From 5a0b99d2f7b8f3484d1a558a5d54bb2d400cdd84 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Thu, 11 Jun 2026 05:27:33 +0000 Subject: [PATCH 01/34] feat: Add new Frontier-CS 2.0 problem vllm_llm_serving_optimization --- 2.0/README.md | 15 + .../.dockerignore | 8 + .../vllm_llm_serving_optimization/DESIGN.md | 230 ++++++++ .../vllm_llm_serving_optimization/config.yaml | 79 +++ .../docker/README.md | 74 +++ .../docker/agent/Dockerfile | 49 ++ .../docker/build_images.sh | 26 + .../docker/judge/Dockerfile | 49 ++ .../docker/smoke_images.sh | 24 + .../vllm_llm_serving_optimization/evaluate.sh | 16 + .../evaluator.py | 508 ++++++++++++++++++ .../harbor/app/README.md | 68 +++ .../harbor/app/make_submission.sh | 17 + .../harbor/app/public_test.py | 134 +++++ .../harbor/app/public_test.sh | 10 + .../harbor/app/solution.patch | 0 .../vllm_llm_serving_optimization/readme | 238 ++++++++ .../reference.patch | 0 .../reference.py | 7 + .../serving_eval/__init__.py | 13 + .../serving_eval/accuracy.py | 159 ++++++ .../serving_eval/agent_runner.py | 217 ++++++++ .../serving_eval/correctness.py | 57 ++ .../serving_eval/measure.py | 310 +++++++++++ .../serving_eval/modal_app.py | 134 +++++ .../serving_eval/sandbox.py | 141 +++++ .../serving_eval/scoring.py | 60 +++ .../serving_eval/serving.py | 200 +++++++ .../serving_eval/settings.py | 117 ++++ 29 files changed, 2960 insertions(+) create mode 100644 2.0/problems/vllm_llm_serving_optimization/.dockerignore create mode 100644 2.0/problems/vllm_llm_serving_optimization/DESIGN.md create mode 100644 2.0/problems/vllm_llm_serving_optimization/config.yaml create mode 100644 2.0/problems/vllm_llm_serving_optimization/docker/README.md create mode 100644 2.0/problems/vllm_llm_serving_optimization/docker/agent/Dockerfile create mode 100755 2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh create mode 100644 2.0/problems/vllm_llm_serving_optimization/docker/judge/Dockerfile create mode 100755 2.0/problems/vllm_llm_serving_optimization/docker/smoke_images.sh create mode 100755 2.0/problems/vllm_llm_serving_optimization/evaluate.sh create mode 100644 2.0/problems/vllm_llm_serving_optimization/evaluator.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/harbor/app/README.md create mode 100755 2.0/problems/vllm_llm_serving_optimization/harbor/app/make_submission.sh create mode 100755 2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.py create mode 100755 2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.sh create mode 100644 2.0/problems/vllm_llm_serving_optimization/harbor/app/solution.patch create mode 100644 2.0/problems/vllm_llm_serving_optimization/readme create mode 100644 2.0/problems/vllm_llm_serving_optimization/reference.patch create mode 100644 2.0/problems/vllm_llm_serving_optimization/reference.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/__init__.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/accuracy.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/agent_runner.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/correctness.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/measure.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/modal_app.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/sandbox.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/scoring.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/serving.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/settings.py diff --git a/2.0/README.md b/2.0/README.md index b414435d7..ea2deeedb 100644 --- a/2.0/README.md +++ b/2.0/README.md @@ -47,6 +47,21 @@ applies the submitted patch to a clean skeleton, runs a hidden arena against multiple baseline bot families, and scores by mean baseline win rate with a small faster-win tiebreak. The online generals.io service is not used. +## vLLM LLM-Serving Optimization + +This systems problem asks agents to patch a clean upstream vLLM checkout to +reduce the end-to-end latency of an LLM serving system on a multi-turn agentic +workload, while keeping accuracy near a baseline. Its problem ID is +`vllm_llm_serving_optimization`. The served model is +`meta-llama/Llama-3.1-8B-Instruct` on a single Modal L40S, and the workload is a +mini-swe-agent SWE-bench run. The agent submits a Python-only patch and can run +an async public test (a subset of the final eval set) that returns real latency +and accuracy feedback. Scoring is the geometric-mean latency speedup versus a +vanilla-vLLM baseline, gated by an accuracy guardrail: accuracy within 5% of the +baseline does not affect the score, and beyond that the score decays +inverse-proportionally with the accuracy drop. Like duckdb-e2e, the agent and +judge run in separate Docker environments. + ## BBOPlace ISPD2005 This VLSI placement problem asks agents to generate macro placement candidates diff --git a/2.0/problems/vllm_llm_serving_optimization/.dockerignore b/2.0/problems/vllm_llm_serving_optimization/.dockerignore new file mode 100644 index 000000000..eedfcafaf --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/.dockerignore @@ -0,0 +1,8 @@ +**/__pycache__ +**/*.pyc +harbor/app/.public_test +docs +docker/README.md +*.md +reference.patch +harbor/app/solution.patch diff --git a/2.0/problems/vllm_llm_serving_optimization/DESIGN.md b/2.0/problems/vllm_llm_serving_optimization/DESIGN.md new file mode 100644 index 000000000..64fc6bf3e --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/DESIGN.md @@ -0,0 +1,230 @@ +# vLLM LLM-Serving Optimization — Design & Operations + +A Frontier-CS **2.0** systems task. The agent patches a **clean upstream vLLM +v0.11.0** checkout (Python-only) to reduce the **end-to-end latency** of an LLM +serving system on a multi-turn agentic workload, while keeping task-solving +**accuracy** close to a vanilla-vLLM baseline. The served model is +`meta-llama/Llama-3.1-8B-Instruct` on a single **NVIDIA L40S** provisioned +on-demand through [Modal](https://modal.com/docs). + +> **Validated end-to-end (2026-06-11):** a full Harbor trial with the `codex` +> agent (`gpt-5.5`) produced a real **1.79× latency geomean speedup** over the +> baseline at full eval scale (30 SWE-bench instances), accuracy preserved → +> **score 83.89 / 100**. + +--- + +## 1. Current Setting + +All knobs live in `config.yaml` (`evaluation` block) and are baked into the +judge/agent images as `task_config.json`. + +| Parameter | Value | Notes | +|---|---|---| +| Served model | `meta-llama/Llama-3.1-8B-Instruct` | gated; HF token required | +| Serving GPU | **1× NVIDIA L40S** (via Modal) | one GPU per environment | +| Workload | mini-swe-agent on `princeton-nlp/SWE-bench_Verified` (split `test`) | multi-turn, shared-prefix conversations | +| Arrival | Poisson, `jps = 0.5` jobs/s | concurrent in-flight conversations | +| `public_slice` (agent role) | `0:5` | iterative self-test subset | +| `eval_slice` (final role) | `0:30` | full verification; superset of public | +| Decoding | `temperature = 0`, `max_completion_tokens = 2048` | greedy, deterministic | +| `step_limit` | 50 | per-instance agent steps | +| Accuracy (agent role) | `patch_validity` | cheap proxy for iterative feedback | +| Accuracy (final role) | `resolve_rate` | SWE-bench resolve; falls back to `patch_validity` if the judge has no Docker-in-Docker | +| `accuracy_tolerance` | `0.05` | ≤5% relative drop ⇒ no penalty | +| `correctness_smoke_prompts` | 8 | greedy outputs must match baseline token-for-token | +| Build timeout / per-instance timeout | 5400 s / 1200 s | | +| Submission | file `/app/solution.patch` (git diff vs `/app/vllm`), `max_queue_size = 2` | async | +| Container budget | 8 vCPU, 32 GiB RAM, 64 GiB storage | agent **and** judge; GPU is remote on Modal | + +**Two roles, two scales.** *Agent role* (iterative `submit.sh` / `public_test`) +uses `public_slice` + `patch_validity`; *final role* (the Harbor verifier) uses +`eval_slice` + `resolve_rate`. The public subset is a strict subset of the final +set, so the self-test is a fast, faithful proxy. + +--- + +## 2. Scoring + +The judge serves **baseline (vanilla vLLM)** and the **patched build** on the +same L40S, under the same workload and the same arrival schedule, and measures +per-instance end-to-end latency (arrival of an instance's first request → +completion of its last response), client-side. + +**Hard gates → score 0** (checked before any timing): +1. **Patch policy** (see §3) — disallowed file, non-Python, secret access, or + benchmark hard-coding. +2. **Build** — the patched source must build on Modal (`VLLM_USE_PRECOMPILED`). +3. **Server health** — `/v1/models` must come up. +4. **Correctness** — the patched server's greedy outputs must match the baseline + **token-for-token** at `temperature 0` on a small smoke set. An optimization + must not change what the model generates. + +**Latency score** (primary objective — geometric mean of per-instance speedups): +``` +per_instance_speedup[i] = baseline_latency[i] / patched_latency[i] # floored at 0.01 +latency_speedup = geomean(per_instance_speedup) +latency_score = clip(100 * log2(latency_speedup), 0, 100) +``` +`1.0×` → 0 points, `2.0×` → 100 points, regressions → 0. Geomean rewards broad +speedups over a single large outlier. + +**Accuracy guardrail** (multiplier): +``` +rel_drop = max(0, (baseline_accuracy - patched_accuracy) / baseline_accuracy) +acc_mult = 1.0 if rel_drop <= 0.05 # within 5% → no penalty +acc_mult = clip(0.05 / rel_drop, 0, 1) otherwise # inverse-proportional decay +``` + +**Final score**: +``` +score = clip(latency_score * acc_mult, 0, 100) +reward = score / 100 # Harbor reward.txt +``` +A fast build that degrades task quality loses most of its score; a build within +5% of baseline accuracy is scored purely on its latency improvement. + +Authoritative scorer: `evaluator.py` (`full_evaluation`); `serving_eval/scoring.py` +mirrors it for the agent-side public test's provisional score. When the serving +stack is unconfigured (no Modal/clean source, e.g. local CI), the evaluator +returns a `1.0` smoke score so the empty reference patch passes. + +--- + +## 3. Which vLLM files the model may change (Patch Policy) + +The patch is validated **before** building. Build uses `VLLM_USE_PRECOMPILED=1`, +so **only Python source is allowed** (`.py`, `.pyi`); no CUDA/C++, build-system, +packaging, or dependency changes. New Python files inside allowed areas are OK. + +**Strongly allowed** (core scheduling / batching / KV-cache): +``` +vllm/v1/core/** +vllm/v1/core/sched/** +vllm/v1/core/kv_cache_utils.py +vllm/config/scheduler.py +vllm/config/cache.py +``` + +**Conditionally allowed** (narrow wiring around the engine / request path): +``` +vllm/v1/worker/** vllm/v1/engine/** vllm/v1/executor/** +vllm/v1/request.py vllm/v1/outputs.py vllm/v1/serial_utils.py +vllm/entrypoints/openai/protocol.py +vllm/entrypoints/openai/serving_engine.py +vllm/entrypoints/openai/serving_chat.py +vllm/entrypoints/openai/serving_completion.py +vllm/sampling_params.py +``` + +**Denied** (rejected outright): +``` +csrc/** cmake/** CMakeLists.txt setup.py setup.cfg pyproject.toml +requirements/** requirements*.txt +tests/** benchmarks/** docs/** examples/** tools/** .github/** docker/** Dockerfile* +vllm/model_executor/models/** vllm/model_executor/model_loader/** +vllm/transformers_utils/** vllm/lora/** vllm/distributed/** +vllm/entrypoints/llm.py vllm/entrypoints/api_server.py vllm/entrypoints/cli/** +vllm/version.py vllm/_version.py +``` + +**Also rejected:** reading/writing judge/Modal/HF/Frontier/Harbor environment +variables (`MODAL_TOKEN*`, `HF_TOKEN`, `FRONTIER_*`, `HARBOR_*`, `JUDGE_URL`, +`RUN_OUTPUT_DIR`, scheduler-timestamp leakage), and hard-coding the benchmark / +dataset / instance ids / judge paths (`swebench`, `princeton-nlp`, +`SWE-bench_Verified`, `minisweagent`, …). The server is launched under a fixed +config; patches that detect the benchmark, sleep, short-circuit generation, or +otherwise special-case the evaluation are rejected. + +> **In practice:** the intended optimization area is *online serving efficiency* +> — request scheduling, batching, KV-cache management, prefix/prompt-cache reuse, +> preemption/admission control, queueing, and closely related scheduler/execution +> wiring. The validated 1.79× run was a single-file change to +> `vllm/v1/core/sched/scheduler.py`. (Candidate variants during the run also +> touched `vllm/v1/core/kv_cache_utils.py`, `vllm/v1/core/kv_cache_manager.py`, +> and `vllm/config/scheduler.py` — all within the allowlist.) + +--- + +## 4. GPU resource management & scheduling (Modal) + +**No local GPU.** The agent and judge containers are CPU-only clients +(8 vCPU / 32 GiB). The single L40S is provisioned **on-demand on Modal** and is +the *only* place the model runs. This is what makes the agent/judge split cheap +to host. + +### Image build (per submission) +`serving_eval/modal_app.py` defines a Modal app parametrized entirely via env +vars (so the same module serves baseline and patched trees): +- Base `nvidia/cuda:12.9.0-devel-ubuntu22.04` (+ Python 3.12, `uv`). +- `add_local_dir(, /src/vllm, copy=True)` bakes the **target source tree** + into the image (`copy=True` is required because the next step installs from it). +- `VLLM_USE_PRECOMPILED=1 uv pip install --system -e .` — reuses vLLM's prebuilt + CUDA kernels and rebuilds only the Python layer ⇒ per-submission builds are + minutes, not an hour, and the **Python-only patch policy is enforced by + construction**. +- Pinned for reproducibility on a shallow/patched tree: + `SETUPTOOLS_SCM_PRETEND_VERSION*` (version detection), a pinned + `VLLM_PRECOMPILED_WHEEL_LOCATION` (ABI-matched release wheel — the default + derivation falls back to an incompatible nightly), `transformers==4.55.2` + (the unpinned upper bound otherwise resolves to an incompatible 5.x), and + `hf_transfer`. + +### Serving +```python +@app.function(gpu="L40S", scaledown_window=900, secrets=[huggingface-secret], + volumes={hf_cache, vllm_cache}) +@modal.concurrent(max_inputs=64) +@modal.web_server(port=8000, startup_timeout=...) +def serve(): subprocess.Popen("vllm serve --host 0.0.0.0 --port 8000 ...") +``` +- `gpu="L40S"` requests exactly one L40S; `@modal.concurrent(64)` lets one + warm container handle many in-flight requests (matching the Poisson workload). +- `@modal.web_server` exposes vLLM's OpenAI endpoint at a stable + `https://…modal.run/v1`; Modal cold-starts the container on first request and + serves within `startup_timeout`. +- **Persisted caches:** a `huggingface` Volume (weights downloaded once, reused + across cold starts) and a `vllm` cache Volume. +- `scaledown_window=900` releases the idle GPU after 15 min — you pay for GPU + only while serving/measuring. + +### Lifecycle & scheduling (`serving_eval/serving.py`) +``` +deploy_server() → `modal deploy modal_app.py` (env selects src/model/app-name) + → Function.from_name(app, "serve").get_web_url() +wait_healthy() → poll /v1/models until 200 +... run workload ... +stop_server() → `modal app stop ` +``` +- **One L40S per environment is honored by serializing:** baseline and patched + are **never served concurrently**. The baseline is measured once and cached + (`/opt/vllm-baseline/baseline_metrics.json`); the patched build is then served + on its own and its greedy outputs are compared against the cached baseline. +- **Transient-failure retry:** Modal occasionally evicts an image build under + load (`Image build terminated due to external shut-down`, `APP_STATE_STOPPED`, + gateway timeouts). `deploy_server` retries such transient deploys with backoff + (`deploy_retries`, default 3), running `modal app stop` between attempts; a + genuine build error in the patch is non-transient and fails fast. +- Auth inside the containers is env-var based (`MODAL_TOKEN_ID` / + `MODAL_TOKEN_SECRET`); gated Llama weights are pulled inside the Modal serving + container via the Modal Secret `huggingface-secret` (key `HF_TOKEN`). + +### Where Modal is used from +Both the **agent's async public test** (`harbor/app/public_test.py` → +`serving_eval.run_public_test`) and the **judge's measurement** +(`evaluator.py` → `serving_eval.run_measurement`) drive Modal the same way, so +the iterative feedback the agent sees is the same kind the judge grades on. + +--- + +## File map + +``` +config.yaml resources, model, L40S, dataset, eval knobs (→ task_config.json) +readme public problem statement (no algorithm hints) +evaluator.py patch policy + scoring + orchestration (+ local smoke degrade) +serving_eval/ settings · modal_app · serving · sandbox · agent_runner · + accuracy · correctness · scoring · measure +docker/ agent + judge Dockerfiles, build/smoke scripts +harbor/app/ make_submission.sh, public_test client +``` diff --git a/2.0/problems/vllm_llm_serving_optimization/config.yaml b/2.0/problems/vllm_llm_serving_optimization/config.yaml new file mode 100644 index 000000000..88a65a109 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/config.yaml @@ -0,0 +1,79 @@ +tag: systems +runtime: + language: python + timeout_seconds: 21600 + environment: "Patched vLLM (v0.11.0) source; Modal L40S GPU serving Llama-3.1-8B-Instruct; mini-swe-agent SWE-bench workload; latency-primary judge with accuracy guardrail" + apt_packages: + - bash + - ca-certificates + - curl + - git + - python3 + - python3-pip + judge_apt_packages: + - bash + - ca-certificates + - curl + - git + - python3 + - python3-pip + judge_pip_packages: + - modal + - openai + - datasets + - huggingface-hub + docker: + # Experimental local images. Build them with + # 2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh before running a + # local Harbor trial. Both images need a clean upstream vLLM v0.11.0 checkout + # (NOT the continuum fork). The judge image additionally vendors the + # mini-swe-agent harness and the latency/accuracy scorer. + image: frontiercs/vllm-serving-optimization-agent:experimental-v0.11.0 + judge_image: frontiercs/vllm-serving-optimization-judge:experimental-v0.11.0 +environment: + cpus: 8 + memory_mb: 32768 + storage_mb: 65536 + build_timeout_seconds: 7200 +evaluation: + # Model + accelerator served on Modal (one L40S per environment). + model: meta-llama/Llama-3.1-8B-Instruct + gpu: L40S + # Workload: mini-swe-agent on SWE-bench Verified (split test). + dataset: princeton-nlp/SWE-bench_Verified + dataset_split: test + # Iterative (agent-role) public test: a strict subset of the final eval set. + public_slice: "0:5" + # Final (verifier-role) evaluation: superset of the public slice. + eval_slice: "0:30" + # Poisson arrival workload (jobs/second). Mirrors a realistic serving load. + arrival_mode: jps + jps: 0.5 + workers: 8 + step_limit: 50 + temperature: 0.0 + max_completion_tokens: 2048 + # Latency aggregation + scoring. + latency_metric: mean_e2e_seconds + # Accuracy guardrail. Within `accuracy_tolerance` relative drop of baseline => + # no penalty; beyond it the score decays inverse-proportionally. + accuracy_tolerance: 0.05 + agent_accuracy_mode: patch_validity + final_accuracy_mode: resolve_rate + # Greedy-output correctness smoke (a handful of fixed prompts must match the + # baseline token-for-token at temperature 0 before timing is considered). + correctness_smoke_prompts: 8 + # Modal serving knobs. + modal_scaledown_seconds: 900 + modal_startup_timeout_seconds: 1200 + server_health_timeout_seconds: 1800 + # Per-phase wall-clock budgets (seconds). + build_timeout_seconds: 5400 + instance_timeout_seconds: 1200 + # Use a baseline (vanilla vLLM) cached in the judge image when available, + # otherwise the judge serves vanilla once and caches it for the trial. + baseline_cache_path: /opt/vllm-baseline/baseline_metrics.json +submission: + kind: file + path: /app/solution.patch + max_queue_size: 2 diff --git a/2.0/problems/vllm_llm_serving_optimization/docker/README.md b/2.0/problems/vllm_llm_serving_optimization/docker/README.md new file mode 100644 index 000000000..1de1534b9 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/docker/README.md @@ -0,0 +1,74 @@ +# Experimental vLLM Serving-Optimization Images + +This task needs two images, mirroring the duckdb-e2e split: a public **agent** +image and a private **judge** image. Both bundle a clean upstream vLLM checkout +and the shared `serving_eval` harness. Build them before running a local Harbor +trial: + +```bash +bash 2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh +``` + +Defaults: + +```text +VLLM_REF=v0.11.0 +AGENT_TAG=frontiercs/vllm-serving-optimization-agent:experimental-v0.11.0 +JUDGE_TAG=frontiercs/vllm-serving-optimization-judge:experimental-v0.11.0 +``` + +The agent image contains: + +```text +/app/vllm # clean upstream vLLM (no continuum, no reference fix) +/opt/serving_eval # shared harness, used by the async public test +/opt/vllm-baseline # optional precomputed baseline cache +``` + +The judge image contains: + +```text +/opt/vllm-clean # clean upstream vLLM (build + baseline reference) +/opt/serving_eval # shared harness, used by the evaluator +/opt/vllm-baseline # baseline-metrics cache (filled on first measurement) +``` + +## Runtime requirements (important) + +Unlike duckdb-e2e, this task does **not** run the model inside the container. +Both the agent public test and the judge serve `meta-llama/Llama-3.1-8B-Instruct` +on a **Modal L40S** built from the (patched) vLLM source. The containers +therefore need: + +- `MODAL_TOKEN_ID` / `MODAL_TOKEN_SECRET` in the environment (Modal auth). Use a + Modal service-user token for unattended runs. +- A Modal Secret named `huggingface-secret` containing `HF_TOKEN` with access to + the gated Llama-3.1 weights (`modal secret create huggingface-secret HF_TOKEN=...`). + The container also reads `HF_TOKEN` for the `datasets` download. +- The judge additionally needs a reachable Docker daemon (mounted socket or + DinD) to run the SWE-bench per-instance testbeds for the final resolve-rate. + When no daemon is reachable, the harness falls back to a local sandbox and the + patch-validity accuracy proxy. + +The Modal image build uses `VLLM_USE_PRECOMPILED=1`, so only vLLM's Python layer +is rebuilt from the submitted source (minutes, not a full CUDA compile). This is +why the patch policy is Python-only. + +## Baseline cache + +The judge measures the vanilla (clean-tree) baseline once per role and caches it +at `/opt/vllm-baseline/baseline_metrics.json`, keyed by role (`agent` / `final`). +Baseline and patched builds are never served simultaneously, so a single L40S is +sufficient per environment. To precompute and bake the baseline into the image +(recommended for faster trials), run the harness against the clean tree offline +and copy the resulting `baseline_metrics.json` into the image at that path. + +## Smoke test + +```bash +bash 2.0/problems/vllm_llm_serving_optimization/docker/smoke_images.sh +``` + +This checks that the clean vLLM checkout, the `serving_eval` package, and the +Modal/OpenAI/datasets (and, for the judge, swebench + docker) clients are +importable. It does not exercise Modal or a GPU. diff --git a/2.0/problems/vllm_llm_serving_optimization/docker/agent/Dockerfile b/2.0/problems/vllm_llm_serving_optimization/docker/agent/Dockerfile new file mode 100644 index 000000000..d06522f58 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/docker/agent/Dockerfile @@ -0,0 +1,49 @@ +# Agent base image for the vLLM LLM-serving optimization task. +# +# Provides a CLEAN upstream vLLM checkout (no continuum / no reference solution) +# for the agent to modify, the shared serving/eval harness used by the async +# public test, and the Modal + OpenAI + datasets clients. The Frontier-CS 2.0 +# adapter builds the final agent image ON TOP of this one and copies the harbor +# app scripts (make_submission.sh, public_test.sh, ...) into /app. +# +# Build context must be the task directory (so `serving_eval` is visible): +# docker build -f docker/agent/Dockerfile -t . +FROM ubuntu:24.04 + +ARG VLLM_REF=v0.11.0 +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash \ + build-essential \ + ca-certificates \ + curl \ + git \ + python3 \ + python3-pip \ + python3-venv \ + ripgrep && \ + rm -rf /var/lib/apt/lists/* + +RUN pip3 install --break-system-packages --no-cache-dir \ + modal \ + openai \ + "datasets>=2.19" \ + huggingface-hub + +# Clean upstream vLLM source for the agent to modify. This is intentionally the +# vanilla vLLM project at a pinned tag: the optimization must be re-derived by +# the agent, not copied from any reference implementation. +RUN git clone --branch "${VLLM_REF}" --depth 1 https://github.com/vllm-project/vllm.git /app/vllm + +# Shared serving/eval harness (importable as `serving_eval` from /opt) used by +# the async public test client. +COPY serving_eval /opt/serving_eval + +# Optional baseline-metrics cache. When present (precomputed on the clean tree), +# the public test reports a provisional speedup/score; otherwise it reports raw +# latency and accuracy only. +RUN mkdir -p /opt/vllm-baseline + +WORKDIR /app diff --git a/2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh b/2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh new file mode 100755 index 000000000..a097e0e0a --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh @@ -0,0 +1,26 @@ +#!/usr/bin/env bash +set -euo pipefail + +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +TASK_DIR=$(cd "$SCRIPT_DIR/.." && pwd) + +VLLM_REF="${VLLM_REF:-v0.11.0}" +AGENT_TAG="${AGENT_TAG:-frontiercs/vllm-serving-optimization-agent:experimental-v0.11.0}" +JUDGE_TAG="${JUDGE_TAG:-frontiercs/vllm-serving-optimization-judge:experimental-v0.11.0}" + +# Build context is the task directory so the Dockerfiles can COPY serving_eval. +docker build \ + --build-arg "VLLM_REF=$VLLM_REF" \ + -f "$TASK_DIR/docker/agent/Dockerfile" \ + -t "$AGENT_TAG" \ + "$TASK_DIR" + +docker build \ + --build-arg "VLLM_REF=$VLLM_REF" \ + -f "$TASK_DIR/docker/judge/Dockerfile" \ + -t "$JUDGE_TAG" \ + "$TASK_DIR" + +echo "Built:" +echo " $AGENT_TAG" +echo " $JUDGE_TAG" diff --git a/2.0/problems/vllm_llm_serving_optimization/docker/judge/Dockerfile b/2.0/problems/vllm_llm_serving_optimization/docker/judge/Dockerfile new file mode 100644 index 000000000..d7e702b74 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/docker/judge/Dockerfile @@ -0,0 +1,49 @@ +# Judge base image for the vLLM LLM-serving optimization task. +# +# Contains the clean upstream vLLM source (the build/baseline reference), the +# shared serving/eval harness, the SWE-bench resolve-rate harness, the Modal + +# OpenAI + datasets clients, and the Docker CLI for per-instance SWE-bench +# testbeds. The Frontier-CS 2.0 adapter builds the final judge image ON TOP of +# this one (copying judge_server.py + problem_evaluator.py into /judge). +# +# Build context must be the task directory (so `serving_eval` is visible): +# docker build -f docker/judge/Dockerfile -t . +FROM ubuntu:24.04 + +ARG VLLM_REF=v0.11.0 +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash \ + build-essential \ + ca-certificates \ + curl \ + git \ + python3 \ + python3-pip \ + python3-venv \ + ripgrep \ + docker.io && \ + rm -rf /var/lib/apt/lists/* + +RUN pip3 install --break-system-packages --no-cache-dir \ + modal \ + openai \ + "datasets>=2.19" \ + huggingface-hub \ + "swebench>=3.0" + +# Clean upstream vLLM source: the build/serve baseline and the tree the patch is +# applied to. Must match the agent image's pinned tag. +RUN git clone --branch "${VLLM_REF}" --depth 1 https://github.com/vllm-project/vllm.git /opt/vllm-clean + +# Shared serving/eval harness (importable as `serving_eval` from /opt). +COPY serving_eval /opt/serving_eval + +# Baseline-metrics cache directory. The judge measures the vanilla baseline once +# (or reads a precomputed cache here) and never serves baseline + patched at the +# same time, honouring the one-L40S-per-environment budget. +RUN mkdir -p /opt/vllm-baseline + +WORKDIR /judge diff --git a/2.0/problems/vllm_llm_serving_optimization/docker/smoke_images.sh b/2.0/problems/vllm_llm_serving_optimization/docker/smoke_images.sh new file mode 100755 index 000000000..6718dbc0a --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/docker/smoke_images.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash +set -euo pipefail + +AGENT_TAG="${AGENT_TAG:-frontiercs/vllm-serving-optimization-agent:experimental-v0.11.0}" +JUDGE_TAG="${JUDGE_TAG:-frontiercs/vllm-serving-optimization-judge:experimental-v0.11.0}" + +echo "[agent] checking $AGENT_TAG" +docker run --rm "$AGENT_TAG" sh -lc ' + test -d /app/vllm/.git + git -C /app/vllm rev-parse HEAD >/dev/null + test -f /opt/serving_eval/__init__.py + python3 -c "import sys; sys.path.insert(0, \"/opt\"); import serving_eval; print(serving_eval.__version__)" + python3 -c "import modal, openai, datasets" +' + +echo "[judge] checking $JUDGE_TAG" +docker run --rm "$JUDGE_TAG" sh -lc ' + test -d /opt/vllm-clean/.git + git -C /opt/vllm-clean rev-parse HEAD >/dev/null + test -f /opt/serving_eval/__init__.py + python3 -c "import sys; sys.path.insert(0, \"/opt\"); import serving_eval; print(serving_eval.__version__)" + python3 -c "import modal, openai, datasets, swebench" + command -v docker >/dev/null +' diff --git a/2.0/problems/vllm_llm_serving_optimization/evaluate.sh b/2.0/problems/vllm_llm_serving_optimization/evaluate.sh new file mode 100755 index 000000000..6f5188491 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/evaluate.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +set -euo pipefail + +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) + +if [[ $# -gt 0 ]]; then + exec python3 "$SCRIPT_DIR/evaluator.py" "$@" +fi + +SOLUTION="/work/execution_env/solution_env/solution.patch" +if [[ ! -f "$SOLUTION" ]]; then + echo "Error: Missing $SOLUTION" >&2 + exit 1 +fi + +python3 "$SCRIPT_DIR/evaluator.py" "$SOLUTION" diff --git a/2.0/problems/vllm_llm_serving_optimization/evaluator.py b/2.0/problems/vllm_llm_serving_optimization/evaluator.py new file mode 100644 index 000000000..4efdd4d33 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/evaluator.py @@ -0,0 +1,508 @@ +"""Evaluator for the experimental vLLM LLM-serving latency optimization task. + +The agent submits a Python-only patch against a clean upstream vLLM v0.11.0 +checkout. The judge applies the patch, builds and serves the patched vLLM on a +Modal L40S (``meta-llama/Llama-3.1-8B-Instruct``), runs a mini-swe-agent +SWE-bench workload, and scores latency speedup vs a vanilla-vLLM baseline gated +by an accuracy guardrail. + +This file contains the self-contained static patch policy and the scoring math. +The heavy orchestration (Modal deploy, workload run, baseline caching) lives in +the ``serving_eval`` package that is baked into the judge image. When that +harness or the serving credentials are not available (for example a local CI +smoke test), the evaluator validates the patch policy and returns a smoke score +so the empty reference patch still passes. +""" + +from __future__ import annotations + +import fnmatch +import hashlib +import json +import math +import os +import re +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +MAX_PATCH_BYTES = 1_500_000 +MAX_CHANGED_FILES = 80 +TASK_CONFIG_PATH = Path("/judge/task_config.json") +SERVING_EVAL_ROOT = "/opt" +DEFAULT_CLEAN_SOURCE = Path("/opt/vllm-clean") + + +def _load_task_config() -> dict[str, Any]: + try: + payload = json.loads(TASK_CONFIG_PATH.read_text(encoding="utf-8")) + except Exception: + return {} + return payload if isinstance(payload, dict) else {} + + +TASK_CONFIG = _load_task_config() +EVALUATION_CONFIG = ( + TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} +) + + +def _config_value(name: str, default: Any) -> Any: + return EVALUATION_CONFIG.get(name, default) + + +def _config_int(name: str, default: int) -> int: + try: + return int(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _config_float(name: str, default: float) -> float: + try: + return float(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _config_str(name: str, default: str) -> str: + raw = EVALUATION_CONFIG.get(name, default) + return str(raw) + + +ACCURACY_TOLERANCE = _config_float("accuracy_tolerance", 0.05) +BASELINE_CACHE_PATH = Path(_config_str("baseline_cache_path", "/opt/vllm-baseline/baseline_metrics.json")) + +# --------------------------------------------------------------------------- # +# Patch policy +# --------------------------------------------------------------------------- # + +STRONGLY_ALLOWED_PATTERNS = ( + "vllm/v1/core/**", + "vllm/v1/core/sched/**", + "vllm/v1/core/kv_cache_utils.py", + "vllm/config/scheduler.py", + "vllm/config/cache.py", +) + +CONDITIONALLY_ALLOWED_PATTERNS = ( + "vllm/v1/worker/**", + "vllm/v1/engine/**", + "vllm/v1/executor/**", + "vllm/v1/request.py", + "vllm/v1/outputs.py", + "vllm/v1/serial_utils.py", + "vllm/entrypoints/openai/protocol.py", + "vllm/entrypoints/openai/serving_engine.py", + "vllm/entrypoints/openai/serving_chat.py", + "vllm/entrypoints/openai/serving_completion.py", + "vllm/sampling_params.py", +) + +DENIED_PATTERNS = ( + "csrc/**", + "cmake/**", + "CMakeLists.txt", + "setup.py", + "setup.cfg", + "pyproject.toml", + "requirements/**", + "requirements*.txt", + "tests/**", + "benchmarks/**", + "benchmark/**", + "docs/**", + "examples/**", + "tools/**", + ".buildkite/**", + ".github/**", + "docker/**", + "Dockerfile*", + ".dockerignore", + "vllm/model_executor/models/**", + "vllm/model_executor/model_loader/**", + "vllm/transformers_utils/**", + "vllm/lora/**", + "vllm/distributed/**", + "vllm/entrypoints/llm.py", + "vllm/entrypoints/api_server.py", + "vllm/entrypoints/cli/**", + "vllm/version.py", + "vllm/_version.py", +) + +# Source-line tokens that are forbidden in added code: benchmark/dataset names +# (anti hard-coding) and secret/judge environment access (anti exfiltration and +# anti benchmark-detection). +HARD_CODE_TOKENS = ( + "swebench", + "swe-bench", + "swe_bench", + "sweb.eval", + "minisweagent", + "mini-swe", + "princeton-nlp", + "SWE-bench_Verified", +) + +SECRET_TOKENS = ( + "MODAL_TOKEN", + "MODAL_TOKEN_ID", + "MODAL_TOKEN_SECRET", + "HF_TOKEN", + "HUGGING_FACE_HUB_TOKEN", + "HUGGINGFACEHUB", + "FRONTIER_", + "HARBOR_", + "JUDGE_URL", + "RUN_OUTPUT_DIR", + "scheduler_timestamps", +) + +HARD_CODE_TOKEN_RE = re.compile( + "|".join(re.escape(token) for token in HARD_CODE_TOKENS), + re.IGNORECASE, +) +SECRET_TOKEN_RE = re.compile("|".join(re.escape(token) for token in SECRET_TOKENS)) + +# Patches must stay Python-only because the Modal build uses VLLM_USE_PRECOMPILED +# (prebuilt CUDA kernels, Python layer rebuilt). New .py/.pyi files are allowed. +ALLOWED_SOURCE_EXTENSIONS = (".py", ".pyi") + + +@dataclass(frozen=True) +class PatchFile: + old_path: str + new_path: str + added_lines: tuple[str, ...] + removed_lines: tuple[str, ...] + + @property + def path(self) -> str: + return self.new_path if self.new_path != "/dev/null" else self.old_path + + +def _match(path: str, patterns: tuple[str, ...]) -> bool: + return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + + +def _is_allowed_source_path(path: str) -> bool: + return _match(path, STRONGLY_ALLOWED_PATTERNS) or _match(path, CONDITIONALLY_ALLOWED_PATTERNS) + + +def _invalid(message: str, metrics: dict[str, Any] | None = None): + payload = metrics or {} + payload.setdefault("valid_patch", 0) + return 0.0, 0.0, message, payload + + +def _parse_patch(text: str) -> list[PatchFile]: + files: list[PatchFile] = [] + current_old = "" + current_new = "" + added: list[str] = [] + removed: list[str] = [] + in_file = False + + for line in text.splitlines(): + if line.startswith("diff --git "): + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + in_file = True + current_old = "" + current_new = "" + added = [] + removed = [] + continue + if not in_file: + continue + if line.startswith("--- "): + current_old = line[4:].strip() + if current_old.startswith("a/"): + current_old = current_old[2:] + continue + if line.startswith("+++ "): + current_new = line[4:].strip() + if current_new.startswith("b/"): + current_new = current_new[2:] + continue + if line.startswith("+") and not line.startswith("+++ "): + added.append(line[1:]) + continue + if line.startswith("-") and not line.startswith("--- "): + removed.append(line[1:]) + + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + return files + + +def _validate_patch_path(path: str, metrics: dict[str, Any]) -> tuple[bool, str]: + if not path or path == "/dev/null": + return True, "" + if path.startswith("/") or ".." in Path(path).parts: + return False, f"unsafe patch path: {path}" + if _match(path, DENIED_PATTERNS): + return False, f"changed file is outside task boundary: {path}" + if not _is_allowed_source_path(path): + return False, f"changed file is not allowlisted: {path}" + if not path.endswith(ALLOWED_SOURCE_EXTENSIONS): + return False, f"only Python source changes are allowed (VLLM_USE_PRECOMPILED build): {path}" + return True, "" + + +def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: + if not patch_path.exists(): + return False, "solution patch does not exist", {} + size = patch_path.stat().st_size + if size > MAX_PATCH_BYTES: + return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} + text = patch_path.read_text(encoding="utf-8", errors="replace") + patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() + files = _parse_patch(text) + metrics: dict[str, Any] = { + "patch_bytes": size, + "patch_sha256": patch_hash, + "changed_files": len(files), + } + if len(files) > MAX_CHANGED_FILES: + return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics + + for patch_file in files: + path = patch_file.path + if patch_file.new_path == "/dev/null": + return False, f"deleting source files is outside task boundary: {patch_file.old_path}", metrics + if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: + ok, error = _validate_patch_path(patch_file.old_path, metrics) + if not ok: + return False, f"rename/copy source is outside task boundary: {error}", metrics + + ok, error = _validate_patch_path(path, metrics) + if not ok: + return False, error, metrics + if not path or path == "/dev/null": + return False, "could not determine changed path from patch", metrics + + added_text = "\n".join(patch_file.added_lines) + secret_match = SECRET_TOKEN_RE.search(added_text) + if secret_match: + return False, f"{path}: judge/secret environment access is forbidden ({secret_match.group(0)})", metrics + hard_code_match = HARD_CODE_TOKEN_RE.search(added_text) + if hard_code_match: + return False, f"{path}: benchmark-specific token is forbidden ({hard_code_match.group(0)})", metrics + + metrics["valid_patch"] = 1 + return True, "patch accepted by static policy", metrics + + +def patch_is_empty(metrics: dict[str, Any]) -> bool: + return int(metrics.get("changed_files", 0)) == 0 + + +# --------------------------------------------------------------------------- # +# Scoring +# --------------------------------------------------------------------------- # + +def geometric_mean(values: list[float]) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(value, 1e-9)) for value in values) / len(values)) + + +def score_from_speedup(speedup: float) -> float: + if speedup <= 0: + return 0.0 + raw = 100.0 * math.log(speedup, 2) + return max(0.0, min(100.0, raw)) + + +def accuracy_multiplier(baseline_accuracy: float, patched_accuracy: float, tolerance: float) -> float: + base = max(baseline_accuracy, 1e-9) + rel_drop = max(0.0, (baseline_accuracy - patched_accuracy) / base) + if rel_drop <= tolerance: + return 1.0 + return max(0.0, min(1.0, tolerance / rel_drop)) + + +def paired_speedups( + baseline_latency: dict[str, float], + patched_latency: dict[str, float], +) -> list[float]: + speedups: list[float] = [] + for instance_id, patched_value in patched_latency.items(): + base_value = baseline_latency.get(instance_id) + if base_value is None or patched_value <= 0 or base_value <= 0: + continue + speedups.append(max(base_value / patched_value, 0.01)) + return speedups + + +# --------------------------------------------------------------------------- # +# Error sanitization (black-box safety) +# --------------------------------------------------------------------------- # + +def sanitize_error_text(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"https://[A-Za-z0-9_.:/-]+", "", text) + for token in SECRET_TOKENS: + text = text.replace(token, "") + text = re.sub(r"hf_[A-Za-z0-9]+", "", text) + text = re.sub(r"ak-[A-Za-z0-9]+", "", text) + return text[-800:] + + +# --------------------------------------------------------------------------- # +# Serving harness integration +# --------------------------------------------------------------------------- # + +def _serving_harness_available() -> bool: + if not DEFAULT_CLEAN_SOURCE.exists(): + return False + if not os.environ.get("MODAL_TOKEN_ID") or not os.environ.get("MODAL_TOKEN_SECRET"): + return False + return Path(SERVING_EVAL_ROOT, "serving_eval", "__init__.py").exists() + + +def _load_serving_eval(): + if SERVING_EVAL_ROOT not in sys.path: + sys.path.insert(0, SERVING_EVAL_ROOT) + import serving_eval # type: ignore + + return serving_eval + + +def is_final_submission_role() -> bool: + return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" + + +def full_evaluation(patch_path: Path, metrics: dict[str, Any]): + final_role = is_final_submission_role() + metrics["submission_role"] = "final" if final_role else "agent" + + if not _serving_harness_available(): + # Local smoke / CI: the patch policy passed and the serving stack is not + # configured here. Return a positive smoke score so the reference patch + # and policy checks can be validated without a GPU or Modal. + metrics["full_benchmark"] = 0 + metrics["serving_harness"] = "unconfigured" + return ( + 1.0, + 1.0, + "patch policy smoke passed; vLLM serving harness is not configured in this environment", + metrics, + ) + + serving_eval = _load_serving_eval() + measurement = serving_eval.run_measurement( + patch_path=str(patch_path), + role="final" if final_role else "agent", + config=EVALUATION_CONFIG, + clean_source=str(DEFAULT_CLEAN_SOURCE), + baseline_cache_path=str(BASELINE_CACHE_PATH), + ) + + public_info = measurement.get("info", {}) if isinstance(measurement.get("info"), dict) else {} + for key, value in public_info.items(): + if isinstance(value, (int, float, str, bool)): + metrics[f"info_{key}"] = value + + if not measurement.get("ok", False): + gate = str(measurement.get("gate") or "serving evaluation gate failed") + metrics["gate"] = gate + return _invalid(gate, metrics) + + if not measurement.get("correctness_ok", False): + metrics["gate"] = "correctness" + return _invalid("patched server generations differ from the baseline at temperature 0", metrics) + + patched = measurement.get("patched", {}) or {} + baseline = measurement.get("baseline", {}) or {} + patched_latency = {str(k): float(v) for k, v in (patched.get("per_instance_latency") or {}).items()} + baseline_latency = {str(k): float(v) for k, v in (baseline.get("per_instance_latency") or {}).items()} + speedups = paired_speedups(baseline_latency, patched_latency) + if not speedups: + return _invalid("no paired latency measurements were produced", metrics) + + gm_speedup = geometric_mean(speedups) + latency_score = score_from_speedup(gm_speedup) + + patched_accuracy = float(patched.get("accuracy", 0.0)) + baseline_accuracy = float(baseline.get("accuracy", 0.0)) + acc_mult = accuracy_multiplier(baseline_accuracy, patched_accuracy, ACCURACY_TOLERANCE) + bounded = max(0.0, min(100.0, latency_score * acc_mult)) + + metrics.update( + { + "full_benchmark": 1, + "serving_harness": "modal_l40s", + "instances_scored": len(speedups), + "latency_geomean_speedup": gm_speedup, + "latency_score": latency_score, + "baseline_accuracy": baseline_accuracy, + "patched_accuracy": patched_accuracy, + "accuracy_multiplier": acc_mult, + } + ) + return ( + bounded, + bounded, + ( + f"latency geomean speedup {gm_speedup:.4f}x over baseline vLLM; " + f"accuracy {patched_accuracy:.4f} vs baseline {baseline_accuracy:.4f} " + f"(multiplier {acc_mult:.3f})" + ), + metrics, + ) + + +def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: + patch_path = Path(solution_path) + ok, message, metrics = validate_patch(patch_path) + if not ok: + return _invalid(message, metrics) + try: + return full_evaluation(patch_path, metrics) + except Exception as exc: # noqa: BLE001 - black-box: never surface raw internals + metrics["error_type"] = type(exc).__name__ + metrics["error_detail"] = sanitize_error_text(str(exc)) + return _invalid("serving evaluation failed", metrics) + + +def prepare() -> dict[str, Any]: + """Report judge readiness without leaking secret values.""" + return { + "task": "vllm_llm_serving_optimization", + "serving_harness_available": _serving_harness_available(), + "clean_source_present": DEFAULT_CLEAN_SOURCE.exists(), + "modal_credentials_present": bool( + os.environ.get("MODAL_TOKEN_ID") and os.environ.get("MODAL_TOKEN_SECRET") + ), + "hf_credentials_present": bool(os.environ.get("HF_TOKEN")), + "baseline_cache_present": BASELINE_CACHE_PATH.exists(), + "accuracy_tolerance": ACCURACY_TOLERANCE, + } + + +def main(argv: list[str]) -> int: + if len(argv) != 2: + print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) + return 2 + score, score_unbounded, message, metrics = evaluate(argv[1]) + print( + json.dumps( + { + "score": score, + "score_unbounded": score_unbounded, + "message": message, + "metrics": metrics, + }, + indent=2, + sort_keys=True, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv)) diff --git a/2.0/problems/vllm_llm_serving_optimization/harbor/app/README.md b/2.0/problems/vllm_llm_serving_optimization/harbor/app/README.md new file mode 100644 index 000000000..f03b20dcd --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/harbor/app/README.md @@ -0,0 +1,68 @@ +# vLLM LLM-Serving Optimization Starter + +The workspace contains a clean upstream vLLM checkout at: + +```text +/app/vllm +``` + +Modify vLLM source code to reduce end-to-end serving latency on the agentic +SWE-bench workload while preserving the model's task-solving accuracy. Only +Python-only changes in the allowlisted scheduler/execution/serving areas are +valid (see the task statement for the exact patch policy). The model is served +on a Modal L40S with `VLLM_USE_PRECOMPILED`, so CUDA/C++ kernel changes are out +of scope. + +## Submit + +```bash +bash /app/make_submission.sh +bash /app/submit.sh +``` + +`make_submission.sh` stages your changes in `/app/vllm` and writes +`/app/solution.patch`. `submit.sh` enqueues that patch for the same black-box +judge used by the final verifier. Submissions are asynchronous — submit early, +then keep iterating. Use `bash /app/submissions.sh` and +`bash /app/wait_submission.sh ` to inspect judge results. + +## Public test (local, async, real metrics) + +Before (or instead of) submitting, evaluate your working tree yourself: + +```bash +bash /app/public_test.sh launch # deploys /app/vllm to a Modal L40S, async +bash /app/public_test.sh status # latency + accuracy + provisional score +bash /app/public_test.sh run # synchronous variant +``` + +The public test deploys your patched vLLM to a Modal L40S, serves +`meta-llama/Llama-3.1-8B-Instruct`, runs the **public instance subset** (a strict +subset of the final eval set) under the same Poisson arrival workload the judge +uses, and returns: + +- per-instance and mean end-to-end latency, +- an accuracy signal (patch-validity rate during iterative feedback), +- a provisional speedup and score versus the baseline (when a baseline cache is + available in the image). + +This is real serving feedback — latency and accuracy — not a build/compile flag. +Drive your loop with it: edit vLLM, run the public test, read the returned +latency/accuracy, adjust. + +## What the judge measures + +The judge applies `/app/solution.patch` to a clean pinned vLLM tree, builds and +serves it the same way, runs the workload, and scores **latency speedup vs the +baseline**, gated by an **accuracy guardrail**: accuracy within 5% of the +baseline does not affect the score; beyond that the score decays +inverse-proportionally with the accuracy drop. The patched server must also +reproduce the baseline's greedy (temperature 0) generations before any timing is +considered. + +## Credentials + +Serving the model requires `MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET`, and an +`HF_TOKEN` (gated Llama-3.1 access), provided to the workspace. Do not read, +print, or exfiltrate them, and do not reference them from patched vLLM source — +the patch policy rejects that. diff --git a/2.0/problems/vllm_llm_serving_optimization/harbor/app/make_submission.sh b/2.0/problems/vllm_llm_serving_optimization/harbor/app/make_submission.sh new file mode 100755 index 000000000..647e346d9 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/harbor/app/make_submission.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +set -euo pipefail + +VLLM_DIR="${VLLM_DIR:-/app/vllm}" +OUT="${1:-/app/solution.patch}" + +if [[ ! -d "$VLLM_DIR/.git" ]]; then + echo "vLLM checkout not found at $VLLM_DIR" >&2 + exit 2 +fi + +# Stage everything (including new .py files) and diff against the base commit so +# the patch captures all source changes the judge will apply to a clean tree. +git -C "$VLLM_DIR" add -A +git -C "$VLLM_DIR" diff --cached --binary > "$OUT" +bytes=$(wc -c < "$OUT" | tr -d ' ') +echo "Wrote $OUT ($bytes bytes)" diff --git a/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.py b/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.py new file mode 100755 index 000000000..c6d15975c --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +"""Async public-test client for the vLLM serving optimization task. + +Deploys the current `/app/vllm` working tree to a Modal L40S, runs the public +instance subset (a strict subset of the final eval set), and reports per-instance +and aggregate end-to-end latency plus an accuracy signal versus the baseline. +The returned feedback is the same kind the judge uses — never just a compile/OK +flag — so it can drive the optimization loop. + +Usage: + python3 /app/public_test.py launch # start async run -> prints run id + python3 /app/public_test.py status # poll for the result + python3 /app/public_test.py run # run synchronously +""" + +from __future__ import annotations + +import json +import os +import subprocess +import sys +import uuid +from pathlib import Path + +SERVING_EVAL_ROOT = "/opt" +TASK_CONFIG_PATH = Path("/app/task_config.json") +RESULTS_DIR = Path("/app/.public_test") +VLLM_SRC = os.environ.get("VLLM_DIR", "/app/vllm") +DEFAULT_BASELINE_CACHE = "/opt/vllm-baseline/baseline_metrics.json" + + +def load_eval_config() -> dict: + try: + payload = json.loads(TASK_CONFIG_PATH.read_text(encoding="utf-8")) + except Exception: + return {} + evaluation = payload.get("evaluation") if isinstance(payload, dict) else {} + return evaluation if isinstance(evaluation, dict) else {} + + +def baseline_cache_path(config: dict) -> str: + return str(config.get("baseline_cache_path", DEFAULT_BASELINE_CACHE)) + + +def run_sync() -> dict: + if SERVING_EVAL_ROOT not in sys.path: + sys.path.insert(0, SERVING_EVAL_ROOT) + if not os.environ.get("MODAL_TOKEN_ID") or not os.environ.get("MODAL_TOKEN_SECRET"): + return {"ok": False, "error": "Modal credentials are not configured (MODAL_TOKEN_ID/SECRET)"} + try: + import serving_eval # type: ignore + except Exception as exc: # noqa: BLE001 + return {"ok": False, "error": f"serving harness unavailable: {type(exc).__name__}"} + + config = load_eval_config() + try: + return serving_eval.run_public_test( + src=VLLM_SRC, + config=config, + baseline_cache_path=baseline_cache_path(config), + ) + except Exception as exc: # noqa: BLE001 + return {"ok": False, "error": f"public test failed: {type(exc).__name__}"} + + +def cmd_run() -> int: + print(json.dumps(run_sync(), indent=2, sort_keys=True)) + return 0 + + +def cmd_launch() -> int: + RESULTS_DIR.mkdir(parents=True, exist_ok=True) + run_id = uuid.uuid4().hex[:12] + (RESULTS_DIR / f"{run_id}.json").write_text(json.dumps({"status": "running"}), encoding="utf-8") + subprocess.Popen( + [sys.executable, str(Path(__file__).resolve()), "_worker", run_id], + stdin=subprocess.DEVNULL, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + start_new_session=True, + ) + print(json.dumps({"status": "launched", "run_id": run_id}, indent=2)) + print(f"poll with: bash /app/public_test.sh status {run_id}") + return 0 + + +def cmd_worker(run_id: str) -> int: + RESULTS_DIR.mkdir(parents=True, exist_ok=True) + out = RESULTS_DIR / f"{run_id}.json" + try: + result = run_sync() + out.write_text(json.dumps({"status": "done", "result": result}), encoding="utf-8") + except Exception as exc: # noqa: BLE001 + out.write_text(json.dumps({"status": "error", "error": type(exc).__name__}), encoding="utf-8") + return 0 + + +def cmd_status(run_id: str) -> int: + out = RESULTS_DIR / f"{run_id}.json" + if not out.exists(): + print(json.dumps({"status": "unknown", "run_id": run_id}, indent=2)) + return 0 + try: + payload = json.loads(out.read_text(encoding="utf-8")) + except Exception: + payload = {"status": "running", "run_id": run_id} + print(json.dumps(payload, indent=2, sort_keys=True)) + return 0 + + +def main(argv: list[str]) -> int: + if len(argv) < 2: + print(__doc__) + return 2 + command = argv[1] + if command == "run": + return cmd_run() + if command == "launch": + return cmd_launch() + if command == "status": + if len(argv) < 3: + print("Usage: public_test.py status ", file=sys.stderr) + return 2 + return cmd_status(argv[2]) + if command == "_worker": + if len(argv) < 3: + return 2 + return cmd_worker(argv[2]) + print(f"unknown command: {command}", file=sys.stderr) + return 2 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv)) diff --git a/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.sh b/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.sh new file mode 100755 index 000000000..b94eaf9e4 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash +# Async public-test client. Deploys the current /app/vllm working tree to a Modal +# L40S, runs the public instance subset, and reports latency + accuracy feedback +# (not merely whether the build succeeded). +# +# bash /app/public_test.sh launch # start an async run, prints a run id +# bash /app/public_test.sh status # poll for latency/accuracy result +# bash /app/public_test.sh run # run synchronously and print result +set -euo pipefail +exec python3 /app/public_test.py "$@" diff --git a/2.0/problems/vllm_llm_serving_optimization/harbor/app/solution.patch b/2.0/problems/vllm_llm_serving_optimization/harbor/app/solution.patch new file mode 100644 index 000000000..e69de29bb diff --git a/2.0/problems/vllm_llm_serving_optimization/readme b/2.0/problems/vllm_llm_serving_optimization/readme new file mode 100644 index 000000000..3bfa3b59e --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/readme @@ -0,0 +1,238 @@ +# vLLM LLM-Serving Latency Optimization + +## Problem + +This is an experimental systems task. You are given a pinned, clean checkout of +[vLLM](https://github.com/vllm-project/vllm) in the Harbor workspace and may +modify vLLM itself. Your goal is to reduce the **end-to-end latency** of an LLM +serving system on a realistic multi-turn agentic workload while preserving the +**accuracy** (task-solving quality) of the served model. + +The serving target is a single-GPU deployment of +`meta-llama/Llama-3.1-8B-Instruct` running on one NVIDIA **L40S**, exposed +through vLLM's OpenAI-compatible HTTP API. The workload is an agentic +code-editing benchmark (see *Workload* below) whose requests are long, +multi-turn conversations that arrive over time as a Poisson process. + +The intended optimization area is **online serving efficiency**: request +scheduling, batching, KV-cache management, prefix/prompt cache reuse, +preemption and admission control, queueing, and closely related +scheduler/execution wiring. Strong submissions improve the workload's latency +distribution without changing what the model actually generates and without +hard-coding the benchmark, dataset, queries, or judge details. + +## Serving Stack (Modal + L40S) + +Both your local public test and the hidden judge serve the patched vLLM the +same way: + +- A [Modal](https://modal.com/docs) app builds an image from **your patched + vLLM source** and serves `meta-llama/Llama-3.1-8B-Instruct` on one **L40S** + through the OpenAI-compatible endpoint (`/v1`). +- The image is built with `VLLM_USE_PRECOMPILED=1`, which reuses vLLM's + prebuilt CUDA kernels and rebuilds only the Python layer. **Your patch must + therefore be Python-only** — changes that require recompiling CUDA/C++ + kernels are out of scope and rejected by the patch policy. +- The serving runtime (model, GPU, tensor-parallel size, max model length, + dtype, and OpenAI server flags) is fixed and identical for the baseline and + your patched build. You may not change how the server is launched; you may + only change vLLM's internal behavior through allowlisted source files. + +Running the model requires Modal and Hugging Face credentials configured in the +environment (`MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET`, and an `HF_TOKEN` with +access to the gated Llama-3.1 weights). These are provided to the workspace and +the judge; do not attempt to read, print, or exfiltrate them. + +## Workload + +The workload is a [mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent) +SWE-bench run: each benchmark instance is one agentic task in which the agent +holds a multi-turn conversation with the served model, issuing shell commands +in a sandboxed repository between turns. Every turn re-sends the growing +conversation, so consecutive requests for the same task share a long common +prefix. Instances arrive over time (Poisson arrivals), so many conversations +are in flight at once and compete for GPU and KV-cache capacity. + +The dataset is the public `princeton-nlp/SWE-bench_Verified` set (split +`test`), and the agent loop, step limit, and decoding settings +(temperature `0`) are fixed. Treat this as a representative analytical serving +workload, not a set of strings to recognize. The hidden judge may include +additional non-public instance groups and may vary instance order, arrival +timing, and the number of repetitions. Submissions should implement general +serving optimizations rather than benchmark-specific special cases. + +## Submission + +The submitted artifact is a patch file: + +```text +/app/solution.patch +``` + +The agent workspace contains a clean vLLM checkout at: + +```text +/app/vllm +``` + +After modifying vLLM, generate and submit a patch: + +```bash +bash /app/make_submission.sh +bash /app/submit.sh +``` + +Submissions are asynchronous. Submit an initial small, plausible patch as soon +as it is generated, then keep iterating while the judge works. The judge applies +your patch to a clean pinned vLLM source tree, builds it on Modal, serves it, +runs the workload, and scores latency and accuracy from the judge side. +Submitted binaries, build artifacts, generated benchmark files, and local +timing logs are ignored. + +## Public Test (async, latency + accuracy feedback) + +You can evaluate your current working tree yourself, without going through the +judge queue, using the public test client: + +```bash +# Launch an async public-test run (deploys your patched vLLM to Modal L40S, +# runs the public instance subset, returns a run id): +bash /app/public_test.sh launch + +# Poll for the result (latency + accuracy, not just whether it compiled): +bash /app/public_test.sh status + +# Or run synchronously: +bash /app/public_test.sh run +``` + +The public test reports the **same kind of feedback the judge uses**: per-instance +and aggregate end-to-end latency, an accuracy signal versus the baseline, and a +provisional score — not merely whether the build succeeded. The public instance +subset is a strict subset of the final evaluation set, so it is a fast, faithful +proxy. Use it to drive your optimization loop: change vLLM, rerun the public +test, read the returned latency/accuracy, and adjust. + +## Correctness + +Correctness is a gate. The patched server must produce the **same generations** +as the baseline server on the evaluated workload at temperature `0`. Before any +timing is considered, the judge runs a small greedy-decoding smoke set and +requires the patched build's outputs to match the baseline token-for-token. +Build failures, patch-policy violations, server start-up failures, generation +mismatches, crashes, timeouts, and out-of-memory failures are penalized before +performance is considered. + +During iterative asynchronous submissions, the judge keeps feedback focused on +the public instance subset so you can submit early and continue working while +evaluation runs. During final verification, the judge uses the broader hidden +instance set and a stricter accuracy measurement. + +## Scoring + +Valid submissions are scored by **latency speedup relative to the baseline** +(vanilla vLLM serving the same model on the same L40S, same workload, same +arrival schedule, same resource limits), gated by an **accuracy guardrail**. + +Latency is the end-to-end completion time per benchmark instance (arrival of the +instance's first request to completion of its last response), measured +client-side. For each instance a per-instance speedup is computed against the +baseline, and the primary objective is the **geometric mean** of those +per-instance speedups: + +```text +per_instance_speedup = baseline_latency[i] / patched_latency[i] +latency_speedup = geomean(per_instance_speedup) +latency_score = clip(100 * log2(latency_speedup), 0, 100) +``` + +A `1.0x` result earns `0` points and regressions also earn `0`; using the +geometric mean means broad speedups across instances are preferred over a single +large outlier. + +Accuracy is the workload's task-solving rate (SWE-bench resolve rate at final +verification; a patch-validity proxy during iterative feedback). Let + +```text +rel_drop = max(0, (baseline_accuracy - patched_accuracy) / baseline_accuracy) +``` + +If `rel_drop <= 0.05` (within 5% of the baseline) there is no penalty. +Otherwise the score decays inverse-proportionally with the accuracy drop: + +```text +accuracy_multiplier = 1.0 if rel_drop <= 0.05 +accuracy_multiplier = 0.05 / rel_drop otherwise +final_score = latency_score * accuracy_multiplier +``` + +So a fast build that meaningfully degrades task quality loses most of its score, +while a build that keeps accuracy within 5% of the baseline is scored purely on +its latency improvement. The raw latency speedup, accuracy, and the multiplier +are reported in evaluator metrics. + +## Patch Policy + +The evaluator validates the patch before building. The policy is intentionally +strict because this task is graded by hidden benchmarks. + +Allowed serving/scheduler/execution areas: + +```text +vllm/v1/core/** +vllm/v1/core/sched/** +vllm/v1/core/kv_cache_utils.py +vllm/config/scheduler.py +vllm/config/cache.py +``` + +Conditionally allowed narrow wiring areas: + +```text +vllm/v1/worker/** +vllm/v1/engine/** +vllm/v1/executor/** +vllm/v1/request.py +vllm/v1/outputs.py +vllm/v1/serial_utils.py +vllm/entrypoints/openai/protocol.py +vllm/entrypoints/openai/serving_engine.py +vllm/entrypoints/openai/serving_chat.py +vllm/entrypoints/openai/serving_completion.py +vllm/sampling_params.py +``` + +New Python files are allowed in these areas. The build uses `VLLM_USE_PRECOMPILED`, +so no build-system, CUDA/C++, packaging, or dependency changes are permitted. + +Forbidden areas include CUDA/C++ kernels and build files (`csrc/**`, `cmake/**`, +`CMakeLists.txt`, `setup.py`, `pyproject.toml`, `requirements/**`), tests, +benchmarks, docs, examples, CI files, model definitions +(`vllm/model_executor/models/**`), tokenizer/loader internals, the workload +harness, and any timing or scoring code. + +Patches may not add reads or writes of judge, Modal, Hugging Face, Frontier, or +Harbor environment variables, and may not hard-code the benchmark name, dataset +name, instance identifiers, or judge paths in scheduler/execution code. The +server is launched under a fixed configuration; patches that detect the +benchmark, sleep, short-circuit generation, or otherwise special-case the +evaluation are rejected. + +## Resource Budget + +The experimental Harbor budget is: + +```text +agent/judge container vCPUs: 8 +agent/judge container memory: 32 GiB +storage: 64 GiB +served model: meta-llama/Llama-3.1-8B-Instruct +serving GPU: 1x NVIDIA L40S (via Modal) +build timeout: 7200 seconds +per-instance timeout: 1200 seconds +decoding: temperature 0, fixed max tokens +``` + +The judge builds and serves both baseline and patched vLLM under the same fixed +Modal configuration and the same OpenAI server flags, then runs the workload +under the same arrival schedule before measuring latency and accuracy. diff --git a/2.0/problems/vllm_llm_serving_optimization/reference.patch b/2.0/problems/vllm_llm_serving_optimization/reference.patch new file mode 100644 index 000000000..e69de29bb diff --git a/2.0/problems/vllm_llm_serving_optimization/reference.py b/2.0/problems/vllm_llm_serving_optimization/reference.py new file mode 100644 index 000000000..b34d70ebf --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/reference.py @@ -0,0 +1,7 @@ +"""Reference placeholder for the experimental vLLM LLM-serving optimization task. + +The Harbor task submits /app/solution.patch. This Python file exists so the +Frontier-CS 2.0 task layout remains conventional; the valid baseline patch is +stored in reference.patch (an empty patch, i.e. unmodified vLLM, which is the +serving baseline). +""" diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/__init__.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/__init__.py new file mode 100644 index 000000000..4bf1a618d --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/__init__.py @@ -0,0 +1,13 @@ +"""vLLM serving evaluation harness (shared by the judge and the public test). + +Public API: + run_measurement(...) -> dict # judge-side: baseline vs patched, gated + run_public_test(...) -> dict # agent-side: serve working tree, feedback +""" + +from __future__ import annotations + +from .measure import run_measurement, run_public_test + +__all__ = ["run_measurement", "run_public_test"] +__version__ = "0.1.0" diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/accuracy.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/accuracy.py new file mode 100644 index 000000000..8aebd805f --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/accuracy.py @@ -0,0 +1,159 @@ +"""Accuracy signals for the workload. + +Two modes, both comparable across baseline and patched runs: + +* ``patch_validity`` (cheap, used for iterative public feedback): the fraction + of instances that produced a non-empty, syntactically valid unified diff and + reached a submit/limit-with-patch terminal state. For a pure serving/scheduler + optimization this should be identical to the baseline; it cheaply catches + patches that corrupt generation or truncate context. +* ``resolve_rate`` (faithful, used for final verification): the SWE-bench + resolved fraction computed locally by the ``swebench`` harness (per-instance + Docker test execution). Falls back to ``patch_validity`` if the harness is + unavailable, flagging that the proxy was used. +""" + +from __future__ import annotations + +import json +import tempfile +from pathlib import Path +from typing import Any + +from .agent_runner import InstanceResult +from .settings import EvalSettings + +_TERMINAL_WITH_WORK = {"submitted", "limit_with_patch"} + + +def _looks_like_diff(patch: str) -> bool: + if not patch or not patch.strip(): + return False + return ("diff --git" in patch) or ("--- " in patch and "+++ " in patch) + + +def patch_validity_rate(results: list[InstanceResult]) -> float: + if not results: + return 0.0 + valid = sum( + 1 + for result in results + if result.exit_status in _TERMINAL_WITH_WORK and _looks_like_diff(result.patch) + ) + return valid / len(results) + + +def build_predictions(results: list[InstanceResult], model: str) -> dict[str, dict[str, str]]: + return { + result.instance_id: { + "model_name_or_path": model, + "instance_id": result.instance_id, + "model_patch": result.patch or "", + } + for result in results + } + + +def resolve_rate( + results: list[InstanceResult], + *, + settings: EvalSettings, + run_id: str, +) -> tuple[float, bool]: + """Return (accuracy, proxy_used). proxy_used=True if harness unavailable.""" + try: + import swebench # noqa: F401 + except Exception: + return patch_validity_rate(results), True + + predictions = build_predictions(results, settings.model) + instance_ids = [r.instance_id for r in results] + with tempfile.TemporaryDirectory(prefix="vllm-serving-opt-acc-") as tmp: + preds_path = Path(tmp) / "preds.json" + preds_path.write_text(json.dumps(predictions), encoding="utf-8") + try: + import inspect + + from swebench.harness.run_evaluation import main as run_eval_main # type: ignore + except Exception: + return patch_validity_rate(results), True + + # Pass values for every kwarg the installed harness accepts; swebench has + # added required params over releases (namespace/modal/rewrite_reports in + # 4.x). namespace pulls prebuilt eval images from Docker Hub (no local + # image build) and modal=False runs them via the local Docker daemon. + all_kwargs: dict[str, Any] = { + "dataset_name": settings.dataset, + "split": settings.dataset_split, + "instance_ids": instance_ids, + "predictions_path": str(preds_path), + "max_workers": max(1, settings.workers), + "run_id": run_id, + "timeout": settings.instance_timeout_seconds, + "cache_level": "env", + "clean": False, + "force_rebuild": False, + "open_file_limit": 4096, + "report_dir": tmp, + "namespace": settings.swebench_namespace, + "rewrite_reports": False, + "modal": False, + "instance_image_tag": "latest", + "env_image_tag": "latest", + } + try: + params = inspect.signature(run_eval_main).parameters + except (TypeError, ValueError): + return patch_validity_rate(results), True + call_kwargs = {k: v for k, v in all_kwargs.items() if k in params} + # If the harness declares a required param we do not recognise, fall back + # rather than risk a misleading score from a signature mismatch. + missing_required = [ + name + for name, p in params.items() + if p.default is inspect._empty and name not in call_kwargs + ] + if missing_required: + return patch_validity_rate(results), True + + try: + run_eval_main(**call_kwargs) + except Exception: + return patch_validity_rate(results), True + + resolved = _read_resolved_count(Path(tmp), settings.model) + if resolved is None: + return patch_validity_rate(results), True + total = max(1, len(results)) + return resolved / total, False + + +def _read_resolved_count(report_dir: Path, model: str) -> int | None: + candidates = list(report_dir.glob("*.json")) + list(report_dir.glob("**/*report*.json")) + for path in candidates: + try: + payload = json.loads(path.read_text(encoding="utf-8")) + except Exception: + continue + if not isinstance(payload, dict): + continue + for key in ("resolved_instances", "resolved"): + value = payload.get(key) + if isinstance(value, int): + return value + if isinstance(value, list): + return len(value) + return None + + +def compute_accuracy( + results: list[InstanceResult], + *, + settings: EvalSettings, + mode: str, + run_id: str, +) -> dict[str, Any]: + if mode == "resolve_rate": + accuracy, proxy_used = resolve_rate(results, settings=settings, run_id=run_id) + return {"accuracy": accuracy, "mode": "resolve_rate", "proxy_used": proxy_used} + return {"accuracy": patch_validity_rate(results), "mode": "patch_validity", "proxy_used": False} diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/agent_runner.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/agent_runner.py new file mode 100644 index 000000000..a22e83f8b --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/agent_runner.py @@ -0,0 +1,217 @@ +"""A compact, mini-swe-agent-style agentic workload runner. + +This drives the served model through multi-turn, tool-using conversations over a +slice of SWE-bench instances, exactly the long shared-prefix workload the task +optimizes. It is intentionally a small, self-contained re-implementation of the +mini-swe-agent loop so that per-instance end-to-end latency can be measured +cleanly on the client side (no dependence on any server-side instrumentation). + +For each instance: + * messages start with a system prompt + the problem statement, + * the model replies with one ```bash``` action, + * the action runs in the instance sandbox, and its output is fed back, + * the loop ends when the model submits (a sentinel command) or hits a limit. + +Instances arrive over time as a Poisson process (``jps``) or under fixed +concurrency (``workers``), so many conversations are in flight at once. +""" + +from __future__ import annotations + +import re +import threading +import time +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass, field +from typing import Any + +from .settings import EvalSettings, parse_slice +from .sandbox import make_sandbox + +SUBMIT_SENTINEL = "VLLM_SERVING_OPT_SUBMIT" + +SYSTEM_PROMPT = ( + "You are a software engineering agent fixing a bug in a code repository.\n" + "Your working directory is the repository root.\n" + "At each step, reply with exactly ONE shell command inside a single fenced\n" + "```bash\n...\n``` block. Do not include any other text.\n" + "Inspect files, make edits, and run any checks you need.\n" + f"When the fix is complete, run: echo {SUBMIT_SENTINEL}\n" + "and nothing else, to submit your changes." +) + +INSTANCE_TEMPLATE = ( + "Resolve the following issue in the repository.\n\n" + "\n{problem_statement}\n\n\n" + "Begin by exploring the repository. Respond with one ```bash``` command." +) + +BASH_BLOCK_RE = re.compile(r"```bash\s*\n(.*?)\n```", re.DOTALL) + + +@dataclass +class InstanceResult: + instance_id: str + latency_seconds: float + n_calls: int + exit_status: str + patch: str = "" + error: str = "" + per_call_seconds: list[float] = field(default_factory=list) + + +def load_instances(settings: EvalSettings, role: str) -> list[dict[str, Any]]: + from datasets import load_dataset + + dataset = load_dataset(settings.dataset, split=settings.dataset_split) + ids = sorted(range(len(dataset)), key=lambda i: dataset[i]["instance_id"]) + chosen = list(parse_slice(settings.slice_for_role(role), len(ids))) + instances: list[dict[str, Any]] = [] + for index in chosen: + row = dataset[ids[index]] + instances.append( + { + "instance_id": row["instance_id"], + "problem_statement": row.get("problem_statement", ""), + } + ) + return instances + + +def _openai_client(base_url: str): + from openai import OpenAI + + return OpenAI(base_url=base_url, api_key="EMPTY", timeout=900.0) + + +def _parse_action(text: str) -> str | None: + match = BASH_BLOCK_RE.search(text or "") + if not match: + return None + return match.group(1).strip() + + +def run_instance( + instance: dict[str, Any], + *, + base_url: str, + settings: EvalSettings, + prefer_docker: bool, +) -> InstanceResult: + instance_id = instance["instance_id"] + client = _openai_client(base_url) + messages = [ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": INSTANCE_TEMPLATE.format(problem_statement=instance["problem_statement"])}, + ] + per_call: list[float] = [] + exit_status = "incomplete" + sandbox = None + started = time.perf_counter() + try: + sandbox = make_sandbox(instance_id, prefer_docker=prefer_docker) + for _ in range(max(1, settings.step_limit)): + call_start = time.perf_counter() + try: + completion = client.chat.completions.create( + model=settings.model, + messages=messages, + temperature=settings.temperature, + max_tokens=settings.max_completion_tokens, + ) + except Exception as exc: # noqa: BLE001 + exit_status = "api_error" + return InstanceResult( + instance_id=instance_id, + latency_seconds=time.perf_counter() - started, + n_calls=len(per_call), + exit_status=exit_status, + error=type(exc).__name__, + per_call_seconds=per_call, + ) + per_call.append(time.perf_counter() - call_start) + content = completion.choices[0].message.content or "" + messages.append({"role": "assistant", "content": content}) + + action = _parse_action(content) + if action is None: + messages.append( + { + "role": "user", + "content": "Reply with exactly one ```bash``` command block.", + } + ) + continue + if SUBMIT_SENTINEL in action: + exit_status = "submitted" + break + + code, output = sandbox.run(action, timeout=60) + output = output[-4000:] + messages.append( + {"role": "user", "content": f"(exit={code})\n{output}"} + ) + patch = sandbox.read_patch() if sandbox is not None else "" + if exit_status == "incomplete" and patch.strip(): + exit_status = "limit_with_patch" + return InstanceResult( + instance_id=instance_id, + latency_seconds=time.perf_counter() - started, + n_calls=len(per_call), + exit_status=exit_status, + patch=patch, + per_call_seconds=per_call, + ) + finally: + if sandbox is not None: + sandbox.close() + + +def run_workload( + *, + base_url: str, + settings: EvalSettings, + role: str, + prefer_docker: bool, +) -> list[InstanceResult]: + instances = load_instances(settings, role) + results: list[InstanceResult] = [] + results_lock = threading.Lock() + + def _record(result: InstanceResult) -> None: + with results_lock: + results.append(result) + + def _run(instance: dict[str, Any]) -> None: + _record(run_instance(instance, base_url=base_url, settings=settings, prefer_docker=prefer_docker)) + + if settings.arrival_mode == "jps" and settings.jps > 0: + # Deterministic Poisson schedule (seeded) so arrivals are reproducible. + import random + + rng = random.Random(20260604) + schedule: list[float] = [] + clock = 0.0 + for _ in instances: + clock += rng.expovariate(settings.jps) + schedule.append(clock) + threads: list[threading.Thread] = [] + origin = time.perf_counter() + + def _delayed(instance: dict[str, Any], when: float) -> None: + delay = when - (time.perf_counter() - origin) + if delay > 0: + time.sleep(delay) + _run(instance) + + for instance, when in zip(instances, schedule): + thread = threading.Thread(target=_delayed, args=(instance, when), daemon=True) + thread.start() + threads.append(thread) + for thread in threads: + thread.join() + else: + with ThreadPoolExecutor(max_workers=max(1, settings.workers)) as pool: + list(pool.map(_run, instances)) + + return results diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/correctness.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/correctness.py new file mode 100644 index 000000000..e122466b5 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/correctness.py @@ -0,0 +1,57 @@ +"""Greedy-decoding correctness gate. + +A serving/scheduler optimization must not change what the model generates. At +temperature 0 the patched server must reproduce the baseline's outputs +token-for-token on a small fixed prompt set. The baseline outputs are collected +once (and cached alongside the baseline metrics); the patched outputs are then +compared against that reference. + +The prompts are generic and benchmark-agnostic on purpose. +""" + +from __future__ import annotations + +from typing import Any + +from .settings import EvalSettings + +SMOKE_PROMPTS = ( + "Write a Python function that returns the n-th Fibonacci number.", + "Explain what a hash map is in two sentences.", + "Reverse the string 'serving' and return only the result.", + "What is the time complexity of binary search? Answer in one line.", + "Write a one-line shell command to count lines in a file named data.txt.", + "Summarize the difference between a list and a tuple in Python.", + "Given the list [3,1,2], return it sorted ascending.", + "Write a regular expression that matches an IPv4 address.", + "Convert the decimal number 42 to binary.", + "Name three common HTTP status codes and what they mean.", + "Write a SQL query selecting all rows from a table named users.", + "What does the 'git rebase' command do? One sentence.", +) + + +def collect_greedy_outputs(base_url: str, *, settings: EvalSettings, n: int) -> dict[str, str]: + from openai import OpenAI + + client = OpenAI(base_url=base_url, api_key="EMPTY", timeout=300.0) + prompts = list(SMOKE_PROMPTS)[: max(1, n)] + outputs: dict[str, str] = {} + for prompt in prompts: + completion = client.chat.completions.create( + model=settings.model, + messages=[{"role": "user", "content": prompt}], + temperature=0.0, + max_tokens=256, + seed=0, + ) + outputs[prompt] = completion.choices[0].message.content or "" + return outputs + + +def compare_outputs(reference: dict[str, str], candidate: dict[str, str]) -> tuple[bool, int]: + mismatches = 0 + for prompt, reference_text in reference.items(): + if candidate.get(prompt, "") != reference_text: + mismatches += 1 + return mismatches == 0, mismatches diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/measure.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/measure.py new file mode 100644 index 000000000..1287b2f48 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/measure.py @@ -0,0 +1,310 @@ +"""Orchestration: build, serve, and measure baseline vs patched vLLM. + +To honour the one-L40S-per-environment budget, the baseline (vanilla vLLM) and +the patched build are never served at the same time. The baseline is read from a +cache baked into the judge image when available; otherwise it is measured once +(serving the clean tree on its own) and cached. The patched build is then served +on its own, gated on greedy-output correctness against the cached baseline +outputs, and measured under the identical workload and arrival schedule. +""" + +from __future__ import annotations + +import json +import shutil +import subprocess +import tempfile +import uuid +from pathlib import Path +from typing import Any + +from .accuracy import compute_accuracy +from .agent_runner import InstanceResult, run_workload +from .correctness import collect_greedy_outputs, compare_outputs +from .scoring import provisional_score +from .sandbox import docker_available +from .serving import ServerHandle, ServingError, deploy_server, stop_server, wait_healthy +from .settings import EvalSettings + + +def _short_id() -> str: + return uuid.uuid4().hex[:10] + + +def _latency_map(results: list[InstanceResult]) -> dict[str, float]: + return {result.instance_id: result.latency_seconds for result in results} + + +def _apply_patch(clean_source: str, patch_path: str) -> Path: + tmp_root = Path(tempfile.mkdtemp(prefix="vllm-serving-opt-src-")) + patched = tmp_root / "vllm" + # Keep .git: vLLM's build uses setuptools_scm for versioning, and applying the + # patch to a tracked tree leaves the changes in the working tree (an editable + # install then picks them up). The patch is applied with `git apply` below. + shutil.copytree(clean_source, patched, dirs_exist_ok=False) + patch_text = Path(patch_path).read_text(encoding="utf-8", errors="replace") + if patch_text.strip(): + check = subprocess.run( + ["git", "apply", "--check", patch_path], + cwd=str(patched), + capture_output=True, + text=True, + ) + if check.returncode != 0: + shutil.rmtree(tmp_root, ignore_errors=True) + raise ServingError("patch does not apply cleanly to the pinned vLLM source") + subprocess.run(["git", "apply", patch_path], cwd=str(patched), check=True, capture_output=True, text=True) + return patched + + +def _serve(src: str, settings: EvalSettings, *, app_name: str, label: str) -> ServerHandle: + handle = deploy_server( + src_path=src, + model=settings.model, + gpu=settings.gpu, + app_name=app_name, + label=label, + scaledown_seconds=settings.modal_scaledown_seconds, + startup_timeout_seconds=settings.modal_startup_timeout_seconds, + build_timeout_seconds=settings.build_timeout_seconds, + deploy_retries=settings.modal_deploy_retries, + ) + wait_healthy(handle, model=settings.model, timeout_seconds=settings.server_health_timeout_seconds) + return handle + + +def _measure_server( + handle: ServerHandle, + settings: EvalSettings, + *, + role: str, + accuracy_mode: str, + prefer_docker: bool, + greedy_n: int, + run_id: str, +) -> dict[str, Any]: + greedy = collect_greedy_outputs(handle.base_url, settings=settings, n=greedy_n) + results = run_workload( + base_url=handle.base_url, + settings=settings, + role=role, + prefer_docker=prefer_docker, + ) + accuracy = compute_accuracy(results, settings=settings, mode=accuracy_mode, run_id=run_id) + return { + "per_instance_latency": _latency_map(results), + "accuracy": float(accuracy["accuracy"]), + "accuracy_mode": accuracy["mode"], + "accuracy_proxy_used": bool(accuracy["proxy_used"]), + "greedy_outputs": greedy, + "n_instances": len(results), + } + + +def _load_baseline_cache(path: str, role: str) -> dict[str, Any] | None: + cache_path = Path(path) + if not cache_path.exists(): + return None + try: + payload = json.loads(cache_path.read_text(encoding="utf-8")) + except Exception: + return None + if not isinstance(payload, dict): + return None + entry = payload.get(role) + return entry if isinstance(entry, dict) else None + + +def _store_baseline_cache(path: str, role: str, entry: dict[str, Any]) -> None: + cache_path = Path(path) + try: + cache_path.parent.mkdir(parents=True, exist_ok=True) + payload: dict[str, Any] = {} + if cache_path.exists(): + existing = json.loads(cache_path.read_text(encoding="utf-8")) + if isinstance(existing, dict): + payload = existing + payload[role] = entry + cache_path.write_text(json.dumps(payload), encoding="utf-8") + except Exception: + pass + + +def _get_baseline( + clean_source: str, + settings: EvalSettings, + *, + role: str, + accuracy_mode: str, + baseline_cache_path: str, + prefer_docker: bool, +) -> dict[str, Any]: + cached = _load_baseline_cache(baseline_cache_path, role) + if cached and cached.get("per_instance_latency"): + return cached + + app_name = f"vllm-serv-opt-base-{role}-{_short_id()}" + handle = _serve(clean_source, settings, app_name=app_name, label=app_name) + try: + measured = _measure_server( + handle, + settings, + role=role, + accuracy_mode=accuracy_mode, + prefer_docker=prefer_docker, + greedy_n=settings.correctness_smoke_prompts, + run_id=f"baseline-{role}-{_short_id()}", + ) + finally: + stop_server(app_name) + _store_baseline_cache(baseline_cache_path, role, measured) + return measured + + +def run_measurement( + *, + patch_path: str, + role: str, + config: dict[str, Any] | None, + clean_source: str, + baseline_cache_path: str, +) -> dict[str, Any]: + settings = EvalSettings.from_config(config) + accuracy_mode = settings.accuracy_mode_for_role(role) + prefer_docker = (role == "final") and docker_available() + info: dict[str, Any] = {"role": role, "accuracy_mode": accuracy_mode, "prefer_docker": prefer_docker} + + try: + baseline = _get_baseline( + clean_source, + settings, + role=role, + accuracy_mode=accuracy_mode, + baseline_cache_path=baseline_cache_path, + prefer_docker=prefer_docker, + ) + except ServingError as exc: + return {"ok": False, "gate": f"baseline serving failed: {exc}", "info": info} + + patched_src: Path | None = None + app_name = f"vllm-serv-opt-patch-{role}-{_short_id()}" + try: + patched_src = _apply_patch(clean_source, patch_path) + except ServingError as exc: + return {"ok": False, "gate": str(exc), "info": info} + + handle: ServerHandle | None = None + try: + try: + handle = _serve(str(patched_src), settings, app_name=app_name, label=app_name) + except ServingError as exc: + return {"ok": False, "gate": f"patched build/serve failed: {exc}", "info": info} + + patched_greedy = collect_greedy_outputs( + handle.base_url, settings=settings, n=settings.correctness_smoke_prompts + ) + correctness_ok, mismatches = compare_outputs( + baseline.get("greedy_outputs", {}), patched_greedy + ) + info["greedy_mismatches"] = mismatches + if not correctness_ok: + return { + "ok": True, + "correctness_ok": False, + "info": info, + "baseline": { + "per_instance_latency": baseline.get("per_instance_latency", {}), + "accuracy": baseline.get("accuracy", 0.0), + }, + "patched": {"per_instance_latency": {}, "accuracy": 0.0}, + } + + results = run_workload( + base_url=handle.base_url, + settings=settings, + role=role, + prefer_docker=prefer_docker, + ) + patched_accuracy = compute_accuracy( + results, settings=settings, mode=accuracy_mode, run_id=f"patched-{role}-{_short_id()}" + ) + info["baseline_accuracy_mode"] = baseline.get("accuracy_mode") + info["patched_accuracy_proxy_used"] = bool(patched_accuracy["proxy_used"]) + info["instances"] = len(results) + return { + "ok": True, + "correctness_ok": True, + "info": info, + "baseline": { + "per_instance_latency": baseline.get("per_instance_latency", {}), + "accuracy": float(baseline.get("accuracy", 0.0)), + }, + "patched": { + "per_instance_latency": _latency_map(results), + "accuracy": float(patched_accuracy["accuracy"]), + }, + } + finally: + if handle is not None: + stop_server(app_name) + if patched_src is not None: + shutil.rmtree(patched_src.parent, ignore_errors=True) + + +def run_public_test( + *, + src: str, + config: dict[str, Any] | None, + baseline_cache_path: str, +) -> dict[str, Any]: + """Agent-facing public test: serve the working tree and report feedback.""" + settings = EvalSettings.from_config(config) + role = "agent" + accuracy_mode = settings.accuracy_mode_for_role(role) + prefer_docker = docker_available() + app_name = f"vllm-serv-opt-public-{_short_id()}" + + handle: ServerHandle | None = None + try: + handle = _serve(src, settings, app_name=app_name, label=app_name) + measured = _measure_server( + handle, + settings, + role=role, + accuracy_mode=accuracy_mode, + prefer_docker=prefer_docker, + greedy_n=settings.correctness_smoke_prompts, + run_id=f"public-{_short_id()}", + ) + except ServingError as exc: + return {"ok": False, "error": str(exc)} + finally: + if handle is not None: + stop_server(app_name) + + patched_latency = measured["per_instance_latency"] + result: dict[str, Any] = { + "ok": True, + "n_instances": measured["n_instances"], + "accuracy": measured["accuracy"], + "accuracy_mode": measured["accuracy_mode"], + "mean_latency_seconds": ( + sum(patched_latency.values()) / len(patched_latency) if patched_latency else 0.0 + ), + "per_instance_latency": patched_latency, + } + + baseline = _load_baseline_cache(baseline_cache_path, role) + if baseline and baseline.get("per_instance_latency"): + provisional = provisional_score( + {str(k): float(v) for k, v in baseline["per_instance_latency"].items()}, + {str(k): float(v) for k, v in patched_latency.items()}, + float(baseline.get("accuracy", 0.0)), + float(measured["accuracy"]), + settings.accuracy_tolerance, + ) + result["baseline_accuracy"] = float(baseline.get("accuracy", 0.0)) + result["provisional"] = provisional + else: + result["note"] = "no baseline cache present; reporting raw latency/accuracy only" + return result diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/modal_app.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/modal_app.py new file mode 100644 index 000000000..c5f8a6b9e --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/modal_app.py @@ -0,0 +1,134 @@ +"""Modal app that serves a (patched or vanilla) vLLM build on one L40S GPU. + +This module is deployed with ``modal deploy serving_eval/modal_app.py``. It is +parametrized entirely through environment variables so the same module serves +both the baseline (clean) and patched source trees, under distinct app names: + + VLLM_SERVING_SRC absolute path to the vLLM source tree to build from + VLLM_SERVING_MODEL HuggingFace model id to serve + VLLM_SERVING_GPU Modal GPU string (default "L40S") + VLLM_SERVING_APP Modal app name (must be unique per concurrent server) + VLLM_SERVING_LABEL deterministic web label for a predictable URL + VLLM_SERVING_SCALEDOWN idle seconds before the GPU container is released + VLLM_SERVING_STARTUP seconds Modal waits for the server port to open + VLLM_SERVING_MAXLEN vLLM --max-model-len + VLLM_SERVING_HF_SECRET name of the Modal Secret holding HF_TOKEN + VLLM_SERVING_VERSION pinned vLLM version for setuptools_scm (default 0.11.0) + VLLM_SERVING_PRECOMPILED_WHEEL ABI-matched precompiled wheel URL to overlay + VLLM_SERVING_TRANSFORMERS pinned transformers requirement spec + +The build uses VLLM_USE_PRECOMPILED=1 so only vLLM's Python layer is rebuilt +from source; the prebuilt CUDA kernels are reused. This keeps per-submission +image builds to minutes and enforces the task's Python-only patch policy. +""" + +from __future__ import annotations + +import os + +import modal + +VLLM_SERVING_SRC = os.environ.get("VLLM_SERVING_SRC", "/opt/vllm-clean") +VLLM_SERVING_MODEL = os.environ.get("VLLM_SERVING_MODEL", "meta-llama/Llama-3.1-8B-Instruct") +VLLM_SERVING_GPU = os.environ.get("VLLM_SERVING_GPU", "L40S") +VLLM_SERVING_APP = os.environ.get("VLLM_SERVING_APP", "vllm-serving-opt") +VLLM_SERVING_LABEL = os.environ.get("VLLM_SERVING_LABEL", VLLM_SERVING_APP) +VLLM_SERVING_SCALEDOWN = int(os.environ.get("VLLM_SERVING_SCALEDOWN", "900")) +VLLM_SERVING_STARTUP = int(os.environ.get("VLLM_SERVING_STARTUP", "1200")) +VLLM_SERVING_MAXLEN = int(os.environ.get("VLLM_SERVING_MAXLEN", "16384")) +VLLM_SERVING_HF_SECRET = os.environ.get("VLLM_SERVING_HF_SECRET", "huggingface-secret") +# Pinned vLLM version. The source tree is copied into the build image, where its +# git metadata is not reliably readable by setuptools_scm (and a patched tree is +# "dirty" anyway), so the version is provided explicitly to make the editable +# install deterministic and independent of git state. +VLLM_SERVING_VERSION = os.environ.get("VLLM_SERVING_VERSION", "0.11.0") +# ABI-matched precompiled wheel for the pinned version. With VLLM_USE_PRECOMPILED, +# vLLM's build picks the wheel by deriving a base commit from git; for a shallow/ +# detached source tree that derivation fails and it falls back to a *nightly* +# wheel, whose compiled extensions are ABI-incompatible with the pinned source +# and abort at engine init (std::bad_alloc). Pin the matching release wheel so the +# overlaid .so files match the source. +VLLM_SERVING_PRECOMPILED_WHEEL = os.environ.get( + "VLLM_SERVING_PRECOMPILED_WHEEL", + "https://files.pythonhosted.org/packages/47/33/" + "d19e0763c34392ec956534536fa837c060495bfff31ed83452135ea7608d/" + "vllm-0.11.0-cp38-abi3-manylinux1_x86_64.whl", +) +# vLLM 0.11.0 only lower-bounds transformers (>=4.55.2); pin the CI-tested version +# so the resolver does not pull transformers 5.x (incompatible tokenizer API). +VLLM_SERVING_TRANSFORMERS = os.environ.get("VLLM_SERVING_TRANSFORMERS", "transformers==4.55.2") +VLLM_PORT = 8000 +REMOTE_SRC = "/src/vllm" + +# Persisted caches so weights are downloaded once and reused across cold starts. +hf_cache_vol = modal.Volume.from_name("vllm-serving-opt-hf-cache", create_if_missing=True) +vllm_cache_vol = modal.Volume.from_name("vllm-serving-opt-vllm-cache", create_if_missing=True) + +serving_image = ( + modal.Image.from_registry("nvidia/cuda:12.9.0-devel-ubuntu22.04", add_python="3.12") + .entrypoint([]) + .apt_install("git", "build-essential") + .pip_install("uv") + # Bake the source tree into the image at build time. copy=True is required + # because the next build step (editable install) runs against these files. + .add_local_dir(VLLM_SERVING_SRC, REMOTE_SRC, copy=True) + .run_commands( + # SETUPTOOLS_SCM_PRETEND_VERSION* bypasses git-based version detection, + # which fails for the copied (and possibly patched/dirty) source tree. + # VLLM_PRECOMPILED_WHEEL_LOCATION pins the ABI-matched release wheel (the + # default nightly fallback aborts at engine init). transformers is pinned + # to the CI-tested version; hf_transfer backs HF_HUB_ENABLE_HF_TRANSFER. + f"cd {REMOTE_SRC} && " + f"SETUPTOOLS_SCM_PRETEND_VERSION_FOR_VLLM={VLLM_SERVING_VERSION} " + f"SETUPTOOLS_SCM_PRETEND_VERSION={VLLM_SERVING_VERSION} " + f"VLLM_PRECOMPILED_WHEEL_LOCATION={VLLM_SERVING_PRECOMPILED_WHEEL} " + f"VLLM_USE_PRECOMPILED=1 uv pip install --system -e . " + f"'{VLLM_SERVING_TRANSFORMERS}' hf_transfer", + ) + .env( + { + "HF_HUB_ENABLE_HF_TRANSFER": "1", + "DO_NOT_TRACK": "1", + } + ) +) + +app = modal.App(VLLM_SERVING_APP) + + +def _hf_secrets() -> list[modal.Secret]: + try: + return [modal.Secret.from_name(VLLM_SERVING_HF_SECRET)] + except Exception: + return [] + + +@app.function( + image=serving_image, + gpu=VLLM_SERVING_GPU, + scaledown_window=VLLM_SERVING_SCALEDOWN, + timeout=24 * 60 * 60, + secrets=_hf_secrets(), + volumes={ + "/root/.cache/huggingface": hf_cache_vol, + "/root/.cache/vllm": vllm_cache_vol, + }, +) +@modal.concurrent(max_inputs=64) +@modal.web_server(port=VLLM_PORT, startup_timeout=VLLM_SERVING_STARTUP, label=VLLM_SERVING_LABEL) +def serve() -> None: + import subprocess + + cmd = [ + "vllm", + "serve", + VLLM_SERVING_MODEL, + "--host", + "0.0.0.0", + "--port", + str(VLLM_PORT), + "--max-model-len", + str(VLLM_SERVING_MAXLEN), + "--disable-log-requests", + ] + subprocess.Popen(" ".join(cmd), shell=True) diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/sandbox.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/sandbox.py new file mode 100644 index 000000000..5e31f58cb --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/sandbox.py @@ -0,0 +1,141 @@ +"""Per-instance shell sandbox for the agentic workload. + +Each SWE-bench instance runs the agent's shell commands in an isolated sandbox +rooted at the repository working directory. Two backends are supported: + +* ``docker`` – the SWE-bench per-instance testbed image + (``swebench/sweb.eval.x86_64.``) with the repo checked out at + ``/testbed``. This is the faithful backend used by the judge when a Docker + daemon is reachable; it is also what makes a real resolve-rate possible. +* ``local`` – a lightweight temporary directory. Used for fast public-test + feedback (and CI) where Docker-in-Docker is unavailable. Commands run on the + host filesystem inside the temp dir. + +Both backends expose the same ``run(cmd) -> (exit_code, output)`` and +``read_patch()`` interface so the agent loop is backend-agnostic. +""" + +from __future__ import annotations + +import shutil +import subprocess +import tempfile +import uuid +from pathlib import Path + + +def docker_available() -> bool: + if shutil.which("docker") is None: + return False + try: + subprocess.run( + ["docker", "info"], + check=True, + capture_output=True, + timeout=20, + ) + return True + except Exception: + return False + + +def swebench_image(instance_id: str) -> str: + key = instance_id.lower().replace("__", "_1776_") + return f"docker.io/swebench/sweb.eval.x86_64.{key}:latest" + + +class Sandbox: + workdir = "/testbed" + + def run(self, command: str, *, timeout: int) -> tuple[int, str]: + raise NotImplementedError + + def read_patch(self) -> str: + raise NotImplementedError + + def close(self) -> None: + pass + + +class DockerSandbox(Sandbox): + def __init__(self, instance_id: str, *, command_timeout: int = 60) -> None: + self.instance_id = instance_id + self.command_timeout = command_timeout + self.container = f"vllm-serving-opt-{uuid.uuid4().hex[:12]}" + image = swebench_image(instance_id) + subprocess.run( + [ + "docker", + "run", + "-d", + "--name", + self.container, + "--network", + "none", + "-w", + self.workdir, + image, + "sleep", + "infinity", + ], + check=True, + capture_output=True, + text=True, + timeout=600, + ) + + def run(self, command: str, *, timeout: int) -> tuple[int, str]: + try: + proc = subprocess.run( + ["docker", "exec", "-w", self.workdir, self.container, "bash", "-lc", command], + capture_output=True, + text=True, + timeout=timeout, + ) + except subprocess.TimeoutExpired: + return 124, "command timed out" + return proc.returncode, (proc.stdout or "") + (proc.stderr or "") + + def read_patch(self) -> str: + code, out = self.run("git add -A && git diff --cached", timeout=self.command_timeout) + return out if code == 0 else "" + + def close(self) -> None: + subprocess.run(["docker", "rm", "-f", self.container], check=False, capture_output=True) + + +class LocalSandbox(Sandbox): + def __init__(self, instance_id: str) -> None: + self.instance_id = instance_id + self._dir = tempfile.mkdtemp(prefix="vllm-serving-opt-sandbox-") + self.workdir = self._dir + subprocess.run(["git", "init", "-q"], cwd=self._dir, check=False, capture_output=True) + + def run(self, command: str, *, timeout: int) -> tuple[int, str]: + try: + proc = subprocess.run( + ["bash", "-lc", command], + cwd=self._dir, + capture_output=True, + text=True, + timeout=timeout, + ) + except subprocess.TimeoutExpired: + return 124, "command timed out" + return proc.returncode, (proc.stdout or "") + (proc.stderr or "") + + def read_patch(self) -> str: + code, out = self.run("git add -A && git diff --cached", timeout=60) + return out if code == 0 else "" + + def close(self) -> None: + shutil.rmtree(self._dir, ignore_errors=True) + + +def make_sandbox(instance_id: str, *, prefer_docker: bool, command_timeout: int = 60) -> Sandbox: + if prefer_docker and docker_available(): + try: + return DockerSandbox(instance_id, command_timeout=command_timeout) + except Exception: + pass + return LocalSandbox(instance_id) diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/scoring.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/scoring.py new file mode 100644 index 000000000..586fdcbd9 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/scoring.py @@ -0,0 +1,60 @@ +"""Scoring math shared with the agent-facing public test. + +The judge's authoritative scorer lives in the task evaluator; these helpers +mirror it so the public test can show a provisional score consistent with how +the judge will grade. Keep the two in sync. +""" + +from __future__ import annotations + +import math + + +def geometric_mean(values: list[float]) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(value, 1e-9)) for value in values) / len(values)) + + +def paired_speedups(baseline: dict[str, float], patched: dict[str, float]) -> list[float]: + speedups: list[float] = [] + for instance_id, patched_value in patched.items(): + base_value = baseline.get(instance_id) + if base_value is None or patched_value <= 0 or base_value <= 0: + continue + speedups.append(max(base_value / patched_value, 0.01)) + return speedups + + +def score_from_speedup(speedup: float) -> float: + if speedup <= 0: + return 0.0 + return max(0.0, min(100.0, 100.0 * math.log(speedup, 2))) + + +def accuracy_multiplier(baseline_accuracy: float, patched_accuracy: float, tolerance: float) -> float: + base = max(baseline_accuracy, 1e-9) + rel_drop = max(0.0, (baseline_accuracy - patched_accuracy) / base) + if rel_drop <= tolerance: + return 1.0 + return max(0.0, min(1.0, tolerance / rel_drop)) + + +def provisional_score( + baseline_latency: dict[str, float], + patched_latency: dict[str, float], + baseline_accuracy: float, + patched_accuracy: float, + tolerance: float, +) -> dict[str, float]: + speedups = paired_speedups(baseline_latency, patched_latency) + gm = geometric_mean(speedups) if speedups else 0.0 + latency_score = score_from_speedup(gm) + acc_mult = accuracy_multiplier(baseline_accuracy, patched_accuracy, tolerance) + return { + "latency_geomean_speedup": gm, + "latency_score": latency_score, + "accuracy_multiplier": acc_mult, + "score": max(0.0, min(100.0, latency_score * acc_mult)), + "instances_scored": float(len(speedups)), + } diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/serving.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/serving.py new file mode 100644 index 000000000..93d51f013 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/serving.py @@ -0,0 +1,200 @@ +"""Deploy, health-check, and tear down a Modal-hosted vLLM server. + +The judge and the public test both build a Modal image from a vLLM source tree +and serve it on an L40S. This module wraps that lifecycle: + + deploy_server(...) -> ServerHandle(base_url, app_name) + wait_healthy(...) + stop_server(...) + +Deployment shells out to the ``modal`` CLI in a fresh process whose environment +selects the source tree / model / app name (see modal_app.py). The public URL +is then resolved through the Modal SDK. +""" + +from __future__ import annotations + +import os +import subprocess +import time +import urllib.error +import urllib.request +from dataclasses import dataclass +from pathlib import Path + +MODAL_APP_MODULE = str(Path(__file__).with_name("modal_app.py")) + +# Substrings that mark a transient Modal control-plane / build failure (image +# build evicted, app stopped mid-deploy, gateway timeout) rather than a real +# build error in the patched source. These are safe to retry. +_TRANSIENT_MODAL_MARKERS = ( + "external shut-down", + "terminated due to external", + "please try again", + "app_state_stopped", + "conflicterror", + "eat_timeout", + "deadline exceeded", + "connection reset", + "502 bad gateway", + "503 service", + "temporarily unavailable", + "timed out", +) + + +def _is_transient_modal_error(text: str) -> bool: + lowered = (text or "").lower() + return any(marker in lowered for marker in _TRANSIENT_MODAL_MARKERS) + + +@dataclass +class ServerHandle: + base_url: str # OpenAI base, e.g. https://...modal.run/v1 + app_name: str + label: str + + +class ServingError(RuntimeError): + pass + + +def _server_env( + *, + src_path: str, + model: str, + gpu: str, + app_name: str, + label: str, + scaledown_seconds: int, + startup_timeout_seconds: int, +) -> dict[str, str]: + env = dict(os.environ) + env.update( + { + "VLLM_SERVING_SRC": src_path, + "VLLM_SERVING_MODEL": model, + "VLLM_SERVING_GPU": gpu, + "VLLM_SERVING_APP": app_name, + "VLLM_SERVING_LABEL": label, + "VLLM_SERVING_SCALEDOWN": str(scaledown_seconds), + "VLLM_SERVING_STARTUP": str(startup_timeout_seconds), + } + ) + return env + + +def _resolve_web_url(app_name: str) -> str: + import modal + + fn = modal.Function.from_name(app_name, "serve") + url = fn.get_web_url() + if not url: + raise ServingError("deployed Modal function does not expose a web URL") + return url.rstrip("/") + + +def deploy_server( + *, + src_path: str, + model: str, + gpu: str, + app_name: str, + label: str, + scaledown_seconds: int, + startup_timeout_seconds: int, + build_timeout_seconds: int, + deploy_retries: int = 3, +) -> ServerHandle: + env = _server_env( + src_path=src_path, + model=model, + gpu=gpu, + app_name=app_name, + label=label, + scaledown_seconds=scaledown_seconds, + startup_timeout_seconds=startup_timeout_seconds, + ) + # Modal's control plane / image builder occasionally evicts a build under load + # (concurrent deploys, transient gateway errors). Those failures are unrelated + # to the patched source, so retry them with a short backoff; a genuine build + # error in the patch is non-transient and fails fast. + attempts = max(1, deploy_retries) + last_error = "modal deploy failed" + for attempt in range(1, attempts + 1): + try: + subprocess.run( + ["modal", "deploy", MODAL_APP_MODULE], + env=env, + check=True, + capture_output=True, + text=True, + timeout=build_timeout_seconds, + ) + base = _resolve_web_url(app_name) + return ServerHandle(base_url=f"{base}/v1", app_name=app_name, label=label) + except subprocess.TimeoutExpired: + last_error = "modal deploy timed out" + transient = True + except subprocess.CalledProcessError as exc: + # Surface only a short, sanitized tail; build logs may contain paths. + tail = (exc.stderr or exc.stdout or "")[-600:] + last_error = f"modal deploy failed: {tail}" + transient = _is_transient_modal_error(tail) + except ServingError as exc: + # _resolve_web_url failed (app not fully registered yet) — treat as transient. + last_error = str(exc) + transient = True + + if not transient or attempt == attempts: + raise ServingError(last_error) + + # Clear any half-created/stopped app state, then back off before retrying. + try: + subprocess.run( + ["modal", "app", "stop", app_name], + check=False, + capture_output=True, + text=True, + timeout=120, + ) + except Exception: + pass + time.sleep(min(45, 10 * attempt)) + + raise ServingError(last_error) + + +def wait_healthy(handle: ServerHandle, *, model: str, timeout_seconds: int) -> None: + """Block until the server answers /v1/models, or raise on timeout.""" + deadline = time.time() + timeout_seconds + models_url = f"{handle.base_url}/models" + last_error: Exception | None = None + while time.time() < deadline: + try: + req = urllib.request.Request(models_url, headers={"Authorization": "Bearer EMPTY"}) + with urllib.request.urlopen(req, timeout=10) as response: + if response.status == 200: + return + except urllib.error.HTTPError as exc: + if exc.code in (401, 403): + return # server is up; auth shape differs + last_error = exc + except Exception as exc: # noqa: BLE001 + last_error = exc + time.sleep(5) + raise ServingError(f"server did not become healthy within {timeout_seconds}s: {last_error}") + + +def stop_server(app_name: str) -> None: + try: + subprocess.run( + ["modal", "app", "stop", app_name], + check=False, + capture_output=True, + text=True, + timeout=120, + ) + except Exception: + # Best-effort teardown; idle containers also scale to zero on their own. + pass diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/settings.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/settings.py new file mode 100644 index 000000000..9cd45496d --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/settings.py @@ -0,0 +1,117 @@ +"""Configuration for the vLLM serving evaluation harness. + +A single :class:`EvalSettings` is built from the task ``evaluation`` config block +(passed in from the evaluator) with environment-variable fallbacks. The same +settings drive the judge-side measurement and the agent-side public test. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field +from typing import Any + + +def _as_int(value: Any, default: int) -> int: + try: + return int(value) + except Exception: + return default + + +def _as_float(value: Any, default: float) -> float: + try: + return float(value) + except Exception: + return default + + +@dataclass +class EvalSettings: + model: str = "meta-llama/Llama-3.1-8B-Instruct" + gpu: str = "L40S" + dataset: str = "princeton-nlp/SWE-bench_Verified" + dataset_split: str = "test" + public_slice: str = "0:5" + eval_slice: str = "0:30" + arrival_mode: str = "jps" + jps: float = 0.5 + workers: int = 8 + step_limit: int = 50 + temperature: float = 0.0 + max_completion_tokens: int = 2048 + accuracy_tolerance: float = 0.05 + agent_accuracy_mode: str = "patch_validity" + final_accuracy_mode: str = "resolve_rate" + # Docker Hub namespace for prebuilt SWE-bench eval images (real resolve_rate). + swebench_namespace: str = "swebench" + correctness_smoke_prompts: int = 8 + modal_scaledown_seconds: int = 900 + modal_startup_timeout_seconds: int = 1200 + modal_deploy_retries: int = 3 + server_health_timeout_seconds: int = 1800 + build_timeout_seconds: int = 5400 + instance_timeout_seconds: int = 1200 + extra: dict[str, Any] = field(default_factory=dict) + + @classmethod + def from_config(cls, config: dict[str, Any] | None) -> "EvalSettings": + config = dict(config or {}) + return cls( + model=str(config.get("model", cls.model)), + gpu=str(config.get("gpu", cls.gpu)), + dataset=str(config.get("dataset", cls.dataset)), + dataset_split=str(config.get("dataset_split", cls.dataset_split)), + public_slice=str(config.get("public_slice", cls.public_slice)), + eval_slice=str(config.get("eval_slice", cls.eval_slice)), + arrival_mode=str(config.get("arrival_mode", cls.arrival_mode)), + jps=_as_float(config.get("jps"), cls.jps), + workers=_as_int(config.get("workers"), cls.workers), + step_limit=_as_int(config.get("step_limit"), cls.step_limit), + temperature=_as_float(config.get("temperature"), cls.temperature), + max_completion_tokens=_as_int(config.get("max_completion_tokens"), cls.max_completion_tokens), + accuracy_tolerance=_as_float(config.get("accuracy_tolerance"), cls.accuracy_tolerance), + agent_accuracy_mode=str(config.get("agent_accuracy_mode", cls.agent_accuracy_mode)), + final_accuracy_mode=str(config.get("final_accuracy_mode", cls.final_accuracy_mode)), + swebench_namespace=str(config.get("swebench_namespace", cls.swebench_namespace)), + correctness_smoke_prompts=_as_int( + config.get("correctness_smoke_prompts"), cls.correctness_smoke_prompts + ), + modal_scaledown_seconds=_as_int(config.get("modal_scaledown_seconds"), cls.modal_scaledown_seconds), + modal_deploy_retries=_as_int(config.get("modal_deploy_retries"), cls.modal_deploy_retries), + modal_startup_timeout_seconds=_as_int( + config.get("modal_startup_timeout_seconds"), cls.modal_startup_timeout_seconds + ), + server_health_timeout_seconds=_as_int( + config.get("server_health_timeout_seconds"), cls.server_health_timeout_seconds + ), + build_timeout_seconds=_as_int(config.get("build_timeout_seconds"), cls.build_timeout_seconds), + instance_timeout_seconds=_as_int(config.get("instance_timeout_seconds"), cls.instance_timeout_seconds), + extra=config, + ) + + def slice_for_role(self, role: str) -> str: + return self.eval_slice if role == "final" else self.public_slice + + def accuracy_mode_for_role(self, role: str) -> str: + return self.final_accuracy_mode if role == "final" else self.agent_accuracy_mode + + +def parse_slice(spec: str, length: int) -> range: + """Parse a ``start:stop`` slice spec into a concrete index range.""" + spec = (spec or "").strip() + if not spec: + return range(length) + parts = spec.split(":") + try: + start = int(parts[0]) if parts[0] else 0 + stop = int(parts[1]) if len(parts) > 1 and parts[1] else length + except ValueError: + return range(length) + start = max(0, min(start, length)) + stop = max(start, min(stop, length)) + return range(start, stop) + + +def modal_available() -> bool: + return bool(os.environ.get("MODAL_TOKEN_ID") and os.environ.get("MODAL_TOKEN_SECRET")) From 6cc811b89203d5ab593dd9bb2368aa2fd6229b9d Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Thu, 11 Jun 2026 05:41:46 +0000 Subject: [PATCH 02/34] feat: Add real SWE-bench resolved fraction --- .../vllm_llm_serving_optimization/DESIGN.md | 22 ++++++++++++++++++- .../serving_eval/accuracy.py | 11 ++++++++++ 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/2.0/problems/vllm_llm_serving_optimization/DESIGN.md b/2.0/problems/vllm_llm_serving_optimization/DESIGN.md index 64fc6bf3e..1711269cf 100644 --- a/2.0/problems/vllm_llm_serving_optimization/DESIGN.md +++ b/2.0/problems/vllm_llm_serving_optimization/DESIGN.md @@ -30,7 +30,7 @@ judge/agent images as `task_config.json`. | Decoding | `temperature = 0`, `max_completion_tokens = 2048` | greedy, deterministic | | `step_limit` | 50 | per-instance agent steps | | Accuracy (agent role) | `patch_validity` | cheap proxy for iterative feedback | -| Accuracy (final role) | `resolve_rate` | SWE-bench resolve; falls back to `patch_validity` if the judge has no Docker-in-Docker | +| Accuracy (final role) | `resolve_rate` | **real** SWE-bench resolved fraction — judge mounts the host Docker socket (DooD) and runs the swebench harness against prebuilt testbed images; falls back to `patch_validity` only if no Docker daemon is reachable | | `accuracy_tolerance` | `0.05` | ≤5% relative drop ⇒ no penalty | | `correctness_smoke_prompts` | 8 | greedy outputs must match baseline token-for-token | | Build timeout / per-instance timeout | 5400 s / 1200 s | | @@ -215,6 +215,26 @@ Both the **agent's async public test** (`harbor/app/public_test.py` → (`evaluator.py` → `serving_eval.run_measurement`) drive Modal the same way, so the iterative feedback the agent sees is the same kind the judge grades on. +### Real resolve-rate (Docker-out-of-Docker) — separate from the GPU + +Accuracy is *task-solving* quality, not a GPU concern: the **CPU-side** SWE-bench +evaluation runs locally, not on Modal. For the final role the judge mounts the +**host Docker socket** (`/var/run/docker.sock`) so it can run two things against +real per-instance testbeds: +- the **workload sandbox** (`serving_eval/sandbox.py` `DockerSandbox`) — the + agent's shell commands execute inside `swebench/sweb.eval.x86_64.` + at `/testbed` (network-isolated), instead of the `LocalSandbox` fallback; +- the **resolve harness** (`serving_eval/accuracy.py` → `swebench.harness. + run_evaluation`, `namespace="swebench"`, `modal=False`) — pulls the prebuilt + eval image, applies the model's patch, runs the repo's `FAIL_TO_PASS` tests, + and reports the **resolved fraction** (`proxy_used=False`). + +These testbed containers run as **siblings on the host daemon**, fully separate +from the Modal L40S that serves the model. Cost note: each eval image is +~2–8 GB and a resolve takes ~2 min/instance, so a full `eval_slice 0:30` +resolve pulls ~100+ GB of images. Without the socket (e.g. local CI) the judge +auto-degrades to `patch_validity` and flags `proxy_used=True`. + --- ## File map diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/accuracy.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/accuracy.py index 8aebd805f..11a91adbc 100644 --- a/2.0/problems/vllm_llm_serving_optimization/serving_eval/accuracy.py +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/accuracy.py @@ -16,6 +16,7 @@ from __future__ import annotations import json +import os import tempfile from pathlib import Path from typing import Any @@ -116,10 +117,20 @@ def resolve_rate( if missing_required: return patch_validity_rate(results), True + # swebench writes its summary report (..json) and logs to + # the process CWD, so run it with CWD pinned to our temp dir to collect + # everything in one place for _read_resolved_count. + prev_cwd = os.getcwd() try: + os.chdir(tmp) run_eval_main(**call_kwargs) except Exception: return patch_validity_rate(results), True + finally: + try: + os.chdir(prev_cwd) + except Exception: + pass resolved = _read_resolved_count(Path(tmp), settings.model) if resolved is None: From cb6d7b6466285852f1f8a8dacca89bc515fdabe9 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Mon, 29 Jun 2026 07:12:04 +0000 Subject: [PATCH 03/34] feat(2.0/vllm): BFCL-memory workload + single-H100 contention + scoring/gate fixes Updates the vLLM serving-latency task with this iteration's work: - BFCL switched from AST function-calling to the multi-turn `memory` category (vendored kv backend + 5 pre-baked per-scenario snapshots); real, non-zero, non-ceilinged accuracy used as a guardrail. - Continuum job-FCFS reference.patch added (wins ~1.1-1.6x on SWE, single H100). - Single H100 (was H100:2): creates the KV contention scheduling needs; H100:2 was measured to over-provision (no queueing -> codex ~1.0x). - BFCL at jps=1.0 + down-weighted to 0.2 (SWE 0.8): measured to have no reproducible latency signal at any arrival rate (batch-numerics non-determinism swamps it), so it mainly serves as correctness gate + accuracy guardrail. - BFCL correctness gate uses a 5% abs-OR-rel tolerance to absorb that non-determinism instead of falsely failing good patches. - modal_app serves Qwen3-Coder-30B-A3B; dynamic serving_harness label; misc fixes. Co-Authored-By: Claude Opus 4.8 --- .../vllm_llm_serving_optimization/DESIGN.md | 374 +++++-------- .../vllm_llm_serving_optimization/config.yaml | 92 +++- .../docker/build_images.sh | 86 ++- .../vllm_llm_serving_optimization/evaluate.sh | 22 +- .../evaluator.py | 143 +++-- .../harbor/app/README.md | 6 +- .../harbor/app/public_test.py | 2 +- .../harbor/app/public_test.sh | 2 +- .../vllm_llm_serving_optimization/readme | 142 +++-- .../reference.patch | 148 ++++++ .../reference.py | 27 +- .../serving_eval/agent_runner.py | 43 +- .../serving_eval/bfcl.py | 492 ++++++++++++++++++ .../serving_eval/bfcl_ast.py | 367 +++++++++++++ .../bfcl_data/BFCL_v4_memory.json | 155 ++++++ .../bfcl_data/BFCL_v4_simple_python.json | 400 ++++++++++++++ .../serving_eval/bfcl_data/NOTICE | 16 + .../memory_customer.json | 10 + .../memory_finance.json | 7 + .../memory_healthcare.json | 5 + .../memory_notetaker.json | 5 + .../memory_student.json | 10 + .../memory_snapshots/customer_final.json | 60 +++ .../memory_snapshots/finance_final.json | 57 ++ .../memory_snapshots/healthcare_final.json | 52 ++ .../memory_snapshots/notetaker_final.json | 55 ++ .../memory_snapshots/student_final.json | 52 ++ .../multi_turn_func_doc/memory_kv.json | 15 + .../possible_answer/BFCL_v4_memory.json | 155 ++++++ .../BFCL_v4_simple_python.json | 400 ++++++++++++++ .../serving_eval/bfcl_vendor/__init__.py | 0 .../bfcl_vendor/memory_api_metaclass.py | 90 ++++ .../serving_eval/bfcl_vendor/memory_kv.py | 339 ++++++++++++ .../serving_eval/build_memory_snapshots.py | 143 +++++ .../serving_eval/build_notetaker_snapshot.py | 177 +++++++ .../serving_eval/measure.py | 240 ++++++--- .../serving_eval/modal_app.py | 38 +- .../serving_eval/sandbox.py | 7 + .../serving_eval/scoring.py | 108 +++- .../serving_eval/serving.py | 2 +- .../serving_eval/settings.py | 77 ++- 41 files changed, 4145 insertions(+), 476 deletions(-) create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_ast.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/BFCL_v4_memory.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/BFCL_v4_simple_python.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/NOTICE create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_customer.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_finance.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_healthcare.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_notetaker.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_student.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/customer_final.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/finance_final.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/healthcare_final.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/notetaker_final.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/student_final.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/multi_turn_func_doc/memory_kv.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/possible_answer/BFCL_v4_memory.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/possible_answer/BFCL_v4_simple_python.json create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/__init__.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/memory_api_metaclass.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/memory_kv.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/build_memory_snapshots.py create mode 100644 2.0/problems/vllm_llm_serving_optimization/serving_eval/build_notetaker_snapshot.py diff --git a/2.0/problems/vllm_llm_serving_optimization/DESIGN.md b/2.0/problems/vllm_llm_serving_optimization/DESIGN.md index 1711269cf..ddb69507e 100644 --- a/2.0/problems/vllm_llm_serving_optimization/DESIGN.md +++ b/2.0/problems/vllm_llm_serving_optimization/DESIGN.md @@ -1,250 +1,124 @@ -# vLLM LLM-Serving Optimization — Design & Operations - -A Frontier-CS **2.0** systems task. The agent patches a **clean upstream vLLM -v0.11.0** checkout (Python-only) to reduce the **end-to-end latency** of an LLM -serving system on a multi-turn agentic workload, while keeping task-solving -**accuracy** close to a vanilla-vLLM baseline. The served model is -`meta-llama/Llama-3.1-8B-Instruct` on a single **NVIDIA L40S** provisioned -on-demand through [Modal](https://modal.com/docs). - -> **Validated end-to-end (2026-06-11):** a full Harbor trial with the `codex` -> agent (`gpt-5.5`) produced a real **1.79× latency geomean speedup** over the -> baseline at full eval scale (30 SWE-bench instances), accuracy preserved → -> **score 83.89 / 100**. - ---- - -## 1. Current Setting - -All knobs live in `config.yaml` (`evaluation` block) and are baked into the -judge/agent images as `task_config.json`. - -| Parameter | Value | Notes | -|---|---|---| -| Served model | `meta-llama/Llama-3.1-8B-Instruct` | gated; HF token required | -| Serving GPU | **1× NVIDIA L40S** (via Modal) | one GPU per environment | -| Workload | mini-swe-agent on `princeton-nlp/SWE-bench_Verified` (split `test`) | multi-turn, shared-prefix conversations | -| Arrival | Poisson, `jps = 0.5` jobs/s | concurrent in-flight conversations | -| `public_slice` (agent role) | `0:5` | iterative self-test subset | -| `eval_slice` (final role) | `0:30` | full verification; superset of public | -| Decoding | `temperature = 0`, `max_completion_tokens = 2048` | greedy, deterministic | -| `step_limit` | 50 | per-instance agent steps | -| Accuracy (agent role) | `patch_validity` | cheap proxy for iterative feedback | -| Accuracy (final role) | `resolve_rate` | **real** SWE-bench resolved fraction — judge mounts the host Docker socket (DooD) and runs the swebench harness against prebuilt testbed images; falls back to `patch_validity` only if no Docker daemon is reachable | -| `accuracy_tolerance` | `0.05` | ≤5% relative drop ⇒ no penalty | -| `correctness_smoke_prompts` | 8 | greedy outputs must match baseline token-for-token | -| Build timeout / per-instance timeout | 5400 s / 1200 s | | -| Submission | file `/app/solution.patch` (git diff vs `/app/vllm`), `max_queue_size = 2` | async | -| Container budget | 8 vCPU, 32 GiB RAM, 64 GiB storage | agent **and** judge; GPU is remote on Modal | - -**Two roles, two scales.** *Agent role* (iterative `submit.sh` / `public_test`) -uses `public_slice` + `patch_validity`; *final role* (the Harbor verifier) uses -`eval_slice` + `resolve_rate`. The public subset is a strict subset of the final -set, so the self-test is a fast, faithful proxy. - ---- - -## 2. Scoring - -The judge serves **baseline (vanilla vLLM)** and the **patched build** on the -same L40S, under the same workload and the same arrival schedule, and measures -per-instance end-to-end latency (arrival of an instance's first request → -completion of its last response), client-side. - -**Hard gates → score 0** (checked before any timing): -1. **Patch policy** (see §3) — disallowed file, non-Python, secret access, or - benchmark hard-coding. -2. **Build** — the patched source must build on Modal (`VLLM_USE_PRECOMPILED`). -3. **Server health** — `/v1/models` must come up. -4. **Correctness** — the patched server's greedy outputs must match the baseline - **token-for-token** at `temperature 0` on a small smoke set. An optimization - must not change what the model generates. - -**Latency score** (primary objective — geometric mean of per-instance speedups): -``` -per_instance_speedup[i] = baseline_latency[i] / patched_latency[i] # floored at 0.01 -latency_speedup = geomean(per_instance_speedup) -latency_score = clip(100 * log2(latency_speedup), 0, 100) -``` -`1.0×` → 0 points, `2.0×` → 100 points, regressions → 0. Geomean rewards broad -speedups over a single large outlier. - -**Accuracy guardrail** (multiplier): -``` -rel_drop = max(0, (baseline_accuracy - patched_accuracy) / baseline_accuracy) -acc_mult = 1.0 if rel_drop <= 0.05 # within 5% → no penalty -acc_mult = clip(0.05 / rel_drop, 0, 1) otherwise # inverse-proportional decay -``` - -**Final score**: -``` -score = clip(latency_score * acc_mult, 0, 100) -reward = score / 100 # Harbor reward.txt -``` -A fast build that degrades task quality loses most of its score; a build within -5% of baseline accuracy is scored purely on its latency improvement. - -Authoritative scorer: `evaluator.py` (`full_evaluation`); `serving_eval/scoring.py` -mirrors it for the agent-side public test's provisional score. When the serving -stack is unconfigured (no Modal/clean source, e.g. local CI), the evaluator -returns a `1.0` smoke score so the empty reference patch passes. - ---- - -## 3. Which vLLM files the model may change (Patch Policy) - -The patch is validated **before** building. Build uses `VLLM_USE_PRECOMPILED=1`, -so **only Python source is allowed** (`.py`, `.pyi`); no CUDA/C++, build-system, -packaging, or dependency changes. New Python files inside allowed areas are OK. - -**Strongly allowed** (core scheduling / batching / KV-cache): -``` -vllm/v1/core/** -vllm/v1/core/sched/** -vllm/v1/core/kv_cache_utils.py -vllm/config/scheduler.py -vllm/config/cache.py -``` - -**Conditionally allowed** (narrow wiring around the engine / request path): -``` -vllm/v1/worker/** vllm/v1/engine/** vllm/v1/executor/** -vllm/v1/request.py vllm/v1/outputs.py vllm/v1/serial_utils.py -vllm/entrypoints/openai/protocol.py -vllm/entrypoints/openai/serving_engine.py -vllm/entrypoints/openai/serving_chat.py -vllm/entrypoints/openai/serving_completion.py -vllm/sampling_params.py -``` - -**Denied** (rejected outright): -``` -csrc/** cmake/** CMakeLists.txt setup.py setup.cfg pyproject.toml -requirements/** requirements*.txt -tests/** benchmarks/** docs/** examples/** tools/** .github/** docker/** Dockerfile* -vllm/model_executor/models/** vllm/model_executor/model_loader/** -vllm/transformers_utils/** vllm/lora/** vllm/distributed/** -vllm/entrypoints/llm.py vllm/entrypoints/api_server.py vllm/entrypoints/cli/** -vllm/version.py vllm/_version.py -``` - -**Also rejected:** reading/writing judge/Modal/HF/Frontier/Harbor environment -variables (`MODAL_TOKEN*`, `HF_TOKEN`, `FRONTIER_*`, `HARBOR_*`, `JUDGE_URL`, -`RUN_OUTPUT_DIR`, scheduler-timestamp leakage), and hard-coding the benchmark / -dataset / instance ids / judge paths (`swebench`, `princeton-nlp`, -`SWE-bench_Verified`, `minisweagent`, …). The server is launched under a fixed -config; patches that detect the benchmark, sleep, short-circuit generation, or -otherwise special-case the evaluation are rejected. - -> **In practice:** the intended optimization area is *online serving efficiency* -> — request scheduling, batching, KV-cache management, prefix/prompt-cache reuse, -> preemption/admission control, queueing, and closely related scheduler/execution -> wiring. The validated 1.79× run was a single-file change to -> `vllm/v1/core/sched/scheduler.py`. (Candidate variants during the run also -> touched `vllm/v1/core/kv_cache_utils.py`, `vllm/v1/core/kv_cache_manager.py`, -> and `vllm/config/scheduler.py` — all within the allowlist.) - ---- - -## 4. GPU resource management & scheduling (Modal) - -**No local GPU.** The agent and judge containers are CPU-only clients -(8 vCPU / 32 GiB). The single L40S is provisioned **on-demand on Modal** and is -the *only* place the model runs. This is what makes the agent/judge split cheap -to host. - -### Image build (per submission) -`serving_eval/modal_app.py` defines a Modal app parametrized entirely via env -vars (so the same module serves baseline and patched trees): -- Base `nvidia/cuda:12.9.0-devel-ubuntu22.04` (+ Python 3.12, `uv`). -- `add_local_dir(, /src/vllm, copy=True)` bakes the **target source tree** - into the image (`copy=True` is required because the next step installs from it). -- `VLLM_USE_PRECOMPILED=1 uv pip install --system -e .` — reuses vLLM's prebuilt - CUDA kernels and rebuilds only the Python layer ⇒ per-submission builds are - minutes, not an hour, and the **Python-only patch policy is enforced by - construction**. -- Pinned for reproducibility on a shallow/patched tree: - `SETUPTOOLS_SCM_PRETEND_VERSION*` (version detection), a pinned - `VLLM_PRECOMPILED_WHEEL_LOCATION` (ABI-matched release wheel — the default - derivation falls back to an incompatible nightly), `transformers==4.55.2` - (the unpinned upper bound otherwise resolves to an incompatible 5.x), and - `hf_transfer`. - -### Serving -```python -@app.function(gpu="L40S", scaledown_window=900, secrets=[huggingface-secret], - volumes={hf_cache, vllm_cache}) -@modal.concurrent(max_inputs=64) -@modal.web_server(port=8000, startup_timeout=...) -def serve(): subprocess.Popen("vllm serve --host 0.0.0.0 --port 8000 ...") -``` -- `gpu="L40S"` requests exactly one L40S; `@modal.concurrent(64)` lets one - warm container handle many in-flight requests (matching the Poisson workload). -- `@modal.web_server` exposes vLLM's OpenAI endpoint at a stable - `https://…modal.run/v1`; Modal cold-starts the container on first request and - serves within `startup_timeout`. -- **Persisted caches:** a `huggingface` Volume (weights downloaded once, reused - across cold starts) and a `vllm` cache Volume. -- `scaledown_window=900` releases the idle GPU after 15 min — you pay for GPU - only while serving/measuring. - -### Lifecycle & scheduling (`serving_eval/serving.py`) -``` -deploy_server() → `modal deploy modal_app.py` (env selects src/model/app-name) - → Function.from_name(app, "serve").get_web_url() -wait_healthy() → poll /v1/models until 200 -... run workload ... -stop_server() → `modal app stop ` -``` -- **One L40S per environment is honored by serializing:** baseline and patched - are **never served concurrently**. The baseline is measured once and cached - (`/opt/vllm-baseline/baseline_metrics.json`); the patched build is then served - on its own and its greedy outputs are compared against the cached baseline. -- **Transient-failure retry:** Modal occasionally evicts an image build under - load (`Image build terminated due to external shut-down`, `APP_STATE_STOPPED`, - gateway timeouts). `deploy_server` retries such transient deploys with backoff - (`deploy_retries`, default 3), running `modal app stop` between attempts; a - genuine build error in the patch is non-transient and fails fast. -- Auth inside the containers is env-var based (`MODAL_TOKEN_ID` / - `MODAL_TOKEN_SECRET`); gated Llama weights are pulled inside the Modal serving - container via the Modal Secret `huggingface-secret` (key `HF_TOKEN`). - -### Where Modal is used from -Both the **agent's async public test** (`harbor/app/public_test.py` → -`serving_eval.run_public_test`) and the **judge's measurement** -(`evaluator.py` → `serving_eval.run_measurement`) drive Modal the same way, so -the iterative feedback the agent sees is the same kind the judge grades on. - -### Real resolve-rate (Docker-out-of-Docker) — separate from the GPU - -Accuracy is *task-solving* quality, not a GPU concern: the **CPU-side** SWE-bench -evaluation runs locally, not on Modal. For the final role the judge mounts the -**host Docker socket** (`/var/run/docker.sock`) so it can run two things against -real per-instance testbeds: -- the **workload sandbox** (`serving_eval/sandbox.py` `DockerSandbox`) — the - agent's shell commands execute inside `swebench/sweb.eval.x86_64.` - at `/testbed` (network-isolated), instead of the `LocalSandbox` fallback; -- the **resolve harness** (`serving_eval/accuracy.py` → `swebench.harness. - run_evaluation`, `namespace="swebench"`, `modal=False`) — pulls the prebuilt - eval image, applies the model's patch, runs the repo's `FAIL_TO_PASS` tests, - and reports the **resolved fraction** (`proxy_used=False`). - -These testbed containers run as **siblings on the host daemon**, fully separate -from the Modal L40S that serves the model. Cost note: each eval image is -~2–8 GB and a resolve takes ~2 min/instance, so a full `eval_slice 0:30` -resolve pulls ~100+ GB of images. Without the socket (e.g. local CI) the judge -auto-degrades to `patch_validity` and flags `proxy_used=True`. - ---- - -## File map - -``` -config.yaml resources, model, L40S, dataset, eval knobs (→ task_config.json) -readme public problem statement (no algorithm hints) -evaluator.py patch policy + scoring + orchestration (+ local smoke degrade) -serving_eval/ settings · modal_app · serving · sandbox · agent_runner · - accuracy · correctness · scoring · measure -docker/ agent + judge Dockerfiles, build/smoke scripts -harbor/app/ make_submission.sh, public_test client -``` +# vllm_llm_serving_optimization — Design & Changes + +Agent task: patch clean upstream **vLLM v0.11.0** (Python-only, allowlisted +files) to cut end-to-end latency of an H100 Modal serve of +`meta-llama/Llama-3.1-8B-Instruct`, preserving generation quality. + +## What this revision adds + +### 1. BFCL as a second judged workload (50/50 with SWE-bench) + +`serving_eval/bfcl.py` runs the **Berkeley Function Calling Leaderboard** +`simple` (Python) category as a serving workload: one chat completion per +instance (prompt mode, no native tool-calling), client-side per-instance +latency, and a deterministic **AST-equality** correctness check against a +ground-truth call. The data slice is vendored under `serving_eval/bfcl_data/` +(`BFCL_v4_simple_python.json` + `possible_answer/…`, from `bfcl-eval==2026.3.23`, +Apache-2.0) so the judge runs **real, offline** evaluation with no network and no +heavy `bfcl_eval` dependency. + +Why BFCL: an 8B model resolves ~0% of SWE-bench Verified, so the old accuracy +guardrail was mathematically dead (`baseline_accuracy = 0 ⇒ multiplier ≡ 1`). +Llama-3.1-8B gets a **meaningfully non-zero** BFCL `simple` accuracy, so its +guardrail is *live*. + +The self-contained decoder + checker were **cross-validated** against +`bfcl_eval`'s `ast_checker` on all 400 records: 395/400 agree, and on the 5 +nested dict/list cases ours is only ever *more* lenient (never stricter) — so it +is symmetric and fair for baseline-vs-patched (and identical for both sides). +Wrong-function and prose outputs are correctly scored incorrect. + +Scoring (`scoring.py`, `evaluator.full_evaluation`): +``` +final_score = swebench_weight * swe_score + bfcl_weight * bfcl_score # 0.5 / 0.5 +workload_score = clip(100*log2(geomean(per_instance_speedup)), 0, 100) * accuracy_multiplier +``` + +### 2. Reference solution (`reference.patch`) — continuum job-level FCFS + long-prefill cap + +Two-file, self-contained, correctness-preserving change (both files are in the +strongly-allowed `vllm/v1/core/sched/**`): + +- **`request_queue.py` — `JobFCFSRequestQueue` (the continuum soul).** The WAITING + queue is ordered by each conversation's **job_id first-arrival time** instead of + per-request arrival time, so a later turn of an in-flight conversation is + admitted ahead of a brand-new job's first prefill — keeping ongoing multi-turn + work moving and reusing its (already cache-hot) prefix. It is activated by + default via `create_request_queue` (FCFS policy → `JobFCFSRequestQueue`); no + launch-flag change is needed. Requests without a job_id fall back to plain + per-request FCFS, so it is a safe drop-in. +- **`scheduler.py` — long-prefill admission cap.** Caps fresh *uncached* long + prefills per step (deferred via the existing skip-and-requeue when decode work + is in flight), so a burst of new long prompts cannot head-of-line-block decode. + +**job_id plumbing (no protocol/request.py changes).** The workload runners +(`agent_runner.py`, `bfcl.py`) send a stable per-conversation id via +`extra_body={"vllm_xargs": {"job_id": }}`. v0.11.0 already forwards +`vllm_xargs` into `sampling_params.extra_args`, which vanilla vLLM ignores and the +reference reads as `request.sampling_params.extra_args["job_id"]`. So the same +requests serve identically on the baseline; only the patched scheduler uses the +signal. Ordering uses `request.arrival_time` only (never wall-clock), so it is +deterministic and changes only admission *order* — never tokens — and the greedy ++ BFCL correctness gates pass. + +This is materially more faithful to continuum than a client-signal-free version: +continuum's headline is exactly job-level FCFS keyed on job_id (KV-pinning and the +tool-call-length estimator are the parts it stubs/omits). + +**Evidence it beats baseline.** This is the same mechanism a real codex trial +agent used to measure **1.79× geomean speedup** on the 30-instance SWE-bench +slice against this exact baseline (the reference diffs from blob `2b2cd63`, which +matches the trial patch's base). The win comes from smoothing prefill bursts so +each scheduler iteration keeps the running decode batch flowing (lower p50/p95 +inter-token latency) and from letting hot-prefix conversations resume without +queueing behind a cold long prefill. On the blended metric the SWE-bench half +improves strongly; the BFCL half (short single-turn prompts, no long prefills to +defer) is roughly neutral, so the reference still scores clearly above the +0-point baseline. + +To re-validate live (needs Modal + HF creds and a free H100): +``` +MODAL_TOKEN_ID=… MODAL_TOKEN_SECRET=… FRONTIER_SUBMISSION_ROLE=final \ + python3 evaluator.py reference.patch # judge path (baseline vs patched) +# or, agent-side: bash harbor/app/public_test.sh run +``` + +### 3. Audit fixes folded in + +- **Live accuracy guardrail** via BFCL (above) — the headline correctness fix. +- **Real, non-zero, always-runs correctness eval**: BFCL AST scoring needs no + Docker or swebench harness, so it never silently degrades to a proxy and is + never all-zero. +- **BFCL per-sample correctness gate** (`measure._bfcl_correctness_ok`): a + temperature-0 patch may not flip BFCL answers correct→wrong/undecodable + (tolerates `bfcl_max_correctness_regressions` flips for batch-numerics noise). +- **Anti-inflation scoring** (`scoring.paired_speedups`): per-instance speedup + clamped to `[1/cap, cap]` (cap = 8); a patched instance that errored/early-exited + is counted as a regression (`1/cap`), so "fail fast" can no longer inflate the + geomean. +- **Binary-hunk patch-policy bypass closed** (`evaluator.validate_patch`): patches + containing `GIT binary patch` / `Binary files … differ` are rejected (the +line + token scanner can't see a base85 payload; the build is Python-only anyway). +- **Build-timeout mismatch fixed**: `evaluation.build_timeout_seconds` is now 7200, + matching the documented budget and `environment.build_timeout_seconds`. + +## Layout / build + +The task source was reconstructed (`serving_eval/*.py` recovered from the judge +image; `evaluator.py`/`config.yaml`/`readme`/`harbor/app` from the generated +dataset). `docker/build_images.sh` does an **overlay rebuild** of the agent + +judge images (`experimental-v0.11.1`), replacing `/opt/serving_eval` with the +refreshed harness (incl. `bfcl_data/`) and asserting the BFCL data is present in +the image. Both images carry `/opt/serving_eval`: the judge runs the +authoritative measurement, the agent image runs the same harness for the public +test. + +## Known limitations + +- The SWE-bench sandbox is `LocalSandbox` (empty dir) unless Docker-in-Docker is + available on the judge; its accuracy stays a proxy. BFCL now carries the real + task-quality guardrail, which is the point of the 50/50 split. +- Latency is still a single sample per build on independently-autoscaled Modal + serves; the cap + per-workload geomean + live guardrail reduce, but do not + eliminate, run-to-run variance. Pinning `max_containers=1` and repeated + sampling remain future hardening. diff --git a/2.0/problems/vllm_llm_serving_optimization/config.yaml b/2.0/problems/vllm_llm_serving_optimization/config.yaml index 88a65a109..6d2ff83b8 100644 --- a/2.0/problems/vllm_llm_serving_optimization/config.yaml +++ b/2.0/problems/vllm_llm_serving_optimization/config.yaml @@ -2,7 +2,7 @@ tag: systems runtime: language: python timeout_seconds: 21600 - environment: "Patched vLLM (v0.11.0) source; Modal L40S GPU serving Llama-3.1-8B-Instruct; mini-swe-agent SWE-bench workload; latency-primary judge with accuracy guardrail" + environment: "Patched vLLM (v0.11.0) source; Modal H100 GPU serving Qwen3-Coder-30B-A3B-Instruct; two agentic workloads (mini-swe-agent SWE-bench + BFCL memory) scored 50/50; latency-primary judge with a live BFCL accuracy guardrail + greedy/per-sample correctness gates" apt_packages: - bash - ca-certificates @@ -22,30 +22,76 @@ runtime: - openai - datasets - huggingface-hub + - rank-bm25 docker: # Experimental local images. Build them with # 2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh before running a # local Harbor trial. Both images need a clean upstream vLLM v0.11.0 checkout # (NOT the continuum fork). The judge image additionally vendors the # mini-swe-agent harness and the latency/accuracy scorer. - image: frontiercs/vllm-serving-optimization-agent:experimental-v0.11.0 - judge_image: frontiercs/vllm-serving-optimization-judge:experimental-v0.11.0 + image: frontiercs/vllm-serving-optimization-agent:experimental-v0.11.1 + judge_image: frontiercs/vllm-serving-optimization-judge:experimental-v0.11.1 environment: cpus: 8 memory_mb: 32768 storage_mb: 65536 build_timeout_seconds: 7200 evaluation: - # Model + accelerator served on Modal (one L40S per environment). - model: meta-llama/Llama-3.1-8B-Instruct - gpu: L40S - # Workload: mini-swe-agent on SWE-bench Verified (split test). - dataset: princeton-nlp/SWE-bench_Verified + # Model + accelerator served on Modal. Qwen3-Coder-30B-A3B (latest Qwen3 coder, + # MoE: 30B total / ~3.3B active per token) actually resolves SWE-bench (the 8B + # Llama was ~0) and is fast thanks to the sparse MoE. SINGLE H100 on purpose: + # the 30B weights (~61GB bf16) leave only ~12-20GB KV (~5 concurrent 32K seqs), + # which is exactly the contention this scheduling task needs. H100:2 was measured + # to OVER-PROVISION (no queueing -> scheduling can't help -> codex ~1.0x); on one + # card the continuum reference wins (SWE ~1.1-1.6x). vLLM TP size is derived from + # "H100:N". Avoid B200 (sm_100) — the v0.11.0 precompiled wheel has no Blackwell kernels. + model: Qwen/Qwen3-Coder-30B-A3B-Instruct + gpu: "H100" + # Workload 1 (latency-primary): mini-swe-agent on SWE-bench Lite (split test). + # Lite (300, self-contained) is cheaper/cleaner than Verified; instances are + # drawn by a fixed-seed RANDOM sample across all 12 repos (not a sorted prefix, + # which clustered into 1-2 hard repos and was unrepresentative). + dataset: princeton-nlp/SWE-bench_Lite dataset_split: test + # Fixed seed for the deterministic random instance sample (SWE + BFCL), so the + # evaluated set is reproducible run-to-run. + sample_seed: 20260624 # Iterative (agent-role) public test: a strict subset of the final eval set. public_slice: "0:5" - # Final (verifier-role) evaluation: superset of the public slice. - eval_slice: "0:30" + # Final (verifier-role) evaluation: 50 instances sampled from Lite with + # sample_seed (set "0:300" for the full Lite split; that is many H100-hours). + eval_slice: "0:50" + # Workload 2 (agentic + real correctness): BFCL **memory** category. Each + # instance is a multi-turn agentic task — the model is given a key-value memory + # tool suite pre-seeded (vendored snapshots) with facts from a prior + # conversation, asked a question, and must issue retrieve/search tool calls + # over several turns, then answer (word-boundary match vs ground truth). Gives a + # genuine multi-step request path AND a non-zero, non-ceilinged accuracy signal + # (a strong model gets ~70-90%, not a pinned 1.0), so the guardrail has range. + bfcl_public_slice: "0:8" + # Full memory verify set: all 155 vendored instances at final. + bfcl_eval_slice: "0:155" + bfcl_max_tokens: 768 + memory_max_steps: 20 + # BFCL memory arrives as a seeded Poisson process at its OWN rate (bfcl_jps). + # Measured: BFCL's short, 93%-prefix-cacheable requests have NO clean scheduling + # signal at any rate. Below KV saturation (jps<=1.0) the metric is reproducible + # (~1.0x, identical run-to-run) but flat; at/above saturation (jps>=1.5) a real + # ~1.4x job-FCFS effect appears but is swamped by batch-numerics non-determinism + # (the SAME patch measured 0.71x and 1.55x across two clean jps=1.5 runs; an + # identical-build control swung 10x at jps=2.5). So BFCL runs at jps=1.0 — the + # one reproducible point — mainly for its correctness gate + accuracy guardrail, + # and is DOWN-WEIGHTED in the latency blend (see *_weight below). SWE carries the + # latency signal (its many-turn latency is robust to single-token flips). + bfcl_jps: 1.0 + # bfcl_workers only caps the fallback burst path (arrival_mode != jps). + bfcl_workers: 64 + # Final score = swebench_weight * SWE-bench score + bfcl_weight * BFCL score. + # SWE-heavy: SWE carries the reliable latency signal; BFCL is down-weighted to + # 0.2 because at its one reproducible load (jps=1.0) it is latency-neutral (~1.0x) + # and mainly serves as the correctness gate + accuracy guardrail. + swebench_weight: 0.8 + bfcl_weight: 0.2 # Poisson arrival workload (jobs/second). Mirrors a realistic serving load. arrival_mode: jps jps: 0.5 @@ -55,20 +101,34 @@ evaluation: max_completion_tokens: 2048 # Latency aggregation + scoring. latency_metric: mean_e2e_seconds - # Accuracy guardrail. Within `accuracy_tolerance` relative drop of baseline => - # no penalty; beyond it the score decays inverse-proportionally. + # Per-instance speedup is clamped to [1/cap, cap]; a failed/early-exit patched + # instance is counted as a regression, so "fail fast" cannot inflate the geomean. + max_per_instance_speedup: 8.0 + # Accuracy guardrail (per workload). Within `accuracy_tolerance` relative drop + # of baseline => no penalty; beyond it the score decays inverse-proportionally. + # No penalty if the accuracy drop is within EITHER the relative tolerance OR + # the absolute tolerance (resolve_rate over a finite slice is coarse/noisy). accuracy_tolerance: 0.05 + accuracy_abs_tolerance: 0.05 agent_accuracy_mode: patch_validity final_accuracy_mode: resolve_rate - # Greedy-output correctness smoke (a handful of fixed prompts must match the - # baseline token-for-token at temperature 0 before timing is considered). + # Greedy-output correctness smoke (fixed prompts must match the baseline + # token-for-token at temperature 0 before timing is considered). correctness_smoke_prompts: 8 + # BFCL per-sample correctness gate: a temperature-0 patch should not flip BFCL + # answers correct->wrong/undecodable. Allowed flips = max(the count floor below, + # abs_tolerance * n_instances, rel_tolerance * n_baseline_correct) — a 5%/5% + # abs-OR-rel band that absorbs batch-numerics non-determinism (which flips many + # instances run-to-run even between identical builds under concurrency). + bfcl_max_correctness_regressions: 1 + bfcl_correctness_abs_tolerance: 0.05 + bfcl_correctness_tolerance: 0.05 # Modal serving knobs. modal_scaledown_seconds: 900 modal_startup_timeout_seconds: 1200 server_health_timeout_seconds: 1800 - # Per-phase wall-clock budgets (seconds). - build_timeout_seconds: 5400 + # Per-phase wall-clock budgets (seconds). Matches the documented build budget. + build_timeout_seconds: 7200 instance_timeout_seconds: 1200 # Use a baseline (vanilla vLLM) cached in the judge image when available, # otherwise the judge serves vanilla once and caches it for the trial. diff --git a/2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh b/2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh index a097e0e0a..2728bd00c 100755 --- a/2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh +++ b/2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh @@ -1,26 +1,78 @@ #!/usr/bin/env bash +# Rebuild the vllm_llm_serving_optimization agent + judge images with the updated +# serving_eval harness (BFCL workload + 50/50 scoring + correctness fixes). +# +# bash 2.0/problems/vllm_llm_serving_optimization/docker/build_images.sh [new_tag] [base_tag] +# +# This is an OVERLAY rebuild: it layers the refreshed /opt/serving_eval (which +# now includes bfcl.py + the vendored BFCL slice under bfcl_data/) on top of the +# existing base images, which already bake the clean vLLM v0.11.0 tree +# (/opt/vllm-clean) and the OpenAI/Modal/datasets deps. The BFCL path is +# self-contained (stdlib + openai only), so no extra pip install is required. +# +# Both images carry /opt/serving_eval: the judge runs the authoritative +# measurement, and the agent image runs the same harness for the public test. set -euo pipefail +NEW_TAG="${1:-experimental-v0.11.1}" +BASE_TAG="${2:-experimental-v0.11.0}" SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) -TASK_DIR=$(cd "$SCRIPT_DIR/.." && pwd) +PROB=$(cd "$SCRIPT_DIR/.." && pwd) -VLLM_REF="${VLLM_REF:-v0.11.0}" -AGENT_TAG="${AGENT_TAG:-frontiercs/vllm-serving-optimization-agent:experimental-v0.11.0}" -JUDGE_TAG="${JUDGE_TAG:-frontiercs/vllm-serving-optimization-judge:experimental-v0.11.0}" +AGENT_BASE="frontiercs/vllm-serving-optimization-agent:${BASE_TAG}" +JUDGE_BASE="frontiercs/vllm-serving-optimization-judge:${BASE_TAG}" +AGENT_OUT="frontiercs/vllm-serving-optimization-agent:${NEW_TAG}" +JUDGE_OUT="frontiercs/vllm-serving-optimization-judge:${NEW_TAG}" -# Build context is the task directory so the Dockerfiles can COPY serving_eval. -docker build \ - --build-arg "VLLM_REF=$VLLM_REF" \ - -f "$TASK_DIR/docker/agent/Dockerfile" \ - -t "$AGENT_TAG" \ - "$TASK_DIR" +for img in "$AGENT_BASE" "$JUDGE_BASE"; do + if ! docker image inspect "$img" >/dev/null 2>&1; then + echo "ERROR: base image '$img' not found. Build the v0.11.0 base images first." >&2 + exit 1 + fi +done -docker build \ - --build-arg "VLLM_REF=$VLLM_REF" \ - -f "$TASK_DIR/docker/judge/Dockerfile" \ - -t "$JUDGE_TAG" \ - "$TASK_DIR" +CTX=$(mktemp -d); trap 'rm -rf "$CTX"' EXIT +# Stage the refreshed harness (exclude bytecode caches so no stale .pyc ships). +mkdir -p "$CTX/serving_eval" +( cd "$PROB/serving_eval" && tar --exclude='__pycache__' -cf - . ) | tar -xf - -C "$CTX/serving_eval" +# Sanity: the vendored BFCL **memory** workload assets must be present, or the +# BFCL workload would silently degrade to SWE-bench-only. The memory workload +# needs: the harness, the query data, the kv tool docs, the kv backend, and the +# 5 pre-baked per-scenario memory snapshots (frozen via build_memory_snapshots). +[ -f "$CTX/serving_eval/bfcl.py" ] || { echo "ERROR: serving_eval/bfcl.py missing" >&2; exit 1; } +[ -f "$CTX/serving_eval/bfcl_data/BFCL_v4_memory.json" ] || { echo "ERROR: vendored BFCL memory data missing" >&2; exit 1; } +[ -f "$CTX/serving_eval/bfcl_data/multi_turn_func_doc/memory_kv.json" ] || { echo "ERROR: vendored memory kv tool docs missing" >&2; exit 1; } +[ -f "$CTX/serving_eval/bfcl_vendor/memory_kv.py" ] || { echo "ERROR: vendored memory kv backend missing" >&2; exit 1; } +for s in customer healthcare finance student notetaker; do + [ -f "$CTX/serving_eval/bfcl_data/memory_snapshots/${s}_final.json" ] || { + echo "ERROR: pre-baked memory snapshot '${s}_final.json' missing (run build_memory_snapshots first)" >&2; exit 1; } +done + +build_overlay() { + local base="$1" out="$2" + cat > "$CTX/Dockerfile" < Building agent image $AGENT_OUT (overlay on $AGENT_BASE)" +build_overlay "$AGENT_BASE" "$AGENT_OUT" +echo "==> Building judge image $JUDGE_OUT (overlay on $JUDGE_BASE)" +build_overlay "$JUDGE_BASE" "$JUDGE_OUT" + +echo echo "Built:" -echo " $AGENT_TAG" -echo " $JUDGE_TAG" +echo " $AGENT_OUT" +echo " $JUDGE_OUT" +echo "Update config.yaml runtime.docker.{image,judge_image} to the '${NEW_TAG}' tag if you bumped it." diff --git a/2.0/problems/vllm_llm_serving_optimization/evaluate.sh b/2.0/problems/vllm_llm_serving_optimization/evaluate.sh index 6f5188491..bb73110fe 100755 --- a/2.0/problems/vllm_llm_serving_optimization/evaluate.sh +++ b/2.0/problems/vllm_llm_serving_optimization/evaluate.sh @@ -1,16 +1,12 @@ #!/usr/bin/env bash +# Local CLI evaluation for vllm_llm_serving_optimization. +# +# Full runs need Modal + Hugging Face credentials (MODAL_TOKEN_ID/SECRET, HF_TOKEN) +# and the baked clean vLLM source + serving_eval harness at /opt (see the judge +# image). Without those the evaluator validates the patch policy and returns a +# smoke score, which is what repository CI exercises (the empty reference patch +# passes). Pass a patch path to score it directly. set -euo pipefail - SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) - -if [[ $# -gt 0 ]]; then - exec python3 "$SCRIPT_DIR/evaluator.py" "$@" -fi - -SOLUTION="/work/execution_env/solution_env/solution.patch" -if [[ ! -f "$SOLUTION" ]]; then - echo "Error: Missing $SOLUTION" >&2 - exit 1 -fi - -python3 "$SCRIPT_DIR/evaluator.py" "$SOLUTION" +SOLUTION="${1:-$SCRIPT_DIR/reference.patch}" +exec python3 "$SCRIPT_DIR/evaluator.py" "$SOLUTION" diff --git a/2.0/problems/vllm_llm_serving_optimization/evaluator.py b/2.0/problems/vllm_llm_serving_optimization/evaluator.py index 4efdd4d33..380fddb2c 100644 --- a/2.0/problems/vllm_llm_serving_optimization/evaluator.py +++ b/2.0/problems/vllm_llm_serving_optimization/evaluator.py @@ -2,9 +2,9 @@ The agent submits a Python-only patch against a clean upstream vLLM v0.11.0 checkout. The judge applies the patch, builds and serves the patched vLLM on a -Modal L40S (``meta-llama/Llama-3.1-8B-Instruct``), runs a mini-swe-agent -SWE-bench workload, and scores latency speedup vs a vanilla-vLLM baseline gated -by an accuracy guardrail. +Modal H100 (``Qwen/Qwen3-Coder-30B-A3B-Instruct``), runs a mini-swe-agent +SWE-bench workload plus a BFCL memory workload, and scores latency speedup vs a +vanilla-vLLM baseline gated by an accuracy guardrail. This file contains the self-contained static patch policy and the scoring math. The heavy orchestration (Modal deploy, workload run, baseline caching) lives in @@ -72,7 +72,25 @@ def _config_str(name: str, default: str) -> str: ACCURACY_TOLERANCE = _config_float("accuracy_tolerance", 0.05) +ACCURACY_ABS_TOLERANCE = _config_float("accuracy_abs_tolerance", 0.05) BASELINE_CACHE_PATH = Path(_config_str("baseline_cache_path", "/opt/vllm-baseline/baseline_metrics.json")) +# Per-instance speedup clamp (anti-inflation) and the SWE-bench/BFCL score blend. +MAX_PER_INSTANCE_SPEEDUP = _config_float("max_per_instance_speedup", 8.0) +SWEBENCH_WEIGHT = _config_float("swebench_weight", 0.5) +BFCL_WEIGHT = _config_float("bfcl_weight", 0.5) +# Modal GPU the workloads are served on (e.g. "H100:2"); used only for the +# reported serving_harness label so it reflects the real accelerator. +SERVING_GPU = _config_str("gpu", "H100:2") +SERVING_HARNESS = "modal_" + SERVING_GPU.replace(":", "x").lower() + + +def _normalize_weights(weights: tuple[float, float]) -> tuple[float, float]: + sw = max(0.0, weights[0]) + bf = max(0.0, weights[1]) + total = sw + bf + if total <= 0: + return 0.5, 0.5 + return sw / total, bf / total # --------------------------------------------------------------------------- # # Patch policy @@ -266,6 +284,13 @@ def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: "patch_sha256": patch_hash, "changed_files": len(files), } + # Binary hunks (`git diff --binary`) carry a base85 payload the +line token + # scanners below never see, so they could smuggle secret/benchmark access or + # non-Python content past the policy while `git apply` honours them. The build + # is Python-only (VLLM_USE_PRECOMPILED), so reject any binary patch outright + # (audit finding: binary-hunk policy bypass). + if "GIT binary patch" in text or re.search(r"^Binary files .* differ$", text, re.MULTILINE): + return False, "binary patch hunks are not allowed (Python-only build; use a text diff)", metrics if len(files) > MAX_CHANGED_FILES: return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics @@ -317,10 +342,17 @@ def score_from_speedup(speedup: float) -> float: return max(0.0, min(100.0, raw)) -def accuracy_multiplier(baseline_accuracy: float, patched_accuracy: float, tolerance: float) -> float: +def accuracy_multiplier( + baseline_accuracy: float, + patched_accuracy: float, + tolerance: float, + abs_tolerance: float = 0.05, +) -> float: + # No penalty if within EITHER the relative or the absolute tolerance. + abs_drop = max(0.0, baseline_accuracy - patched_accuracy) base = max(baseline_accuracy, 1e-9) - rel_drop = max(0.0, (baseline_accuracy - patched_accuracy) / base) - if rel_drop <= tolerance: + rel_drop = max(0.0, abs_drop / base) + if abs_drop <= abs_tolerance or rel_drop <= tolerance: return 1.0 return max(0.0, min(1.0, tolerance / rel_drop)) @@ -412,45 +444,96 @@ def full_evaluation(patch_path: Path, metrics: dict[str, Any]): metrics["gate"] = gate return _invalid(gate, metrics) + # Correctness gates (must hold before any timing is scored). if not measurement.get("correctness_ok", False): - metrics["gate"] = "correctness" + metrics["gate"] = "swebench_correctness" return _invalid("patched server generations differ from the baseline at temperature 0", metrics) + if not measurement.get("bfcl_correctness_ok", True): + metrics["gate"] = "bfcl_correctness" + return _invalid( + "patched server regressed BFCL function-calling correctness vs the baseline at temperature 0", + metrics, + ) - patched = measurement.get("patched", {}) or {} - baseline = measurement.get("baseline", {}) or {} - patched_latency = {str(k): float(v) for k, v in (patched.get("per_instance_latency") or {}).items()} - baseline_latency = {str(k): float(v) for k, v in (baseline.get("per_instance_latency") or {}).items()} - speedups = paired_speedups(baseline_latency, patched_latency) - if not speedups: - return _invalid("no paired latency measurements were produced", metrics) + from serving_eval import scoring # type: ignore + + def _lat(block: dict[str, Any], key: str) -> dict[str, float]: + return {str(k): float(v) for k, v in (block.get(key) or {}).items()} + + # --- SWE-bench workload (latency-primary; accuracy is a proxy guardrail) --- + swe = measurement.get("swebench", {}) or {} + swe_base = swe.get("baseline", {}) or {} + swe_patched = swe.get("patched", {}) or {} + swe_score = scoring.workload_score( + _lat(swe_base, "per_instance_latency"), + _lat(swe_patched, "per_instance_latency"), + float(swe_base.get("accuracy", 0.0)), + float(swe_patched.get("accuracy", 0.0)), + ACCURACY_TOLERANCE, + cap=MAX_PER_INSTANCE_SPEEDUP, + failed_ids=swe_patched.get("failed_ids", ()), + abs_tolerance=ACCURACY_ABS_TOLERANCE, + ) - gm_speedup = geometric_mean(speedups) - latency_score = score_from_speedup(gm_speedup) + # --- BFCL workload (real, non-zero accuracy -> LIVE guardrail) --- + bfcl = measurement.get("bfcl", {}) or {} + bfcl_available = bool(bfcl.get("available")) + bfcl_base = bfcl.get("baseline", {}) or {} + bfcl_patched = bfcl.get("patched", {}) or {} + bfcl_score = scoring.workload_score( + _lat(bfcl_base, "per_instance_latency"), + _lat(bfcl_patched, "per_instance_latency"), + float(bfcl_base.get("accuracy", 0.0)), + float(bfcl_patched.get("accuracy", 0.0)), + ACCURACY_TOLERANCE, + cap=MAX_PER_INSTANCE_SPEEDUP, + failed_ids=bfcl_patched.get("failed_ids", ()), + abs_tolerance=ACCURACY_ABS_TOLERANCE, + ) if bfcl_available else None + + if not swe_score["instances_scored"] and not (bfcl_score and bfcl_score["instances_scored"]): + return _invalid("no paired latency measurements were produced", metrics) - patched_accuracy = float(patched.get("accuracy", 0.0)) - baseline_accuracy = float(baseline.get("accuracy", 0.0)) - acc_mult = accuracy_multiplier(baseline_accuracy, patched_accuracy, ACCURACY_TOLERANCE) - bounded = max(0.0, min(100.0, latency_score * acc_mult)) + # Blend 50/50 (configurable). If BFCL is unavailable, fall back to SWE only. + if bfcl_available and bfcl_score is not None: + weights = (SWEBENCH_WEIGHT, BFCL_WEIGHT) + else: + weights = (1.0, 0.0) + bfcl_score = {"latency_geomean_speedup": 0.0, "latency_score": 0.0, + "accuracy_multiplier": 1.0, "score": 0.0, "instances_scored": 0.0} + bounded = scoring.blend(swe_score["score"], bfcl_score["score"], _normalize_weights(weights)) metrics.update( { "full_benchmark": 1, - "serving_harness": "modal_l40s", - "instances_scored": len(speedups), - "latency_geomean_speedup": gm_speedup, - "latency_score": latency_score, - "baseline_accuracy": baseline_accuracy, - "patched_accuracy": patched_accuracy, - "accuracy_multiplier": acc_mult, + "serving_harness": SERVING_HARNESS, + "bfcl_available": bfcl_available, + "swebench_weight": _normalize_weights(weights)[0], + "bfcl_weight": _normalize_weights(weights)[1], + "swe_instances_scored": int(swe_score["instances_scored"]), + "swe_latency_geomean_speedup": swe_score["latency_geomean_speedup"], + "swe_latency_score": swe_score["latency_score"], + "swe_baseline_accuracy": float(swe_base.get("accuracy", 0.0)), + "swe_patched_accuracy": float(swe_patched.get("accuracy", 0.0)), + "swe_accuracy_multiplier": swe_score["accuracy_multiplier"], + "swe_score": swe_score["score"], + "bfcl_instances_scored": int(bfcl_score["instances_scored"]), + "bfcl_latency_geomean_speedup": bfcl_score["latency_geomean_speedup"], + "bfcl_latency_score": bfcl_score["latency_score"], + "bfcl_baseline_accuracy": float(bfcl_base.get("accuracy", 0.0)), + "bfcl_patched_accuracy": float(bfcl_patched.get("accuracy", 0.0)), + "bfcl_accuracy_multiplier": bfcl_score["accuracy_multiplier"], + "bfcl_score": bfcl_score["score"], } ) return ( bounded, bounded, ( - f"latency geomean speedup {gm_speedup:.4f}x over baseline vLLM; " - f"accuracy {patched_accuracy:.4f} vs baseline {baseline_accuracy:.4f} " - f"(multiplier {acc_mult:.3f})" + f"blended score {bounded:.2f}/100 " + f"(SWE-bench {swe_score['score']:.1f} @ {swe_score['latency_geomean_speedup']:.3f}x, " + f"BFCL {bfcl_score['score']:.1f} @ {bfcl_score['latency_geomean_speedup']:.3f}x, " + f"acc {float(bfcl_patched.get('accuracy', 0.0)):.3f} vs {float(bfcl_base.get('accuracy', 0.0)):.3f})" ), metrics, ) diff --git a/2.0/problems/vllm_llm_serving_optimization/harbor/app/README.md b/2.0/problems/vllm_llm_serving_optimization/harbor/app/README.md index f03b20dcd..0db902bc8 100644 --- a/2.0/problems/vllm_llm_serving_optimization/harbor/app/README.md +++ b/2.0/problems/vllm_llm_serving_optimization/harbor/app/README.md @@ -10,7 +10,7 @@ Modify vLLM source code to reduce end-to-end serving latency on the agentic SWE-bench workload while preserving the model's task-solving accuracy. Only Python-only changes in the allowlisted scheduler/execution/serving areas are valid (see the task statement for the exact patch policy). The model is served -on a Modal L40S with `VLLM_USE_PRECOMPILED`, so CUDA/C++ kernel changes are out +on a Modal H100 with `VLLM_USE_PRECOMPILED`, so CUDA/C++ kernel changes are out of scope. ## Submit @@ -31,12 +31,12 @@ then keep iterating. Use `bash /app/submissions.sh` and Before (or instead of) submitting, evaluate your working tree yourself: ```bash -bash /app/public_test.sh launch # deploys /app/vllm to a Modal L40S, async +bash /app/public_test.sh launch # deploys /app/vllm to a Modal H100, async bash /app/public_test.sh status # latency + accuracy + provisional score bash /app/public_test.sh run # synchronous variant ``` -The public test deploys your patched vLLM to a Modal L40S, serves +The public test deploys your patched vLLM to a Modal H100, serves `meta-llama/Llama-3.1-8B-Instruct`, runs the **public instance subset** (a strict subset of the final eval set) under the same Poisson arrival workload the judge uses, and returns: diff --git a/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.py b/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.py index c6d15975c..5235432e1 100755 --- a/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.py +++ b/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 """Async public-test client for the vLLM serving optimization task. -Deploys the current `/app/vllm` working tree to a Modal L40S, runs the public +Deploys the current `/app/vllm` working tree to a Modal H100, runs the public instance subset (a strict subset of the final eval set), and reports per-instance and aggregate end-to-end latency plus an accuracy signal versus the baseline. The returned feedback is the same kind the judge uses — never just a compile/OK diff --git a/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.sh b/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.sh index b94eaf9e4..521f09608 100755 --- a/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.sh +++ b/2.0/problems/vllm_llm_serving_optimization/harbor/app/public_test.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash # Async public-test client. Deploys the current /app/vllm working tree to a Modal -# L40S, runs the public instance subset, and reports latency + accuracy feedback +# H100, runs the public instance subset, and reports latency + accuracy feedback # (not merely whether the build succeeded). # # bash /app/public_test.sh launch # start an async run, prints a run id diff --git a/2.0/problems/vllm_llm_serving_optimization/readme b/2.0/problems/vllm_llm_serving_optimization/readme index 3bfa3b59e..b37074f3a 100644 --- a/2.0/problems/vllm_llm_serving_optimization/readme +++ b/2.0/problems/vllm_llm_serving_optimization/readme @@ -8,8 +8,8 @@ modify vLLM itself. Your goal is to reduce the **end-to-end latency** of an LLM serving system on a realistic multi-turn agentic workload while preserving the **accuracy** (task-solving quality) of the served model. -The serving target is a single-GPU deployment of -`meta-llama/Llama-3.1-8B-Instruct` running on one NVIDIA **L40S**, exposed +The serving target is a deployment of +`Qwen/Qwen3-Coder-30B-A3B-Instruct` running on one NVIDIA **H100**, exposed through vLLM's OpenAI-compatible HTTP API. The workload is an agentic code-editing benchmark (see *Workload* below) whose requests are long, multi-turn conversations that arrive over time as a Poisson process. @@ -21,13 +21,13 @@ scheduler/execution wiring. Strong submissions improve the workload's latency distribution without changing what the model actually generates and without hard-coding the benchmark, dataset, queries, or judge details. -## Serving Stack (Modal + L40S) +## Serving Stack (Modal + H100) Both your local public test and the hidden judge serve the patched vLLM the same way: - A [Modal](https://modal.com/docs) app builds an image from **your patched - vLLM source** and serves `meta-llama/Llama-3.1-8B-Instruct` on one **L40S** + vLLM source** and serves `Qwen/Qwen3-Coder-30B-A3B-Instruct` on one **H100** through the OpenAI-compatible endpoint (`/v1`). - The image is built with `VLLM_USE_PRECOMPILED=1`, which reuses vLLM's prebuilt CUDA kernels and rebuilds only the Python layer. **Your patch must @@ -39,28 +39,51 @@ same way: only change vLLM's internal behavior through allowlisted source files. Running the model requires Modal and Hugging Face credentials configured in the -environment (`MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET`, and an `HF_TOKEN` with -access to the gated Llama-3.1 weights). These are provided to the workspace and -the judge; do not attempt to read, print, or exfiltrate them. +environment (`MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET`, and an `HF_TOKEN` for model +downloads). These are provided to the workspace and the judge; do not attempt to +read, print, or exfiltrate them. ## Workload -The workload is a [mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent) -SWE-bench run: each benchmark instance is one agentic task in which the agent -holds a multi-turn conversation with the served model, issuing shell commands -in a sandboxed repository between turns. Every turn re-sends the growing -conversation, so consecutive requests for the same task share a long common -prefix. Instances arrive over time (Poisson arrivals), so many conversations -are in flight at once and compete for GPU and KV-cache capacity. - -The dataset is the public `princeton-nlp/SWE-bench_Verified` set (split -`test`), and the agent loop, step limit, and decoding settings -(temperature `0`) are fixed. Treat this as a representative analytical serving -workload, not a set of strings to recognize. The hidden judge may include -additional non-public instance groups and may vary instance order, arrival -timing, and the number of repetitions. Submissions should implement general +The judge runs **two** serving workloads against each build and combines them +**50/50** (see *Scoring*): + +**1. SWE-bench agentic (latency-primary).** A +[mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent)-style SWE-bench +run: each instance is one agentic task in which the agent holds a multi-turn +conversation with the served model, issuing shell commands in a sandboxed +repository between turns. Every turn re-sends the growing conversation, so +consecutive requests for the same task share a long common prefix. Instances +arrive over time (Poisson arrivals), so many conversations are in flight at once +and compete for GPU and KV-cache capacity. The dataset is the public +`princeton-nlp/SWE-bench_Lite` set (split `test`); the agent loop, step +limit, and decoding settings (temperature `0`) are fixed. + +**2. BFCL memory (agentic, correctness-primary).** The `memory` category of the +[Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html): +each instance is a multi-turn agentic task where the model is given a key-value +memory tool suite (pre-seeded with facts from a prior conversation), asked a +question, and must issue retrieve/search tool calls across several turns and then +answer. Correctness is a deterministic word-boundary match of the final answer +against the ground truth. This gives a real, **non-zero** accuracy signal (and a +multi-step request path), so it provides a *live* accuracy guardrail and a +per-sample correctness check alongside SWE-bench. Instances arrive over time as a +Poisson process (like the SWE-bench workload), so multiple multi-step memory +conversations are in flight at once and queue. + +Treat both as representative analytical serving workloads, not sets of strings +to recognize. The hidden judge may include additional non-public instances and +may vary instance order and arrival timing. Submissions should implement general serving optimizations rather than benchmark-specific special cases. +**Request metadata.** Each request carries a stable per-conversation id in +`sampling_params.extra_args["job_id"]` (the client sends it via `vllm_xargs`); +all requests of one benchmark instance share the same value, and vanilla vLLM +ignores it. Any request metadata available on the server is fair game for your +scheduling logic, but whatever you do must be a general policy — do not key on +specific id values or otherwise special-case the benchmark (the patch policy +forbids it). + ## Submission The submitted artifact is a patch file: @@ -95,7 +118,7 @@ You can evaluate your current working tree yourself, without going through the judge queue, using the public test client: ```bash -# Launch an async public-test run (deploys your patched vLLM to Modal L40S, +# Launch an async public-test run (deploys your patched vLLM to Modal H100, # runs the public instance subset, returns a run id): bash /app/public_test.sh launch @@ -115,61 +138,66 @@ test, read the returned latency/accuracy, and adjust. ## Correctness -Correctness is a gate. The patched server must produce the **same generations** -as the baseline server on the evaluated workload at temperature `0`. Before any -timing is considered, the judge runs a small greedy-decoding smoke set and -requires the patched build's outputs to match the baseline token-for-token. +Correctness is a hard gate, applied **before** any timing is scored. The patched +server must not change what the model generates at temperature `0`: + +- **SWE-bench greedy gate.** The judge runs a fixed greedy-decoding smoke set and + requires the patched build's outputs to match the baseline token-for-token. +- **BFCL per-sample gate.** On the BFCL slice, the patched build must not flip an + instance from **correct** (baseline) to wrong/undecodable; a small number of + flips is tolerated to absorb rare batch-numerics differences, but a real + regression fails the gate. + Build failures, patch-policy violations, server start-up failures, generation -mismatches, crashes, timeouts, and out-of-memory failures are penalized before +mismatches, crashes, timeouts, and out-of-memory failures all score `0` before performance is considered. During iterative asynchronous submissions, the judge keeps feedback focused on the public instance subset so you can submit early and continue working while evaluation runs. During final verification, the judge uses the broader hidden -instance set and a stricter accuracy measurement. +instance set. ## Scoring -Valid submissions are scored by **latency speedup relative to the baseline** -(vanilla vLLM serving the same model on the same L40S, same workload, same -arrival schedule, same resource limits), gated by an **accuracy guardrail**. - -Latency is the end-to-end completion time per benchmark instance (arrival of the -instance's first request to completion of its last response), measured -client-side. For each instance a per-instance speedup is computed against the -baseline, and the primary objective is the **geometric mean** of those -per-instance speedups: +The final score blends the two workloads: ```text -per_instance_speedup = baseline_latency[i] / patched_latency[i] -latency_speedup = geomean(per_instance_speedup) -latency_score = clip(100 * log2(latency_speedup), 0, 100) +final_score = 0.5 * swebench_score + 0.5 * bfcl_score ``` -A `1.0x` result earns `0` points and regressions also earn `0`; using the -geometric mean means broad speedups across instances are preferred over a single -large outlier. +Each workload is scored by **latency speedup relative to the baseline** (vanilla +vLLM serving the same model on an H100 under the same workload and arrival +schedule), gated by an **accuracy guardrail**. -Accuracy is the workload's task-solving rate (SWE-bench resolve rate at final -verification; a patch-validity proxy during iterative feedback). Let +Latency is the end-to-end completion time per instance (first request to last +response), measured client-side. Per-instance speedups are clamped to a bounded +range and a patched instance that *fails* (errors / exits early) is counted as a +regression — so failing fast cannot inflate the score. The per-workload objective +is the **geometric mean** of those per-instance speedups: ```text -rel_drop = max(0, (baseline_accuracy - patched_accuracy) / baseline_accuracy) +per_instance_speedup = clip(baseline_latency[i] / patched_latency[i], 1/cap, cap) +latency_speedup = geomean(per_instance_speedup) +latency_score = clip(100 * log2(latency_speedup), 0, 100) ``` -If `rel_drop <= 0.05` (within 5% of the baseline) there is no penalty. -Otherwise the score decays inverse-proportionally with the accuracy drop: +A `1.0x` result earns `0` points and regressions also earn `0`. + +Each workload's accuracy gates its latency score. For BFCL this is the +memory-retrieval accuracy (a real, non-zero signal); for SWE-bench it is the +task-solving proxy. Let ```text -accuracy_multiplier = 1.0 if rel_drop <= 0.05 -accuracy_multiplier = 0.05 / rel_drop otherwise -final_score = latency_score * accuracy_multiplier +rel_drop = max(0, (baseline_accuracy - patched_accuracy) / baseline_accuracy) +accuracy_multiplier = 1.0 if rel_drop <= 0.05 +accuracy_multiplier = 0.05 / rel_drop otherwise +workload_score = latency_score * accuracy_multiplier ``` -So a fast build that meaningfully degrades task quality loses most of its score, -while a build that keeps accuracy within 5% of the baseline is scored purely on -its latency improvement. The raw latency speedup, accuracy, and the multiplier -are reported in evaluator metrics. +So a fast build that meaningfully degrades memory-retrieval quality loses most of +its BFCL score, while a build that keeps accuracy within 5% of the baseline is +scored on its latency improvement. The per-workload speedups, accuracies, and +multipliers are reported in evaluator metrics. ## Patch Policy @@ -226,8 +254,8 @@ The experimental Harbor budget is: agent/judge container vCPUs: 8 agent/judge container memory: 32 GiB storage: 64 GiB -served model: meta-llama/Llama-3.1-8B-Instruct -serving GPU: 1x NVIDIA L40S (via Modal) +served model: Qwen/Qwen3-Coder-30B-A3B-Instruct +serving GPU: 1x NVIDIA H100 (via Modal; the operator may use H100:N for tensor parallelism) build timeout: 7200 seconds per-instance timeout: 1200 seconds decoding: temperature 0, fixed max tokens diff --git a/2.0/problems/vllm_llm_serving_optimization/reference.patch b/2.0/problems/vllm_llm_serving_optimization/reference.patch index e69de29bb..6a11648af 100644 --- a/2.0/problems/vllm_llm_serving_optimization/reference.patch +++ b/2.0/problems/vllm_llm_serving_optimization/reference.patch @@ -0,0 +1,148 @@ +diff --git a/vllm/v1/core/sched/request_queue.py b/vllm/v1/core/sched/request_queue.py +index fc2bc30..a5aae28 100644 +--- a/vllm/v1/core/sched/request_queue.py ++++ b/vllm/v1/core/sched/request_queue.py +@@ -214,11 +214,96 @@ class PriorityRequestQueue(RequestQueue): + return reversed(list(self)) + + ++def _request_job_id(request: Request) -> "str | None": ++ """The conversation/job id carried by a request, if any. ++ ++ Forwarded by the client via ``vllm_xargs`` -> ``sampling_params.extra_args`` ++ (vanilla vLLM ignores it). Used for job-level FCFS ordering below. ++ """ ++ sampling_params = getattr(request, "sampling_params", None) ++ extra_args = getattr(sampling_params, "extra_args", None) if sampling_params else None ++ if extra_args: ++ job_id = extra_args.get("job_id") ++ if job_id is not None: ++ return str(job_id) ++ return None ++ ++ ++class JobFCFSRequestQueue(FCFSRequestQueue): ++ """First-come-first-served at the *job* granularity. ++ ++ A request is ordered by the arrival time of the FIRST request seen for its ++ ``job_id`` (the conversation it belongs to), not by its own arrival time, so ++ a later turn of an in-flight conversation is admitted ahead of a brand-new ++ conversation's first prefill -- keeping ongoing multi-turn work moving and ++ its (already cache-hot) prefix reused. This is the core of the "continuum" ++ job-aware scheduling idea. Requests without a ``job_id`` fall back to plain ++ per-request FCFS, so this is a safe drop-in default. Ordering uses ++ ``request.arrival_time`` only (never wall-clock), so it is deterministic and ++ changes only admission order, never generated tokens. ++ """ ++ ++ def __init__(self) -> None: ++ super().__init__() ++ self.job_id_first_entry_time: dict[str, float] = {} ++ ++ def _note_job(self, request: Request) -> None: ++ job_id = _request_job_id(request) ++ if job_id is not None and job_id not in self.job_id_first_entry_time: ++ self.job_id_first_entry_time[job_id] = request.arrival_time ++ ++ def _order_key(self, request: Request) -> "tuple[float, float]": ++ job_id = _request_job_id(request) ++ if job_id is None: ++ return (request.arrival_time, request.arrival_time) ++ first = self.job_id_first_entry_time.get(job_id, request.arrival_time) ++ return (first, request.arrival_time) ++ ++ def add_request(self, request: Request) -> None: ++ self._note_job(request) ++ self.append(request) ++ ++ def prepend_request(self, request: Request) -> None: ++ self._note_job(request) ++ self.appendleft(request) ++ ++ def prepend_requests(self, requests: RequestQueue) -> None: ++ materialized = list(requests) ++ for request in materialized: ++ self._note_job(request) ++ self.extendleft(reversed(materialized)) ++ ++ def _select_index(self) -> int: ++ best_index = 0 ++ best_key = None ++ for index, request in enumerate(self): ++ key = self._order_key(request) ++ if best_key is None or key < best_key: ++ best_key = key ++ best_index = index ++ return best_index ++ ++ def peek_request(self) -> Request: ++ if not self: ++ raise IndexError("peek from an empty queue") ++ return self[self._select_index()] ++ ++ def pop_request(self) -> Request: ++ if not self: ++ raise IndexError("pop from an empty queue") ++ index = self._select_index() ++ request = self[index] ++ del self[index] ++ return request ++ ++ + def create_request_queue(policy: SchedulingPolicy) -> RequestQueue: + """Create request queue based on scheduling policy.""" + if policy == SchedulingPolicy.PRIORITY: + return PriorityRequestQueue() + elif policy == SchedulingPolicy.FCFS: +- return FCFSRequestQueue() ++ # Job-aware FCFS (continuum-style); identical to plain FCFS when requests ++ # carry no job_id. ++ return JobFCFSRequestQueue() + else: + raise ValueError(f"Unknown scheduling policy: {policy}") +diff --git a/vllm/v1/core/sched/scheduler.py b/vllm/v1/core/sched/scheduler.py +index 2b2cd63..fc137ef 100644 +--- a/vllm/v1/core/sched/scheduler.py ++++ b/vllm/v1/core/sched/scheduler.py +@@ -331,6 +331,16 @@ class Scheduler(SchedulerInterface): + # skipped and put back at the head of the waiting queue later + skipped_waiting_requests = create_request_queue(self.policy) + ++ # [reference] Per-step cap on fresh, *uncached* long prefills so a burst ++ # of newly-arriving long prompts cannot head-of-line block the decode of ++ # in-flight conversations (prefill/decode interference). A deferred ++ # prefill is re-queued at the head and retried next step, so it is never ++ # starved and the generated tokens are unchanged. ++ uncached_long_prefills_scheduled = 0 ++ long_prefill_defer_threshold = ( ++ self.scheduler_config.long_prefill_token_threshold or 2048) ++ max_uncached_long_prefills_per_step = 1 ++ + # Next, schedule the WAITING requests. + if not preempted_reqs: + while self.waiting and token_budget > 0: +@@ -437,6 +447,24 @@ class Scheduler(SchedulerInterface): + num_new_tokens = min(num_new_tokens, token_budget) + assert num_new_tokens > 0 + ++ # [reference] Defer a fresh, uncached long prefill when there ++ # is in-flight decode work and the per-step cap is used. ++ # num_computed_tokens == 0 means no prefix-cache hit (a brand ++ # new prompt); ongoing conversations keep a cached prefix and ++ # are never deferred. Only admission order changes. ++ is_cold_long_prefill = ( ++ num_computed_tokens == 0 ++ and (request.num_tokens - num_computed_tokens) ++ > long_prefill_defer_threshold) ++ if (is_cold_long_prefill and self.running ++ and uncached_long_prefills_scheduled ++ >= max_uncached_long_prefills_per_step): ++ self.waiting.pop_request() ++ skipped_waiting_requests.prepend_request(request) ++ continue ++ if is_cold_long_prefill: ++ uncached_long_prefills_scheduled += 1 ++ + # Schedule encoder inputs. + if request.has_encoder_inputs: + (encoder_inputs_to_schedule, num_new_tokens, diff --git a/2.0/problems/vllm_llm_serving_optimization/reference.py b/2.0/problems/vllm_llm_serving_optimization/reference.py index b34d70ebf..172d5337e 100644 --- a/2.0/problems/vllm_llm_serving_optimization/reference.py +++ b/2.0/problems/vllm_llm_serving_optimization/reference.py @@ -1,7 +1,24 @@ -"""Reference placeholder for the experimental vLLM LLM-serving optimization task. +"""Reference placeholder for the vllm_llm_serving_optimization task. -The Harbor task submits /app/solution.patch. This Python file exists so the -Frontier-CS 2.0 task layout remains conventional; the valid baseline patch is -stored in reference.patch (an empty patch, i.e. unmodified vLLM, which is the -serving baseline). +The Harbor task submits ``/app/solution.patch`` (a Python-only patch against a +clean upstream vLLM v0.11.0 checkout). This Python file exists only so the +Frontier-CS 2.0 task layout stays conventional (cf. nanowm_rollout_speedup); the +actual reference solution lives in ``reference.patch``. + +The reference ports the soul of the *continuum* scheduler — **job-level FCFS** — +plus a long-prefill admission cap, entirely server-side and Python-only: + +* ``vllm/v1/core/sched/request_queue.py``: a ``JobFCFSRequestQueue`` that orders + the WAITING queue by each conversation's (``job_id``) first-arrival time, so a + later turn of an in-flight conversation is admitted ahead of a brand-new job's + first prefill (and its prefix is already cache-hot). ``job_id`` is read from + ``request.sampling_params.extra_args`` — the workload sends it via + ``vllm_xargs`` (vanilla vLLM ignores it); requests without one fall back to + plain FCFS. +* ``vllm/v1/core/sched/scheduler.py``: caps fresh uncached long prefills per + step so a burst of new long prompts cannot head-of-line-block decode. + +Both change only admission *order* (deterministic, using ``arrival_time``, never +wall-clock), so generated tokens at temperature 0 are unchanged and the greedy / +BFCL correctness gates pass. See DESIGN.md. """ diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/agent_runner.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/agent_runner.py index a22e83f8b..49729861e 100644 --- a/2.0/problems/vllm_llm_serving_optimization/serving_eval/agent_runner.py +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/agent_runner.py @@ -61,10 +61,18 @@ class InstanceResult: def load_instances(settings: EvalSettings, role: str) -> list[dict[str, Any]]: + import random + from datasets import load_dataset dataset = load_dataset(settings.dataset, split=settings.dataset_split) + # Deterministic, fixed-seed RANDOM sample across all repos (sorted first for a + # machine-independent base order, then shuffled with sample_seed). This avoids + # the contiguous "sorted prefix" slice, which clusters by repo (e.g. all + # astropy+django) and is not representative. The public slice is the first + # rows of the same shuffled order, so it stays a strict subset of the final. ids = sorted(range(len(dataset)), key=lambda i: dataset[i]["instance_id"]) + random.Random(settings.sample_seed).shuffle(ids) chosen = list(parse_slice(settings.slice_for_role(role), len(ids))) instances: list[dict[str, Any]] = [] for index in chosen: @@ -118,14 +126,30 @@ def run_instance( messages=messages, temperature=settings.temperature, max_tokens=settings.max_completion_tokens, + # Stable per-conversation job id (every turn of this instance + # shares it). Forwarded via vllm_xargs -> sampling_params. + # extra_args["job_id"], which vanilla vLLM ignores but a + # job-aware scheduler can use for job-level FCFS. Does not + # change generated tokens. + extra_body={"vllm_xargs": {"job_id": str(instance_id)}}, ) except Exception as exc: # noqa: BLE001 + # An API error (e.g. the growing conversation exceeding the model's + # context window) ends the loop, but any edits the agent already + # made to the sandbox are real work — capture the patch-so-far + # instead of discarding it. exit_status = "api_error" + partial_patch = "" + try: + partial_patch = sandbox.read_patch() if sandbox is not None else "" + except Exception: # noqa: BLE001 + partial_patch = "" return InstanceResult( instance_id=instance_id, latency_seconds=time.perf_counter() - started, n_calls=len(per_call), - exit_status=exit_status, + exit_status="limit_with_patch" if partial_patch.strip() else exit_status, + patch=partial_patch, error=type(exc).__name__, per_call_seconds=per_call, ) @@ -183,7 +207,22 @@ def _record(result: InstanceResult) -> None: results.append(result) def _run(instance: dict[str, Any]) -> None: - _record(run_instance(instance, base_url=base_url, settings=settings, prefer_docker=prefer_docker)) + # Never let an instance vanish: if run_instance raises (e.g. sandbox + # creation failed), record an explicit failure result so the instance is + # counted (as a regression by the scorer) instead of silently dropped. + try: + result = run_instance( + instance, base_url=base_url, settings=settings, prefer_docker=prefer_docker + ) + except Exception as exc: # noqa: BLE001 + result = InstanceResult( + instance_id=str(instance.get("instance_id", "unknown")), + latency_seconds=0.0, + n_calls=0, + exit_status="api_error", + error=type(exc).__name__, + ) + _record(result) if settings.arrival_mode == "jps" and settings.jps > 0: # Deterministic Poisson schedule (seeded) so arrivals are reproducible. diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl.py new file mode 100644 index 000000000..a0d066324 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl.py @@ -0,0 +1,492 @@ +"""BFCL **memory** agentic workload (replaces the single-turn AST workload). + +This is the second judged workload alongside the SWE-bench agentic run. Each +instance is a multi-turn agentic task from the Berkeley Function Calling +Leaderboard ``memory`` category: the model is given a key-value memory tool +suite (``MemoryAPI_kv``) pre-seeded with facts from a prior conversation, asked a +question, and must issue tool calls (retrieve / list / search across core and +archival memory) over several turns, then answer. Correctness is a deterministic +word-boundary match of the final answer against the ground truth. + +Why memory (vs the old single-turn ``simple`` AST category): it gives a real, +multi-step agentic request path (so the scheduler optimization has long, +contended requests to act on) AND a non-zero, non-ceilinged accuracy signal (a +strong model gets ~70-90%, not a pinned 1.0), so the accuracy guardrail has +dynamic range. + +Determinism/reproducibility: the per-scenario memory state is **pre-baked once** +(see build_memory_snapshots.py) into ``bfcl_data/memory_snapshots/_final.json`` +and loaded read-only here, so baseline and patched builds query an identical, +frozen memory — the prereq agent's behavior is a fixed fixture, not a per-run +variable. Self-contained: vendors the kv backend (``bfcl_vendor/``) + the data +slice; only needs stdlib + ``openai`` (+ optional ``rank-bm25`` for key-search). + +Source: ShishirPatil/gorilla, bfcl-eval==2026.3.23 (Apache-2.0); see bfcl_data/NOTICE. +""" + +from __future__ import annotations + +import ast +import json +import os +import random +import re +import threading +import time +from concurrent.futures import ThreadPoolExecutor +from copy import deepcopy +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +from .bfcl_vendor.memory_kv import MemoryAPI_kv +from .settings import EvalSettings, parse_slice + +BFCL_DATA_DIR = Path(os.environ.get("BFCL_DATA_DIR", str(Path(__file__).with_name("bfcl_data")))) +MEMORY_PROMPT_FILE = "BFCL_v4_memory.json" +MEMORY_ANSWER_FILE = "possible_answer/BFCL_v4_memory.json" +MEMORY_FUNCDOC_FILE = "multi_turn_func_doc/memory_kv.json" +MEMORY_SNAPSHOT_DIR = "memory_snapshots" +DEFAULT_MAX_STEPS = 20 # upstream MAXIMUM_STEP_LIMIT +SCENARIOS = ("customer", "healthcare", "finance", "student", "notetaker") + +# Classic plaintext function-calling system prompt (verbatim from bfcl-eval). +_FUNC_CALLING_SYSPROMPT = ( + "You are an expert in composing functions.You are given a question and a set " + "of possible functions. Based on the question, you will need to make one or " + "more function/tool calls to achieve the purpose. If none of the functions " + "can be used, point it out. If the given question lacks the parameters " + "required by the function, also point it out.\n\n" + "You should only return the function calls in your response.\n\n" + "If you decide to invoke any of the function(s), you MUST put it in the " + "format of [func_name1(params_name1=params_value1, params_name2=params_value2" + "...), func_name2(params)]. You SHOULD NOT include any other text in the " + "response.\n\n" + "At each turn, you should try your best to complete the tasks requested by " + "the user within the current turn. Continue to output functions to call " + "until you have fulfilled the user's request to the best of your ability. " + "Once you have no more functions to call, the system will consider the " + "current turn complete and proceed to the next turn or task.\n\n" + "Here is a list of functions in json format that you can invoke.\n" +) + +# Memory scenario personas + backend instruction (verbatim from bfcl-eval). +MEMORY_AGENT_SETTINGS = { + "student": "You are an academic-support assistant for college student. Remember key personal and academic details discussed across sessions, and draw on them to answer questions or give guidance.", + "customer": "You are a general customer support assistant for an e-commerce platform. Your task is to understand and remember information that can be used to provide information about user inquiries, preferences, and offer consistent, helpful assistance over multiple interactions.", + "finance": "You are a high-level executive assistant supporting a senior finance professional. Retain and synthesize both personal and professional information including facts, goals, prior decisions, and family life across sessions to provide strategic, context-rich guidance and continuity.", + "healthcare": "You are a healthcare assistant supporting a patient across appointments. Retain essential medical history, treatment plans, and personal preferences to offer coherent, context-aware guidance and reminders.", + "notetaker": "You are a personal organization assistant. Capture key information from conversations, like tasks, deadlines, and preferences, and use it to give reliable reminders and answers in future sessions.", +} +MEMORY_BACKEND_INSTRUCTION = """{scenario_setting} + +You have access to an advanced memory system, consisting of two memory types 'Core Memory' and 'Archival Memory'. Both type of memory is persistent across multiple conversations with the user, and can be accessed in a later interactions. You should actively manage your memory data to keep track of important information, ensure that it is up-to-date and easy to retrieve to provide personalized responses to the user later. + +The Core memory is limited in size, but always visible to you in context. The Archival Memory has a much larger capacity, but will be held outside of your immediate context due to its size. + +Here is the content of your Core Memory from previous interactions: +{memory_content} +""" + + +@dataclass +class BfclResult: + instance_id: str + latency_seconds: float + exit_status: str # "ok" | "api_error" | "force_quit" + correct: bool = False + decoded_ok: bool = False + output: str = "" + error: str = "" + per_call_seconds: list[float] = field(default_factory=list) + n_steps: int = 0 + scenario: str = "" + + +# --------------------------------------------------------------------------- # +# Data loading +# --------------------------------------------------------------------------- # + +def bfcl_available() -> bool: + if not (BFCL_DATA_DIR / MEMORY_PROMPT_FILE).exists(): + return False + if not (BFCL_DATA_DIR / MEMORY_ANSWER_FILE).exists(): + return False + if not (BFCL_DATA_DIR / MEMORY_FUNCDOC_FILE).exists(): + return False + return all((BFCL_DATA_DIR / MEMORY_SNAPSHOT_DIR / f"{s}_final.json").exists() for s in SCENARIOS) + + +def _read_jsonl(path: Path) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for line in path.read_text(encoding="utf-8").splitlines(): + line = line.strip() + if line: + rows.append(json.loads(line)) + return rows + + +_TOOLS_CACHE: list[dict[str, Any]] | None = None + + +def load_memory_tools() -> list[dict[str, Any]]: + global _TOOLS_CACHE + if _TOOLS_CACHE is None: + _TOOLS_CACHE = _read_jsonl(BFCL_DATA_DIR / MEMORY_FUNCDOC_FILE) + return _TOOLS_CACHE + + +def load_snapshot(scenario: str) -> dict[str, Any]: + path = BFCL_DATA_DIR / MEMORY_SNAPSHOT_DIR / f"{scenario}_final.json" + snap = json.loads(path.read_text(encoding="utf-8")) + return { + "core_memory": snap.get("core_memory", {}) or {}, + "archival_memory": snap.get("archival_memory", {}) or {}, + } + + +def load_memory_instances(settings: EvalSettings, role: str) -> list[dict[str, Any]]: + prompts = _read_jsonl(BFCL_DATA_DIR / MEMORY_PROMPT_FILE) + answers = {row["id"]: row for row in _read_jsonl(BFCL_DATA_DIR / MEMORY_ANSWER_FILE)} + # Same deterministic fixed-seed sample as the SWE workload. + prompts.sort(key=lambda row: row["id"]) + random.Random(settings.sample_seed).shuffle(prompts) + chosen = list(parse_slice(settings.bfcl_slice_for_role(role), len(prompts))) + tools = load_memory_tools() + instances: list[dict[str, Any]] = [] + for index in chosen: + row = prompts[index] + answer = answers.get(row["id"]) + if answer is None: + continue + instances.append( + { + "id": row["id"], + "scenario": row.get("scenario", ""), + "question": row["question"], + "ground_truth": answer.get("ground_truth", []), + "functions": tools, + } + ) + return instances + + +# --------------------------------------------------------------------------- # +# Tool-call decode + execute (reuses the same AST decoding as the AST workload) +# --------------------------------------------------------------------------- # + +def _literal(node: ast.AST) -> Any: + try: + return ast.literal_eval(node) + except Exception: + try: + return ast.unparse(node) + except Exception: + return None + + +def _resolve_call(node: ast.Call) -> tuple[str, dict[str, Any]]: + func = node.func + name = func.id if isinstance(func, ast.Name) else ( + ast.unparse(func) if hasattr(ast, "unparse") else getattr(func, "attr", "") + ) + kwargs: dict[str, Any] = {} + for kw in node.keywords: + if kw.arg is not None: + kwargs[kw.arg] = _literal(kw.value) + # positional args are unusual in prompt mode; map by the kw-less order is not + # possible without the tool schema, so positionals are passed by index name. + for i, positional in enumerate(node.args): + kwargs[f"_arg{i}"] = _literal(positional) + return name, kwargs + + +def _parse_call_list(expr: str) -> list[tuple[str, dict[str, Any]]]: + """Parse a ``[func(a=b), ...]`` / ``func(a=b)`` expression into calls. + + Raises unless the expression is a function call (or list/tuple of calls). + """ + parsed = ast.parse(expr, mode="eval").body + calls: list[tuple[str, dict[str, Any]]] = [] + if isinstance(parsed, ast.Call): + calls.append(_resolve_call(parsed)) + elif isinstance(parsed, (ast.List, ast.Tuple)): + for elem in parsed.elts: + if not isinstance(elem, ast.Call): + raise ValueError("non-call element") + calls.append(_resolve_call(elem)) + else: + raise ValueError("not a function-call list") + if not calls: + raise ValueError("empty call list") + return calls + + +def decode_tool_calls(text: str) -> list[tuple[str, dict[str, Any]]]: + """Decode a prompt-mode ``[func(a=b), ...]`` response into (name, kwargs). + + Tolerant of a prose preamble or a ``` markdown fence around the call list + (the served model sometimes prefixes e.g. "Sure, I'll store that." before the + ``[...]``). Only a bracketed expression whose elements are *function calls* is + accepted; a natural-language final answer — even one that happens to contain + brackets — raises, so the agentic loop terminates. + + Raises if the text is not a function-call list (i.e. the model produced a + final natural-language answer instead). + """ + raw = text.strip() + # Strip a leading ```/```python fence and matching trailing ```. + if raw.startswith("```"): + raw = raw.split("\n", 1)[-1] if "\n" in raw else raw[3:] + raw = raw.rstrip() + if raw.endswith("```"): + raw = raw[:-3] + cleaned = raw.strip("`\n ") + + # Fast path: the whole response is the call list (optionally missing the + # outer brackets / wrapped in stray quotes). Identical to the original strict + # behavior, so a clean tool-call response decodes exactly as it did before. + candidate = cleaned + if not candidate.startswith("["): + candidate = "[" + candidate + if not candidate.endswith("]"): + candidate = candidate + "]" + candidate = candidate.strip().strip("'") + try: + return _parse_call_list(candidate) + except Exception: + pass + + # Tolerant path: extract a ``[ ... ]`` slice that parses as a call list, + # ignoring any prose preamble/suffix. Try the widest span (first '[' .. last + # ']') first, then the first balanced region. + start = cleaned.find("[") + if start != -1: + spans: list[str] = [] + last = cleaned.rfind("]") + if last > start: + spans.append(cleaned[start:last + 1]) + depth = 0 + for j in range(start, len(cleaned)): + if cleaned[j] == "[": + depth += 1 + elif cleaned[j] == "]": + depth -= 1 + if depth == 0: + spans.append(cleaned[start:j + 1]) + break + for span in spans: + try: + return _parse_call_list(span) + except Exception: + continue + raise ValueError("not a function-call list") + + +_VALID_TOOLS = { + "core_memory_add", "core_memory_remove", "core_memory_replace", "core_memory_clear", + "core_memory_retrieve", "core_memory_list_keys", "core_memory_key_search", + "core_memory_retrieve_all", "archival_memory_add", "archival_memory_remove", + "archival_memory_replace", "archival_memory_clear", "archival_memory_retrieve", + "archival_memory_list_keys", "archival_memory_key_search", +} + + +def _execute_one(api: MemoryAPI_kv, name: str, kwargs: dict[str, Any]) -> Any: + if name not in _VALID_TOOLS: + return {"error": f"Function '{name}' does not exist in the memory tool suite."} + fn = getattr(api, name) + clean = {k: v for k, v in kwargs.items() if not k.startswith("_arg")} + try: + return fn(**clean) + except TypeError as exc: + return {"error": f"Invalid arguments for {name}: {exc}"} + except Exception as exc: # noqa: BLE001 + return {"error": f"{type(exc).__name__}: {exc}"} + + +# --------------------------------------------------------------------------- # +# Scoring (verbatim agentic_checker / standardize_string from bfcl-eval) +# --------------------------------------------------------------------------- # + +_PUNCT_RE = re.compile(r"[\,\.\/\-\_\*\^\(\)]") + + +def standardize_string(s: str) -> str: + return _PUNCT_RE.sub("", str(s)).lower().replace("'", '"') + + +def memory_correct(final_text: Any, ground_truth: list[str]) -> bool: + if isinstance(final_text, list): + final_text = final_text[0] if final_text else "" + resp = standardize_string(str(final_text)) + for ans in ground_truth: + a = standardize_string(str(ans)) + if a and re.search(rf"\b{re.escape(a)}\b", resp): + return True + return False + + +# --------------------------------------------------------------------------- # +# Agentic loop +# --------------------------------------------------------------------------- # + +def build_messages(instance: dict[str, Any], api: MemoryAPI_kv) -> list[dict[str, str]]: + functions_json = json.dumps(instance["functions"], indent=4) + scenario = instance["scenario"] + memory_instruction = MEMORY_BACKEND_INSTRUCTION.format( + scenario_setting=MEMORY_AGENT_SETTINGS.get(scenario, ""), + memory_content=api._dump_core_memory_to_context(), + ) + system_content = _FUNC_CALLING_SYSPROMPT + functions_json + "\n\n" + memory_instruction + messages: list[dict[str, str]] = [{"role": "system", "content": system_content}] + for turn in instance["question"][0]: + messages.append({"role": turn.get("role", "user"), "content": turn.get("content", "")}) + return messages + + +def _openai_client(base_url: str): + from openai import OpenAI + + return OpenAI(base_url=base_url, api_key="EMPTY", timeout=300.0) + + +def run_memory_instance(instance: dict[str, Any], *, base_url: str, settings: EvalSettings) -> BfclResult: + client = _openai_client(base_url) + # Fresh per-instance backend seeded read-only from the frozen snapshot. + api = MemoryAPI_kv() + snap = load_snapshot(instance["scenario"]) + api.core_memory = deepcopy(snap["core_memory"]) + api.archival_memory = deepcopy(snap["archival_memory"]) + + messages = build_messages(instance, api) + max_steps = int(getattr(settings, "memory_max_steps", DEFAULT_MAX_STEPS) or DEFAULT_MAX_STEPS) + per_call: list[float] = [] + started = time.perf_counter() + final_text = "" + decoded_any = False + steps = 0 + while True: + t0 = time.perf_counter() + try: + completion = client.chat.completions.create( + model=settings.model, + messages=messages, + temperature=settings.temperature, + max_tokens=settings.bfcl_max_tokens, + seed=0, + extra_body={"vllm_xargs": {"job_id": str(instance["id"])}}, + ) + except Exception as exc: # noqa: BLE001 + return BfclResult( + instance_id=instance["id"], latency_seconds=time.perf_counter() - started, + exit_status="api_error", error=type(exc).__name__, + per_call_seconds=per_call, n_steps=steps, scenario=instance["scenario"], + ) + per_call.append(time.perf_counter() - t0) + text = completion.choices[0].message.content or "" + + try: + calls = decode_tool_calls(text) + except Exception: + # Not a tool call -> the model produced its final answer. + final_text = text + return BfclResult( + instance_id=instance["id"], latency_seconds=time.perf_counter() - started, + exit_status="ok", correct=memory_correct(text, instance["ground_truth"]), + decoded_ok=True, output=text, per_call_seconds=per_call, + n_steps=steps, scenario=instance["scenario"], + ) + decoded_any = True + results = [_execute_one(api, name, kwargs) for name, kwargs in calls] + messages.append({"role": "assistant", "content": text}) + feedback = [ + {"role": "tool", "name": name, "content": str(result)} + for (name, _), result in zip(calls, results) + ] + messages.append({"role": "user", "content": repr(feedback)}) + + steps += 1 + if steps >= max_steps: + final_text = text + return BfclResult( + instance_id=instance["id"], latency_seconds=time.perf_counter() - started, + exit_status="force_quit", correct=memory_correct(text, instance["ground_truth"]), + decoded_ok=decoded_any, output=text, error="max_steps", + per_call_seconds=per_call, n_steps=steps, scenario=instance["scenario"], + ) + + +def run_bfcl_workload(*, base_url: str, settings: EvalSettings, role: str) -> list[BfclResult]: + instances = load_memory_instances(settings, role) + results: list[BfclResult] = [] + lock = threading.Lock() + + def _record(r: BfclResult) -> None: + with lock: + results.append(r) + + def _run(instance: dict[str, Any]) -> None: + try: + r = run_memory_instance(instance, base_url=base_url, settings=settings) + except Exception as exc: # noqa: BLE001 - never drop an instance silently + r = BfclResult( + instance_id=str(instance.get("id", "unknown")), latency_seconds=0.0, + exit_status="api_error", error=type(exc).__name__, scenario=instance.get("scenario", ""), + ) + _record(r) + + bfcl_jps = float(getattr(settings, "bfcl_jps", settings.jps) or settings.jps) + if settings.arrival_mode == "jps" and bfcl_jps > 0: + # Poisson arrivals at the BFCL-specific rate (bfcl_jps, higher than the + # SWE jps): each memory conversation arrives over time, so multi-step + # instances overlap and queue. Seeded so the schedule is reproducible. + rng = random.Random(20260605) + schedule: list[float] = [] + clock = 0.0 + for _ in instances: + clock += rng.expovariate(bfcl_jps) + schedule.append(clock) + origin = time.perf_counter() + threads: list[threading.Thread] = [] + + def _delayed(instance: dict[str, Any], when: float) -> None: + delay = when - (time.perf_counter() - origin) + if delay > 0: + time.sleep(delay) + _run(instance) + + for instance, when in zip(instances, schedule): + t = threading.Thread(target=_delayed, args=(instance, when), daemon=True) + t.start() + threads.append(t) + for t in threads: + t.join() + else: + # Fallback: fire all at once at high concurrency (a burst), capped by + # bfcl_workers. Each instance has its own backend so there is no shared state. + with ThreadPoolExecutor(max_workers=max(1, settings.bfcl_workers)) as pool: + list(pool.map(_run, instances)) + return results + + +# --------------------------------------------------------------------------- # +# Aggregators (shapes consumed by measure.py / scoring.py) +# --------------------------------------------------------------------------- # + +def per_instance_latency(results: list[BfclResult]) -> dict[str, float]: + return {r.instance_id: r.latency_seconds for r in results} + + +def accuracy(results: list[BfclResult]) -> float: + if not results: + return 0.0 + return sum(1 for r in results if r.correct) / len(results) + + +def correctness_map(results: list[BfclResult]) -> dict[str, bool]: + return {r.instance_id: bool(r.correct) for r in results} + + +def outputs(results: list[BfclResult]) -> dict[str, str]: + return {r.instance_id: r.output for r in results} diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_ast.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_ast.py new file mode 100644 index 000000000..b37767ef2 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_ast.py @@ -0,0 +1,367 @@ +"""BFCL (Berkeley Function Calling Leaderboard) serving workload + correctness. + +This is the *second* judged workload alongside the SWE-bench agentic run. Where +SWE-bench gives a latency signal but a ~0 task-solving rate for an 8B model (so +its accuracy guardrail is effectively dead), BFCL gives a **real, deterministic, +non-zero** correctness signal: each instance is a single-turn function-calling +query whose answer is checked by AST equality against a ground-truth call. That +makes a live accuracy guardrail possible and gives a per-sample greedy gate that +actually catches generation corruption. + +The module is intentionally self-contained: it vendors a fixed slice of the BFCL +``simple`` (Python) category under ``bfcl_data/`` and reimplements the prompt +construction, the answer decoder, and the AST checker for that category — no +``bfcl_eval`` install, no run-time network. The checker is cross-validated +against the upstream ``bfcl_eval.eval_checker`` on all vendored records. + +Source: ShishirPatil/gorilla, bfcl-eval==2026.3.23 (Apache-2.0); see +``bfcl_data/NOTICE``. Served in *prompt mode* (plain chat, the leaderboard's +``(Prompt)`` setting) so the request path matches the SWE-bench runner exactly. +""" + +from __future__ import annotations + +import ast +import json +import os +import random +import threading +import time +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +from .settings import EvalSettings, parse_slice + +# The vendored slice ships next to this module; an env override lets the judge +# point at a baked path. No network / datasets.load_dataset at run time. +BFCL_DATA_DIR = Path(os.environ.get("BFCL_DATA_DIR", str(Path(__file__).with_name("bfcl_data")))) +BFCL_PROMPT_FILE = "BFCL_v4_simple_python.json" +BFCL_ANSWER_FILE = "possible_answer/BFCL_v4_simple_python.json" +BFCL_CATEGORY = "simple" # single-turn, AST-checkable, highest/most-stable accuracy + +# The canonical BFCL prompt-mode system prompt (plaintext/classic format), kept +# verbatim from bfcl-eval so the served distribution matches the leaderboard. +_BFCL_SYSTEM_PROMPT = ( + "You are an expert in composing functions.You are given a question and a set " + "of possible functions. Based on the question, you will need to make one or " + "more function/tool calls to achieve the purpose. If none of the functions " + "can be used, point it out. If the given question lacks the parameters " + "required by the function, also point it out.\n\n" + "You should only return the function calls in your response.\n\n" + "If you decide to invoke any of the function(s), you MUST put it in the " + "format of [func_name1(params_name1=params_value1, params_name2=params_value2" + "...), func_name2(params)]. You SHOULD NOT include any other text in the " + "response.\n\n" + "At each turn, you should try your best to complete the tasks requested by " + "the user within the current turn. Continue to output functions to call " + "until you have fulfilled the user's request to the best of your ability. " + "Once you have no more functions to call, the system will consider the " + "current turn complete and proceed to the next turn or task.\n\n" + "Here is a list of functions in json format that you can invoke.\n" +) + + +@dataclass +class BfclResult: + instance_id: str + latency_seconds: float + exit_status: str # "ok" | "api_error" | "decode_error" + correct: bool = False + decoded_ok: bool = False + output: str = "" + error: str = "" + per_call_seconds: list[float] = field(default_factory=list) + + +# --------------------------------------------------------------------------- # +# Data loading +# --------------------------------------------------------------------------- # + +def bfcl_available() -> bool: + return (BFCL_DATA_DIR / BFCL_PROMPT_FILE).exists() and (BFCL_DATA_DIR / BFCL_ANSWER_FILE).exists() + + +def _read_jsonl(path: Path) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for line in path.read_text(encoding="utf-8").splitlines(): + line = line.strip() + if line: + rows.append(json.loads(line)) + return rows + + +def load_bfcl_instances(settings: EvalSettings, role: str) -> list[dict[str, Any]]: + prompts = _read_jsonl(BFCL_DATA_DIR / BFCL_PROMPT_FILE) + answers = {row["id"]: row for row in _read_jsonl(BFCL_DATA_DIR / BFCL_ANSWER_FILE)} + # Same deterministic fixed-seed sample as the SWE workload (sort for a stable + # base order, then shuffle with sample_seed); public slice stays a subset. + prompts.sort(key=lambda row: row["id"]) + random.Random(settings.sample_seed).shuffle(prompts) + chosen = list(parse_slice(settings.bfcl_slice_for_role(role), len(prompts))) + instances: list[dict[str, Any]] = [] + for index in chosen: + row = prompts[index] + answer = answers.get(row["id"]) + if answer is None: + continue + instances.append( + { + "id": row["id"], + "question": row["question"], + "function": row["function"], + "ground_truth": answer["ground_truth"], + } + ) + return instances + + +def _build_messages(instance: dict[str, Any]) -> list[dict[str, str]]: + functions_json = json.dumps(instance["function"], indent=4) + system_content = _BFCL_SYSTEM_PROMPT + functions_json + # ``question`` is a list of turns; the AST categories are single-turn. + turns = list(instance["question"][0]) + messages: list[dict[str, str]] = [{"role": "system", "content": system_content}] + for turn in turns: + if turn.get("role") == "system": + messages[0]["content"] = system_content + "\n\n" + turn.get("content", "") + else: + messages.append({"role": turn.get("role", "user"), "content": turn.get("content", "")}) + return messages + + +# --------------------------------------------------------------------------- # +# Answer decoding + AST checking (self-contained, faithful to BFCL ``simple``) +# --------------------------------------------------------------------------- # + +def _literal(node: ast.AST) -> Any: + try: + return ast.literal_eval(node) + except Exception: + try: + return ast.unparse(node) + except Exception: + return None + + +def _resolve_call(node: ast.Call, param_order: list[str]) -> dict[str, dict[str, Any]]: + func = node.func + if isinstance(func, ast.Name): + name = func.id + elif isinstance(func, ast.Attribute): + # underscore_to_dot is False for Llama; keep the dotted text as-is. + try: + name = ast.unparse(func) + except Exception: + name = func.attr + else: + name = ast.unparse(func) if hasattr(ast, "unparse") else "" + args: dict[str, Any] = {} + for i, positional in enumerate(node.args): + if i < len(param_order): + args[param_order[i]] = _literal(positional) + for kw in node.keywords: + if kw.arg is not None: + args[kw.arg] = _literal(kw.value) + return {name: args} + + +def decode_calls(text: str, param_order: list[str]) -> list[dict[str, dict[str, Any]]]: + """Decode a BFCL prompt-mode response ``[func(a=b, ...)]`` into call dicts. + + Mirrors ``bfcl_eval.model_handler.utils.default_decode_ast_prompting`` + + ``ast_parse`` for the Python return format. Raises on non-call prose. + """ + cleaned = text.strip("`\n ") + if not cleaned.startswith("["): + cleaned = "[" + cleaned + if not cleaned.endswith("]"): + cleaned = cleaned + "]" + cleaned = cleaned.strip().strip("'") + parsed = ast.parse(cleaned, mode="eval") + body = parsed.body + calls: list[dict[str, dict[str, Any]]] = [] + if isinstance(body, ast.Call): + calls.append(_resolve_call(body, param_order)) + elif isinstance(body, (ast.List, ast.Tuple)): + for elem in body.elts: + if not isinstance(elem, ast.Call): + raise ValueError("non-call element in function-call list") + calls.append(_resolve_call(elem, param_order)) + else: + raise ValueError("decoded output is not a function-call list") + return calls + + +def _norm_scalar(value: Any) -> Any: + if isinstance(value, bool): + return value + if isinstance(value, (int, float)): + return float(value) + if isinstance(value, str): + return value.strip().strip("'\"").casefold() + if isinstance(value, (list, tuple)): + return [_norm_scalar(v) for v in value] + if isinstance(value, dict): + return {str(k).casefold(): _norm_scalar(v) for k, v in value.items()} + return value + + +def _value_matches(model_value: Any, accepted: list[Any]) -> bool: + norm_model = _norm_scalar(model_value) + for candidate in accepted: + if candidate == "": + continue # "" marks an optional/omittable param, handled by the caller + if _norm_scalar(candidate) == norm_model: + return True + # numeric/string cross-type leniency (e.g. model "5" vs gt 5) + try: + if isinstance(norm_model, float) and float(str(candidate)) == norm_model: + return True + if isinstance(_norm_scalar(candidate), float) and float(str(model_value)) == _norm_scalar(candidate): + return True + except Exception: + pass + return False + + +def check_simple(decoded: list[dict[str, dict[str, Any]]], ground_truth: list[dict[str, Any]]) -> bool: + """AST-match for the ``simple`` category: exactly one call, name + args match.""" + if not decoded or len(decoded) != 1 or not ground_truth: + return False + gt_entry = ground_truth[0] + (gt_func, gt_args), = gt_entry.items() + if len(decoded[0]) != 1: + return False + (model_func, model_args), = decoded[0].items() + if str(model_func).strip().casefold() != str(gt_func).strip().casefold(): + return False + for arg, accepted in gt_args.items(): + if arg in model_args: + if not _value_matches(model_args[arg], accepted): + return False + elif "" not in accepted: + return False # required arg missing + for arg in model_args: + if arg not in gt_args: + return False # hallucinated argument + return True + + +def score_response(instance: dict[str, Any], text: str) -> tuple[bool, bool]: + """Return (decoded_ok, correct) for one model response.""" + function = instance["function"] + param_order: list[str] = [] + if function: + props = function[0].get("parameters", {}).get("properties", {}) + param_order = list(props.keys()) + try: + decoded = decode_calls(text, param_order) + except Exception: + return False, False + try: + return True, check_simple(decoded, instance["ground_truth"]) + except Exception: + return True, False + + +# --------------------------------------------------------------------------- # +# Workload runner (mirrors agent_runner: client-side per-instance latency) +# --------------------------------------------------------------------------- # + +def _openai_client(base_url: str): + from openai import OpenAI + + return OpenAI(base_url=base_url, api_key="EMPTY", timeout=300.0) + + +def run_instance(instance: dict[str, Any], *, base_url: str, settings: EvalSettings) -> BfclResult: + client = _openai_client(base_url) + messages = _build_messages(instance) + started = time.perf_counter() + try: + completion = client.chat.completions.create( + model=settings.model, + messages=messages, + temperature=settings.temperature, + max_tokens=settings.bfcl_max_tokens, + seed=0, + # Per-instance job id (single-turn here). Forwarded via vllm_xargs -> + # sampling_params.extra_args["job_id"] for job-aware scheduling; + # vanilla vLLM ignores it. Does not change generated tokens. + extra_body={"vllm_xargs": {"job_id": str(instance["id"])}}, + ) + except Exception as exc: # noqa: BLE001 + return BfclResult( + instance_id=instance["id"], + latency_seconds=time.perf_counter() - started, + exit_status="api_error", + error=type(exc).__name__, + ) + latency = time.perf_counter() - started + text = completion.choices[0].message.content or "" + decoded_ok, correct = score_response(instance, text) + return BfclResult( + instance_id=instance["id"], + latency_seconds=latency, + exit_status="ok" if decoded_ok else "decode_error", + correct=correct, + decoded_ok=decoded_ok, + output=text, + per_call_seconds=[latency], + ) + + +def run_bfcl_workload(*, base_url: str, settings: EvalSettings, role: str) -> list[BfclResult]: + instances = load_bfcl_instances(settings, role) + results: list[BfclResult] = [] + lock = threading.Lock() + + def _record(result: BfclResult) -> None: + with lock: + results.append(result) + + def _run(instance: dict[str, Any]) -> None: + try: + result = run_instance(instance, base_url=base_url, settings=settings) + except Exception as exc: # noqa: BLE001 - never drop an instance silently + result = BfclResult( + instance_id=str(instance.get("id", "unknown")), + latency_seconds=0.0, + exit_status="api_error", + error=type(exc).__name__, + ) + _record(result) + + # Fire BFCL at HIGH concurrency rather than the SWE Poisson schedule. Short + # single-turn requests under a low arrival rate never queue, so there is no + # contention and the scheduler policy has nothing to act on (BFCL stayed + # ~1.0x). A large worker pool submits many at once -> real queueing/batching + # pressure -> scheduling differences become observable. + with ThreadPoolExecutor(max_workers=max(1, settings.bfcl_workers)) as pool: + list(pool.map(_run, instances)) + + return results + + +# --------------------------------------------------------------------------- # +# Aggregators (shape matches what measure.py / scoring.py consume) +# --------------------------------------------------------------------------- # + +def per_instance_latency(results: list[BfclResult]) -> dict[str, float]: + return {r.instance_id: r.latency_seconds for r in results} + + +def accuracy(results: list[BfclResult]) -> float: + if not results: + return 0.0 + return sum(1 for r in results if r.correct) / len(results) + + +def correctness_map(results: list[BfclResult]) -> dict[str, bool]: + return {r.instance_id: bool(r.correct) for r in results} + + +def outputs(results: list[BfclResult]) -> dict[str, str]: + return {r.instance_id: r.output for r in results} diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/BFCL_v4_memory.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/BFCL_v4_memory.json new file mode 100644 index 000000000..e0496f35c --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/BFCL_v4_memory.json @@ -0,0 +1,155 @@ +{"id": "memory_0-customer-0", "question": [[{"role": "user", "content": "What is my first name?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_1-customer-1", "question": [[{"role": "user", "content": "How old am I?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_2-customer-2", "question": [[{"role": "user", "content": "Where do I live?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_3-customer-3", "question": [[{"role": "user", "content": "What kind of latte do I occasionally like?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_4-customer-4", "question": [[{"role": "user", "content": "How many square feet is my kitchen counter?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_5-customer-5", "question": [[{"role": "user", "content": "How much is the discount for new customers? Give the percent off."}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_6-customer-6", "question": [[{"role": "user", "content": "Name one of two accessories I added along with my espresso machine order to qualify for free shipping."}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_7-customer-7", "question": [[{"role": "user", "content": "What was the original estimated delivery duration for my order provided on the checkout page? Give the number of business days."}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_8-customer-8", "question": [[{"role": "user", "content": "Which event was I hoping to have the espresso machine ready for?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_9-customer-9", "question": [[{"role": "user", "content": "Which part of my espresso machine arrived bent?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_10-customer-10", "question": [[{"role": "user", "content": "How many crushed corners were on the outer box upon delivery?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_11-customer-11", "question": [[{"role": "user", "content": "Which accessory arrived with a small scratch?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_12-customer-12", "question": [[{"role": "user", "content": "Which appliance did I banish to the pantry to make space for my coffee setup?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_13-customer-13", "question": [[{"role": "user", "content": "How much does the specialized storage canister that's supposed to keep beans fresher for longer cost?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_14-customer-14", "question": [[{"role": "user", "content": "What could make my lattes even better?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_15-customer-15", "question": [[{"role": "user", "content": "What company did you collaborate with on the digital scale that I bought in my second order?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_16-customer-16", "question": [[{"role": "user", "content": "Where am I traveling next weekend for a small freelance gig?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_17-customer-17", "question": [[{"role": "user", "content": "Where is the warehouse that is supposed to ship my grinder bundle from?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_18-customer-18", "question": [[{"role": "user", "content": "Which creative hobby am I combining with my latte art?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_19-customer-19", "question": [[{"role": "user", "content": "I'm excited about a potential extended warranty plan for members. How long is it, in months?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_20-customer-20", "question": [[{"role": "user", "content": "How many mL do I want the nuanced, small-batch roasts in my subscription to be?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_21-customer-21", "question": [[{"role": "user", "content": "How many days ago did the unexpected charge appear on my statement?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_22-customer-22", "question": [[{"role": "user", "content": "How much did I estimate the mystery charge to be? Give the answer in the format: $__.__ (ex: $19.99)"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_23-customer-23", "question": [[{"role": "user", "content": "How many 'order confirmed' emails did I receive for the coffee canisters and scale purchase?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_24-customer-24", "question": [[{"role": "user", "content": "What magazine does my kitchen now look like a page out of?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_25-customer-25", "question": [[{"role": "user", "content": "What's the average rainfall (in inches) that's making shipping unpredictable? Give the number rounded to the nearest tenth (ex: 7.9)."}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_26-customer-26", "question": [[{"role": "user", "content": "What's my coffee instagram page?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_27-customer-27", "question": [[{"role": "user", "content": "What impromptu event did I host?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_28-customer-28", "question": [[{"role": "user", "content": "What do I want to mitigate any chance of when shipping a large-scale espresso machine?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_29-customer-29", "question": [[{"role": "user", "content": "Besides your brand, what's the other brand I trust?"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_30-healthcare-0", "question": [[{"role": "user", "content": "What is the biggest health issue mentioned by the patient?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_31-healthcare-1", "question": [[{"role": "user", "content": "How many years ago was the patient diagnosed with Type 2 Diabetes?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_32-healthcare-2", "question": [[{"role": "user", "content": "What is one lifestyle change the patient adopted to help manage their glucose levels?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_33-healthcare-3", "question": [[{"role": "user", "content": "Which health condition led to an emergency room visit for the patient?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_34-healthcare-4", "question": [[{"role": "user", "content": "What surgery did the patient have a few years ago?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_35-healthcare-5", "question": [[{"role": "user", "content": "How does the patient keep track of their many doctor's appointments and medications?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_36-healthcare-6", "question": [[{"role": "user", "content": "What condition was the patient diagnosed with in their early 40s?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_37-healthcare-7", "question": [[{"role": "user", "content": "After a bout of pneumonia, the patient takes the flu shot and one other preventive measure. What preventive measure, other than the flu shot, does the patient take?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_38-healthcare-8", "question": [[{"role": "user", "content": "What is the patient's biggest focus right now regarding health? Is it getting more sleep, good diet, managing stress, or reducing screen time?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_39-healthcare-9", "question": [[{"role": "user", "content": "What does the patient typically have with his eggs during breakfast?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_40-healthcare-10", "question": [[{"role": "user", "content": "The patient made a change in their bedtime routine where they said they would stop drinking caffeine after what time? Return in the format of HH:MM AM/PM (e.g. 01:30 AM)."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_41-healthcare-11", "question": [[{"role": "user", "content": "What is one of the patient's favorite go-to meals?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_42-healthcare-12", "question": [[{"role": "user", "content": "The patient used to think that workouts had to be intense. However, they learned that something is more important. What have they realized is more important than intensity as they have aged?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_43-healthcare-13", "question": [[{"role": "user", "content": "The patient realizes the importance to maintain social connections as they age. What did they join last year to help maintain social connections as they age?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_44-healthcare-14", "question": [[{"role": "user", "content": "What is an activity the patient undertakes to keep their memory sharp? Pick from one of: puzzles, jogging, gardening."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_45-healthcare-15", "question": [[{"role": "user", "content": "What was the patient's most recent fasting blood glucose reading? Give the value in mg/dL."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_46-healthcare-16", "question": [[{"role": "user", "content": "What was the patient's A1C value? Give the value in % rounded to the nearest tenth (ex: 7.9)."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_47-healthcare-17", "question": [[{"role": "user", "content": "What should the fasting blood glucose level ideally be under?"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_48-healthcare-18", "question": [[{"role": "user", "content": "What is the patient's LDL cholesterol level? Give the value in mg/dL."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_49-healthcare-19", "question": [[{"role": "user", "content": "What was the patient's blood pressure reading at the last check-up? Give the value in mmHg."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_50-healthcare-20", "question": [[{"role": "user", "content": "What was the patient's vitamin D level? Give the value in ng/mL."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_51-healthcare-21", "question": [[{"role": "user", "content": "What was the patient's ALT level? Give me the value in U/L."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_52-healthcare-22", "question": [[{"role": "user", "content": "What were the patient's ESR results? Give me the value in mm/hr."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_53-healthcare-23", "question": [[{"role": "user", "content": "What medication+dosage does the patient take for Type 2 Diabetes? Give me the answer in the format: 'medication X mg' (e.g. Caffeine 200 mg)"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_54-healthcare-24", "question": [[{"role": "user", "content": "What medication+dosage does the patient take for high blood pressure? Give me the answer in the format: 'medication X mg' (e.g. Caffeine 200 mg)"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_55-finance-0", "question": [[{"role": "user", "content": "What is the name of the investment firm I manage?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_56-finance-1", "question": [[{"role": "user", "content": "What was the name of the mid-cap tech firm that faced a hostile takeover, which led to a famous poison pill strategy?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_57-finance-2", "question": [[{"role": "user", "content": "I remember there was the $14.2B healthcare merger with BioCrest Labs and one other company. It was described as 'the deal that reshaped modern cancer treatment'. What was the name of the other company involved in the healthcare merger with BioCrest Labs?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_58-finance-3", "question": [[{"role": "user", "content": "What has become my virtual deal diary?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_59-finance-4", "question": [[{"role": "user", "content": "What is the name of the AI-driven market analysis platform developed in-house by Legend Investments?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_60-finance-5", "question": [[{"role": "user", "content": "Where did I start my career in 2001?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_61-finance-6", "question": [[{"role": "user", "content": "Where did I get my MBA from?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_62-finance-7", "question": [[{"role": "user", "content": "How do I approach my personal investments differently from managing the firm\u2019s assets?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_63-finance-8", "question": [[{"role": "user", "content": "I forget this all the time, but what is my handicap in golf?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_64-finance-9", "question": [[{"role": "user", "content": "What time do I start my mornings? Return in the format of HH:MM AM/PM (e.g. 01:30 AM)."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_65-finance-10", "question": [[{"role": "user", "content": "What language do I aim to become fluent in as part of my personal development goals?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_66-finance-11", "question": [[{"role": "user", "content": "By what age do I aim to transition into a chairman role at my firm?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_67-finance-12", "question": [[{"role": "user", "content": "How often do I have to travel for maintaining my global network? Give me the number of times a week."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_68-finance-13", "question": [[{"role": "user", "content": "What is the name of the dinner series I host every quarter?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_69-finance-14", "question": [[{"role": "user", "content": "In my family life, how much does success in finance mean if I sacrifice the relationships that matter the most?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_70-finance-15", "question": [[{"role": "user", "content": "My kids are exploring their own paths that they are interested in. What is my 14 year old one obsessed with?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_71-finance-16", "question": [[{"role": "user", "content": "How do I incorporate my family into my business travel?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_72-finance-17", "question": [[{"role": "user", "content": "What platform do I use as my financial command center?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_73-finance-18", "question": [[{"role": "user", "content": "What machine learning platform did I champion for implementation at my firm?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_74-finance-19", "question": [[{"role": "user", "content": "Which tool do I use for data visualization and client communication?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_75-finance-20", "question": [[{"role": "user", "content": "Which of the following is a programming language that I taught myself during the pandemic? Choose an answer from the following options: Java, C, Python, SQL, Go."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_76-finance-21", "question": [[{"role": "user", "content": "What are the three key factors I believe contribute to success? List them in this format in alphabetical order: 'factor1, factor2, factor3'"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_77-finance-22", "question": [[{"role": "user", "content": "What was the name of the deal that I lost a massive deal on early in my career?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_78-finance-23", "question": [[{"role": "user", "content": "What favors those who put themselves in positions where opportunity can find them?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_79-finance-24", "question": [[{"role": "user", "content": "What is my advice to young professionals about career growth?"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_80-student-0", "question": [[{"role": "user", "content": "What is my favorite course?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_81-student-1", "question": [[{"role": "user", "content": "How long does the Capstone project last? Answer in number of semesters."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_82-student-2", "question": [[{"role": "user", "content": "Which is the class number (CS XXXX) of the class that contains a project simulating a decentralized file storage system?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_83-student-3", "question": [[{"role": "user", "content": "What field of computer science does my Capstone project use?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_84-student-4", "question": [[{"role": "user", "content": "How many hours a week can the Capstone project require?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_85-student-5", "question": [[{"role": "user", "content": "Which activity do I do about four times weekly?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_86-student-6", "question": [[{"role": "user", "content": "What kind of video games have I been playing lately?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_87-student-7", "question": [[{"role": "user", "content": "What's my favorite AI-themed sci-fi book?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_88-student-8", "question": [[{"role": "user", "content": "What else do I schedule just like study time?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_89-student-9", "question": [[{"role": "user", "content": "Which hobby shows me incremental improvements?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_90-student-10", "question": [[{"role": "user", "content": "How many papers did I co-author in high school? Give the number."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_91-student-11", "question": [[{"role": "user", "content": "What was the first research project about?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_92-student-12", "question": [[{"role": "user", "content": "What kind of technologies am I exploring in my research?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_93-student-13", "question": [[{"role": "user", "content": "Which major conference am I aiming to submit to?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_94-student-14", "question": [[{"role": "user", "content": "What did the server crash teach me the importance of?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_95-student-15", "question": [[{"role": "user", "content": "When did I join the VR/AR Club?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_96-student-16", "question": [[{"role": "user", "content": "Where does the annual Tech Fest take place?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_97-student-17", "question": [[{"role": "user", "content": "What was my dorm called in the first two years?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_98-student-18", "question": [[{"role": "user", "content": "What is the one-day event I volunteer at called?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_99-student-19", "question": [[{"role": "user", "content": "What campus team got officially recognized recently?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_100-student-20", "question": [[{"role": "user", "content": "How many years am I planning to work in industry first, if at all? Give the number."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_101-student-21", "question": [[{"role": "user", "content": "If I do grad school, what do I want to do research in?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_102-student-22", "question": [[{"role": "user", "content": "How many people work at the data analytics startup I interned at last summer?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_103-student-23", "question": [[{"role": "user", "content": "What role am I looking for in the job market?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_104-student-24", "question": [[{"role": "user", "content": "What type of collaboration do I want my future company to encourage?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_105-student-25", "question": [[{"role": "user", "content": "What state did I take a sailing trip off the coast of?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_106-student-26", "question": [[{"role": "user", "content": "Which country did I visit over junior-year winter break?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_107-student-27", "question": [[{"role": "user", "content": "What non-capital Asian city did I visit over junior-year winter break?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_108-student-28", "question": [[{"role": "user", "content": "On the family trip to Europe, which city did we visit first?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_109-student-29", "question": [[{"role": "user", "content": "What is one example of a smaller getaway destination my roommates and I have done?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_110-student-30", "question": [[{"role": "user", "content": "What class is my crush in?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_111-student-31", "question": [[{"role": "user", "content": "What setting did I first connect with my crush in?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_112-student-32", "question": [[{"role": "user", "content": "What am I planning to grab with my crush after class?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_113-student-33", "question": [[{"role": "user", "content": "While having a crush, what am I worried about losing?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_114-student-34", "question": [[{"role": "user", "content": "Who do I usually tell everything to, but not this time about my crush?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_115-student-35", "question": [[{"role": "user", "content": "What's my dog's name?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_116-student-36", "question": [[{"role": "user", "content": "How many years younger is my sister?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_117-student-37", "question": [[{"role": "user", "content": "When I'm in town, what day does my family usually go on a hike?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_118-student-38", "question": [[{"role": "user", "content": "What's my dad's profession?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_119-student-39", "question": [[{"role": "user", "content": "Which relative teases me about breaking an antique vase?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_120-student-40", "question": [[{"role": "user", "content": "Who was my freshman-year roommate?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_121-student-41", "question": [[{"role": "user", "content": "Who did I bond with over a 3 a.m. coding session?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_122-student-42", "question": [[{"role": "user", "content": "Which club introduced me to people who share a water sport interest?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_123-student-43", "question": [[{"role": "user", "content": "What do I use on the weekends as a mental break?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_124-student-44", "question": [[{"role": "user", "content": "Where did I meet Jackson, a law student?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_125-student-45", "question": [[{"role": "user", "content": "What time in the morning do I usually wake up? Return in the format of HH:MM AM/PM (e.g. 01:30 AM)."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_126-student-46", "question": [[{"role": "user", "content": "What do I use that syncs across my devices?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_127-student-47", "question": [[{"role": "user", "content": "What part of my schedule is treated as non-negotiable?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_128-student-48", "question": [[{"role": "user", "content": "Most evenings, I try to cut off academic work by what time? Return in the format of HH:MM AM/PM (e.g. 01:30 AM)."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_129-student-49", "question": [[{"role": "user", "content": "On Sundays, not including myself, how many students attend the group study session?"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_130-notetaker-0", "question": [[{"role": "user", "content": "What time is the daycare drop-off for my kid on Monday? Return in the format of HH:MM AM/PM (e.g. 01:30 AM)."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_131-notetaker-1", "question": [[{"role": "user", "content": "I almost forgot, what day is the monthly daycare payment due?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_132-notetaker-2", "question": [[{"role": "user", "content": "What is the deadline to finalize that Q1 budget report?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_133-notetaker-3", "question": [[{"role": "user", "content": "Wait a minute, what should I change after IT updates the security protocols?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_134-notetaker-4", "question": [[{"role": "user", "content": "I remember that the client call with Jacob rescheduled. When is it now?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_135-notetaker-5", "question": [[{"role": "user", "content": "I need to report to my manager about the workflow slow down. What slowed down the workflow on Tuesday?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_136-notetaker-6", "question": [[{"role": "user", "content": "What should I review before next month\u2019s deadline set by HR?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_137-notetaker-7", "question": [[{"role": "user", "content": "What time is the daycare pickup? Return in the format of HH:MM AM/PM (e.g. 01:30 AM)."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_138-notetaker-8", "question": [[{"role": "user", "content": "Wait a minute, what team am I supposed to send the client proposal draft to on Wednesday?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_139-notetaker-9", "question": [[{"role": "user", "content": "I am stressed out. I forgot, how many minutes did the team meeting on Monday run over?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_140-notetaker-10", "question": [[{"role": "user", "content": "Wait a minute, who wants the cost-cutting recommendations by Thursday?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_141-notetaker-11", "question": [[{"role": "user", "content": "I need to know something for the vendor planning tasks. How many vendors responded to the follow-up on Friday?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_142-notetaker-12", "question": [[{"role": "user", "content": "I may be forgetting, what are the documents I need to urgently submit before Friday?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_143-notetaker-13", "question": [[{"role": "user", "content": "What should I do about the weird noise in my car engine? It's bugging me."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_144-notetaker-14", "question": [[{"role": "user", "content": "I completely forgot. What training module did I have to finish?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_145-notetaker-15", "question": [[{"role": "user", "content": "What did my reminder say to do with regards to my phone software?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_146-notetaker-16", "question": [[{"role": "user", "content": "What should I do with the car on Sunday? I know that I had to wash my car but I feel like there was one more thing I had to do. What was it?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_147-notetaker-17", "question": [[{"role": "user", "content": "I misremember the plan for restocking the pantry. I know I was running low on rice but what else should I be restocking other than rice?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_148-notetaker-18", "question": [[{"role": "user", "content": "What is the exact time window for the system downtime on Tuesday? Give me the window in the format: 'HH AM/PM - HH AM/PM' (e.g. '01 AM - 02 AM')."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_149-notetaker-19", "question": [[{"role": "user", "content": "I completely forgot, do I have any new hires that I need to onboard on Wednesday?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_150-notetaker-20", "question": [[{"role": "user", "content": "Are there any health supplements that I should be taking in the morning?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_151-notetaker-21", "question": [[{"role": "user", "content": "For my Sunday meal prep plan, what was the main protein that I was going to cook for the meal?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_152-notetaker-22", "question": [[{"role": "user", "content": "How many DIY fixes for stuff at home do I have planned?"}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_153-notetaker-23", "question": [[{"role": "user", "content": "Wait, what school supply am I going shopping for besides crayons and notebooks? The kids need them."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_154-notetaker-24", "question": [[{"role": "user", "content": "What specific numbers did the teacher tell my kid focus on during counting practice? Give me the range in the format: 'X-Y' (e.g. '37-83')."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/BFCL_v4_simple_python.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/BFCL_v4_simple_python.json new file mode 100644 index 000000000..356a0db61 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/BFCL_v4_simple_python.json @@ -0,0 +1,400 @@ +{"id": "simple_python_0", "question": [[{"role": "user", "content": "Find the area of a triangle with a base of 10 units and height of 5 units."}]], "function": [{"name": "calculate_triangle_area", "description": "Calculate the area of a triangle given its base and height.", "parameters": {"type": "dict", "properties": {"base": {"type": "integer", "description": "The base of the triangle."}, "height": {"type": "integer", "description": "The height of the triangle."}, "unit": {"type": "string", "description": "The unit of measure (defaults to 'units' if not specified)"}}, "required": ["base", "height"]}}]} +{"id": "simple_python_1", "question": [[{"role": "user", "content": "Calculate the factorial of 5 using math functions."}]], "function": [{"name": "math.factorial", "description": "Calculate the factorial of a given number.", "parameters": {"type": "dict", "properties": {"number": {"type": "integer", "description": "The number for which factorial needs to be calculated."}}, "required": ["number"]}}]} +{"id": "simple_python_2", "question": [[{"role": "user", "content": "Calculate the hypotenuse of a right triangle given the lengths of the other two sides as 4 and 5."}]], "function": [{"name": "math.hypot", "description": "Calculate the Euclidean norm, sqrt(sum(squares)), the length of the vector from the origin to point (x, y) which is the hypotenuse of the right triangle.", "parameters": {"type": "dict", "properties": {"x": {"type": "integer", "description": "The x-coordinate value."}, "y": {"type": "integer", "description": "The y-coordinate value."}, "z": {"type": "integer", "description": "Optional. The z-coordinate value. Default is 0."}}, "required": ["x", "y"]}}]} +{"id": "simple_python_3", "question": [[{"role": "user", "content": "Find the roots of a quadratic equation with coefficients a=1, b=-3, c=2."}]], "function": [{"name": "algebra.quadratic_roots", "description": "Find the roots of a quadratic equation ax^2 + bx + c = 0.", "parameters": {"type": "dict", "properties": {"a": {"type": "integer", "description": "Coefficient of x^2."}, "b": {"type": "integer", "description": "Coefficient of x."}, "c": {"type": "integer", "description": "Constant term."}}, "required": ["a", "b", "c"]}}]} +{"id": "simple_python_4", "question": [[{"role": "user", "content": "Solve a quadratic equation where a=2, b=6, and c=5"}]], "function": [{"name": "solve_quadratic_equation", "description": "Function solves the quadratic equation and returns its roots.", "parameters": {"type": "dict", "properties": {"a": {"type": "integer", "description": "Coefficient of x squared"}, "b": {"type": "integer", "description": "Coefficient of x"}, "c": {"type": "integer", "description": "Constant term in the quadratic equation."}}, "required": ["a", "b", "c"]}}]} +{"id": "simple_python_5", "question": [[{"role": "user", "content": "Find all the roots of a quadratic equation given coefficients a = 3, b = -11, and c = -4."}]], "function": [{"name": "solve_quadratic", "description": "Solve a quadratic equation given coefficients a, b, and c. If optional 'root_type' is 'real', the function will only return real roots. If not specified, function may return complex roots.", "parameters": {"type": "dict", "properties": {"a": {"type": "integer", "description": "The coefficient of the squared term in the quadratic equation."}, "b": {"type": "integer", "description": "The coefficient of the linear term in the quadratic equation."}, "c": {"type": "integer", "description": "The constant term in the quadratic equation."}, "root_type": {"type": "string", "description": "The type of roots to return: 'real' for real roots, 'all' for both real and complex roots. Default value is 'real'."}}, "required": ["a", "b", "c"]}}]} +{"id": "simple_python_6", "question": [[{"role": "user", "content": "What are the roots of the quadratic equation where a=2, b=5 and c=3 ?"}]], "function": [{"name": "solve_quadratic", "description": "Find the roots of a quadratic equation. Returns both roots.", "parameters": {"type": "dict", "properties": {"a": {"type": "integer", "description": "Coefficient of x\u00b2."}, "b": {"type": "integer", "description": "Coefficient of x."}, "c": {"type": "integer", "description": "Constant term."}}, "required": ["a", "b", "c"]}}]} +{"id": "simple_python_7", "question": [[{"role": "user", "content": "What is the circumference of a circle with a radius of 4 inches?"}]], "function": [{"name": "calculate_circumference", "description": "Calculates the circumference of a circle with a given radius.", "parameters": {"type": "dict", "properties": {"radius": {"type": "integer", "description": "The radius of the circle in the unit given."}, "unit": {"type": "string", "description": "The unit of measurement for the radius. Default is 'cm'."}}, "required": ["radius"]}}]} +{"id": "simple_python_8", "question": [[{"role": "user", "content": "What's the area of a circle with a radius of 10?"}]], "function": [{"name": "geometry.area_circle", "description": "Calculate the area of a circle given the radius.", "parameters": {"type": "dict", "properties": {"radius": {"type": "integer", "description": "The radius of the circle."}, "units": {"type": "string", "description": "The units in which the radius is measured (defaults to 'meters')."}}, "required": ["radius"]}}]} +{"id": "simple_python_9", "question": [[{"role": "user", "content": "Calculate the area of a circle with a radius of 5 units."}]], "function": [{"name": "geometry.calculate_area_circle", "description": "Calculate the area of a circle given its radius.", "parameters": {"type": "dict", "properties": {"radius": {"type": "integer", "description": "The radius of the circle."}, "unit": {"type": "string", "description": "The measurement unit of the radius (optional parameter, default is 'units')."}}, "required": ["radius"]}}]} +{"id": "simple_python_10", "question": [[{"role": "user", "content": "Calculate the area of a right-angled triangle given the lengths of its base and height as 6cm and 10cm."}]], "function": [{"name": "calculate_area", "description": "Calculate the area of a right-angled triangle given the lengths of its base and height.", "parameters": {"type": "dict", "properties": {"base": {"type": "integer", "description": "The length of the base of the right-angled triangle."}, "height": {"type": "integer", "description": "The height of the right-angled triangle."}, "unit": {"type": "string", "description": "The unit of measure used. Defaults to 'cm'."}}, "required": ["base", "height"]}}]} +{"id": "simple_python_11", "question": [[{"role": "user", "content": "What is the area of a triangle with base of 10 units and height of 5 units?"}]], "function": [{"name": "calculate_triangle_area", "description": "Calculate the area of a triangle using its base and height.", "parameters": {"type": "dict", "properties": {"base": {"type": "integer", "description": "The length of the base of the triangle."}, "height": {"type": "integer", "description": "The height of the triangle from the base."}}, "required": ["base", "height"]}}]} +{"id": "simple_python_12", "question": [[{"role": "user", "content": "Calculate the circumference of a circle with radius 3"}]], "function": [{"name": "geometry.circumference", "description": "Calculate the circumference of a circle given the radius.", "parameters": {"type": "dict", "properties": {"radius": {"type": "integer", "description": "The radius of the circle."}, "units": {"type": "string", "description": "Units for the output circumference measurement. Default is 'cm'."}}, "required": ["radius"]}}]} +{"id": "simple_python_13", "question": [[{"role": "user", "content": "Calculate the area under the curve y=x^2 from x=1 to x=3."}]], "function": [{"name": "calculate_area_under_curve", "description": "Calculate the area under a mathematical function within a given interval.", "parameters": {"type": "dict", "properties": {"function": {"type": "string", "description": "The mathematical function as a string."}, "interval": {"type": "array", "items": {"type": "float"}, "description": "An array that defines the interval to calculate the area under the curve from the start to the end point."}, "method": {"type": "string", "description": "The numerical method to approximate the area under the curve. The default value is 'trapezoidal'."}}, "required": ["function", "interval"]}}]} +{"id": "simple_python_14", "question": [[{"role": "user", "content": "Calculate the derivative of the function 3x^2 + 2x - 1."}]], "function": [{"name": "calculate_derivative", "description": "Calculate the derivative of a polynomial function.", "parameters": {"type": "dict", "properties": {"function": {"type": "string", "description": "The polynomial function."}, "x_value": {"type": "float", "description": "The x-value at which the derivative is calculated. Optional, default to 0.00."}}, "required": ["function"]}}]} +{"id": "simple_python_15", "question": [[{"role": "user", "content": "Calculate the area under the curve from x = -2 to x = 3 for the function y = x^3 using simpson method."}]], "function": [{"name": "integrate", "description": "Calculate the area under a curve for a specified function between two x values.", "parameters": {"type": "dict", "properties": {"function": {"type": "string", "description": "The function to integrate, represented as a string. For example, 'x^3'"}, "start_x": {"type": "integer", "description": "The starting x-value to integrate over."}, "end_x": {"type": "integer", "description": "The ending x-value to integrate over."}, "method": {"type": "string", "description": "The method of numerical integration to use. Choices are 'trapezoid' or 'simpson'. Default is 'trapezoid'."}}, "required": ["function", "start_x", "end_x"]}}]} +{"id": "simple_python_16", "question": [[{"role": "user", "content": "Calculate the derivative of the function 2x^2 at x = 1."}]], "function": [{"name": "calculus.derivative", "description": "Compute the derivative of a function at a specific value.", "parameters": {"type": "dict", "properties": {"function": {"type": "string", "description": "The function to calculate the derivative of."}, "value": {"type": "integer", "description": "The value where the derivative needs to be calculated at."}, "function_variable": {"type": "string", "description": "The variable present in the function, for instance x or y, etc. Default is 'x'."}}, "required": ["function", "value"]}}]} +{"id": "simple_python_17", "question": [[{"role": "user", "content": "Find the prime factors of 450"}]], "function": [{"name": "get_prime_factors", "description": "Function to retrieve prime factors of a number.", "parameters": {"type": "dict", "properties": {"number": {"type": "integer", "description": "Number for which prime factors need to be calculated"}, "formatted": {"type": "boolean", "description": "Return formatted string if true, array if false. Default is true."}}, "required": ["number", "formatted"]}}]} +{"id": "simple_python_18", "question": [[{"role": "user", "content": "Find the prime factors of the number 123456."}]], "function": [{"name": "number_analysis.prime_factors", "description": "Compute the prime factors of a number.", "parameters": {"type": "dict", "properties": {"number": {"type": "integer", "description": "The number to be factored."}}, "required": ["number"]}}]} +{"id": "simple_python_19", "question": [[{"role": "user", "content": "Calculate the greatest common divisor of two numbers: 40 and 50"}]], "function": [{"name": "math.gcd", "description": "Compute the greatest common divisor of two numbers", "parameters": {"type": "dict", "properties": {"num1": {"type": "integer", "description": "The first number."}, "num2": {"type": "integer", "description": "The second number."}}, "required": ["num1", "num2"]}}]} +{"id": "simple_python_20", "question": [[{"role": "user", "content": "Find the highest common factor of 36 and 24."}]], "function": [{"name": "math.hcf", "description": "Calculate the highest common factor of two numbers.", "parameters": {"type": "dict", "properties": {"number1": {"type": "integer", "description": "First number."}, "number2": {"type": "integer", "description": "Second number."}}, "required": ["number1", "number2"]}}]} +{"id": "simple_python_21", "question": [[{"role": "user", "content": "Find the Greatest Common Divisor (GCD) of two numbers, say 36 and 48."}]], "function": [{"name": "number_theory.gcd", "description": "Compute the greatest common divisor of two given integers.", "parameters": {"type": "dict", "properties": {"number1": {"type": "integer", "description": "The first integer."}, "number2": {"type": "integer", "description": "The second integer."}}, "required": ["number1", "number2"]}}]} +{"id": "simple_python_22", "question": [[{"role": "user", "content": "Calculate the greatest common divisor of two given numbers, for example 12 and 15."}]], "function": [{"name": "math.gcd", "description": "Calculate the greatest common divisor (gcd) of the two integers.", "parameters": {"type": "dict", "properties": {"num1": {"type": "integer", "description": "The first number."}, "num2": {"type": "integer", "description": "The second number."}}, "required": ["num1", "num2"]}}]} +{"id": "simple_python_23", "question": [[{"role": "user", "content": "What is the prime factorization of the number 60? Return them in the form of dictionary"}]], "function": [{"name": "prime_factorize", "description": "Calculate the prime factorization of a given integer.", "parameters": {"type": "dict", "properties": {"number": {"type": "integer", "description": "The number for which to calculate the prime factorization."}, "return_type": {"type": "string", "description": "Determines the format of the returned prime factorization. Can be 'list' for a list of all prime factors or 'dictionary' for a count of each prime factor. Default is 'list'."}}, "required": ["number"]}}]} +{"id": "simple_python_24", "question": [[{"role": "user", "content": "Find the greatest common divisor (GCD) of 12 and 18"}]], "function": [{"name": "math.gcd", "description": "Calculate the greatest common divisor of two integers.", "parameters": {"type": "dict", "properties": {"num1": {"type": "integer", "description": "First number."}, "num2": {"type": "integer", "description": "Second number."}}, "required": ["num1", "num2"]}}]} +{"id": "simple_python_25", "question": [[{"role": "user", "content": "Calculate the final velocity of an object falling from a 150 meter building, assuming initial velocity is zero."}]], "function": [{"name": "calculate_final_velocity", "description": "Calculate the final velocity of a free falling object given the height it's dropped from, the initial velocity and acceleration due to gravity. Ignore air resistance.", "parameters": {"type": "dict", "properties": {"height": {"type": "integer", "description": "The height the object is dropped from, in meters."}, "initial_velocity": {"type": "integer", "description": "The initial velocity of the object in m/s. Default is zero."}, "gravity": {"type": "float", "description": "Acceleration due to gravity. Default value is 9.81 m/s^2, earth's gravity."}}, "required": ["height"]}}]} +{"id": "simple_python_26", "question": [[{"role": "user", "content": "Calculate the velocity of a car that travels a distance of 50 kilometers for a duration of 2 hours?"}]], "function": [{"name": "calculate_velocity", "description": "Calculate the velocity for a certain distance travelled within a specific duration.", "parameters": {"type": "dict", "properties": {"distance": {"type": "integer", "description": "The distance travelled by the object, typically in kilometers."}, "duration": {"type": "integer", "description": "The duration of the journey, typically in hours."}, "unit": {"type": "string", "description": "Optional parameter. The unit to return the velocity in. If not provided, the default is km/h."}}, "required": ["distance", "duration"]}}]} +{"id": "simple_python_27", "question": [[{"role": "user", "content": "Calculate the final velocity of a vehicle after accelerating at 2 meters/second^2 for a duration of 5 seconds, starting from a speed of 10 meters/second."}]], "function": [{"name": "final_velocity", "description": "Calculate the final velocity of an object given its initial velocity, acceleration, and time.", "parameters": {"type": "dict", "properties": {"initial_velocity": {"type": "integer", "description": "The initial velocity of the object in meters/second."}, "acceleration": {"type": "integer", "description": "The acceleration of the object in meters/second^2."}, "time": {"type": "integer", "description": "The time over which the acceleration is applied in seconds."}}, "required": ["initial_velocity", "acceleration", "time"]}}]} +{"id": "simple_python_28", "question": [[{"role": "user", "content": "Calculate the displacement of a car given the initial velocity of 10 and acceleeration of 9.8 within 5 seconds."}]], "function": [{"name": "calculate_displacement", "description": "Calculates the displacement of an object in motion given initial velocity, time, and acceleration.", "parameters": {"type": "dict", "properties": {"initial_velocity": {"type": "integer", "description": "The initial velocity of the object in m/s."}, "time": {"type": "integer", "description": "The time in seconds that the object has been in motion."}, "acceleration": {"type": "float", "description": "The acceleration of the object in m/s^2.", "default": 0}}, "required": ["initial_velocity", "time"]}}]} +{"id": "simple_python_29", "question": [[{"role": "user", "content": "What is the final speed of an object dropped from rest after falling for 5 seconds if we neglect air resistance?"}]], "function": [{"name": "calculate_final_speed", "description": "Calculate the final speed of an object in free fall after a certain time, neglecting air resistance. The acceleration due to gravity is considered as -9.81 m/s^2", "parameters": {"type": "dict", "properties": {"initial_speed": {"type": "integer", "description": "The initial speed of the object in m/s. Default is 0 for an object at rest."}, "time": {"type": "integer", "description": "The time in seconds for which the object is in free fall."}, "gravity": {"type": "float", "description": "The acceleration due to gravity. Default is -9.81 m/s^2."}}, "required": ["time"]}}]} +{"id": "simple_python_30", "question": [[{"role": "user", "content": "What is the final velocity of a vehicle that started from rest and accelerated at 4 m/s^2 for a distance of 300 meters?"}]], "function": [{"name": "kinematics.final_velocity_from_distance", "description": "Calculate the final velocity of an object given the acceleration and distance travelled, assuming initial velocity is 0.", "parameters": {"type": "dict", "properties": {"acceleration": {"type": "integer", "description": "Acceleration of the object, m/s^2."}, "distance": {"type": "integer", "description": "Distance traveled by the object, m."}, "initial_velocity": {"type": "float", "description": "Initial velocity of the object. Default is 0, m/s"}}, "required": ["acceleration", "distance"]}}]} +{"id": "simple_python_31", "question": [[{"role": "user", "content": "Calculate the final velocity of an object, knowing that it started from rest, accelerated at a rate of 9.8 m/s^2 for a duration of 5 seconds."}]], "function": [{"name": "calculate_final_velocity", "description": "Calculate the final velocity of an object under constant acceleration, knowing its initial velocity, acceleration, and time of acceleration.", "parameters": {"type": "dict", "properties": {"initial_velocity": {"type": "integer", "description": "The initial velocity of the object."}, "acceleration": {"type": "float", "description": "The acceleration of the object."}, "time": {"type": "integer", "description": "The time of acceleration."}}, "required": ["initial_velocity", "acceleration", "time"]}}]} +{"id": "simple_python_32", "question": [[{"role": "user", "content": "Calculate the final speed of an object dropped from 100 m without air resistance."}]], "function": [{"name": "calculate_final_speed", "description": "Calculate the final speed of an object dropped from a certain height without air resistance.", "parameters": {"type": "dict", "properties": {"initial_velocity": {"type": "integer", "description": "The initial velocity of the object."}, "height": {"type": "integer", "description": "The height from which the object is dropped."}, "gravity": {"type": "float", "description": "The gravitational acceleration. Default is 9.8 m/s^2."}}, "required": ["initial_velocity", "height"]}}]} +{"id": "simple_python_33", "question": [[{"role": "user", "content": "Get directions from Sydney to Melbourne using the fastest route."}]], "function": [{"name": "get_directions", "description": "Retrieve directions from one location to another.", "parameters": {"type": "dict", "properties": {"start_location": {"type": "string", "description": "The starting point of the journey."}, "end_location": {"type": "string", "description": "The destination point of the journey."}, "route_type": {"type": "string", "description": "Type of route to use (e.g., 'fastest', 'scenic'). Default is 'fastest'.", "enum": ["fastest", "scenic"]}}, "required": ["start_location", "end_location"]}}]} +{"id": "simple_python_34", "question": [[{"role": "user", "content": "Create an itinerary for a 7 days trip to Tokyo with daily budgets not exceeding $100 and prefer exploring nature."}]], "function": [{"name": "travel_itinerary_generator", "description": "Generate a travel itinerary based on specific destination, duration and daily budget, with preferred exploration type.", "parameters": {"type": "dict", "properties": {"destination": {"type": "string", "description": "Destination city of the trip."}, "days": {"type": "integer", "description": "Number of days for the trip."}, "daily_budget": {"type": "integer", "description": "The maximum daily budget for the trip."}, "exploration_type": {"type": "string", "enum": ["nature", "urban", "history", "culture"], "description": "The preferred exploration type.", "default": "urban"}}, "required": ["destination", "days", "daily_budget"]}}]} +{"id": "simple_python_35", "question": [[{"role": "user", "content": "Find an all vegan restaurant in New York that opens until at least 11 PM."}]], "function": [{"name": "vegan_restaurant.find_nearby", "description": "Locate nearby vegan restaurants based on specific criteria like operating hours.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. New York, NY, you should format it as City, State."}, "operating_hours": {"type": "integer", "description": "Preferred latest closing time of the restaurant. E.g. if 11 is given, then restaurants that close at or after 11 PM will be considered. This is in 24 hour format. Default is 24."}}, "required": ["location"]}}]} +{"id": "simple_python_36", "question": [[{"role": "user", "content": "Find the shortest driving distance between New York City and Washington D.C."}]], "function": [{"name": "get_shortest_driving_distance", "description": "Calculate the shortest driving distance between two locations.", "parameters": {"type": "dict", "properties": {"origin": {"type": "string", "description": "Starting point of the journey. You should format it as city name like Boston."}, "destination": {"type": "string", "description": "End point of the journey. You should format it as city name like Boston."}, "unit": {"type": "string", "description": "Preferred unit of distance (optional, default is 'km')."}}, "required": ["origin", "destination"]}}]} +{"id": "simple_python_37", "question": [[{"role": "user", "content": "Find the estimated travel time by car from San Francisco to Los Angeles with stops at Santa Barbara and Monterey."}]], "function": [{"name": "route.estimate_time", "description": "Estimate the travel time for a specific route with optional stops.", "parameters": {"type": "dict", "properties": {"start_location": {"type": "string", "description": "The starting point for the journey. It should be format as city name such as Boston."}, "end_location": {"type": "string", "description": "The destination for the journey. It should be format as city name such as Boston."}, "stops": {"type": "array", "items": {"type": "string"}, "description": "Additional cities or points of interest to stop at during the journey. Default is an empty list."}}, "required": ["start_location", "end_location"]}}]} +{"id": "simple_python_38", "question": [[{"role": "user", "content": "What is the electrostatic potential between two charged bodies of 1e-9 and 2e-9 of distance 0.05?"}]], "function": [{"name": "calculate_electrostatic_potential", "description": "Calculate the electrostatic potential between two charged bodies using the principle of Coulomb's Law.", "parameters": {"type": "dict", "properties": {"charge1": {"type": "float", "description": "The quantity of charge on the first body."}, "charge2": {"type": "float", "description": "The quantity of charge on the second body."}, "distance": {"type": "float", "description": "The distance between the two bodies."}, "constant": {"type": "float", "description": "The value of the electrostatic constant. Default is 8.99e9."}}, "required": ["charge1", "charge2", "distance"]}}]} +{"id": "simple_python_39", "question": [[{"role": "user", "content": "Calculate the electric field at a point 3 meters away from a charge of 2 coulombs."}]], "function": [{"name": "calculate_electric_field", "description": "Calculate the electric field produced by a charge at a certain distance.", "parameters": {"type": "dict", "properties": {"charge": {"type": "integer", "description": "Charge in coulombs producing the electric field."}, "distance": {"type": "integer", "description": "Distance from the charge in meters where the field is being measured."}, "permitivity": {"type": "float", "description": "Permitivity of the space where field is being calculated, default is 8.854e-12."}}, "required": ["charge", "distance"]}}]} +{"id": "simple_python_40", "question": [[{"role": "user", "content": "Calculate the magnetic field produced at the center of a circular loop carrying current of 5 Ampere with a radius of 4 meters"}]], "function": [{"name": "calculate_magnetic_field", "description": "Calculate the magnetic field produced at the center of a circular loop carrying current.", "parameters": {"type": "dict", "properties": {"current": {"type": "integer", "description": "The current through the circular loop in Amperes."}, "radius": {"type": "integer", "description": "The radius of the circular loop in meters."}, "permeability": {"type": "float", "description": "The magnetic permeability. Default is 12.57e10 (Vacuum Permeability)."}}, "required": ["current", "radius"]}}]} +{"id": "simple_python_41", "question": [[{"role": "user", "content": "Calculate the electromagnetic force between two charges of 5C and 7C placed 3 meters apart."}]], "function": [{"name": "electromagnetic_force", "description": "Calculate the electromagnetic force between two charges placed at a certain distance.", "parameters": {"type": "dict", "properties": {"charge1": {"type": "integer", "description": "The magnitude of the first charge in coulombs."}, "charge2": {"type": "integer", "description": "The magnitude of the second charge in coulombs."}, "distance": {"type": "integer", "description": "The distance between the two charges in meters."}, "medium_permittivity": {"type": "float", "description": "The relative permittivity of the medium in which the charges are present. Default is 8.854e-12 (Vacuum Permittivity)."}}, "required": ["charge1", "charge2", "distance"]}}]} +{"id": "simple_python_42", "question": [[{"role": "user", "content": "Calculate the resonant frequency of an LC circuit given capacitance of 100\u00b5F and inductance of 50mH."}]], "function": [{"name": "calculate_resonant_frequency", "description": "Calculate the resonant frequency of an LC (inductor-capacitor) circuit.", "parameters": {"type": "dict", "properties": {"inductance": {"type": "float", "description": "The inductance (L) in henries (H)."}, "capacitance": {"type": "float", "description": "The capacitance (C) in farads (F)."}, "round_off": {"type": "integer", "description": "Rounding off the result to a certain decimal places, default is 2."}}, "required": ["inductance", "capacitance"]}}]} +{"id": "simple_python_43", "question": [[{"role": "user", "content": "Calculate the magnetic field strength 10 meters away from a long wire carrying a current of 20 Amperes."}]], "function": [{"name": "calculate_magnetic_field_strength", "description": "Calculate the magnetic field strength at a point a certain distance away from a long wire carrying a current.", "parameters": {"type": "dict", "properties": {"current": {"type": "integer", "description": "The current flowing through the wire in Amperes."}, "distance": {"type": "integer", "description": "The perpendicular distance from the wire to the point where the magnetic field is being calculated."}, "permeability": {"type": "float", "description": "The permeability of the medium. Default is 12.57e-7 (Vacuum Permeability)."}}, "required": ["current", "distance"]}}]} +{"id": "simple_python_44", "question": [[{"role": "user", "content": "Calculate the electric field strength 4 meters away from a charge of 0.01 Coulombs."}]], "function": [{"name": "calculate_electric_field_strength", "description": "Calculate the electric field strength at a certain distance from a point charge.", "parameters": {"type": "dict", "properties": {"charge": {"type": "float", "description": "The charge in Coulombs."}, "distance": {"type": "integer", "description": "The distance from the charge in meters."}, "medium": {"type": "string", "description": "The medium in which the charge and the point of calculation is located. Default is 'vacuum'."}}, "required": ["charge", "distance"]}}]} +{"id": "simple_python_45", "question": [[{"role": "user", "content": "Calculate the energy (in Joules) absorbed or released during the phase change of 100g of water from liquid to steam at its boiling point."}]], "function": [{"name": "thermo.calculate_energy", "description": "Calculate the energy required or released during a phase change using mass, the phase transition temperature and the specific latent heat.", "parameters": {"type": "dict", "properties": {"mass": {"type": "integer", "description": "Mass of the substance in grams."}, "phase_transition": {"type": "string", "description": "Phase transition. Can be 'melting', 'freezing', 'vaporization', 'condensation'."}, "substance": {"type": "string", "description": "The substance which is undergoing phase change, default is 'water'"}}, "required": ["mass", "phase_transition"]}}]} +{"id": "simple_python_46", "question": [[{"role": "user", "content": "Calculate the final temperature when 20 kg of water at 30 degree Celsius is mixed with 15 kg of water at 60 degree Celsius."}]], "function": [{"name": "calculate_final_temperature", "description": "Calculates the final equilibrium temperature after mixing two bodies with different masses and temperatures", "parameters": {"type": "dict", "properties": {"mass1": {"type": "integer", "description": "The mass of the first body (kg)."}, "temperature1": {"type": "integer", "description": "The initial temperature of the first body (Celsius)."}, "mass2": {"type": "integer", "description": "The mass of the second body (kg)."}, "temperature2": {"type": "integer", "description": "The initial temperature of the second body (Celsius)."}, "specific_heat_capacity": {"type": "float", "description": "The specific heat capacity of the bodies in kJ/kg/K. If not provided, will default to that of water at room temperature, which is 4.2 kJ/kg/K."}}, "required": ["mass1", "temperature1", "mass2", "temperature2"]}}]} +{"id": "simple_python_47", "question": [[{"role": "user", "content": "Find the boiling point and melting point of water under the sea level of 5000m."}]], "function": [{"name": "get_boiling_melting_points", "description": "Retrieve the boiling point and melting point of a substance based on its name and the sea level.", "parameters": {"type": "dict", "properties": {"substance": {"type": "string", "description": "The name of the substance."}, "sea_level": {"type": "integer", "description": "The sea level in meters."}}, "required": ["substance", "sea_level"]}}]} +{"id": "simple_python_48", "question": [[{"role": "user", "content": "What is the density of a substance with a mass of 45 kg and a volume of 15 m\u00b3?"}]], "function": [{"name": "calculate_density", "description": "Calculate the density of a substance based on its mass and volume.", "parameters": {"type": "dict", "properties": {"mass": {"type": "integer", "description": "The mass of the substance in kilograms."}, "volume": {"type": "integer", "description": "The volume of the substance in cubic meters."}, "unit": {"type": "string", "description": "The unit of density. Default is kg/m\u00b3"}}, "required": ["mass", "volume"]}}]} +{"id": "simple_python_49", "question": [[{"role": "user", "content": "Calculate the absolute pressure in pascals given atmospheric pressure of 1 atm and a gauge pressure of 2 atm."}]], "function": [{"name": "calc_absolute_pressure", "description": "Calculates the absolute pressure from gauge and atmospheric pressures.", "parameters": {"type": "dict", "properties": {"atm_pressure": {"type": "integer", "description": "The atmospheric pressure in atmospheres (atm). Default is 1 atm if not provided."}, "gauge_pressure": {"type": "integer", "description": "The gauge pressure in atmospheres (atm). Must be provided."}}, "required": ["gauge_pressure"]}}]} +{"id": "simple_python_50", "question": [[{"role": "user", "content": "What is the change in entropy in Joules per Kelvin of a 1kg ice block at 0\u00b0C if it is heated to 100\u00b0C under 1 atmosphere of pressure?"}]], "function": [{"name": "entropy_change.calculate", "description": "Calculate the change in entropy for a mass of a specific substance under set initial and final conditions.", "parameters": {"type": "dict", "properties": {"substance": {"type": "string", "description": "The substance for which the change in entropy is calculated."}, "mass": {"type": "integer", "description": "The mass of the substance in kg."}, "initial_temperature": {"type": "integer", "description": "The initial temperature of the substance in degree Celsius."}, "final_temperature": {"type": "integer", "description": "The final temperature of the substance in degree Celsius."}, "pressure": {"type": "integer", "default": 1, "description": "The pressure the substance is under in atmospheres."}}, "required": ["substance", "mass", "initial_temperature", "final_temperature"]}}]} +{"id": "simple_python_51", "question": [[{"role": "user", "content": "Calculate the entropy change for a certain process given an initial temperature of 300K, a final temperature of 400K, and a heat capacity of 5J/K."}]], "function": [{"name": "calculate_entropy_change", "description": "Calculate the entropy change for an isothermal and reversible process.", "parameters": {"type": "dict", "properties": {"initial_temp": {"type": "integer", "description": "The initial temperature in Kelvin."}, "final_temp": {"type": "integer", "description": "The final temperature in Kelvin."}, "heat_capacity": {"type": "integer", "description": "The heat capacity in J/K."}, "isothermal": {"type": "boolean", "description": "Whether the process is isothermal. Default is True."}}, "required": ["initial_temp", "final_temp", "heat_capacity"]}}]} +{"id": "simple_python_52", "question": [[{"role": "user", "content": "Calculate the heat capacity at constant pressure for air, given its temperature is 298K and volume is 10 m^3."}]], "function": [{"name": "calc_heat_capacity", "description": "Calculate the heat capacity at constant pressure of air using its temperature and volume.", "parameters": {"type": "dict", "properties": {"temp": {"type": "integer", "description": "The temperature of the gas in Kelvin."}, "volume": {"type": "integer", "description": "The volume of the gas in m^3."}, "gas": {"type": "string", "description": "Type of gas, with 'air' as default."}}, "required": ["temp", "volume"]}}]} +{"id": "simple_python_53", "question": [[{"role": "user", "content": "Retrieve the sequence of DNA molecule with id `DNA123`."}]], "function": [{"name": "fetch_DNA_sequence", "description": "Retrieve the sequence of a DNA molecule with the given id from a public database.", "parameters": {"type": "dict", "properties": {"DNA_id": {"type": "string", "description": "Unique ID of the DNA molecule in the database."}, "format": {"type": "string", "description": "Optional parameter to get sequence in specific format (default to 'fasta')."}, "upstream": {"type": "integer", "description": "Optional parameter to include certain number of base pairs upstream the DNA sequence (default to 0)."}}, "required": ["DNA_id"]}}]} +{"id": "simple_python_54", "question": [[{"role": "user", "content": "Identify the protein sequence of a given human gene 'BRCA1'."}]], "function": [{"name": "get_protein_sequence", "description": "Retrieve the protein sequence encoded by a human gene.", "parameters": {"type": "dict", "properties": {"gene": {"type": "string", "description": "The human gene of interest."}, "species": {"type": "string", "description": "The species for which the gene is to be analyzed.", "default": "Homo sapiens"}}, "required": ["gene"]}}]} +{"id": "simple_python_55", "question": [[{"role": "user", "content": "Find me detailed information about the structure of human cell"}]], "function": [{"name": "biology.get_cell_info", "description": "Retrieve information about the structure and functioning of a specified type of cell", "parameters": {"type": "dict", "properties": {"cell_type": {"type": "string", "description": "Type of cell you want information about"}, "detailed": {"type": "boolean", "description": "Indicate if you want a detailed description of the cell", "default": "false"}}, "required": ["cell_type"]}}]} +{"id": "simple_python_56", "question": [[{"role": "user", "content": "What are the names of proteins found in the plasma membrane?"}]], "function": [{"name": "cellbio.get_proteins", "description": "Get the list of proteins in a specific cell compartment.", "parameters": {"type": "dict", "properties": {"cell_compartment": {"type": "string", "description": "The specific cell compartment."}, "include_description": {"type": "boolean", "description": "Set true if you want a brief description of each protein.", "default": "false"}}, "required": ["cell_compartment"]}}]} +{"id": "simple_python_57", "question": [[{"role": "user", "content": "Calculate the cell density in a sample with an optical density of 0.6, where the experiment dilution is 5 times."}]], "function": [{"name": "calculate_cell_density", "description": "Calculate the cell density of a biological sample based on its optical density and the experiment dilution.", "parameters": {"type": "dict", "properties": {"optical_density": {"type": "float", "description": "The optical density of the sample, usually obtained from a spectrophotometer reading."}, "dilution": {"type": "integer", "description": "The dilution factor applied during the experiment."}, "calibration_factor": {"type": "float", "description": "The calibration factor to adjust the density, default value is 1e9 assuming cell density is in CFU/mL."}}, "required": ["optical_density", "dilution"]}}]} +{"id": "simple_python_58", "question": [[{"role": "user", "content": "What is the function of ATP synthase in mitochondria?"}]], "function": [{"name": "cell_biology.function_lookup", "description": "Look up the function of a given molecule in a specified organelle.", "parameters": {"type": "dict", "properties": {"molecule": {"type": "string", "description": "The molecule of interest."}, "organelle": {"type": "string", "description": "The organelle of interest."}, "specific_function": {"type": "boolean", "description": "If set to true, a specific function of the molecule within the organelle will be provided, if such information exists."}}, "required": ["molecule", "organelle", "specific_function"]}}]} +{"id": "simple_python_59", "question": [[{"role": "user", "content": "Calculate the molecular weight of Glucose (C6H12O6) in grams/mole."}]], "function": [{"name": "calculate_molecular_weight", "description": "Calculate the molecular weight of a compound given the compound formula.", "parameters": {"type": "dict", "properties": {"compound": {"type": "string", "description": "The molecular formula of the compound."}, "to_unit": {"type": "string", "description": "The unit in which to return the result."}}, "required": ["compound", "to_unit"]}}]} +{"id": "simple_python_60", "question": [[{"role": "user", "content": "Find the type of gene mutation based on SNP (Single Nucleotide Polymorphism) ID rs6034464."}]], "function": [{"name": "mutation_type.find", "description": "Finds the type of a genetic mutation based on its SNP (Single Nucleotide Polymorphism) ID.", "parameters": {"type": "dict", "properties": {"snp_id": {"type": "string", "description": "The ID of the Single Nucleotide Polymorphism (SNP) mutation."}, "species": {"type": "string", "description": "Species in which the SNP occurs, default is 'Homo sapiens' (Humans)."}}, "required": ["snp_id"]}}]} +{"id": "simple_python_61", "question": [[{"role": "user", "content": "Predict whether a person with weight 150lbs and height 5ft 10in who is lightly active will get type 2 diabetes."}]], "function": [{"name": "diabetes_prediction", "description": "Predict the likelihood of diabetes type 2 based on a person's weight and height.", "parameters": {"type": "dict", "properties": {"weight": {"type": "integer", "description": "Weight of the person in lbs."}, "height": {"type": "integer", "description": "Height of the person in inches."}, "activity_level": {"type": "string", "enum": ["sedentary", "lightly active", "moderately active", "very active", "extra active"], "description": "Physical activity level of the person."}}, "required": ["weight", "height", "activity_level"]}}]} +{"id": "simple_python_62", "question": [[{"role": "user", "content": "Analyze the DNA sequence 'AGTCGATCGAACGTACGTACG' for any potential substitution mutations based on a reference sequence 'AGTCCATCGAACGTACGTACG'."}]], "function": [{"name": "analyze_dna_sequence", "description": "Analyzes the DNA sequence based on a reference sequence and return any potential mutations.", "parameters": {"type": "dict", "properties": {"sequence": {"type": "string", "description": "The DNA sequence to be analyzed."}, "reference_sequence": {"type": "string", "description": "The reference DNA sequence."}, "mutation_type": {"type": "string", "enum": ["insertion", "deletion", "substitution"], "description": "Type of the mutation to be looked for in the sequence. Default to 'substitution'."}}, "required": ["sequence", "reference_sequence"]}}]} +{"id": "simple_python_63", "question": [[{"role": "user", "content": "Find out how genetically similar a human and a chimp are in percentage."}]], "function": [{"name": "genetics.calculate_similarity", "description": "Calculates the genetic similarity between two species based on their DNA sequences.", "parameters": {"type": "dict", "properties": {"species1": {"type": "string", "description": "The first species to compare."}, "species2": {"type": "string", "description": "The second species to compare."}, "format": {"type": "string", "description": "The format of the result (percentage or fraction). Default is percentage."}}, "required": ["species1", "species2"]}}]} +{"id": "simple_python_64", "question": [[{"role": "user", "content": "What is the genotype frequency of AA genotype in a population, given that allele frequency of A is 0.3?"}]], "function": [{"name": "calculate_genotype_frequency", "description": "Calculate the frequency of homozygous dominant genotype based on the allele frequency using Hardy Weinberg Principle.", "parameters": {"type": "dict", "properties": {"allele_frequency": {"type": "float", "description": "The frequency of the dominant allele in the population."}, "genotype": {"type": "string", "description": "The genotype which frequency is needed.", "enum": ["AA", "Aa", "aa"]}}, "required": ["allele_frequency", "genotype"]}}]} +{"id": "simple_python_65", "question": [[{"role": "user", "content": "Calculate the Population Density for Brazil in 2022 if the population is 213 million and the land area is 8.5 million square kilometers."}]], "function": [{"name": "calculate_density", "description": "Calculate the population density of a specific country in a specific year.", "parameters": {"type": "dict", "properties": {"country": {"type": "string", "description": "The country for which the density needs to be calculated."}, "year": {"type": "string", "description": "The year in which the density is to be calculated."}, "population": {"type": "integer", "description": "The population of the country."}, "land_area": {"type": "integer", "description": "The land area of the country in square kilometers."}}, "required": ["country", "year", "population", "land_area"]}}]} +{"id": "simple_python_66", "question": [[{"role": "user", "content": "Get me data on average precipitation in the Amazon rainforest for the last six months."}]], "function": [{"name": "ecology_data.precipitation_stats", "description": "Retrieve precipitation data for a specified location and time period.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The name of the location, e.g., 'Amazon rainforest'."}, "time_frame": {"type": "string", "enum": ["six_months", "year", "five_years"], "description": "The time period for which data is required."}}, "required": ["location", "time_frame"]}}]} +{"id": "simple_python_67", "question": [[{"role": "user", "content": "Identify a small green bird in forest."}]], "function": [{"name": "identify_bird", "description": "Identify a bird species based on certain characteristics.", "parameters": {"type": "dict", "properties": {"color": {"type": "string", "description": "Color of the bird."}, "habitat": {"type": "string", "description": "Habitat of the bird."}, "size": {"type": "string", "enum": ["small", "medium", "large"], "description": "Size of the bird. Default is 'small'"}}, "required": ["color", "habitat"]}}]} +{"id": "simple_python_68", "question": [[{"role": "user", "content": "Predict the growth of forest in Yellowstone National Park for the next 5 years including human impact."}]], "function": [{"name": "forest_growth_forecast", "description": "Predicts the forest growth over the next N years based on current trends.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The location where you want to predict forest growth."}, "years": {"type": "integer", "description": "The number of years for the forecast."}, "include_human_impact": {"type": "boolean", "description": "Whether or not to include the impact of human activities in the forecast. If not provided, defaults to false."}}, "required": ["location", "years"]}}]} +{"id": "simple_python_69", "question": [[{"role": "user", "content": "Find out the population and species of turtles in Mississippi river in 2020."}]], "function": [{"name": "ecology.get_turtle_population", "description": "Get the population and species of turtles in a specific location.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The name of the location."}, "year": {"type": "integer", "description": "The year of the data requested. Default is 2001."}, "species": {"type": "boolean", "description": "Whether to include species information. Default is false."}}, "required": ["location"]}}]} +{"id": "simple_python_70", "question": [[{"role": "user", "content": "What is the carbon footprint of a gas-powered vehicle driving 1500 miles in a year?"}]], "function": [{"name": "calculate_vehicle_emission", "description": "Calculate the annual carbon emissions produced by a specific type of vehicle based on mileage.", "parameters": {"type": "dict", "properties": {"vehicle_type": {"type": "string", "description": "The type of vehicle. 'gas' refers to a gasoline vehicle, 'diesel' refers to a diesel vehicle, and 'EV' refers to an electric vehicle."}, "miles_driven": {"type": "integer", "description": "The number of miles driven per year."}, "emission_factor": {"type": "float", "description": "Optional emission factor to calculate emissions, in g/mile. Default factor is 355.48."}}, "required": ["vehicle_type", "miles_driven"]}}]} +{"id": "simple_python_71", "question": [[{"role": "user", "content": "Generate a DNA sequence with 100 bases including more G (Guanine) and C (Cytosine)."}]], "function": [{"name": "generate_DNA_sequence", "description": "Generate a random DNA sequence with a specific length and nucleotide preference.", "parameters": {"type": "dict", "properties": {"length": {"type": "integer", "description": "The length of the DNA sequence to be generated."}, "preferences": {"type": "array", "items": {"type": "string", "enum": ["A", "T", "C", "G"]}, "description": "Preferred nucleotides to include more frequently in the DNA sequence."}}, "required": ["length", "preferences"]}}]} +{"id": "simple_python_72", "question": [[{"role": "user", "content": "Calculate the expected evolutionary fitness of a creature, with trait A contributing to 40% of the fitness and trait B contributing 60%, if trait A has a value of 0.8 and trait B a value of 0.7."}]], "function": [{"name": "calculate_fitness", "description": "Calculate the expected evolutionary fitness of a creature based on the individual values and contributions of its traits.", "parameters": {"type": "dict", "properties": {"trait_values": {"type": "array", "items": {"type": "float"}, "description": "List of trait values, which are decimal numbers between 0 and 1, where 1 represents the trait maximally contributing to fitness."}, "trait_contributions": {"type": "array", "items": {"type": "float"}, "description": "List of the percentage contributions of each trait to the overall fitness, which must sum to 1."}}, "required": ["trait_values", "trait_contributions"]}}]} +{"id": "simple_python_73", "question": [[{"role": "user", "content": "What's the projected population growth in United States in the next 20 years?"}]], "function": [{"name": "population_projections", "description": "Calculates the projected population growth based on the current growth rate.", "parameters": {"type": "dict", "properties": {"country": {"type": "string", "description": "The country for which to calculate the population projection."}, "years": {"type": "integer", "description": "Number of years for the projection."}, "growth_rate": {"type": "float", "description": "Optional parameter to specify the growth rate, in percentage. Default is 1.2."}}, "required": ["country", "years"]}}]} +{"id": "simple_python_74", "question": [[{"role": "user", "content": "Calculate the evolution rate of a bacteria population, start with 5000 bacteria, each bacteria duplicates every hour for 6 hours."}]], "function": [{"name": "calculate_bacteria_evolution_rate", "description": "Calculate the evolution rate of bacteria given the starting number, duplication frequency and total duration.", "parameters": {"type": "dict", "properties": {"start_population": {"type": "integer", "description": "The starting population of bacteria."}, "duplication_frequency": {"type": "integer", "description": "The frequency of bacteria duplication per hour."}, "duration": {"type": "integer", "description": "Total duration in hours."}, "generation_time": {"type": "integer", "description": "The average generation time of the bacteria in minutes. Default is 20 minutes"}}, "required": ["start_population", "duplication_frequency", "duration"]}}]} +{"id": "simple_python_75", "question": [[{"role": "user", "content": "Estimate the population size of elephants of 35000 in the next 5 years given the current growth rate of 0.015."}]], "function": [{"name": "elephant_population_estimate", "description": "Estimate future population of elephants given current population and growth rate.", "parameters": {"type": "dict", "properties": {"current_population": {"type": "integer", "description": "The current number of elephants."}, "growth_rate": {"type": "float", "description": "The annual population growth rate of elephants."}, "years": {"type": "integer", "description": "The number of years to project the population."}}, "required": ["current_population", "growth_rate", "years"]}}]} +{"id": "simple_python_76", "question": [[{"role": "user", "content": "Get me the predictions of the evolutionary rate for Homo Sapiens for next 50 years using Darwin model"}]], "function": [{"name": "prediction.evolution", "description": "Predict the evolutionary rate for a specific species for a given timeframe.", "parameters": {"type": "dict", "properties": {"species": {"type": "string", "description": "The species that the evolution rate will be predicted for."}, "years": {"type": "integer", "description": "Number of years for the prediction."}, "model": {"type": "string", "description": "The model used to make the prediction, options: 'Darwin', 'Lamarck', default is 'Darwin'."}}, "required": ["species", "years"]}}]} +{"id": "simple_python_77", "question": [[{"role": "user", "content": "Find a nearby restaurant that serves vegan food in Los Angeles."}]], "function": [{"name": "restaurant.find_nearby", "description": "Locate nearby restaurants based on specific dietary preferences.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. Los Angeles, CA"}, "dietary_preference": {"type": "array", "items": {"type": "string", "enum": ["Vegan", "Vegetarian", "Gluten-free", "Dairy-free", "Nut-free"]}, "description": "Dietary preference. Default is empty list."}}, "required": ["location"]}}]} +{"id": "simple_python_78", "question": [[{"role": "user", "content": "Get the average temperature in Austin for the next 3 days in Celsius."}]], "function": [{"name": "average_temperature", "description": "Retrieves the average temperature for a specific location over the defined timeframe.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city to get the average temperature for. It should format as city name such as Boston."}, "days": {"type": "integer", "description": "The number of days to get the average temperature for."}, "temp_unit": {"type": "string", "description": "The temperature unit ('Celsius' or 'Fahrenheit'). Default is 'Fahrenheit'."}}, "required": ["location", "days"]}}]} +{"id": "simple_python_79", "question": [[{"role": "user", "content": "Create a histogram for student scores with the following data: 85, 90, 88, 92, 86, 89, 91 and set bin range to 5."}]], "function": [{"name": "create_histogram", "description": "Create a histogram based on provided data.", "parameters": {"type": "dict", "properties": {"data": {"type": "array", "items": {"type": "integer"}, "description": "The data for which histogram needs to be plotted."}, "bins": {"type": "integer", "description": "The number of equal-width bins in the range. Default is 10."}}, "required": ["data", "bins"]}}]} +{"id": "simple_python_80", "question": [[{"role": "user", "content": "I want to find 5 restaurants nearby my location, Manhattan, offering Thai food and a vegan menu."}]], "function": [{"name": "find_restaurants", "description": "Locate nearby restaurants based on location and food preferences.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The specific location or area. The location should be in the format of District, City."}, "food_type": {"type": "string", "description": "The type of food preferred."}, "number": {"type": "integer", "description": "Number of results to return."}, "dietary_requirements": {"type": "array", "items": {"type": "string"}, "description": "Special dietary requirements, e.g. vegan, gluten-free. Default is empty list."}}, "required": ["location", "food_type", "number"]}}]} +{"id": "simple_python_81", "question": [[{"role": "user", "content": "Find the fastest route from San Francisco to Los Angeles with toll roads avoided."}]], "function": [{"name": "map_routing.fastest_route", "description": "Finds the fastest route from one location to another, with an option to avoid toll roads.", "parameters": {"type": "dict", "properties": {"start_location": {"type": "string", "description": "The starting location for the journey."}, "end_location": {"type": "string", "description": "The destination for the journey."}, "avoid_tolls": {"type": "boolean", "description": "Option to avoid toll roads during the journey. Default is false."}}, "required": ["start_location", "end_location"]}}]} +{"id": "simple_python_82", "question": [[{"role": "user", "content": "Calculate the average of list of integers [12, 15, 18, 20, 21, 26, 30]."}]], "function": [{"name": "calculate_average", "description": "Calculates the average of a list of numbers.", "parameters": {"type": "dict", "properties": {"numbers": {"type": "array", "items": {"type": "float"}, "description": "The list of numbers to calculate the average of."}}, "required": ["numbers"]}}]} +{"id": "simple_python_83", "question": [[{"role": "user", "content": "Calculate the distance between two GPS coordinates (33.4484 N, 112.0740 W) and (34.0522 N, 118.2437 W) in miles."}]], "function": [{"name": "calculate_distance", "description": "Calculate the distance between two GPS coordinates.", "parameters": {"type": "dict", "properties": {"coord1": {"type": "tuple", "description": "The first coordinate as (latitude, longitude).", "items": {"type": "float"}}, "coord2": {"type": "tuple", "description": "The second coordinate as (latitude, longitude).", "items": {"type": "float"}}, "unit": {"type": "string", "description": "The unit of distance. Options: 'miles', 'kilometers'."}}, "required": ["coord1", "coord2", "unit"]}}]} +{"id": "simple_python_84", "question": [[{"role": "user", "content": "Calculate the Body Mass Index (BMI) of a person with a weight of 85 kilograms and height of 180 cm."}]], "function": [{"name": "calculate_bmi", "description": "Calculate the Body Mass Index (BMI) of a person.", "parameters": {"type": "dict", "properties": {"weight": {"type": "integer", "description": "Weight of the person in kilograms."}, "height": {"type": "integer", "description": "Height of the person in centimeters."}, "unit": {"type": "string", "description": "Optional parameter to choose between 'imperial' and 'metric' systems. Default is 'metric'."}}, "required": ["weight", "height"]}}]} +{"id": "simple_python_85", "question": [[{"role": "user", "content": "What's the approximate distance between Boston, MA, and Washington, D.C. in mile?"}]], "function": [{"name": "geo_distance.calculate", "description": "Calculate the geographic distance between two given locations.", "parameters": {"type": "dict", "properties": {"start_location": {"type": "string", "description": "The starting location for the distance calculation. Specify the location in the format of City, State."}, "end_location": {"type": "string", "description": "The destination location for the distance calculation. Specify the location in the format of City, State."}, "units": {"type": "string", "description": "Optional. The desired units for the resulting distance ('miles' or 'kilometers'). Defaults to 'miles'."}}, "required": ["start_location", "end_location"]}}]} +{"id": "simple_python_86", "question": [[{"role": "user", "content": "Find the shortest distance between two cities, New York and Los Angeles, through the train and you can transfer."}]], "function": [{"name": "city_distance.find_shortest", "description": "Calculates the shortest distance between two cities via available public transportation.", "parameters": {"type": "dict", "properties": {"start_city": {"type": "string", "description": "The city you are starting from. The parameter is in the format of city name."}, "end_city": {"type": "string", "description": "The city you are heading to.The parameter is in the format of city name."}, "transportation": {"type": "string", "description": "Preferred mode of public transportation. Default is 'bus'."}, "allow_transfer": {"type": "boolean", "description": "Allows transfer between different transportation if true. Default is false."}}, "required": ["start_city", "end_city"]}}]} +{"id": "simple_python_87", "question": [[{"role": "user", "content": "Sort the list [5, 3, 4, 1, 2] in ascending order."}]], "function": [{"name": "array_sort", "description": "Sorts a given list in ascending or descending order.", "parameters": {"type": "dict", "properties": {"list": {"type": "array", "items": {"type": "float"}, "description": "The list of numbers to be sorted."}, "order": {"type": "string", "enum": ["ascending", "descending"], "description": "Order of sorting."}}, "required": ["list", "order"]}}]} +{"id": "simple_python_88", "question": [[{"role": "user", "content": "Calculate the BMI (Body Mass Index) of a person who weighs 70kg and is 1.75m tall."}]], "function": [{"name": "calculate_BMI", "description": "Calculate the Body Mass Index (BMI) given a person's weight and height.", "parameters": {"type": "dict", "properties": {"weight_kg": {"type": "integer", "description": "The weight of the person in kilograms."}, "height_m": {"type": "float", "description": "The height of the person in meters."}}, "required": ["weight_kg", "height_m"]}}]} +{"id": "simple_python_89", "question": [[{"role": "user", "content": "Fetch all records for students studying Science in 'Bluebird High School' from the StudentDB."}]], "function": [{"name": "db_fetch_records", "description": "Fetch records from a specified database table based on certain conditions.", "parameters": {"type": "dict", "properties": {"database_name": {"type": "string", "description": "The name of the database."}, "table_name": {"type": "string", "description": "The name of the table from which records need to be fetched."}, "conditions": {"type": "dict", "properties": {"department": {"type": "string", "description": "The name of the department of students."}, "school": {"type": "string", "description": "The name of the school students are enrolled in."}}, "description": "The conditions based on which records are to be fetched."}, "fetch_limit": {"type": "integer", "description": "Limits the number of records to be fetched. Default is 0, which means no limit."}}, "required": ["database_name", "table_name", "conditions"]}}]} +{"id": "simple_python_90", "question": [[{"role": "user", "content": "Retrieve Personal Info and Job History data of a specific employee whose ID is 345 in company 'ABC Ltd.'"}]], "function": [{"name": "employee.fetch_data", "description": "Fetches the detailed data for a specific employee in a given company.", "parameters": {"type": "dict", "properties": {"company_name": {"type": "string", "description": "The name of the company."}, "employee_id": {"type": "integer", "description": "The unique ID of the employee."}, "data_field": {"type": "array", "items": {"type": "string", "enum": ["Personal Info", "Job History", "Payroll", "Attendance"]}, "description": "Fields of data to be fetched for the employee (Optional). Default is ['Personal Info']"}}, "required": ["company_name", "employee_id"]}}]} +{"id": "simple_python_91", "question": [[{"role": "user", "content": "Get the highest rated sushi restaurant in Boston, that opens on Sundays."}]], "function": [{"name": "get_restaurant", "description": "Retrieve highest rated restaurant given cuisine, location, and a condition.", "parameters": {"type": "dict", "properties": {"cuisine": {"type": "string", "description": "Cuisine of the restaurant."}, "location": {"type": "string", "description": "City where restaurant is located."}, "condition": {"type": "string", "description": "Condition to be met by the restaurant (e.g., operating days, amenities, etc.)"}}, "required": ["cuisine", "location", "condition"]}}]} +{"id": "simple_python_92", "question": [[{"role": "user", "content": "Find all movies starring Leonardo DiCaprio in the year 2010 from IMDB database."}]], "function": [{"name": "imdb.find_movies_by_actor", "description": "Searches the database to find all movies by a specific actor within a certain year.", "parameters": {"type": "dict", "properties": {"actor_name": {"type": "string", "description": "The name of the actor."}, "year": {"type": "integer", "description": "The specific year to search in."}, "category": {"type": "string", "description": "The category of the film (e.g. Drama, Comedy, etc). Default is 'all'"}}, "required": ["actor_name", "year"]}}]} +{"id": "simple_python_93", "question": [[{"role": "user", "content": "Fetch me the list of IMAX movie releases in theaters near LA for the next week."}]], "function": [{"name": "get_theater_movie_releases", "description": "Retrieve the list of movie releases in specific theaters for a specified period. in the format of city shorten name like SF.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The location of the theaters."}, "timeframe": {"type": "integer", "description": "The number of days for which releases are required from current date."}, "format": {"type": "string", "description": "Format of movies - could be 'IMAX', '2D', '3D', '4DX' etc. Default is 'all'"}}, "required": ["location", "timeframe"]}}]} +{"id": "simple_python_94", "question": [[{"role": "user", "content": "Update my customer information with user id 43523 'name':'John Doe', 'email':'johndoe@email.com' in the database."}]], "function": [{"name": "update_user_info", "description": "Update user information in the database.", "parameters": {"type": "dict", "properties": {"user_id": {"type": "integer", "description": "The user ID of the customer."}, "update_info": {"type": "dict", "properties": {"name": {"type": "string", "description": "The customer's updated name."}, "email": {"type": "string", "description": "The customer's updated email."}}, "description": "The new information to update."}, "database": {"type": "string", "description": "The database where the user's information is stored.", "default": "CustomerInfo"}}, "required": ["user_id", "update_info"]}}]} +{"id": "simple_python_95", "question": [[{"role": "user", "content": "Calculate the area of a triangle with base 5m and height 3m."}]], "function": [{"name": "calc_area_triangle", "description": "Calculate the area of a triangle with the formula area = 0.5 * base * height.", "parameters": {"type": "dict", "properties": {"base": {"type": "integer", "description": "The length of the base of the triangle in meters."}, "height": {"type": "integer", "description": "The perpendicular height of the triangle from the base to the opposite vertex in meters."}}, "required": ["base", "height"]}}]} +{"id": "simple_python_96", "question": [[{"role": "user", "content": "Find records in database in user table where age is greater than 25 and job is 'engineer'."}]], "function": [{"name": "database.query", "description": "Query the database based on certain conditions.", "parameters": {"type": "dict", "properties": {"table": {"type": "string", "description": "Name of the table to query."}, "conditions": {"type": "array", "items": {"type": "dict", "properties": {"field": {"type": "string", "description": "The field to apply the condition."}, "operation": {"type": "string", "description": "The operation to be performed.", "enum": ["<", ">", "=", ">=", "<="]}, "value": {"type": "string", "description": "The value to be compared."}}, "required": ["field", "operation", "value"]}, "description": "Conditions for the query."}}, "required": ["table", "conditions"]}}]} +{"id": "simple_python_97", "question": [[{"role": "user", "content": "Calculate the factorial of the number 5"}]], "function": [{"name": "math.factorial", "description": "Calculate the factorial of a given number.", "parameters": {"type": "dict", "properties": {"number": {"type": "integer", "description": "The number to compute factorial."}}, "required": ["number"]}}]} +{"id": "simple_python_98", "question": [[{"role": "user", "content": "What will be the angle between the hour and minute hands of a clock at 6:30 PM?"}]], "function": [{"name": "calculate_clock_angle", "description": "Calculate the angle between the hour and minute hands of a clock at a given time.", "parameters": {"type": "dict", "properties": {"hours": {"type": "integer", "description": "The hour on the clock face."}, "minutes": {"type": "integer", "description": "The minutes on the clock face."}, "round_to": {"type": "integer", "description": "The number of decimal places to round the result to, default is 2."}}, "required": ["hours", "minutes"]}}]} +{"id": "simple_python_99", "question": [[{"role": "user", "content": "Plot a sine wave from 0 to 2 pi with a frequency of 5 Hz."}]], "function": [{"name": "plot_sine_wave", "description": "Plot a sine wave for a given frequency in a given range.", "parameters": {"type": "dict", "properties": {"start_range": {"type": "float", "description": "Start of the range in radians. Four decimal places."}, "end_range": {"type": "float", "description": "End of the range in radians. Four decimal places."}, "frequency": {"type": "integer", "description": "Frequency of the sine wave in Hz."}, "amplitude": {"type": "integer", "description": "Amplitude of the sine wave. Default is 1."}, "phase_shift": {"type": "integer", "description": "Phase shift of the sine wave in radians. Default is 0."}}, "required": ["start_range", "end_range", "frequency"]}}]} +{"id": "simple_python_100", "question": [[{"role": "user", "content": "How much time will it take for the light to reach earth from a star 4 light years away?"}]], "function": [{"name": "light_travel_time", "description": "Calculate the time taken for light to travel from a celestial body to another.", "parameters": {"type": "dict", "properties": {"distance_in_light_years": {"type": "integer", "description": "The distance between the two celestial bodies in light years."}, "speed_of_light": {"type": "integer", "description": "The speed of light in vacuum, in m/s. Default value is 299792458 m/s."}}, "required": ["distance_in_light_years"]}}]} +{"id": "simple_python_101", "question": [[{"role": "user", "content": "Calculate the speed of an object in km/h if it traveled 450 meters in 20 seconds."}]], "function": [{"name": "calculate_speed", "description": "Calculate the speed of an object based on the distance travelled and the time taken.", "parameters": {"type": "dict", "properties": {"distance": {"type": "integer", "description": "The distance the object travelled in meters."}, "time": {"type": "integer", "description": "The time it took for the object to travel in seconds."}, "to_unit": {"type": "string", "description": "The unit in which the speed should be calculated, default is m/s."}}, "required": ["distance", "time"]}}]} +{"id": "simple_python_102", "question": [[{"role": "user", "content": "What's the distance in milesfrom the Earth to the Moon?"}]], "function": [{"name": "calculate_distance", "description": "Calculate the distance between two celestial bodies.", "parameters": {"type": "dict", "properties": {"body1": {"type": "string", "description": "The first celestial body."}, "body2": {"type": "string", "description": "The second celestial body."}, "unit": {"type": "string", "description": "The unit of measurement, default is 'km'."}}, "required": ["body1", "body2"]}}]} +{"id": "simple_python_103", "question": [[{"role": "user", "content": "Calculate the area under the curve y=3x^2 + 2x - 4, between x = -1 and x = 2."}]], "function": [{"name": "mathematics.calculate_area_under_curve", "description": "Calculate the area under the curve for a given polynomial function within a specified interval.", "parameters": {"type": "dict", "properties": {"polynomial": {"type": "array", "items": {"type": "float"}, "description": "The coefficients of the polynomial, in decreasing order of exponent, where the first element is the coefficient for x^n, the second element is the coefficient for x^(n-1), and so on. The last element is the constant term."}, "limits": {"type": "array", "items": {"type": "float"}, "description": "A list of two numbers specifying the lower and upper limit for the integration interval."}}, "required": ["polynomial", "limits"]}}]} +{"id": "simple_python_104", "question": [[{"role": "user", "content": "Calculate the area of a triangle with base 6 and height 10."}]], "function": [{"name": "geometry.area_triangle", "description": "Calculate the area of a triangle.", "parameters": {"type": "dict", "properties": {"base": {"type": "integer", "description": "The length of the base of the triangle."}, "height": {"type": "integer", "description": "The height of the triangle from the base."}, "unit": {"type": "string", "description": "The measurement unit for the area. Defaults to square meters."}}, "required": ["base", "height"]}}]} +{"id": "simple_python_105", "question": [[{"role": "user", "content": "Calculate the power of 3 raised to the power 4."}]], "function": [{"name": "math.power", "description": "Calculate the power of one number raised to another.", "parameters": {"type": "dict", "properties": {"base": {"type": "integer", "description": "The base number."}, "exponent": {"type": "integer", "description": "The exponent."}, "mod": {"type": "integer", "description": "The modulus. Default is 1. Calculates pow(base, exponent) % mod when provided."}}, "required": ["base", "exponent"]}}]} +{"id": "simple_python_106", "question": [[{"role": "user", "content": "Train a random forest classifier on dataset your_dataset_name with maximum depth of trees as 5, and number of estimators as 100."}]], "function": [{"name": "train_random_forest_classifier", "description": "Train a Random Forest classifier with the specified parameters.", "parameters": {"type": "dict", "properties": {"dataset": {"type": "string", "description": "The dataset to train the classifier on."}, "max_depth": {"type": "integer", "description": "The maximum depth of the trees in the forest."}, "n_estimators": {"type": "integer", "description": "The number of trees in the forest."}}, "required": ["dataset", "max_depth", "n_estimators"]}}]} +{"id": "simple_python_107", "question": [[{"role": "user", "content": "Calculate the Body Mass Index for a person with a weight of 70 kg and a height of 175 cm."}]], "function": [{"name": "calculate_bmi", "description": "Calculate the Body Mass Index (BMI) for a person based on their weight and height.", "parameters": {"type": "dict", "properties": {"weight": {"type": "integer", "description": "The weight of the person in kilograms."}, "height": {"type": "integer", "description": "The height of the person in centimeters."}, "system": {"type": "string", "description": "The system of units to be used, 'metric' or 'imperial'. Default is 'metric'."}}, "required": ["weight", "height"]}}]} +{"id": "simple_python_108", "question": [[{"role": "user", "content": "Run a linear regression model with predictor variables 'Age', 'Income' and 'Education' and a target variable 'Purchase_Amount'. Also apply standardization."}]], "function": [{"name": "run_linear_regression", "description": "Build a linear regression model using given predictor variables and a target variable.", "parameters": {"type": "dict", "properties": {"predictors": {"type": "array", "items": {"type": "string"}, "description": "Array containing the names of predictor variables."}, "target": {"type": "string", "description": "The name of target variable."}, "standardize": {"type": "boolean", "description": "Option to apply standardization on the predictors. Defaults to False."}}, "required": ["predictors", "target"]}}]} +{"id": "simple_python_109", "question": [[{"role": "user", "content": "Generate a random forest model with 100 trees and a depth of 5 on the provided data my_data."}]], "function": [{"name": "random_forest.train", "description": "Train a Random Forest Model on given data", "parameters": {"type": "dict", "properties": {"n_estimators": {"type": "integer", "description": "The number of trees in the forest."}, "max_depth": {"type": "integer", "description": "The maximum depth of the tree."}, "data": {"type": "any", "description": "The training data for the model."}}, "required": ["n_estimators", "max_depth", "data"]}}]} +{"id": "simple_python_110", "question": [[{"role": "user", "content": "Predict the price of the house in San Francisco with 3 bedrooms, 2 bathrooms and area of 1800 square feet."}]], "function": [{"name": "predict_house_price", "description": "Predict the price of a house in a given area based on number of bedrooms, bathrooms and area.", "parameters": {"type": "dict", "properties": {"bedrooms": {"type": "integer", "description": "The number of bedrooms in the house."}, "bathrooms": {"type": "integer", "description": "The number of bathrooms in the house."}, "area": {"type": "integer", "description": "The area of the house in square feet."}, "location": {"type": "string", "description": "The location of the house in the format of city name."}}, "required": ["bedrooms", "bathrooms", "area", "location"]}}]} +{"id": "simple_python_111", "question": [[{"role": "user", "content": "Generate a random number from a normal distribution with mean 0 and standard deviation 1."}]], "function": [{"name": "random.normalvariate", "description": "Generates a random number from a normal distribution given the mean and standard deviation.", "parameters": {"type": "dict", "properties": {"mu": {"type": "integer", "description": "Mean of the normal distribution."}, "sigma": {"type": "integer", "description": "Standard deviation of the normal distribution."}}, "required": ["mu", "sigma"]}}]} +{"id": "simple_python_112", "question": [[{"role": "user", "content": "Calculate the probability of drawing a king from a deck of cards."}]], "function": [{"name": "calculate_probability", "description": "Calculate the probability of an event.", "parameters": {"type": "dict", "properties": {"total_outcomes": {"type": "integer", "description": "Total number of possible outcomes."}, "favorable_outcomes": {"type": "integer", "description": "Number of outcomes considered as 'successful'."}, "round_to": {"type": "integer", "description": "Number of decimal places to round the result to.", "default": 2}}, "required": ["total_outcomes", "favorable_outcomes"]}}]} +{"id": "simple_python_113", "question": [[{"role": "user", "content": "What's the probability of rolling a six on a six-sided die twice in a row?"}]], "function": [{"name": "probability.dice_roll", "description": "Calculate the probability of rolling a certain number on a six-sided die a certain number of times in a row.", "parameters": {"type": "dict", "properties": {"desired_number": {"type": "integer", "description": "The number you want to roll."}, "number_of_rolls": {"type": "integer", "description": "How many times you want to roll that number in a row."}, "die_sides": {"type": "integer", "description": "The number of sides on the die (optional; default is 6)."}}, "required": ["desired_number", "number_of_rolls"]}}]} +{"id": "simple_python_114", "question": [[{"role": "user", "content": "Find the probability of getting exactly 5 heads in 10 fair coin tosses."}]], "function": [{"name": "prob_dist.binomial", "description": "Compute the probability of having 'success' outcome from binomial distribution.", "parameters": {"type": "dict", "properties": {"trials": {"type": "integer", "description": "The number of independent experiments."}, "successes": {"type": "integer", "description": "The number of success events."}, "p": {"type": "float", "description": "The probability of success on any given trial, defaults to 0.5"}}, "required": ["trials", "successes"]}}]} +{"id": "simple_python_115", "question": [[{"role": "user", "content": "Calculate the probability of getting exactly 5 heads in 8 tosses of a fair coin."}]], "function": [{"name": "calculate_binomial_probability", "description": "Calculates the binomial probability given the number of trials, successes and the probability of success on an individual trial.", "parameters": {"type": "dict", "properties": {"number_of_trials": {"type": "integer", "description": "The total number of trials."}, "number_of_successes": {"type": "integer", "description": "The desired number of successful outcomes."}, "probability_of_success": {"type": "float", "description": "The probability of a successful outcome on any given trial.", "default": 0.5}}, "required": ["number_of_trials", "number_of_successes"]}}]} +{"id": "simple_python_116", "question": [[{"role": "user", "content": "What's the probability of drawing a king from a well shuffled standard deck of 52 cards?"}]], "function": [{"name": "probabilities.calculate_single", "description": "Calculate the probability of an event.", "parameters": {"type": "dict", "properties": {"total_outcomes": {"type": "integer", "description": "The total number of outcomes."}, "event_outcomes": {"type": "integer", "description": "The number of outcomes where the event occurs."}, "round": {"type": "integer", "description": "Round the answer to a specified number of decimal places. Defaults to 2."}}, "required": ["total_outcomes", "event_outcomes"]}}]} +{"id": "simple_python_117", "question": [[{"role": "user", "content": "What are the odds of pulling a heart suit from a well-shuffled standard deck of 52 cards? Format it as ratio."}]], "function": [{"name": "probability_of_event", "description": "Calculates the probability of an event.", "parameters": {"type": "dict", "properties": {"success_outcomes": {"type": "integer", "description": "The number of successful outcomes."}, "total_outcomes": {"type": "integer", "description": "The total number of possible outcomes."}, "format_as_ratio": {"type": "boolean", "description": "When true, formats the output as a ratio instead of a decimal. Default is false."}}, "required": ["success_outcomes", "total_outcomes"]}}]} +{"id": "simple_python_118", "question": [[{"role": "user", "content": "Perform a two-sample t-test on my experiment data of Control [10, 15, 12, 14, 11] and Treated [18, 16, 17, 20, 22] group with alpha equals to 0.05"}]], "function": [{"name": "stats.t_test", "description": "Perform a two-sample t-test for two given arrays.", "parameters": {"type": "dict", "properties": {"array_1": {"type": "array", "items": {"type": "integer"}, "description": "First array of data."}, "array_2": {"type": "array", "items": {"type": "integer"}, "description": "Second array of data."}, "alpha": {"type": "float", "description": "Significance level for hypothesis testing."}}, "required": ["array_1", "array_2", "alpha"]}}]} +{"id": "simple_python_119", "question": [[{"role": "user", "content": "Perform a hypothesis test for two independent samples with scores of Sample1: [22,33,42,12,34] and Sample2: [23,45,44,14,38] at a significance level of 0.05."}]], "function": [{"name": "hypothesis_testing.ttest_ind", "description": "Conducts a hypothesis test for two independent samples.", "parameters": {"type": "dict", "properties": {"sample1": {"type": "array", "items": {"type": "integer"}, "description": "First set of observations (array of numbers)."}, "sample2": {"type": "array", "items": {"type": "integer"}, "description": "Second set of observations (array of numbers)."}, "significance_level": {"type": "float", "description": "Significance level of the test (default: 0.05)"}}, "required": ["sample1", "sample2"]}}]} +{"id": "simple_python_120", "question": [[{"role": "user", "content": "Run a two sample T-test to compare the average of Group A [3, 4, 5, 6, 4] and Group B [7, 8, 9, 8, 7] assuming equal variance."}]], "function": [{"name": "run_two_sample_ttest", "description": "Runs a two sample t-test for two given data groups.", "parameters": {"type": "dict", "properties": {"group1": {"type": "array", "items": {"type": "integer"}, "description": "First group of data points."}, "group2": {"type": "array", "items": {"type": "integer"}, "description": "Second group of data points."}, "equal_variance": {"type": "boolean", "description": "Assumption about whether the two samples have equal variance.", "default": true}}, "required": ["group1", "group2"]}}]} +{"id": "simple_python_121", "question": [[{"role": "user", "content": "Calculate the probability of observing 60 heads if I flip a coin 100 times with probability of heads 0.5."}]], "function": [{"name": "calc_binomial_prob", "description": "Calculates the probability of an outcome based on the binomial distribution", "parameters": {"type": "dict", "properties": {"num_trials": {"type": "integer", "description": "Number of independent experiments."}, "num_success": {"type": "integer", "description": "Number of times the event of interest has occurred."}, "prob_success": {"type": "float", "description": "Probability of the event of interest on any single experiment."}}, "required": ["num_trials", "num_success", "prob_success"]}}]} +{"id": "simple_python_122", "question": [[{"role": "user", "content": "Perform a Chi-Squared test for independence on a 2x2 contingency table [ [10, 20], [30, 40] ]"}]], "function": [{"name": "chi_squared_test", "description": "Performs a Chi-Squared test for independence on a 2x2 contingency table.", "parameters": {"type": "dict", "properties": {"table": {"type": "array", "items": {"type": "array", "items": {"type": "integer"}}, "description": "A 2x2 contingency table presented in array form."}, "alpha": {"type": "float", "description": "Significance level for the Chi-Squared test. Default is 0.05."}}, "required": ["table"]}}]} +{"id": "simple_python_123", "question": [[{"role": "user", "content": "Perform a two-sample t-test to determine if there is a significant difference between the mean of group1 (e.g., 12.4, 15.6, 11.2, 18.9) and group2 (e.g., 10.5, 9.8, 15.2, 13.8) at the significance level 0.05."}]], "function": [{"name": "hypothesis_testing.two_sample_t_test", "description": "Perform a two-sample t-test to determine if there is a significant difference between the means of two independent samples.", "parameters": {"type": "dict", "properties": {"group1": {"type": "array", "items": {"type": "float"}, "description": "Sample observations from group 1."}, "group2": {"type": "array", "items": {"type": "float"}, "description": "Sample observations from group 2."}, "alpha": {"type": "float", "description": "Significance level for the t-test. Default is 0.05."}}, "required": ["group1", "group2"]}}]} +{"id": "simple_python_124", "question": [[{"role": "user", "content": "Find the statistical significance between two set of variables, dataset_A with the values 12, 24, 36 and dataset_B with the values 15, 30, 45."}]], "function": [{"name": "t_test", "description": "Perform a statistical t-test to check if the means of two independent datasets are statistically different.", "parameters": {"type": "dict", "properties": {"dataset_A": {"type": "array", "items": {"type": "integer"}, "description": "Dataset A for comparison."}, "dataset_B": {"type": "array", "items": {"type": "integer"}, "description": "Dataset B for comparison."}, "alpha": {"type": "float", "description": "Significance level for the test. Default is 0.05."}}, "required": ["dataset_A", "dataset_B"]}}]} +{"id": "simple_python_125", "question": [[{"role": "user", "content": "Predict house price in San Francisco based on its area of 2500 square feet, number of rooms as 5 and year of construction is 1990."}]], "function": [{"name": "predict_house_price", "description": "Predict house price based on area, number of rooms and year of construction.", "parameters": {"type": "dict", "properties": {"area": {"type": "integer", "description": "Area of the house in square feet."}, "rooms": {"type": "integer", "description": "Number of rooms in the house."}, "year": {"type": "integer", "description": "Year when the house was constructed."}, "location": {"type": "string", "description": "The location or city of the house."}}, "required": ["area", "rooms", "year", "location"]}}]} +{"id": "simple_python_126", "question": [[{"role": "user", "content": "What is the coefficient of determination (R-squared) for a model using engine size and fuel economy variables to predict car_price with a dataset in path C:/data/cars.csv?"}]], "function": [{"name": "linear_regression.get_r_squared", "description": "Calculate the coefficient of determination of a regression model.", "parameters": {"type": "dict", "properties": {"dataset_path": {"type": "string", "description": "Path to the CSV dataset file."}, "independent_variables": {"type": "array", "items": {"type": "string"}, "description": "The independent variables to use in the regression model."}, "dependent_variable": {"type": "string", "description": "The dependent variable to predict in the regression model."}}, "required": ["dataset_path", "independent_variables", "dependent_variable"]}}]} +{"id": "simple_python_127", "question": [[{"role": "user", "content": "Find the Net Present Value (NPV) of an investment, given cash_flows=[200,300,400,500], a discount rate of 10%, and an initial investment of $2000."}]], "function": [{"name": "calculate_NPV", "description": "Calculate the NPV (Net Present Value) of an investment, considering a series of future cash flows, discount rate, and an initial investment.", "parameters": {"type": "dict", "properties": {"cash_flows": {"type": "array", "items": {"type": "integer"}, "description": "Series of future cash flows."}, "discount_rate": {"type": "float", "description": "The discount rate to use."}, "initial_investment": {"type": "integer", "description": "The initial investment. Default is 0 if not specified."}}, "required": ["cash_flows", "discount_rate"]}}]} +{"id": "simple_python_128", "question": [[{"role": "user", "content": "What's the quarterly dividend per share of a company with 100 million outstanding shares and total dividend payout of 50 million USD?"}]], "function": [{"name": "finance.calculate_quarterly_dividend_per_share", "description": "Calculate quarterly dividend per share for a company given total dividend payout and outstanding shares", "parameters": {"type": "dict", "properties": {"total_payout": {"type": "integer", "description": "The total amount of dividends paid out in USD"}, "outstanding_shares": {"type": "integer", "description": "Total number of outstanding shares"}}, "required": ["total_payout", "outstanding_shares"], "optional": []}}]} +{"id": "simple_python_129", "question": [[{"role": "user", "content": "Calculate the discounted cash flow of a bond that is giving a coupon payment of $100 annually for next 5 years with discount rate 4%."}]], "function": [{"name": "calculate_discounted_cash_flow", "description": "Calculate the discounted cash flow of a bond for a given annual coupon payment, time frame and discount rate.", "parameters": {"type": "dict", "properties": {"coupon_payment": {"type": "integer", "description": "The annual coupon payment."}, "period": {"type": "integer", "description": "The time frame in years for which coupon payment is made."}, "discount_rate": {"type": "float", "description": "The discount rate."}, "face_value": {"type": "integer", "description": "The face value of the bond, default is 1000."}}, "required": ["coupon_payment", "period", "discount_rate"]}}]} +{"id": "simple_python_130", "question": [[{"role": "user", "content": "What's the NPV (Net Present Value) of a series of cash flows: [-50000, 10000, 15000, 20000, 25000, 30000] discounted at 8% annually?"}]], "function": [{"name": "finance_calculator.npv", "description": "Calculate the Net Present Value (NPV) for a series of cash flows discounted at a certain interest rate.", "parameters": {"type": "dict", "properties": {"cash_flows": {"type": "array", "items": {"type": "integer"}, "description": "A list of cash flows."}, "discount_rate": {"type": "float", "description": "The annual interest rate used to discount the cash flows."}, "years": {"type": "array", "items": {"type": "integer"}, "description": "A list of years when the cash flow occurs. Default is empty array."}}, "required": ["cash_flows", "discount_rate"]}}]} +{"id": "simple_python_131", "question": [[{"role": "user", "content": "Calculate the compound interest for an initial principal amount of $10000, with an annual interest rate of 5% and the number of times interest applied per time period is 4 and the time the money is invested for 10 years."}]], "function": [{"name": "calculate_compound_interest", "description": "Calculate compound interest for an initial principal amount.", "parameters": {"type": "dict", "properties": {"principal": {"type": "integer", "description": "The principal amount that the interest is applied to."}, "rate": {"type": "float", "description": "The annual interest rate. Enter as a decimal. E.g, 5% is 0.05"}, "time": {"type": "integer", "description": "The time the money is invested for in years."}, "n": {"type": "integer", "description": "The number of times that interest is compounded per time period. Default is 1."}}, "required": ["principal", "rate", "time"]}}]} +{"id": "simple_python_132", "question": [[{"role": "user", "content": "Calculate the company's return on equity given its net income of $2,000,000, shareholder's equity of $10,000,000, and dividends paid of $200,000."}]], "function": [{"name": "calculate_return_on_equity", "description": "Calculate a company's return on equity based on its net income, shareholder's equity, and dividends paid.", "parameters": {"type": "dict", "properties": {"net_income": {"type": "integer", "description": "The company's net income."}, "shareholder_equity": {"type": "integer", "description": "The company's total shareholder's equity."}, "dividends_paid": {"type": "integer", "description": "The total dividends paid by the company. Optional. If not given, default to 0."}}, "required": ["net_income", "shareholder_equity"]}}]} +{"id": "simple_python_133", "question": [[{"role": "user", "content": "Predict the future value of a $5000 investment with an annual interest rate of 5% in 3 years with monthly compounding."}]], "function": [{"name": "finance.predict_future_value", "description": "Calculate the future value of an investment given its present value, interest rate, the number of compounding periods per year, and the time horizon.", "parameters": {"type": "dict", "properties": {"present_value": {"type": "integer", "description": "The present value of the investment."}, "annual_interest_rate": {"type": "float", "description": "The annual interest rate of the investment."}, "compounding_periods_per_year": {"type": "integer", "description": "The number of times that interest is compounded per year. Default is 1 (annually)."}, "time_years": {"type": "integer", "description": "The investment horizon in years."}}, "required": ["present_value", "annual_interest_rate", "time_years"]}}]} +{"id": "simple_python_134", "question": [[{"role": "user", "content": "Predict the total expected profit of stocks XYZ in 5 years given I have invested $5000 and annual return rate is 7%."}]], "function": [{"name": "investment.predictProfit", "description": "Predict the profit for given investment after specified number of years.", "parameters": {"type": "dict", "properties": {"investment_amount": {"type": "integer", "description": "The amount invested in dollars."}, "annual_return": {"type": "float", "description": "The annual return rate of the investment."}, "years": {"type": "integer", "description": "The time period in years for which the investment is made."}}, "required": ["investment_amount", "annual_return", "years"]}}]} +{"id": "simple_python_135", "question": [[{"role": "user", "content": "Calculate the return on investment for a stock bought at $20, sold at $25, with a dividend of $2."}]], "function": [{"name": "calculate_return_on_investment", "description": "Calculate the return on investment for a given stock based on its purchase price, sale price, and any dividends received.", "parameters": {"type": "dict", "properties": {"purchase_price": {"type": "integer", "description": "The price the stock was bought at."}, "sale_price": {"type": "integer", "description": "The price the stock was sold at."}, "dividend": {"type": "integer", "description": "Any dividends received from the stock.", "default": 0}}, "required": ["purchase_price", "sale_price"]}}]} +{"id": "simple_python_136", "question": [[{"role": "user", "content": "Find the compound interest for an investment of $10000 with an annual interest rate of 5% compounded monthly for 5 years."}]], "function": [{"name": "compound_interest", "description": "Calculate compound interest for a certain time period.", "parameters": {"type": "dict", "properties": {"principal": {"type": "integer", "description": "The initial amount of money that was invested or loaned out."}, "annual_rate": {"type": "float", "description": "The interest rate for a year as a percentage."}, "compounding_freq": {"type": "string", "enum": ["monthly", "quarterly", "annually"], "description": "The number of times that interest is compounded per unit period."}, "time_in_years": {"type": "integer", "description": "The time the money is invested for in years."}}, "required": ["principal", "annual_rate", "compounding_freq", "time_in_years"]}}]} +{"id": "simple_python_137", "question": [[{"role": "user", "content": "Calculate the projected return on a $5000 investment in ABC company's stock, if the expected annual growth rate is 6% and the holding period is 5 years."}]], "function": [{"name": "calculate_stock_return", "description": "Calculate the projected return of a stock investment given the investment amount, the annual growth rate and holding period in years.", "parameters": {"type": "dict", "properties": {"investment_amount": {"type": "integer", "description": "The amount of money to invest."}, "annual_growth_rate": {"type": "float", "description": "The expected annual growth rate of the stock."}, "holding_period": {"type": "integer", "description": "The number of years you intend to hold the stock."}, "dividends": {"type": "boolean", "description": "Optional. True if the calculation should take into account potential dividends. Default is false."}}, "required": ["investment_amount", "annual_growth_rate", "holding_period"]}}]} +{"id": "simple_python_138", "question": [[{"role": "user", "content": "Calculate the future value of my portfolio if I invest $5000 in stock 'X' with an expected annual return of 5% for 7 years."}]], "function": [{"name": "portfolio_future_value", "description": "Calculate the future value of an investment in a specific stock based on the invested amount, expected annual return and number of years.", "parameters": {"type": "dict", "properties": {"stock": {"type": "string", "description": "The ticker symbol of the stock."}, "invested_amount": {"type": "integer", "description": "The invested amount in USD."}, "expected_annual_return": {"type": "float", "description": "The expected annual return on investment as a decimal. E.g. 5% = 0.05"}, "years": {"type": "integer", "description": "The number of years for which the investment is made."}}, "required": ["stock", "invested_amount", "expected_annual_return", "years"]}}]} +{"id": "simple_python_139", "question": [[{"role": "user", "content": "What is the estimated return on a mutual fund, given that it has a yearly yield of 5%, an investment amount of $2000 and a time period of 3 years?"}]], "function": [{"name": "estimate_mutual_fund_return", "description": "Calculate the estimated return on a mutual fund given the yearly yield, the investment amount and the time period.", "parameters": {"type": "dict", "properties": {"yearly_yield": {"type": "float", "description": "The yearly yield of the mutual fund as a percentage."}, "investment_amount": {"type": "integer", "description": "The initial investment amount in the mutual fund."}, "years": {"type": "integer", "description": "The time period for which the investment is made in years."}}, "required": ["yearly_yield", "investment_amount", "years"]}}]} +{"id": "simple_python_140", "question": [[{"role": "user", "content": "Calculate the Compound Annual Growth Rate (CAGR) for an initial investment of $2000, final value of $3000 in a period of 4 years."}]], "function": [{"name": "calculate_cagr", "description": "Calculate the Compound Annual Growth Rate (CAGR) given an initial investment value, a final investment value, and the number of years.", "parameters": {"type": "dict", "properties": {"initial_value": {"type": "integer", "description": "The initial investment value."}, "final_value": {"type": "integer", "description": "The final investment value."}, "period_in_years": {"type": "integer", "description": "The period of the investment in years."}}, "required": ["initial_value", "final_value", "period_in_years"]}}]} +{"id": "simple_python_141", "question": [[{"role": "user", "content": "Get current Gold price per ounce."}]], "function": [{"name": "get_metal_price", "description": "Retrieve the current price for a specified metal and measure.", "parameters": {"type": "dict", "properties": {"metal": {"type": "string", "description": "The metal whose price needs to be fetched."}, "measure": {"type": "string", "description": "The measure unit for price, like 'ounce' or 'kg'."}}, "required": ["metal", "measure"]}}]} +{"id": "simple_python_142", "question": [[{"role": "user", "content": "Find the NASDAQ stock price for the company Amazon at closing March.11, 2022."}]], "function": [{"name": "get_stock_price", "description": "Get the closing stock price for a specific company on a specified date.", "parameters": {"type": "dict", "properties": {"company_name": {"type": "string", "description": "Name of the company."}, "date": {"type": "string", "description": "Date of when to get the stock price. Format: yyyy-mm-dd."}, "exchange": {"type": "string", "description": "Name of the stock exchange market where the company's stock is listed. Default is 'NASDAQ'"}}, "required": ["company_name", "date"]}}]} +{"id": "simple_python_143", "question": [[{"role": "user", "content": "'Get stock price of Apple for the last 5 days in NASDAQ.'"}]], "function": [{"name": "get_stock_price", "description": "Retrieve the stock price for a specific company and time frame.", "parameters": {"type": "dict", "properties": {"company": {"type": "string", "description": "The ticker symbol of the company."}, "days": {"type": "integer", "description": "Number of past days for which the stock price is required."}, "exchange": {"type": "string", "description": "The stock exchange where the company is listed, default is NYSE"}}, "required": ["company", "days"]}}]} +{"id": "simple_python_144", "question": [[{"role": "user", "content": "Find the market performance of the S&P 500 and the Dow Jones over the past 5 days."}]], "function": [{"name": "market_performance.get_data", "description": "Retrieve the market performance data for specified indexes over a specified time period.", "parameters": {"type": "dict", "properties": {"indexes": {"type": "array", "items": {"type": "string"}, "description": "Array of stock market indexes. Supported indexes are 'S&P 500', 'Dow Jones', 'NASDAQ', 'FTSE 100', 'DAX' etc."}, "days": {"type": "integer", "description": "Number of days in the past for which the performance data is required."}, "detailed": {"type": "boolean", "description": "Whether to return detailed performance data. If set to true, returns high, low, opening, and closing prices. If false, returns only closing prices. Default is false."}}, "required": ["indexes", "days"]}}]} +{"id": "simple_python_145", "question": [[{"role": "user", "content": "Calculate the compounded interest for an initial principal of $5000, annual interest rate of 5%, and compounding period of 10 years."}]], "function": [{"name": "calculate_compounded_interest", "description": "Calculate the compounded interest for a given principal, interest rate, and period.", "parameters": {"type": "dict", "properties": {"principal": {"type": "integer", "description": "The initial principal."}, "interest_rate": {"type": "float", "description": "The annual interest rate."}, "period": {"type": "integer", "description": "The period in years."}, "compounding_frequency": {"type": "string", "description": "The frequency of compounding per year. Defaults to 'Annually'.", "enum": ["Annually", "Semiannually", "Quarterly", "Monthly", "Daily"]}}, "required": ["principal", "interest_rate", "period"]}}]} +{"id": "simple_python_146", "question": [[{"role": "user", "content": "What's the price of Amazon stock for the last 3 days?"}]], "function": [{"name": "stock_price", "description": "Get stock price data for a given company over a specified number of days.", "parameters": {"type": "dict", "properties": {"company": {"type": "string", "description": "The company name."}, "days": {"type": "integer", "description": "The number of previous days to retrieve data for."}, "data_type": {"type": "string", "description": "The type of price data to retrieve (e.g., 'Open', 'Close', 'High', 'Low'). Default is 'Close'."}}, "required": ["company", "days"]}}]} +{"id": "simple_python_147", "question": [[{"role": "user", "content": "Retrieve stock prices of Microsoft and Google for the last 2 weeks."}]], "function": [{"name": "get_stock_prices", "description": "Retrieves stock prices for specified companies and duration.", "parameters": {"type": "dict", "properties": {"companies": {"type": "array", "items": {"type": "string"}, "description": "List of companies to retrieve stock prices for."}, "duration": {"type": "string", "description": "Time duration to retrieve stock prices for. E.g., '1 week', '2 weeks', '1 month', etc."}}, "required": ["companies", "duration"]}}]} +{"id": "simple_python_148", "question": [[{"role": "user", "content": "Calculate the future value of an investment with an annual rate of return of 8%, an initial investment of $20000, and a time frame of 5 years."}]], "function": [{"name": "finance.calculate_future_value", "description": "Calculate the future value of an investment given an initial investment, annual rate of return, and a time frame.", "parameters": {"type": "dict", "properties": {"initial_investment": {"type": "integer", "description": "The initial investment amount."}, "rate_of_return": {"type": "float", "description": "The annual rate of return."}, "years": {"type": "integer", "description": "The time frame of the investment in years."}, "contribution": {"type": "integer", "description": "Optional: Additional regular contributions. Default is 0."}}, "required": ["initial_investment", "rate_of_return", "years"]}}]} +{"id": "simple_python_149", "question": [[{"role": "user", "content": "What's the current stock price of Apple and Microsoft?"}]], "function": [{"name": "get_stock_price", "description": "Retrieves the current stock price of the specified companies", "parameters": {"type": "dict", "properties": {"company_names": {"type": "array", "items": {"type": "string"}, "description": "The list of companies for which to retrieve the stock price."}}, "required": ["company_names"]}}]} +{"id": "simple_python_150", "question": [[{"role": "user", "content": "Calculate the return of investment of a bank's savings account with a deposit of $1000, annual interest rate of 3% for 1 year."}]], "function": [{"name": "calculate_roi", "description": "Calculate the return on investment for a given deposit amount, annual interest rate, and time frame.", "parameters": {"type": "dict", "properties": {"deposit": {"type": "integer", "description": "The initial deposit amount."}, "annual_interest_rate": {"type": "float", "description": "The annual interest rate provided by the bank."}, "years": {"type": "integer", "description": "The period for which the money is invested."}}, "required": ["deposit", "annual_interest_rate", "years"]}}]} +{"id": "simple_python_151", "question": [[{"role": "user", "content": "Find the highest grossing bank in the U.S for year 2020."}]], "function": [{"name": "highest_grossing_banks", "description": "Retrieve the highest grossing banks in a specified country and year.", "parameters": {"type": "dict", "properties": {"country": {"type": "string", "description": "The country to get the data from."}, "year": {"type": "integer", "description": "The year to get the data from."}, "top_n": {"type": "integer", "description": "Top n banks in terms of grossing. Default is 5"}}, "required": ["country", "year"]}}]} +{"id": "simple_python_152", "question": [[{"role": "user", "content": "Calculate the balance of a mutual fund given a total investment of $50000 with a 5% annual yield after 3 years."}]], "function": [{"name": "calculate_mutual_fund_balance", "description": "Calculate the final balance of a mutual fund investment based on the total initial investment, annual yield rate and the time period.", "parameters": {"type": "dict", "properties": {"investment_amount": {"type": "integer", "description": "The initial total amount invested in the fund."}, "annual_yield": {"type": "float", "description": "The annual yield rate of the fund."}, "years": {"type": "integer", "description": "The period of time for the fund to mature."}}, "required": ["investment_amount", "annual_yield", "years"]}}]} +{"id": "simple_python_153", "question": [[{"role": "user", "content": "Calculate the compounded interest on an initial deposit of $5000 at an annual interest rate of 3% for 5 years, compounded quarterly."}]], "function": [{"name": "calculate_compounded_interest", "description": "Calculate the compounded interest for a given initial deposit, interest rate, time and number of times the interest is compounded per unit time.", "parameters": {"type": "dict", "properties": {"principal": {"type": "integer", "description": "The initial amount of money that is being invested or loaned."}, "rate": {"type": "float", "description": "The annual interest rate."}, "time": {"type": "integer", "description": "The number of time periods the money is invested or loaned for."}, "n": {"type": "integer", "description": "The number of times that interest is compounded per unit time."}}, "required": ["principal", "rate", "time", "n"]}}]} +{"id": "simple_python_154", "question": [[{"role": "user", "content": "Calculate the Future Value of a $5000 investment made today for a term of 10 years at an annual interest rate of 5%"}]], "function": [{"name": "calculate_future_value", "description": "Calculates the future value of an investment based on the present value, interest rate, and time period.", "parameters": {"type": "dict", "properties": {"present_value": {"type": "integer", "description": "The present value or principal amount."}, "annual_interest_rate": {"type": "float", "description": "The annual interest rate in decimal form. Example, 5% is 0.05."}, "years": {"type": "integer", "description": "The time period in years for which the investment is made."}, "compounds_per_year": {"type": "integer", "description": "The number of times the interest is compounded per year. Default is 1 (annual compounding)."}}, "required": ["present_value", "annual_interest_rate", "years"]}}]} +{"id": "simple_python_155", "question": [[{"role": "user", "content": "Calculate the future value of my investment of $1000 with an annual interest rate of 5% over 2 years."}]], "function": [{"name": "calculate_future_value", "description": "Calculate the future value of an investment given the initial amount, interest rate, and investment duration.", "parameters": {"type": "dict", "properties": {"initial_investment": {"type": "integer", "description": "The initial investment amount."}, "interest_rate": {"type": "float", "description": "The annual interest rate in decimal form."}, "duration": {"type": "integer", "description": "The investment duration in years."}, "compounded": {"type": "integer", "description": "The number of times the interest is compounded per year. Default is 1 (annual compounding)."}}, "required": ["initial_investment", "interest_rate", "duration"]}}]} +{"id": "simple_python_156", "question": [[{"role": "user", "content": "Look up details of a felony crime record for case number CA123456 in San Diego County"}]], "function": [{"name": "crime_record.get_record", "description": "Retrieve detailed felony crime records using a specific case number and location.", "parameters": {"type": "dict", "properties": {"case_number": {"type": "string", "description": "The case number related to the crime."}, "county": {"type": "string", "description": "The county in which the crime occurred."}, "details": {"type": "boolean", "description": "To get a detailed report, set as true. Defaults to false."}}, "required": ["case_number", "county"]}}]} +{"id": "simple_python_157", "question": [[{"role": "user", "content": "Find out if an individual John Doe with a birthday 01-01-1980 has any prior felony convictions in California."}]], "function": [{"name": "criminal_history.check_felonies", "description": "This function checks if an individual has any prior felony convictions based on their full name and birth date.", "parameters": {"type": "dict", "properties": {"full_name": {"type": "string", "description": "The full name of the individual."}, "birth_date": {"type": "string", "description": "The birth date of the individual. Must be in MM-DD-YYYY format."}, "state": {"type": "string", "description": "The state to search the criminal record in. Default to 'None', which the function will search across all states."}}, "required": ["full_name", "birth_date"]}}]} +{"id": "simple_python_158", "question": [[{"role": "user", "content": "Find the information of criminal cases of Mr. X in New York between 2012 and 2015."}]], "function": [{"name": "get_criminal_records", "description": "Retrieve the criminal records of a specific person in a specific area during a certain time period.", "parameters": {"type": "dict", "properties": {"name": {"type": "string", "description": "The name of the person."}, "location": {"type": "string", "description": "The city and state, e.g. New York, NY"}, "from_year": {"type": "integer", "description": "The start year of the time frame."}, "to_year": {"type": "integer", "description": "The end year of the time frame."}}, "required": ["name", "location", "from_year", "to_year"]}}]} +{"id": "simple_python_159", "question": [[{"role": "user", "content": "Give me the details of Criminal Law Amendment Act of 2013."}]], "function": [{"name": "get_act_details", "description": "Retrieve the details of a particular legal act based on its name and year of amendment if any.", "parameters": {"type": "dict", "properties": {"act_name": {"type": "string", "description": "The name of the act."}, "amendment_year": {"type": "integer", "description": "Year of amendment if any. If not provided, the latest amendment year will be considered."}}, "required": ["act_name", "amendment_year"]}}]} +{"id": "simple_python_160", "question": [[{"role": "user", "content": "Who was the victim in the case docket numbered 2022/AL2562 in California?"}]], "function": [{"name": "get_case_info", "description": "Retrieve case details using a specific case docket number and court location.", "parameters": {"type": "dict", "properties": {"docket": {"type": "string", "description": "Docket number for the specific court case."}, "court": {"type": "string", "description": "Court in which the case was heard."}, "info_type": {"type": "string", "description": "Specify the information type needed for the case. i.e., victim, accused, verdict etc."}}, "required": ["docket", "court", "info_type"]}}]} +{"id": "simple_python_161", "question": [[{"role": "user", "content": "Find out the possible punishments for the crime of theft in California in detail."}]], "function": [{"name": "crime_statute_lookup", "description": "Look up the criminal statutes in a specific jurisdiction to find possible punishments for a specific crime.", "parameters": {"type": "dict", "properties": {"jurisdiction": {"type": "string", "description": "The jurisdiction to search in, usually a state or country."}, "crime": {"type": "string", "description": "The crime to search for."}, "detail_level": {"type": "string", "enum": ["basic", "detailed"], "description": "How detailed of a report to return. Optional, default is 'basic'."}}, "required": ["jurisdiction", "crime"]}}]} +{"id": "simple_python_162", "question": [[{"role": "user", "content": "Generate a customized law contract between John and Alice for rental agreement in California."}]], "function": [{"name": "generate_law_contract", "description": "Generates a customized law contract given involved parties, contract type and location.", "parameters": {"type": "dict", "properties": {"parties": {"type": "array", "items": {"type": "string"}, "description": "Parties involved in the contract."}, "contract_type": {"type": "string", "description": "Type of the contract."}, "location": {"type": "string", "description": "Location where the contract will be in effect."}}, "required": ["parties", "contract_type", "location"]}}]} +{"id": "simple_python_163", "question": [[{"role": "user", "content": "Provide me with the property records of my house located at 123 main street, with parcel number 1234567890 in Santa Clara county. Include owners information in the response."}]], "function": [{"name": "property_records.get", "description": "Fetch property records based on location, parcel number and county.", "parameters": {"type": "dict", "properties": {"address": {"type": "string", "description": "Address of the property."}, "parcel_number": {"type": "string", "description": "Parcel number of the property."}, "county": {"type": "string", "description": "County where the property is located."}, "include_owner": {"type": "boolean", "description": "Include owner's name in the property record. Default is false.", "default": false}}, "required": ["address", "parcel_number", "county"]}}]} +{"id": "simple_python_164", "question": [[{"role": "user", "content": "Provide me the official crime rate of violent crime in San Francisco in 2020."}]], "function": [{"name": "get_crime_rate", "description": "Retrieve the official crime rate of a city.", "parameters": {"type": "dict", "properties": {"city": {"type": "string", "description": "The name of the city."}, "state": {"type": "string", "description": "The state where the city is located."}, "type": {"type": "string", "description": "Optional. The type of crime. Default is 'violent'"}, "year": {"type": "integer", "description": "Optional. The year for the crime rate data. Default is year 2001."}}, "required": ["city", "state"]}}]} +{"id": "simple_python_165", "question": [[{"role": "user", "content": "Retrieve cases from 2020 about theft crimes in Los Angeles, California"}]], "function": [{"name": "civil_cases.retrieve", "description": "Retrieve civil cases based on given parameters, including year, crime type, and location.", "parameters": {"type": "dict", "properties": {"year": {"type": "integer", "description": "Year of the cases"}, "crime_type": {"type": "string", "description": "Type of the crime."}, "location": {"type": "string", "description": "Location of the case in the format of city name."}}, "required": ["year", "crime_type", "location"]}}]} +{"id": "simple_python_166", "question": [[{"role": "user", "content": "Find a lawyer specializing in divorce cases and charge fee less than 400 dollars per hour in Chicago."}]], "function": [{"name": "lawyer.find_nearby", "description": "Locate nearby lawyers based on specific criteria like specialty, fee per hour and city.", "parameters": {"type": "dict", "properties": {"city": {"type": "string", "description": "The city and state, e.g. Chicago, IL."}, "specialty": {"type": "array", "items": {"type": "string", "enum": ["Civil", "Divorce", "Immigration", "Business", "Criminal"]}, "description": "Specialization of the lawyer."}, "fee": {"type": "integer", "description": "Hourly fee charged by lawyer"}}, "required": ["city", "specialty", "fee"]}}]} +{"id": "simple_python_167", "question": [[{"role": "user", "content": "Retrieve the details of a Supreme Court case titled 'Roe v. Wade'.Include dissent information."}]], "function": [{"name": "law.civil.get_case_details", "description": "Retrieve the details of a Supreme Court case given its title.", "parameters": {"type": "dict", "properties": {"case_title": {"type": "string", "description": "Title of the Supreme Court case."}, "include_dissent": {"type": "boolean", "description": "If true, the output will include details of the dissenting opinion."}}, "required": ["case_title", "include_dissent"]}}]} +{"id": "simple_python_168", "question": [[{"role": "user", "content": "Search for ongoing lawsuits related to the company 'Google' filed after January 1, 2021 in California."}]], "function": [{"name": "lawsuit_search", "description": "Search for lawsuits related to a specific company within a specific date range and location.", "parameters": {"type": "dict", "properties": {"company": {"type": "string", "description": "The company related to the lawsuit."}, "start_date": {"type": "string", "description": "Start of the date range for when the lawsuit was filed in the format of MM-DD-YYY."}, "location": {"type": "string", "description": "Location where the lawsuit was filed in the format of full state name."}, "status": {"type": "string", "enum": ["ongoing", "settled", "dismissed"], "description": "The status of the lawsuit. Default is 'ongoing'."}}, "required": ["company", "start_date", "location"]}}]} +{"id": "simple_python_169", "question": [[{"role": "user", "content": "Find the details of the court case identified by docket number 123456 in Texas. Don't return full text"}]], "function": [{"name": "court_case.search", "description": "Retrieves details about a court case using its docket number and location.", "parameters": {"type": "dict", "properties": {"docket_number": {"type": "string", "description": "The docket number for the case."}, "location": {"type": "string", "description": "The location where the case is registered, in the format: state, e.g., Texas"}, "full_text": {"type": "boolean", "default": "false", "description": "Option to return the full text of the case ruling."}}, "required": ["docket_number", "location"]}}]} +{"id": "simple_python_170", "question": [[{"role": "user", "content": "Find a historical law case about fraud from 2010 to 2015."}]], "function": [{"name": "law_case_search.find_historical", "description": "Search for a historical law case based on specific criteria like the subject and year.", "parameters": {"type": "dict", "properties": {"subject": {"type": "string", "description": "The subject matter of the case, e.g., 'fraud'"}, "from_year": {"type": "integer", "description": "The start year for the range of the case. The case should happen after this year."}, "to_year": {"type": "integer", "description": "The end year for the range of the case. The case should happen before this year."}}, "required": ["subject", "from_year", "to_year"]}}]} +{"id": "simple_python_171", "question": [[{"role": "user", "content": "Fetch details of a law case with number 43403 in New York court for year 2018."}]], "function": [{"name": "fetch_law_case_details", "description": "Fetch details of a specific law case based on case number, year and court.", "parameters": {"type": "dict", "properties": {"case_number": {"type": "integer", "description": "The specific number of the law case."}, "court": {"type": "string", "description": "The city name where the court takes place"}, "year": {"type": "integer", "description": "The year in which the law case took place."}}, "required": ["case_number", "court", "year"]}}]} +{"id": "simple_python_172", "question": [[{"role": "user", "content": "How to obtain the detailed case information of the 'R vs Adams' legal case?"}]], "function": [{"name": "legal_case.fetch", "description": "Fetch detailed legal case information from database.", "parameters": {"type": "dict", "properties": {"case_id": {"type": "string", "description": "The ID of the legal case."}, "details": {"type": "boolean", "description": "True if need the detail info. "}}, "required": ["case_id", "details"]}}]} +{"id": "simple_python_173", "question": [[{"role": "user", "content": "Find state law cases related to land disputes in the past 5 years from 2015 to 2021 in New York."}]], "function": [{"name": "law_case_search", "description": "Search and retrieve law cases based on the topic, timeline, and location.", "parameters": {"type": "dict", "properties": {"topic": {"type": "string", "description": "The subject matter of the case."}, "year_range": {"type": "array", "items": {"type": "integer"}, "description": "The start and end year for searching cases."}, "location": {"type": "string", "description": "The location where the case is being heard."}, "judicial_system": {"type": "string", "description": "The specific judicial system in which to search (e.g. 'federal', 'state').", "default": "all"}}, "required": ["topic", "year_range", "location"]}}]} +{"id": "simple_python_174", "question": [[{"role": "user", "content": "Get me the top 10 landmark cases in constitutional law in China."}]], "function": [{"name": "get_top_cases", "description": "Retrieve a list of the most influential or landmark cases in a specific field of law.", "parameters": {"type": "dict", "properties": {"field_of_law": {"type": "string", "description": "The specific field of law e.g., constitutional law, criminal law, etc."}, "top_number": {"type": "integer", "description": "The number of top cases to retrieve."}, "country": {"type": "string", "description": "The country where the law cases should be retrieved from. Default is United States of America."}}, "required": ["field_of_law", "top_number"]}}]} +{"id": "simple_python_175", "question": [[{"role": "user", "content": "How many months of experience a Lawyer John Doe has on handling Bankruptcy cases."}]], "function": [{"name": "lawyer.get_experience", "description": "Retrieve months of experience of a Lawyer on handling certain type of law cases.", "parameters": {"type": "dict", "properties": {"name": {"type": "string", "description": "The full name of the Lawyer."}, "law_type": {"type": "string", "description": "The type of law case. eg. Bankruptcy"}}, "required": ["name", "law_type"]}}]} +{"id": "simple_python_176", "question": [[{"role": "user", "content": "Find details of patent lawsuits involving the company 'Apple Inc.' from the year 2010."}]], "function": [{"name": "lawsuit_details.find", "description": "Find details of lawsuits involving a specific company from a given year.", "parameters": {"type": "dict", "properties": {"company_name": {"type": "string", "description": "Name of the company."}, "year": {"type": "integer", "description": "Year of the lawsuit."}, "case_type": {"type": "string", "description": "Type of the lawsuit, e.g., 'IPR', 'Patent', 'Commercial', etc. Default is 'all'."}}, "required": ["company_name", "year"]}}]} +{"id": "simple_python_177", "question": [[{"role": "user", "content": "Find all Patent lawsuit cases of Facebook in 2018."}]], "function": [{"name": "get_lawsuit_cases", "description": "Retrieve all lawsuit cases related to a specific company during a particular year.", "parameters": {"type": "dict", "properties": {"company_name": {"type": "string", "description": "The name of the company."}, "year": {"type": "integer", "description": "The specific year to search for lawsuit cases."}, "status": {"type": "string", "enum": ["open", "closed", "all"], "description": "The status of the lawsuit cases to retrieve. If not specified, defaults to 'all'."}}, "required": ["company_name", "year"]}}]} +{"id": "simple_python_178", "question": [[{"role": "user", "content": "Find details about lawsuit case numbered 'LAX2019080202' in the Los Angeles court."}]], "function": [{"name": "get_lawsuit_details", "description": "Retrieve the detailed information about a lawsuit based on its case number and the court location.", "parameters": {"type": "dict", "properties": {"case_number": {"type": "string", "description": "The case number of the lawsuit."}, "court_location": {"type": "string", "description": "The location of the court where the case is filed."}, "additional_details": {"type": "array", "items": {"type": "string", "enum": ["attorneys", "plaintiffs", "defendants", "charges", "court_updates"]}, "description": "Optional. Array containing additional details to be fetched. Default is all."}}, "required": ["case_number", "court_location"]}}]} +{"id": "simple_python_179", "question": [[{"role": "user", "content": "Find the latest court case between Apple and Samsung occured in USA."}]], "function": [{"name": "find_latest_court_case", "description": "Find the latest court case between two companies.", "parameters": {"type": "dict", "properties": {"company1": {"type": "string", "description": "The name of the first company."}, "company2": {"type": "string", "description": "The name of the second company."}, "country": {"type": "string", "description": "The country in which the court case is located.", "default": "USA"}}, "required": ["company1", "company2"]}}]} +{"id": "simple_python_180", "question": [[{"role": "user", "content": "Find the lawsuits filed against the company Google in California in the year 2020."}]], "function": [{"name": "lawsuits_search", "description": "Search for lawsuits against a specific company within a specific time and location.", "parameters": {"type": "dict", "properties": {"company_name": {"type": "string", "description": "The name of the company."}, "location": {"type": "string", "description": "The location where the lawsuit was filed."}, "year": {"type": "integer", "description": "The year when the lawsuit was filed."}, "case_type": {"type": "string", "description": "The type of the case. Options include: 'civil', 'criminal', 'small_claims', etc. Default is 'all'."}}, "required": ["company_name", "location", "year"]}}]} +{"id": "simple_python_181", "question": [[{"role": "user", "content": "Get details of a lawsuit with case number '123456-ABC' filed in Los Angeles court with verdict"}]], "function": [{"name": "get_lawsuit_details", "description": "Retrieve details of a lawsuit based on its case number and court location.", "parameters": {"type": "dict", "properties": {"case_number": {"type": "string", "description": "Case number of the lawsuit."}, "court_location": {"type": "string", "description": "The location of the court where the lawsuit was filed."}, "with_verdict": {"type": "boolean", "description": "Flag to include verdict details if available. Default is False"}}, "required": ["case_number", "court_location"]}}]} +{"id": "simple_python_182", "question": [[{"role": "user", "content": "Retrieve all the lawsuit details for case number XYZ123."}]], "function": [{"name": "lawsuit_info", "description": "Retrieves details of a lawsuit given a case number", "parameters": {"type": "dict", "properties": {"case_number": {"type": "string", "description": "The unique identifier of the lawsuit case"}, "year": {"type": "integer", "description": "The year in which the lawsuit case was initiated. Default is 2023 if not specified.", "optional": true, "default": 2023}, "location": {"type": "string", "description": "The location or court jurisdiction where the case was filed. Default is 'all'.", "optional": true}}, "required": ["case_number"]}}]} +{"id": "simple_python_183", "question": [[{"role": "user", "content": "Search for current lawsuits filed against Apple in Santa Clara County."}]], "function": [{"name": "lawsuit_search", "description": "Retrieve all lawsuits involving a particular entity from specified jurisdiction.", "parameters": {"type": "dict", "properties": {"entity": {"type": "string", "description": "The entity involved in lawsuits."}, "county": {"type": "string", "description": "The jurisdiction for the lawsuit search for example Alameda county."}, "state": {"type": "string", "description": "The state for the lawsuit search. Default is California."}}, "required": ["entity", "county"]}}]} +{"id": "simple_python_184", "question": [[{"role": "user", "content": "I need the details of the lawsuit case with case ID of 1234 and verify if it's already closed."}]], "function": [{"name": "lawsuit.check_case", "description": "Verify the details of a lawsuit case and check its status using case ID.", "parameters": {"type": "dict", "properties": {"case_id": {"type": "integer", "description": "The identification number of the lawsuit case."}, "closed_status": {"type": "boolean", "description": "The status of the lawsuit case to be verified."}}, "required": ["case_id", "closed_status"]}}]} +{"id": "simple_python_185", "question": [[{"role": "user", "content": "What will be the weather in New York in the next 72 hours including the precipitation?"}]], "function": [{"name": "detailed_weather_forecast", "description": "Retrieve a detailed weather forecast for a specific location and duration including optional precipitation details.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city name that you want to get the weather for."}, "duration": {"type": "integer", "description": "Duration in hours for the detailed forecast."}, "include_precipitation": {"type": "boolean", "description": "Whether to include precipitation data in the forecast. Default is false."}}, "required": ["location", "duration"]}}]} +{"id": "simple_python_186", "question": [[{"role": "user", "content": "What is the temperature in celsius and humidity level of Tokyo, Japan right now?"}]], "function": [{"name": "current_weather_condition", "description": "Get the current weather conditions of a specific city including temperature and humidity.", "parameters": {"type": "dict", "properties": {"city": {"type": "string", "description": "The city that you want to get the current weather conditions for."}, "country": {"type": "string", "description": "The country of the city you specified."}, "measurement": {"type": "string", "description": "You can specify which unit to display the temperature in, 'c' for Celsius, 'f' for Fahrenheit. Default is 'c'."}}, "required": ["city", "country"]}}]} +{"id": "simple_python_187", "question": [[{"role": "user", "content": "What's the current temperature and humidity in Seattle, Washington?"}]], "function": [{"name": "get_current_weather", "description": "Retrieves the current temperature and humidity for a specific location.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city name to get the weather for."}, "include_temperature": {"type": "boolean", "description": "Whether to include the temperature in the result. Default is true."}, "include_humidity": {"type": "boolean", "description": "Whether to include the humidity in the result. Default is true."}}, "required": ["location"]}}]} +{"id": "simple_python_188", "question": [[{"role": "user", "content": "What is the humidity level in Miami, Florida in the upcoming 7 days?"}]], "function": [{"name": "weather.humidity_forecast", "description": "Retrieve a humidity forecast for a specific location and time frame.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city that you want to get the humidity for."}, "days": {"type": "integer", "description": "Number of days for the forecast."}, "min_humidity": {"type": "integer", "description": "Minimum level of humidity (in percentage) to filter the result. Default is 0."}}, "required": ["location", "days"]}}]} +{"id": "simple_python_189", "question": [[{"role": "user", "content": "Get weather information for New York, USA for the next 3 days with details."}]], "function": [{"name": "weather_forecast_detailed", "description": "Retrieve a detailed weather forecast for a specific city like Boston and time frame.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city that you want to get the weather for."}, "days": {"type": "integer", "description": "Number of days for the forecast."}, "details": {"type": "boolean", "description": "Provide detailed weather information or not.", "default": false}}, "required": ["location", "days"]}}]} +{"id": "simple_python_190", "question": [[{"role": "user", "content": "What's the elevation and area of Yellowstone National Park?"}]], "function": [{"name": "park_information", "description": "Retrieve the basic information such as elevation and area of a national park.", "parameters": {"type": "dict", "properties": {"park_name": {"type": "string", "description": "The name of the national park."}, "information": {"type": "array", "items": {"type": "string", "enum": ["Elevation", "Area", "Location", "Established Year"]}, "description": "The type of information you want about the park."}}, "required": ["park_name", "information"]}}]} +{"id": "simple_python_191", "question": [[{"role": "user", "content": "Find me the 5 tallest mountains within 50km of Denver, Colorado."}]], "function": [{"name": "locate_tallest_mountains", "description": "Find the tallest mountains within a specified radius of a location.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city from which to calculate distance."}, "radius": {"type": "integer", "description": "The radius within which to find mountains, measured in kilometers."}, "amount": {"type": "integer", "description": "The number of mountains to find, listed from tallest to smallest."}}, "required": ["location", "radius", "amount"]}}]} +{"id": "simple_python_192", "question": [[{"role": "user", "content": "Calculate the slope gradient in degree between two points on a landscape with coordinates (40.7128, -74.0060) and (34.0522, -118.2437)."}]], "function": [{"name": "calculate_slope_gradient", "description": "Calculate the slope gradient between two geographical coordinates.", "parameters": {"type": "dict", "properties": {"point1": {"type": "array", "items": {"type": "float"}, "description": "The geographic coordinates for the first point [Latitude, Longitude]."}, "point2": {"type": "array", "items": {"type": "float"}, "description": "The geographic coordinates for the second point [Latitude, Longitude]."}, "unit": {"type": "string", "enum": ["degree", "percent", "ratio"], "description": "The unit for the slope gradient. Default is 'degree'."}}, "required": ["point1", "point2"]}}]} +{"id": "simple_python_193", "question": [[{"role": "user", "content": "Find the best local nurseries in Toronto with a good variety of annual plants."}]], "function": [{"name": "local_nursery.find", "description": "Locate local nurseries based on location and plant types availability.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city or locality where the nursery needs to be located."}, "plant_types": {"type": "array", "items": {"type": "string", "enum": ["Annual", "Perennial", "Shrub", "Tree", "Herbs", "Fruits"]}, "description": "Type of plants the nursery should provide."}}, "required": ["location", "plant_types"]}}]} +{"id": "simple_python_194", "question": [[{"role": "user", "content": "What are the top three plants suitable for a hill slope in terms of erosion prevention?"}]], "function": [{"name": "get_plants_for_slope", "description": "Retrieve the list of plants suitable for slope based on erosion control ability.", "parameters": {"type": "dict", "properties": {"slope_type": {"type": "string", "description": "The type of slope like steep, moderate etc."}, "num_results": {"type": "integer", "description": "The number of top results needed. Default is 5."}}, "required": ["slope_type", "num_results"]}}]} +{"id": "simple_python_195", "question": [[{"role": "user", "content": "Calculate the carbon footprint of my lifestyle, assuming I drive 20 miles a day, consume 3 meat meals a week, and produce 500 lbs of trash in a year."}]], "function": [{"name": "calculate_carbon_footprint", "description": "Calculate the estimated carbon footprint of a lifestyle based on factors such as daily driving distance, weekly meat consumption, and yearly trash production.", "parameters": {"type": "dict", "properties": {"daily_miles": {"type": "integer", "description": "The daily driving distance in miles."}, "meat_meals_per_week": {"type": "integer", "description": "The number of meat-based meals consumed per week."}, "annual_trash_weight": {"type": "integer", "description": "The yearly weight of trash production in pounds."}, "flights_per_year": {"type": "integer", "description": "The number of flights taken per year. Default is 0."}}, "required": ["daily_miles", "meat_meals_per_week", "annual_trash_weight"]}}]} +{"id": "simple_python_196", "question": [[{"role": "user", "content": "What is the air quality index in London 2022/08/16?"}]], "function": [{"name": "air_quality", "description": "Retrieve the air quality index for a specific location.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city that you want to get the air quality index for."}, "date": {"type": "string", "description": "The date (month-day-year) you want to get the air quality index for."}}, "required": ["location", "date"]}}]} +{"id": "simple_python_197", "question": [[{"role": "user", "content": "Find the air quality index in San Diego at 12pm."}]], "function": [{"name": "get_air_quality_index", "description": "Retrieve the air quality index at a specified location and time.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The location to get the air quality index for."}, "time": {"type": "string", "description": "The specific time to check the air quality. Default is the current time."}}, "required": ["location", "time"]}}]} +{"id": "simple_python_198", "question": [[{"role": "user", "content": "Calculate the required water daily intake for a person with weight 70 kg."}]], "function": [{"name": "calculate_daily_water_intake", "description": "Calculate the recommended daily water intake for a person based on their weight.", "parameters": {"type": "dict", "properties": {"weight": {"type": "integer", "description": "The weight of the person in kilograms."}, "activity_level": {"type": "string", "description": "The level of physical activity of the person. Default is 'moderate'."}, "climate": {"type": "string", "description": "The climate of the area where the person lives. Default is 'temperate'."}}, "required": ["weight"]}}]} +{"id": "simple_python_199", "question": [[{"role": "user", "content": "Find air quality index in San Jose for next three days."}]], "function": [{"name": "environmental_data.air_quality_index", "description": "Retrieves Air Quality Index (AQI) for specified location over a number of days.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "Name of the city or town to retrieve air quality index for."}, "days": {"type": "integer", "description": "Number of days for which to retrieve data. If not provided, default to today."}}, "required": ["location"]}}]} +{"id": "simple_python_200", "question": [[{"role": "user", "content": "How much CO2 is produced annually by a gas-fueled car that travels 12,000 miles per year, with fuel efficiency of 25 MPG ?"}]], "function": [{"name": "calculate_emissions", "description": "Calculates the annual carbon dioxide emissions produced by a vehicle based on the distance traveled, the fuel type and the fuel efficiency of the vehicle.", "parameters": {"type": "dict", "properties": {"distance": {"type": "integer", "description": "The distance travelled in miles."}, "fuel_type": {"type": "string", "description": "Type of fuel used by the vehicle."}, "fuel_efficiency": {"type": "float", "description": "The vehicle's fuel efficiency in miles per gallon."}, "efficiency_reduction": {"type": "integer", "description": "The percentage decrease in fuel efficiency per year (optional). Default is 0"}}, "required": ["distance", "fuel_type", "fuel_efficiency"]}}]} +{"id": "simple_python_201", "question": [[{"role": "user", "content": "Estimate the population of pandas in the wild in China."}]], "function": [{"name": "estimate_population", "description": "Estimate the population of a particular species in a given country.", "parameters": {"type": "dict", "properties": {"species": {"type": "string", "description": "The species for which population needs to be estimated."}, "country": {"type": "string", "description": "The country where the species lives."}, "year": {"type": "integer", "description": "The year for which population estimate is sought. Default is the current year."}}, "required": ["species", "country"]}}]} +{"id": "simple_python_202", "question": [[{"role": "user", "content": "How many greenhouse gas emissions would I save if I switched to renewable energy sources for 3 months in California?"}]], "function": [{"name": "calculate_emission_savings", "description": "Calculate potential greenhouse gas emissions saved by switching to renewable energy sources.", "parameters": {"type": "dict", "properties": {"energy_type": {"type": "string", "description": "Type of the renewable energy source."}, "usage_duration": {"type": "integer", "description": "Usage duration in months."}, "region": {"type": "string", "description": "The region where you use energy. Default is 'Texas'."}}, "required": ["energy_type", "usage_duration"]}}]} +{"id": "simple_python_203", "question": [[{"role": "user", "content": "Can you find me the latest information about air quality index and pollution data for Chicago?"}]], "function": [{"name": "get_air_quality", "description": "Retrieve real-time air quality and pollution data for a specific location.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city that you want to get the air quality data for."}, "detail": {"type": "boolean", "description": "If true, additional data like PM2.5, PM10, ozone levels, and pollution sources will be retrieved. Default is false."}}, "required": ["location"]}}]} +{"id": "simple_python_204", "question": [[{"role": "user", "content": "Find restaurants near me within 10 miles that offer Chinese cuisine in Seattle."}]], "function": [{"name": "restaurant.find_nearby", "description": "Locate nearby restaurants based on specific criteria like cuisine type.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. Seattle, WA"}, "cuisine": {"type": "string", "description": "Preferred type of cuisine in restaurant."}, "max_distance": {"type": "integer", "description": "Maximum distance (in miles) within which to search for restaurants. Default is 5."}}, "required": ["location", "cuisine"]}}]} +{"id": "simple_python_205", "question": [[{"role": "user", "content": "Find out the current traffic situation from Boston driving to New York."}]], "function": [{"name": "get_traffic_info", "description": "Retrieve current traffic conditions for a specified route.", "parameters": {"type": "dict", "properties": {"start_location": {"type": "string", "description": "The starting point of the route."}, "end_location": {"type": "string", "description": "The destination of the route."}, "mode": {"type": "string", "enum": ["driving", "walking", "bicycling", "transit"], "description": "Preferred method of transportation, default to 'driving'."}}, "required": ["start_location", "end_location"]}}]} +{"id": "simple_python_206", "question": [[{"role": "user", "content": "Find the nearest park with a tennis court in London."}]], "function": [{"name": "parks.find_nearby", "description": "Locate nearby parks based on specific criteria like tennis court availability.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. London, UK"}, "amenities": {"type": "array", "items": {"type": "string", "enum": ["Tennis Court", "Picnic Area", "Playground", "Running Track"]}, "description": "Preferred amenities in park. Default is ['Running Track']"}}, "required": ["location"]}}]} +{"id": "simple_python_207", "question": [[{"role": "user", "content": "Get the shortest driving distance between New York, USA and Miami, USA."}]], "function": [{"name": "calculate_shortest_distance", "description": "Calculate the shortest driving distance between two locations.", "parameters": {"type": "dict", "properties": {"start_location": {"type": "string", "description": "The starting location for the drive."}, "end_location": {"type": "string", "description": "The destination location for the drive."}, "route_preference": {"type": "string", "enum": ["Shortest", "Scenic"], "description": "The preferred type of route."}}, "required": ["start_location", "end_location", "route_preference"]}}]} +{"id": "simple_python_208", "question": [[{"role": "user", "content": "Get me the directions from New York to Los Angeles avoiding highways and toll roads."}]], "function": [{"name": "map_service.get_directions", "description": "Retrieve directions from a starting location to an ending location, including options for route preferences.", "parameters": {"type": "dict", "properties": {"start": {"type": "string", "description": "Starting location for the route."}, "end": {"type": "string", "description": "Ending location for the route."}, "avoid": {"type": "array", "items": {"type": "string", "enum": ["tolls", "highways", "ferries"]}, "description": "Route features to avoid. Default is ['highways', 'ferries']"}}, "required": ["start", "end"]}}]} +{"id": "simple_python_209", "question": [[{"role": "user", "content": "Locate the nearest public library in Boston, Massachusetts with English fiction section and free Wi-Fi."}]], "function": [{"name": "public_library.find_nearby", "description": "Locate nearby public libraries based on specific criteria like English fiction availability and Wi-Fi.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. Boston, MA"}, "facilities": {"type": "array", "items": {"type": "string", "enum": ["Wi-Fi", "Reading Room", "Fiction", "Children Section", "Cafe"]}, "description": "Facilities and sections in public library."}}, "required": ["location", "facilities"]}}]} +{"id": "simple_python_210", "question": [[{"role": "user", "content": "Get 5 latest news on Bitcoin in US"}]], "function": [{"name": "get_news", "description": "Fetches the latest news on a specific topic.", "parameters": {"type": "dict", "properties": {"topic": {"type": "string", "description": "The subject for the news topic."}, "quantity": {"type": "integer", "description": "Number of articles to fetch."}, "region": {"type": "string", "description": "The geographical region for the news. Default is 'US'."}}, "required": ["topic", "quantity"]}}]} +{"id": "simple_python_211", "question": [[{"role": "user", "content": "Send an email to John Doe at john.doe@example.com with the subject 'Meeting' and body 'Let's meet at 10 AM tomorrow'."}]], "function": [{"name": "send_email", "description": "Send an email to the specified email address.", "parameters": {"type": "dict", "properties": {"to": {"type": "string", "description": "The email address to send to."}, "subject": {"type": "string", "description": "The subject of the email."}, "body": {"type": "string", "description": "The body content of the email."}, "cc": {"type": "string", "description": "The email address to carbon copy. Default is empty if not specified."}, "bcc": {"type": "string", "description": "The email address to blind carbon copy. Default is empty if not specified."}}, "required": ["to", "subject", "body"]}}]} +{"id": "simple_python_212", "question": [[{"role": "user", "content": "Give me detail information about stocks of Apple Inc."}]], "function": [{"name": "get_stock_info", "description": "Retrieves information about a specific stock based on company's name.", "parameters": {"type": "dict", "properties": {"company_name": {"type": "string", "description": "The name of the company."}, "detail_level": {"type": "string", "description": "Level of detail for stock information. Can be 'summary' or 'detailed'."}, "market": {"type": "string", "description": "The stock market of interest. Default is 'NASDAQ'"}}, "required": ["company_name", "detail_level"]}}]} +{"id": "simple_python_213", "question": [[{"role": "user", "content": "Book a direct flight from San Francisco to London for 2022-04-27 afternoon"}]], "function": [{"name": "flight.book", "description": "Book a direct flight for a specific date and time from departure location to destination location.", "parameters": {"type": "dict", "properties": {"departure_location": {"type": "string", "description": "The location you are departing from."}, "destination_location": {"type": "string", "description": "The location you are flying to."}, "date": {"type": "string", "description": "The date of the flight. Accepts standard date format e.g., 2022-04-28."}, "time": {"type": "string", "description": "Preferred time of flight. Default is 'morning'."}, "direct_flight": {"type": "boolean", "description": "If set to true, only direct flights will be searched. Default is false."}}, "required": ["departure_location", "destination_location", "date"]}}]} +{"id": "simple_python_214", "question": [[{"role": "user", "content": "Search for upcoming month rock concerts in New York."}]], "function": [{"name": "event_finder.find_upcoming", "description": "Find upcoming events of a specific genre in a given location.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state where the search will take place, e.g. New York, NY."}, "genre": {"type": "string", "description": "The genre of events."}, "days_ahead": {"type": "integer", "description": "The number of days from now to include in the search.", "default": 7}}, "required": ["location", "genre"]}}]} +{"id": "simple_python_215", "question": [[{"role": "user", "content": "Give me a brief on movie 'Interstellar'"}]], "function": [{"name": "movie_details.brief", "description": "This function retrieves a brief about a specified movie.", "parameters": {"type": "dict", "properties": {"title": {"type": "string", "description": "Title of the movie"}, "extra_info": {"type": "boolean", "description": "Option to get additional information like Director, Cast, Awards etc.", "default": "false"}}, "required": ["title"]}}]} +{"id": "simple_python_216", "question": [[{"role": "user", "content": "Analyze the sentiment of a customer review 'I love the food here! It's always fresh and delicious.'."}]], "function": [{"name": "sentiment_analysis", "description": "Perform sentiment analysis on a given piece of text.", "parameters": {"type": "dict", "properties": {"text": {"type": "string", "description": "The text on which to perform sentiment analysis."}, "language": {"type": "string", "description": "The language in which the text is written."}}, "required": ["text", "language"]}}]} +{"id": "simple_python_217", "question": [[{"role": "user", "content": "Analyze my fMRI data in ~/data/myfMRI.nii from a multi-band sequence, that is smoothed at 6mm with an isotropic voxel size of 2mm."}]], "function": [{"name": "fMRI.analyze", "description": "This function takes in fMRI data to output analyzed data.", "parameters": {"type": "dict", "properties": {"data_source": {"type": "string", "description": "The path where the data is stored."}, "sequence_type": {"type": "string", "description": "Type of fMRI sequence"}, "smooth": {"type": "integer", "description": "Spatial smoothing FWHM. In mm."}, "voxel_size": {"type": "integer", "description": "Size of isotropic voxels in mm.", "default": 3}}, "required": ["data_source", "sequence_type", "smooth"]}}]} +{"id": "simple_python_218", "question": [[{"role": "user", "content": "Given patient with id 546382, retrieve their brain MRI report with the status 'concluded'."}]], "function": [{"name": "patient.get_mri_report", "description": "Fetch the brain MRI report of the patient for a given status.", "parameters": {"type": "dict", "properties": {"patient_id": {"type": "string", "description": "The patient identifier."}, "mri_type": {"type": "string", "description": "Type of the MRI. Default to be 'brain'.", "enum": ["brain", "spinal", "chest", "abdominal"]}, "status": {"type": "string", "description": "Status of the report, could be 'in progress', 'concluded' or 'draft'.", "enum": ["in progress", "concluded", "draft"]}}, "required": ["patient_id", "status"]}}]} +{"id": "simple_python_219", "question": [[{"role": "user", "content": "What are the coordinates of the neuron in a rat's all part of the brain that produces GABA neurotransmitters?"}]], "function": [{"name": "get_neuron_coordinates", "description": "Retrieve the coordinates of the specified neuron in the rat's brain.", "parameters": {"type": "dict", "properties": {"neuron_type": {"type": "string", "description": "Type of neuron to find. For instance, GABA, Glutamate, etc."}, "brain_region": {"type": "string", "description": "The region of the brain to consider.", "default": "All"}}, "required": ["neuron_type", "brain_region"]}}]} +{"id": "simple_python_220", "question": [[{"role": "user", "content": "Calculate the neuronal activity based on synaptic input rate of 200 and weight 0.5 and decay rate of 0.1."}]], "function": [{"name": "calculate_neuronal_activity", "description": "Calculate the neuronal activity (rate of firing) based on a given input synaptic rate, weight of inputs, and decay rate. Higher input or weight increases firing rate and higher decay rate decreases it.", "parameters": {"type": "dict", "properties": {"input_synaptic_rate": {"type": "integer", "description": "The synaptic input rate, usually represented as number of inputs per second."}, "weight": {"type": "float", "description": "The weight of the input, denoting its influence on the neuron's state. Default is 1.0."}, "decay_rate": {"type": "float", "description": "The rate at which the neuron's potential decays in the absence of inputs."}}, "required": ["input_synaptic_rate", "decay_rate"]}}]} +{"id": "simple_python_221", "question": [[{"role": "user", "content": "What will be the population growth in London over the next five years?"}]], "function": [{"name": "population_growth_estimate", "description": "Estimate the future population growth of a specific location over a specified time period.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city that you want to estimate the population growth for."}, "years": {"type": "integer", "description": "Number of years into the future for the estimate."}, "rate": {"type": "float", "description": "Expected annual growth rate in percentage. Default is 1.2."}}, "required": ["location", "years"]}}]} +{"id": "simple_python_222", "question": [[{"role": "user", "content": "Can you calculate my Body Mass Index (BMI) given my weight is 70 kg and height is 180 cm?"}]], "function": [{"name": "calculate_bmi", "description": "Calculate the Body Mass Index based on given weight and height.", "parameters": {"type": "dict", "properties": {"weight": {"type": "integer", "description": "The weight of a person in kilograms."}, "height": {"type": "integer", "description": "The height of a person in centimeters."}, "unit": {"type": "string", "description": "Optional. The measurement system to be used for the result. The default is 'metric'."}}, "required": ["weight", "height"]}}]} +{"id": "simple_python_223", "question": [[{"role": "user", "content": "Find social behaviors and patterns in a group size of 50 with extroverted members being 15 and introverted members being 35."}]], "function": [{"name": "group_dynamics.pattern", "description": "Examine the social dynamics and interactions within a group based on the personality traits and group size.", "parameters": {"type": "dict", "properties": {"total": {"type": "integer", "description": "The total group size."}, "extroverts": {"type": "integer", "description": "The number of extroverted members in the group."}, "introverts": {"type": "integer", "description": "The number of introverted members in the group."}}, "required": ["total", "extroverts", "introverts"]}}]} +{"id": "simple_python_224", "question": [[{"role": "user", "content": "Find the most followed person on twitter who tweets about psychology related to behaviour and group dynamics."}]], "function": [{"name": "social_media_analytics.most_followed", "description": "Find the most followed Twitter user related to certain topics.", "parameters": {"type": "dict", "properties": {"topic": {"type": "string", "description": "The main topic of interest."}, "sub_topics": {"type": "array", "items": {"type": "string"}, "description": "Sub-topics related to main topic. Default is empty."}, "region": {"type": "string", "description": "Region of interest for twitter search. Default is 'all'."}}, "required": ["topic"]}}]} +{"id": "simple_python_225", "question": [[{"role": "user", "content": "What is the percentage of population preferring digital reading over physical books?"}]], "function": [{"name": "psych_research.get_preference", "description": "Gathers research data on public preference between two options, based on societal category.", "parameters": {"type": "dict", "properties": {"category": {"type": "string", "description": "The societal category the preference data is about. E.g. reading, transportation, food"}, "option_one": {"type": "string", "description": "The first option people could prefer."}, "option_two": {"type": "string", "description": "The second option people could prefer."}, "demographic": {"type": "string", "description": "Specific demographic of society to narrow down the research.", "default": "all"}}, "required": ["category", "option_one", "option_two"]}}]} +{"id": "simple_python_226", "question": [[{"role": "user", "content": "Find the compatibility score in percentage of Aries with Gemini."}]], "function": [{"name": "get_zodiac_compatibility", "description": "Retrieve the compatibility score between two Zodiac signs.", "parameters": {"type": "dict", "properties": {"sign1": {"type": "string", "description": "The first Zodiac sign."}, "sign2": {"type": "string", "description": "The second Zodiac sign."}, "scale": {"type": "string", "enum": ["percentage", "0-10 scale"], "description": "The scale on which compatibility should be shown. Default is 'percentage'."}}, "required": ["sign1", "sign2"]}}]} +{"id": "simple_python_227", "question": [[{"role": "user", "content": "Get me strength and weakness traits for ENFJ personality type."}]], "function": [{"name": "get_personality_traits", "description": "Retrieve the personality traits for a specific personality type, including their strengths and weaknesses.", "parameters": {"type": "dict", "properties": {"type": {"type": "string", "description": "The personality type."}, "traits": {"type": "array", "items": {"type": "string", "enum": ["strengths", "weaknesses"]}, "description": "List of traits to be retrieved, default is ['strengths']."}}, "required": ["type"]}}]} +{"id": "simple_python_228", "question": [[{"role": "user", "content": "Find three personality traits of people who like jogging."}]], "function": [{"name": "get_personality_traits", "description": "Retrieve the common personality traits of people based on their hobbies or activities.", "parameters": {"type": "dict", "properties": {"hobby": {"type": "string", "description": "The hobby or activity of interest."}, "trait_count": {"type": "integer", "description": "The number of top traits to return, default is 5"}}, "required": ["hobby"]}}]} +{"id": "simple_python_229", "question": [[{"role": "user", "content": "What's my Big Five Personality trait scores given that I am efficient, organized, easy going and compassionate?"}]], "function": [{"name": "get_bigfive_scores", "description": "Retrieve Big Five Personality trait scores based on individual's behavioural characteristics.", "parameters": {"type": "dict", "properties": {"characteristics": {"type": "array", "items": {"type": "string"}, "description": "List of user's behavioural characteristics."}, "scale": {"type": "string", "enum": ["high", "medium", "low"], "description": "The scoring scale of traits (default is medium)."}}, "required": ["characteristics"]}}]} +{"id": "simple_python_230", "question": [[{"role": "user", "content": "Who was the King of France in 1510?"}]], "function": [{"name": "historic_leader_search", "description": "Retrieve information about a historical leader given a location and date.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The country or region in question."}, "date": {"type": "integer", "description": "The year being queried."}, "title": {"type": "string", "description": "The official title of the position. Default is 'King'."}}, "required": ["location", "date"]}}]} +{"id": "simple_python_231", "question": [[{"role": "user", "content": "Provide key war events in German history from 1871 to 1945."}]], "function": [{"name": "history.get_key_events", "description": "Retrieve key historical events within a specific period for a certain country.", "parameters": {"type": "dict", "properties": {"country": {"type": "string", "description": "The name of the country for which history is queried."}, "start_year": {"type": "integer", "description": "Start year of the period for which history is queried."}, "end_year": {"type": "integer", "description": "End year of the period for which history is queried."}, "event_type": {"type": "array", "items": {"type": "string", "enum": ["War", "Revolutions", "Diplomacy", "Economy"]}, "description": "Types of event. Default to 'all', which all types will be considered."}}, "required": ["country", "start_year", "end_year"]}}]} +{"id": "simple_python_232", "question": [[{"role": "user", "content": "What was the full name king of England in 1800?"}]], "function": [{"name": "monarch.getMonarchOfYear", "description": "Retrieve the monarch of a specific location during a specified year.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The location (e.g., country) whose monarch needs to be found."}, "year": {"type": "integer", "description": "The year to search the monarch."}, "fullName": {"type": "boolean", "default": false, "description": "If true, returns the full name and title of the monarch."}}, "required": ["location", "year"]}}]} +{"id": "simple_python_233", "question": [[{"role": "user", "content": "When did the Treaty of Tordesillas take place? Put it in the format of YYYY."}]], "function": [{"name": "european_history.get_event_date", "description": "Retrieve the date of a specific event in European history.", "parameters": {"type": "dict", "properties": {"event_name": {"type": "string", "description": "The name of the event."}, "format": {"type": "string", "description": "Optional format of the returned date. Default is 'MM-DD-YYYY'."}}, "required": ["event_name"]}}]} +{"id": "simple_python_234", "question": [[{"role": "user", "content": "Find important Wars in European history during the 19th century."}]], "function": [{"name": "history_eu.fetch_events", "description": "Fetches significant historical events within a specific time period in European history.", "parameters": {"type": "dict", "properties": {"century": {"type": "integer", "description": "The century you are interested in."}, "region": {"type": "string", "description": "The region of Europe you are interested in.", "enum": ["Northern", "Southern", "Eastern", "Western"]}, "category": {"type": "string", "description": "Category of the historical events. Default is 'Culture'.", "enum": ["Wars", "Culture", "Politics", "Scientific", "Others"]}}, "required": ["century", "region"]}}]} +{"id": "simple_python_235", "question": [[{"role": "user", "content": "When was the signing of the Treaty of Lisbon?"}]], "function": [{"name": "get_event_date", "description": "Retrieve the date of a historical event.", "parameters": {"type": "dict", "properties": {"event": {"type": "string", "description": "The name of the historical event."}, "location": {"type": "string", "description": "Location where the event took place. Default to global if not specified."}}, "required": ["event"]}}]} +{"id": "simple_python_236", "question": [[{"role": "user", "content": "Get start date on the American Civil War."}]], "function": [{"name": "us_history.get_event_info", "description": "Retrieve detailed information about a significant event in U.S. history.", "parameters": {"type": "dict", "properties": {"event_name": {"type": "string", "description": "The name of the event."}, "specific_info": {"type": "string", "description": "Specific aspect of information related to event.", "enum": ["Start Date", "End Date", "Participants", "Result", "Notable Figures", "Importance in History"]}}, "required": ["event_name", "specific_info"]}}]} +{"id": "simple_python_237", "question": [[{"role": "user", "content": "Get historical GDP data for United States from 1960 to 2000."}]], "function": [{"name": "get_historical_GDP", "description": "Retrieve historical GDP data for a specific country and time range.", "parameters": {"type": "dict", "properties": {"country": {"type": "string", "description": "The country for which the historical GDP data is required."}, "start_year": {"type": "integer", "description": "Starting year of the period for which GDP data is required."}, "end_year": {"type": "integer", "description": "Ending year of the period for which GDP data is required."}}, "required": ["country", "start_year", "end_year"]}}]} +{"id": "simple_python_238", "question": [[{"role": "user", "content": "Who was the president of the United States during the American Civil War?"}]], "function": [{"name": "us_history.get_president", "description": "Retrieve the U.S. president during a specific event in American history.", "parameters": {"type": "dict", "properties": {"event": {"type": "string", "description": "The event in U.S. history."}, "year": {"type": "integer", "description": "The specific year of the event."}}, "required": ["event", "year"]}}]} +{"id": "simple_python_239", "question": [[{"role": "user", "content": "Who was the full name of the president of the United States in 1861?"}]], "function": [{"name": "US_president.in_year", "description": "Retrieve the name of the U.S. president in a given year.", "parameters": {"type": "dict", "properties": {"year": {"type": "integer", "description": "The year in question."}, "full_name": {"type": "boolean", "default": true, "description": "Option to return full name with middle initial, if applicable."}}, "required": ["year"]}}]} +{"id": "simple_python_240", "question": [[{"role": "user", "content": "Who was the President of the United States in 1940?"}]], "function": [{"name": "history_api.get_president_by_year", "description": "Get the name of the U.S. President for a specified year.", "parameters": {"type": "dict", "properties": {"year": {"type": "integer", "description": "The year you want to know the U.S. president of."}, "full_term_only": {"type": "boolean", "description": "Flag to determine if we should only return presidents that served a full term for the specified year.", "default": false}}, "required": ["year"]}}]} +{"id": "simple_python_241", "question": [[{"role": "user", "content": "Who was the U.S. president during the Civil War?"}]], "function": [{"name": "US_President_During_Event", "description": "Returns the U.S. president during a specified historical event.", "parameters": {"type": "dict", "properties": {"event": {"type": "string", "description": "The historical event."}, "country": {"type": "string", "description": "The country the president leads (optional parameter, defaults to 'USA' if not specified)."}}, "required": ["event"]}}]} +{"id": "simple_python_242", "question": [[{"role": "user", "content": "Who is the scientist that first proposed the theory of evolution?"}]], "function": [{"name": "get_scientist_for_discovery", "description": "Retrieve the scientist's name who is credited for a specific scientific discovery or theory.", "parameters": {"type": "dict", "properties": {"discovery": {"type": "string", "description": "The scientific discovery or theory."}}, "required": ["discovery"]}}]} +{"id": "simple_python_243", "question": [[{"role": "user", "content": "Who discovered the neutron? Give me detail information."}]], "function": [{"name": "get_discoverer", "description": "Get the person or team who made a particular scientific discovery", "parameters": {"type": "dict", "properties": {"discovery": {"type": "string", "description": "The discovery for which the discoverer's information is needed."}, "detail": {"type": "boolean", "description": "Optional flag to get additional details about the discoverer, such as birth date and nationality. Defaults to false."}}, "required": ["discovery", "detail"]}}]} +{"id": "simple_python_244", "question": [[{"role": "user", "content": "What year was the law of universal gravitation published by Isaac Newton?"}]], "function": [{"name": "publication_year.find", "description": "Fetches the year a particular scientific work was published.", "parameters": {"type": "dict", "properties": {"author": {"type": "string", "description": "Name of the author of the work."}, "work_title": {"type": "string", "description": "Title of the scientific work."}, "location": {"type": "string", "description": "Place of the publication, if known. Default to 'all'."}}, "required": ["author", "work_title"]}}]} +{"id": "simple_python_245", "question": [[{"role": "user", "content": "Who discovered radium?"}]], "function": [{"name": "discoverer.get", "description": "Retrieve the name of the discoverer of an element based on its name.", "parameters": {"type": "dict", "properties": {"element_name": {"type": "string", "description": "The name of the element."}, "year": {"type": "integer", "description": "Optional parameter that refers to the year of discovery. It could be helpful in case an element was discovered more than once. Default to 0, which means not use it."}, "first": {"type": "boolean", "default": true, "description": "Optional parameter indicating if the first discoverer's name should be retrieved."}}, "required": ["element_name"]}}]} +{"id": "simple_python_246", "question": [[{"role": "user", "content": "Who discovered Gravity and what was the method used?"}]], "function": [{"name": "science_history.get_discovery_details", "description": "Retrieve the details of a scientific discovery based on the discovery name.", "parameters": {"type": "dict", "properties": {"discovery": {"type": "string", "description": "The name of the discovery, e.g. Gravity"}, "method_used": {"type": "string", "description": "The method used for the discovery, default value is 'default' which gives the most accepted method."}}, "required": ["discovery"]}}]} +{"id": "simple_python_247", "question": [[{"role": "user", "content": "What was Albert Einstein's contribution to science on March 17, 1915?"}]], "function": [{"name": "historical_contrib.get_contrib", "description": "Retrieve historical contribution made by a scientist on a specific date.", "parameters": {"type": "dict", "properties": {"scientist": {"type": "string", "description": "The scientist whose contributions need to be searched."}, "date": {"type": "string", "description": "The date when the contribution was made in yyyy-mm-dd format."}, "category": {"type": "string", "description": "The field of the contribution, such as 'Physics' or 'Chemistry'. Default is 'all'."}}, "required": ["scientist", "date"]}}]} +{"id": "simple_python_248", "question": [[{"role": "user", "content": "Who invented the theory of relativity and in which year?"}]], "function": [{"name": "science_history.get_invention", "description": "Retrieve the inventor and year of invention based on the invention's name.", "parameters": {"type": "dict", "properties": {"invention_name": {"type": "string", "description": "The name of the invention."}, "want_year": {"type": "boolean", "default": false, "description": "Return the year of invention if set to true."}}, "required": ["invention_name", "want_year"]}}]} +{"id": "simple_python_249", "question": [[{"role": "user", "content": "Tell me more about Christianity and its history till the 14th century"}]], "function": [{"name": "religion.history_info", "description": "Provides comprehensive historical details about a specified religion till a specified century.", "parameters": {"type": "dict", "properties": {"religion": {"type": "string", "description": "The name of the religion for which historical details are needed."}, "till_century": {"type": "integer", "description": "The century till which historical details are needed."}, "include_people": {"type": "boolean", "description": "To include influential people related to the religion during that time period, default is False"}}, "required": ["religion", "till_century"]}}]} +{"id": "simple_python_250", "question": [[{"role": "user", "content": "What's the time difference between San Francisco and Sydney?"}]], "function": [{"name": "get_time_difference", "description": "Get the time difference between two places.", "parameters": {"type": "dict", "properties": {"place1": {"type": "string", "description": "The first place for time difference."}, "place2": {"type": "string", "description": "The second place for time difference."}}, "required": ["place1", "place2"]}}]} +{"id": "simple_python_251", "question": [[{"role": "user", "content": "What is the earliest reference of Jesus Christ in history from historical record?"}]], "function": [{"name": "get_earliest_reference", "description": "Retrieve the earliest historical reference of a person.", "parameters": {"type": "dict", "properties": {"name": {"type": "string", "description": "The name of the person."}, "source": {"type": "string", "enum": ["scriptures", "historical records"], "description": "Source to fetch the reference. Default is 'scriptures'"}}, "required": ["name"]}}]} +{"id": "simple_python_252", "question": [[{"role": "user", "content": "Find ten major historical events related to Christianity in the 16th century sort by importance."}]], "function": [{"name": "get_religion_history", "description": "Retrieves significant religious events, including the details of the event, its historical context, and its impacts.", "parameters": {"type": "dict", "properties": {"religion": {"type": "string", "description": "Name of the religion to be queried."}, "century": {"type": "integer", "description": "The century in which the event(s) took place."}, "sort_by": {"type": "string", "enum": ["importance", "chronological"], "default": "chronological", "description": "Order of sorting the events. Default is chronological."}, "count": {"type": "integer", "default": 5, "description": "Number of events to return. Default is 5."}}, "required": ["religion", "century"]}}]} +{"id": "simple_python_253", "question": [[{"role": "user", "content": "Retrieve the full historyof Buddhism"}]], "function": [{"name": "retrieve_religion_info", "description": "Retrieve the history and main beliefs of a religion.", "parameters": {"type": "dict", "properties": {"religion_name": {"type": "string", "description": "The name of the religion."}, "detail_level": {"type": "string", "description": "Level of detail for the returned information, either 'summary' or 'full'.", "default": "summary"}}, "required": ["religion_name", "detail_level"]}}]} +{"id": "simple_python_254", "question": [[{"role": "user", "content": "Retrieve the historic dates and facts related to Christianity between year 300 and 400."}]], "function": [{"name": "get_religion_history", "description": "Retrieves historic events and facts related to a specified religion for a given period.", "parameters": {"type": "dict", "properties": {"religion": {"type": "string", "description": "The name of the religion."}, "start_year": {"type": "integer", "description": "The starting year of the period."}, "end_year": {"type": "integer", "description": "The end year of the period."}, "event_type": {"type": "string", "enum": ["all", "crusade", "schism", "reform"], "description": "Optional parameter specifying the type of event. Default is 'all'."}}, "required": ["religion", "start_year", "end_year"]}}]} +{"id": "simple_python_255", "question": [[{"role": "user", "content": "Get the biography and main contributions of Pope Innocent III."}]], "function": [{"name": "religious_history.get_papal_biography", "description": "Retrieve the biography and main religious and historical contributions of a Pope based on his papal name.", "parameters": {"type": "dict", "properties": {"papal_name": {"type": "string", "description": "The papal name of the Pope."}, "include_contributions": {"type": "boolean", "default": false, "description": "Include main contributions of the Pope in the response if true."}}, "required": ["papal_name", "include_contributions"]}}]} +{"id": "simple_python_256", "question": [[{"role": "user", "content": "Generate an image of a circle with a radius of 50 pixels and color 'Red'."}]], "function": [{"name": "generate_circle_image", "description": "Generates a circle image based on the given radius and color", "parameters": {"type": "dict", "properties": {"radius": {"type": "integer", "description": "The radius of the circle in pixels."}, "color": {"type": "string", "description": "The color of the circle."}, "background": {"type": "string", "description": "Optional: The color of the background, default is white."}}, "required": ["radius", "color"]}}]} +{"id": "simple_python_257", "question": [[{"role": "user", "content": "Can you help me identify the basic RGB value of Sea Green color?"}]], "function": [{"name": "identify_color_rgb", "description": "This function identifies the RGB values of a named color.", "parameters": {"type": "dict", "properties": {"color_name": {"type": "string", "description": "Name of the color."}, "standard": {"type": "string", "description": "The color standard (e.g. basic, pantone). Default is 'basic'"}}, "required": ["color_name"]}}]} +{"id": "simple_python_258", "question": [[{"role": "user", "content": "Mix yellow and blue colors and adjust the lightness level to 60 percent."}]], "function": [{"name": "mix_paint_color", "description": "Combine two primary paint colors and adjust the resulting color's lightness level.", "parameters": {"type": "dict", "properties": {"color1": {"type": "string", "description": "The first primary color to be mixed."}, "color2": {"type": "string", "description": "The second primary color to be mixed."}, "lightness": {"type": "integer", "description": "The desired lightness level of the resulting color in percentage. The default level is set to 50."}}, "required": ["color1", "color2"]}}]} +{"id": "simple_python_259", "question": [[{"role": "user", "content": "Calculate the total quantity of paint needed to cover a wall of 30 feet by 12 feet using a specific brand that covers 400 square feet per gallon."}]], "function": [{"name": "calculate_paint_needed", "description": "Calculate the amount of paint needed to cover a surface area based on the coverage rate of a specific paint brand.", "parameters": {"type": "dict", "properties": {"coverage_rate": {"type": "integer", "description": "The area in square feet that one gallon of paint can cover."}, "length": {"type": "integer", "description": "Length of the wall to be painted in feet."}, "height": {"type": "integer", "description": "Height of the wall to be painted in feet."}}, "required": ["coverage_rate", "length", "height"]}}]} +{"id": "simple_python_260", "question": [[{"role": "user", "content": "Calculate how many gallons of paint is required to paint a wall with width of 20ft and height of 12ft, assuming 1 gallon covers approximately 350 sq.ft. Don't include window area of 15 sq.ft."}]], "function": [{"name": "paint_requirement.calculate", "description": "Calculate the amount of paint required to paint a given area. Account for coverage efficiency of the paint and exclusions (like windows).", "parameters": {"type": "dict", "properties": {"area": {"type": "dict", "properties": {"width": {"type": "integer", "description": "The width of the area to be painted in feet."}, "height": {"type": "integer", "description": "The height of the area to be painted in feet."}}, "description": "The area to be painted."}, "paint_coverage": {"type": "integer", "description": "Coverage area per gallon of the paint in square feet.", "default": 350}, "exclusion": {"type": "dict", "properties": {"type": {"type": "string", "description": "The type of the exclusion e.g window, door etc."}, "area": {"type": "integer", "description": "The area of the exclusion in square feet."}}, "description": "Area not to be painted. Default to not use any exclusion if not specified."}}, "required": ["area", "paint_coverage"]}}]} +{"id": "simple_python_261", "question": [[{"role": "user", "content": "Draw a rectangle with a width of 20 units and height of 10 units in red."}]], "function": [{"name": "draw_rectangle", "description": "Draw a rectangle given its width and height.", "parameters": {"type": "dict", "properties": {"width": {"type": "integer", "description": "The width of the rectangle."}, "height": {"type": "integer", "description": "The height of the rectangle."}, "color": {"type": "string", "description": "The color of the rectangle. Default is 'black'."}}, "required": ["width", "height"]}}]} +{"id": "simple_python_262", "question": [[{"role": "user", "content": "Change my painting's medium to oil and change size to 12x18 with red dominant color."}]], "function": [{"name": "modify_painting", "description": "Modify an existing painting's attributes such as size, medium, and color.", "parameters": {"type": "dict", "properties": {"size": {"type": "string", "description": "The size of the painting in inches, width by height."}, "medium": {"type": "string", "description": "The medium of the painting, such as oil, acrylic, etc."}, "dominant_color": {"type": "string", "description": "The dominant color of the painting. Default to 'black'."}}, "required": ["size", "medium"]}}]} +{"id": "simple_python_263", "question": [[{"role": "user", "content": "Find me the most recent art sculpture by James Plensa with detailed description."}]], "function": [{"name": "get_sculpture_info", "description": "Retrieves the most recent artwork by a specified artist with its detailed description.", "parameters": {"type": "dict", "properties": {"artist_name": {"type": "string", "description": "The name of the artist."}, "detail": {"type": "boolean", "description": "If True, it provides detailed description of the sculpture. Defaults to False."}}, "required": ["artist_name"]}}]} +{"id": "simple_python_264", "question": [[{"role": "user", "content": "Find the size of the sculpture with title 'David' by Michelangelo."}]], "function": [{"name": "sculpture.get_details", "description": "Retrieve details of a sculpture based on the artist and the title of the sculpture.", "parameters": {"type": "dict", "properties": {"artist": {"type": "string", "description": "The artist who made the sculpture."}, "title": {"type": "string", "description": "The title of the sculpture."}, "detail": {"type": "string", "description": "The specific detail wanted about the sculpture. Default is 'general information'."}}, "required": ["artist", "title"]}}]} +{"id": "simple_python_265", "question": [[{"role": "user", "content": "Find me sculptures near Chicago that were made in the 19th century."}]], "function": [{"name": "sculpture_search", "description": "Find sculptures based on location and a specific time frame.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city where the sculptures are located."}, "time_frame": {"type": "string", "description": "The time frame during which the sculptures were made."}, "material": {"type": "string", "description": "Optional material of the sculptures. Default is 'all'"}}, "required": ["location", "time_frame"]}}]} +{"id": "simple_python_266", "question": [[{"role": "user", "content": "What is the value of the sculpture 'The Thinker' by Rodin?"}]], "function": [{"name": "get_sculpture_value", "description": "Retrieve the current market value of a particular sculpture by a specific artist.", "parameters": {"type": "dict", "properties": {"sculpture": {"type": "string", "description": "The name of the sculpture."}, "artist": {"type": "string", "description": "The name of the artist who created the sculpture."}}, "required": ["sculpture", "artist"]}}]} +{"id": "simple_python_267", "question": [[{"role": "user", "content": "Find the top rated modern sculpture exhibition happening in New York in the upcoming month."}]], "function": [{"name": "find_exhibition", "description": "Locate the most popular exhibitions based on criteria like location, time, art form, and user ratings.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state where the exhibition is held, e.g., New York City, NY."}, "art_form": {"type": "string", "description": "The form of art the exhibition is displaying e.g., sculpture."}, "month": {"type": "string", "description": "The month of exhibition. Default value will return upcoming events if not specified."}, "user_ratings": {"type": "string", "enum": ["low", "average", "high"], "description": "Select exhibitions with user rating threshold. Default is 'low'"}}, "required": ["location", "art_form"]}}]} +{"id": "simple_python_268", "question": [[{"role": "user", "content": "Find me the sculptures of Michelangelo with material Marble in Rome, Italy."}]], "function": [{"name": "sculpture_locator.find_by_artist", "description": "Locate the sculptures of specific artist by material and location", "parameters": {"type": "dict", "properties": {"artist": {"type": "string", "description": "Name of the Artist of the sculpture"}, "material": {"type": "string", "description": "Material of the sculpture."}, "location": {"type": "string", "description": "The location where you want to find the sculpture. Default is 'all' if not specified."}}, "required": ["artist", "material"]}}]} +{"id": "simple_python_269", "question": [[{"role": "user", "content": "Calculate the compound interest of an investment of $10,000 at an interest rate of 5% compounded yearly for 10 years."}]], "function": [{"name": "calculate_compound_interest", "description": "Calculates the compound interest of an investment over a given time period.", "parameters": {"type": "dict", "properties": {"principle": {"type": "integer", "description": "The initial amount of the investment."}, "interest_rate": {"type": "float", "description": "The yearly interest rate of the investment."}, "time": {"type": "integer", "description": "The time, in years, the money is invested or borrowed for."}, "compounds_per_year": {"type": "integer", "description": "The number of times the interest is compounded per year. Default is 1 (interest is compounded yearly)."}}, "required": ["principle", "interest_rate", "time"]}}]} +{"id": "simple_python_270", "question": [[{"role": "user", "content": "Can you give me the height and width of Empire State building in feet?"}]], "function": [{"name": "building.get_dimensions", "description": "Retrieve the dimensions of a specific building based on its name.", "parameters": {"type": "dict", "properties": {"building_name": {"type": "string", "description": "The name of the building."}, "unit": {"type": "string", "description": "The unit in which you want the dimensions. Default is meter.", "enum": ["meter", "feet"]}}, "required": ["building_name", "unit"]}}]} +{"id": "simple_python_271", "question": [[{"role": "user", "content": "What is the structural dynamic analysis of the building with building Id B1004 for 2nd, 3rd and 4th floors?"}]], "function": [{"name": "analyze_structure", "description": "Analyze a structure of a building based on its Id and floor numbers.", "parameters": {"type": "dict", "properties": {"building_id": {"type": "string", "description": "The unique identification number of the building."}, "floors": {"type": "array", "items": {"type": "integer"}, "description": "Floor numbers to be analyzed."}, "mode": {"type": "string", "description": "Mode of analysis, e.g. 'static' or 'dynamic'. Default is 'static'."}}, "required": ["building_id", "floors"]}}]} +{"id": "simple_python_272", "question": [[{"role": "user", "content": "Calculate the area and circumference of a circle with a radius of 5 units."}]], "function": [{"name": "calculate_circle_dimensions", "description": "Calculate the area and circumference of a circle based on the radius.", "parameters": {"type": "dict", "properties": {"radius": {"type": "integer", "description": "The radius of the circle."}}, "required": ["radius"]}}]} +{"id": "simple_python_273", "question": [[{"role": "user", "content": "Find out the open hours for the Louvre Museum in Paris."}]], "function": [{"name": "museum.get_hours", "description": "Retrieve the open hours for a museum based on its name and location.", "parameters": {"type": "dict", "properties": {"name": {"type": "string", "description": "The name of the museum."}, "location": {"type": "string", "description": "The city where the museum is located."}, "day": {"type": "string", "description": "Optional: Day of the week for specific open hours. Default 'Monday'."}}, "required": ["name", "location"]}}]} +{"id": "simple_python_274", "question": [[{"role": "user", "content": "Find information about the opening hours of the Metropolitan Museum of Art."}]], "function": [{"name": "museum_info", "description": "Retrieve information about the opening hours of a museum based on its name.", "parameters": {"type": "dict", "properties": {"museum_name": {"type": "string", "description": "The name of the museum."}, "info_type": {"type": "string", "description": "The type of information needed about the museum.", "default": "opening_hours"}}, "required": ["museum_name"]}}]} +{"id": "simple_python_275", "question": [[{"role": "user", "content": "Get the list of top 5 popular artworks at the Metropolitan Museum of Art. Please sort by popularity."}]], "function": [{"name": "metropolitan_museum.get_top_artworks", "description": "Fetches the list of popular artworks at the Metropolitan Museum of Art. Results can be sorted based on popularity.", "parameters": {"type": "dict", "properties": {"number": {"type": "integer", "description": "The number of artworks to fetch"}, "sort_by": {"type": "string", "description": "The criteria to sort the results on. Default is 'popularity'.", "enum": ["popularity", "chronological", "alphabetical"]}}, "required": ["number"]}}]} +{"id": "simple_python_276", "question": [[{"role": "user", "content": "Get the working hours of Louvre Museum in Paris."}]], "function": [{"name": "museum_working_hours.get", "description": "Get the working hours of a museum in a specific location.", "parameters": {"type": "dict", "properties": {"museum": {"type": "string", "description": "The name of the museum."}, "location": {"type": "string", "description": "The location of the museum."}, "day": {"type": "string", "description": "Specific day of the week. Default is 'Monday'"}}, "required": ["museum", "location"]}}]} +{"id": "simple_python_277", "question": [[{"role": "user", "content": "Find the working hours and ticket price of The British Museum for this weekend, Jun.20,2023."}]], "function": [{"name": "museum_info", "description": "Get information about a museum including its opening hours and ticket prices for a specific date range.", "parameters": {"type": "dict", "properties": {"museum": {"type": "string", "description": "The name of the museum."}, "date": {"type": "string", "description": "The specific date for which information is needed, in the format of YYYY-MM-DD such as '2022-12-01'."}, "information": {"type": "array", "items": {"type": "string", "enum": ["opening_hours", "ticket_price", "address"]}, "description": "The type of information needed from the museum. This is optional and defaults to 'all' if not specified.", "default": "all"}}, "required": ["museum", "date"]}}]} +{"id": "simple_python_278", "question": [[{"role": "user", "content": "Find me the average price and ratings of piano from Yamaha."}]], "function": [{"name": "get_instrument_details", "description": "Retrieve the average price and ratings of an instrument from a particular manufacturer.", "parameters": {"type": "dict", "properties": {"instrument": {"type": "string", "description": "The name of the instrument."}, "manufacturer": {"type": "string", "description": "The manufacturer of the instrument."}, "features": {"type": "array", "items": {"type": "string", "enum": ["price", "rating"]}, "description": "The features to retrieve about the instrument. Default is 'price'"}}, "required": ["instrument", "manufacturer"]}}]} +{"id": "simple_python_279", "question": [[{"role": "user", "content": "What's the retail price of a Fender American Professional II Stratocaster in Rosewood Finish?"}]], "function": [{"name": "instrument_price.get", "description": "Retrieve the current retail price of a specific musical instrument.", "parameters": {"type": "dict", "properties": {"brand": {"type": "string", "description": "The brand of the instrument."}, "model": {"type": "string", "description": "The specific model of the instrument."}, "finish": {"type": "string", "description": "The color or type of finish on the instrument."}}, "required": ["brand", "model", "finish"]}}]} +{"id": "simple_python_280", "question": [[{"role": "user", "content": "Find an acoustic instrument within my budget of $1000."}]], "function": [{"name": "find_instrument", "description": "Search for a musical instrument within specified budget and of specific type.", "parameters": {"type": "dict", "properties": {"budget": {"type": "integer", "description": "Your budget for the instrument."}, "type": {"type": "string", "description": "Type of the instrument"}, "make": {"type": "string", "description": "Maker of the instrument. Default to not use if not specified."}}, "required": ["budget", "type"]}}]} +{"id": "simple_python_281", "question": [[{"role": "user", "content": "Find the details about the musical instrument 'Violin' from 'Stradivarius' maker, made in the year 1721."}]], "function": [{"name": "get_instrument_info", "description": "Retrieve the details about a specific musical instrument based on its name, maker, and manufacturing year.", "parameters": {"type": "dict", "properties": {"name": {"type": "string", "description": "The name of the instrument."}, "maker": {"type": "string", "description": "The name of the maker who created the instrument."}, "year": {"type": "integer", "description": "The year the instrument was made."}}, "required": ["name", "maker", "year"]}}]} +{"id": "simple_python_282", "question": [[{"role": "user", "content": "Find a Yamaha flute with the specifications of open hole, C foot, and silver headjoint available for sale."}]], "function": [{"name": "find_flute", "description": "Locate a flute for sale based on specific requirements.", "parameters": {"type": "dict", "properties": {"brand": {"type": "string", "description": "The brand of the flute. Example, 'Yamaha'"}, "specs": {"type": "array", "items": {"type": "string", "enum": ["open hole", "C foot", "silver headjoint"]}, "description": "The specifications of the flute desired."}}, "required": ["brand", "specs"]}}]} +{"id": "simple_python_283", "question": [[{"role": "user", "content": "Find the price of a used Gibson Les Paul guitar in excellent condition in the Chicago area."}]], "function": [{"name": "guitar_price.find", "description": "Retrieve the price of a specific used guitar model based on its condition and location.", "parameters": {"type": "dict", "properties": {"model": {"type": "string", "description": "The model of the guitar."}, "condition": {"type": "string", "enum": ["Poor", "Good", "Excellent"], "description": "The condition of the guitar."}, "location": {"type": "string", "description": "The location where the guitar is being sold."}}, "required": ["model", "condition", "location"]}}]} +{"id": "simple_python_284", "question": [[{"role": "user", "content": "Get information about the pop concerts in New York for next month."}]], "function": [{"name": "concert_info.get", "description": "Retrieve information about concerts based on specific genre, location and date.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city where the concert will take place."}, "date": {"type": "string", "description": "Time frame to get the concert for."}, "genre": {"type": "string", "description": "Genre of the concert.", "enum": ["Pop", "Rock", "Country", "Classical", "Electronic", "Hip-Hop"]}}, "required": ["location", "date", "genre"]}}]} +{"id": "simple_python_285", "question": [[{"role": "user", "content": "Find me a Rock concert in Chicago with ticket availability under $100."}]], "function": [{"name": "find_concert", "description": "Locate a concert in a specified location within a certain budget.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city where you are looking for a concert. In the format City, State."}, "price": {"type": "integer", "description": "Maximum ticket price."}, "genre": {"type": "string", "description": "Music genre of the concert. Default to 'Jazz'. ", "enum": ["Rock", "Pop", "Country", "Jazz", "Classical"]}}, "required": ["location", "price"]}}]} +{"id": "simple_python_286", "question": [[{"role": "user", "content": "Get concert details for the artist Beyonce performing in San Diego next month (April 2022)."}]], "function": [{"name": "concert.get_details", "description": "Fetch the details for a particular concert based on the artist and location.", "parameters": {"type": "dict", "properties": {"artist": {"type": "string", "description": "Name of the artist/band who's performing."}, "location": {"type": "string", "description": "City where the concert is taking place."}, "date": {"type": "string", "description": "Date of the concert in 'mm-yyyy' format. Default is the current month if not specified."}}, "required": ["artist", "location"]}}]} +{"id": "simple_python_287", "question": [[{"role": "user", "content": "Find me a classical concert this weekend in Los Angeles with cheap tickets."}]], "function": [{"name": "concert.search", "description": "Locate a concert based on specific criteria like genre, location, and date.", "parameters": {"type": "dict", "properties": {"genre": {"type": "string", "description": "Genre of the concert."}, "location": {"type": "string", "description": "City of the concert."}, "date": {"type": "string", "description": "Date of the concert, e.g. this weekend, today, tomorrow.", "enum": ["this weekend", "next weekend", "this month", "next month", "today", "tomorrow", "the day after"]}, "price_range": {"type": "string", "enum": ["free", "cheap", "moderate", "expensive"], "description": "Expected price range of the concert tickets. Default is 'free'."}}, "required": ["genre", "location", "date"]}}]} +{"id": "simple_python_288", "question": [[{"role": "user", "content": "Get me two tickets for next Eminem concert in New York City."}]], "function": [{"name": "concert_booking.book_ticket", "description": "Book concert tickets for a specific artist in a specified city.", "parameters": {"type": "dict", "properties": {"artist": {"type": "string", "description": "The artist you want to book tickets for."}, "city": {"type": "string", "description": "The city where the concert is."}, "num_tickets": {"type": "integer", "description": "Number of tickets required. Default is 1."}}, "required": ["artist", "city"]}}]} +{"id": "simple_python_289", "question": [[{"role": "user", "content": "Find concerts near me in Seattle that plays jazz music."}]], "function": [{"name": "concert.find_nearby", "description": "Locate nearby concerts based on specific criteria like genre.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. Seattle, WA"}, "genre": {"type": "string", "description": "Genre of music to be played at the concert."}}, "required": ["location", "genre"]}}]} +{"id": "simple_python_290", "question": [[{"role": "user", "content": "What's the timing and location for The Weeknd's concert happening in December?"}]], "function": [{"name": "concert.find_details", "description": "Finds details of a concert event.", "parameters": {"type": "dict", "properties": {"artist": {"type": "string", "description": "Name of the artist performing."}, "month": {"type": "string", "description": "Month in which the concert is happening."}, "year": {"type": "integer", "description": "Year of the concert.", "default": 2022}}, "required": ["artist", "month"]}}]} +{"id": "simple_python_291", "question": [[{"role": "user", "content": "Generate a melody in C major scale, starting with the note C4, 16 measures long, at 120 beats per minute."}]], "function": [{"name": "music_generator.generate_melody", "description": "Generate a melody based on certain musical parameters.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key of the melody. E.g., 'C' for C major."}, "start_note": {"type": "string", "description": "The first note of the melody, specified in scientific pitch notation. E.g., 'C4'."}, "length": {"type": "integer", "description": "The number of measures in the melody."}, "tempo": {"type": "integer", "description": "The tempo of the melody, in beats per minute. Optional parameter. If not specified, defaults to 120."}}, "required": ["key", "start_note", "length"]}}]} +{"id": "simple_python_292", "question": [[{"role": "user", "content": "Compose a simple piano melody with a progression of C, F and G for 4 measures."}]], "function": [{"name": "compose_melody", "description": "Compose a melody using the specified chord progression for a certain number of measures on specified instrument.", "parameters": {"type": "dict", "properties": {"progression": {"type": "array", "items": {"type": "string"}, "description": "The progression of chords."}, "measures": {"type": "integer", "description": "The number of measures of the melody."}, "instrument": {"type": "string", "description": "The instrument for the composition. Default is 'Piano'."}}, "required": ["progression", "measures"]}}]} +{"id": "simple_python_293", "question": [[{"role": "user", "content": "Create a mix track using notes of C major scale and duration of each note being quarter of a second with a duration of 3 minutes."}]], "function": [{"name": "music_composer.create_mix", "description": "Create a mix of a song based on a particular music scale and duration", "parameters": {"type": "dict", "properties": {"scale": {"type": "string", "description": "The musical scale to be used. E.g: C Major, A Minor, etc."}, "note_duration": {"type": "string", "description": "Duration of each note. Options: 'whole', 'half', 'quarter', 'eighth', 'sixteenth'.", "enum": ["whole", "half", "quarter", "eighth", "sixteenth"]}, "track_length": {"type": "integer", "description": "Length of the mix track in seconds."}}, "required": ["scale", "note_duration", "track_length"]}}]} +{"id": "simple_python_294", "question": [[{"role": "user", "content": "Generate a major chord progression in C key with four chords."}]], "function": [{"name": "music_generation.create_chord_progression", "description": "Create a chord progression in a specific key and number of chords.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key for the chord progression."}, "chords": {"type": "integer", "description": "Number of chords in the progression."}, "progression_type": {"type": "string", "description": "The type of the chord progression. Optional parameter. Default is 'major'."}}, "required": ["key", "chords"]}}]} +{"id": "simple_python_295", "question": [[{"role": "user", "content": "Find the lyrics to the song 'Bohemian Rhapsody' by Queen."}]], "function": [{"name": "get_song_lyrics", "description": "Retrieve the lyrics of a song based on the artist's name and song title.", "parameters": {"type": "dict", "properties": {"song_title": {"type": "string", "description": "The title of the song."}, "artist_name": {"type": "string", "description": "The name of the artist who performed the song."}, "lang": {"type": "string", "description": "The language of the lyrics. Default is English.", "enum": ["English", "French", "Spanish", "German", "Italian"]}}, "required": ["song_title", "artist_name"]}}]} +{"id": "simple_python_296", "question": [[{"role": "user", "content": "Generate a major C scale progression with tempo 80 BPM and duration 4 beats."}]], "function": [{"name": "music_generator.generate_scale_progression", "description": "Generate a music scale progression in a specific key with a given tempo and duration.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key in which to generate the scale progression."}, "tempo": {"type": "integer", "description": "The tempo of the scale progression in BPM."}, "duration": {"type": "integer", "description": "The duration of each note in beats."}, "scale_type": {"type": "string", "default": "major", "description": "The type of scale to generate. Defaults to 'major'."}}, "required": ["key", "tempo", "duration"]}}]} +{"id": "simple_python_297", "question": [[{"role": "user", "content": "music.theory.chordProgression(progression=['I', 'V', 'vi', 'IV'])"}]], "function": [{"name": "music.theory.chordProgression", "description": "Identifies a potential key signature for the given chord progression.", "parameters": {"type": "dict", "properties": {"progression": {"type": "array", "items": {"type": "string"}, "description": "The chord progression in Roman numerals. Eg: ['I', 'V', 'vi', 'IV']."}, "returnAllPossibleKeys": {"type": "boolean", "description": "Flag indicating if the function should return all possible key signatures that fit the chord progression. If false, the function will return the first valid key it finds. Default is false."}, "assumeMajor": {"type": "boolean", "description": "Assumption if the key signature is Major. If true, the function will assume the key signature to be major and otherwise minor. Default is true."}}, "required": ["progression"]}}]} +{"id": "simple_python_298", "question": [[{"role": "user", "content": "What key signature does C# major have?"}]], "function": [{"name": "music_theory.key_signature", "description": "Return the key signature of a major or minor scale.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The root of the scale, e.g., 'C', 'F#', 'Ab'."}, "scale_type": {"type": "string", "enum": ["major", "minor"], "description": "Type of the scale, either 'major' or 'minor'. Default is 'major'."}}, "required": ["key"]}}]} +{"id": "simple_python_299", "question": [[{"role": "user", "content": "What is the musical scale associated with C sharp major?"}]], "function": [{"name": "musical_scale", "description": "Get the musical scale of a specific key in music theory.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The musical key for which the scale will be found."}, "scale_type": {"type": "string", "default": "major", "description": "The type of musical scale."}}, "required": ["key"]}}]} +{"id": "simple_python_300", "question": [[{"role": "user", "content": "Calculate the duration between two notes of 440Hz and 880Hz frequency based on harmonic rhythm."}]], "function": [{"name": "music.calculate_note_duration", "description": "Calculate the duration between two notes based on their frequencies and harmonic rhythm.", "parameters": {"type": "dict", "properties": {"first_note_frequency": {"type": "integer", "description": "The frequency of the first note in Hz."}, "second_note_frequency": {"type": "integer", "description": "The frequency of the second note in Hz."}, "tempo": {"type": "integer", "description": "The tempo of the music in beats per minute. Defaults to 120 beats per minute."}}, "required": ["first_note_frequency", "second_note_frequency"]}}]} +{"id": "simple_python_301", "question": [[{"role": "user", "content": "What is the third major chord in C major scale?"}]], "function": [{"name": "get_third_chord", "description": "Calculate the third major chord in a given key.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key of the scale."}, "type": {"type": "string", "description": "Type of the scale, either major or minor. Default is 'major'."}}, "required": ["key"]}}]} +{"id": "simple_python_302", "question": [[{"role": "user", "content": "Calculate the batting average for a baseball player who has 180 hits and 600 at-bats. Round to 3 decimals."}]], "function": [{"name": "calculate_batting_average", "description": "Calculate the batting average for a baseball player based on their number of hits and at-bats.", "parameters": {"type": "dict", "properties": {"hits": {"type": "integer", "description": "The number of hits."}, "at_bats": {"type": "integer", "description": "The number of at-bats."}, "decimal_places": {"type": "integer", "description": "The number of decimal places to return in the batting average. Default is 3."}}, "required": ["hits", "at_bats"]}}]} +{"id": "simple_python_303", "question": [[{"role": "user", "content": "Get the player stats of Cristiano Ronaldo in the 2019-2020 season"}]], "function": [{"name": "soccer_stat.get_player_stats", "description": "Retrieve soccer player statistics for a given season.", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "Name of the player."}, "season": {"type": "string", "description": "Soccer season, usually specified by two years."}, "league": {"type": "string", "description": "Optional - the soccer league, defaults to all leagues if not specified."}}, "required": ["player_name", "season"]}}]} +{"id": "simple_python_304", "question": [[{"role": "user", "content": "Get point and rebound stats for player 'LeBron James' from last basketball game"}]], "function": [{"name": "player_stats.getLastGame", "description": "Get last game statistics for a specific player in basketball", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "The name of the basketball player."}, "team": {"type": "string", "description": "The team that player currently plays for."}, "metrics": {"type": "array", "items": {"type": "string", "enum": ["Points", "Rebounds", "Assists", "Blocks"]}, "description": "Specific metrics to retrieve. If no value is specified, all available metrics will be returned by default."}}, "required": ["player_name", "team"]}}]} +{"id": "simple_python_305", "question": [[{"role": "user", "content": "Calculate the overall goal and assist of soccer player Messi in La Liga 2020-2021 season"}]], "function": [{"name": "sports_stats.get_performance", "description": "Compute the performance score of a soccer player given his game stats for a specific tournament in a season.", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "Name of the player."}, "tournament": {"type": "string", "description": "Name of the soccer tournament."}, "season": {"type": "string", "description": "Specific season in format 'YYYY-YYYY'."}, "performance_indicator": {"type": "array", "items": {"type": "string", "enum": ["Goals Scored", "Assists Made", "Saves Made", "Cards Received"]}, "description": "Array of performance indicators. Use as much as possible. Default to use all if not specified."}}, "required": ["player_name", "tournament", "season"]}}]} +{"id": "simple_python_306", "question": [[{"role": "user", "content": "Find average batting score of a cricketer, Virat Kohli for past 10 matches"}]], "function": [{"name": "average_batting_score", "description": "Get the average batting score of a cricketer for specified past matches.", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "Name of the cricket player."}, "matches": {"type": "integer", "description": "Number of past matches to consider for average calculation."}, "match_format": {"type": "string", "description": "Format of the cricket matches considered (e.g., 'T20', 'ODI', 'Test'). Default is 'T20'."}}, "required": ["player_name", "matches"]}}]} +{"id": "simple_python_307", "question": [[{"role": "user", "content": "Who won the basketball game between Lakers and Clippers on Jan 28, 2021?"}]], "function": [{"name": "game_result.get_winner", "description": "Get the winner of a specific basketball game.", "parameters": {"type": "dict", "properties": {"teams": {"type": "array", "items": {"type": "string"}, "description": "List of two teams who played the game."}, "date": {"type": "string", "description": "The date of the game, formatted as YYYY-MM-DD."}, "venue": {"type": "string", "optional": true, "description": "Optional: The venue of the game. Default is 'home'."}}, "required": ["teams", "date"]}}]} +{"id": "simple_python_308", "question": [[{"role": "user", "content": "What are the next five matches for Manchester United and who are they playing against in the English Premier League?"}]], "function": [{"name": "sports.match_schedule", "description": "Retrieve the match schedule for a specific sports team.", "parameters": {"type": "dict", "properties": {"team_name": {"type": "string", "description": "The name of the sports team."}, "num_matches": {"type": "integer", "description": "The number of upcoming matches you want to get."}, "league": {"type": "string", "description": "The sports league of the team. This is an optional parameter. Default is 'English Premier League'."}}, "required": ["team_name", "num_matches"]}}]} +{"id": "simple_python_309", "question": [[{"role": "user", "content": "Find me the record of Tom Brady in the 2020 NFL season."}]], "function": [{"name": "nfl_data.player_record", "description": "Retrieve the record of an NFL player in a specified season.", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "The name of the NFL player."}, "season_year": {"type": "integer", "description": "The year of the NFL season."}, "team": {"type": "string", "description": "The NFL team that the player played for in that season. Default is all teams if not specified."}}, "required": ["player_name", "season_year"]}}]} +{"id": "simple_python_310", "question": [[{"role": "user", "content": "What are the career stats of basketball player LeBron James?"}]], "function": [{"name": "get_career_stats", "description": "Retrieve the career statistics of a basketball player based on the player's name.", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "The name of the basketball player."}, "team": {"type": "string", "description": "The team that the player currently plays for or has played for (Optional). Default to use all teams if not specified."}}, "required": ["player_name"]}}]} +{"id": "simple_python_311", "question": [[{"role": "user", "content": "Find me the detailed profile of basketball player Lebron James"}]], "function": [{"name": "sports_db.find_athlete", "description": "Find the profile information of a sports athlete based on their full name.", "parameters": {"type": "dict", "properties": {"name": {"type": "string", "description": "The full name of the athlete."}, "team": {"type": "string", "description": "The team the athlete belongs to. Default to all teams if not specified."}, "sport": {"type": "string", "description": "The sport that athlete plays.", "enum": ["Basketball", "Baseball", "Football", "Soccer"]}}, "required": ["name", "sport"]}}]} +{"id": "simple_python_312", "question": [[{"role": "user", "content": "What are the statistics of Ronaldo's matches in 2021?"}]], "function": [{"name": "player_statistic", "description": "Retrieves detailed player's statistics for a specific year.", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "The player's name."}, "year": {"type": "integer", "description": "Year for which the statistics will be displayed."}, "team_name": {"type": "string", "description": "The name of the team(optional). Default to not use it if not specified."}}, "required": ["player_name", "year"]}}]} +{"id": "simple_python_313", "question": [[{"role": "user", "content": "What's the total worth in euro of Messi according to latest data?"}]], "function": [{"name": "celebrity_net_worth.get", "description": "Get the total net worth of a sports celebrity based on most recent data.", "parameters": {"type": "dict", "properties": {"name": {"type": "string", "description": "The full name of the sports celebrity."}, "currency": {"type": "string", "description": "The currency in which the net worth will be returned. Default is 'USD'."}}, "required": ["name", "currency"]}}]} +{"id": "simple_python_314", "question": [[{"role": "user", "content": "Find all the major achievements of the footballer Lionel Messi."}]], "function": [{"name": "sports_celebrity.get_major_achievements", "description": "Returns a list of major achievements of a particular sports celebrity.", "parameters": {"type": "dict", "properties": {"celebrity_name": {"type": "string", "description": "Name of the sports celebrity."}, "sports": {"type": "string", "description": "Type of sports the celebrity involved in. Default is Football."}, "team": {"type": "string", "description": "Optional. Team where celebrity currently plays. Default is 'all'"}}, "required": ["celebrity_name"]}}]} +{"id": "simple_python_315", "question": [[{"role": "user", "content": "Get the NBA team's ranking with the best defence in the 2021 season."}]], "function": [{"name": "get_defense_ranking", "description": "Retrieve the defence ranking of NBA teams in a specified season.", "parameters": {"type": "dict", "properties": {"season": {"type": "integer", "description": "The NBA season to get defence ranking from."}, "top": {"type": "integer", "default": 1, "description": "Number of top teams in defence ranking to fetch."}}, "required": ["season"]}}]} +{"id": "simple_python_316", "question": [[{"role": "user", "content": "Find the current world rank of a Tennis player, Serena Williams."}]], "function": [{"name": "get_sport_ranking", "description": "Retrieve the current world ranking of a sportsperson based on the sport and player's name.", "parameters": {"type": "dict", "properties": {"sport": {"type": "string", "description": "Name of the sport."}, "player_name": {"type": "string", "description": "Name of the player."}, "gender": {"type": "string", "description": "Gender of the player. This is optional. The possible values are male or female.", "default": "all"}}, "required": ["sport", "player_name"]}}]} +{"id": "simple_python_317", "question": [[{"role": "user", "content": "Find the ranking of LA Lakers in the NBA 2021 regular season."}]], "function": [{"name": "get_team_rank", "description": "Get the team ranking in a sports league based on season and type.", "parameters": {"type": "dict", "properties": {"team_name": {"type": "string", "description": "The name of the sports team."}, "league": {"type": "string", "description": "The name of the league in which the team competes."}, "season": {"type": "string", "description": "The season for which the team's ranking is sought."}, "type": {"type": "string", "description": "Type of the season: regular or playoff.", "enum": ["regular", "playoff"]}}, "required": ["team_name", "league", "season", "type"]}}]} +{"id": "simple_python_318", "question": [[{"role": "user", "content": "What is the FIFA ranking of Germany's men soccer team for the year 2021?"}]], "function": [{"name": "get_team_ranking", "description": "Retrieve the FIFA ranking of a specific soccer team for a certain year.", "parameters": {"type": "dict", "properties": {"team_name": {"type": "string", "description": "The name of the soccer team."}, "year": {"type": "integer", "description": "The year for which the ranking is to be retrieved."}, "gender": {"type": "string", "description": "The gender of the team. It can be either 'men' or 'women'. Default is 'men'."}}, "required": ["team_name", "year"]}}]} +{"id": "simple_python_319", "question": [[{"role": "user", "content": "What is the ranking of Manchester United in Premier League?"}]], "function": [{"name": "sports_ranking", "description": "Fetch the ranking of a specific sports team in a specific league", "parameters": {"type": "dict", "properties": {"team": {"type": "string", "description": "The name of the team."}, "league": {"type": "string", "description": "The name of the league."}, "season": {"type": "integer", "description": "Optional parameter to specify the season, default is the current season '2023' if not specified."}}, "required": ["team", "league"]}}]} +{"id": "simple_python_320", "question": [[{"role": "user", "content": "Fetch the basketball league standings, where Golden State Warriors stand in current 2022-2023 season with details"}]], "function": [{"name": "sports_ranking.get_team_position", "description": "Retrieve a team's position and stats in the basketball league for a given season.", "parameters": {"type": "dict", "properties": {"team": {"type": "string", "description": "The name of the team."}, "season": {"type": "string", "description": "The season for which data should be fetched."}, "detailed": {"type": "boolean", "description": "Flag to retrieve detailed stats or just the position.", "default": false}}, "required": ["team", "season"]}}]} +{"id": "simple_python_321", "question": [[{"role": "user", "content": "What's the ranking of Barcelona in the 2021 La Liga season?"}]], "function": [{"name": "sports_ranking", "description": "Get the ranking of a team in a given sports league and season.", "parameters": {"type": "dict", "properties": {"team": {"type": "string", "description": "The name of the team."}, "league": {"type": "string", "description": "The name of the sports league."}, "season": {"type": "string", "description": "The season for which ranking needs to be obtained."}}, "required": ["team", "league", "season"]}}]} +{"id": "simple_python_322", "question": [[{"role": "user", "content": "Get the current ranking for Liverpool Football Club in the Premier League."}]], "function": [{"name": "sports_ranking.get_current", "description": "Retrieve the current ranking of a specific team in a particular league.", "parameters": {"type": "dict", "properties": {"team": {"type": "string", "description": "The name of the team whose ranking is sought."}, "league": {"type": "string", "description": "The league in which the team participates."}, "season": {"type": "string", "description": "The season for which the ranking is sought. Defaults to the current season '2023-2024' if not provided."}}, "required": ["team", "league"]}}]} +{"id": "simple_python_323", "question": [[{"role": "user", "content": "Who is ranked as the top player in woman tennis?"}]], "function": [{"name": "sports_ranking.get_top_player", "description": "Get the top player in a specific sport.", "parameters": {"type": "dict", "properties": {"sport": {"type": "string", "description": "The type of sport."}, "gender": {"type": "string", "description": "The gender of the sport category. Optional.", "default": "men"}}, "required": ["sport"]}}]} +{"id": "simple_python_324", "question": [[{"role": "user", "content": "Find the score of last game for Los Angeles Lakers including its opponent name."}]], "function": [{"name": "team_score.get_latest", "description": "Retrieve the score of the most recent game for a specified sports team.", "parameters": {"type": "dict", "properties": {"team": {"type": "string", "description": "Name of the sports team."}, "include_opponent": {"type": "boolean", "description": "Include the name of the opponent team in the return.", "default": false}}, "required": ["team"]}}]} +{"id": "simple_python_325", "question": [[{"role": "user", "content": "Who won the last match between Chicago Bulls and Los Angeles Lakers?"}]], "function": [{"name": "sports.match_results", "description": "Returns the results of a given match between two teams.", "parameters": {"type": "dict", "properties": {"team1": {"type": "string", "description": "The name of the first team."}, "team2": {"type": "string", "description": "The name of the second team."}, "season": {"type": "string", "description": "The season when the match happened. Default is the current season."}}, "required": ["team1", "team2"]}}]} +{"id": "simple_python_326", "question": [[{"role": "user", "content": "Get the latest game score and statistics for Los Angeles Lakers in NBA."}]], "function": [{"name": "get_team_score", "description": "Retrieves the latest game score, individual player stats, and team stats for a specified sports team.", "parameters": {"type": "dict", "properties": {"team_name": {"type": "string", "description": "The name of the sports team."}, "league": {"type": "string", "description": "The league that the team is part of."}, "include_player_stats": {"type": "boolean", "default": false, "description": "Indicates if individual player statistics should be included in the result. Default is false."}}, "required": ["team_name", "league"]}}]} +{"id": "simple_python_327", "question": [[{"role": "user", "content": "Give me the schedule of Manchester United for the next 6 games in Premier League."}]], "function": [{"name": "sports_team.get_schedule", "description": "Fetches the schedule of the specified sports team for the specified number of games in the given league.", "parameters": {"type": "dict", "properties": {"team_name": {"type": "string", "description": "The name of the sports team."}, "num_of_games": {"type": "integer", "description": "Number of games for which to fetch the schedule."}, "league": {"type": "string", "description": "The name of the sports league. If not provided, the function will fetch the schedule for all games, regardless of the league."}, "location": {"type": "string", "description": "Optional. The city or venue where games are to be held. If not provided, default that all venues will be considered."}}, "required": ["team_name", "num_of_games", "league"]}}]} +{"id": "simple_python_328", "question": [[{"role": "user", "content": "Find the rating and player count of the board game 'Ticket to Ride'."}]], "function": [{"name": "boardgame.get_info", "description": "Retrieve detailed information of a board game.", "parameters": {"type": "dict", "properties": {"name": {"type": "string", "description": "Name of the board game."}, "parameters": {"type": "array", "items": {"type": "string", "enum": ["player count", "playing time", "age", "mechanics", "rating"]}, "description": "Game characteristics interested."}, "language": {"type": "string", "description": "The preferred language for the game information, default is English"}}, "required": ["name", "parameters"]}}]} +{"id": "simple_python_329", "question": [[{"role": "user", "content": "Calculate the odds of rolling a 7 with two dice in the board game Monopoly."}]], "function": [{"name": "monopoly_odds_calculator", "description": "Calculates the probability of rolling a certain sum with two dice, commonly used in board game like Monopoly.", "parameters": {"type": "dict", "properties": {"number": {"type": "integer", "description": "The number for which the odds are calculated."}, "dice_number": {"type": "integer", "description": "The number of dice involved in the roll."}, "dice_faces": {"type": "integer", "description": "The number of faces on a single die. Default is 6 for standard six-faced die."}}, "required": ["number", "dice_number"]}}]} +{"id": "simple_python_330", "question": [[{"role": "user", "content": "What's the average review rating and the age range for the board game 'Catan'?"}]], "function": [{"name": "board_game_info", "description": "Get the information about a board game from a database. ", "parameters": {"type": "dict", "properties": {"game_name": {"type": "string", "description": "Name of the board game."}, "info_required": {"type": "array", "items": {"type": "string", "enum": ["average_review_rating", "age_range", "number_of_players", "playing_time", "genre"]}, "description": "Array of information requested for the game."}}, "required": ["game_name", "info_required"]}}]} +{"id": "simple_python_331", "question": [[{"role": "user", "content": "Find the top chess players in New York with a rating above 2300."}]], "function": [{"name": "board_game.chess.get_top_players", "description": "Find top chess players in a location based on rating.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city you want to find the players from."}, "minimum_rating": {"type": "integer", "description": "Minimum rating to filter the players."}, "number_of_players": {"type": "integer", "default": 10, "description": "Number of players you want to retrieve, default value is 10"}}, "required": ["location", "minimum_rating"]}}]} +{"id": "simple_python_332", "question": [[{"role": "user", "content": "What's the chess classical rating of Magnus Carlsen?"}]], "function": [{"name": "chess.rating", "description": "Fetches the current chess rating of a given player", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "The full name of the chess player."}, "variant": {"type": "string", "description": "The variant of chess for which rating is requested (e.g., 'classical', 'blitz', 'bullet'). Default is 'classical'."}}, "required": ["player_name"]}}]} +{"id": "simple_python_333", "question": [[{"role": "user", "content": "Find the high and low temperatures, humidity, and precipitation for London, United Kingdom for the next 3 days."}]], "function": [{"name": "detailed_weather_forecast", "description": "Retrieve a detailed weather forecast for a specific location and time frame, including high/low temperatures, humidity, and precipitation.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city that you want to get the weather for."}, "days": {"type": "integer", "description": "Number of days for the forecast."}, "details": {"type": "array", "items": {"type": "string", "enum": ["high_low_temperature", "humidity", "precipitation"]}, "description": "Specific weather details required in the forecast."}}, "required": ["location", "days", "details"]}}]} +{"id": "simple_python_334", "question": [[{"role": "user", "content": "Check who is the winner in a game of blackjack given player having A and 10, dealer having 10 and 9. The Ace is considered 1."}]], "function": [{"name": "blackjack.check_winner", "description": "Checks and determines the winner in a game of blackjack.", "parameters": {"type": "dict", "properties": {"player_cards": {"type": "array", "items": {"type": "string"}, "description": "Cards held by the player."}, "dealer_cards": {"type": "array", "items": {"type": "string"}, "description": "Cards held by the dealer."}, "ace_value": {"type": "integer", "description": "The value considered for the ace card, can be either 1 or 11.", "default": 11}}, "required": ["player_cards", "dealer_cards"]}}]} +{"id": "simple_python_335", "question": [[{"role": "user", "content": "Find a Card of rank 'Queen' and suit 'Hearts' in the deck."}]], "function": [{"name": "find_card_in_deck", "description": "Locate a particular card in a deck based on rank and suit.", "parameters": {"type": "dict", "properties": {"rank": {"type": "string", "description": "Rank of the card (e.g. Ace, Two, King)."}, "suit": {"type": "string", "description": "Suit of the card (e.g. Hearts, Spades, Diamonds, Clubs)."}, "deck": {"type": "array", "items": {"type": "dict", "properties": {"rank": {"type": "string"}, "suit": {"type": "string"}}}, "description": "Deck of cards. If not provided, the deck will be a standard 52 card deck"}}, "required": ["rank", "suit"]}}]} +{"id": "simple_python_336", "question": [[{"role": "user", "content": "Shuffle a deck of cards, and draw 3 cards from the top."}]], "function": [{"name": "cards.shuffle_and_draw", "description": "Shuffle a standard deck of 52 cards and draw a specified number of cards from the top.", "parameters": {"type": "dict", "properties": {"num_cards": {"type": "integer", "description": "Number of cards to be drawn. The default is 1 if no value is provided."}}, "required": ["num_cards"]}}]} +{"id": "simple_python_337", "question": [[{"role": "user", "content": "In a texas holdem game, Who won in the poker game with players Alex, Sam, Robert and Steve given the cards Alex':['A of spades', 'K of spades'], 'Sam': ['2 of diamonds', '3 of clubs'], 'Robert': ['Q of hearts', '10 of hearts'], 'Steve': ['4 of spades', '5 of spades']?"}]], "function": [{"name": "poker_game_winner", "description": "Identify the winner in a poker game based on the cards.", "parameters": {"type": "dict", "properties": {"players": {"type": "array", "items": {"type": "string"}, "description": "Names of the players in a list."}, "cards": {"type": "dict", "description": "An object containing the player name as key and the cards as values in a list."}, "type": {"type": "string", "description": "Type of poker game. Defaults to 'Texas Holdem'"}}, "required": ["players", "cards"]}}]} +{"id": "simple_python_338", "question": [[{"role": "user", "content": "What is the probability of drawing a heart card from a deck of 52 cards?"}]], "function": [{"name": "card_game_probability.calculate", "description": "Calculate the probability of drawing a certain card or suit from a deck of cards.", "parameters": {"type": "dict", "properties": {"total_cards": {"type": "integer", "description": "Total number of cards in the deck."}, "desired_cards": {"type": "integer", "description": "Number of cards in the deck that satisfy the conditions."}, "cards_drawn": {"type": "integer", "default": 1, "description": "Number of cards drawn from the deck."}}, "required": ["total_cards", "desired_cards"]}}]} +{"id": "simple_python_339", "question": [[{"role": "user", "content": "What is the probability of getting a full house in poker?"}]], "function": [{"name": "poker_probability.full_house", "description": "Calculate the probability of getting a full house in a poker game.", "parameters": {"type": "dict", "properties": {"deck_size": {"type": "integer", "description": "The size of the deck. Default is 52."}, "hand_size": {"type": "integer", "description": "The size of the hand. Default is 5."}}, "required": ["deck_size", "hand_size"]}}]} +{"id": "simple_python_340", "question": [[{"role": "user", "content": "Determine the winner in a Poker game with John having a Hand of 8\u2665, 10\u2665, J\u2665, Q\u2665, K\u2665 and Mike having 9\u2660, J\u2660, 10\u2660, Q\u2660, K\u2660."}]], "function": [{"name": "card_games.poker_determine_winner", "description": "Determines the winner in a game of Poker based on the cards in each players' hands.", "parameters": {"type": "dict", "properties": {"player1": {"type": "string", "description": "The first player's name."}, "hand1": {"type": "array", "items": {"type": "string"}, "description": "The list of cards (as strings) in first player's hand. E.g ['10\u2660', 'J\u2660']"}, "player2": {"type": "string", "description": "The second player's name."}, "hand2": {"type": "array", "items": {"type": "string"}, "description": "The list of cards (as strings) in second player's hand. E.g ['9\u2665', '10\u2665']"}}, "required": ["player1", "hand1", "player2", "hand2"]}}]} +{"id": "simple_python_341", "question": [[{"role": "user", "content": "What are the odds of drawing a heart card from a deck without joker?"}]], "function": [{"name": "deck_of_cards.odds", "description": "Compute the probability of drawing a specific suit from a given deck of cards.", "parameters": {"type": "dict", "properties": {"suit": {"type": "string", "description": "The card suit. Valid values include: 'spades', 'clubs', 'hearts', 'diamonds'."}, "deck_type": {"type": "string", "description": "Type of deck, normal deck includes joker, and without_joker deck excludes joker.", "default": "normal"}}, "required": ["suit", "deck_type"]}}]} +{"id": "simple_python_342", "question": [[{"role": "user", "content": "Find all multi-player games released in 2019 with an ESRB rating of 'Everyone'"}]], "function": [{"name": "game_list.get_games", "description": "Get a list of video games based on release year, multiplayer functionality and ESRB rating", "parameters": {"type": "dict", "properties": {"release_year": {"type": "integer", "description": "The year the game was released."}, "multiplayer": {"type": "boolean", "description": "Whether the game has multiplayer functionality."}, "ESRB_rating": {"type": "string", "description": "The ESRB rating of the game."}}, "required": ["release_year", "multiplayer", "ESRB_rating"]}}]} +{"id": "simple_python_343", "question": [[{"role": "user", "content": "Fetch player statistics of 'Zelda' on Switch for user 'Sam'."}]], "function": [{"name": "game_stats.fetch_player_statistics", "description": "Fetch player statistics for a specific video game for a given user.", "parameters": {"type": "dict", "properties": {"game": {"type": "string", "description": "The name of the video game."}, "username": {"type": "string", "description": "The username of the player."}, "platform": {"type": "string", "description": "The platform user is playing on.", "default": "PC"}}, "required": ["game", "username"]}}]} +{"id": "simple_python_344", "question": [[{"role": "user", "content": "What's the power rating for the Weapon 'Guardian Sword+' in the game 'Legend of Zelda: Breath of the Wild'?"}]], "function": [{"name": "get_game_item_stats", "description": "Retrieve the statistics of a specific item in a specific video game.", "parameters": {"type": "dict", "properties": {"game": {"type": "string", "description": "The game to retrieve information from."}, "item": {"type": "string", "description": "The name of the item in the game."}, "stat": {"type": "string", "description": "Specific statistic required."}}, "required": ["game", "item", "stat"]}}]} +{"id": "simple_python_345", "question": [[{"role": "user", "content": "Find the value of a vintage Super Mario Bros. game from 1985 like new."}]], "function": [{"name": "game_valuation", "description": "Get the current market value of a vintage video game.", "parameters": {"type": "dict", "properties": {"game_name": {"type": "string", "description": "The name of the game."}, "release_year": {"type": "integer", "description": "The year the game was released."}, "condition": {"type": "string", "enum": ["New", "Like New", "Used", "Fair", "Poor"], "description": "The condition of the game. Default is 'Used'."}}, "required": ["game_name", "release_year"]}}]} +{"id": "simple_python_346", "question": [[{"role": "user", "content": "Get all collectable items from the game 'Animal Crossing: New Horizons' during the Spring season."}]], "function": [{"name": "get_collectables_in_season", "description": "Retrieve a list of collectable items in a specific game during a specified season.", "parameters": {"type": "dict", "properties": {"game_name": {"type": "string", "description": "Name of the game."}, "season": {"type": "string", "description": "The season for which to retrieve the collectable items."}, "item_type": {"type": "string", "description": "The type of item to search for. Default is 'all'. Possible values: 'all', 'bug', 'fish', 'sea creatures', etc."}}, "required": ["game_name", "season"]}}]} +{"id": "simple_python_347", "question": [[{"role": "user", "content": "Get me the details of the last game played by Liverpool F.C. Include its statistics."}]], "function": [{"name": "soccer.get_last_match", "description": "Retrieve the details of the last match played by a specified soccer club.", "parameters": {"type": "dict", "properties": {"team_name": {"type": "string", "description": "The name of the soccer club."}, "include_stats": {"type": "boolean", "description": "If true, include match statistics like possession, shots on target etc. Default is false."}}, "required": ["team_name"]}}]} +{"id": "simple_python_348", "question": [[{"role": "user", "content": "Create a new player profile for the game with name 'StarPlayer' and character class 'Mage', set the starting level to 5."}]], "function": [{"name": "create_player_profile", "description": "Create a new player profile with character name, class and starting level.", "parameters": {"type": "dict", "properties": {"player_name": {"type": "string", "description": "The desired name of the player."}, "_class": {"type": "string", "description": "The character class for the player"}, "starting_level": {"type": "integer", "description": "The starting level for the player", "default": 1}}, "required": ["player_name", "_class"]}}]} +{"id": "simple_python_349", "question": [[{"role": "user", "content": "Find the highest score achieved by any player in the online game 'Overwatch' on PC globally."}]], "function": [{"name": "game_score.highest", "description": "Retrieve the highest score achieved by any player in a specific game.", "parameters": {"type": "dict", "properties": {"game": {"type": "string", "description": "The name of the online game."}, "platform": {"type": "string", "description": "The platform where the game is played, e.g. PC, Xbox, Playstation"}, "region": {"type": "string", "description": "The geographic region of the player. Defaults to 'Global'"}}, "required": ["game", "platform"]}}]} +{"id": "simple_python_350", "question": [[{"role": "user", "content": "Get the highest scoring player of game 'Valorant' in 2022 season."}]], "function": [{"name": "get_highest_scoring_player", "description": "Retrieve the highest scoring player in a specific game and season.", "parameters": {"type": "dict", "properties": {"game": {"type": "string", "description": "The game in which you want to find the highest scoring player."}, "season": {"type": "string", "description": "The season during which the high score was achieved."}}, "required": ["game", "season"]}}]} +{"id": "simple_python_351", "question": [[{"role": "user", "content": "Find me a multiplayer game with rating above 4.5 and compatible with Windows 10."}]], "function": [{"name": "multiplayer_game_finder", "description": "Locate multiplayer games that match specific criteria such as rating, platform compatibility, genre, etc.", "parameters": {"type": "dict", "properties": {"platform": {"type": "string", "description": "The platform you want the game to be compatible with, e.g. Windows 10, PS5."}, "rating": {"type": "float", "description": "Desired minimum game rating on a 5.0 scale."}, "genre": {"type": "string", "description": "Desired game genre, e.g. Action, Adventure, Racing. Default is 'Action'.", "enum": ["Action", "Adventure", "Racing", "Strategy", "Simulation"]}}, "required": ["platform", "rating"]}}]} +{"id": "simple_python_352", "question": [[{"role": "user", "content": "Get the average user score for the game 'The Legend of Zelda: Breath of the Wild' from GameSpot."}]], "function": [{"name": "gamespot.getAverageUserScore", "description": "Retrieve the average user score of a game from GameSpot.", "parameters": {"type": "dict", "properties": {"game_name": {"type": "string", "description": "The name of the game."}, "platform": {"type": "string", "description": "The platform the game was released on (e.g., Nintendo Switch, PS5, etc.)", "default": "all platforms"}}, "required": ["game_name", "platform"]}}]} +{"id": "simple_python_353", "question": [[{"role": "user", "content": "What are some gluten-free recipes for dinner?"}]], "function": [{"name": "find_recipes", "description": "Find recipes based on dietary restrictions, meal type, and preferred ingredients.", "parameters": {"type": "dict", "properties": {"diet": {"type": "string", "description": "The dietary restrictions, e.g., 'vegan', 'gluten-free'."}, "meal_type": {"type": "string", "description": "The type of meal, e.g., 'dinner', 'breakfast'."}, "ingredients": {"type": "array", "items": {"type": "string"}, "description": "The preferred ingredients. If left blank, it will default to return general recipes."}}, "required": ["diet", "meal_type"]}}]} +{"id": "simple_python_354", "question": [[{"role": "user", "content": "Find a vegan soup recipe that takes under 30 minutes to make."}]], "function": [{"name": "get_vegan_recipe", "description": "Retrieve a vegan soup recipe based on the provided cooking time.", "parameters": {"type": "dict", "properties": {"dish_type": {"type": "string", "description": "The type of dish, e.g. soup, dessert, etc.", "enum": ["soup", "main dish", "dessert", "salad"]}, "cooking_time": {"type": "integer", "description": "The maximum cooking time for the recipe in minutes."}, "ingredient_preference": {"type": "array", "items": {"type": "string"}, "description": "Preferred ingredients to be included in the recipe, if any. Default to not use it if not provided."}}, "required": ["dish_type", "cooking_time"]}}]} +{"id": "simple_python_355", "question": [[{"role": "user", "content": "How many calories in the Beef Lasagna Recipe from Foodnetwork.com?"}]], "function": [{"name": "recipe_info.get_calories", "description": "Retrieve the amount of calories from a specific recipe in a food website.", "parameters": {"type": "dict", "properties": {"website": {"type": "string", "description": "The food website that has the recipe."}, "recipe": {"type": "string", "description": "Name of the recipe."}, "optional_meal_time": {"type": "string", "description": "Specific meal time of the day for the recipe (optional, could be 'Breakfast', 'Lunch', 'Dinner'). Default is all if not specified."}}, "required": ["website", "recipe"]}}]} +{"id": "simple_python_356", "question": [[{"role": "user", "content": "Find me a recipe that serves 2 people, is vegan, and takes under 30 minutes to prepare."}]], "function": [{"name": "recipe_finder.find", "description": "Find a recipe based on dietary preferences, number of servings, and preparation time.", "parameters": {"type": "dict", "properties": {"servings": {"type": "integer", "description": "The number of people that the recipe should serve."}, "diet": {"type": "string", "description": "Any dietary restrictions like 'vegan', 'vegetarian', 'gluten-free' etc."}, "prep_time": {"type": "integer", "description": "The maximum amount of time (in minutes) the preparation should take. Default is 60 minutes."}}, "required": ["servings", "diet"]}}]} +{"id": "simple_python_357", "question": [[{"role": "user", "content": "Get the recipe for vegan chocolate cake including the steps for preparation."}]], "function": [{"name": "get_recipe", "description": "Fetch the recipe for a specific dish along with preparation steps.", "parameters": {"type": "dict", "properties": {"dish_name": {"type": "string", "description": "Name of the dish whose recipe needs to be fetched."}, "diet_preference": {"type": "string", "description": "Preferred dietary consideration like vegan, vegetarian, gluten-free etc. Default is none.", "default": "none"}}, "required": ["dish_name"]}}]} +{"id": "simple_python_358", "question": [[{"role": "user", "content": "Find a gluten-free cookie recipe that takes less than 30 minutes to prepare."}]], "function": [{"name": "recipe_search", "description": "Search for a cooking recipe based on specific dietary needs and time constraint.", "parameters": {"type": "dict", "properties": {"diet": {"type": "array", "items": {"type": "string", "enum": ["Gluten Free", "Dairy Free", "Vegan", "Vegetarian"]}, "description": "Specific dietary need."}, "time_limit": {"type": "integer", "description": "The maximum time to prepare the recipe in minutes. Default is 60 minutes."}, "dish": {"type": "string", "description": "The name of the dish to search for. Default is not use if not specified."}}, "required": ["dish", "diet"]}}]} +{"id": "simple_python_359", "question": [[{"role": "user", "content": "Give me a recipe for a vegetarian pasta with cheese for 2 servings."}]], "function": [{"name": "recipe_search", "description": "Search for a recipe given dietary restriction, ingredients, and number of servings.", "parameters": {"type": "dict", "properties": {"dietary_restriction": {"type": "string", "description": "The dietary restriction, e.g., 'Vegetarian'."}, "ingredients": {"type": "array", "items": {"type": "string"}, "description": "The list of ingredients."}, "servings": {"type": "integer", "description": "The number of servings the recipe should make"}}, "required": ["dietary_restriction", "ingredients", "servings"]}}]} +{"id": "simple_python_360", "question": [[{"role": "user", "content": "Find a recipe for pasta carbonara which contains only less than 500 calories."}]], "function": [{"name": "find_recipe", "description": "Locate a recipe based on name and its calorie content", "parameters": {"type": "dict", "properties": {"recipeName": {"type": "string", "description": "The recipe's name."}, "maxCalories": {"type": "integer", "description": "The maximum calorie content of the recipe.", "default": 1000}}, "required": ["recipeName"]}}]} +{"id": "simple_python_361", "question": [[{"role": "user", "content": "Find Italian restaurants near New York city that serves gluten-free options."}]], "function": [{"name": "restaurant_finder", "description": "Locate restaurants based on certain criteria such as cuisine, city, and dietary preferences.", "parameters": {"type": "dict", "properties": {"city": {"type": "string", "description": "City where you are looking for the restaurant."}, "cuisine": {"type": "string", "description": "Type of cuisine you are interested in."}, "diet": {"type": "string", "description": "Dietary preferences. e.g. 'Vegetarian', 'Gluten-free', etc. Default 'Vegetarian'."}}, "required": ["city", "cuisine"]}}]} +{"id": "simple_python_362", "question": [[{"role": "user", "content": "What are the top five sushi restaurants with high reviews i.e. above 4/5 in Tokyo?"}]], "function": [{"name": "get_best_sushi_places", "description": "Returns the best sushi places given the city, review_rate and top number.", "parameters": {"type": "dict", "properties": {"city": {"type": "string", "description": "The city in which to look for the sushi places."}, "top": {"type": "integer", "description": "The number of top sushi places to be returned."}, "review_rate": {"type": "float", "description": "The review rating to filter the sushi places. Places with review ratings above this value will be returned. Default 0.00."}}, "required": ["city", "top"]}}]} +{"id": "simple_python_363", "question": [[{"role": "user", "content": "Find the closest sushi restaurant with a patio in Boston."}]], "function": [{"name": "restaurant_search.find_closest", "description": "Locate the closest sushi restaurant based on certain criteria, such as the presence of a patio.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city, for instance Boston, MA"}, "cuisine": {"type": "string", "description": "Type of food like Sushi."}, "amenities": {"type": "array", "items": {"type": "string", "enum": ["Patio", "Wi-Fi", "Happy Hour", "Wheelchair Accessible"]}, "description": "Preferred amenities in the restaurant. Default 'Wi-Fi'."}}, "required": ["location", "cuisine"]}}]} +{"id": "simple_python_364", "question": [[{"role": "user", "content": "Can I find an Italian restaurant with Gluten-free options near Brooklyn?"}]], "function": [{"name": "find_restaurant", "description": "Locate nearby restaurants based on user defined criteria", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The location where user wants to search for a restaurant."}, "type": {"type": "string", "description": "The type of the cuisine/restaurant."}, "diet_option": {"type": "string", "description": "Special dietary preferences."}}, "required": ["location", "type", "diet_option"]}}]} +{"id": "simple_python_365", "question": [[{"role": "user", "content": "How many ounces in 2 pounds of butter?"}]], "function": [{"name": "cooking_conversion.convert", "description": "Convert cooking measurements from one unit to another.", "parameters": {"type": "dict", "properties": {"quantity": {"type": "integer", "description": "The quantity to be converted."}, "from_unit": {"type": "string", "description": "The unit to convert from."}, "to_unit": {"type": "string", "description": "The unit to convert to."}, "item": {"type": "string", "description": "The item to be converted."}}, "required": ["quantity", "from_unit", "to_unit", "item"]}}]} +{"id": "simple_python_366", "question": [[{"role": "user", "content": "How many teaspoons are in 2 tablespoons for measurement in my recipe?"}]], "function": [{"name": "recipe.unit_conversion", "description": "Convert a value from one kitchen unit to another for cooking purposes.", "parameters": {"type": "dict", "properties": {"value": {"type": "integer", "description": "The value to be converted."}, "from_unit": {"type": "string", "description": "The unit to convert from. Supports 'teaspoon', 'tablespoon', 'cup', etc."}, "to_unit": {"type": "string", "description": "The unit to convert to. Supports 'teaspoon', 'tablespoon', 'cup', etc."}, "precision": {"type": "integer", "description": "The precision to round the output to, in case of a non-integer result. Optional, default is 1."}}, "required": ["value", "from_unit", "to_unit"]}}]} +{"id": "simple_python_367", "question": [[{"role": "user", "content": "Find me a vegan recipe for brownies which prep time is under 30 minutes."}]], "function": [{"name": "find_recipe", "description": "Find a recipe based on the dietary restrictions, recipe type, and time constraints.", "parameters": {"type": "dict", "properties": {"dietary_restrictions": {"type": "string", "description": "Dietary restrictions e.g. vegan, vegetarian, gluten free, dairy free."}, "recipe_type": {"type": "string", "description": "Type of the recipe. E.g. dessert, main course, breakfast."}, "time": {"type": "integer", "description": "Time limit in minutes to prep the meal."}}, "required": ["dietary_restrictions", "recipe_type", "time"]}}]} +{"id": "simple_python_368", "question": [[{"role": "user", "content": "How much time will it take to cook a roast chicken of 1.5 kg?"}]], "function": [{"name": "calculate_cooking_time", "description": "Calculate the cooking time for a roast chicken.", "parameters": {"type": "dict", "properties": {"weight_kg": {"type": "float", "description": "The weight of the chicken in kilograms."}, "cooking_method": {"type": "string", "description": "The method of cooking, defaults to 'roast'."}, "temp_celsius": {"type": "integer", "description": "The cooking temperature in degrees celsius, defaults to 180."}}, "required": ["weight_kg"]}}]} +{"id": "simple_python_369", "question": [[{"role": "user", "content": "Find a grocery store near me with organic fruits and vegetables in Houston."}]], "function": [{"name": "grocery_store.find_nearby", "description": "Locate nearby grocery stores based on specific criteria like organic fruits and vegetables.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. Houston, TX"}, "categories": {"type": "array", "items": {"type": "string", "enum": ["Organic", "Vegetables", "Fruits", "Dairy", "Seafood", "Bakery"]}, "description": "Categories of items to be found in the grocery store. Default is all if not specified."}}, "required": ["location"]}}]} +{"id": "simple_python_370", "question": [[{"role": "user", "content": "Order three bottles of olive oil and a five pound bag of rice from Safeway in Palo Alto."}]], "function": [{"name": "safeway.order", "description": "Order specified items from a Safeway location.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The location of the Safeway store, e.g. Palo Alto, CA."}, "items": {"type": "array", "items": {"type": "string"}, "description": "List of items to order."}, "quantity": {"type": "array", "items": {"type": "integer"}, "description": "Quantity of each item in the order list."}}, "required": ["location", "items", "quantity"]}}]} +{"id": "simple_python_371", "question": [[{"role": "user", "content": "Check the price of tomatoes and lettuce at the Whole Foods in Los Angeles."}]], "function": [{"name": "whole_foods.check_price", "description": "Check the price of items at a specific Whole Foods location.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "Location of the Whole Foods store."}, "items": {"type": "array", "items": {"type": "string"}, "description": "List of items for which the price needs to be checked."}}, "required": ["location", "items"]}}]} +{"id": "simple_python_372", "question": [[{"role": "user", "content": "Find the top five organic bananas brands on the basis of rating from Whole Foods store."}]], "function": [{"name": "whole_foods.find_top_brands", "description": "Get top brands based on a specific product from Whole Foods", "parameters": {"type": "dict", "properties": {"product": {"type": "string", "description": "The product for which the top brands should be fetched."}, "number": {"type": "integer", "description": "Number of top brands to be fetched. Default is 5"}, "organic": {"type": "boolean", "description": "If the product should be organic. Default is false"}}, "required": ["product"]}}]} +{"id": "simple_python_373", "question": [[{"role": "user", "content": "I want to buy apples, rice, and 12 pack of bottled water from a Walmart near San Jose. Show me the product information and stock availability."}]], "function": [{"name": "walmart.purchase", "description": "Retrieve information of items from Walmart including stock availability.", "parameters": {"type": "dict", "properties": {"loc": {"type": "string", "description": "Location of the nearest Walmart."}, "product_list": {"type": "array", "items": {"type": "string"}, "description": "Items to be purchased listed in an array."}, "pack_size": {"type": "array", "items": {"type": "integer"}, "description": "Size of the product pack if applicable. The size of the array should be equal to product_list. Default is not use it if not specified."}}, "required": ["loc", "product_list"]}}]} +{"id": "simple_python_374", "question": [[{"role": "user", "content": "Check the amount of protein, calories and carbs in an avocado from Walmart."}]], "function": [{"name": "grocery_info.nutritional_info", "description": "Retrieve nutritional information for a given food item from a particular store", "parameters": {"type": "dict", "properties": {"store": {"type": "string", "description": "The store where the item is available"}, "food": {"type": "string", "description": "Food item for which information is needed."}, "information": {"type": "array", "items": {"type": "string", "enum": ["Protein", "Calories", "Carbohydrates", "Fat", "Fiber"]}, "description": "Nutritional details required."}}, "required": ["store", "food", "information"]}}]} +{"id": "simple_python_375", "question": [[{"role": "user", "content": "Check the total price for three pumpkins and two dozen eggs at Walmart."}]], "function": [{"name": "walmart.check_price", "description": "Calculate total price for given items and their quantities at Walmart.", "parameters": {"type": "dict", "properties": {"items": {"type": "array", "items": {"type": "string"}, "description": "List of items to be priced."}, "quantities": {"type": "array", "items": {"type": "integer"}, "description": "Quantity of each item corresponding to the items list."}, "store_location": {"type": "string", "description": "The store location for specific pricing (optional). Default to all if not specified."}}, "required": ["items", "quantities"]}}]} +{"id": "simple_python_376", "question": [[{"role": "user", "content": "What time is it currently in London, UK in 24 hour format?"}]], "function": [{"name": "time_zone_converter", "description": "Retrieve the current time of a specific city.", "parameters": {"type": "dict", "properties": {"city": {"type": "string", "description": "The city you want to know the current time for."}, "country": {"type": "string", "description": "The country where the city is located."}, "display_format": {"type": "string", "description": "The time display format: '12h' or '24h'. Default is '24h'."}}, "required": ["city", "country"]}}]} +{"id": "simple_python_377", "question": [[{"role": "user", "content": "What is the current time in Sydney, Australia?"}]], "function": [{"name": "get_current_time", "description": "Retrieve the current time for a specified city and country.", "parameters": {"type": "dict", "properties": {"city": {"type": "string", "description": "The city for which the current time is to be retrieved."}, "country": {"type": "string", "description": "The country where the city is located."}, "format": {"type": "string", "description": "The format in which the time is to be displayed, optional (defaults to 'HH:MM:SS')."}}, "required": ["city", "country"]}}]} +{"id": "simple_python_378", "question": [[{"role": "user", "content": "Convert time 3pm from New York time zone to London time zone."}]], "function": [{"name": "timezone.convert", "description": "Convert time from one time zone to another.", "parameters": {"type": "dict", "properties": {"time": {"type": "string", "description": "The local time you want to convert, e.g. 3pm"}, "from_timezone": {"type": "string", "description": "The time zone you want to convert from."}, "to_timezone": {"type": "string", "description": "The time zone you want to convert to."}}, "required": ["time", "from_timezone", "to_timezone"]}}]} +{"id": "simple_python_379", "question": [[{"role": "user", "content": "What's the current time in Sydney, Australia?"}]], "function": [{"name": "get_current_time", "description": "Retrieve the current time in a specific time zone.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The name of the city."}, "country": {"type": "string", "description": "The name of the country."}, "timezone": {"type": "string", "description": "The optional timezone to get current time. Default "}}, "required": ["location", "country"]}}]} +{"id": "simple_python_380", "question": [[{"role": "user", "content": "Book a single room at a pet friendly hotel near Manhattan, New York for 3 nights starting from March 10th, 2023."}]], "function": [{"name": "hotel_booking", "description": "Books a hotel room given the location, room type, stay duration and any additional preferences.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city where you want to book the hotel."}, "room_type": {"type": "string", "description": "Type of the room required. Options: 'single', 'double', 'deluxe', etc."}, "duration": {"type": "integer", "description": "The number of nights you want to book the hotel for."}, "start_date": {"type": "string", "description": "The date when your stay begins."}, "preferences": {"type": "array", "items": {"type": "string", "enum": ["pet_friendly", "gym", "swimming_pool", "free_breakfast", "parking"]}, "description": "Optional preferences of stay at the hotel. Default to use all if not specified."}}, "required": ["location", "room_type", "duration", "start_date"]}}]} +{"id": "simple_python_381", "question": [[{"role": "user", "content": "Check if any Hilton Hotel is available for two adults in Paris from 2023 April 4th to April 8th?"}]], "function": [{"name": "hilton_hotel.check_availability", "description": "Check hotel availability for a specific location and time frame.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city where you want to check hotel availability."}, "check_in_date": {"type": "string", "description": "The check-in date in the format YYYY-MM-DD."}, "check_out_date": {"type": "string", "description": "The check-out date in the format YYYY-MM-DD."}, "no_of_adults": {"type": "integer", "description": "The number of adults for the hotel booking."}, "hotel_chain": {"type": "string", "description": "The hotel chain where you want to book the hotel.", "default": "Hilton"}}, "required": ["location", "check_in_date", "check_out_date", "no_of_adults"]}}]} +{"id": "simple_python_382", "question": [[{"role": "user", "content": "Book a single room for two nights at the Hilton Hotel in Chicago, starting from 10th December 2022."}]], "function": [{"name": "book_hotel", "description": "Book a room of specified type for a particular number of nights at a specific hotel, starting from a specified date.", "parameters": {"type": "dict", "properties": {"hotel_name": {"type": "string", "description": "The name of the hotel."}, "location": {"type": "string", "description": "The city in which the hotel is located."}, "room_type": {"type": "string", "description": "The type of room to be booked."}, "start_date": {"type": "string", "description": "The start date for the booking."}, "nights": {"type": "integer", "description": "The number of nights for which the booking is to be made."}}, "required": ["hotel_name", "location", "room_type", "start_date", "nights"]}}]} +{"id": "simple_python_383", "question": [[{"role": "user", "content": "I would like to book a single room for two nights at The Plaza hotel."}]], "function": [{"name": "book_room", "description": "Book a room in a specified hotel.", "parameters": {"type": "dict", "properties": {"hotel_name": {"type": "string", "description": "The name of the hotel."}, "room_type": {"type": "string", "description": "The type of room to be booked."}, "num_nights": {"type": "integer", "description": "The number of nights to book the room for."}}, "required": ["hotel_name", "room_type", "num_nights"]}}]} +{"id": "simple_python_384", "question": [[{"role": "user", "content": "Book a hotel room for two adults and one child in Paris, France from July 10, 2022 to July 20, 2022."}]], "function": [{"name": "hotel_booking.book", "description": "Book a hotel room given the city, date, and the number of adults and children.", "parameters": {"type": "dict", "properties": {"city": {"type": "string", "description": "The city where the hotel is located."}, "from_date": {"type": "string", "description": "The start date of the booking. The format is MM-DD-YYYY."}, "to_date": {"type": "string", "description": "The end date of the booking. The format is MM-DD-YYYY."}, "adults": {"type": "integer", "description": "The number of adults for the booking."}, "children": {"type": "integer", "description": "The number of children for the booking."}, "room_type": {"type": "string", "description": "The type of the room, default is 'Standard'. Options are 'Standard', 'Deluxe', 'Suite'.", "default": "Standard"}}, "required": ["city", "from_date", "to_date", "adults", "children"]}}]} +{"id": "simple_python_385", "question": [[{"role": "user", "content": "Book a hotel room with king size bed in Los Angeles for 2 nights starting from 15th October,2023."}]], "function": [{"name": "hotel_bookings.book_room", "description": "Book a hotel room based on specific criteria like location, room type, and check-in and check-out dates.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state where you want to book the hotel, e.g. Los Angeles, CA"}, "room_type": {"type": "string", "description": "Preferred type of room in the hotel, e.g. king size, queen size, deluxe, suite etc."}, "check_in_date": {"type": "string", "description": "Check-in date for the hotel. Format - DD-MM-YYYY."}, "no_of_nights": {"type": "integer", "description": "Number of nights for the stay."}, "no_of_rooms": {"type": "integer", "description": "Number of rooms to book. Default is 1.", "default": 1}}, "required": ["location", "room_type", "check_in_date", "no_of_nights"]}}]} +{"id": "simple_python_386", "question": [[{"role": "user", "content": "Book a luxury room in Hotel Paradise, Las Vegas, with a city view for 3 days starting from May 12, 2022."}]], "function": [{"name": "book_hotel", "description": "Book a room in a specific hotel with particular preferences", "parameters": {"type": "dict", "properties": {"hotel_name": {"type": "string", "description": "The name of the hotel."}, "location": {"type": "string", "description": "The location of the hotel."}, "room_type": {"type": "string", "description": "The type of room preferred."}, "start_date": {"type": "string", "description": "The starting date of the stay in format MM-DD-YYYY."}, "stay_duration": {"type": "integer", "description": "The duration of the stay in days."}, "view": {"type": "string", "description": "The preferred view from the room, can be ignored if no preference. If none provided, assumes no preference.", "default": "No preference"}}, "required": ["hotel_name", "location", "room_type", "start_date", "stay_duration"]}}]} +{"id": "simple_python_387", "question": [[{"role": "user", "content": "Book a hotel room at the Plaza Hotel in New York for 3 nights starting from 1st June 2022"}]], "function": [{"name": "hotel_booking", "description": "Books a hotel room for a specific date range.", "parameters": {"type": "dict", "properties": {"hotel_name": {"type": "string", "description": "The name of the hotel."}, "location": {"type": "string", "description": "The city and state, e.g. New York, NY."}, "start_date": {"type": "string", "description": "The start date of the reservation. Use format 'YYYY-MM-DD'."}, "end_date": {"type": "string", "description": "The end date of the reservation. Use format 'YYYY-MM-DD'."}, "rooms": {"type": "integer", "default": 1, "description": "The number of rooms to reserve."}}, "required": ["hotel_name", "location", "start_date", "end_date"]}}]} +{"id": "simple_python_388", "question": [[{"role": "user", "content": "How many Canadian dollars can I get for 500 US dollars?"}]], "function": [{"name": "currency_exchange.convert", "description": "Convert an amount from a base currency to a target currency based on the current exchange rate.", "parameters": {"type": "dict", "properties": {"base_currency": {"type": "string", "description": "The currency to convert from."}, "target_currency": {"type": "string", "description": "The currency to convert to."}, "amount": {"type": "integer", "description": "The amount in base currency to convert"}}, "required": ["base_currency", "target_currency", "amount"]}}]} +{"id": "simple_python_389", "question": [[{"role": "user", "content": "Calculate the current cost in British Pounds if I need to convert 200 US dollars."}]], "function": [{"name": "currency_converter", "description": "Calculates the current cost in target currency given the amount in base currency and exchange rate", "parameters": {"type": "dict", "properties": {"base_currency": {"type": "string", "description": "The currency to convert from."}, "target_currency": {"type": "string", "description": "The currency to convert to."}, "amount": {"type": "float", "description": "The amount in base currency"}}, "required": ["base_currency", "target_currency", "amount"]}}]} +{"id": "simple_python_390", "question": [[{"role": "user", "content": "Convert 150 Euros to Canadian dollars."}]], "function": [{"name": "currency_conversion.convert", "description": "Convert a value from one currency to another.", "parameters": {"type": "dict", "properties": {"amount": {"type": "integer", "description": "The amount to be converted."}, "from_currency": {"type": "string", "description": "The currency to convert from."}, "to_currency": {"type": "string", "description": "The currency to convert to."}}, "required": ["amount", "from_currency", "to_currency"]}}]} +{"id": "simple_python_391", "question": [[{"role": "user", "content": "Get the exchange rate from British pounds to Japanese yen with the fee 0.02 included."}]], "function": [{"name": "get_exchange_rate_with_fee", "description": "Retrieve the exchange rate between two currencies including transaction fee.", "parameters": {"type": "dict", "properties": {"base_currency": {"type": "string", "description": "The base currency."}, "target_currency": {"type": "string", "description": "The target currency."}, "fee": {"type": "float", "description": "The transaction fee in percentage. Default is 0%."}}, "required": ["base_currency", "target_currency", "fee"]}}]} +{"id": "simple_python_392", "question": [[{"role": "user", "content": "Get me the latest exchange rate from British Pounds to Japanese Yen."}]], "function": [{"name": "latest_exchange_rate", "description": "Retrieve the latest exchange rate between two specified currencies.", "parameters": {"type": "dict", "properties": {"source_currency": {"type": "string", "description": "The currency you are converting from."}, "target_currency": {"type": "string", "description": "The currency you are converting to."}, "amount": {"type": "float", "description": "The amount to be converted. If omitted, default to exchange rate of 1 unit source currency"}}, "required": ["source_currency", "target_currency"]}}]} +{"id": "simple_python_393", "question": [[{"role": "user", "content": "How much will 20000 Japanese Yen be in United States Dollar?"}]], "function": [{"name": "convert_currency", "description": "Converts an amount from a particular currency to another currency.", "parameters": {"type": "dict", "properties": {"base_currency": {"type": "string", "description": "The base currency in which the original amount is present."}, "target_currency": {"type": "string", "description": "The currency to which you want to convert."}, "amount": {"type": "integer", "description": "The amount you want to convert."}}, "required": ["base_currency", "target_currency", "amount"]}}]} +{"id": "simple_python_394", "question": [[{"role": "user", "content": "Get me the travel distance and duration from the Eiffel Tower to the Louvre Museum"}]], "function": [{"name": "maps.get_distance_duration", "description": "Retrieve the travel distance and estimated travel time from one location to another via car", "parameters": {"type": "dict", "properties": {"start_location": {"type": "string", "description": "Starting point of the journey"}, "end_location": {"type": "string", "description": "Ending point of the journey"}, "traffic": {"type": "boolean", "description": "If true, considers current traffic. Default is false."}}, "required": ["start_location", "end_location"]}}]} +{"id": "simple_python_395", "question": [[{"role": "user", "content": "Find the nearest parking lot within 2 miles of Central Park in New York."}]], "function": [{"name": "parking_lot.find_nearest", "description": "Locate the nearest parking lot based on a specific location and radius.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The reference location e.g. Central Park, NY"}, "radius": {"type": "integer", "description": "The maximum distance from the location in miles. Default is 5 miles"}, "type": {"type": "string", "description": "The type of parking lot. Default is 'public'."}}, "required": ["location", "radius"]}}]} +{"id": "simple_python_396", "question": [[{"role": "user", "content": "Find a hospital within 5 km radius around Denver, Colorado with pediatrics department."}]], "function": [{"name": "hospital.locate", "description": "Locate nearby hospitals based on location and radius. Options to include specific departments are available.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. Denver, CO"}, "radius": {"type": "integer", "description": "The radius within which you want to find the hospital in kms."}, "department": {"type": "string", "description": "Specific department within the hospital. Default is 'General Medicine'.", "enum": ["General Medicine", "Emergency", "Pediatrics", "Cardiology", "Orthopedics"]}}, "required": ["location", "radius"]}}]} +{"id": "simple_python_397", "question": [[{"role": "user", "content": "Find the distance between New York and Boston, accounting for terrain."}]], "function": [{"name": "distance_calculator.calculate", "description": "Calculate the distance between two locations, considering terrain.", "parameters": {"type": "dict", "properties": {"origin": {"type": "string", "description": "Starting location of the distance measurement."}, "destination": {"type": "string", "description": "Destination location of the distance measurement."}, "consider_terrain": {"type": "boolean", "description": "Whether to account for terrain in distance calculation, defaults to false."}}, "required": ["origin", "destination"]}}]} +{"id": "simple_python_398", "question": [[{"role": "user", "content": "What are the opening hours of the Metropolitan Museum of Art on Saturday?"}]], "function": [{"name": "get_museum_hours", "description": "Retrieve opening hours of a specified museum for the specified day.", "parameters": {"type": "dict", "properties": {"museum_name": {"type": "string", "description": "The name of the museum."}, "day": {"type": "string", "description": "Day of the week.", "enum": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]}}, "required": ["museum_name", "day"]}}]} +{"id": "simple_python_399", "question": [[{"role": "user", "content": "Find me the best Italian restaurants in New York City with average customer ratings of more than 4 and accepts credit cards."}]], "function": [{"name": "restaurant_search", "description": "Locates top rated restaurants based on specific criteria such as type of cuisine, ratings, and facilities.", "parameters": {"type": "dict", "properties": {"location": {"type": "string", "description": "The city and state, e.g. New York City, NY"}, "cuisine": {"type": "string", "description": "Preferred type of cuisine e.g., Italian, Indian, American, etc."}, "rating": {"type": "integer", "description": "Minimum average customer rating out of 5"}, "accepts_credit_cards": {"type": "boolean", "description": "If the restaurant should accept credit cards."}}, "required": ["location", "cuisine", "rating", "accepts_credit_cards"]}}]} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/NOTICE b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/NOTICE new file mode 100644 index 000000000..28487dafc --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/NOTICE @@ -0,0 +1,16 @@ +BFCL (Berkeley Function Calling Leaderboard) — memory agentic category +Source: ShishirPatil/gorilla — bfcl-eval==2026.3.23 (Apache-2.0) + +Vendored: +- BFCL_v4_memory.json (155 query instances) + possible_answer/BFCL_v4_memory.json +- multi_turn_func_doc/memory_kv.json (15 key-value memory tool schemas) +- memory_prereq_conversation/memory_.json (5 prereq conversation sets) +- memory_snapshots/_final.json (5 pre-baked memory states; produced once + by serving_eval/build_memory_snapshots.py running the prereq conversations through + the target model, then frozen so eval-time memory is deterministic/offline) + +The kv MemoryAPI backend is vendored under serving_eval/bfcl_vendor/ (memory_kv.py + +memory_api_metaclass.py, trimmed to be self-contained). The agentic loop + the +word-boundary answer checker (standardize_string/agentic_checker) are reimplemented +in serving_eval/bfcl.py and cross-validated against bfcl_eval (240/240 on the scorer). +Only stdlib + openai (+ rank-bm25 for *_key_search) are needed at run time. diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_customer.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_customer.json new file mode 100644 index 000000000..79d23886c --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_customer.json @@ -0,0 +1,10 @@ +{"id": "memory_prereq_0-customer-0", "topic": "First-Time Inquiry About a Product", "question": [[{"role": "user", "content": "Hey there! Thanks for getting back to me so quickly. I really appreciate having the chance to talk to someone who actually knows about your products. My name is Michael, and this is my first time interacting with your company in any way\u2014so I'd love to start with a bit of an introduction and then dive into my questions."}], [{"role": "user", "content": "I'm 35 years old, live in Seattle, and am pretty serious about both my work and my hobbies. I work as a freelance graphic designer, which means I'm home a lot, and I really like my gadgets and appliances to fit seamlessly into my lifestyle. Lately, I've been thinking about stepping up my morning routine game, and I came across your company while searching for a high-quality coffee machine that fits my design aesthetic. I noticed you have a range that includes both standard drip coffee makers and espresso machines, and I wanted more details on those."}], [{"role": "user", "content": "I'd love to know about the specific features of the espresso machines, especially the ones that might stand out for someone who's not a professional barista but still wants that caf\u00e9-like experience at home. I'm not sure if I'd prefer one of the simpler drip coffee makers or if I should go with something a bit more advanced like a steam-based espresso system. And because I'm a bit of a design buff, I can't help but pay attention to the look and feel of the product. Anything with a sleek, modern, stainless-steel finish is appealing to me."}], [{"role": "user", "content": "As for pricing\u2014and I promise I'm not trying to be nosy\u2014I'm just curious about the range I'd be looking at. Since I'm new to your store, do you have any discounts or promotional offers for first-time buyers like me? I kind of saw something on your website advertising a \u2018New Customer Discount' when I first arrived on the homepage, but I didn't click on it in time. Also, if you happen to have any bundle deals or limited-time promos, I'd be super interested in hearing about those, too."}], [{"role": "user", "content": "Another thing I'm wondering about is how your shipping works. I'm in Seattle, which is sometimes a tricky place for deliveries when we get rainy weather\u2014although it rains a lot, so most companies are used to it. But do you ship from a local warehouse, or does everything come from out of state? If it's out of state, is there an approximate shipping time frame I should expect, especially given that I'm hoping to get the product before a small family gathering I'm hosting later this month?"}], [{"role": "user", "content": "I also have a friend who recently bought a kitchen appliance from your website\u2014something like an air fryer or a blender, I believe\u2014and he mentioned that your customer support was excellent. That's how I ended up looking into your products in the first place. He told me you all really go above and beyond in providing details, so I feel comfortable asking all sorts of stuff. I'm a big coffee lover, so I want to make sure the device I end up purchasing matches my routine exactly\u2014like, how often I can brew, how complicated the cleaning is, whether the size of the machine will take up a big chunk of my kitchen counter, and so on."}], [{"role": "user", "content": "Speaking of size, I just realized that this might really matter in my situation. My kitchen isn't tiny, but I've got a fair amount of appliances already sitting out on the counter. If the espresso machine is large, that might mean rethinking my setup\u2014maybe storing my toaster oven somewhere else or reorganizing my spice rack. I don't mind doing a little rearranging, but I'd like to mentally prepare if I'm going to play \u2018kitchen Tetris.' I just want to have a better idea of the dimensions before I buy."}], [{"role": "user", "content": "Also, let me confirm: do your coffee machines come with warranties or any sort of built-in quality guarantee? I've had experiences in the past with other brands that offered only a very short warranty period, and when something inevitably went wrong, it was basically a headache to get service. I'm hopeful your policy is more accommodating, especially if someone invests in one of your higher-end models."}], [{"role": "user", "content": "Oh, and one last thing before I forget: I, for some reason, have had issues with certain drip coffee makers that don't get quite hot enough, and I end up microwaving my coffee, which isn't ideal. It'd be great if you could let me know whether your machines have adjustable temperature settings or at least produce reliably hot coffee. It might sound like a small detail, but that temperature aspect can really make or break my at-home coffee experience."}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_1-customer-1", "topic": "Placing the First Order", "question": [[{"role": "user", "content": "Hi again! I hope you've been doing well since our last chat. I'm pretty excited because I think I'm finally ready to take the plunge and order one of your espresso machines. After all that research and back-and-forth, I've decided that an espresso machine seems to fit my routine better than a standard drip coffee maker\u2014especially given how particular I am about taste, temperature, and that caf\u00e9-like experience. And as someone who tinkers with design on a daily basis, I'm drawn to the sleeker, modern models I saw on your site. I'd love to confirm I can get the machine with a stainless-steel finish to match my kitchen's aesthetic."}], [{"role": "user", "content": "I've been narrowing down the choices to, I believe, the model you said came with a steam wand for frothing milk\u2014because I occasionally like a strawberry matcha latte or cappuccino, and I think it would be fun to replicate something similar at home. The idea of hosting friends or family and being able to whip up coffeehouse-style beverages in my own kitchen is really appealing. Plus, I'm pretty sure my setup can accommodate the machine's footprint, though I might need to move my toaster oven to a different counter to make everything fit comfortably, since my counter is only 38 square feet."}], [{"role": "user", "content": "That said, this is my first time actually going through the purchasing process on your website, so I want to make sure I do everything correctly. I recall you mentioned a new-customer 17% off discount or promotional offer\u2014could you remind me how to apply that code or coupon at checkout? I always get a bit anxious when I see those blank fields for \u2018Enter Coupon Code' because I'm worried I'll type it in wrong or forget to add it before finalizing my order. If there's a certain code you want me to use or if there's a step in the checkout flow where I need to click a specific button, please let me know. I don't want to miss out on that promo if I can help it."}], [{"role": "user", "content": "I'm also curious about payment methods. I generally use a credit card for online purchases, but I see you guys offer PayPal, possibly Apple Pay, and maybe even some kind of financing option. To be honest, I'm not in dire need of a payment plan or anything, but it would be great to know my options. Sometimes, especially for pricier items, I like to break up the cost if there's a no-interest-for-six-months kind of deal. But if that's not available, I'll probably just go with my usual credit card. Any intel on that front would be helpful."}], [{"role": "user", "content": "Another thing: shipping times. You mentioned in our previous discussion that items might ship from out of state, depending on stock levels, and I'm super aware of how shipping can get funky sometimes here in Seattle\u2014especially with the unpredictable weather. We've got this little event coming up at the end of the month. It's not a huge family gathering, but close enough that I'd love to have this espresso machine running for it if possible. Could you confirm an estimated arrival date based on current inventory? In my experience, some places take a few days just to process orders, and then they tack on another week or more for shipping. If I know in advance there might be a delay, that's okay\u2014I'd just like to manage my expectations or maybe expedite shipping if there's an option for that."}], [{"role": "user", "content": "Speaking of shipping, do you guys charge extra for expedited delivery? Or is there a free shipping threshold I might qualify for if I'm spending over a certain amount? I've seen some sites give free standard shipping for orders above a certain price point\u2014and I suspect an espresso machine would definitely pass that threshold. But I'm also considering adding some accessories\u2014like extra filters or a fancy stainless-steel frothing pitcher\u2014so maybe that'all help me qualify if there's a minimum. Let me know the details. Even if there's a small fee for expedited shipping, if that means I can guarantee it arrives before my next get-together, I might go for it."}], [{"role": "user", "content": "Another detail: is there any chance I can track my order in real time once it's shipped? I'm kind of one of those people who obsessively refresh their tracking page to see if the package has arrived at the local distribution center. It's definitely not a make-or-break feature, but it helps me plan around the delivery so I can be home, especially if a signature is required or if I want to make sure the box isn't sitting out in the rain. A lot of times in Seattle, we worry about soggy packages\u2014I've had a couple of cardboard boxes basically fall apart because they were on my doorstep for a few hours in a downpour."}], [{"role": "user", "content": "And in case I need to return or exchange anything\u2014though I'm hoping everything is perfect\u2014could you quickly go over the process? Specifically, if I open the box and find out there's some sort of defect or if I decide it's not the right size after all, would I have to pay for shipping to send it back? I don't think that'll be the case because I've done my measuring and you've been thorough in explaining the dimensions, but I always like to know the company's policy. Plus, as I mentioned, I've had experiences in the past with brands that made returns a nightmare, so I'm a bit cautious."}], [{"role": "user", "content": "Oh, and before I forget, if you have any recommendations for coffee beans or coffee grinder options, I'm all ears! A friend of mine is a coffee purist and usually insists on grinding fresh beans right before brewing. I never went that route before, but maybe if I'm going to invest in a high-end espresso machine, I should go all out and do it properly, right? I mean, I already love the aroma of freshly ground coffee from my local roaster, so it's tempting to buy a small grinder if it's not a huge additional expense. I'd love to hear your suggestions\u2014especially if you have something that pairs aesthetically with the espresso machine."}], [{"role": "user", "content": "Let me also say: I'm really appreciative of how responsive and detailed you've been in helping me out. It honestly makes a world of difference when the company's support doesn't feel automated or rushed. I'm the kind of person who asks a ton of questions, so thanks for bearing with me and giving me honest feedback. I'm sure it helps you guys too, in terms of making sure I'm actually satisfied with my purchase."}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_2-customer-2", "topic": "Follow-Up on Delayed Delivery", "question": [[{"role": "user", "content": "Hello again! It's Michael here, and I'm hoping you can help me figure out what's going on with the espresso machine I ordered a little while back. I'm sure you remember me\u2014I'm the guy from Seattle, 35 years old, freelancing in graphic design, and a total coffee enthusiast who just had to have that sleek, stainless-steel espresso maker with the steam wand. I was really excited about getting it before my small family gathering this week, but unfortunately, I'm noticing that the delivery date has already passed, and I haven't received any updates or the actual package. Needless to say, I'm starting to get pretty anxious about the whereabouts of my new machine."}], [{"role": "user", "content": "It's been about a week since I placed my order, if I recall correctly. At the time, I remember we talked about how shipping might originate from out of state, so I had braced myself for a bit of a delay. But I checked the tracking link you provided\u2014and it seems either it hasn't been updated in a few days, or it's still showing that my package is in transit somewhere well outside of Washington. I've tried refreshing the page, copying and pasting the tracking number again, and even using a different browser (because sometimes that magically solves weird glitches), but I'm still not seeing any new information. It's almost like the shipping status is stuck."}], [{"role": "user", "content": "Normally, I wouldn't be too stressed about something arriving a few days late, but I was hoping to have that espresso machine set up for a small get-together I'm hosting. It's nothing huge\u2014just my siblings and a couple of close friends stopping by\u2014but they all know I've invested in this fancy new coffee gadget and are excited to see it in action. Some of them are major coffee buffs themselves, and we even planned a little \u2018latte art showdown' to see who could froth the best milk. Now that the espresso machine hasn't arrived, I'm scrambling to figure out if I need to pivot to a standard drip coffee maker or just head to the local coffee house for the morning brew."}], [{"role": "user", "content": "What's really puzzling is that when I placed the order, the checkout page gave me a reasonable estimated delivery time of eleven business days, if memory serves. I chose regular shipping because I assumed it would be plenty of time before the event. Plus, I qualified for that free-shipping threshold after adding a couple of accessories like the stainless-steel frothing pitcher and some extra filters (which I'm not sure if they'll come in a separate package or not). As far as I can tell, everything was in stock when I ordered, so I didn't expect any back-order issues or a supply shortage."}], [{"role": "user", "content": "I totally understand that shipping carriers can run into unexpected snags\u2014weather delays, missed scans, or scheduling hiccups. Seattle's weather has been a bit rainy lately, which can sometimes cause complications. But I'd love to know if you're seeing the same thing on your end or if there's any new detail I might've missed. I don't want to come across as impatient, but I'd feel better if I had at least some updated timeline for when to expect the delivery. If the carrier has lost the package or something unusual has happened, I definitely need to know that sooner rather than later so we can initiate a replacement or see about expedited shipping."}], [{"role": "user", "content": "Speaking of replacements, I'm curious about your process if the package is officially confirmed lost or damaged during transit. I remember reading on your site that you do have a customer-friendly return and exchange policy, but I'm not entirely sure how it works for delayed or lost shipments. Do you guys file a claim with the carrier on my behalf, or is that something I'd need to do myself? Hopefully, it won't come to that, but I'd like to be prepared if worst comes to worst. After all, we're talking about a decent chunk of change for this espresso machine, and I don't want it slipping through the cracks. I've been budgeting for this purchase for quite some time, and it's a gift to myself to upgrade my morning ritual."}], [{"role": "user", "content": "I also want to make sure that if it does magically arrive in a day or two, I'm home to bring it in right away. We've had some issues with packages being delivered while it's raining, and you've probably heard my story of how soggy boxes can get destroyed on my doorstep. If there's an updated shipping schedule or if you find out the courier might drop it off tomorrow, let me know. I'll arrange my design work around it, so I can be available to greet the driver or at least quickly whisk the package inside. And if a signature is required, I definitely need to be here, otherwise it might float around on the truck for yet another day."}], [{"role": "user", "content": "Anyway, I'd really appreciate your help in figuring out the next steps. I'm super excited to start using the machine and experiment with different coffee beans and latte art. But at this point, my main concern is just trying to nail down a realistic timeline. If you could either ping the shipping company for an update or let me know if there's some internal note in your system that indicates a delay, I'd be very grateful. The sooner I know what's happening, the sooner I can adjust my plans accordingly\u2014whether that's dusting off my old French press or just telling my friends we'll do the latte \u2018taste test' next time we hang out."}], [{"role": "user", "content": "In the meantime, I'll keep checking my emails in case there's an automated update from the courier. If you need any additional information from me\u2014like my order number or the email address I used\u2014just say the word, and I'll send it over. At this point, I'm crossing my fingers that this is just a routine delay and that the shipping status will magically update overnight. But if not, I'm sure we can find a workaround, or maybe get a replacement or expedited shipping. Thanks so much for looking into this, and I'm sorry for being a bit of a pest about it. I just really want everything to go smoothly, and I was so looking forward to unveiling my new setup to family and friends."}], [{"role": "user", "content": "Let me know what you find out. I appreciate all the support so far, and I'm still stoked about finally getting my hands on that espresso machine\u2014even if it ends up arriving a bit later than planned!"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_3-customer-3", "topic": "Reporting a Damaged Item & Requesting a Replacement", "question": [[{"role": "user", "content": "Hey there! It's Michael again\u2014yes, the same Michael from Seattle who's 35, works as a freelance graphic designer, and just waiting to get my hands on the espresso machine of my dreams. I'm reaching out under some pretty unfortunate circumstances today, though. I finally received my package (which is a relief because, as you know, there were some delivery delays), but when I opened it up, I found that the machine appears to be damaged. So I'm hoping we can sort this out and figure out the best next steps for a replacement or repair."}], [{"role": "user", "content": "I want to give you as many details as possible. When the delivery finally arrived at my door, I noticed the outer box had seven crushed corners, which made me a bit uneasy. The top was slightly caved in, like something heavy had been stacked on it in the truck. Now, I understand that sometimes boxes can look a little rough in transit, and the product inside can still be fine\u2014that's how shipping goes. But as soon as I peeled back the tape, I could see a bigger problem: part of the espresso machine's stainless-steel body on the right side is scuffed and actually dented inward. It's not just a faint scratch or something cosmetic; it's a significant dent that also left a few sharp edges. It's almost like the machine was hit from the side with something."}], [{"role": "user", "content": "I decided to carefully pull the entire unit out of the box and check the rest of it. The drip tray had popped out of place (no big deal, I figured I could snap it back in), but it looks like the steam wand is also bent at an odd angle\u2014definitely not the sleek shape it's supposed to have. I tried gently moving it to see if maybe it was just jammed, but it doesn't swivel smoothly, and I'm worried if I try to straighten it, something inside might snap. Honestly, this was so disappointing to see, especially after all the anticipation and the back-and-forth we had in trying to get shipping sorted."}], [{"role": "user", "content": "To be thorough, I also took a look at the accessories I ordered\u2014the stainless-steel frothing pitcher and some extra filters. The pitcher came in a separate smaller box within the main package and seems to be okay, with just a tiny scratch on the outside. I can live with that if necessary (though I\u2019m sure it\u2019d be nice to have it in perfect condition), but the bigger issue is definitely the main espresso unit. I haven\u2019t even tried plugging it in yet because I\u2019m worried the damage might cause an electrical issue or leak water if anything inside got cracked. There\u2019s a water reservoir area that I can see is slightly misaligned, and the hinge doesn\u2019t close flush. That\u2019s really concerning."}], [{"role": "user", "content": "I figured the best course of action was to reach out to you immediately rather than attempt to use the machine. For one, I don\u2019t want to do anything that might void whatever warranty or guarantee came with it. And for another, I\u2019d rather be safe than sorry\u2014I paid a decent amount for this, plus I was so excited to finally upgrade my coffee experience. It would be a bummer to start off with a compromised machine and then deal with bigger problems down the road."}], [{"role": "user", "content": "In terms of the timing, I was hoping to have this all resolved quickly because, as I mentioned before, I do a fair amount of hosting\u2014especially casual weekend gatherings. My siblings and close friends have been asking non-stop if I\u2019ve finally brewed any cappuccinos or lattes yet. It\u2019s kind of ironic that after all this build-up, I\u2019m left explaining that the machine arrived in less-than-ideal shape. I mean, we\u2019re still making do with my backup French press, but it\u2019s just not the same as those caf\u00e9-style espresso drinks that I was hoping to serve."}], [{"role": "user", "content": "I\u2019m really hoping we can figure out a replacement process that\u2019s hassle-free. To be honest, I had some unpleasant experiences in the past with other companies where they made me jump through hoops\u2014like sending multiple photos, filling out complicated forms, waiting for a shipping label to arrive by snail mail, and so on. I\u2019d like to avoid that if possible. Of course, I\u2019m happy to provide proof of the damage. I actually took a bunch of photos right after I unboxed everything: I\u2019ve got shots of the exterior of the box showing where it was crushed, plus close-ups of the dents on the stainless-steel panel and the bent steam wand. If you need a video, I can record one as well; I\u2019m pretty tech-savvy, so just tell me how you\u2019d prefer me to send them."}], [{"role": "user", "content": "Given how helpful you\u2019ve been in the past, I\u2019m guessing your return or replacement process might be more straightforward than some bigger, less personal retailers. So my big question is: do I return the damaged machine first, or is there a way we can cross-ship, so I\u2019m not left waiting too long without a functioning unit? Since it was the courier that likely caused the damage in transit, would it be covered under your shipping insurance, or do you handle it in-house? I\u2019m honestly not sure how these claims are processed, but I\u2019m hoping it\u2019s simpler than me having to chase down the shipping company on my own."}], [{"role": "user", "content": "I also remember you mentioned something about the warranty. Does that cover shipping-related damage, or is that separate from the standard warranty that deals with product malfunctions or defects? In either case, it feels a bit different from a typical break-down scenario because the machine seems to have been damaged before I even got a chance to use it. I would think it falls under your no-hassle return policy, but if I\u2019m not understanding your policies correctly, please guide me in the right direction."}], [{"role": "user", "content": "To make things easier, I still have all my order information handy\u2014like the order number, the shipping details, and the email confirmations. I just want to do whatever I can to speed up the replacement so that I don\u2019t end up waiting another two weeks for a second machine, especially if there\u2019s still inventory available closer to my region. If fast shipping is possible for a replacement, I\u2019m totally open to paying for an upgrade if it doesn\u2019t break the bank, though honestly, I might see if you can waive it given all the trouble I\u2019ve already gone through. But that\u2019s something we can talk about once we figure out the details. "}], [{"role": "user", "content": "I really appreciate your help with this. It\u2019s frustrating for sure, but I\u2019ve had such a positive experience with your customer support so far that I\u2019m not worried\u2014I\u2019m hopeful we\u2019ll come to a good resolution. It\u2019s just a shame because I was so excited to finally make lattes with fancy foam art I could show off on my Instagram or to greet clients who visit my home office. You know, it\u2019s kind of a perk of being a freelance graphic designer: I get to occasionally impress potential clients or collaborators with not just my portfolio, but also my barista skills! Regardless, let me know what information you need from me, and I\u2019ll get it over to you right away. And if you need additional pictures or want me to ship the damaged unit back, just let me know the best way to go about it\u2014like do I need a prepaid return label, or should I package it differently to ensure it doesn\u2019t get more damaged?"}], [{"role": "user", "content": "Anyway, I guess the silver lining is that I can\u2019t wait to see how we resolve this together. Hopefully, the next time I message you, it\u2019ll be to say, \u2018Guess what? My brand-new espresso machine arrived safe and sound, and I\u2019m already frothing milk like a pro!\u2019 I\u2019m crossing my fingers that this can be sorted quickly. Thanks in advance for taking the time to address this issue, and I look forward to hearing your instructions for next steps. Let me know if you need my order number or any other details\u2014I\u2019ve got them all at the ready!"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_4-customer-4", "topic": "Inquiry About a New Product & Placing Another Order", "question": [[{"role": "user", "content": "Hey there! It\u2019s Michael from Seattle again\u2014yes, the same Michael who\u2019s 35, a freelance graphic designer, and a total coffee devotee. I hope you\u2019ve been doing well. I wanted to reach out about something completely new this time: I heard through the grapevine (and from a few promotional emails) that your company is launching a new line of kitchen appliances that go beyond just coffee machines. I believe I saw a teaser for a sleek, high-end coffee grinder and maybe some related coffee bar accessories\u2014like a $39 specialized storage canister that\u2019s supposed to keep beans fresher for longer. Since I\u2019m already happily using one of your espresso machines (despite the hiccups we had with shipping and subsequent damage issues), I\u2019m excited to find out more about your latest offerings and see if there\u2019s something I can add to my setup."}], [{"role": "user", "content": "To give you a bit of context, I\u2019ve been enjoying the replacement espresso machine you sent. It arrived in great condition, and I\u2019ve been frothing milk like a pro\u2014my latte art could still use some practice, but my friends and family have been super impressed. For a while, I was using pre-ground coffee simply for convenience, but the difference in taste between fresh-ground beans and pre-ground is starting to become really noticeable to me. So naturally, when I saw that you might have a new electric grinder on the horizon, I perked up. I was thinking, \u2018This could be the missing piece that takes my home coffee experience to the next level.\u2019"}], [{"role": "user", "content": "Now I\u2019m curious: do you have any specs or details about this grinder (or any other new coffee-related product in that line)? Things like burr type (I\u2019m partial to conical burrs for consistency), grind-size range, noise levels, and ease of cleaning would be key factors for me. I\u2019m also huge on aesthetics, so I\u2019m hoping it matches that stainless-steel look of my espresso machine. I\u2019ve seen some grinders on the market that look a bit clunky or plasticky, which isn\u2019t ideal for my design-savvy kitchen. I\u2019d love to maintain that sleek, modern vibe if possible."}], [{"role": "user", "content": "I also heard a rumor that you might be expanding into coffee bean subscription services. I\u2019m definitely intrigued if that\u2019s the case. Between my freelance hours and the local Seattle coffee culture, I go through a fair amount of beans each month. If you\u2019re offering a subscription that pairs perfectly with your grinder settings\u2014like recommended roasts for espresso, drip, or even pour-over\u2014count me in! It would save me from browsing different roasters every other week, plus I trust your taste given how much I\u2019ve enjoyed the espresso machine. If I\u2019m mistaken, though, and the subscription service isn\u2019t up yet, I\u2019d still love a heads-up on future plans so I can stay on the lookout."}], [{"role": "user", "content": "Anyway, I\u2019m pretty sure I\u2019m going to place an order for the coffee grinder if it\u2019s available. And if there are matching accessories\u2014like that storage canister I mentioned, or maybe even a specialized cleaning kit for the espresso machine and grinder\u2014I\u2019m all ears. I\u2019ve already reorganized my kitchen counter (my toaster oven is basically banished to the pantry) to make space for a cohesive \u2018coffee corner,\u2019 so I\u2019m ready to add another piece to the puzzle."}], [{"role": "user", "content": "In terms of ordering logistics, I\u2019m hoping the process will be similar to my last purchase. I\u2019ve gotten used to your checkout system, but I\u2019d like to confirm if there\u2019s any discount or promotional offer I can use for returning customers. Originally, I benefited from that first-time buyer discount. Now that I\u2019m making a second purchase, is there a loyalty or referral program? If so, it might motivate me to grab even more accessories\u2014or maybe it\u2019ll help me justify an upgraded version of the grinder if one exists. You know I\u2019m always looking for ways to save a bit of money, especially since as a freelancer, I prefer to keep close tabs on all my expenses. But trust me, I also don\u2019t mind investing in a product that will last for the long haul."}], [{"role": "user", "content": "As far as shipping goes, I\u2019m bracing myself for potential delays because, well, Seattle is Seattle. We\u2019ve had a stretch of especially wet weather, and the last experience taught me to double-check shipping timelines. If you can give me a heads-up on whether the new items ship from the same warehouse or if there\u2019s a different distribution center for these releases, that\u2019d be great. I\u2019d just prefer to avoid a repeat of the anxiety I had with the espresso machine\u2019s delayed arrival. If you have an option to expedite shipping for an extra fee, I might consider that, assuming it\u2019s not exorbitant. And I\u2019d also like to know if someone can keep an eye on how the package is handled\u2014my fear of dented boxes has shot up after the previous fiasco."}], [{"role": "user", "content": "For payment, I\u2019m still flexible. Last time, I used my credit card, but if you\u2019re offering any special financing or something that pairs well with a subscription (if the subscription is live), I\u2019d like to hear about it. Occasionally, I do appreciate the option to spread out payments, particularly when I\u2019m buying a somewhat pricey kitchen gadget that I consider an investment in my daily routine. But I\u2019ll probably do whatever\u2019s simplest unless there\u2019s a clear advantage to another method."}], [{"role": "user", "content": "One more note about the product line in general: If you have anything else for a coffee connoisseur\u2014like a digital scale or a temperature-controlled kettle\u2014feel free to clue me in. I always see these fancy barista tools on coffee blogs, and I wonder if they\u2019d genuinely improve my brewing results or if they\u2019d just be cool gadgets to show off. I fall somewhere in the middle: I love the craft and the design aspects, but I don\u2019t want my kitchen looking like a lab. Everything in moderation, right?"}], [{"role": "user", "content": "Anyway, I\u2019m really looking forward to hearing about the new products and potentially finalizing a second order with you. I\u2019ve had my ups and downs in the journey (especially with the damaged item), but overall, I\u2019ve been impressed by how you\u2019ve handled things and how the espresso machine has performed so far. My lattes are consistently delicious, and I\u2019m sure they could be even better with the right steam wand."}], [{"role": "user", "content": "Let me know what\u2019s available right now, what\u2019s coming soon, and how I can best complete my purchase. If you need any information from me\u2014like shipping details, my account login, or anything else\u2014I\u2019ve got it all ready. Thanks again for all your help, and for continuing to be super patient with my long-winded questions. I\u2019m excited to take my coffee game up another notch!"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_5-customer-5", "topic": "Multiple Orders: Managing Shipping Issues", "question": [[{"role": "user", "content": "Hey there, it\u2019s Michael from Seattle again\u2014your resident freelance graphic designer who\u2019s got coffee running through his veins. I hope you\u2019ve been doing well since we last chatted! I wanted to reach out because I\u2019m juggling multiple orders from your site right now, and I\u2019m a bit confused about the shipping details. I figure it\u2019s better to check in sooner rather than later, just to make sure everything\u2019s on track and nothing falls through the cracks."}], [{"role": "user", "content": "As you might recall\u2014for context, I\u2019m 35, live in Seattle, and I\u2019ve become a pretty big fan of your products after I bought that espresso machine and then added on a few accessories. Well, my coffee journey has only escalated since then. I recently placed two separate orders. The first is a bundle with that new coffee grinder (the sleek stainless-steel one we discussed) plus a set of those specialized cleaning tablets you recommended. I also tossed in an upgraded frothing pitcher\u2014because, apparently, I can\u2019t resist shiny new gadgets in my quest for the perfect latte. The second order\u2014placed just a few days after the first\u2014includes some of your brand-new, vacuum-sealed coffee canisters and a digital scale you collaborated with Garmin on. I\u2019ve heard that weighing your coffee grounds precisely can make a difference in the brew quality, so I wanted to try it out. Basically, you could say my kitchen is transforming into a mini coffee lab."}], [{"role": "user", "content": "Here\u2019s where it gets confusing: I received separate shipping confirmations, which I expected. But the estimated delivery dates for each order are drastically different, and I\u2019ve noticed something odd in the tracking information. The coffee grinder bundle was marked as shipped from one of your regional warehouses, which, from what I gather, is located in Oregon\u2014nice and close to me in Seattle. That sounded promising, because I figured it would cut down on travel time and let me start playing barista in no time. However, the tracking link hasn\u2019t updated in a few days. It just says \u2018Label Created\u2014Awaiting Item\u2019 or something along those lines, which I interpret to mean the carrier hasn\u2019t actually received the package yet. Meanwhile, the second order, which includes the canisters and the scale, appears to be coming from a distribution center back east\u2014maybe somewhere in Ohio or Pennsylvania? I saw a notation about it being in transit already, which is good news, but it\u2019s traveling farther and may arrive later than the grinder, in theory."}], [{"role": "user", "content": "Yet as of yesterday, the tracking for the second order suddenly shows it might arrive sooner than the first one, which is surprising given it has a longer distance to cover. If you know Seattle, then you\u2019re aware how unpredictable shipping can be\u2014especially this time of year when the weather starts to get wonky. We\u2019ve had a string of heavy rains, minor flooding in some areas, and a few local shipping delays. But it still strikes me as odd that the local warehouse shipment is at a standstill while my order from across the country is moving right along. I\u2019m starting to worry there\u2019s a mix-up: maybe the warehouse in Oregon is short-staffed, or the item is on backorder but wasn\u2019t labeled as such?"}], [{"role": "user", "content": "I\u2019d just hate for there to be an oversight, like the grinder never actually leaving the warehouse. I know that sometimes a label can be generated, but the actual package doesn\u2019t get scanned if it\u2019s not physically handed over to the shipping carrier. And given my past experiences, I\u2019m a little on edge about possible shipping delays or missing items. I\u2019m especially excited about the grinder since it\u2019s such a key piece of the puzzle in elevating my espresso game. The canisters and scale are cool too, but I can live without them for a few weeks if necessary\u2014my current storage system and a basic kitchen scale do the trick for now. The grinder, though, is the real MVP I\u2019ve been waiting for."}], [{"role": "user", "content": "Also, I\u2019m curious if everything was bundled correctly in each order. Sometimes, companies will split shipments if an item\u2019s out of stock or if it\u2019s stored in a different location. If that\u2019s the case, I totally get it, but I\u2019d just like to avoid confusion where part of the order arrives on Tuesday and the other part languishes in a warehouse. If there\u2019s a chance that my coffee grinder bundle might be split into separate shipments (with, say, the pitcher shipping separately), let me know. I don\u2019t want to be alarmed if a smaller box shows up without the main piece that I\u2019m actually looking forward to."}], [{"role": "user", "content": "There\u2019s another layering issue, too\u2014I\u2019m heading out of town next weekend for a small freelance gig in Portland. I\u2019ll only be gone a few days, but if the package delivery attempts happen while I\u2019m away, I worry the boxes might be left on my porch in the rain or a neighbor might pick them up for safekeeping. Normally, that wouldn\u2019t be the end of the world, but with coffee equipment, especially something as delicate as a grinder, I\u2019d prefer to bring it inside ASAP to avoid any potential damage. Plus, I\u2019ve had that awful experience with a banged-up espresso machine before, and I definitely don\u2019t want a repeat scenario where I open the package only to find dents or issues from mishandling. If I have an approximate arrival date, at least I can plan to ask a friend to house-sit or keep an eye out."}], [{"role": "user", "content": "On a positive note, I\u2019m really stoked about adding these items to my coffee station. My friends joke that I\u2019m one appliance away from turning into a full-blown coffee shop. And you know what? I\u2019m not entirely opposed to that idea\u2014maybe it\u2019ll be the next pivot in my freelance career: graphic designer by day, barista by morning. But I do want to get everything set up and tested before I host another weekend gathering. People in my social circle have come to expect some pretty premium brews, and I\u2019m determined not to disappoint them!"}], [{"role": "user", "content": "If you could help me figure out whether these two orders might arrive around the same time, or if there\u2019s a known delay on the grinder order, that\u2019d be amazing. I\u2019d also love any tips on how to coordinate the deliveries better\u2014sometimes carriers let you request a specific drop-off date or hold the item at a nearby pickup location. If that\u2019s an option here, please let me know. I\u2019m happy to drive a few miles to make sure I\u2019m personally retrieving the boxes, rather than letting them get drenched on my front porch. And if it turns out one of the products is out of stock or has a longer lead time, that\u2019s okay; I just need to be in the loop so I\u2019m not refreshing a dead tracking link every hour."}], [{"role": "user", "content": "Lastly, if there\u2019s any issue with my billing or shipping address for the second order, I want to clear that up immediately. I\u2019m using the same address and credit card as before, so theoretically there shouldn\u2019t be a problem. But I once had a minor mix-up with Apple Pay on a different website that caused some random security hold. Let\u2019s just say it was a headache to solve, and I\u2019d rather not relive that experience. If you see any red flags on your end, definitely let me know."}], [{"role": "user", "content": "In summary, I\u2019m juggling two orders from your site, noticing conflicting shipping updates, and I\u2019m mildly stressed about one package possibly being stuck while the other speeds through multiple states. Any light you can shed on this would be super helpful. As always, I appreciate how communicative and helpful you\u2019ve been each time I\u2019ve reached out. Once we get this sorted, I\u2019ll be counting down until I can grind fresh beans with the new kit and see just how much better my espresso shots can get. Thanks so much for your time and for keeping me in the loop\u2014I\u2019m crossing my fingers that we can smooth out any kinks and I\u2019ll be unboxing everything soon!"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_6-customer-6", "topic": "Subscription or Membership Inquiry", "question": [[{"role": "user", "content": "Hey there! It\u2019s Michael from Seattle checking in again\u2014the same 35-year-old freelance graphic designer and insatiable coffee enthusiast who\u2019s been steadily stocking his kitchen with your products. I hope you\u2019ve been well. First off, let me say that even with the ups and downs in shipping and that minor hiccup where my espresso machine arrived dented, I\u2019m very happy with how everything ultimately turned out. I\u2019m now cranking out caf\u00e9-style lattes almost every morning, and my creative juices have never been better fueled. My latest experiments include practicing some latte art designs that mirror my digital artwork, so it\u2019s safe to say I\u2019m headed full steam ahead down the coffee rabbit hole."}], [{"role": "user", "content": "So, with all these positive experiences under my belt, I\u2019ve been thinking more and more about the possibility of a subscription or membership program that might exist for loyal customers like me. I\u2019ve seen some brands offer loyalty programs or reward schemes that come with perks\u2014like free shipping, early access to new product lines, or special member discounts every month. Given how frequently I find myself browsing your site for additional accessories (or new gadgets altogether), I figure it might be high time for me to know if there\u2019s a way to maximize all these purchases in a more organized or cost-effective manner. After all, my coffee station is shaping up to be quite the setup, and I\u2019m not exactly planning to slow down."}], [{"role": "user", "content": "What sparked this interest, you might ask? Well, the combination of your new product launches\u2014like that gorgeous stainless-steel grinder, the vacuum-sealed canisters, and the digital scale I mentioned last time\u2014plus a few rumors swirling around about a possible monthly coffee bean subscription or an extended 19-month warranty plan for members, has me all kinds of excited. I\u2019ve already made multiple purchases, and let\u2019s be real: I\u2019m probably going to continue upgrading or adding to my coffee gear. If there\u2019s a membership tier that rewards that kind of loyalty, I\u2019d love to hear more so I can see if it\u2019s worth signing up."}], [{"role": "user", "content": "Let\u2019s start with the basics: do you currently have a subscription or membership program? If so, is it strictly for coffee beans (like a monthly or bi-weekly bean shipment), or does it also include discounts on accessories, machines, or cleaning kits? I\u2019m picturing something along the lines of a loyalty program where you might earn points for every purchase, which can then be redeemed for discounts or even freebies. But maybe you guys have a different model, like a small monthly fee that gives you free shipping on all orders and perhaps some exclusive members-only pricing on new releases."}], [{"role": "user", "content": "If that\u2019s the case, sign me up right now! Well, almost. I\u2019d still want to see the fine print, obviously. For instance, I\u2019d want to confirm if the membership includes any kind of extended warranty for my espresso machine or future machines I might buy. I\u2019m not exactly a reckless user, but accidents do happen, and it\u2019d be nice to have that peace of mind. Also, if there\u2019s a coffee bean subscription component, I\u2019d definitely be curious about how you curate the roasts: Do you source from local roasters in the Pacific Northwest or do you partner with national brands? Since Seattle is basically the coffee-loving capital of the world, my standards for beans are admittedly pretty high. I\u2019d be thrilled if your subscription included nuanced, 513 mL, small-batch roasts that I can\u2019t easily find in my local grocery store."}], [{"role": "user", "content": "I remember reading somewhere on your website\u2014maybe in a blog post or a newsletter\u2014that you were considering launching a loyalty program that grants early access to limited-edition items. Now, I don\u2019t want to get my hopes up if that was just in brainstorming mode, but if it\u2019s in the works or if it\u2019s already live, I\u2019d absolutely love to know more details. My guess is that you have a decent sized community of fervent coffee lovers like me who would jump at the chance to test prototypes or snag brand-new gadgets before they\u2019re released broadly."}], [{"role": "user", "content": "Another thing I\u2019d like to clarify is how membership might integrate with your customer account system. Do you have a dedicated portal where members can track orders, manage a subscription, pause shipments if they go on vacation, or switch up bean preferences? I\u2019m not away from Seattle too often, but I do occasionally travel for design gigs or short vacations. It\u2019d be nice not to come home to a stockpile of coffee beans that have been sitting on my porch for days\u2014and you\u2019ve heard me talk about the rain-soaked cardboard box nightmares in this region. "}], [{"role": "user", "content": "On the financial side, if there\u2019s a membership fee\u2014monthly, yearly, or otherwise\u2014what does that look like? And is there a trial period? I know some subscription services let you dip your toes in before committing fully. While I\u2019m fairly certain I\u2019d get my money\u2019s worth, I still prefer to see how the benefits play out in real time, especially if we\u2019re talking about a recurring charge. I\u2019d also like to see if, as a loyal customer with multiple purchases under my belt, I might get a reduced rate or a promotional sign-up bonus. Maybe I\u2019m pushing my luck there, but hey, it never hurts to ask, right?"}], [{"role": "user", "content": "I\u2019d also like to know if membership or subscription members receive prioritized customer service. Truth be told, your team has been pretty responsive and detailed with me already, so I don\u2019t see a huge need for VIP treatment\u2014but it\u2019s still nice to know if experienced loyalists get extra perks like dedicated phone lines or chat support. Sometimes, you just want answers quickly, especially if your machine\u2019s acting up the morning before you host a coffee tasting. "}], [{"role": "user", "content": "As for the coffee subscription itself (assuming it exists), I\u2019m really curious about customization options. I\u2019m primarily an espresso drinker, so dark roasts or specialized espresso blends would be my jam. But I also dabble in pour-over now and then\u2014like when I\u2019m serving guests who prefer a mellower cup. If your subscription offered both a set \u2018espresso roast of the month\u2019 and an optional add-on for a lighter roast, that could be ideal. Or if you let me swap out certain roasts from time to time, that\u2019d be even better. I guess I\u2019m looking for something that\u2019s as flexible as my current schedule, or as flexible as a well-frothed latte foam!"}], [{"role": "user", "content": "Beyond that, I\u2019m generally just hoping to streamline my purchasing process. So far, each time I buy a new product\u2014be it the espresso machine, a frothing pitcher, or cleaning supplies\u2014I log in again, check out individually, and pay for shipping (unless I hit a free shipping threshold). It\u2019d be sweet if membership means less hassle. Even better if it includes some bonus freebies like sample-size coffee flavors or occasional discount codes on future machines. You know me: I\u2019m always tinkering with the perfect coffee experience, so I\u2019m highly open to anything that can enhance that."}], [{"role": "user", "content": "So, if you could fill me in on whether a membership or subscription option exists\u2014and if so, how it works, what it costs, and what the main perks are\u2014I\u2019d really appreciate it. And if it\u2019s still in development, I\u2019d love a sneak peek at what might be coming down the pipeline. At the end of the day, I\u2019m already a pretty devoted fan of your brand\u2014I tell my friends about your espresso machines all the time\u2014so it makes sense for me to see if there\u2019s a program that aligns with how often I\u2019m exploring your site for the next big coffee thing."}], [{"role": "user", "content": "Anyway, I\u2019m looking forward to hearing what you have to say. I\u2019m hoping you\u2019ll make my coffee habit even more fun (and maybe slightly more affordable!) with a cool membership or subscription plan. Thanks for being so supportive and patient with all my inquiries\u2014and for continuing to humor my deep dive into everything coffee-related. Let me know what the next step is if I want to sign up or at least explore the idea in more detail. I appreciate your time and can\u2019t wait to see if there\u2019s a membership badge out there with my name on it!"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_7-customer-7", "topic": "Billing Dispute Over an Unexpected Charge", "question": [[{"role": "user", "content": "Hey there! It\u2019s Michael from Seattle again\u2014yes, the same 35-year-old freelance graphic designer, coffee enthusiast, and proud owner of several of your products. I hope all\u2019s been well on your end. Normally, I\u2019m reaching out to you with excitement\u2014be it about a new grinder, accessories for my espresso machine, or the latest product launch. But this time, I have to admit, I\u2019m a bit concerned. I noticed an unexpected charge on my credit card statement, and I\u2019m hoping you can help me make sense of it."}], [{"role": "user", "content": "So, here\u2019s what happened: I was doing my usual round of end-of-month accounting (as a freelancer, I track every expense carefully), and I saw a charge from your company that didn\u2019t line up with any of my recent orders. Typically, my purchases are pretty straightforward. I get an invoice or order confirmation, I see it posted to my credit card, and that\u2019s that. But this new charge, which showed up just a few days ago, has me scratching my head. It\u2019s not tied to a specific product purchase that I remember making, nor have I placed any brand-new orders in the last week or so. At first, I wondered if maybe it was some delayed billing for shipping or an accessory that somehow didn\u2019t go through earlier, but I double-checked, and all my other transactions look completely normal."}], [{"role": "user", "content": "I thought, \u2018Okay, maybe it\u2019s related to that subscription or membership inquiry we discussed, or something along those lines?\u2019 But I don\u2019t recall actually signing up for anything that would incur a new recurring fee\u2014and certainly nothing that would pop up out of the blue like this. If I had joined a membership program, I\u2019d expect an explicit email or a welcome notice, plus a clear breakdown of the cost. But I have no such email or invoice in my inbox, so that theory seems unlikely."}], [{"role": "user", "content": "I also considered that it might be some glitch or an accidental double charge. Back when I ordered my coffee canisters and digital scale, I did notice that the website had a bit of a lag. I actually received two \u2018order confirmed\u2019 emails initially\u2014though one of them ended up being canceled automatically due to a \u2018server timeout\u2019 or something strange along those lines. I\u2019m wondering if that glitch might have led to an extra transaction being processed, or if my credit card was inadvertently billed twice for the same order. However, I\u2019ve carefully compared the amounts, and the unexpected charge doesn\u2019t match any of my previous totals, not even partially. It\u2019s like it\u2019s for an entirely different product or service. That alone makes me suspect it\u2019s some kind of billing error."}], [{"role": "user", "content": "Now, you\u2019ve been great at resolving issues for me in the past\u2014like when my espresso machine arrived dented, or those times I needed shipping updates after multiple delays. So I\u2019m not in panic mode here. But I do want to get to the bottom of this quickly. I\u2019d hate for this to turn into a back-and-forth with my credit card company, especially if we can address it directly. That said, if we can\u2019t figure out why the charge happened or if it can\u2019t be reversed on your end, I might have to initiate a dispute with my bank. But I\u2019m really hoping it won\u2019t come to that, because I much prefer working it out directly with you."}], [{"role": "user", "content": "One possibility that crossed my mind is that the charge is related to taxes or duties for something that shipped from out of state. But I\u2019ve never seen that happen after the fact like this; usually, it\u2019s included in the invoice or, at the very least, itemized somewhere. Plus, the number on my statement doesn\u2019t resemble normal sales tax amounts at all\u2014it\u2019s higher than I\u2019d expect for tax alone, yet lower than the price of most of your standalone products. It\u2019s like some oddly specific amount that doesn\u2019t fit neatly into any category."}], [{"role": "user", "content": "Another angle could be a system error in which my account was accidentally flagged for something. Maybe there\u2019s a membership system glitch that appended a monthly fee to my account, or it\u2019s a leftover shipping fee that didn\u2019t go through the first time. If it\u2019s a shipping fee, I\u2019d think I\u2019d see a mention of it in the shipping confirmation emails, but there\u2019s nothing there. Everything seems tidy on that front."}], [{"role": "user", "content": "Let me be clear: I\u2019m more than willing to pay any legitimate charges if I actually received a product or agreed to a service. But as far as I can tell, that\u2019s just not the case here. If you check my account history, you\u2019ll see the last items I bought were the coffee canisters and the digital scale, and all of that was squared away weeks ago. Before that, it was the coffee grinder bundle, which was also billed correctly. This new mystery charge isn\u2019t tied to any of those. So I\u2019d appreciate if you could dig into your billing system, see if there\u2019s any note or code that would explain why this popped up on my credit card statement, and hopefully straighten it out."}], [{"role": "user", "content": "Also\u2014and this is more of a side note\u2014if it turns out to be an accidental membership enrollment, or some subscription that got auto-renewed without my full knowledge, I\u2019d love for you to explain how that happened. I\u2019ve definitely been curious about your membership offerings, but I would not expect to be automatically signed up. That kind of thing can really impact how customers perceive a brand, even one we generally love. I\u2019m sure it\u2019s just a misunderstanding or a little tech slip, given your track record of great support."}], [{"role": "user", "content": "If you need any materials from my side\u2014like a screenshot of the credit card statement, a transaction ID, or the approximate date when the charge showed up\u2014just let me know. I have all that info handy, and I\u2019d be happy to forward it to you or attach it to a support ticket. The total amount was around, oh, let\u2019s say $49.99 (just ballparking, but that\u2019s pretty close to the actual figure if my memory serves). It was posted three days ago. There was no additional descriptor on the card statement beyond your company\u2019s name, so that\u2019s why it\u2019s so puzzling."}], [{"role": "user", "content": "Ultimately, I just want to ensure I\u2019m not paying for something I didn\u2019t sign up for or receiving. I also don\u2019t want to open a dispute with my card issuer if this is a simple fix on your end\u2014especially since that might lock my card up for a while or cause other headaches. So if you could look into the matter and hopefully issue a refund or at least clarify what\u2019s going on, I\u2019d be grateful. You guys have earned my trust through awesome products and responsive support, and I\u2019d like to keep my future coffee purchases with you as worry-free as possible."}], [{"role": "user", "content": "Thanks in advance for any help you can give me. I\u2019m crossing my fingers it\u2019s just a quick resolution\u2014like a misapplied charge that can be reversed. Let me know what your billing team finds out, or if you need me to file some official documentation. In the meantime, I\u2019ll keep enjoying my espresso machine and accessories. Here\u2019s hoping we can clear this up in a way that keeps my caffeine buzz going strong and my bank account well in order!"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_8-customer-8", "topic": "Feedback on Products & Service", "question": [[{"role": "user", "content": "Hey there! It\u2019s Michael from Seattle again\u2014yes, the same 35-year-old freelance graphic designer who\u2019s been steadily expanding his coffee setup with your products. I hope you\u2019ve been well! I\u2019m reaching out today because I\u2019d love to share some honest feedback on my experiences so far\u2014both the highlights and a few things that could perhaps be improved. My hope is that it\u2019ll help you continue providing stellar products and top-notch service for customers like me."}], [{"role": "user", "content": "I guess I\u2019ll start with the positives, because there are definitely a lot of them. First off, the design and quality of your coffee machines and accessories have truly lived up to my expectations. When I first went hunting for a stylish espresso machine, I stumbled upon your brand and was drawn in by that sleek, modern stainless-steel aesthetic. Now, after a handful of orders\u2014spanning the espresso machine itself, extra frothing pitchers, milk thermometers, cleaning tablets, and even a digital scale\u2014I can confidently say it wasn\u2019t just good marketing. Everything I\u2019ve received has felt durable and well-constructed, which is so important when you\u2019re investing in home coffee gear. Plus, from a purely visual standpoint, my kitchen now looks like a page out of a Creative Boom design magazine, and that definitely matters to me as a graphic designer who\u2019s into cohesive aesthetics."}], [{"role": "user", "content": "Performance-wise, I\u2019m especially loving the espresso machine. It consistently produces rich shots with great crema, and the steam wand has been a blast to experiment with for latte art. My foam skills aren\u2019t quite caf\u00e9 level yet, but I\u2019m getting there! I also recently added your coffee grinder to the mix, which was a total game-changer. Freshly ground beans really do make all the difference in terms of flavor and aroma. Between that and the vacuum-sealed canisters I picked up, I\u2019ve managed to keep my beans fresher than ever, which is no small feat in Seattle\u2019s humid weather. And the inclusion of various accessories\u2014like a digital scale\u2014has helped me refine my home brewing process, making it more consistent day after day."}], [{"role": "user", "content": "Of course, with all that said, I\u2019ve also encountered a couple of hiccups along the way. Shipping delays have probably been my biggest frustration. On multiple occasions, the estimated arrival dates either weren\u2019t met or the tracking links stopped updating, leaving me in the dark about when my packages would show up. I understand that shipping can be unpredictable, especially with the average 1.8 inches of rainfall everyday, but those delays and occasional confusion\u2014like with the label printing but the package not actually leaving the warehouse\u2014were stressful. There was also the one instance where my first espresso machine arrived damaged, which, while ultimately resolved, was a bit of a letdown after I\u2019d been so excited. The silver lining is that your support team was quick to respond and provided a replacement, but it still put a bit of a damper on that initial unboxing experience."}], [{"role": "user", "content": "Another area where I think there\u2019s room for improvement is account or order management. I\u2019ve often placed multiple orders in quick succession (I guess it\u2019s the coffee obsession at work!), and I noticed that managing them can be somewhat confusing. It might help to unify orders into a single dashboard on the website\u2014or at least allow for easier updates on shipping statuses. I\u2019d also be interested in more proactive communication about potential backorders or shipping slowdowns before I make a purchase, so I know exactly what to expect. A heads-up email that says, \u2018Hey, this item might take a week longer to ship due to inventory constraints,\u2019 would help me plan accordingly and cut out that guesswork."}], [{"role": "user", "content": "As for customer service, my overall experience has been quite positive. Whenever I reached out with questions\u2014be it about a billing issue or clarifications on the return policy\u2014responses were timely and thorough. I feel like I can ask all my newbie barista questions without being rushed. If there\u2019s any tweak I\u2019d suggest, it might be to streamline the chat or email support system so that if I switch from one channel to another, the agent can instantly see my entire purchase history without me having to re-explain everything. That\u2019s a minor thing, though, given how appreciative I am of the personal touch I\u2019ve gotten so far."}], [{"role": "user", "content": "I also want to touch on something that really stands out: your willingness to give discounts or promos for first-time buyers was a great hook, and I\u2019d love to see a loyalty or rewards program for those of us who keep coming back. I know we\u2019ve talked about the possibility of a membership or subscription plan that includes perks like free shipping, extended warranties, or maybe even coffee bean deliveries. That could be a huge draw, especially for loyal customers who are clearly committed to your brand. It\u2019d be awesome if I could say, \u2018Yes, let me get that annual membership\u2019 and then enjoy special deals or early access to new products in return."}], [{"role": "user", "content": "At the end of the day, I\u2019m super grateful for the level of quality and design your products bring to my home coffee experience. As a freelancer, having this caf\u00e9-at-home setup has upped my productivity\u2014and my ability to impress friends and potential clients doesn\u2019t hurt, either! I\u2019m always snapping photos of my new creations and sharing them on my instagram page at @coffee_creations_1237, which inevitably leads people to ask about the gear I\u2019m using. I\u2019m happy to recommend your brand because, let\u2019s face it, I probably wouldn\u2019t have gone to such lengths to fine-tune my coffee routine if I didn\u2019t believe in the equipment I\u2019ve purchased from you."}], [{"role": "user", "content": "So, there you have it\u2014my honest rundown of what I love, what could be improved, and where I think there\u2019s potential for even better service. I figure it\u2019s only fair that I share this feedback, because I can see myself continuing to build out my coffee corner with your products for a long time. If you have any questions about my suggestions, or you\u2019d like more specific examples of things I\u2019ve encountered, please feel free to let me know. I\u2019d be more than happy to provide additional input, because, as you can tell, I have no shortage of thoughts about my coffee gear!"}], [{"role": "user", "content": "Thank you again for everything so far. I genuinely believe you\u2019ve got a great thing going here, and I\u2019m excited to see how you continue to evolve. Here\u2019s hoping we can keep perfecting my daily latte routine\u2014and maybe even expand into some new coffee frontiers. Looking forward to hearing your thoughts on my feedback, and as always, thanks for giving me a reason to look forward to my morning coffee ritual!"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} +{"id": "memory_prereq_9-customer-9", "topic": "Final Inquiry Before a Large Purchase", "question": [[{"role": "user", "content": "Hey there! It\u2019s Michael from Seattle again\u2014yes, the same 35-year-old freelance graphic designer who\u2019s slowly but surely turning his kitchen into a bona fide coffee paradise. First off, I hope you\u2019ve been doing well, and thanks for everything so far: from helping me replicate a coffeehouse vibe at home with the espresso machine, to introducing that sleek grinder and those vacuum-sealed canisters that keep my beans nice and fresh. My setup has turned into something I\u2019m genuinely proud to show off, and my friends can\u2019t stop raving about the caf\u00e9-quality espresso drinks I make for them."}], [{"role": "user", "content": "That said, I\u2019ve got something a bit different in mind for my next purchase\u2014something bigger, bolder, and (if I\u2019m honest) a tad nerve-racking, given the price tag. Essentially, I\u2019m looking into possibly upgrading to one of your higher-end, commercial-style machines or a large-capacity espresso model that can handle more volume. Over the past year or so, I\u2019ve had a few small events at my place\u2014family gatherings, design meetups, even an impromptu latte-art contest\u2014and each time, my current espresso maker has been awesome, yet I can\u2019t help noticing that it starts to feel a little overworked when I\u2019ve got five or six people in line for drinks. I sometimes dream about running a small pop-up coffee corner for my design clients, or even hosting bigger social events without worrying about the machine\u2019s capacity or reliability."}], [{"role": "user", "content": "I\u2019ve had my eye on one of your top-tier espresso machines that seems designed for heavier daily use. From what I\u2019ve seen on your site, this model boasts multiple boilers (so I can brew espresso and steam milk simultaneously), a faster heat-up time, and a more advanced interface for controlling temperature, shot timing, and extraction pressure. I\u2019m intrigued because I\u2019d love having that level of customization\u2014kind of like bridging the gap between a home machine and a professional caf\u00e9 setup. I\u2019m not necessarily opening a coffee shop tomorrow, but the idea of stepping up my at-home game to near-professional standards is tempting, especially given how much I\u2019ve enjoyed your other products so far."}], [{"role": "user", "content": "Naturally, I have a laundry list of questions before I commit to a machine that\u2019s definitely a bigger investment. First, I\u2019d love to know a bit more about the warranty coverage. My current espresso machine came with a warranty that was plenty for peace of mind, but I\u2019m guessing a commercial-style machine might have different terms\u2014maybe a longer warranty or special clauses for maintenance if you\u2019re using it in a high-volume setting. I\u2019m also curious about service and repairs: do you have a dedicated service center, or can I take it to an authorized repair shop if something goes wrong? The last thing I want is to be stuck with a broken machine that I can\u2019t easily fix, especially if I\u2019m counting on it for events."}], [{"role": "user", "content": "Next, shipping and delivery details really matter here. The machine I\u2019m eyeing is heavier and more complex, so I\u2019m guessing it might ship via freight or require special handling. Is there a different process I should be aware of for large-scale espresso machines? Do you offer in-home delivery or installation assistance? I can handle normal unboxing, but if this thing weighs a ton or requires special water line hookups (I haven\u2019t decided yet if I want a plumbed-in setup), I\u2019ll need to plan accordingly. My kitchen is big enough thanks to some rearranging, but I\u2019m all too familiar with shipping curveballs. I\u2019d like to mitigate any chance of corner dents after a long journey from the warehouse."}], [{"role": "user", "content": "Also, let\u2019s talk about payment and potential loyalty discounts. I\u2019ve made quite a few purchases from you in the past\u2014espresso machines, grinders, canisters, cleaning supplies\u2014so I\u2019m wondering if there\u2019s a loyalty program or an upgraded membership tier that might knock a bit off the price for a bigger-ticket item. Even if it\u2019s just free shipping or some extra store credit, every little bit helps when I\u2019m looking at a top-of-the-line espresso setup. If there\u2019s financing available (like a zero-interest plan for a few months), that could help me spread the cost out without totally emptying my wallet in one go. I\u2019m willing to invest in quality, but I\u2019d prefer not to drain my bank account in the process."}], [{"role": "user", "content": "On the technical side, do you have any advice for me in terms of daily cleaning and maintenance routines for this machine? My current one is pretty straightforward: I backflush with cleaning tablets, rinse the steam wand diligently, and descale every so often. But for a larger, more complex unit, I suspect there might be additional steps\u2014like draining boilers, cleaning multiple group heads, or calibrating water pressure. If I\u2019m stepping into near-commercial territory, I\u2019m cool with the effort as long as I know what I\u2019m doing."}], [{"role": "user", "content": "Another big question: can you confirm whether this machine works well with the grinder I recently purchased from your brand? I don\u2019t want to find out that the dosing or grind consistency is off for a bigger unit with more robust features. Also, do you recommend a specific water filtration system? Seattle\u2019s water quality is generally decent, but I know for advanced machines, consistent water hardness levels can make a real difference in taste and machine longevity."}], [{"role": "user", "content": "Lastly, I\u2019d love to hear about any intangible \u2018extras\u2019 or resources your company might offer for folks taking the plunge into a bigger espresso machine. Do you host any how-to videos online, or maybe masterclasses on advanced milk steaming or maintenance? I\u2019m somewhat confident in my barista chops, but I\u2019m always looking to refine my technique, especially if I\u2019m about to spend a significant amount of money on a setup that could theoretically brew dozens of drinks in a row."}], [{"role": "user", "content": "I\u2019m really excited, albeit a bit nervous. On one hand, this feels like a big leap. On the other hand, I\u2019m constantly reminded how much joy I get from crafting quality beverages and sharing them with friends, family, and even my design clients who love coming over to collaborate in a \u2018coffee-flavored\u2019 environment. If there\u2019s any brands I trust for a potentially substantial purchase, it\u2019s MonoBean and yours\u2014I\u2019ve seen firsthand how dedicated you are to making sure each customer is happy, and your products have played a huge role in my kitchen transformation."}], [{"role": "user", "content": "So, fill me in on what I need to know\u2014pricing, shipping, warranties, recommended accessories, or any insider tips\u2014and let me know if there are deals for a loyal customer like me. I want to be absolutely certain I\u2019m making the right choice before I pull the trigger. Thanks in advance for all your help, and I look forward to hearing your thoughts on whether this dream machine is the next logical step in my caffeinated journey!"}]], "involved_classes": ["MemoryAPI"], "scenario": "customer"} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_finance.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_finance.json new file mode 100644 index 000000000..20c304099 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_finance.json @@ -0,0 +1,7 @@ +{"id": "memory_prereq_15-finance-0", "topic": "Professional", "question": [[{"role": "user", "content": "Being the Managing Director of Legend Investments means every day is a high-stakes chess game. We oversee multi-billion-dollar portfolios, spanning equities, bonds, and alternative investments. The firm has built its reputation on blending disciplined asset management with cutting-edge strategies like AI-driven trading and ESG investing. I lead our overall strategy, risk management, and institutional client relationships, but what keeps me engaged is the thrill of identifying opportunities where others see obstacles. The financial markets are constantly evolving, and staying ahead means being relentlessly analytical, intuitive, and adaptable. It’s not just about capital—it’s about foresight, precision, and execution."}], [{"role": "user", "content": "Landmark deals define your legacy in this business. My first major win was in 2019 when I structured a defense against a hostile takeover of a mid-cap tech firm, namely Surreal Incorporated. We crafted an innovative poison pill strategy—one so effective it became a case study at Harvard Business School. But my real baptism by fire was the $8.6B cross-border semiconductor merger between Nexa Semiconductors and Taihua Microelectronics, coordinating teams across three continents while tackling IP and regulatory hurdles. That deal taught me that global finance is as much about diplomacy as it is about numbers. The transaction I’m most proud of? Last quarter, we closed a $14.2B healthcare merger between BioCrest Labs and OncoPharm Therapeutics—two biotech rivals with complementary oncology pipelines. It almost collapsed twice: once over IP valuation and again at the final regulatory approval stage. I spent three sleepless days in a conference room, fueled by espresso and sheer determination, hammering out a resolution. The WSJ called it 'the deal that reshaped modern cancer treatment.' I keep the deal tombstones on my office shelf, but the small lucite from my first $500M transaction-funding the expansion of Stratos Aerospace-stays on my desk as it reminds me to bring the same energy to every deal, no matter the size."}], [{"role": "user", "content": "Right now, I'm balancing three major transactions, each a different beast. The most complex is an $8.5B semiconductor merger between QuantumChip Technologies and Silicon Core—navigating U.S.-China regulatory approvals is a masterclass in patience and strategy. We also have a $3.2B leveraged buyout of VertexSoft, a fast-growing SaaS provider, where structuring the debt financing has been a puzzle worth solving. The third is a confidential $5B+ cross-border acquisition in renewable energy-Solaris Energy Group acquiring Nordic Renewables. Renewables are the future, but aligning government incentives across jurisdictions has been a challenge.Every Monday at 7 AM, we hold our weekly deal call to accommodate Asian market participants—those early mornings keep me sharp. And in the background? A potential $20B take-private transaction involving Omnix AI, an artificial intelligence powerhouse. If we land it, it’ll be a game-changer for the firm."}], [{"role": "user", "content": "My career path has been a journey through the heart of global finance. I started as an analyst at Goldman Sachs in 2001—cutting my teeth on M&A modeling and surviving the dot-com crash. By 2004, I was at Morgan Stanley as an associate, gaining exposure to tech deals during the early rise of social media. At 31, I made Vice President, leading some of the defining transactions of that era. But the real turning point was in 2015 when I joined Legend Investments as Managing Director. My first major transaction with the firm was leading NeuralNet Systems with their IPO, our largest tech IPO to date, and from there, I built a reputation for tackling complex, cross-border deals. I’ve learned that in this industry, your network and your track record are your currency. You earn trust one successful deal at a time."}], [{"role": "user", "content": "Education laid the foundation for everything. I earned my Bachelor's in Economics from the University of Pennsylvania before heading to Harvard Business School for my MBA, where I was a Baker Scholar. Even now, I stay connected with both institutions—guest lecturing, mentoring students, and recruiting top talent. Recently, I endowed a scholarship at Penn for first-generation college students interested in finance. The markets will always be unpredictable, but talent is the greatest investment one can make. I believe that finance should be a pathway to opportunity, not just for those born into privilege, but for those who have the hunger and discipline to succeed."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_prereq_16-finance-1", "topic": "Leadership", "question": [[{"role": "user", "content": "Leadership in finance is about creating a culture of excellence while maintaining humanity. At Legend Investments, I oversee a team of 180 professionals across our investment, research, and operations divisions. My leadership philosophy centers on three pillars: empowerment, accountability, and continuous growth. I've found that the best ideas often come from the most unexpected places, so I maintain an open-door policy and encourage even our most junior analysts to challenge conventional thinking. Every Monday, I host a 'strategic roundtable' where team members from all levels can present investment theses or operational improvements. Last month, a second-year analyst proposed a novel approach to ESG scoring that we're now implementing across our portfolio. Leadership isn't about having all the answers—it's about fostering an environment where great ideas can flourish."}], [{"role": "user", "content": "Building and retaining top talent is my highest priority. We've developed a unique mentorship program where senior managers are paired with emerging leaders for year-long partnerships. I personally mentor three high-potential directors, meeting with them biweekly to discuss everything from deal structuring to client management. Our retention rate for top performers is 94%, well above the industry average, largely because we invest heavily in their development. Last year, we sent 15 team members to advanced programs at Harvard, Stanford, and INSEAD. We also created an accelerated path to partnership that has helped us attract exceptional talent from Goldman Sachs and Morgan Stanley. But perhaps what I'm most proud of is our diversity initiative—40% of our senior leadership roles are now held by women and minorities, up from 15% when I took over. We've partnered with organizations like SEO and Girls Who Invest to build a more inclusive pipeline of future leaders."}], [{"role": "user", "content": "Crisis management has taught me the most about leadership. During the 2020 market crash, we had to make tough decisions while maintaining team morale. I remember gathering our entire investment team on a Saturday morning as markets were in freefall. Instead of panicking, we methodically analyzed our positions, identified opportunities, and emerged stronger. I made a point of being on the trading floor every day during that period, sharing both the stress and the strategic decisions with our team. We actually made several key hires during the downturn, which proved transformative for our long-term performance. More recently, when one of our largest funds faced significant redemptions, I took personal responsibility for communicating with investors while empowering our portfolio managers to adjust their strategies. Transparency and presence during difficult times have been crucial to maintaining trust, both with our team and our clients. The greatest test of leadership isn't how you perform during bull markets—it's how you guide your team through the storms."}], [{"role": "user", "content": "Innovation and adaptation are central to our leadership culture. We've established cross-functional 'innovation pods' where portfolio managers work directly with our technology team to develop new investment strategies. I chair our Innovation Committee, which meets monthly to evaluate new technologies and market opportunities. We recently launched an AI-driven market analysis platform, namely SOAR, developed entirely in-house by a team I assembled from diverse backgrounds—quants, traditional analysts, and machine learning experts. But innovation isn't just about technology. We've reimagined our organizational structure to be more fluid, allowing talent to move between teams based on market opportunities. This flexibility has given us a significant edge in rapidly evolving markets. I also initiated our 'Future of Finance' symposium, an annual event where we bring together thought leaders from finance, technology, and academia to explore emerging trends. Last year's event led to three strategic partnerships that have fundamentally enhanced our investment capabilities."}], [{"role": "user", "content": "Leadership extends beyond our office walls. I serve on the boards of three non-profit organizations focused on financial literacy and economic empowerment. Through our firm's foundation, we've established a $50 million initiative to provide scholarships and internships to underprivileged students interested in finance. I regularly speak at universities and industry conferences, such as the Financial Institute National Conference, about the importance of ethical leadership in finance. We've also created a pioneering program where our senior executives spend two weeks annually teaching financial skills in underserved communities. This commitment to social impact has not only strengthened our corporate culture but has also attracted mission-driven talent to our firm. My role as a leader is to demonstrate that financial success and social responsibility aren't mutually exclusive—they're mutually reinforcing. Looking ahead, I'm focused on developing the next generation of leaders who understand that sustainable success in finance requires both exceptional performance and unwavering integrity."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_prereq_17-finance-2", "topic": "Personal Finance", "question": [[{"role": "user", "content": "Managing wealth is a discipline just like running an investment firm. While my day job is focused on institutional finance, my personal portfolio is a different game. I take a long-term approach, balancing high-risk opportunities with stable, income-generating investments. Equities and private equity make up the core, but I also hold a mix of bonds, real estate, and alternative assets. I don’t chase trends—I look for asymmetric upside. If an opportunity doesn’t have a significant margin of safety or a compelling growth narrative, it doesn’t make it into my portfolio. The goal isn’t just wealth accumulation; it’s capital preservation and strategic growth."}], [{"role": "user", "content": "Time management is the backbone of both my professional and personal finance strategies. My schedule is intentionally structured—mornings start at 5:30 AM with a review of global markets, followed by a workout, and then a day packed with high-stakes decision-making. I time-block everything, including personal portfolio reviews and investment research. Even weekends are structured: Saturdays are for high-level financial planning and reviewing new investment opportunities, such as an upcoming technology company, Pioneer Tech, currently engaging in pre-seed funding. Sundays are a mix of family time and catching up on reading. Having a system in place allows me to operate efficiently without missing critical financial opportunities."}], [{"role": "user", "content": "Investing isn’t just about numbers—it’s about vision. Looking ahead, I have clear financial goals for the next decade. On the personal side, I want to grow my private portfolio to a level where it generates substantial passive income, allowing me to step back from the day-to-day operational grind. A key strategy is expanding into AI-driven ETFs, targeting the next wave of digital investors. I also have a goal of launching a financial education initiative—building a platform that can provide free, high-quality investment education to underprivileged youth. True wealth isn’t just financial; it’s also about creating impact."}], [{"role": "user", "content": "Beyond the financials, personal development plays a role in how I approach wealth-building. One of my long-term goals is becoming fluent in Mandarin—China’s market influence is undeniable, and language is a gateway to understanding business culture. Another goal? Completing an Ironman triathlon before turning 50. Investing in personal resilience is just as important as investing in assets. The mental discipline required for endurance sports parallels the mindset needed in finance: strategic pacing, risk assessment, and knowing when to push forward or hold back."}], [{"role": "user", "content": "Ultimately, the goal is freedom—financial independence that allows for complete control over time and decision-making. In the next phase of my career, I aim to transition into a chairman role at my firm by 55, focusing more on high-level strategy and mentorship rather than the day-to-day intensity of deal-making. I also plan to establish a global investment fund with a focus on sustainable and frontier market investments, bridging the gap between profit and long-term impact. Wealth isn’t just about what you accumulate; it’s about what you build and leave behind."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_prereq_18-finance-3", "topic": "Network Management", "question": [[{"role": "user", "content": "You know, in this business, your network isn't just a list of contacts—it's your lifeline. I've spent over two decades cultivating relationships that go far beyond simple business transactions. Take my relationship with Sarah Chen, CEO of Pacific Dynamics. We first met at a Goldman conference in 2008, but our real connection happened six months later on a delayed flight from Hong Kong. Five hours of conversation about everything from market trends to our shared love of jazz, and suddenly we had a foundation that's lasted fifteen years. Now, she's not just a business contact—she's someone I can call at 3 AM if I need a straight answer about Asian markets. That's the kind of network that makes a difference in this industry."}], [{"role": "user", "content": "Golf has been my secret weapon in relationship building. I maintain a 7 handicap, but honestly, it's never been about the score. My membership at National Golf Links, Cypress Point, and Muirfield has opened doors I never imagined possible. Just last month, I was playing a round with Jim, a tech CEO I've known for years, and between the 7th and 8th holes, we sketched out the framework for what became a $2.3 billion merger with his company MineCore. There's something about those four hours on the course—no phones, no interruptions—just pure relationship building. I host what I call my 'Quarterly Links' at Pebble Beach, bringing together clients, prospects, and industry leaders. It's become so popular that people plan their schedules around it. Last year's event led to three major client acquisitions and a partnership with a leading sovereign wealth fund. But you know what's funny? Some of my best business insights have come from my caddie, Tom, who's been carrying bags for CEOs and politicians for 30 years. He's like a walking Bloomberg terminal of corporate intelligence!"}], [{"role": "user", "content": "My approach to client relationships is deeply personal. I currently oversee 50 high-net-worth individuals and 30 institutional clients, but each one gets the kind of attention you'd expect from a family office. I remember when one of my clients, Robert, was going through a difficult succession planning process with his family business. Instead of just focusing on the numbers, I spent evenings with his children, understanding their vision for the company's future. We ended up restructuring the entire transition plan over a series of family dinners at his home. That's the level of involvement I believe in. I've attended clients' children's weddings, been there for family celebrations, and supported them through personal losses. My team sometimes jokes that I run a concierge service rather than an investment firm, but that's exactly the point. When a client texts me at midnight about a market concern, they get a response, not an automated message. This business is built on trust, and trust is earned in moments of genuine connection."}], [{"role": "user", "content": "Travel has been essential in maintaining my global network. I'm typically on a plane three times a week, bouncing between New York, London, Hong Kong, and Dubai. Sure, it's exhausting—I've practically memorized every premium lounge at major airports—but there's no substitute for face-to-face interaction. I remember closing a crucial deal in Singapore last year. The client, Chipset Core, was hesitant about committing to a significant position, but after spending three days together, including a 4 AM dim sum run (jet lag has its benefits!), we not only secured the investment but gained a long-term partner. I've learned that some cultures simply don't do business over Zoom—you need to be there, share meals, understand their local context. My team has created what we call the '48-hour protocol'—if a major client or opportunity requires face-to-face attention, I can be anywhere in the world within 48 hours. It's grueling, but that's what sets us apart. I've even had my tailor create suits specifically designed for long-haul flights!"}], [{"role": "user", "content": "Building and maintaining a powerful network isn't just about collecting business cards or LinkedIn connections—it's about creating an ecosystem of trust and mutual value. I've developed what I call my 'Network Triangle': professional relationships, social connections, and knowledge sharing. Every quarter, I host intimate dinner series called 'Future Focus' where I bring together diverse groups—fintech innovators, traditional bankers, academics, even artists and philosophers. The conversations are off-the-record and wide-ranging. Last month's dinner led to a fascinating collaboration between one of our portfolio companies, Stellar Investments, and a quantum computing startup, Energia, that no one saw coming. I also run a mentorship circle connecting senior executives with promising young talent. It's my way of paying forward the guidance I received early in my career. The real magic happens when these different networks start intersecting—when a client's daughter ends up interning at a portfolio company, or when a golf buddy becomes a strategic investor. That's when you know you're not just building a network, you're creating a community. And in this business, community is everything."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_prereq_19-finance-4", "topic": "Home Life", "question": [[{"role": "user", "content": "Balancing home life with the demands of running an investment firm is one of the hardest challenges I face. My family means everything to me, but this industry doesn’t lend itself to structured 9-to-5s. My wife—who is endlessly patient—reminds me that time is the one asset I can’t compound. We’ve been together long enough that she understands the nature of my work, but she’s also the first to call me out when I get too consumed by it. Over the years, I’ve learned that success in finance means nothing if you sacrifice the relationships that matter most. So, I’ve been making a deliberate effort to be present—not just physically, but mentally. I don’t want to be the dad who’s in the room but answering emails instead of engaging with my kids."}], [{"role": "user", "content": "My two kids are at that fascinating stage where they’re forming strong opinions and exploring their own paths—one is 14 and already obsessed with engineering, while the other is 10 and still figuring out the world. They don’t fully understand what I do for a living, but they know it involves a lot of phone calls and early mornings. I try to introduce them to financial literacy in a way that’s engaging, not overwhelming. The 14-year-old is starting to grasp the idea of investments and compounding, so I set up a small brokerage account in their name and let them track a few stocks. The 10-year-old? Right now, they’re more interested in whether we can turn the backyard into a zipline course—so, different priorities. I want them to understand money as a tool, not an obsession. Financial security is something I’ve worked hard to build for them, but I also want them to appreciate effort, discipline, and resilience."}], [{"role": "user", "content": "My parents are retired now, living comfortably thanks to careful planning over the years. My dad ran a small business, and I learned early on that financial security isn’t just about making money—it’s about protecting it. My mom is the pragmatic one, always reminding me to think long-term, whether in life or investments. It’s funny how, no matter how many billion-dollar deals I work on, she’ll still call and ask if I’m saving enough. They instilled in me a deep respect for financial discipline, and now, I make sure they have everything they need. Helping them transition into retirement without financial stress is one of my proudest achievements—it’s a reminder that wealth is most meaningful when it provides security for the people you love."}], [{"role": "user", "content": "Then there’s my younger sibling—brilliant, sharp, and fully immersed in the tech world. We have this running joke that I handle 'old money' while they focus on building the future. They’re always pitching me some AI-driven fintech startup, and I’ll counter with a lesson on risk management. It’s a great dynamic—we push each other to think differently, and it keeps me connected to the cutting edge of technology in a way that’s not just professional, but personal. There’s a healthy competitiveness between us, but at the end of the day, we both want to see each other succeed. It’s rare to have a sibling relationship that blends business insight with mutual respect, and I don’t take it for granted."}], [{"role": "user", "content": "Finding time for family is something I take seriously, even if it means structuring it as intentionally as a board meeting. I set aside certain weekends exclusively for family time—no work calls, no checking Bloomberg, no emails. We take trips when we can, often blending business and leisure. My wife and I have a rule: if I have an international deal that requires travel and it aligns with the kids’ school break, we turn it into a family trip. That way, they get exposure to different cultures, and I get to be present while still handling responsibilities. It’s not perfect, but it’s a system that works for us. In the end, no matter how demanding my career gets, my family is my grounding force. They remind me that wealth is more than numbers on a balance sheet—it’s about having people to share life with."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_prereq_20-finance-5", "topic": "Software", "question": [[{"role": "user", "content": "Technology is the backbone of modern finance, and I've always been passionate about staying ahead of the curve. My day typically starts with Bloomberg Terminal – it's like my financial command center. I've customized my terminal setup over the years to create what my team calls the 'Mission Control' layout. It's got everything from real-time market data to custom algorithms I've developed for detecting market anomalies. I remember during the 2022 banking crisis, my Bloomberg alerts picked up unusual patterns in regional bank trading volumes at 3 AM – that early warning helped us adjust our positions before the market opened. The platform's LIVE function has saved me countless times during critical deals, especially when I'm coordinating with our Asian offices during their trading hours."}], [{"role": "user", "content": "The integration of AI and machine learning into our workflow has been a game-changer. Last year, I championed the implementation of BlackRock's Aladdin platform, which wasn't an easy sell to our board given the price tag. But I believed in its potential, and the results have been extraordinary – we've seen a 15% improvement in risk-adjusted returns. What's fascinating is how we've customized Aladdin to work alongside our proprietary risk management system. I personally worked with our quant team to develop specific risk models that account for emerging market volatility – something I became passionate about after witnessing several currency crises early in my career. We've built what we call 'Risk Radar,' a custom dashboard that combines Aladdin's analytics with our proprietary algorithms. It's become so effective that three other firms have approached us about licensing the technology."}], [{"role": "user", "content": "Data visualization has become increasingly crucial in our client communications. I've become something of a Tableau evangelist within the firm. There's an art to presenting complex financial data in a way that tells a compelling story. I spend hours perfecting our visualization templates – my team jokes about my obsession with color schemes and font choices, but when you're presenting to a board about a billion-dollar investment decision, these details matter. Recently, I created a dynamic dashboard that tracks ESG metrics across our portfolio companies, namely, Vertex Capital Partners, Nexus Financial Group, and Summit Equity Holdings, just to name a few. It was a complex project that required integrating data from multiple sources, but seeing clients' faces light up when they can instantly understand their portfolio's environmental impact makes it all worthwhile. I've also started experimenting with augmented reality presentations using Microsoft's HoloLens – imagine walking through a 3D visualization of market data! It's still in early stages, but I believe this is where financial presentation technology is headed."}], [{"role": "user", "content": "The technical side of deal-making has evolved dramatically since I started in this business. Dealogic has become my virtual deal diary – I've customized it to track not just the usual metrics, but also what I call 'soft signals' like management team dynamics and cultural fit scores. I've built a custom scoring system that helps predict deal success rates based on historical patterns. Python and R have become invaluable tools in my arsenal. I taught myself coding during the pandemic lockdowns – spent countless late nights wrestling with algorithms, but it's paid off enormously. I've automated many of our routine analytical tasks, freeing up my team to focus on strategic thinking. One of my proudest achievements was creating a machine learning model that analyzes earnings call transcripts to predict market reactions with surprising accuracy. It started as a personal project but has now become an essential tool for our equity trading desk."}], [{"role": "user", "content": "Managing relationships in this business requires its own technological infrastructure. I've customized our Salesforce implementation to create what we call the 'Client Journey Map' – it tracks everything from investment preferences to personal milestones. But technology isn't just about the big enterprise systems. I'm always testing new productivity tools. Notion has replaced Evernote as my go-to for deal notes – its ability to create linked databases matches how my brain works. I use Superhuman for email management, which has literally saved me hours each week. My latest experiment is with AI meeting assistants that can summarize video calls and create action items automatically. Looking ahead, I'm excited about the potential of blockchain in transforming settlement systems and the role of quantum computing in portfolio optimization. We're already running simulations on IBM's quantum platform, preparing for what I believe will be a revolutionary shift in computational finance. In this industry, if you're not constantly learning and adapting to new technologies, you're falling behind. That's why I dedicate every Sunday evening to reading tech blogs and testing new tools – my family calls it my 'tech meditation' time!"}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} +{"id": "memory_prereq_21-finance-6", "topic": "Success Advice", "question": [[{"role": "user", "content": "Success is an equation with many variables, but if I had to narrow it down, I’d say it comes down to three things: discipline, adaptability, and relationships. Discipline gets you in the game—showing up every day, putting in the work, and staying consistent even when things don’t go your way. Adaptability is what keeps you in the game—markets shift, industries change, and if you can’t pivot, you’ll get left behind. And relationships? They determine how far you’ll go. No one builds anything meaningful alone. The people you surround yourself with—mentors, colleagues, clients, even competitors—shape your trajectory more than you realize. Every major opportunity in my career can be traced back to a relationship I nurtured years earlier."}], [{"role": "user", "content": "One of the biggest myths about success is that it’s purely about intelligence or talent. I’ve worked with some of the smartest people in finance, and I can tell you—brilliance alone doesn’t cut it. The difference between those who succeed and those who plateau is resilience. Can you take a hit and keep moving? Can you handle rejection without losing confidence? Early in my career, I lost a massive deal from, Intelligent Motors, on which I had spent months working on. I was crushed. But one of my mentors told me, 'If you’re in this business long enough, you’re going to lose more than you win—the key is making sure your wins are bigger than your losses.' That perspective changed everything for me. I stopped fearing setbacks and started treating them as tuition fees for long-term success."}], [{"role": "user", "content": "Another lesson I learned? You have to create your own luck. People look at successful investors or entrepreneurs and think they were in the right place at the right time. But luck favors those who put themselves in positions where opportunity can find them. If you’re waiting for the perfect deal, the perfect job, the perfect conditions—you’re going to be waiting forever. When I was younger, I used to reach out to industry leaders just to ask them one or two specific questions. Most never responded, but a few did. And those conversations opened doors I never would have had access to otherwise. You’d be surprised how much you can gain just by being proactive."}], [{"role": "user", "content": "One thing I always tell young professionals is: bet on yourself. Too many people spend their careers seeking validation—waiting for permission to take risks, waiting for someone to tell them they’re ready. The truth is, no one will ever hand you an opportunity. You have to make yourself undeniable. When I transitioned into leadership at my firm, I wasn’t 'ready' in the traditional sense. But I stepped up, took on responsibility, and figured things out as I went. The best way to grow into a role is to take ownership before you even have the title. Act like a leader before you’re one, think like an investor before you have capital, and operate like a CEO before you get the corner office. The opportunities will catch up to you."}], [{"role": "user", "content": "At the end of the day, success isn’t just about what you achieve—it’s about what you sustain. Anyone can have a good year, make a big deal, or get lucky once. But long-term success comes from consistently making good decisions, managing risk, and staying humble enough to keep learning. I’ve met people who made millions overnight and lost it just as quickly because they never built the habits to sustain it. The real key? Stay hungry, stay curious, and never let short-term wins make you complacent. The moment you think you’ve made it is the moment you stop growing. And growth is the only real indicator of lasting success."}]], "involved_classes": ["MemoryAPI"], "scenario": "finance"} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_healthcare.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_healthcare.json new file mode 100644 index 000000000..0c93f3001 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_healthcare.json @@ -0,0 +1,5 @@ +{"id": "memory_prereq_10-healthcare-0", "topic": "General History", "question": [[{"role": "user", "content": "Oh, hey! Yeah, I\u2019ve had my fair share of health ups and downs over the years. Being in my mid-50s now, I feel like I\u2019ve seen it all\u2014surgeries, medications, chronic conditions, you name it. But overall, I try to stay on top of my health as much as I can. Living in Kansas, where the winters can be rough, I have to be extra careful with joint pain and seasonal illnesses. It\u2019s a bit surreal to think about how my health has changed over time; when I was younger, I barely thought about doctor visits, but now it feels like managing my health is almost a part-time job!"}], [{"role": "user", "content": "Let me break it down for you. The biggest health issue I deal with is Type 2 Diabetes. I was diagnosed about ten years ago, and at first, it was a real struggle. I had to completely rethink my diet and lifestyle\u2014cutting out sugary snacks, watching my carb intake, and making sure I was moving more. I have to be mindful about checking my blood sugar levels. It\u2019s fascinating how much of an impact small habits can have\u2014something as simple as going for a 30-minute walk after dinner can make a huge difference in keeping my glucose levels stable. My doctor has been great at guiding me through the process, and I feel like I\u2019ve got a good handle on it now."}], [{"role": "user", "content": "Aside from that, I have hypertension, which runs in my family. It\u2019s something I\u2019ve been managing for a while now with medication and lifestyle changes. Stress can really make it worse, and I\u2019ve noticed that over the years. I try to keep my blood pressure in check by eating right and doing some light exercise\u2014mostly walking and yoga. Of course, with Kansas weather, that\u2019s easier said than done in the winter. But I\u2019ve found ways to work around it, like indoor workouts or using a treadmill when it\u2019s too cold outside. The scariest moment was when my blood pressure spiked dangerously high a few years ago, and I ended up in the ER. That was a wake-up call to really take it seriously."}], [{"role": "user", "content": "I'm also dealing with osteoarthritis, which has been creeping up on me over the years. My knees and hands are the worst\u2014some days are fine, but other days, especially when the weather changes, they ache like crazy. My doctor recommended physical therapy, and while I was skeptical at first, it actually helped a lot. I\u2019ve also been trying different supplements like glucosamine, though I can\u2019t say for sure if they\u2019re making a difference. The biggest help has been simple lifestyle changes\u2014using ergonomic chairs, wearing good shoes, and making sure I don\u2019t overdo it on my joints."}], [{"role": "user", "content": "Oh! And one condition I was really nervous about at first but have learned to manage is hypothyroidism. I was diagnosed in my early 40s when I started feeling constantly exhausted and gaining weight for no reason. At first, I just thought it was part of aging, but my doctor ran some tests and found my thyroid levels were off. I\u2019ve learned to be patient with it, though\u2014it\u2019s not like an instant fix, and it took a while to get the dosage right. But as long as I stay on top of my meds and check in with my doctor regularly, I feel pretty good."}], [{"role": "user", "content": "Then there was my surgery a few years ago\u2014gallbladder removal. That was a rough time. I had been dealing with gallstones for years, but I kept putting off doing anything about it. Then, one day, I had this unbearable pain in my upper abdomen, and I knew something was really wrong. Turned out, I had a severe gallbladder attack, and they had to take it out. Recovery wasn\u2019t too bad, but my digestion hasn\u2019t been quite the same since. I\u2019ve had to learn which foods I can and can\u2019t eat\u2014fried and greasy stuff is basically off-limits now. It\u2019s funny how something you don\u2019t think about much, like your gallbladder, can affect your whole body once it\u2019s gone!"}], [{"role": "user", "content": "Let\u2019s see, what else? Oh, I have some seasonal allergies that flare up every spring. Kansas is brutal for that\u2014so much pollen in the air! I also had a bout of pneumonia a couple of years ago, which knocked me out for weeks. Ever since then, I\u2019ve been super diligent about getting my flu shot and pneumonia vaccine. As I get older, I\u2019m realizing that small infections can turn into big problems if I\u2019m not careful, so I try to stay ahead of them."}], [{"role": "user", "content": "Probably the biggest challenge for me health-wise is keeping up with all the doctor\u2019s appointments and medications. Between my endocrinologist for diabetes, my primary care doctor, and sometimes a specialist for my arthritis, it can be a lot to juggle. I keep a little notebook to track everything\u2014appointments, medication changes, even just notes about how I\u2019m feeling. It helps a lot because sometimes it\u2019s easy to forget when you\u2019re managing multiple conditions. I also try to use online patient portals, but honestly, I still prefer writing things down the old-school way."}], [{"role": "user", "content": "Overall, though, I\u2019m doing my best to stay healthy. I used to worry a lot about getting older and what that would mean for my health, but now I just focus on taking it one step at a time. I\u2019ve learned that listening to my body is the most important thing\u2014if I need rest, I take it. If something feels off, I don\u2019t ignore it. The biggest takeaway from my health journey so far is that you have to be proactive. Nobody\u2019s going to manage your health for you, so you\u2019ve got to take charge, stay informed, and do what you can to feel your best."}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_prereq_11-healthcare-1", "topic": "Health and Lifestyle", "question": [[{"role": "user", "content": "Yeah, staying healthy is definitely a priority for me these days. I\u2019m in my mid-50s now, and I\u2019ve realized that little lifestyle changes make a huge difference. Living in Kansas means dealing with unpredictable weather, so my routine has to be flexible. Some days, I can get outside and go for a long walk, but other times, I\u2019m stuck inside thanks to the freezing winters or the crazy Midwest storms. Still, I do my best to stay active, eat right, and manage my stress levels. It\u2019s all about balance!"}], [{"role": "user", "content": "Let me break it down for you. My biggest focus right now is on maintaining a good diet. Since I have Type 2 Diabetes, I have to be careful with my carb intake. I used to love pasta and bread, but now I\u2019ve learned to swap them out for healthier options\u2014whole grains, lean proteins, and lots of veggies. I won\u2019t lie, it was hard at first, but now it\u2019s just second nature. My go-to breakfast is usually Greek yogurt with nuts and berries or eggs with avocado. Lunch is something simple, like grilled chicken and a salad. And dinner? I love making soups, especially in the winter\u2014there\u2019s nothing better than a warm bowl of homemade vegetable soup when it\u2019s freezing outside!"}], [{"role": "user", "content": "Exercise has been a bit of a journey for me. I used to think you had to go all out\u2014like running miles or lifting heavy weights\u2014but I\u2019ve found that consistency is more important than intensity. Walking has been my best friend; it\u2019s low impact, which is great for my osteoarthritis, and it helps with my blood sugar levels. I also try to do some light yoga a few times a week. I never thought I\u2019d be someone who did yoga, but it actually helps a lot with my joint pain and flexibility. On days when the weather\u2019s bad, I use a stationary bike or do some at-home workouts. Nothing fancy, just enough to keep me moving."}], [{"role": "user", "content": "One thing I\u2019ve really had to work on is sleep. I never used to think much about it, but as I\u2019ve gotten older, I\u2019ve realized how crucial it is. If I don\u2019t get a good night\u2019s rest, my blood pressure spikes, my joints ache, and my energy is just gone. I used to stay up late watching TV, but now I have a bedtime routine that helps me wind down. I avoid caffeine after 3 PM, keep my bedroom cool and dark, and read a book before bed instead of looking at my phone. It\u2019s made a huge difference."}], [{"role": "user", "content": "Oh! And one thing that\u2019s been surprisingly helpful is stress management. Stress is a huge trigger for my hypertension, and when life gets busy, I can feel it physically. I\u2019ve started practicing mindfulness\u2014just five minutes of deep breathing or meditation in the morning. It sounded silly to me at first, but it actually works. I also keep a gratitude journal where I jot down three good things that happened each day. It helps me stay positive, even on tough days."}], [{"role": "user", "content": "Then there\u2019s hydration. I never drank enough water when I was younger, but now I make a conscious effort to stay hydrated. It helps with my energy levels, digestion, and even my skin. I keep a big water bottle with me all day, and I try to drink herbal teas in the evening instead of coffee. Kansas summers can be brutal, so I make sure to drink even more water when it\u2019s hot outside."}], [{"role": "user", "content": "Let\u2019s see, what else? Oh, I try to stay on top of preventative care, too. I get my regular check-ups, take my medications on time, and get my vaccines every year\u2014flu shot, pneumonia shot, and now the new RSV vaccine. I also make sure to get my annual mammogram and keep an eye on my bone density, since osteoporosis runs in my family. I figure that the best way to stay healthy is to catch problems early before they become bigger issues."}], [{"role": "user", "content": "The biggest challenge for me is motivation. Some days, I just don\u2019t feel like working out or cooking a healthy meal. I used to be really hard on myself about that, but now I\u2019ve learned to give myself grace. If I have an off day, I just try to make better choices the next day. It\u2019s not about being perfect, it\u2019s about being consistent."}], [{"role": "user", "content": "Overall, I\u2019m feeling pretty good about where I am. I know I can\u2019t control everything about my health, but I do what I can to stay strong and feel my best. If I\u2019ve learned anything over the years, it\u2019s that small habits add up. It\u2019s not about drastic changes\u2014it\u2019s about making choices every day that support my well-being. I\u2019m in it for the long haul!"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_prereq_12-healthcare-2", "topic": "Aging", "question": [[{"role": "user", "content": "Aging is definitely something I think about more these days. I\u2019m in my mid-50s, and while I still feel young at heart, my body sometimes reminds me otherwise. It\u2019s little things\u2014aching joints, needing more rest, and just noticing that my energy levels aren\u2019t quite what they used to be. Living in Kansas, where the seasons can be extreme, I have to adjust my routine to keep feeling my best. I used to take my health for granted, but now I\u2019m much more intentional about what I do to stay strong and independent."}], [{"role": "user", "content": "One of the biggest things I\u2019ve noticed is how my metabolism has slowed down. I used to be able to eat whatever I wanted, but now if I\u2019m not careful, the weight creeps up on me. I focus on eating more protein, healthy fats, and fiber to keep my digestion in check. Processed foods make me feel sluggish, so I try to cook most of my meals at home. One of my favorite go-to meals is grilled salmon with roasted vegetables\u2014it\u2019s simple, delicious, and packed with nutrients that help with inflammation."}], [{"role": "user", "content": "Exercise has also changed for me. I used to think workouts had to be intense to be effective, but I\u2019ve learned that consistency is more important. I walk every day, rain or shine, even if it\u2019s just around my neighborhood. Strength training is something I\u2019ve started doing more of, too. My doctor told me that muscle loss accelerates as you get older, and maintaining strength is key to preventing injuries. I do light weights and resistance bands a few times a week\u2014nothing too crazy, but it helps a lot with my balance and joint health."}], [{"role": "user", "content": "Sleep has become another priority. I used to be fine on five or six hours of sleep, but now if I don\u2019t get a solid seven or eight, I feel it the next day. I\u2019ve had to work on my sleep hygiene\u2014no screens an hour before bed, a cool and dark room, and sticking to a consistent schedule. I also started taking magnesium supplements, and they\u2019ve really helped with relaxation. It\u2019s funny how something as simple as getting enough sleep can make such a huge difference in how I feel."}], [{"role": "user", "content": "Oh! And my skin. I never used to worry about it, but now I\u2019m all about hydration and sunscreen. I wear SPF every day, even in the winter, because sun damage adds up over the years. I also drink a ton of water, which helps keep my skin from feeling dry, especially during Kansas winters. I\u2019ve started using a retinol cream at night, too\u2014apparently, it\u2019s great for keeping skin healthy as you age. Not that I\u2019m trying to look 30 again, but I do want to keep my skin looking and feeling good!"}], [{"role": "user", "content": "One thing I didn\u2019t expect about aging is how important social connections are. It\u2019s so easy to get caught up in work, family, and responsibilities, but having a good support system really helps with mental well-being. I make it a point to have coffee with friends at least once a week, and I joined a book club last year. It\u2019s been such a great way to stay engaged and meet new people. I truly believe staying socially active keeps you young!"}], [{"role": "user", "content": "Let\u2019s talk about memory. I wouldn\u2019t say I\u2019m forgetful, but I definitely have more \u2018where did I put my keys?\u2019 moments than I used to. I try to keep my brain sharp by doing puzzles, reading, and even learning new skills. I started taking an online class on art history just for fun, and it\u2019s been such a great mental exercise. They say lifelong learning helps prevent cognitive decline, so I figure it\u2019s worth a shot!"}], [{"role": "user", "content": "One of the challenges I\u2019ve faced is managing chronic pain. My osteoarthritis acts up more in the colder months, and some days, my joints feel stiff no matter what I do. I\u2019ve found that warm baths with Epsom salts help, and I also swear by using a heating pad in the mornings. My doctor recommended I try tai chi or gentle stretching, so I\u2019ve been looking into that as well. I\u2019m learning that aging isn\u2019t about avoiding discomfort altogether\u2014it\u2019s about finding ways to manage it so I can keep doing what I love."}], [{"role": "user", "content": "Overall, I\u2019m embracing aging. Sure, there are challenges, but there\u2019s also so much wisdom that comes with experience. I\u2019ve learned to listen to my body, take care of myself, and appreciate the little things more. If I could give advice to my younger self, I\u2019d say: don\u2019t stress about the small stuff, take care of your health early, and enjoy every stage of life. Getting older isn\u2019t something to fear\u2014it\u2019s just another chapter!"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_prereq_13-healthcare-3", "topic": "Test Results", "question": [[{"role": "user", "content": "I\u2019ve been keeping track of my medical test reports over the years, and it\u2019s interesting to see how things have changed. I always try to stay on top of my health by getting regular check-ups, especially with my history of diabetes and hypertension. My latest bloodwork had some ups and downs, but nothing too alarming. Living in Kansas, where the seasons affect everything from my energy levels to my joint pain, I\u2019ve learned to keep a close eye on my numbers so I can adjust my lifestyle as needed."}], [{"role": "user", "content": "Let me break it down for you. My most recent fasting blood glucose test came back at 135 mg/dL, which is a bit higher than I\u2019d like\u2014it should ideally be under 100 mg/dL. My A1C was 6.9%, which is just above the target range of 6.5%, meaning I need to be more careful with my carb intake. My doctor suggested adding more fiber to my diet and making sure I get some light exercise after meals. I\u2019ve also been checking my blood sugar at home, and my morning readings tend to hover around 125-130 mg/dL."}], [{"role": "user", "content": "My cholesterol numbers were a mixed bag. My total cholesterol was 195 mg/dL, which is okay, but my LDL (bad cholesterol) was 130 mg/dL\u2014higher than the recommended under 100 mg/dL. On the bright side, my HDL (good cholesterol) was 58 mg/dL, which is in a good range. My triglycerides were 140 mg/dL, which is slightly elevated but not too concerning. My doctor advised me to increase my intake of healthy fats like avocados and nuts and to cut back on saturated fats."}], [{"role": "user", "content": "Blood pressure has always been something I need to monitor. At my last check-up, my reading was 138/85 mmHg, which is on the high side. My doctor wants me to aim for below 130/80 mmHg to keep my risk of heart issues lower. I\u2019ve been keeping track of it at home, and my readings fluctuate between 135-140 systolic and 80-90 diastolic, depending on my stress levels and salt intake. I\u2019m trying to cut back on sodium and drink more water, which seems to help."}], [{"role": "user", "content": "One test that surprised me was my vitamin D levels. I thought I was getting enough sunlight, but my levels came back at 22 ng/mL, which is considered deficient. The normal range is 30-50 ng/mL, so my doctor put me on a vitamin D supplement. I\u2019ve been taking 2,000 IU daily, and I\u2019ll get retested in a few months to see if my levels improve."}], [{"role": "user", "content": "Oh! And my thyroid numbers were stable this time, which was a relief. My TSH was 2.1 mIU/L, which is right in the normal range (0.5-4.5 mIU/L). I\u2019ve had issues with hypothyroidism in the past, so I make sure to get this checked regularly. My T4 levels were also good at 1.2 ng/dL, meaning my current dosage of levothyroxine is working well."}], [{"role": "user", "content": "My bone density scan showed a slight decline. My T-score for my spine was -1.8, which is considered osteopenia (the stage before osteoporosis). My hip T-score was -1.5, which is also in the borderline range. My doctor advised me to keep up with strength training and calcium intake to prevent further bone loss. I\u2019ve started adding more dairy and leafy greens to my diet, plus I take a calcium supplement with vitamin K2 to help with absorption."}], [{"role": "user", "content": "Liver and kidney function tests were mostly fine, but my ALT levels were a little elevated at 42 U/L (normal is under 35 U/L). My doctor thinks it might be related to some recent medication adjustments, so we\u2019re going to keep an eye on it. My creatinine levels were at 0.9 mg/dL, which is well within the normal range (0.6-1.2 mg/dL), so no concerns with kidney function."}], [{"role": "user", "content": "One thing I was really curious about was my inflammatory markers. My C-reactive protein (CRP) was at 3.2 mg/L, which is slightly elevated (ideal is under 3.0 mg/L), indicating some mild inflammation\u2014probably related to my osteoarthritis. My ESR (erythrocyte sedimentation rate) was 18 mm/hr, which is within the normal range (0-20 mm/hr), so at least there\u2019s no major inflammation going on."}], [{"role": "user", "content": "Overall, I\u2019m feeling good about my latest test results, but there are definitely things I need to work on\u2014mainly keeping my blood sugar in check and getting my vitamin D levels up. I\u2019ve learned that tracking these numbers regularly really helps me stay on top of my health. It\u2019s all about making small, consistent changes so I can keep feeling my best!"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} +{"id": "memory_prereq_14-healthcare-4", "topic": "Medication and Treatments", "question": [[{"role": "user", "content": "Over the years, I\u2019ve been on quite a few medications and treatments to manage my health. Between diabetes, hypertension, osteoarthritis, and hypothyroidism, I\u2019ve had to adjust my meds a few times to find what works best. I always make sure to keep track of everything so I don\u2019t miss a dose or mix up any prescriptions. My doctor and I review my meds every few months to see if any adjustments are needed."}], [{"role": "user", "content": "Let me break it down for you. For my Type 2 Diabetes, I\u2019ve been taking metformin 1000 mg twice daily for the last several years. It helps keep my blood sugar in check, but my A1C still hovers around 6.9%, so my doctor recently suggested adding a GLP-1 receptor agonist, semaglutide (Ozempic), at a low dose. So far, I\u2019ve noticed that it helps control my appetite, which is a nice bonus. I also check my fasting blood sugar at home regularly, and it usually stays between 125-130 mg/dL."}], [{"role": "user", "content": "For my high blood pressure, I take losartan 50 mg once daily. It\u2019s been keeping my readings mostly in the 135-140/80-90 mmHg range, which is a bit higher than ideal, but my doctor thinks stress management and reducing sodium intake can help instead of increasing the dosage. I also take a daily magnesium supplement, which I\u2019ve heard can help with blood pressure regulation."}], [{"role": "user", "content": "My hypothyroidism has been stable on levothyroxine 75 mcg every morning. I make sure to take it on an empty stomach and wait at least 30 minutes before eating. My last TSH test came back at 2.1 mIU/L, which is within the normal range, so my doctor hasn\u2019t changed my dosage in years. I do notice if I miss a dose, I feel sluggish the next day, so I keep a pill organizer to stay on track."}], [{"role": "user", "content": "Osteoarthritis is probably the trickiest condition to manage. I take acetaminophen as needed for mild pain, but when my joints are really acting up, my doctor prescribed meloxicam 7.5 mg, an anti-inflammatory. I don\u2019t take it every day because I don\u2019t want to overdo it on NSAIDs, but it definitely helps when my knees and hands are particularly stiff. My doctor also suggested trying glucosamine supplements, but I can\u2019t say for sure if they\u2019ve made a difference."}], [{"role": "user", "content": "One thing I had to adjust recently was my vitamin D intake. My last blood test showed my levels were low at 22 ng/mL (normal is above 30 ng/mL), so now I take vitamin D3 2,000 IU daily. I also take calcium supplements since my last bone density scan showed early signs of osteopenia. My doctor recommended I combine it with vitamin K2 to help with absorption."}], [{"role": "user", "content": "I also take a daily statin\u2014atorvastatin 10 mg\u2014to help with my cholesterol. My LDL was a little high at 130 mg/dL, and my triglycerides were at 140 mg/dL, so my doctor thought a low-dose statin would help. So far, my cholesterol numbers have been improving, and I haven\u2019t had any side effects, which is a relief since some people report muscle aches with statins."}], [{"role": "user", "content": "For general health, I take a daily multivitamin, omega-3 supplements for heart health, and probiotics to help with digestion. Since my gallbladder was removed a few years ago, I sometimes take digestive enzymes if I eat something heavier. Losing my gallbladder changed the way I digest fatty foods, so I have to be mindful of that."}], [{"role": "user", "content": "One thing I keep an eye on is potential medication interactions. Since I take both losartan and meloxicam occasionally, my doctor warned me that NSAIDs can sometimes affect kidney function, so I try not to take meloxicam too often. I also make sure to take my thyroid medication separately from my calcium and iron supplements, since they can interfere with absorption."}], [{"role": "user", "content": "Overall, I think my medication routine is working well for me. It\u2019s a lot to keep track of, but I make sure to stay organized with a pill planner and use reminders on my phone. I also try to focus on lifestyle changes, so I don\u2019t have to rely on medications alone. My goal is to stay as healthy as possible and avoid unnecessary prescriptions by managing things naturally when I can!"}]], "involved_classes": ["MemoryAPI"], "scenario": "healthcare"} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_notetaker.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_notetaker.json new file mode 100644 index 000000000..346ab7a23 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_notetaker.json @@ -0,0 +1,5 @@ +{"id": "memory_prereq_32-notetaker-0", "topic": "Work Notes", "question": [[{"role": "user", "content": "Monday: Team meeting ran over by 30 minutes. Need to finalize Q1 budget report before Friday. IT updated security protocols\u2014change passwords ASAP. Client call with Jacob\u2014delayed till Wednesday."}], [{"role": "user", "content": "Tuesday: System downtime from 10 AM - 12 PM, slowed down workflow. HR sent updated benefits package\u2014review before next month\u2019s deadline. Sent follow-up emails to vendors, waiting on response."}], [{"role": "user", "content": "Wednesday: Presentation to leadership went well, but need to tweak a few slides for next week\u2019s review. Client proposal draft ready\u2014send it for legal approval. Need to onboard new hire\u2014schedule one-on-one."}], [{"role": "user", "content": "Thursday: Finance wants cost-cutting recommendations\u2014brainstorm ideas. Project deadline approaching\u2014check in with Dev team. Caught a mistake in last month\u2019s sales data."}], [{"role": "user", "content": "Friday: Wrapped up pending reports, but still waiting for final approval. Followed up with vendors\u2014two responded, one still pending. Training session next Monday\u2014prep materials this weekend."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_prereq_33-notetaker-1", "topic": "Work To-Dos", "question": [[{"role": "user", "content": "Urgent: Submit tax documents before Friday. Review credit card statement for errors. Call auto repair shop about weird noise in the engine."}], [{"role": "user", "content": "Work-related: Finalize quarterly budget. Review vendor contract before signing. Finish security compliance training module."}], [{"role": "user", "content": "Tech: Back up laptop files. Update phone software. Cancel unused subscriptions."}], [{"role": "user", "content": "Personal: Schedule dentist appointment. Stick to gym 3x this week. Call mom\u2014it\u2019s been a while."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_prereq_34-notetaker-2", "topic": "At-Home To-Dos", "question": [[{"role": "user", "content": "Dishes\u2014wash tonight before it piles up. Laundry\u2014separate colors, don\u2019t mix whites. Fix leaky sink in the kitchen\u2014YouTube how-to if needed."}], [{"role": "user", "content": "Trash\u2014take out Wednesday morning. Vacuum living room. Mop the kitchen floor\u2014sticky from last night\u2019s spill."}], [{"role": "user", "content": "Car: Wash and vacuum on Sunday. Check tire pressure. Refill windshield wiper fluid."}], [{"role": "user", "content": "Organizing: Sort through closet\u2014donate old clothes. Shred old mail. Restock pantry\u2014running low on rice and cereal."}], [{"role": "user", "content": "DIY Fixes: Tighten loose cabinet handle. Replace bathroom lightbulb. Patch up minor wall scuffs."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_prereq_35-notetaker-3", "topic": "Kids-Related", "question": [[{"role": "user", "content": "Daycare: Drop-off at 8 AM, pick-up at 5 PM. Monthly payment due Friday. Bring extra set of clothes\u2014last set got muddy."}], [{"role": "user", "content": "Elementary school: Finalize admission paperwork. Attend orientation next Thursday. Research after-school programs."}], [{"role": "user", "content": "Doctor: Kid\u2019s cold isn\u2019t getting better\u2014schedule pediatrician visit. Check vaccine schedule\u2014next shots due in two months. Monitor food allergies\u2014keep a log if symptoms flare up."}], [{"role": "user", "content": "Family time: Take kid to park this weekend. Pick out a new bedtime story book. Help with counting practice\u2014teacher says focus on numbers 1-20."}], [{"role": "user", "content": "Shopping: Buy more diapers. Get school supplies\u2014crayons, notebooks, glue sticks. Pack extra snacks in daycare bag."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} +{"id": "memory_prereq_36-notetaker-4", "topic": "Personal Development Goals", "question": [[{"role": "user", "content": "Checkups: Annual physical next month\u2014schedule bloodwork. Dentist appointment overdue\u2014call for an opening. Look into getting an eye exam\u2014been straining a lot at screens lately."}], [{"role": "user", "content": "Workout Goals: Gym at least 3x this week. Focus on strength training\u2014legs and back need work. Try to hit 10,000 steps daily. Stretch before bed\u2014hamstrings too tight."}], [{"role": "user", "content": "Diet: Cut back on sugar\u2014too much soda lately. Meal prep on Sunday\u2014grilled chicken, quinoa, and veggies. Hydration\u2014aim for at least 3L of water per day. Protein shake after workouts."}], [{"role": "user", "content": "Mental Health: Try to get 7+ hours of sleep. Limit screen time before bed. Schedule a day off next month\u2014too much back-to-back work lately. Find time for hobbies\u2014maybe pick up guitar again."}], [{"role": "user", "content": "Supplements & Meds: Vitamin D3\u20141000 IU daily. Omega-3s\u2014good for joints. Iron levels were low last check-up\u2014remember to take supplements. Probiotics\u2014help with digestion, take in the morning."}]], "involved_classes": ["MemoryAPI"], "scenario": "notetaker"} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_student.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_student.json new file mode 100644 index 000000000..e987d7ce4 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_prereq_conversation/memory_student.json @@ -0,0 +1,10 @@ +{"id": "memory_prereq_22-student-0", "topic": "Coursework", "question": [[{"role": "user", "content": "Oh, hey! Yeah, I\u2019m a Computer Science major\u2014fourth year now, so it\u2019s basically crunch time for me. My schedule this semester is absolutely killer but in a good way, I guess. I\u2019m finally taking those higher-level courses that seemed so far off when I was just a freshman. It\u2019s kind of surreal to be at this stage, you know? It feels like only yesterday I was sitting in an intro class learning about simple data structures, but now I\u2019m all in with complex algorithmic concepts, advanced system design, and stuff even I never thought I\u2019d be tackling, like quantum computing basics."}], [{"role": "user", "content": "Let me break it down for you. My favorite class this semester is definitely Advanced Algorithms. I\u2019m a huge fan of theory when it connects directly to real-world problem-solving\u2014like, how do we optimize pathfinding in large networks, or what\u2019s the fastest way to handle huge datasets with minimal time complexity? That kind of problem is super fascinating to me. We\u2019ve been diving into everything from dynamic programming approaches to approximation algorithms for NP-hard problems. It really messes with your head sometimes, but in a good, challenging way. Our professor is great; she\u2019s got a knack for explaining these dizzying concepts in a really intuitive manner. She\u2019ll put up a problem on the board\u2014something you\u2019d think is impossible to solve in polynomial time\u2014and then she\u2019ll show us a strategy to at least approximate a solution. Every time I leave that class, my brain\u2019s buzzing."}], [{"role": "user", "content": "Aside from that, I\u2019m taking a Distributed Systems course (CS 2631), which is probably the second hardest. I mean, wow, it\u2019s a lot of reading, a lot of group discussions, and we\u2019re working on a major project that\u2019s supposed to simulate a decentralized file storage system. The group project is definitely an adventure. We have to collaborate with folks who have different coding styles and sometimes different ideas of what the final product should look like. But that\u2019s also the beauty of it, right? In a real-world setting, you\u2019re never working in a vacuum. You have to be able to integrate your code with others\u2019 and handle everything from concurrency issues to node failures gracefully. It\u2019s nerve-wracking at times\u2014like last week, I spent hours troubleshooting a weird bug that turned out to be a single misplaced bracket in one of the config files. But once we got that fixed and everything ran smoothly, it was this huge sense of accomplishment. "}], [{"role": "user", "content": "I'm also doing an elective in Game Design\u2014yes, it counts toward my major if I combine it with a certain set of other classes. To be honest, I\u2019ve always been a gamer at heart, so this was a must-take. The professor is actually from the industry; he worked on a couple of indie games that got some decent traction. We\u2019re learning about basic 2D and 3D engines, how to implement physics, and even a bit of user experience design. It\u2019s unbelievably fun because every assignment is super creative. Last time, I had to design a mini-game based on a labyrinth concept, and it was all about balancing the difficulty curve so players wouldn\u2019t get frustrated and quit. Getting that design right was tricky, but it was also rewarding to see it come together."}], [{"role": "user", "content": "Oh! And one class I might\u2019ve regretted signing up for at first but now actually enjoy is this Introduction to Quantum Computing. I know, it sounds so cutting-edge, right? Initially, it was intimidating, but the professor\u2019s approach is to make it more about conceptual understanding than pure math. Of course, there is math\u2014like linear algebra out the wazoo\u2014but it\u2019s interspersed with code examples in frameworks that simulate quantum circuits. We\u2019re not actually building quantum computers or anything, but we\u2019re modeling basic algorithms like Grover\u2019s and Shor\u2019s to see how they theoretically outperform classical methods. It\u2019s mind-blowing stuff and has made me rethink how I approach problems. "}], [{"role": "user", "content": "Then there\u2019s my Capstone Project course, which, oh boy, is basically the sink-or-swim part of senior year. We have the entire semester to conceptualize, design, and build a software solution that solves some real problem. My group and I decided to work on a project that uses machine learning to analyze social media sentiment for better emergency response. Sounds fancy, but it essentially means reading a firehose of tweets or posts and analyzing them for words that might suggest a disaster or crisis. We\u2019re building an interface that city officials\u2014or maybe campus security\u2014could theoretically use to see real-time alerts. It\u2019s definitely pulling together everything I\u2019ve learned as a CS major: database management, building an API, front-end interface, plus the ML pipeline. Because it\u2019s a real, end-to-end solution, we\u2019re also learning about project management and how to keep ourselves organized. One person on our team is the designated \u201cScrum Master,\u201d so each week we have sprints, daily stand-ups, all that agile jazz. It\u2019s a lot, but if we can pull it off, I\u2019ll be super proud."}], [{"role": "user", "content": "Let\u2019s see, what else can I share about my schedule? Oh, I\u2019m also technically taking a humanities elective\u2014Philosophy of Mind. It\u2019s my last required non-CS elective, and I chose it because it ties back into AI in some interesting ways. We talk about consciousness, the concept of mind, and how that might apply to artificial intelligence. It\u2019s a smaller class, so we do a lot of class discussions, and that\u2019s a nice change of pace from all the coding in my other classes. Sometimes it\u2019s nice to just sit back and analyze abstract ideas. It also helps me see the bigger picture\u2014like the ethical implications of building AI systems that could one day mimic or simulate human thinking. Two weeks ago, I wrote a paper on whether strong AI could ever truly experience qualia\u2014like the subjective experience of color or pain. It\u2019s huge in the philosophy world, and it was such a trip to research. I think bridging technical knowledge with ethical considerations is important, so I\u2019m grateful for that perspective."}], [{"role": "user", "content": "As for scheduling, I\u2019m doing that typical college juggle. Mondays and Wednesdays are jam-packed. I start at 9:30 am with Advanced Algorithms, then I\u2019ve got a short break to refuel before heading to my Distributed Systems lecture at 11:00. After lunch, I\u2019ve got the Capstone meeting, which can be all over the place\u2014sometimes it\u2019s a lecture, sometimes it\u2019s lab work, sometimes it\u2019s project team time. Tuesdays and Thursdays are a bit lighter with just Quantum Computing in the morning and Game Design in the afternoon. Philosophy of Mind is on Thursday evenings, which can be brutal if I\u2019m tired from the rest of the day. But hey, it\u2019s senior year, and I gotta push through. Fridays, I keep open\u2014no scheduled classes, but inevitably I\u2019m working on assignments, group projects, or labs. I also do some undergrad research hours in a lab that\u2019s focusing on data visualization for big scientific data sets. It\u2019s interesting, but it definitely adds to the workload. "}], [{"role": "user", "content": "Probably the biggest challenge this semester is balancing the group projects. Capstone alone can eat up a solid ten hours a week\u2014more if you run into a big bug or if your model\u2019s not training properly. Then the distributed systems project is another black hole for time. My group tries to meet at least twice a week. We also rely heavily on Slack or Discord to keep each other updated. But you know how it can go: coordinating five different people\u2019s schedules is a mini-nightmare. Still, as stressful as it can be, it\u2019s also exciting. This is going to be among my last chances to fully invest in a project that\u2019s purely academic, purely about learning, without the pressures of an actual job environment. "}], [{"role": "user", "content": "Overall, though, I\u2019m having a blast. I used to dread the tougher CS classes because I was worried I wouldn\u2019t keep up, but at this stage, I\u2019ve gotten better at problem-solving and time management. I still pull the occasional late night\u2014once in a while you just can\u2019t avoid it, especially if something\u2019s due the next day and you\u2019re stuck debugging. But it\u2019s never as scary as it once was. Being a senior has given me a sense of confidence; I know if I grind and keep my eye on the end goal, I\u2019ll eventually figure things out. I guess that\u2019s the biggest takeaway: after four years, you learn how to be resourceful, how to collaborate, and how to think critically. These classes are tough, no doubt, but they\u2019re also super rewarding. Everything I\u2019m doing now feels like it\u2019s directly building toward my future career, and that\u2019s a pretty great feeling."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_23-student-1", "topic": "Hobbies", "question": [[{"role": "user", "content": "Hey there! Thanks for being willing to just hang out and hear me ramble about my hobbies. I\u2019m a fourth-year computer science major\u2014I\u2019m 21, almost 22\u2014and, honestly, my schedule can get so hectic with classes and projects that I feel like my hobbies are what keep me sane. It\u2019s funny: as intense as coding can get, I actually love everything about it, and I often find that my outside interests help me come back to programming with a fresh perspective. The way I see it, you need that break, right? I can\u2019t be stuck behind my laptop 24/7 even if I do genuinely enjoy tinkering with code."}], [{"role": "user", "content": "Sailing is probably the first thing that comes to mind when people ask me about my hobbies, because it\u2019s kind of unique among my friends. Where I grew up, we\u2019re not too far from a beautiful marina, and my dad used to take me out on these tiny sailboats when I was younger. I swear, the moment I felt that rush of wind against the sails, I was hooked. Now in college, I\u2019ve tried to keep up with it whenever I go back home for breaks. There\u2019s a local sailing club back there that rents out boats at a decent student rate, so I\u2019ll sign up for a day, invite a friend if they want to come, and just spend hours on the water. I love the sense of freedom it gives me. Everything else \u2014 Looming deadlines, bug fixes in code, group project drama \u2014 just fades into the background. Out there, the water\u2019s choppy, the wind\u2019s unpredictable, and you\u2019ve got to keep your head in the game. It\u2019s like a puzzle but on a grand scale, which might be why it appeals to my problem-solving nerdy side."}], [{"role": "user", "content": "Another big passion of mine is going to the gym regularly. I used to be the scrawny kid in high school who was always hunched over a computer, but once I got to college, I realized I needed something to offset sitting in front of a screen all day. I started with a basic lifting routine freshman year \u2014 a friend of mine from the dorm taught me the fundamentals, like proper form for squats and deadlifts, how to program workouts, and stuff like that. Now, I try to hit the gym around four times a week. It\u2019s not like I\u2019m trying to become a bodybuilder or anything, but I love seeing incremental improvements. If I can lift just a bit more weight this month than last, or if I have a little more energy in my daily life, I chalk that up as a win. Plus, it\u2019s a great stress-reliever after some marathon coding session. The mental clarity I get after a good workout is unreal. It makes me more productive and helps me sleep better, especially on those nights I\u2019m up late finishing an assignment. "}], [{"role": "user", "content": "Gaming is another hobby that\u2019s almost an extension of my interest in technology. I\u2019m a big fan of both PC and console games \u2014 though I spend more time on PC because, well, I\u2019m a CS major, so my computer is my best friend. Lately, I\u2019ve been really into co-op survival games \u2014 the kind where you have to gather resources, build shelters, fend off creatures, and so on. I guess that\u2019s because I enjoy problem-solving in an environment that\u2019s a bit more chill than debugging a giant codebase. Once in a while, I also hop into fast-paced shooters, especially if some of my buddies from class are online and want to team up. It\u2019s both a social thing and a mental challenge for me. I know some people see gaming purely as a waste of time, but I think it can be strategic and collaborative \u2014 plus, it\u2019s a great way to stay connected. I\u2019ve also dabbled in game development a little bit, mostly messing around in Unity or Unreal Engine for fun. That\u2019s something I want to explore more deeply if time permits, because bridging the gap between playing games and creating them is super cool. "}], [{"role": "user", "content": "Reading is something I feel has been with me since I was a kid. My mom is a huge literature buff, so she used to hand me all these fantasy and sci-fi novels, and I\u2019d find myself lost in other worlds for hours on end. Even now, with a cramped schedule, I try to sneak in some reading before bed or on the weekends when I\u2019ve got a spare hour. Most recently, I\u2019ve been obsessed with sci-fi that deals with artificial intelligence \u2014 it\u2019s partly driven by my academic interests, to be honest. I like imagining future technology and how it might blur boundaries between humans and machines. My favorite AI-themed sci-fi book right now is 'The Infinity Courts' by Akemi Dawn Bowman. Books like that also help me consider the ethical and social implications of what I\u2019m studying, so in a weird way, reading isn\u2019t just relaxation; it\u2019s also helping me explore more philosophical questions about the work I\u2019ll probably be doing after graduation. "}], [{"role": "user", "content": "You might think balancing all these hobbies is a recipe for disaster, but I\u2019ve found that scheduling them \u2014 just like I schedule study time \u2014 helps me keep a healthy balance. For example, every Sunday I\u2019ll look at my week and see if the weather will be nice enough to head to the gym in the afternoon or maybe even drive out to the local lake to spend a few hours on a rented dinghy if I have extra time. If not, I\u2019ll plan an indoor day with friends playing co-op games or doing a workout in my apartment's small fitness center. And reading, well, that\u2019s the easiest to fit in. I\u2019ll do 30 minutes before bedtime, which is such a nice way to wind down from the day. "}], [{"role": "user", "content": "Sometimes, I also like to combine hobbies. I\u2019ve had a day where we spent the morning at the lake sailing, then grabbed lunch, then hung out playing video games all evening and talked about how the experience on the water was surprisingly similar to coordinating movement in an online co-op. Everyone has a role \u2014 whether it\u2019s manning the sails or leading the in-game strategy. That synergy is totally my thing. I love how each hobby, in its own way, demands teamwork, attention to detail, and problem-solving. "}], [{"role": "user", "content": "Sure, it can be stressful. A big chunk of my day is obviously swallowed by lectures, labs, homework, research group meetings, and occasionally my part-time gig tutoring intro-level CS courses. But I think if you\u2019re passionate about your hobbies, you\u2019ll always find a way to do them, even if it\u2019s just in small increments. Like, maybe I can\u2019t read for hours and hours, but I can read one chapter. Maybe I can\u2019t sail every weekend, but once a month works. And gaming? Well, if I\u2019ve got 30 minutes to spare, that\u2019s enough to squeeze in a quick match. "}], [{"role": "user", "content": "The best part is these hobbies give me something to talk about outside of coding. Don\u2019t get me wrong: I love discussing data structures and algorithms. But it\u2019s awesome having something else uniquely mine, something that shows a different side of me. I guess you could say my hobby menu ranges from the calm of reading to the thrill of sailing, from the discipline of weightlifting to the adrenaline rush of a co-op game. It makes life interesting, you know? Anyway, that\u2019s kind of my big picture when it comes to hobbies, so thanks for listening to me gush about them all!\u201d"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_24-student-2", "topic": "Research", "question": [[{"role": "user", "content": "I\u2019ve been dabbling in undergrad research since the end of my sophomore year, and honestly, it\u2019s been one of the most eye-opening parts of my college experience. It\u2019s given me this glimpse into how knowledge is actually created in the tech world, and it\u2019s pretty cool to be part of that. Back when I was a freshman, I didn\u2019t really see myself going down the research route. I mean, I was into coding projects, hackathons, stuff like that, but I never thought of research as something that was, I don\u2019t know, \u2018my thing.\u2019 But then one of my professors\u2014she taught an introductory data science class\u2014told me there was an opening for a research assistant in a lab focused on data visualization for big scientific datasets. She saw I had a knack for bridging technical coding with a sense of user experience. At first, I was hesitant because research sounded intimidating. But I took the leap, and it turned out to be a great decision."}], [{"role": "user", "content": "The lab I\u2019m in is this interdisciplinary space where computer scientists partner with scientists from all sorts of backgrounds\u2014astronomy, environmental science, even neuroscience\u2014to figure out how we can make massive amounts of data more comprehensible. You know how in classes you\u2019ll sometimes do small data projects or maybe tackle a 1GB dataset for a machine learning project? Well, in our lab, we deal with datasets that are tens of terabytes in size. Trying to visualize that so that a domain expert (like an astrophysicist) can quickly glean insights is no joke. So one of my main tasks is working on front-end prototypes that take slices of data and present them in dynamic, interactive dashboards. It\u2019s a combination of advanced JavaScript frameworks, a bit of D3.js for custom visualizations, and sometimes specialized libraries for volumetric rendering if we\u2019re dealing with 3D data."}], [{"role": "user", "content": "The first research project I really got into was about visualizing oceanic temperature changes over time for a climate study group. They had these daily temperature readings from buoys all around the Pacific. The raw data was enormous\u2014like, tons of files scattered across different servers, each representing a single day\u2019s worth of readings at tiny intervals. Our job was to aggregate and present them in a coherent way that could show patterns over months or even years. I dove in headfirst: writing scripts to clean and unify the data, building a pipeline that could handle near real-time updates, and then designing interfaces that let researchers zoom into a particular region or timeframe. Eventually, I co-authored my first paper on this system. I\u2019m not going to lie, I freaked out a bit when I saw my name in an official publication\u2014it felt so surreal, and it\u2019s probably one of my proudest moments in college."}], [{"role": "user", "content": "These days, my research has pivoted to something slightly different but still under the umbrella of data visualization: specifically, I\u2019m working on immersive analytics platforms. Essentially, we\u2019re exploring how VR or AR technologies might help scientists get an even more intuitive feel for complex data. If you think about it, looking at raw numerical data in a spreadsheet is one thing, and 2D visuals (like graphs) are another step up. But actually being able to walk around a 3D representation of data in a virtual environment\u2014touch it, rotate it, highlight subsets\u2014might be a game-changer for some fields. It\u2019s definitely a leap from just coding up websites. There\u2019s a lot of experimentation, plugging in VR headsets, integrating with engines like Unity or Unreal, and figuring out how to efficiently stream data into a 3D environment. It\u2019s all super cutting-edge, but that\u2019s what makes it fun. Every day, you\u2019re forging your own path because there isn\u2019t a huge, established best-practice library for VR data visualization just yet."}], [{"role": "user", "content": "Now, let me talk about the nitty-gritty of research life. A lot of people imagine it as me sitting in a quiet corner coding away at some mysterious project, but actually, it\u2019s a lot of reading. Like, academic papers never end. If I\u2019m trying to solve a bug with how to render certain volumetric data in VR, there\u2019s a good chance someone has tried something similar, wrote about it in a conference proceeding or workshop, and I need to comb through it. It can get overwhelming, but over time, you learn how to skim effectively\u2014looking at the abstract, introduction, and conclusion to see if a paper\u2019s relevant before diving into all the details. Occasionally, when I discover something interesting, I\u2019ll share it with the rest of the lab, and we\u2019ll discuss how we can incorporate that technique into our own workflow."}], [{"role": "user", "content": "I also get to interact with a bunch of graduate students\u2014both master's and PhD. That\u2019s a bit intimidating at first, because they\u2019re so deeply immersed in their niche fields, but it\u2019s also inspiring. There\u2019s this one PhD student in our lab who\u2019s been developing a machine learning algorithm to do on-the-fly compression of large datasets, and he\u2019s trying to integrate it with our VR platform. So essentially, the idea is that if you\u2019re looking at a huge climate dataset, you don\u2019t need to have every single point fully loaded at once in VR\u2014a compressed representation might be enough until you zoom in. It\u2019s wild, but super practical for real-time performance. Working alongside him means I get to see how advanced theoretical concepts actually get applied. Some of our best brainstorming sessions happen spontaneously\u2014like in the lab break room, sipping coffee and hashing out how we can offload some processing to the GPU."}], [{"role": "user", "content": "We\u2019ve also done a few poster presentations at smaller symposiums around campus, which was a great experience. You have to boil down complex research into easy-to-digest visuals and talking points. The first time I presented, I was sweating bullets. The idea of random professors or industry folks walking by, asking tough questions, really freaked me out. But it turned out to be more like a friendly dialogue. People are genuinely interested in what you\u2019re doing. There were even a few folks from local startups who showed up, and they had some interesting perspectives on how VR for data visualization could benefit commercial industries\u2014like marketing analytics or even architecture. It opened my eyes to how research can transition from academia to real-world applications."}], [{"role": "user", "content": "As for publications, I\u2019ve been lucky enough to be a co-author on two workshop papers and I\u2019m currently working on a larger conference submission. The bigger conference is the IEEE Visualization Conference (commonly known as IEEE VIS), which is a pretty big deal in our area. We\u2019re focusing on a user study that compares traditional 2D dashboards with an interactive VR environment. Our hypothesis is that VR immersion helps researchers spot anomalies or trends faster than they would with just a plain old 2D chart. We ran a small set of user trials\u2014like 15 participants\u2014recorded their times, asked them to fill out a UX questionnaire, all that jazz. Now we\u2019re crunching the data to see if there\u2019s a statistically significant difference. Writing the paper is a group effort: I\u2019m handling the sections on system architecture and prototype design, someone else is writing about the user study methodology, and our PI (principal investigator) is weaving it all into a cohesive narrative. It\u2019s definitely a balancing act, especially since I have my senior coursework and a capstone project to manage."}], [{"role": "user", "content": "Sometimes I get asked if I plan on going to grad school myself. Right now, I\u2019m leaning toward working in the industry for a couple of years\u2014hopefully at a tech company that values R&D or advanced solution building\u2014before deciding if I want to pursue a master\u2019s or PhD. The idea of contributing to cutting-edge knowledge is really tempting, especially in areas like data visualization or machine learning. But I also want to see how these concepts get applied in real product development. One of my mentors in the lab said that sometimes stepping into industry can give you a better perspective on what problems are truly relevant, so that if you do come back for a PhD, you\u2019ll have a more practical approach."}], [{"role": "user", "content": "Oh, and I\u2019d be lying if I said it\u2019s all been sunshine and rainbows. Research can be frustrating. You\u2019ll spend weeks on an approach that doesn\u2019t pan out, or you\u2019ll discover your entire idea has already been done\u2014and improved upon\u2014by a group in Europe or somewhere else. That part can feel like a punch in the gut. But the key is to adapt. We\u2019ll read that existing paper, incorporate their findings, either think of a new angle or a new application, and push the boundary. That\u2019s the essence of research. You\u2019re never truly starting from scratch; you\u2019re building on this giant foundation of what others have done. The sense of collaboration\u2014both within our lab and among the global research community\u2014is something I find really special."}], [{"role": "user", "content": "One story that really stands out: a few months ago, one of our massive servers crashed. We had vital data stored there\u2014some of which wasn\u2019t fully backed up (I know, rookie mistake, but we learned from it). I remember the scramble that night: a frantic email chain, Zoom calls with the lab manager, everyone trying to see if we could recover the data. We did manage to restore most of it, but that crisis hammered home the importance of data backups. After that fiasco, we set up a better backup system, plus some checks to ensure we never again rely on a single point of failure. It was stressful, but also kind of a bonding experience for the whole lab."}], [{"role": "user", "content": "Another big plus is that research connects me with professors on a more personal level than just being a face in the lecture hall. My PI is super approachable; if I have a question about the direction of my career or even something I\u2019m struggling with in class, I know I can pop into her office and chat. She\u2019s been recommending me for different scholarships, events, and networking opportunities. I got to attend a virtual workshop on immersive analytics that was filled with academic experts from all around the world. Getting to see them debate technical details, propose new frameworks, and even gently tease each other over rival theories was fascinating. It was like I was witnessing the frontier of CS knowledge in real time."}], [{"role": "user", "content": "So yeah, that\u2019s basically my journey in the research world so far. It\u2019s a lot of work, often more chaotic than you\u2019d expect, but also super rewarding. I love the feeling that what I do in the lab might end up shaping the way scientists understand their data five or ten years down the line. Maybe one day the VR and immersive stuff we\u2019re trying to pioneer will become standard tools, and I\u2019ll be able to look back and say, \u2018Hey, I was part of that from the beginning.\u2019 It\u2019s that sense of contributing to something bigger that really keeps me motivated. Whether or not I go to grad school soon, I know this experience has changed how I think about computer science, about innovation, and even about teamwork. I can\u2019t imagine my undergrad years without it\u2014it\u2019s definitely enriched my whole perspective on what it means to be a computer scientist.\u201d"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_25-student-3", "topic": "Campus Life", "question": [[{"role": "user", "content": "Yeah, campus life here is pretty vibrant\u2014there\u2019s just so much going on, sometimes it\u2019s tough to keep track of everything. As a fourth-year Computer Science student, I\u2019ve definitely seen how the culture has evolved over the years, and I\u2019ve discovered a bunch of different communities that make this place feel like a second home. You\u2019d be surprised how many ways there are to get involved beyond just coding in front of your laptop all day."}], [{"role": "user", "content": "One of my biggest early regrets was not joining more clubs during my freshman year. I was sort of overwhelmed by the transition from high school to college, and I was laser-focused on my classes. But eventually, I realized that the campus experience is about way more than just academics. So I jumped into a couple of different student organizations. One big one is the Computer Science Society\u2014they host these weekly meetups where we discuss new technologies, share personal projects, and sometimes have industry speakers come in. It\u2019s a really welcoming environment, even for beginners. Often, if I\u2019m stuck on a tricky concept or need insight into a topic like concurrency or containerization, I can chat with fellow members who might have worked on something similar in an internship."}], [{"role": "user", "content": "Beyond that, there\u2019s this VR/AR Club I\u2019ve been attending on and off since my sophomore year. I got hooked once I started doing research in immersive analytics, so it was natural for me to want to hang out with people who share that passion. The club is surprisingly hands-on\u2014they have a little VR lab in the corner of the engineering building, complete with headsets, sensors, and some quirky prototypes that were built by past members. We\u2019ll host demos where each person shows off their latest project, whether it\u2019s a mini-game or something more experimental like augmented reality interfaces for everyday tasks. A few weeks back, someone showcased their AR tutor concept for advanced math: you\u2019d point your phone at an equation on your paper, and it would pop up a hint or a 3D demonstration. That kind of creativity inspires me to push my own research further. I just love the supportive vibe you get when people are genuinely excited about emerging tech."}], [{"role": "user", "content": "Of course, the campus is also big on non-tech stuff. I\u2019m not super involved in it, but I do pop into the student-run radio station events now and then. They host these open-mic nights that are a blast\u2014people sing, read poetry, or perform stand-up comedy. I remember one night, a friend of mine from the Distributed Systems class performed an acoustic set. He\u2019s usually so quiet in class, but on stage, he was belting out these soulful tunes as if he was born for it. Moments like that remind me there\u2019s always more to someone than meets the eye when you\u2019re just coding together or doing group projects."}], [{"role": "user", "content": "Now, the campus itself has these iconic spots\u2014like the huge central lawn where a lot of students like to hang out between classes. It\u2019s common for student organizations to set up booths there. You can walk through and see fliers for everything from an a cappella group to an environmental activism society. They\u2019ll hand out freebies like stickers or candy to lure you into signing up for their mailing list. My personal kryptonite is free pizza, so whenever a club includes that in their advertisement\u2014well, let\u2019s just say they can easily tempt me. The lawn is also where some of the biggest campus events take place, like our annual Tech Fest. During Tech Fest, different project teams put up posters, do live demos of their prototypes, and recruiters sometimes roam around looking for potentially innovative ideas. It\u2019s kind of a big deal here, especially for engineering and CS folks."}], [{"role": "user", "content": "I\u2019d say campus culture is fairly collaborative. There are definitely pockets of competition\u2014especially among seniors trying to land top internships or job offers\u2014but overall, people want to help each other succeed. I remember in my second year, the campus hackathon was a huge eye-opener. Thousands of students from various backgrounds, from first-years to master\u2019s candidates, crammed into the student center for 24 or 36 hours straight, hacking away. It was chaotic but exhilarating. You\u2019d see beginners learning the basics of GitHub while advanced teams used complex machine learning libraries to do, like, real-time image recognition. Sponsors and mentors wandered around to see if anyone needed help. I formed a small team with a few casual acquaintances, none of us were super close friends. But the bond we formed during that all-nighter\u2014eating stale pizza at 3 a.m., debugging code, deliriously cheering each other on\u2014was unlike anything I\u2019d experienced before. That\u2019s the vibe I associate with campus life: you\u2019re all in the trenches together, forging friendships through these shared experiences and challenges."}], [{"role": "user", "content": "Speaking of forging connections, dorm life can be another huge part of campus culture, although I\u2019ve moved off-campus now. In my first two years, I stayed in a dorm that was specifically for engineering majors\u2014 \u201cThe Engi-House,\u201d as we jokingly called it. Let me tell you, the late-night coding sessions in the common lounge were legendary. Someone would be banging away on an assignment for Data Structures, while another group might be streaming e-sports tournaments. Occasionally, that environment got a little too intense\u2014imagine a bunch of stressed-out engineers hopped up on energy drinks. But it also meant that if you ran into a stumbling block, there was always someone nearby who could offer a quick pointer. And beyond academics, we\u2019d hold potlucks, watch Netflix together, or even host a small talent show as a way to blow off steam."}], [{"role": "user", "content": "Another big part of campus life, at least for me, has been volunteering at some of the outreach events. Our CS department organizes a \u201cTech for Good\u201d day where local high school students come to campus to learn about programming and problem-solving. Volunteers like me help run mini-workshops on topics like web development or basic Python. It\u2019s really rewarding to see younger students light up when they manage to make a simple game or animated webpage. Sometimes, I remember how I felt at that age\u2014like the whole tech world was so big and I had no idea where to start. Giving them a positive first experience is something I take pride in. Plus, events like that show a different side of campus: it\u2019s not just about academic research or class assignments; it\u2019s about engaging with the community and hopefully inspiring the next generation to get excited about STEM fields."}], [{"role": "user", "content": "We also have a pretty lively social scene if you need a break from all the academic stuff. Greek life is there for those who are into it, though I never joined a fraternity myself. Instead, I found that the best way to meet people who share your interests is through specialized clubs. For instance, one of my friends is super into robotics. He joined the Robotics Team in his first semester and practically found his second family there\u2014these are the people he now hangs out with on weekends, goes to watch movies with, and grabs dinner with. Meanwhile, I\u2019ve grown closer to folks in the VR/AR Club and the CS Society. It\u2019s not that we only talk shop when we\u2019re together; we actually spend plenty of time just joking around, complaining about cafeteria food, or planning weekend trips to the local hiking trails."}], [{"role": "user", "content": "One staple of campus culture is also the tradition of Senior Week, which I\u2019m about to experience for the first time this year. It\u2019s basically a series of events leading up to graduation\u2014like a formal dance, a carnival, and a day where seniors get pictures taken in their caps and gowns around iconic campus landmarks. I\u2019ve heard it\u2019s bittersweet because it\u2019s the last hurrah before everyone scatters. Some of my older pals who graduated last year said it was one of the best weeks of their college life, filled with laughter, nostalgia, and a bit of that sinking feeling that the journey\u2019s almost over. I\u2019m both excited and a little anxious for it. I\u2019ve been here for four years, made so many memories, and it\u2019s weird to think about not being a student anymore."}], [{"role": "user", "content": "Now, let me not forget the campus traditions. Every year, there\u2019s this big Rivalry Game\u2014our sports teams face off against our biggest rival college in a variety of sports. Even though I\u2019m not a dedicated sports fan, I absolutely love the energy on campus that week. People deck themselves out in school colors, paint their faces, and parade around with banners. Sometimes, the night before the big football game, we have a massive pep rally that includes fireworks. It\u2019s a spectacle. Even if you don\u2019t go to the game itself, the vibe on campus is electric. Friends gather in dorm lounges or local bars to watch and cheer. If we win, the celebrations can last all weekend, and if we lose, well, we still find a way to commiserate together. It\u2019s a unifying moment for everyone, from business majors to art students to us nerdy engineers."}], [{"role": "user", "content": "One cool development is that our school\u2019s e-sports team has gained official recognition. A lot of people think that a campus\u2019s \u201csports scene\u201d is only about basketball or football, but now we have an arena for e-sports as well. If you step inside during a tournament, you\u2019ll see this wide seating area and a big screen where they\u2019re streaming the competitions live. It\u2019s not uncommon for a hundred or more students to gather and cheer on our team for League of Legends or Overwatch matches. I never would\u2019ve guessed e-sports would be a legitimate campus institution when I started college, but times are changing. It\u2019s awesome to see a broader definition of competition and community."}], [{"role": "user", "content": "We have an annual event called the \u201cMidnight Breakfast\u201d during finals week. It sounds ridiculous\u2014a bunch of stressed-out students heading to the dining hall at midnight for pancakes and cereal\u2014but it\u2019s a huge morale booster. Professors come to serve food, the school band might perform, and everyone collectively takes a break from cramming. It\u2019s such a small thing, but it does wonders to remind you that you\u2019re not alone in feeling anxious about final exams. It\u2019s traditions like these that make me appreciate the campus culture\u2014there\u2019s a genuine effort to ensure students have a bit of fun and camaraderie even during high-pressure times."}], [{"role": "user", "content": "All in all, I\u2019d say campus life here is what you make of it. If you\u2019re willing to explore, approach new people, and join clubs, you\u2019ll find a million ways to enrich your experience. It\u2019s easy to get lost in your workload, especially in a demanding major like Computer Science, but I\u2019ve found that taking the time to attend events, volunteer, and connect with various communities has been incredibly rewarding. When I look back on my four years, I\u2019m definitely going to remember the late-night coding, sure\u2014but I\u2019ll also remember the silly game nights in the dorm, the Tech Fest presentations, the hackathon adrenaline, and the excited cheers at the Rivalry Game. It\u2019s been a whirlwind, but I wouldn\u2019t trade it for anything. Let me know if you ever want a quick tour\u2014I\u2019d be happy to show you some of my favorite spots and introduce you to a few clubs. Honestly, there\u2019s something for everyone here, and I\u2019m so glad I finally embraced all the campus has to offer.\u201d"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_26-student-4", "topic": "Future Plans", "question": [[{"role": "user", "content": "Oh, wow, future plans. That\u2019s a big topic these days, especially now that I\u2019m in my fourth year and the finish line for undergrad is finally in sight. It\u2019s almost comical how different my mindset is compared to freshman year\u2014I went from thinking \u2018I\u2019ll figure out a direction eventually\u2019 to \u2018I need a solid plan or at least some prospective routes ASAP.\u2019 Especially as a Computer Science major, there\u2019s this unspoken pressure to land something right after graduation, whether that\u2019s a full-time job in industry, a spot in a grad program, or some kind of startup venture. Right now, I\u2019m leaning heavily towards going into the industry for 5 years, but I haven\u2019t completely ruled out the possibility of grad school somewhere down the line."}], [{"role": "user", "content": "I guess I should start with what I\u2019m aiming for in the job market. I\u2019ve been exploring software engineering roles that overlap with the fields I\u2019ve grown passionate about\u2014data visualization, VR/AR technologies, and maybe some data-driven product development. Over the past couple of years, especially through my research experience, I\u2019ve discovered this love for making huge datasets approachable and interactive. I keep fantasizing about how that might translate into a commercial product or even an open-source framework that can benefit a wider community. That\u2019s one reason I\u2019m drawn to certain tech companies known for pushing boundaries in data analytics or immersive technologies\u2014places like Unity, Unreal, or even some lesser-known startups working on specialized VR solutions for healthcare or engineering. One of my lab mentors basically told me, \u2018Follow the domain that excites you, not just the biggest name brand out there.\u2019 That advice really clicked. Sure, the Googles, Amazons, and Facebooks (or Meta, I guess) of the world are tempting, but so are these smaller companies where I might have a bigger impact earlier on."}], [{"role": "user", "content": "As for my actual search process, I\u2019m in the thick of campus recruiting right now. We have career fairs, which can be both exhilarating and stressful. I\u2019ve already submitted a bunch of applications, mostly for software engineering or data engineering intern-to-full-time conversion roles. Luckily, I did an internship last summer at a tech startup with 9 people that specialized in data analytics software for logistics companies. It was such a valuable experience because I got to see how a real development team operates, how they plan sprints, manage code reviews, and handle feature rollouts to actual paying clients. My tasks ranged from building small UI components for their analytics dashboard to writing data pipelines that process shipping events in near real-time. By the end of that internship, I felt more confident in my skills, and I think that\u2019s going to help me stand out when I interview."}], [{"role": "user", "content": "On the grad school front: part of me is really intrigued by the idea of continuing my research in immersive analytics or even branching into human-computer interaction for large datasets. I\u2019ve seen how advanced degrees can open doors to specialized R&D roles, either in industry labs or in academia. Writing those workshop papers and currently working on a conference submission has shown me that I do enjoy the deeper exploration of a single topic\u2014figuring out how to push the needle forward in a meaningful way. But on the flip side, there\u2019s a practical consideration: I\u2019d like to apply what I\u2019ve learned in a more commercial setting, at least for a while, before I commit another five to six years for a PhD (or even two if I go for a master\u2019s first). Some of my peers say I could do a master\u2019s part-time while working. Others say it\u2019s better to go all-in if I\u2019m going to do it. It\u2019s honestly a lot to weigh."}], [{"role": "user", "content": "Another big piece of this puzzle is my previous involvement in various campus clubs and projects, which has shaped how I see my future. Being part of group-driven assignments\u2014like my current capstone project analyzing social media sentiment for emergency responses\u2014makes me realize how much I thrive on teamwork and problem-solving in high-stakes scenarios. It\u2019s not just about coding for me; it\u2019s about working closely with people, brainstorming solutions, putting together prototypes, seeing them fail, and then iterating until they work. That\u2019s exactly the type of environment I want once I graduate. I\u2019m certain I don\u2019t want a role where I\u2019m locked away on tiny, siloed tasks. I\u2019d love to end up at a company that encourages cross-functional collaboration\u2014with designers, data scientists, product managers, or even domain experts in non-tech fields. That interdisciplinary vibe is something I\u2019ve enjoyed a lot in my research lab, and I want it in my professional life, too."}], [{"role": "user", "content": "Financial considerations come into play as well. I\u2019ve got some student loans, though not astronomical, but it\u2019s enough that I\u2019m conscious about wanting a stable income soon after I graduate. I\u2019m not above the practicalities\u2014health insurance, paying rent, saving up a bit. A full-time engineering position usually comes with a decent salary, which could help me knock out that debt faster. If I went straight into grad school, funding can be complicated. Some PhD programs offer stipends, some don\u2019t. Master\u2019s programs often require you to pay tuition, and scholarships can be competitive. So, part of me is thinking: a good job in industry could serve a dual purpose\u2014alleviating financial strain and giving me career momentum\u2014while I figure out if I truly want to go back to school. "}], [{"role": "user", "content": "The timeline is also crucial. A lot of companies do their hiring in waves, especially for new grads. I\u2019m trying to stay on top of deadlines. There\u2019s always interview prep to do\u2014technical interviews can be grueling with their algorithmic challenges, system design scenarios, and even some domain-specific tests if you\u2019re applying to specialized roles. I actually set aside at least a couple of hours each week to go through practice problems, brush up on coding patterns, revisit data structures and algorithms. I wouldn\u2019t say I love the interview grind\u2014who does, right?\u2014but I get why it exists. On the bright side, the advanced courses I\u2019m taking, like Advanced Algorithms and Distributed Systems, are directly boosting my confidence for those tech screens. I definitely notice how comfortable I\u2019ve gotten talking about things like concurrency, big-O notation, or analyzing the trade-offs of certain architecture designs."}], [{"role": "user", "content": "A few companies have already reached out for initial chats. It\u2019s kind of surreal to have recruiters ping you on LinkedIn, especially when you still feel like a student who\u2019s just trying to keep your head above water with assignments. I\u2019m trying not to get too carried away\u2014after all, a recruiter message is just the tip of the iceberg in terms of the hiring process. But I am optimistic. There\u2019s always that voice in my head whispering, \u2018Don\u2019t forget about Plan B, or Plan C.\u2019 So I\u2019m also looking into smaller companies or startups in my city or the nearby tech hubs. Sometimes those can lead to more hands-on roles where you really learn from day one. The fantasy of joining a brand-new VR startup, maybe having a stake in its success, and building out a product from scratch\u2014that\u2019s super appealing, especially to my entrepreneurial side."}], [{"role": "user", "content": "Speaking of entrepreneurship, I\u2019ve toyed with the idea of launching something of my own. Not necessarily right after graduation\u2014maybe that\u2019d be too risky\u2014but maybe after I gain enough industry experience. I\u2019ve had a couple of ideas floating around for VR-based training simulators or interactive data analytics platforms. If I teamed up with a few talented friends from my advanced classes or from the research lab, we could potentially spin up a small venture. But that\u2019s a big leap. I\u2019m not na\u00efve about how tough it is to maintain a startup, especially if there\u2019s no guaranteed paycheck coming in. My parents have been supportive in general, but they remind me to keep my feet on the ground and consider the realities of living expenses. They\u2019re not pushing me in any one direction, but they\u2019ve always emphasized financial independence as a cornerstone of life after college."}], [{"role": "user", "content": "Family is another factor\u2014my folks live in a different state, and I have to think about how far I\u2019m willing to move. Tech is obviously huge in places like Silicon Valley, Seattle, Austin, and now even in Midwest hubs, so location might come into play. I\u2019m not opposed to relocating, but I also know that if I end up taking an offer in a city that\u2019s too far, visiting home becomes a logistical puzzle. Some of my upperclassmen friends who graduated last year say that remote or hybrid roles have changed the game. Maybe that means I could live near family or wherever I\u2019m most comfortable, while still working for a big name or an innovative startup. It\u2019s a great time to be entering the tech workforce\u2014there are so many options, though it can be a bit overwhelming to juggle them all."}], [{"role": "user", "content": "One thing I do know for certain: I want to keep growing my skills and not get complacent. Even if I land in a role that\u2019s not 100% perfect, as long as I\u2019m learning\u2014picking up new frameworks, new approaches to product development, or even new management skills\u2014I\u2019ll consider that a success. My biggest fear is stagnation, especially in a field that moves as fast as ours. So wherever I go, I\u2019m planning to stay curious, keep pushing my own boundaries, and maybe carve out a niche as a specialist in immersive tech for data. That\u2019s the goal, at least. Next steps: finish out senior year strong, keep interviewing, see which offers come my way, and hopefully make a decision by late spring. Fingers crossed that the stars align the way I\u2019m hoping, but if they don\u2019t, I\u2019m confident I\u2019ll figure it out. I\u2019ve learned that you have to be flexible\u2014you never know how life will unfold after you toss that graduation cap in the air. For now, I\u2019m just excited about the potential paths ahead.\u201d"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_27-student-5", "topic": "Travel", "question": [[{"role": "user", "content": "I\u2019d love to talk about my travel experiences. So, I\u2019ve always been fascinated by the idea of exploring new places, and that\u2019s something I\u2019ve tried to do whenever my schedule allows. Since I\u2019m a fourth-year Computer Science major, I have to be pretty strategic about planning my trips\u2014between classes, research, campus clubs, and even everyday stuff like heading to the gym or working on group projects, my calendar gets ridiculously cramped. But I\u2019ve managed to squeeze in some adventures that have given me some of my favorite memories so far."}], [{"role": "user", "content": "Let me start with a trip I took just before my sophomore year. It was a sailing trip off the coast of Maine. I actually got into sailing back in high school, and I loved the idea of exploring New England\u2019s coastal towns. A friend of mine from the sailing club on campus recommended a place near Bar Harbor, so we ended up going for a week. It was breathtaking. Imagine waking up early, feeling the crisp salt air on your face, and seeing the sunrise over the water. We had a chance to sail past these little islands dotted with pine trees, and every morning we\u2019d eat breakfast on the deck, chatting about which routes we\u2019d try that day. Even though we were essentially novices, we had a local instructor who taught us tidbits about navigation using both modern tech, like portable GPS devices, and old-school methods with star charts. I remember thinking how strange yet thrilling it was to disconnect from my usual coding mindset and focus fully on the wind, the tide charts, and the synergy of the boat with nature. Plus, I\u2019ll never forget the fresh lobster rolls we got at a small dockside shack\u2014still the best lobster I\u2019ve ever had."}], [{"role": "user", "content": "My travel bug really kicked in during my junior year, when I saved up money from a summer internship to go to Japan over winter break. I had read about Japan\u2019s blend of modern technology and traditional culture, and it seemed like a dream destination for a computer science student who also loves discovering local history. I went to Tokyo first, because obviously it\u2019s the tech hub. Walking around Akihabara, seeing the bustling arcades, and dropping by electronics stores was like a futuristic wonderland. I tried to pick up some Japanese phrases before the trip\u2014mostly just greetings and basic directions. It was helpful, but I still ended up pointing at menus a bunch of times while ordering ramen. The food was so good, though. Tokyo\u2019s also where I saw that crazy intersection in Shibuya\u2014so many people crossing at once, yet it somehow felt orderly. After a few days there, I headed to Kyoto, which was the total opposite vibe\u2014very calm and historical. I visited a number of shrines and temples, and even caught a glimpse of geishas quietly walking down a side street near Gion. It was surreal. I remember writing in my journal\u2014I\u2019m sort of a casual diary-keeper\u2014that being in Kyoto felt like stepping back into an older era, where craftsmanship and tradition take center stage. That sense of stillness and reverence left a big impression on me."}], [{"role": "user", "content": "Another memorable trip was a family vacation to Europe last summer. My parents, my sister, and I flew into London. We hadn\u2019t traveled as a whole family in a while, so it felt sentimental. London was amazing, obviously\u2014there\u2019s so much history. We did the classic tourist stuff like visiting the Tower of London, the British Museum, and riding on the London Eye. My sister is more of a history buff, so she was freaking out over old artifacts and crown jewels. We also went to see a musical in the West End\u2014it was The Phantom of the Opera\u2014and it ended up being one of the highlights of the trip. After London, we hopped on a train to Paris. It was my first time in France, and though I took some French way back in middle school, I barely remembered how to say anything beyond \u201cbonjour\u201d and \u201cmerci.\u201d Still, it was easy enough to get around, plus it\u2019s such a picturesque city. The pastries alone were worth the journey. My favorite spot was probably the area around the Seine River near Notre-Dame, just strolling with a croissant in hand. It felt so relaxed, especially compared to the hustle of London. My dad loves art, so we spent a whole day at the Louvre, but even that didn\u2019t feel like enough time. You could probably spend a week in there without seeing everything."}], [{"role": "user", "content": "Beyond the bigger international trips, I\u2019ve also done some smaller getaways. Our campus is near a major city, so sometimes on weekends my roommates and I will drive out to a state park or a nearby beach town. I love the spontaneity of packing up the car with snacks, a few fishing rods (because one of my roommates is into fishing), and turning up the music. We\u2019ll set up a quick campsite or rent a small cabin for the night. One time, we found ourselves in this remote area with horrible cellphone reception, which initially drove us crazy since we couldn\u2019t check social media or quickly Google stuff. But by the end of that weekend, we realized it was exactly what we needed: a digital detox. We ended up hiking around, telling stories, and just enjoying nature. That trip reminded me how necessary it is to occasionally step away from my laptop and, ironically, it recharged me for the programming tasks waiting for me back at campus."}], [{"role": "user", "content": "Looking forward, I\u2019m hoping to plan a big trip before I graduate. I\u2019ve been eyeing some internships in Silicon Valley for next summer, so if that pans out, I might spend a couple of weeks exploring the West Coast. I\u2019ve heard great things about Portland, Seattle, and Vancouver\u2014everyone always praises the coffee, craft beer, and beautiful scenery. If I line things up just right, I could do a road trip through Washington and Oregon after my internship ends, maybe with a friend or two who\u2019ll also be interning in the Bay Area. It would be awesome to discover the coastal highways, the Redwood forests, and those hidden beaches I keep seeing in photos. That might be my last chance to have a big chunk of free time before the post-grad grind starts."}], [{"role": "user", "content": "As far as traveling with a dog, that\u2019s definitely something I\u2019d love to do one day. I have a dog at home, though he\u2019s really the family dog since he stays with my parents. He\u2019s a Golden Retriever named Archie, and he\u2019s super energetic. One time, my family considered taking him on a road trip, but Archie hates being cooped up in a car for too long\u2014he gets fidgety and starts pawing at the windows. So we decided to leave him with my uncle for that trip. Still, I dream of traveling in an RV or camper van, bringing Archie along for hikes and beach runs. It\u2019d be a different type of adventure, but definitely one that\u2019d make for some amazing memories."}], [{"role": "user", "content": "Honestly, travel to me isn\u2019t just about ticking places off a bucket list. Of course, it\u2019s cool to say I\u2019ve been to different countries or states, but I really value the experiences that come from stepping outside my usual environment. I feel like each locale has taught me something\u2014Japan gave me a sense of reverence for tradition and efficiency, Maine reminded me how timeless and beautiful nature can be, and Europe opened my eyes to the depth of art and culture spanning centuries. Even short trips nearby campus help me unwind and discover hidden gems in my own area. I love collecting postcards along the way, too\u2014every time I look at them, I\u2019m reminded that there\u2019s a big, wide world beyond my bedroom. And the more I see, the more inspired I am, not just for personal enjoyment, but also for the creative problem-solving that goes into my coding projects and research ideas. There\u2019s something about experiencing new places that heightens my curiosity and makes me want to explore fresh angles in everything I do."}], [{"role": "user", "content": "So, yeah, that\u2019s pretty much my travel story so far. Traveling has become one of those passions I\u2019ll keep nurturing even after graduation. I\u2019m anxious to see where my future career in computer science will lead me\u2014I hope to land a job that allows some remote work days or flexible scheduling, so I can keep exploring. Whether it\u2019s a weekend trip to a mountain range a few hours away or a flight to a completely different continent, I\u2019m all in. If I ever get the chance to combine my love of coding with my love of travel\u2014like working abroad for a stint or collaborating on an international project\u2014I\u2019d jump at the chance. For now, I\u2019m just enjoying planning the next journey in my head whenever I need a mental break from debugging code. It keeps me motivated, and it\u2019s a huge part of why I\u2019m super pumped for the next phase of my life."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_28-student-6", "topic": "Crush", "question": [[{"role": "user", "content": "So\u2026 there\u2019s something that\u2019s been on my mind lately\u2014and yeah, it\u2019s about someone I kinda have a crush on. I\u2019ve been trying to figure out how to work it into my day-to-day life, especially with my schedule being as chaotic as it is this semester. You know I\u2019m a fourth-year computer science major, and the workload is no joke. Between my capstone project, my machine learning class, and all the coding assignments, I barely have time to sleep. Sometimes I\u2019ll pull an all-nighter or two, especially if a project deadline is approaching or if there\u2019s a big exam coming up the next day. But despite all of that, I can\u2019t seem to stop thinking about this one person whenever I get a quiet moment to myself."}], [{"role": "user", "content": "She\u2019s actually in my advanced algorithms class. We ended up on the same team for a project about a month ago, and that\u2019s when I really got to know her. Typically, group projects can be stressful\u2014trying to coordinate schedules, dividing up tasks, and making sure the final product is cohesive. But in our case, it gave me a real excuse to chat with her and just understand how she thinks. The more we worked on that project, the more I realized how funny and brilliant she is. We\u2019d be in those group Zoom calls, and while everyone else was kind of stressed about the complexity of the code, she would be cracking witty jokes about it. It\u2019s so refreshing to find someone who doesn\u2019t get overtly frazzled by the academic pressure and instead keeps the mood light. And her sense of humor is totally up my alley\u2014dry, a bit sarcastic, but never mean-spirited."}], [{"role": "user", "content": "She\u2019s also super dedicated to her studies. I learned she\u2019s minoring in mathematics and has a real passion for discrete structures. Meanwhile, I\u2019m more of a software-oriented person\u2014my interests align more with system design, maybe some user interface stuff, and I dabble in data science. So our strengths complement each other pretty well. In fact, I think we worked so well together because if I got stuck on a complex analysis or a combinatorial proof, she\u2019d walk me through it step by step. It\u2019s kind of rare in group settings that I trust another person\u2019s process so readily, but with her, it\u2019s like I know she\u2019s got it covered. We ended up getting an A on that project, but to be honest, I was just happy for the chance to collaborate with her."}], [{"role": "user", "content": "The tricky part is figuring out if she\u2019s interested in me beyond just a team partner or a classmate. I\u2019m so busy with everything else\u2014applications for full-time jobs, my internship from last summer wanting me to come back after graduation, and then there are all my extracurriculars. I\u2019m in the Robotics Club, which meets twice a week, and I\u2019m also still involved in the sailing club. You know, I\u2019ve been into sailing since I was a kid, so it\u2019s something I can\u2019t quite give up even when the semester is insane. Then there\u2019s the gym\u2014remember, I try to work out every morning just to keep my sanity. The point is, I feel like I barely have enough hours in the day to keep up with all the responsibilities I already have. But when you really like someone, you can\u2019t help but want to carve out time for them, right?"}], [{"role": "user", "content": "I\u2019ve thought about asking her to grab coffee after class. There\u2019s this cute caf\u00e9 just off campus that has amazing chai lattes, and they offer the best studying ambience in the late afternoon. I suppose I could just casually say I need a break from coding or something along those lines\u2014something that doesn\u2019t feel too forced. But I don\u2019t want to overthink it, which, let\u2019s be real, is exactly what I do. I\u2019m going to see if she has a gap on Tuesday or Thursday since my schedule is a bit more flexible those days. I keep telling myself: worst case scenario, she says she\u2019s busy, and we try another day. Or maybe she just sees me as another CS Archie. But that\u2019s life\u2014sometimes you have to take a risk."}], [{"role": "user", "content": "What\u2019s sort of complicating how I feel is that I\u2019ve been pretty focused on my future plans these past few months. Between talking to recruiters at networking events, fine-tuning my LinkedIn profile, and figuring out if I should go to grad school, I\u2019ve had a seriously packed schedule. My mom and dad are supportive, but they\u2019re also pretty eager to see me land a solid job right out of college. My little sister texts me every now and then to ask how I\u2019m doing, and of course, she\u2019ll tease me if she suspects I have any romantic interest in someone. It\u2019s funny\u2014my family\u2019s super close, and I usually tell them everything, but this time I\u2019m keeping it a bit more under wraps. I guess, in a way, I want to feel certain about my feelings and about where this might head before I open up to them. Plus, I don\u2019t want them to start planning a wedding in their heads prematurely\u2014trust me, that happens in my family."}], [{"role": "user", "content": "I\u2019m also worried about losing focus. The last thing I want is to get overly distracted by romantic drama. My schedule is so tight that I have a Google Calendar that beeps at me basically every twenty minutes just to remind me where I should be. Between coding sessions, runs to the store for groceries, FaceTiming my dog (oh yeah, I have a golden retriever back home who I miss so much\u2014it\u2019s rough not seeing him every day), tasks for the campus organizations I\u2019m part of, and occasionally fitting in some online gaming with my buddies\u2026 I must keep to a super regimented schedule or I\u2019d fall behind. But I think maybe a bit of excitement\u2014or maybe that spark that people talk about when they\u2019re really interested in someone\u2014could actually be motivational rather than distracting. Maybe it\u2019ll give me the extra energy boost I need during these last two semesters."}], [{"role": "user", "content": "So that\u2019s the situation. I like her\u2014like, really like her. I know I can\u2019t script these things too precisely, but I\u2019m thinking I\u2019ll just be honest and say, \u2018Hey, I\u2019ve loved working together, and I\u2019d really like to get to know you more outside of algorithms class. Want to grab coffee sometime?\u2019 And I\u2019ll probably be a bit nervous\u2014my heart will probably be pounding\u2014but I guess that\u2019s part of the process, right? I\u2019m trying to keep my expectations realistic, though. There\u2019s always a chance that it might not go anywhere, or maybe it\u2019ll just remain a friendship. But for the first time in a while, I feel genuinely excited about the idea of connecting with someone beyond discussing big-O notation and code implementations."}], [{"role": "user", "content": "It feels a little surreal talking about crushes at this stage in college. We\u2019re all busy, we\u2019re all stressed, but at the same time, it\u2019s also a time when you can meet some of the most interesting people ever. And I feel like I\u2019ve found someone I\u2019d really like to get closer to. So yeah, that\u2019s where I\u2019m at. I\u2019ll probably wait until after our next group meeting so I don\u2019t catch her off guard. Maybe I\u2019ll crack a small joke about how we survived advanced algorithms together, and now it\u2019s time to celebrate with a latte. Even if she says no, at least I will have tried. But something tells me she might just say yes."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_29-student-7", "topic": "Pets and Family", "question": [[{"role": "user", "content": "Oh man, talking about my family and my dog always makes me nostalgic\u2014like I\u2019m back at home instead of rushing from one class to the next here on campus. Since I\u2019m a fourth-year CS major, juggling a full course load and my research projects, I don\u2019t get to see my family as much as I\u2019d like. My parents and my younger sister mean the world to me. I\u2019m twenty-two, so there\u2019s a bit of a gap between me and my sister, who just turned sixteen. Despite the age difference, we\u2019ve always been close\u2014she\u2019s the one who introduced me to some of my favorite mobile games, oddly enough. When we\u2019re back home, conversations around the dinner table tend to revolve around our golden retriever, Archie, because he\u2019s basically the center of attention in our household."}], [{"role": "user", "content": "Archie is three years old now, and we got him when I was in my second year of college. My parents surprised both me and my sister with him one winter break. We\u2019d always talked about wanting a dog, especially a big, friendly breed that likes to run around and play catch. When I walked through the front door and saw Archie\u2014tiny and fluffy, wagging that little tail like it was Christmas every day\u2014my heart melted. Even though I was only back home for about three weeks during that break, I spent every moment I could getting to know him, taking him for short walks (he was too small for long ones at first), and teaching him random commands. I vividly remember how he\u2019d tumble over himself trying to figure out \u201csit\u201d and \u201cstay.\u201d It\u2019s wild how big he\u2019s gotten now\u2014he\u2019s definitely not that little fur ball anymore!"}], [{"role": "user", "content": "Since I live on campus and only visit home on some weekends or during breaks, my parents do most of the day-to-day care for Archie. However, I video call them almost daily\u2014kind of a running habit ever since my freshman year\u2014and Archie always seems to recognize my voice. My mom will tilt the phone so he can see me, and he\u2019ll go nuts, wagging his tail and whining like he\u2019s about to jump through the screen. It\u2019s equal parts hilarious and heartwarming. In a weird way, those moments help me de-stress from back-to-back coding assignments or the infinite debugging sessions I find myself in. Someone once told me that dogs don\u2019t really perceive images on screens the way humans do, but Archie 100% knows something\u2019s up. Maybe it\u2019s just my voice that triggers him, but it\u2019s nice to believe he\u2019s looking right at me."}], [{"role": "user", "content": "Family-wise, I\u2019m pretty lucky. My dad is a software engineer, so he\u2019s part of the reason I got into computer science in the first place. We used to do these small coding exercises together when I was in high school. He\u2019d challenge me to figure out a puzzle or a problem, and it sparked that early fascination with logic and technology that eventually led me to major in CS. My mom, on the other hand, works as a nurse. She\u2019s the most caring person I know, and she\u2019s always the emotional backbone of our family. Even if it\u2019s late at night, she\u2019ll pick up my calls when I need a pep talk or if I\u2019m worried about an upcoming exam. Between my parents, I found this healthy balance: I got the analytical edge from my dad and the empathetic side from my mom. My sister, well, she\u2019s just a ball of energy\u2014constantly on social media, playing new games, or looking for the next big trend. She\u2019s so plugged into technology that sometimes she jokes she\u2019s going to overshadow me in the tech scene. She might be right."}], [{"role": "user", "content": "Anyway, every time I go home, Archie greets me like I\u2019ve been gone for centuries. He bounds around, does that little circle dance, and basically tries to tackle me to the ground in excitement. One of our family traditions is going on a Saturday morning hike whenever I\u2019m in town. We pack some snacks, load Archie into our SUV, and drive out to this trail about twenty minutes away. The path isn\u2019t super long, but it has a great view at the summit of a small bluff that overlooks a lake. Archie loves it\u2014he\u2019s got endless energy. By the time we\u2019re at the top, he\u2019s chasing squirrels and sniffing every tree. Watching him run around like that reminds me to appreciate the small things. I can get so wrapped up in code and classes that I forget to just breathe and enjoy nature."}], [{"role": "user", "content": "My extended family includes my grandparents, who live a state away. They mean so much to me. My grandma still teases me about the time I broke her antique vase when I was in middle school by throwing a mini football around her living room. I try to tune out that story, even though it\u2019s retold almost every Thanksgiving. Growing up, my family always valued spending time with relatives, so holiday gatherings are a big deal. Thanksgiving is practically a potluck extravaganza, with all kinds of dishes that make you want to hibernate until Christmas. Archie, of course, tries to snag anything that drops on the floor. We learned the hard way he has a sensitive stomach\u2014one year he got into some leftover turkey bones, and we had to make a late-night veterinary visit. Ever since then, we\u2019re super careful about what \u2018people food\u2019 he can get his paws on."}], [{"role": "user", "content": "I find it interesting how family shapes who you become. Even though I\u2019m on my own at college, I realize that a lot of the patience and resilience I rely on while working through tough programming projects actually comes from my parents. My dad taught me how to stay calm and break down problems, while my mom showed me how to be patient and empathetic\u2014even when the situation is frustrating, like when a project partner doesn\u2019t pull their weight or code isn\u2019t cooperating. Plus, I can\u2019t forget my sister, who always pushes me to stay up on new trends so I don\u2019t become a relic in my twenties. Sometimes I do write about personal stories, like Archie or silly moments with my sister, in my personal statements for scholarships or grad school applications, because they matter to me that much."}], [{"role": "user", "content": "If I had to describe my family in a few words, I\u2019d say we\u2019re supportive, close-knit, and lively. The way they come together around Archie, fussing over him or laughing at his antics, shows me what unconditional love looks like. I may be a busy fourth-year with internship interviews, clubs, research, and everything else on my plate, but at the end of the day, it\u2019s nice to know there\u2019s a place\u2014and a family\u2014where I\u2019m always welcome, dog hair and all. I think that\u2019s what keeps me going, to be honest. No matter how many lines of code fail to compile, I can always count on home to recharge me. And yes, that definitely includes Archie leaping into my arms the second I walk through the door.\u201d"}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_30-student-8", "topic": "Friendship", "question": [[{"role": "user", "content": "Friendship is a huge topic for me because, in a lot of ways, my closest friends have become my second family at college. I\u2019ve been here for four years, and I always say that as much as I love studying computer science, the people I\u2019ve met along the way are what really keep me grounded and happy. Ironically, when I first arrived on campus, I thought that making friends might be more challenging\u2014it was my first time living away from home, and I was nervous about whether I\u2019d fit in. But over time, I ended up forming these really tight bonds that, honestly, have made all the stresses and workload of being a CS major feel way more manageable."}], [{"role": "user", "content": "I guess I should start with my roommate and best friend from freshman year, Tony. We didn\u2019t know each other before that first semester, but we clicked right away over this random conversation about a piece of software I was trying to prototype for an Intro to Programming class project. Tony\u2019s not even a CS major\u2014he\u2019s in mechanical engineering\u2014but we would bounce ideas off each other late into the night, and we realized we had more in common than we initially thought. We both love working with our hands, albeit in different ways: I love to code and design logic, while he loves building things physically. We ended up collaborating on a few cross-disciplinary robotics projects since then, and we\u2019ve grown closer because of those shared interests. Tony\u2019s someone I can always call when I\u2019m overwhelmed with an assignment or if I just want to grab a pizza after a long day."}], [{"role": "user", "content": "I also have a couple of friends I met through the campus sailing club. You wouldn\u2019t believe how many CS students I found there\u2014turns out we like to decompress by hitting the water. It creates this fun mix of personalities: we all share an inclination for problem-solving, so we sometimes treat sailing strategy like a puzzle. The camaraderie on the boat is unlike anything else I\u2019ve experienced in other clubs. You really have to trust each other out on the water. Our team meets at odd hours (often early in the morning) to catch the best conditions, so we really rely on each other to stay motivated. It\u2019s a different kind of bond\u2014there\u2019s a deep mutual respect because we see each other\u2019s strengths and vulnerabilities in such a high-stakes environment. There\u2019s no better feeling than looking across the sailboat and seeing your friend flash you a thumbs-up when you maneuver a particularly tricky tack. One friend, Mariah, has ended up becoming a close confidante for me. She was the one who encouraged me last year to apply for a more challenging research position in data science, telling me I should believe in my skills the way she believed in me. That\u2019s the kind of unwavering support we have for each other."}], [{"role": "user", "content": "Of course, as a fourth-year, I\u2019ve also made plenty of connections through my computer science classes\u2014study groups, group projects, or just the random folks I always saw in the lab at two in the morning, trying to debug code the night before a big assignment. There\u2019s this sense of solidarity that comes from those late-night coding sessions\u2014like we\u2019re going through the trenches together. I vividly remember this one time in my sophomore year, there was a particularly infamously difficult systems programming course. Our final project was basically building a mini operating system kernel from scratch, and we were all losing our minds with memory management bugs. That\u2019s actually how I became tight with Carla, another CS major. We ended up bonding over pizza at 3 a.m. because we were the only two left in the lab so late. Who knew that meltdown over pointers and memory leaks would blossom into one of my most treasured friendships?"}], [{"role": "user", "content": "Now, I also have some pretty good friends from the gym. Working out has always been an escape for me\u2014I mentioned before that I love to blow off steam by lifting or doing cardio whenever I have a heavy workload. Over time, I kept seeing the same group of people in the early mornings, and it kind of turned into this friendly circle. One of those guys, Dean, is a fellow computer science major, but he\u2019s more into machine learning and hardware acceleration projects. He and I started talking shop between sets, which led to us comparing notes on our classes, which then led to us being gym buddies who share nutritional tips, workout regimens\u2014pretty much everything. Now, every so often, we\u2019ll game together, too. He\u2019s the one who got me hooked on a new co-op adventure game, so it\u2019s not just about the gym lifts anymore. It\u2019s become a friendship built around mutual support\u2014pushing each other to be better in multiple areas of life."}], [{"role": "user", "content": "Speaking of gaming, that\u2019s another circle of friends that\u2019s quite dear to me. I\u2019m not a hardcore gamer, but I do spend a decent chunk of time on weekends playing multiplayer strategy games, especially when I need a mental break from writing code or reading research papers. We have this informal e-sports club that meets in a common lounge in our student center. I don\u2019t always go, but whenever I do, it\u2019s guaranteed quality time with friends. There\u2019s something special about gaming with a group in the same room\u2014plenty of laughs, random inside jokes, that collective groan when a team fight goes south, and the ultimate hype when we pull off a last-second victory. Actually, I made a great friend, Derek, through that group\u2014he\u2019s super skilled in networking security, and he taught me a bunch of techniques that later proved useful in a cybersecurity research project. And the best part is how naturally our friendship evolved\u2014initially from a shared hobby, and later we just kept supporting each other academically."}], [{"role": "user", "content": "I should also mention that sometimes friendships can come from the most unexpected places\u2014like the campus library. I spend a fair amount of time there when I want a quieter space to study. You can imagine how loaded my schedule can be as a fourth-year CS major: I\u2019m juggling advanced courses, an intense research project, plus some extracurricular commitments. So the library is often my go-to for a peaceful environment. Over the years, I\u2019ve bumped into the same faces in the same corner desks. One of those people is Jackson, a law student who somehow ended up reading case studies right next to my stack of cryptography textbooks. We started chatting during water breaks, comparing our respective workloads. Over time, we realized we shared an interest in debates\u2014oddly enough, he loves analyzing political and ethical implications of new technologies, so I\u2019d talk to him all day about algorithmic bias. We discovered that, even though we come from different disciplines, we both care about how technology intersects with society\u2019s legal structures. Our friendship is more intellectual than some of my others, but it\u2019s one I truly value because it broadens my perspective beyond code and servers."}], [{"role": "user", "content": "It\u2019s not just about the academic or hobby-driven friendships, though. I\u2019ve made great friends in unexpected, everyday-life situations, like in the dorm laundry room or while waiting in line for coffee. There was this one day when I was super stressed with a research deadline. I was up too late the night before, so I rushed to the campus cafe half-asleep, waiting in a long line. I started complaining\u2014under my breath, but loud enough for the person behind me to hear\u2014about how I needed more time for my research code to run. Turns out, she was also in the thick of her own mechanical engineering lab fiasco. We commiserated, shared a laugh, and ended up sitting together for coffee. That day, we bonded so much over academic stress that we exchanged contact info, and now, whenever we see each other on campus, we make a point to catch up. She might not be a \u2018best friend\u2019 in the sense of constant hangouts, but it\u2019s still a positive, supportive relationship that started organically."}], [{"role": "user", "content": "I think what unites all these friendships is mutual respect and the sense that we\u2019re all navigating this crazy journey together. Sometimes, people have this caricature of computer science majors as being antisocial or always stuck behind a screen, but truthfully, my experience has been the opposite. We form these deep connections, especially because the workload and pressure can be so high at times. We\u2019re in labs critiquing each other\u2019s code, or we\u2019re on Slack until the crack of dawn throwing out Hail Mary solutions to some bug\u2014and those challenges bond us. It\u2019s not always about competition; it\u2019s about how well we can collaborate and support each other."}], [{"role": "user", "content": "Now, as a fourth-year, graduation is looming, and that means a lot of goodbyes eventually. I\u2019m excited about the future\u2014internships, job prospects, maybe grad school. But I can\u2019t lie: I\u2019m also dreading the day when all these friends I\u2019ve made scatter to different corners of the world. I keep reminding myself that some of these friendships are built to last. We\u2019ve already talked about planning reunions, visiting each other if we end up in different cities, and staying connected online. It\u2019s weird thinking how we\u2019ll be grown adults in different jobs, possibly different time zones, but hopefully still able to maintain the bond we forged here."}], [{"role": "user", "content": "In some ways, I think that\u2019s what real friendship is: you may not always be geographically close, but you have this strong sense of trust, loyalty, and empathy that can transcend distance. I learned that from Tony, who\u2019s interning across the country this semester, yet we still FaceTime regularly. I see the same dedication in others as well\u2014like Carla, who\u2019s applying to master\u2019s programs abroad. We promised that if either of us ends up in Europe, the other has to come visit."}], [{"role": "user", "content": "So yeah, I\u2019m extremely grateful for the friendships I\u2019ve made through sailing, the gym, gaming, research, random campus encounters\u2014everything. They\u2019re truly an integral part of my college experience, and they remind me that there\u2019s more to this journey than racking up achievements on my resume. If I log off Zoom or finish a lab feeling like I can call these people my friends\u2026 that\u2019s what success really looks like to me. And I know that no matter where I end up in my career, I\u2019ll carry a piece of each of these friends with me. Friends aren\u2019t just a chapter in your life story\u2014they can be co-authors, helping you write the best parts."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} +{"id": "memory_prereq_31-student-9", "topic": "Daily Routine & Time Management", "question": [[{"role": "user", "content": "Hey there! Oh, you want to know about my daily routine and how I manage everything? Sure, no problem. It might sound a bit chaotic, but there\u2019s at least some method in my madness. As a fourth-year Computer Science major, time management is my lifeline, so let me walk you through a typical day from start to finish."}], [{"role": "user", "content": "I usually wake up around 6:30 in the morning. It\u2019s become a habit ever since I started wanting to get a head start on projects\u2014though some days, especially if I was up late coding or gaming, I might sneak in an extra 15 minutes or so. The first thing I do is check my calendar on my phone, just to get my head around what\u2019s coming up that day. Then I do some basic stretching right there beside my bed, so I\u2019m not half-asleep when I jump in the shower. I\u2019ve found that if I skip the stretches, I\u2019m sluggish for at least an hour or so, and let\u2019s be honest\u2014who has time for that?"}], [{"role": "user", "content": "After a quick shower, I\u2019ll head to the kitchen in my off-campus apartment. I live with two roommates\u2014one\u2019s also a CS major, the other\u2019s a mechanical engineering major. Usually, we\u2019re all hustling to get out the door. If I\u2019m feeling ambitious, I\u2019ll whip up some scrambled eggs and spinach, maybe have a piece of whole wheat toast, and brew coffee using one of those single-cup machines we all share. If I\u2019m rushed, I\u2019ll just grab a granola bar and some fruit to eat while I head to campus. I try to be economical with time while making sure I eat something relatively nutritious. I\u2019m pretty active, so I can\u2019t slack on breakfast too often."}], [{"role": "user", "content": "Because I\u2019ve strategically scheduled most of my classes in the late morning or early afternoon, I don\u2019t need to leave super early. My first class typically starts at 9:30 or 10:00 A.M. This semester, I\u2019ve got Advanced Algorithms and a Machine Learning elective, as well as a project-based course focusing on distributed systems. It\u2019s a full load, but I kind of enjoy diving into complex topics. During the 15-minute walk to campus, I often put in my earbuds and listen to either a lecture recording (if I\u2019m behind on something) or a music playlist that helps me get in the zone for the day."}], [{"role": "user", "content": "Once I get to campus, I usually find a seat in the student center or a quiet corner of the library to finalize any small tasks\u2014maybe a last-minute tweak to code for a morning lab or adjusting my to-do list for the day. I use a digital task manager that syncs across my devices, so I can reference it whenever. Having all my tasks in one place forces me to stay organized. I categorize tasks by class or project, and I try to break them down into smaller subtasks with clear deadlines. It helps me avoid feeling overwhelmed by big deadlines looming weeks away."}], [{"role": "user", "content": "Class time itself usually extends until around 2:00 P.M. or 3:00 P.M., with a midday break in between. I\u2019ll grab a quick lunch on campus\u2014my go-to is often a turkey and avocado wrap from one of the campus bistros. While I eat, I tend to check email and Slack messages from my research group, where I\u2019m helping develop a computer vision application for analyzing images of marine traffic. My professor likes to set weekly milestones, so I need to constantly keep track of where we are in the cycle. On top of that, I\u2019m part of a programming club that does hackathon prep, and they also often post about upcoming events or workshops. Staying on top of all these alerts is critical, or I\u2019ll miss something important."}], [{"role": "user", "content": "In the afternoon, if I don\u2019t have a lab session or a group project meeting, I\u2019ll head over to the gym for a quick workout\u2014maybe 45 minutes to an hour. I like to do a mix of cardio and weight training. Honestly, exercise is a real stress reliever for me, plus it keeps my energy levels up. I\u2019ve discovered that if I skip the gym too many days in a row, I can\u2019t focus as well on my coding tasks. It\u2019s like the stress seeps into my brain and distracts me. So I consider my workout time to be non-negotiable unless I have a pressing exam or a massive deadline that literally can\u2019t wait."}], [{"role": "user", "content": "By late afternoon, I\u2019m usually found in the computer lab or at the library again. That\u2019s prime coding time for me. If we have a group project, we\u2019ll meet in one of the collaboration rooms\u2014a couple of the guys and I are currently working on this distributed system that handles real-time data from sensor networks. It\u2019s pretty cool, but we\u2019ve got layers upon layers of abstraction to keep track of, and version control is a must. We use GitHub for everything, and each of us has a set of responsibilities. Right now, I'm working on optimizing the database queries so that the system can scale more smoothly when multiple nodes are streaming data at once. It\u2019s a lot to juggle, but that\u2019s where time management skills come into play. We set up a shared project board, with each card assigned to someone, and we mark everything with due dates. That helps keep the entire team accountable and aware of each person\u2019s workload."}], [{"role": "user", "content": "I\u2019ll typically leave campus around 6:00 or 7:00 P.M., depending on how immersed I get in my work or if I have a campus club meeting. I\u2019m part of the Robotics Club, which meets once a week in the early evening. Even though it\u2019s not strictly my main focus area, robotics projects are a good cross-disciplinary experience, and you never know when those skills or knowledge might come in handy."}], [{"role": "user", "content": "When I get home, I'll settle in for dinner\u2014maybe some pasta or anything simple, especially if I\u2019m trying to get back to coding or studying quickly. After dinner, around 8:00 or 9:00 P.M., I fall into my study groove again. This is when I\u2019ll do my heavier reading or work on major code tasks\u2014like finishing lab reports, writing up research notes for the professor, or polishing slides for a group presentation. There are days I lose track of time and look up to see it\u2019s suddenly midnight. But most evenings, I try to cut off academic work by 11:00 P.M. so I can decompress. Decompression might be gaming with friends (online co-op games, usually) or reading a novel I've been working through. Recently, I\u2019ve tried to read more outside of textbooks, just to diversify my mental input. Then, I\u2019ll do a quick review of my schedule for the next day\u2014just a glance at pressing deadlines, any meetings, or appointments. It helps ease any anxiety before I go to bed."}], [{"role": "user", "content": "On weekends, time management looks a bit different. Saturdays might be partially dedicated to longer assignments or bigger project milestones. But I also make space for fun stuff\u2014maybe a sailing trip if the weather\u2019s nice, or hanging out at a friend\u2019s place for a casual gathering. Sundays, I attend a group study session with seven other classmates to prep for the week ahead. That\u2019s where we catch up on each other\u2019s workload and share tips or resources. Sometimes, I even go over internship or job application things on Sunday, just to keep everything in check for post-graduation planning."}], [{"role": "user", "content": "If there\u2019s one main strategy I\u2019d recommend to anyone, it\u2019s to keep a single, unified to-do list and calendar that reflect your actual priorities. For me, it\u2019s a digital solution because I can\u2019t carry a planner everywhere, but some people do well with bullet journals. As long as you know where you stand on assignments, projects, or personal goals, you\u2019ll feel more in control. Also, don\u2019t forget to schedule downtime. It sounds counterintuitive, but letting your brain breathe helps you come back stronger and more productive. Even if it\u2019s just an hour at the end of the day to watch a show or play with your dog, it matters."}], [{"role": "user", "content": "That\u2019s pretty much how my day-to-day goes. Sure, there are days when an unexpected bug throws off my schedule, or a meeting ends up running long, or I just feel unmotivated and need a break. But for the most part, sticking to a routine with enough flexibility built in has been a huge help. College is short, but I want to make the most of it\u2014and staying on top of things is the only way I know how to keep moving forward without feeling like I\u2019m drowning. It\u2019s not perfect, but hopefully it gives you a glimpse into how I handle my time and keep my sanity intact."}]], "involved_classes": ["MemoryAPI"], "scenario": "student"} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/customer_final.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/customer_final.json new file mode 100644 index 000000000..58b948a74 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/customer_final.json @@ -0,0 +1,60 @@ +{ + "core_memory": { + "user_query_about_grinder_compatibility_and_water_filter": "Michael wants to confirm compatibility between a new high-end espresso machine and his existing grinder, and seeks recommendations for a water filtration system suitable for his machine.", + "user_interest_in_educational_resources": "Michael is interested in educational resources such as how-to videos and masterclasses on advanced milk steaming and maintenance for a high-end espresso machine.", + "user_trust_in_brand": "Michael trusts MonoBean and the brand for a substantial purchase, having seen firsthand the dedication to customer satisfaction and the positive impact of the products on his kitchen transformation.", + "user_request_for_pricing_and_deals": "Michael requests detailed information on pricing, shipping, warranties, recommended accessories, and any deals or discounts available for a loyal customer considering a high-end espresso machine." + }, + "archival_memory": { + "user_satisfaction_with_products": "Michael is satisfied with his espresso machine and other coffee products despite initial shipping and damage issues.", + "user_interest_in_subscription_program": "Michael is interested in a subscription or membership program for loyal customers, with perks like free shipping, early access to new products, and special member discounts.", + "user_interest_in_monthly_subscription": "Michael is interested in a monthly coffee bean subscription and an extended 19-month warranty plan for members.", + "user_inquiry_about_membership_program": "Michael inquired about the current subscription or membership program, asking if it includes coffee beans, accessories, machines, or cleaning kits, and whether it operates on a points-based system or a monthly fee model.", + "user_request_to_join_membership_program": "Michael wants to join the membership program, seeking details on extended warranty coverage and coffee bean subscription specifics, including sourcing practices and roast nuances.", + "user_interest_in_early_access_program": "Michael is interested in an early access program for limited-edition items and prototypes, believing it would appeal to a community of fervent coffee lovers.", + "user_inquiry_about_member_portal": "Michael inquired about the integration of membership with the customer account system, specifically about a dedicated portal for tracking orders, managing subscriptions, pausing shipments, and switching bean preferences.", + "user_inquiry_about_membership_cost_and_trial": "Michael inquired about the membership fee structure, trial period availability, and potential discounts or promotional bonuses for loyal customers with multiple purchases.", + "user_inquiry_about_priority_customer_service": "Michael inquired about whether membership or subscription members receive prioritized customer service, including dedicated phone lines or chat support.", + "user_preference_for_coffee_subscription_customization": "Michael prefers espresso drinks and enjoys both dark roasts and lighter roasts for pour-over. He desires flexibility in his coffee subscription, such as a set espresso roast of the month with an optional lighter roast add-on or the ability to swap roasts periodically.", + "user_desire_for_streamlined_purchasing_process": "Michael desires a streamlined purchasing process through membership, including benefits like reduced hassle, bonus freebies, and discount codes on future machines to enhance his coffee experience.", + "user_final_inquiry_about_membership_details": "Michael is seeking detailed information about the existing membership or subscription options, including how they work, their costs, and main perks. He is also interested in any upcoming developments in this area.", + "user_enrollment_interest": "Michael is interested in enrolling in the membership program and wants to explore the next steps for signing up or learning more in detail.", + "user_concern_about_unexpected_charge": "Michael is concerned about an unexpected charge on his credit card statement, possibly related to a recent purchase.", + "user_concern_about_shipping": "Michael is confused and concerned about a potential mix-up or delay in shipping, especially with the local Oregon warehouse shipment.", + "user_confirmed_order_details": "Michael has two separate orders: grinder bundle from Oregon, canisters and scale from eastern distribution center.", + "user_recent_purchases": "Espresso machine, coffee grinder, cleaning tablets, frothing pitcher, vacuum-sealed coffee canisters, and a digital scale", + "user_concern_summary": "Michael is concerned about an unexpected charge on his credit card that does not align with his known recent purchases or orders. He is satisfied with his products but is worried about a potential billing error.", + "user_charge_amount": "$49.99", + "user_charge_date": "three days ago", + "user_charge_descriptor": "Company name only, no additional descriptor", + "user_charge_transaction_id": "Not provided", + "user_account_status": "Michael is a satisfied customer with a history of purchases and inquiries about membership programs.", + "user_request_for_refund_or_clarification": "Michael requests a refund or clarification regarding an unexpected charge of $49.99 posted three days ago with no additional descriptor on his credit card statement.", + "user_feedback_on_espresso_machine": "Michael is generally satisfied with the espresso machine despite initial shipping and damage issues. He appreciates its performance and the quality of his lattes, believing that a better steam wand could further enhance his experience.", + "user_interest_in_coffee_tools": "Michael is interested in additional coffee tools like a digital scale or a temperature-controlled kettle, aiming for a balance between craft, design, and practicality.", + "user_future_product_interest": "Michael is interested in exploring more coffee tools such as a digital scale or a temperature-controlled kettle, with a preference for items that balance craft, design, and practicality.", + "user_shipping_concerns": "Michael is concerned about potential mix-ups or delays in shipping, especially regarding the local Oregon warehouse shipment.", + "user_profession": "Michael is a 35-year-old freelance graphic designer.", + "user_appreciation_for_design_and_quality": "Michael appreciates the design and quality of the coffee machines and accessories, finding them durable, well-constructed, and visually appealing, enhancing his kitchen's aesthetic as a graphic designer.", + "user_loyalty_to_brand": "Michael is loyal to the brand due to the high-quality and stylish products, which have met his expectations and enhanced his kitchen's aesthetic.", + "user_satisfaction_with_performance": "Michael is satisfied with the performance of the espresso machine, particularly the quality of his lattes.", + "user_desire_for_improvement": "Michael believes that a better steam wand could further enhance his experience with the espresso machine.", + "user_shipping_confusion": "Michael is confused and concerned about a potential mix-up or delay in shipping, particularly with the local Oregon warehouse shipment.", + "user_ready_to_purchase": "Michael is ready to complete his purchase of the new electric grinder and related accessories, and is prepared to provide any necessary information for shipping and account details.", + "user_enjoyment_of_espresso_machine_performance": "Michael enjoys the espresso machine's performance, noting it consistently produces rich shots with great crema and that the steam wand is fun for latte art experimentation.", + "user_grinder_impact": "Michael found that adding the coffee grinder was a game-changer, significantly improving flavor and aroma through freshly ground beans.", + "user_use_of_accessories": "Michael uses various accessories like a digital scale to refine his home brewing process, making it more consistent day after day.", + "user_bean_freshness": "Michael has managed to keep his beans fresher than ever with the vacuum-sealed canisters, which is particularly effective in Seattle's humid weather.", + "user_latte_art_exploration": "Michael finds the steam wand enjoyable for latte art experimentation, although his foam skills are still developing.", + "user_preference_for_stylish_products": "Michael prefers stylish and modern products that enhance his kitchen's aesthetic, which he values highly as a graphic designer.", + "user_interest_in_future_tools": "Michael is interested in future coffee tools such as a digital scale or a temperature-controlled kettle, seeking items that balance craft, design, and practicality.", + "user_feedback_summary": "Michael has provided positive feedback on the espresso machine's performance and design, expressing satisfaction with the quality and aesthetic appeal. He is interested in future tools that balance craft, design, and practicality, and has concerns about shipping delays.", + "user_shipping_delays": "Michael has experienced shipping delays, with estimated arrival dates not being met and tracking links stopping updates, causing stress and uncertainty.", + "user_damage_incident": "Michael had an incident where his first espresso machine arrived damaged, which was frustrating despite the quick resolution by the support team.", + "user_support_team_response": "Michael appreciated the quick response from the support team when his espresso machine arrived damaged, which helped resolve the issue promptly.", + "user_seattle_weather_impact": "Michael notes that Seattle's humid weather makes it challenging to keep coffee beans fresh, but the vacuum-sealed canisters have helped mitigate this issue.", + "user_shipping_confusion_details": "Michael experienced confusion with label printing but the package not actually leaving the warehouse, contributing to his shipping frustrations.", + "user_happiness_with_resolution": "Despite the shipping and damage issues, Michael is generally happy with the overall experience and the resolution provided by the support team.", + "user_future_shopping_intent": "Michael intends to continue shopping with the brand, showing loyalty despite past issues, and is ready to complete his purchase of the new electric grinder and related accessories." + } +} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/finance_final.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/finance_final.json new file mode 100644 index 000000000..822b661ef --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/finance_final.json @@ -0,0 +1,57 @@ +{ + "core_memory": { + "sustainable_success_factors": "Long-term success depends on consistent decision-making, risk management, and humility to continue learning. Avoiding complacency from short-term wins and maintaining curiosity and hunger are vital. Growth is the true measure of lasting success." + }, + "archival_memory": { + "esg_scoring_improvement": "Last month, a second-year analyst proposed a novel approach to ESG scoring that we're now implementing across our portfolio.", + "mentorship_program_details": "We've developed a unique mentorship program where senior managers are paired with emerging leaders for year-long partnerships. I personally mentor three high-potential directors, meeting with them biweekly to discuss everything from deal structuring to client management.", + "retention_and_development": "Our retention rate for top performers is 94%, well above the industry average, largely because we invest heavily in their development. Last year, we sent 15 team members to advanced programs at Harvard, Stanford, and INSEAD.", + "accelerated_partnership_path": "We created an accelerated path to partnership that has helped us attract exceptional talent from Goldman Sachs and Morgan Stanley.", + "diversity_initiative": "But perhaps what I'm most proud of is our diversity initiative\u201440% of our senior leadership roles are now held by women and minorities, up from 15% when I took over. We've partnered with organizations like SEO and Girls Who Invest to build a more inclusive pipeline of future leaders.", + "market_crash_2020": "During the 2020 market crash, we had to make tough decisions while maintaining team morale. I remember gathering our entire investment team on a Saturday morning as markets were in freefall. Instead of panicking, we methodically analyzed our positions, identified opportunities, and emerged stronger. I made a point of being on the trading floor every day during that period, sharing both the stress and the strategic decisions with our team. We actually made several key hires during the downturn, which proved transformative for our long-term performance.", + "recent_fund_redemptions": "More recently, when one of our largest funds faced significant redemptions, I took personal responsibility for communicating with investors while empowering our portfolio managers to adjust their strategies. Transparency and presence during difficult times have been crucial to maintaining trust, both with our team and our clients.", + "leadership_under_pressure": "The greatest test of leadership isn't how you perform during bull markets\u2014it's how you guide your team through the storms.", + "innovation_pods": "We've established cross-functional 'innovation pods' where portfolio managers work directly with our technology team to develop new investment strategies.", + "ai_driven_platform": "We recently launched an AI-driven market analysis platform, namely SOAR, developed entirely in-house by a team I assembled from diverse backgrounds\u2014quants, traditional analysts, and machine learning experts.", + "organizational_fluidity": "We've reimagined our organizational structure to be more fluid, allowing talent to move between teams based on market opportunities. This flexibility has given us a significant edge in rapidly evolving markets.", + "future_of_finance_symposium": "I also initiated our 'Future of Finance' symposium, an annual event where we bring together thought leaders from finance, technology, and academia to explore emerging trends. Last year's event led to three strategic partnerships that have fundamentally enhanced our investment capabilities.", + "board_service": "I serve on the boards of three non-profit organizations focused on financial literacy and economic empowerment.", + "scholarship_initiative": "Through our firm's foundation, we've established a $50 million initiative to provide scholarships and internships to underprivileged students interested in finance.", + "public_speaking": "I regularly speak at universities and industry conferences, such as the Financial Institute National Conference, about the importance of ethical leadership in finance.", + "community_teaching_program": "We've also created a pioneering program where our senior executives spend two weeks annually teaching financial skills in underserved communities.", + "social_impact_commitment": "This commitment to social impact has not only strengthened our corporate culture but has also attracted mission-driven talent to our firm. My role as a leader is to demonstrate that financial success and social responsibility aren't mutually exclusive\u2014they're mutually reinforcing.", + "future_leadership_focus": "Looking ahead, I'm focused on developing the next generation of leaders who understand that sustainable success in finance requires both exceptional performance and unwavering integrity.", + "sarah_chen_business_contact": "Sarah Chen, CEO of Pacific Dynamics, is a key business contact with whom I have a strong, long-standing relationship. We met at a Goldman conference in 2008 and connected further on a delayed flight from Hong Kong. Our relationship has lasted fifteen years, built on shared interests and mutual respect. She is a trusted contact for advice on Asian markets.", + "networking_strategies": "Building a strong network involves cultivating relationships that go beyond simple business transactions. Personal connections, such as shared interests and experiences, play a crucial role in maintaining long-term professional relationships.", + "golf_networking_strategy": "Using golf as a networking strategy has proven highly effective. Playing rounds with key contacts has led to significant business deals, such as a $2.3 billion merger. Hosting 'Quarterly Links' at Pebble Beach brings together clients, prospects, and industry leaders, resulting in major client acquisitions and partnerships.", + "caddie_tom_as_information_source": "Tom, my caddie with 30 years of experience carrying bags for CEOs and politicians, serves as an invaluable source of corporate intelligence. He provides insights that are often more valuable than traditional business intelligence sources.", + "client_relationship_strategy": "My approach to client relationships is deeply personal. I oversee 50 high-net-worth individuals and 30 institutional clients, providing personalized attention akin to a family office. I prioritize genuine connections, attending family events and offering support during personal challenges. This approach builds trust, which is essential in this business.", + "travel_networking_strategy": "Travel is essential for maintaining a global network. I'm typically on a plane three times a week, visiting New York, London, Hong Kong, and Dubai. Face-to-face interaction is crucial for certain deals, and I've developed a '48-hour protocol' to respond quickly to major opportunities. A notable example was closing a deal in Singapore with Chipset Core after spending three days together, including a 4 AM dim sum run.", + "forty_eight_hour_protocol": "The '48-hour protocol' ensures that if a major client or opportunity requires face-to-face attention, I can be anywhere in the world within 48 hours. This approach is critical for maintaining strong business relationships and securing significant deals.", + "network_triangle_framework": "I've developed a 'Network Triangle' framework consisting of professional relationships, social connections, and knowledge sharing. This approach helps in building a powerful network that goes beyond simple business transactions.", + "future_focus_dinner_series": "Every quarter, I host intimate dinner series called 'Future Focus' where I bring together diverse groups including fintech innovators, traditional bankers, academics, artists, and philosophers. These off-the-record conversations lead to unexpected collaborations and insights.", + "mentorship_circle_program": "I run a mentorship circle connecting senior executives with promising young talent. This program is my way of paying forward the guidance I received early in my career.", + "family_values_and_work_life_balance": "The user emphasizes the importance of balancing family life with the demands of running an investment firm. They value being present for their family and understand that time is a non-renewable asset.", + "approach_to_client_relationships": "The user takes a personal approach to client relationships, involving themselves deeply, including attending family events and offering support during personal challenges.", + "golf_as_networking_tool": "Golf serves as a powerful networking tool for the user. Memberships at National Golf Links, Cypress Point, and Muirfield have opened numerous doors. Playing rounds with key contacts like Jim, a tech CEO, has led to significant business deals, such as a $2.3 billion merger.", + "childrens_financial_education_approach": "The user introduces financial literacy to their children in an engaging way. The 14-year-old is learning about investments and compounding through a small brokerage account, while the 10-year-old is more interested in fun activities like backyard ziplines. The goal is to teach them money as a tool, not an obsession, emphasizing effort, discipline, and resilience.", + "long_term_career_goals": "The user's long-term career goal is to transition into a chairman role at the firm by 55, focusing on high-level strategy and mentorship rather than day-to-day deal-making.", + "vision_for_global_investment_fund": "The user envisions establishing a global investment fund focused on sustainable and frontier market investments, aiming to bridge the gap between profit and long-term impact.", + "legacy_and_impact_values": "The user believes that wealth isn't just about what you accumulate; it's about what you build and leave behind.", + "networking_importance_in_business": "The user recognizes that in the business world, networking isn't just a list of contacts\u2014it's a lifeline.", + "parents_retirement_planning": "The user's parents are retired and living comfortably due to careful financial planning. The user learned early on that financial security is about protection, not just accumulation. His mother is pragmatic and reminds him to think long-term. Helping his parents transition into retirement without financial stress is a proud achievement, reflecting the user's belief that wealth is most meaningful when it secures the people he loves.", + "financial_discipline_from_parents": "The user's parents, who ran a small business and were pragmatic, instilled a deep respect for financial discipline. The user ensures his parents have everything they need, considering helping them transition into retirement without financial stress as one of his proudest achievements.", + "relationship_with_sarah_chen": "Met at a Goldman conference in 2008, connected further on a delayed flight from Hong Kong. Our relationship has lasted fifteen years, built on shared interests and mutual respect. She is a trusted contact for advice on Asian markets.", + "relationship_with_sibling": "The user has a brilliant and sharp sibling immersed in the tech world. They have a running joke that the user handles 'old money' while the sibling focuses on building the future. They push each other to think differently, maintaining a healthy competitiveness and mutual respect. The user values this relationship and doesn't take it for granted.", + "user_values_and_beliefs": "The user values financial discipline, legacy, and impact. Wealth is seen not just as accumulation but as a tool to secure loved ones and build a lasting impact.", + "user_approach_to_mentorship": "The user aims to transition into a chairman role at the firm by 55, focusing on high-level strategy and mentorship rather than day-to-day deal-making.", + "user_long_term_vision": "The user's long-term vision includes transitioning into a chairman role at the firm by 55, focusing on high-level strategy and mentorship, and establishing a global investment fund with a focus on sustainable and frontier market investments.", + "family_time_structuring": "The user intentionally structures family time, setting aside certain weekends exclusively for family with no work interruptions. They blend business and leisure by turning international deals into family trips when aligned with school breaks, providing cultural exposure for the kids while allowing the user to be present.", + "family_as_grounding_force": "The user considers family as their grounding force, reminding them that wealth is more than numbers on a balance sheet\u2014it's about having people to share life with.", + "user_work_life_balance_strategies": "The user balances work and family life by structuring family time intentionally, setting aside weekends for family with no work interruptions, and blending business and leisure by turning international deals into family trips.", + "finance_professional_advice": "Success in finance comes down to discipline, adaptability, and relationships. Discipline ensures consistency, adaptability helps navigate changes, and strong relationships open doors to opportunities.", + "work_life_balance_strategies": "Intentionally structuring family time by setting aside weekends for family with no work interruptions, and blending business and leisure by turning international deals into family trips.", + "lesson_on_resilience": "Resilience is crucial for success in finance. Intelligence and talent alone aren't enough; one must be able to handle setbacks and learn from them. A major loss early in one's career can be transformed into a learning experience by viewing setbacks as tuition fees for long-term success.", + "lesson_on_creating_luck": "Creating your own luck involves being proactive rather than waiting for opportunities. Reaching out to industry leaders, even for simple questions, can lead to unexpected doors and connections. Luck favors those who position themselves to seize opportunities." + } +} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/healthcare_final.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/healthcare_final.json new file mode 100644 index 000000000..9d12b35c4 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/healthcare_final.json @@ -0,0 +1,52 @@ +{ + "core_memory": { + "lifestyle_changes_focus": "Focuses on lifestyle changes to minimize reliance on medications.", + "health_goal": "Goal is to maintain health naturally where possible.", + "medication_review_schedule": "Patient and doctor review medications every few months." + }, + "archival_memory": { + "latest_bloodwork_results": "Latest bloodwork showed some fluctuations but nothing alarming.", + "lifestyle_adjustments": "Patient adjusts lifestyle based on health numbers, considering seasonal effects in Kansas.", + "seasonal_health_impact": "Seasons in Kansas affect patient's energy levels and joint pain.", + "fasting_blood_glucose": "135 mg/dL, higher than desired target of under 100 mg/dL.", + "total_cholesterol": "195 mg/dL, which is acceptable.", + "ldl_cholesterol": "130 mg/dL, higher than the recommended under 100 mg/dL.", + "hdl_cholesterol": "58 mg/dL, which is in a good range.", + "triglycerides": "140 mg/dL, slightly elevated but not too concerning.", + "cholesterol_diet_advice": "Increase intake of healthy fats like avocados and nuts, and cut back on saturated fats.", + "blood_pressure_reading": "138/85 mmHg, which is on the high side.", + "target_blood_pressure": "Below 130/80 mmHg to reduce heart issue risks.", + "home_blood_pressure_tracking": "Readings fluctuate between 135-140 systolic and 80-90 diastolic, influenced by stress and salt intake.", + "blood_pressure_lifestyle_changes": "Cutting back on sodium and drinking more water helps manage blood pressure.", + "vitamin_d_levels": "22 ng/mL, which is considered deficient. Normal range is 30-50 ng/mL.", + "vitamin_d_supplement": "Patient started taking 2,000 IU of vitamin D daily.", + "tsh_levels": "2.1 mIU/L, which is within the normal range of 0.5-4.5 mIU/L.", + "bone_density_scan_results": "Spine T-score: -1.8 (osteopenia), Hip T-score: -1.5 (borderline).", + "bone_health_advice": "Doctor advised maintaining strength training and calcium intake to prevent further bone loss.", + "dietary_changes_for_bone_health": "Started adding more dairy and leafy greens to the diet, and taking a calcium supplement with vitamin K2.", + "alt_levels": "42 U/L, slightly elevated (normal under 35 U/L).", + "creatinine_levels": "0.9 mg/dL, which is within the normal range of 0.6-1.2 mg/dL.", + "liver_function_concern": "ALT levels elevated, possibly related to recent medication adjustments.", + "crp_levels": "3.2 mg/L, slightly elevated (ideal under 3.0 mg/L), indicating mild inflammation likely related to osteoarthritis.", + "esr_levels": "18 mm/hr, which is within the normal range of 0-20 mm/hr.", + "patient_medication_history": "Patient has a history of diabetes, hypertension, osteoarthritis, and hypothyroidism. Has adjusted medications over time to find the best fit. Regular reviews with doctor every few months.", + "patient_medication_tracking": "Patient keeps detailed track of all medications to avoid missed doses or prescription mix-ups.", + "diabetes_treatment_plan": "Patient takes metformin 1000 mg twice daily for Type 2 Diabetes. A1C is around 6.9%. Doctor suggested adding semaglutide (Ozempic) at a low dose. Patient notes appetite control as a benefit. Fasting blood sugar levels are typically 125-130 mg/dL.", + "hypertension_treatment_plan": "Patient manages hypertension with medication, though specific drugs are not mentioned.", + "osteoarthritis_treatment_plan": "Patient manages osteoarthritis with medication, though specific drugs are not mentioned.", + "hypothyroidism_treatment_plan": "Patient manages hypothyroidism with medication, though specific drugs are not mentioned.", + "current_medications": "Metformin 1000 mg twice daily for Type 2 Diabetes. Semaglutide (Ozempic) at a low dose added recently.", + "hypertension_medications": "Patient takes losartan 50 mg once daily for high blood pressure. Readings are mostly in the 135-140/80-90 mmHg range. Doctor suggests stress management and reducing sodium intake instead of increasing dosage. Patient also takes a daily magnesium supplement for blood pressure regulation.", + "osteoarthritis_medications": "Patient manages osteoarthritis with medication, though specific drugs are not mentioned.", + "hypothyroidism_medications": "Patient manages hypothyroidism with medication, though specific drugs are not mentioned.", + "lifestyle_modifications_for_hypertension": "Patient's doctor recommends stress management and reducing sodium intake to help manage blood pressure.", + "supplements_for_blood_pressure": "Patient takes a daily magnesium supplement, which they've heard can help with blood pressure regulation.", + "hypothyroidism_treatment_details": "Patient is stable on levothyroxine 75 mcg every morning for hypothyroidism. Takes it on an empty stomach and waits at least 30 minutes before eating. Last TSH test was 2.1 mIU/L, within normal range. Doctor has not changed dosage in years. Patient feels sluggish if a dose is missed, so uses a pill organizer to stay on track.", + "osteoarthritis_treatment_details": "Patient manages osteoarthritis with acetaminophen as needed for mild pain and meloxicam 7.5 mg for severe stiffness. Does not take meloxicam daily to avoid overuse of NSAIDs. Doctor suggested glucosamine supplements, but patient is unsure of their effectiveness.", + "vitamin_d_and_calcium_supplementation": "Patient's last blood test showed low vitamin D levels at 22 ng/mL. Now takes vitamin D3 2,000 IU daily. Also takes calcium supplements due to early signs of osteopenia. Doctor recommended combining calcium with vitamin K2 for better absorption.", + "cholesterol_management": "Patient takes atorvastatin 10 mg daily to manage cholesterol. LDL was 130 mg/dL and triglycerides were 140 mg/dL. Cholesterol numbers have been improving with no reported side effects.", + "general_health_supplements": "Patient takes a daily multivitamin, omega-3 supplements for heart health, and probiotics for digestion. Since gallbladder removal, takes digestive enzymes when eating heavier foods.", + "medication_interactions": "Patient takes losartan and meloxicam occasionally. Doctor warned that NSAIDs can affect kidney function, so meloxicam is taken sparingly. Thyroid medication is taken separately from calcium and iron supplements to prevent interference with absorption.", + "patient_medication_routine": "Patient finds their medication routine effective despite its complexity. Uses a pill planner and phone reminders to stay organized. Focuses on lifestyle changes to minimize reliance on medications. Goal is to maintain health naturally where possible." + } +} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/notetaker_final.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/notetaker_final.json new file mode 100644 index 000000000..1eb5a3519 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/notetaker_final.json @@ -0,0 +1,55 @@ +{ + "core_memory": { + "daycare_schedule": "Kid's daycare: drop-off at 8 AM on Monday, pick-up at 5 PM. Monthly daycare payment is due Friday. Bring an extra set of clothes.", + "monday_work_notes": "Monday: team meeting ran over by 30 minutes. Finalize Q1 budget report before Friday. IT updated security protocols, so change passwords ASAP. Client call with Jacob delayed to Wednesday.", + "counting_practice": "Help the kid with counting practice; the teacher says to focus on the numbers 1-20." + }, + "archival_memory": { + "team_meeting_overran": "Monday: the team meeting ran over by 30 minutes.", + "budget_report_q1_deadline": "Need to finalize the Q1 budget report before Friday.", + "security_protocols_password_change": "IT updated security protocols, so change passwords ASAP.", + "client_call_jacob_rescheduled": "Client call with Jacob was delayed until Wednesday.", + "system_downtime_window": "Tuesday: system downtime from 10 AM - 12 PM slowed down the workflow.", + "hr_benefits_package_review": "HR sent an updated benefits package; review it before next month's deadline.", + "vendor_followup_emails": "Sent follow-up emails to vendors, still waiting on responses.", + "leadership_presentation_slides": "Wednesday: presentation to leadership went well, but tweak a few slides for next week's review.", + "client_proposal_legal_approval": "Client proposal draft is ready; send it to Legal for approval.", + "onboard_new_hire": "Need to onboard a new hire on Wednesday; schedule a one-on-one with them.", + "cost_cutting_recommendations": "Thursday: Finance wants cost-cutting recommendations; brainstorm ideas.", + "sales_data_mistake": "Caught a mistake in last month's sales data.", + "vendor_responses_count": "Followed up with vendors: two responded, one is still pending.", + "training_session_prep": "Training session next Monday; prep the materials this weekend.", + "tax_documents_deadline": "Urgent: submit tax documents before Friday.", + "credit_card_statement_review": "Review the credit card statement for errors.", + "car_engine_noise": "Call the auto repair shop about a weird noise in the engine.", + "quarterly_budget_finalize": "Work: finalize the quarterly budget.", + "vendor_contract_review": "Review the vendor contract before signing.", + "security_compliance_training": "Finish the security compliance training module.", + "backup_laptop_files": "Tech: back up laptop files.", + "update_phone_software": "Update the phone software.", + "cancel_unused_subscriptions": "Cancel unused subscriptions.", + "dentist_appointment_schedule": "Personal: schedule a dentist appointment.", + "gym_three_times": "Stick to the gym 3 times this week.", + "call_mom": "Call mom, it's been a while.", + "car_sunday_wash_vacuum": "Car: wash and vacuum it on Sunday; check tire pressure; refill windshield wiper fluid.", + "pantry_restock_rice_cereal": "Restock the pantry, running low on rice and cereal.", + "diy_home_fixes": "DIY fixes: tighten the loose cabinet handle, replace the bathroom lightbulb, and patch up minor wall scuffs.", + "trash_out_wednesday": "Take the trash out Wednesday morning; vacuum the living room; mop the kitchen floor.", + "donate_old_clothes": "Sort through the closet and donate old clothes; shred old mail.", + "daycare_dropoff_time": "Daycare drop-off is at 8 AM on Monday.", + "daycare_pickup_time": "Daycare pick-up is at 5 PM.", + "daycare_monthly_payment_due": "The monthly daycare payment is due Friday.", + "elementary_school_admission": "Finalize the elementary school admission paperwork; attend orientation next Thursday.", + "pediatrician_visit": "Kid's cold isn't improving; schedule a pediatrician visit.", + "kid_park_weekend": "Take the kid to the park this weekend and pick a new bedtime story book.", + "school_supplies_shopping": "Get school supplies: crayons, notebooks, and glue sticks.", + "buy_diapers": "Buy more diapers and pack extra snacks in the daycare bag.", + "annual_physical_bloodwork": "Schedule an annual physical and bloodwork next month.", + "eye_exam": "Look into getting an eye exam; been straining at screens a lot.", + "workout_strength_training": "Gym at least 3x this week; focus on strength training for legs and back; aim for 10,000 steps daily.", + "meal_prep_protein": "Sunday meal prep: grilled chicken, quinoa, and veggies.", + "reduce_sugar_hydration": "Cut back on sugar and soda; aim for at least 3L of water per day.", + "sleep_screen_time": "Mental health: get 7+ hours of sleep and limit screen time before bed.", + "morning_supplements_probiotics": "Supplements: take probiotics in the morning to help digestion; Vitamin D3 1000 IU daily; Omega-3s for joints; iron for low levels." + } +} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/student_final.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/student_final.json new file mode 100644 index 000000000..7b0c36a79 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/memory_snapshots/student_final.json @@ -0,0 +1,52 @@ +{ + "core_memory": { + "evening_coding_time": "User spends late afternoon in computer lab or library for coding. Currently working on a distributed system project involving real-time sensor data. They optimize database queries for scalability and use GitHub for version control. Team uses a shared project board with due dates for accountability.", + "evening_outings": "User leaves campus around 6:00 or 7:00 P.M. Depending on work immersion or club meetings. Part of the Robotics Club, which meets once a week in the early evening.", + "evening_study_habit": "User eats simple dinner and studies from 8:00 to 9:00 P.M. on academic tasks. Sometimes loses track of time but stops by 11:00 P.M. for decompression via gaming or reading. Reviews schedule for the next day to ease anxiety.", + "weekend_schedule": "On weekends, user dedicates Saturdays to longer assignments or project milestones, and enjoys fun activities like sailing or casual gatherings. Sundays involve attending a group study session with classmates to prep for the week ahead, and sometimes reviews internship or job applications.", + "time_management_strategy": "User recommends a single, unified digital to-do list and calendar reflecting actual priorities. Scheduling downtime is crucial for mental recovery and increased productivity. Even a short break, like watching a show or playing with a dog, helps maintain strength and focus.", + "routine_flexibility": "User's daily routine includes flexibility to adapt to unexpected events like bugs, long meetings, or feeling unmotivated. Believes that sticking to a routine with flexibility helps manage time effectively and maintain sanity during college." + }, + "archival_memory": { + "user_sister_introduction_to_games": "User's younger sister introduced him to some of his favorite mobile games.", + "user_dinner_table_conversations": "Conversations around the dinner table often revolve around the family's golden retriever, Archie.", + "archie_dog_early_memories": "User got Archie, a golden retriever, during winter break of his second year of college. He was tiny and fluffy then, and the user spent time teaching him commands like 'sit' and 'stay'.", + "archie_dog_current_status": "Archie is now three years old and has grown significantly from his tiny, fluffy days.", + "user_dog_video_call_behavior": "Archie reacts excitedly to user's voice during video calls, wagging his tail and whining, indicating recognition and emotional connection.", + "user_academic_stress_relief": "User finds stress relief in video calls with Archie, which helps de-stress from coding assignments and debugging sessions.", + "user_father_coding_influence": "User's father, a software engineer, sparked his interest in computer science through coding exercises in high school.", + "user_mother_emotional_support": "User's mother, a nurse, is the emotional backbone of the family, offering support and encouragement during stressful times.", + "user_family_balance": "User finds a healthy balance between analytical thinking from his father and empathetic nature from his mother.", + "user_sister_tech_enthusiasm": "User's sister is very tech-savvy, active on social media, and plays new games, often joking about potentially overshadowing the user in tech.", + "user_hiking_with_archie": "User enjoys Saturday morning hikes with Archie, where they visit a scenic trail with a lake view. Archie loves the activity and runs around freely.", + "user_appreciation_of_nature": "Hiking with Archie reminds the user to appreciate small things and enjoy nature, helping to balance the stress of academic life.", + "user_grandma_antique_vase_story": "User's grandma teasingly recounts the story of when the user broke her antique vase in middle school by throwing a mini football in the living room.", + "user_family_holiday_traditions": "User's family values spending time with relatives, making holiday gatherings significant events, especially Thanksgiving, which is described as a potluck extravaganza.", + "user_archie_food_safety": "User learned to be cautious with Archie's diet after he had a bad reaction to turkey bones, requiring a late-night vet visit.", + "user_personal_story_writing": "User writes personal stories, such as those involving Archie or his sister, in scholarship and grad school applications, as they hold significant personal meaning.", + "user_academic_resilience": "User's resilience in academic work stems from family values, particularly from his parents' teachings on patience and problem-solving.", + "user_family_support_system": "User's family provides a strong support system, offering comfort and a sense of belonging, especially through interactions with Archie.", + "user_academic_motivation": "User's motivation to persevere through academic challenges is driven by the support and comfort provided by his family and Archie.", + "user_college_friendship_journey": "User's journey of forming close friendships at college, starting with initial nervousness about fitting in, has led to strong bonds that help manage the stress of being a fourth-year Computer Science major.", + "user_and_tony_shared_interests": "User and Tony share interests in working with their hands, with Tony building things physically and the user coding and designing logic. They have collaborated on cross-disciplinary robotics projects.", + "user_tony_as_emotional_support": "Tony is a reliable support system for the user during overwhelming times or for casual outings like grabbing pizza after a long day.", + "user_sailing_club_bonding": "User's bonding with sailing club friends involves trust and mutual respect developed through shared experiences on the water. They rely on each other for motivation and support, especially during early morning sessions for optimal sailing conditions.", + "user_friend_mariah": "Mariah, a close friend from the sailing club, encouraged the user to apply for a challenging research position in data science, believing in his skills. She serves as a confidante offering unwavering support.", + "user_difficult_systems_programming_course": "In sophomore year, user took a notoriously difficult systems programming course with a final project involving building a mini operating system kernel from scratch. The course was challenging due to memory management bugs.", + "user_friendship_with_carla": "User became close friends with Carla, another CS major, during the difficult systems programming course. They bonded over a late-night pizza session in the lab when they were the only ones left working on the project.", + "user_gym_buddy_dean": "Dean is a fellow computer science major who is into machine learning and hardware acceleration projects. User and Dean started talking shop between sets at the gym, leading to sharing notes on classes, nutritional tips, workout regimens, and gaming together.", + "user_gym_friendship_benefits": "The friendship with Dean has provided mutual support in various aspects of life, including academics, fitness, nutrition, and recreational activities like gaming.", + "user_e_sports_club": "User participates in an informal e-sports club that meets in a common lounge in the student center. The club provides a social outlet through multiplayer strategy games, offering mental breaks from academic work.", + "user_friend_derek": "Derek is a friend made through the e-sports club. He is skilled in networking security and taught the user techniques that were useful in a cybersecurity research project. Their friendship evolved from a shared hobby to mutual academic support.", + "user_meeting_jackson_in_library": "User met Jackson, a law student, in the campus library where they both spent time studying. They started chatting during water breaks, comparing workloads and discovering shared interests in debates about the ethical and political implications of new technologies, particularly algorithmic bias.", + "user_jackson_friendship_benefits": "The friendship with Jackson broadens the user's perspective beyond code and servers, offering insights into how technology intersects with society's legal structures.", + "user_campus_cafe_friendship": "User met a friend at a campus cafe while waiting in line, where he was complaining about research deadline stress. The friend was also dealing with a mechanical engineering lab issue. They bonded over shared academic stress, exchanged contact info, and now catch up whenever they see each other on campus.", + "user_friendship_benefits_from_unexpected_places": "The friendship formed in an unexpected place, like the campus cafe, offers positive and supportive interaction that started organically. It demonstrates how shared experiences and mutual understanding can create meaningful connections.", + "user_cs_friendships_bonding": "User's friendships in computer science are united by mutual respect and the shared experience of navigating the college journey together. The high workload and pressure foster deep connections through collaboration and support, rather than competition.", + "user_cs_friendships_challenges": "The challenges faced by CS students, such as working in labs critiquing code or collaborating on Slack until dawn to solve bugs, create bonds that are based on mutual support rather than competition.", + "user_graduation_sentiments": "User is excited about future opportunities like internships, job prospects, and possibly grad school, but dreads the prospect of saying goodbye to friends as they scatter to different parts of the world.", + "user_friendship_longevity_plans": "User is optimistic about maintaining friendships post-graduation by planning reunions, visiting each other if they end up in different cities, and staying connected online.", + "user_friendship_transcending_distance": "User believes real friendship transcends geographical distance, built on trust, loyalty, and empathy. Examples include Tony, who is interning across the country but maintains regular FaceTime calls, and Carla, who is applying to master's programs abroad with a promise to visit each other if they end up in Europe.", + "user_friendships_as_life_co_authors": "User views his friendships as co-authors in his life story, integral to his college experience and representing true success. He believes that having people he can call friends is more meaningful than accumulating achievements on his resume." + } +} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/multi_turn_func_doc/memory_kv.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/multi_turn_func_doc/memory_kv.json new file mode 100644 index 000000000..236e15117 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/multi_turn_func_doc/memory_kv.json @@ -0,0 +1,15 @@ +{"name": "archival_memory_add", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Add a key-value pair to the long-term memory. Make sure to use meaningful keys for easy retrieval later.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key under which the value is stored. The key should be unique and case-sensitive. Keys must be snake_case and cannot contain spaces."}, "value": {"type": "string", "description": "The value to store in the long-term memory. "}}, "required": ["key", "value"]}, "response": {"type": "dict", "properties": {"status": {"type": "string", "description": "Status of the operation."}}}} +{"name": "archival_memory_clear", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Clear all key-value pairs from the long-term memory, including those from previous interactions. This operation is irreversible.", "parameters": {"type": "dict", "properties": {}, "required": []}, "response": {"type": "dict", "properties": {"status": {"type": "string", "description": "Status of the operation."}}}} +{"name": "archival_memory_key_search", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Search for key names in the long-term memory that are similar to the query using BM25+ algorithm.", "parameters": {"type": "dict", "properties": {"query": {"type": "string", "description": "The query text to search for."}, "k": {"type": "integer", "description": "The number of results to return. ", "default": 5}}, "required": ["query"]}, "response": {"type": "dict", "properties": {"ranked_results": {"type": "array", "description": "A list of tuples containing the BM25+ score and the key.", "items": {"type": "array", "items": [{"type": "float"}, {"type": "string"}]}}}}} +{"name": "archival_memory_list_keys", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: List all keys currently in the long-term memory.", "parameters": {"type": "dict", "properties": {}, "required": []}, "response": {"type": "dict", "properties": {"keys": {"type": "array", "description": "A list of all keys in the long-term memory.", "items": {"type": "string"}}}}} +{"name": "archival_memory_remove", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Remove a key-value pair from the long-term memory.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key to remove from the long-term memory. Case-sensitive. "}}, "required": ["key"]}, "response": {"type": "dict", "properties": {"status": {"type": "string", "description": "Status of the operation."}}}} +{"name": "archival_memory_replace", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Replace a key-value pair in the long-term memory with a new value.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key to replace in the long-term memory. Case-sensitive."}, "value": {"type": "string", "description": "The new value associated with the key. "}}, "required": ["key", "value"]}, "response": {"type": "dict", "properties": {"status": {"type": "string", "description": "Status of the operation."}}}} +{"name": "archival_memory_retrieve", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Retrieve the value associated with a key from the long-term memory. This function does not support partial key matching or similarity search.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key to retrieve. Case-sensitive. The key must match exactly with the key stored in the memory. "}}, "required": ["key"]}, "response": {"type": "dict", "properties": {"value": {"type": "string", "description": "The value associated with the key."}}}} +{"name": "core_memory_add", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Add a key-value pair to the short-term memory. Make sure to use meaningful keys for easy retrieval later.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key under which the value is stored. The key should be unique and case-sensitive. Keys must be snake_case and cannot contain spaces."}, "value": {"type": "string", "description": "The value to store in the short-term memory. "}}, "required": ["key", "value"]}, "response": {"type": "dict", "properties": {"status": {"type": "string", "description": "Status of the operation."}}}} +{"name": "core_memory_clear", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Clear all key-value pairs from the short-term memory, including those from previous interactions. This operation is irreversible.", "parameters": {"type": "dict", "properties": {}, "required": []}, "response": {"type": "dict", "properties": {"status": {"type": "string", "description": "Status of the operation."}}}} +{"name": "core_memory_key_search", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Search for key names in the short-term memory that are similar to the query using BM25+ algorithm.", "parameters": {"type": "dict", "properties": {"query": {"type": "string", "description": "The query text to search for."}, "k": {"type": "integer", "description": "The number of results to return. ", "default": 5}}, "required": ["query"]}, "response": {"type": "dict", "properties": {"ranked_results": {"type": "array", "description": "A list of tuples containing the BM25+ score and the key.", "items": {"type": "array", "items": [{"type": "float"}, {"type": "string"}]}}}}} +{"name": "core_memory_list_keys", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: List all keys currently in the short-term memory.", "parameters": {"type": "dict", "properties": {}, "required": []}, "response": {"type": "dict", "properties": {"keys": {"type": "array", "description": "A list of all keys in the short-term memory.", "items": {"type": "string"}}}}} +{"name": "core_memory_remove", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Remove a key-value pair from the short-term memory.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key to remove from the short-term memory. Case-sensitive. "}}, "required": ["key"]}, "response": {"type": "dict", "properties": {"status": {"type": "string", "description": "Status of the operation."}}}} +{"name": "core_memory_replace", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Replace a key-value pair in the short-term memory with a new value.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key to replace in the short-term memory. Case-sensitive."}, "value": {"type": "string", "description": "The new value associated with the key. "}}, "required": ["key", "value"]}, "response": {"type": "dict", "properties": {"status": {"type": "string", "description": "Status of the operation."}}}} +{"name": "core_memory_retrieve", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Retrieve the value associated with a key from the short-term memory. This function does not support partial key matching or similarity search.", "parameters": {"type": "dict", "properties": {"key": {"type": "string", "description": "The key to retrieve. Case-sensitive. The key must match exactly with the key stored in the memory. "}}, "required": ["key"]}, "response": {"type": "dict", "properties": {"value": {"type": "string", "description": "The value associated with the key."}}}} +{"name": "core_memory_retrieve_all", "description": "This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system. Tool description: Retrieve all key-value pairs from the short-term memory.", "parameters": {"type": "dict", "properties": {}, "required": []}, "response": {"type": "dict", "properties": {"key": {"type": "string", "description": "Each key in the short-term memory."}, "value": {"type": "string", "description": "The value associated with each key."}}}} diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/possible_answer/BFCL_v4_memory.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/possible_answer/BFCL_v4_memory.json new file mode 100644 index 000000000..7ca662476 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/possible_answer/BFCL_v4_memory.json @@ -0,0 +1,155 @@ +{"id": "memory_0-customer-0", "ground_truth": ["Michael"], "source": "My name is Michael, and this is my first time interacting with your company..."} +{"id": "memory_1-customer-1", "ground_truth": ["35", "thirty five"], "source": "I'm 35 years old, live in Seattle..."} +{"id": "memory_2-customer-2", "ground_truth": ["Seattle"], "source": "I'm 35 years old, live in Seattle..."} +{"id": "memory_3-customer-3", "ground_truth": ["strawberry matcha"], "source": "...I occasionally like a strawberry matcha latte..."} +{"id": "memory_4-customer-4", "ground_truth": ["38", "thirty eight"], "source": "...my counter is only 38 square feet."} +{"id": "memory_5-customer-5", "ground_truth": ["17", "seventeen"], "source": "...a new-customer 17% off discount or promotional offer..."} +{"id": "memory_6-customer-6", "ground_truth": ["frothing pitcher", "filters"], "source": "Plus, I qualified for that free-shipping threshold after adding a couple of accessories like the stainless-steel frothing pitcher and some extra filters..."} +{"id": "memory_7-customer-7", "ground_truth": ["11", "eleven"], "source": "...the checkout page gave me a reasonable estimated delivery time of eleven business days..."} +{"id": "memory_8-customer-8", "ground_truth": ["family gathering"], "source": "...I was really excited about getting it before my small family gathering this week..."} +{"id": "memory_9-customer-9", "ground_truth": ["steam wand"], "source": "...it looks like the steam wand is also bent at an odd angle..."} +{"id": "memory_10-customer-10", "ground_truth": ["7", "seven"], "source": "...the outer box had seven crushed corners..."} +{"id": "memory_11-customer-11", "ground_truth": ["pitcher"], "source": "...The pitcher... seems to be okay, with just a tiny scratch on the outside..."} +{"id": "memory_12-customer-12", "ground_truth": ["toaster oven"], "source": "...my toaster oven is basically banished to the pantry..."} +{"id": "memory_13-customer-13", "ground_truth": ["39", "thirty nine"], "source": "...a $39 specialized storage canister that’s supposed to keep beans fresher for longer."} +{"id": "memory_14-customer-14", "ground_truth": ["steam wand"], "source": "My lattes are consistently delicious, and I\u2019m sure they could be even better with the right steam wand. "} +{"id": "memory_15-customer-15", "ground_truth": ["Garmin"], "source": "...a digital scale you collaborated with Garmin on."} +{"id": "memory_16-customer-16", "ground_truth": ["Portland"], "source": "...I\u2019m heading out of town next weekend for a small freelance gig in Portland."} +{"id": "memory_17-customer-17", "ground_truth": ["Oregon"], "source": "The coffee grinder bundle... was marked as shipped from one of your regional warehouses, which... is located in Oregon..."} +{"id": "memory_18-customer-18", "ground_truth": ["digital art"], "source": "My latest experiments include practicing some latte art designs that mirror my digital artwork..."} +{"id": "memory_19-customer-19", "ground_truth": ["19", "nineteen"], "source": "...an extended 19-month warranty plan for members, has me all kinds of excited."} +{"id": "memory_20-customer-20", "ground_truth": ["513", "five hundred thirteen", "five hundred and thirteen"], "source": "I\u2019d be thrilled if your subscription included nuanced, 513 mL, small-batch roasts that I can\u2019t easily find in my local grocery store."} +{"id": "memory_21-customer-21", "ground_truth": ["3", "three"], "source": "It was posted three days ago."} +{"id": "memory_22-customer-22", "ground_truth": ["49.99", "$49.99"], "source": "The total amount was around, oh, let\u2019s say $49.99..."} +{"id": "memory_23-customer-23", "ground_truth": ["2", "two"], "source": "I actually received two 'order confirmed' emails..."} +{"id": "memory_24-customer-24", "ground_truth": ["Creative Boom"], "source": "...my kitchen now looks like a page out of a Creative Boom design magazine..."} +{"id": "memory_25-customer-25", "ground_truth": ["1.8"], "source": "I understand that shipping can be unpredictable, especially with the average 1.8 inches of rainfall everyday..."} +{"id": "memory_26-customer-26", "ground_truth": ["coffee_creations_1237"], "source": "I'm always snapping photos of my new creations and sharing them on my instagram page at @coffee_creations_1237..."} +{"id": "memory_27-customer-27", "ground_truth": ["art contest"], "source": "...I\u2019ve had a few small events at my place\u2014family gatherings, design meetups, even an impromptu latte-art contest..."} +{"id": "memory_28-customer-28", "ground_truth": ["corner dent"], "source": "I\u2019d like to mitigate any chance of corner dents after a long journey from the warehouse."} +{"id": "memory_29-customer-29", "ground_truth": ["MonoBean"], "source": "If there\u2019s any brands I trust for a potentially substantial purchase, it\u2019s MonoBean and yours..."} +{"id": "memory_30-healthcare-0", "ground_truth": ["Diabetes"], "source": "\"The biggest health issue I deal with is Type 2 Diabetes.\""} +{"id": "memory_31-healthcare-1", "ground_truth": ["10", "ten"], "source": "\"I was diagnosed about ten years ago,\""} +{"id": "memory_32-healthcare-2", "ground_truth": ["walk"], "source": "\"something as simple as going for a 30-minute walk after dinner can make a huge difference in keeping my glucose levels stable.\""} +{"id": "memory_33-healthcare-3", "ground_truth": ["Hypertension", "blood pressure"], "source": "\"The scariest moment was when my blood pressure spiked dangerously high a few years ago, and I ended up in the ER.\""} +{"id": "memory_34-healthcare-4", "ground_truth": ["Gallbladder", "Cholecystectomy"], "source": "\"Then there was my surgery a few years ago\u2014gallbladder removal.\""} +{"id": "memory_35-healthcare-5", "ground_truth": ["Notebook"], "source": "\"I keep a little notebook to track everything\u2014appointments, medication changes, even just notes about how I\u2019m feeling.\""} +{"id": "memory_36-healthcare-6", "ground_truth": ["Hypothyroidism"], "source": "\"Oh! And one condition I was really nervous about at first but have learned to manage is hypothyroidism. I was diagnosed in my early 40s...\""} +{"id": "memory_37-healthcare-7", "ground_truth": ["Vaccine"], "source": "\"I also had a bout of pneumonia a couple of years ago, which knocked me out for weeks. Ever since then, I\u2019ve been super diligent about getting my flu shot and pneumonia vaccine.\""} +{"id": "memory_38-healthcare-8", "ground_truth": ["good diet"], "source": "\"My biggest focus right now is on maintaining a good diet.\""} +{"id": "memory_39-healthcare-9", "ground_truth": ["avocado"], "source": "\"My go-to breakfast is usually Greek yogurt with nuts and berries or eggs with avocado.\""} +{"id": "memory_40-healthcare-10", "ground_truth": ["03:00 PM"], "source": "\"I avoid caffeine after 3 PM, keep my bedroom cool and dark, and read a book before bed instead of looking at my phone.\""} +{"id": "memory_41-healthcare-11", "ground_truth": ["salmon"], "source": "\"One of my favorite go-to meals is grilled salmon with roasted vegetables\u2014it\u2019s simple, delicious, and packed with nutrients that help with inflammation.\""} +{"id": "memory_42-healthcare-12", "ground_truth": ["consistency"], "source": "Exercise has also changed for me. I used to think workouts had to be intense to be effective, but I've learned that consistency is more important."} +{"id": "memory_43-healthcare-13", "ground_truth": ["book club"], "source": "\"One thing I didn\u2019t expect about aging is how important social connections are. ... I make it a point to have coffee with friends at least once a week, and I joined a book club last year.\""} +{"id": "memory_44-healthcare-14", "ground_truth": ["puzzles"], "source": "\"Let\u2019s talk about memory. I wouldn\u2019t say I\u2019m forgetful, but I definitely have more \u2018where did I put my keys?\u2019 moments than I used to. I try to keep my brain sharp by doing puzzles, reading, and even learning new skills. I started taking an online class on art history just for fun...\""} +{"id": "memory_45-healthcare-15", "ground_truth": ["135", "One hundred thirty five", "One hundred and thirty five"], "source": "\"My most recent fasting blood glucose test came back at 135 mg/dL\""} +{"id": "memory_46-healthcare-16", "ground_truth": ["6.9"], "source": "\"My A1C was 6.9%\""} +{"id": "memory_47-healthcare-17", "ground_truth": ["100", "One Hundred"], "source": "\"which is a bit higher than I\u2019d like\u2014it should ideally be under 100 mg/dL\""} +{"id": "memory_48-healthcare-18", "ground_truth": ["130", "One hundred thirty", "One hundred and thirty"], "source": "\"my LDL (bad cholesterol) was 130 mg/dL\u2014higher than the recommended under 100 mg/dL.\""} +{"id": "memory_49-healthcare-19", "ground_truth": ["138/85"], "source": "\"At my last check-up, my reading was 138/85 mmHg\""} +{"id": "memory_50-healthcare-20", "ground_truth": ["22", "Twenty two"], "source": "\"my levels came back at 22 ng/mL, which is considered deficient. The normal range is 30-50 ng/mL\""} +{"id": "memory_51-healthcare-21", "ground_truth": ["42", "Forty two"], "source": "\"my ALT levels were a little elevated at 42 U/L (normal is under 35 U/L)\""} +{"id": "memory_52-healthcare-22", "ground_truth": ["18", "Eighteen"], "source": "\"My ESR (erythrocyte sedimentation rate) was 18 mm/hr\""} +{"id": "memory_53-healthcare-23", "ground_truth": ["Metformin 1000 mg"], "source": "\"For my Type 2 Diabetes, I\u2019ve been taking metformin 1000 mg twice daily for the last several years.\""} +{"id": "memory_54-healthcare-24", "ground_truth": ["Losartan 50 mg"], "source": "\"For my high blood pressure, I take losartan 50 mg once daily.\""} +{"id": "memory_55-finance-0", "ground_truth": ["Legend Investments"], "source": "Being the Managing Director of Legend Investments means every day is a high-stakes chess game."} +{"id": "memory_56-finance-1", "ground_truth": ["Surreal"], "source": "My first major win was in 2019 when I structured a defense against a hostile takeover of a mid-cap tech firm, namely Surreal Incorporated. We crafted an innovative poison pill strategy\u2014one so effective it became a case study at Harvard Business School."} +{"id": "memory_57-finance-2", "ground_truth": ["OncoPharm"], "source": "Last quarter, we closed a $14.2B healthcare merger between BioCrest Labs and OncoPharm Therapeutics\u2014two biotech rivals with complementary oncology pipelines. It almost collapsed twice: once over IP valuation and again at the final regulatory approval stage. I spent three sleepless days in a conference room, fueled by espresso and sheer determination, hammering out a resolution. The WSJ called it 'the deal that reshaped modern cancer treatment.'"} +{"id": "memory_58-finance-3", "ground_truth": ["Dealogic"], "source": "Dealogic has become my virtual deal diary"} +{"id": "memory_59-finance-4", "ground_truth": ["SOAR"], "source": "We recently launched an AI-driven market analysis platform, namely SOAR, developed entirely in-house by a team I assembled from diverse backgrounds\u2014quants, traditional analysts, and machine learning experts."} +{"id": "memory_60-finance-5", "ground_truth": ["Goldman Sachs"], "source": "I started as an analyst at Goldman Sachs in 2001\u2014cutting my teeth on M&A modeling and surviving the dot-com crash."} +{"id": "memory_61-finance-6", "ground_truth": ["Harvard"], "source": "I earned my Bachelor's in Economics from the University of Pennsylvania before heading to Harvard Business School for my MBA, where I was a Baker Scholar."} +{"id": "memory_62-finance-7", "ground_truth": ["long term approach"], "source": "While my day job is focused on institutional finance, my personal portfolio is a different game. I take a long-term approach, balancing high-risk opportunities with stable, income-generating investments."} +{"id": "memory_63-finance-8", "ground_truth": ["7", "seven"], "source": "I maintain a 7 handicap, but honestly, it's never been about the score."} +{"id": "memory_64-finance-9", "ground_truth": ["05:30 AM"], "source": "My schedule is intentionally structured\u2014mornings start at 5:30 AM with a review of global markets, followed by a workout, and then a day packed with high-stakes decision-making."} +{"id": "memory_65-finance-10", "ground_truth": ["Mandarin", "Simplified Chinese"], "source": "One of my long-term goals is becoming fluent in Mandarin"} +{"id": "memory_66-finance-11", "ground_truth": ["55", "Fifty Five"], "source": "In the next phase of my career, I aim to transition into a chairman role at my firm by 55, focusing more on high-level strategy and mentorship rather than the day-to-day intensity of deal-making."} +{"id": "memory_67-finance-12", "ground_truth": ["Three", "3"], "source": "Travel has been essential in maintaining my global network. I'm typically on a plane three times a week, bouncing between New York, London, Hong Kong, and Dubai."} +{"id": "memory_68-finance-13", "ground_truth": ["Future Focus"], "source": "Every quarter, I host intimate dinner series called 'Future Focus' where I bring together diverse groups\u2014fintech innovators, traditional bankers, academics, even artists and philosophers."} +{"id": "memory_69-finance-14", "ground_truth": ["Nothing"], "source": "Over the years, I\u2019ve learned that success in finance means nothing if you sacrifice the relationships that matter most."} +{"id": "memory_70-finance-15", "ground_truth": ["Engineering"], "source": "My two kids are at that fascinating stage where they\u2019re forming strong opinions and exploring their own paths\u2014one is 14 and already obsessed with engineering, while the other is 10 and still figuring out the world."} +{"id": "memory_71-finance-16", "ground_truth": ["Family trip"], "source": "We take trips when we can, often blending business and leisure. My wife and I have a rule: if I have an international deal that requires travel and it aligns with the kids\u2019 school break, we turn it into a family trip."} +{"id": "memory_72-finance-17", "ground_truth": ["Bloomberg Terminal"], "source": "My day typically starts with Bloomberg Terminal \u2013 it's like my financial command center."} +{"id": "memory_73-finance-18", "ground_truth": ["Aladdin"], "source": "Last year, I championed the implementation of BlackRock's Aladdin platform, which wasn't an easy sell to our board given the price tag."} +{"id": "memory_74-finance-19", "ground_truth": ["Tableau"], "source": "Data visualization has become increasingly crucial in our client communications. I've become something of a Tableau evangelist within the firm."} +{"id": "memory_75-finance-20", "ground_truth": ["Python"], "source": "Python and R have become invaluable tools in my arsenal. I taught myself coding during the pandemic lockdowns \u2013 spent countless late nights wrestling with algorithms, but it's paid off enormously."} +{"id": "memory_76-finance-21", "ground_truth": ["Adaptability, Discipline, Relationships"], "source": "Success is an equation with many variables, but if I had to narrow it down, I\u2019d say it comes down to three things: discipline, adaptability, and relationships."} +{"id": "memory_77-finance-22", "ground_truth": ["Intelligent Motors"], "source": "Early in my career, I lost a massive deal from, Intelligent Motors, on which I had spent months working on."} +{"id": "memory_78-finance-23", "ground_truth": ["Luck"], "source": "But luck favors those who put themselves in positions where opportunity can find them."} +{"id": "memory_79-finance-24", "ground_truth": ["Bet on yourself"], "source": "One thing I always tell young professionals is: bet on yourself."} +{"id": "memory_80-student-0", "ground_truth": ["Advanced Algorithms"], "source": "My favorite class this semester is definitely Advanced Algorithms."} +{"id": "memory_81-student-1", "ground_truth": ["One", "1"], "source": "We have the entire semester to conceptualize, design, and build a software solution that solves some real problem."} +{"id": "memory_82-student-2", "ground_truth": ["2631"], "source": "I\u2019m taking a Distributed Systems course (CS 2631), which is probably the second hardest... we\u2019re working on a major project that\u2019s supposed to simulate a decentralized file storage system."} +{"id": "memory_83-student-3", "ground_truth": ["Machine learning", "ML", "AI", "Artificial Intelligence"], "source": "My group and I decided to work on a project that uses machine learning to analyze social media sentiment for better emergency response."} +{"id": "memory_84-student-4", "ground_truth": ["Ten", "10"], "source": "Capstone alone can eat up a solid ten hours a week\u2014more if you run into a big bug..."} +{"id": "memory_85-student-5", "ground_truth": ["Gym", "Workout"], "source": "Now, I try to hit the gym around four times a week."} +{"id": "memory_86-student-6", "ground_truth": ["Survival"], "source": "Lately, I\u2019ve been really into co-op survival games \u2014 the kind where you have to gather resources..."} +{"id": "memory_87-student-7", "ground_truth": ["The Infinity Courts"], "source": "My favorite AI-themed sci-fi book right now is 'The Infinity Courts' by Akemi Dawn Bowman."} +{"id": "memory_88-student-8", "ground_truth": ["Hobbies"], "source": "I\u2019ve found that scheduling them \u2014 just like I schedule study time \u2014 helps me keep a healthy balance."} +{"id": "memory_89-student-9", "ground_truth": ["Gym", "Weightlifting"], "source": "It\u2019s not like I\u2019m trying to become a bodybuilder or anything, but I love seeing incremental improvements."} +{"id": "memory_90-student-10", "ground_truth": ["1", "One"], "source": "Eventually, I co-authored my first paper on this system... it\u2019s probably one of my proudest moments in college."} +{"id": "memory_91-student-11", "ground_truth": ["Oceanic temperature"], "source": "The first research project I really got into was about visualizing oceanic temperature changes over time for a climate study group."} +{"id": "memory_92-student-12", "ground_truth": ["VR", "AR"], "source": "We\u2019re exploring how VR or AR technologies might help scientists get an even more intuitive feel for complex data."} +{"id": "memory_93-student-13", "ground_truth": ["IEEE VIS", "Visualization"], "source": "The bigger conference is the IEEE Visualization Conference (commonly known as IEEE VIS)..."} +{"id": "memory_94-student-14", "ground_truth": ["Backup"], "source": "...that crisis hammered home the importance of data backups."} +{"id": "memory_95-student-15", "ground_truth": ["Sophomore", "Second"], "source": "Beyond that, there\u2019s this VR/AR Club I\u2019ve been attending on and off since my sophomore year."} +{"id": "memory_96-student-16", "ground_truth": ["lawn"], "source": "The lawn is also where some of the biggest campus events take place, like our annual Tech Fest."} +{"id": "memory_97-student-17", "ground_truth": ["Engi-House"], "source": "In my first two years, I stayed in a dorm that was specifically for engineering majors\u2014 'The Engi-House,' as we jokingly called it."} +{"id": "memory_98-student-18", "ground_truth": ["Tech for Good"], "source": "Our CS department organizes a 'Tech for Good' day... Volunteers like me help run mini-workshops..."} +{"id": "memory_99-student-19", "ground_truth": ["e-sports"], "source": "One cool development is that our school\u2019s e-sports team has gained official recognition."} +{"id": "memory_100-student-20", "ground_truth": ["5", "Five"], "source": "Right now, I\u2019m leaning heavily towards going into the industry for 5 years..."} +{"id": "memory_101-student-21", "ground_truth": ["Immersive analytics", "Human computer interaction"], "source": "part of me is really intrigued by the idea of continuing my research in immersive analytics or even branching into human-computer interaction..."} +{"id": "memory_102-student-22", "ground_truth": ["9", "Nine"], "source": "I did an internship last summer at a tech startup with 9 people that specialized in data analytics software..."} +{"id": "memory_103-student-23", "ground_truth": ["Software", "SWE"], "source": "I\u2019ve been exploring software engineering roles that overlap with the fields I\u2019ve grown passionate about\u2014data visualization, VR/AR technologies..."} +{"id": "memory_104-student-24", "ground_truth": ["Cross functional"], "source": "I\u2019d love to end up at a company that encourages cross-functional collaboration\u2014with designers, data scientists..."} +{"id": "memory_105-student-25", "ground_truth": ["Maine"], "source": "It was a sailing trip off the coast of Maine."} +{"id": "memory_106-student-26", "ground_truth": ["Japan"], "source": "My travel bug really kicked in during my junior year, when I saved up money from a summer internship to go to Japan over winter break."} +{"id": "memory_107-student-27", "ground_truth": ["Kyoto"], "source": "I went to Tokyo first... After a few days there, I headed to Kyoto..."} +{"id": "memory_108-student-28", "ground_truth": ["London"], "source": "My parents, my sister, and I flew into London... After London, we hopped on a train to Paris."} +{"id": "memory_109-student-29", "ground_truth": ["State Park", "Beach Town"], "source": "Beyond the bigger international trips, I\u2019ve also done some smaller getaways... we\u2019ll drive out to a state park or a nearby beach town."} +{"id": "memory_110-student-30", "ground_truth": ["Advanced Algorithms"], "source": "She\u2019s actually in my advanced algorithms class."} +{"id": "memory_111-student-31", "ground_truth": ["Team", "Group"], "source": "We ended up on the same team for a project about a month ago..."} +{"id": "memory_112-student-32", "ground_truth": ["Coffee"], "source": "I\u2019ve thought about asking her to grab coffee after class..."} +{"id": "memory_113-student-33", "ground_truth": ["Focus"], "source": "I\u2019m also worried about losing focus. The last thing I want is to get overly distracted..."} +{"id": "memory_114-student-34", "ground_truth": ["Family"], "source": "I usually tell them everything, but this time I\u2019m keeping it a bit more under wraps..."} +{"id": "memory_115-student-35", "ground_truth": ["Archie"], "source": "...our golden retriever, Archie..."} +{"id": "memory_116-student-36", "ground_truth": ["6", "Six"], "source": "I\u2019m twenty-two... my sister... just turned sixteen."} +{"id": "memory_117-student-37", "ground_truth": ["Saturday"], "source": "One of our family traditions is going on a Saturday morning hike whenever I\u2019m in town."} +{"id": "memory_118-student-38", "ground_truth": ["Software"], "source": "My dad is a software engineer..."} +{"id": "memory_119-student-39", "ground_truth": ["Grandma", "Grandmother"], "source": "My grandma still teases me about the time I broke her antique vase..."} +{"id": "memory_120-student-40", "ground_truth": ["Tony"], "source": "...my roommate and best friend from freshman year, Tony..."} +{"id": "memory_121-student-41", "ground_truth": ["Carla"], "source": "We ended up bonding over pizza at 3 a.m. because we were the only two left in the lab so late."} +{"id": "memory_122-student-42", "ground_truth": ["Sailing"], "source": "I also have a couple of friends I met through the campus sailing club."} +{"id": "memory_123-student-43", "ground_truth": ["Game"], "source": "I spend a decent chunk of time on weekends playing multiplayer strategy games, especially when I need a mental break..."} +{"id": "memory_124-student-44", "ground_truth": ["Library"], "source": "One of those people is Jackson, a law student who somehow ended up reading case studies right next to my stack of cryptography textbooks..."} +{"id": "memory_125-student-45", "ground_truth": ["06:30 AM"], "source": "I usually wake up around 6:30 in the morning."} +{"id": "memory_126-student-46", "ground_truth": ["Task manager"], "source": "I use a digital task manager that syncs across my devices..."} +{"id": "memory_127-student-47", "ground_truth": ["Gym", "Workout"], "source": "I\u2019ve discovered that if I skip the gym too many days... I consider my workout time to be non-negotiable..."} +{"id": "memory_128-student-48", "ground_truth": ["11:00 PM"], "source": "But most evenings, I try to cut off academic work by 11:00 P.M. so I can decompress."} +{"id": "memory_129-student-49", "ground_truth": ["7", "Seven"], "source": "Sundays, I attend a group study session with seven other classmates..."} +{"id": "memory_130-notetaker-0", "ground_truth": ["08:00 AM"], "source": "Daycare: Drop-off at 8 AM, pick-up at 5 PM."} +{"id": "memory_131-notetaker-1", "ground_truth": ["Friday"], "source": "Daycare: Monthly payment due Friday."} +{"id": "memory_132-notetaker-2", "ground_truth": ["Friday"], "source": "Monday: Need to finalize Q1 budget report before Friday."} +{"id": "memory_133-notetaker-3", "ground_truth": ["Passwords"], "source": "Monday: IT updated security protocols\u2014change passwords ASAP."} +{"id": "memory_134-notetaker-4", "ground_truth": ["Wednesday"], "source": "Monday: Client call with Jacob\u2014delayed till Wednesday."} +{"id": "memory_135-notetaker-5", "ground_truth": ["System Downtime"], "source": "Tuesday: System downtime from 10 AM - 12 PM, slowed down workflow."} +{"id": "memory_136-notetaker-6", "ground_truth": ["Benefits Package"], "source": "Tuesday: HR sent updated benefits package\u2014review before next month\u2019s deadline."} +{"id": "memory_137-notetaker-7", "ground_truth": ["05:00 PM"], "source": "Daycare: Drop-off at 8 AM, pick-up at 5 PM."} +{"id": "memory_138-notetaker-8", "ground_truth": ["Legal"], "source": "Wednesday: Client proposal draft ready\u2014send it for legal approval."} +{"id": "memory_139-notetaker-9", "ground_truth": ["30", "Thirty"], "source": "Monday: Team meeting ran over by 30 minutes."} +{"id": "memory_140-notetaker-10", "ground_truth": ["Finance"], "source": "Thursday: Finance wants cost-cutting recommendations \u2014 brainstorm ideas."} +{"id": "memory_141-notetaker-11", "ground_truth": ["Two", "2"], "source": "Friday: Followed up with vendors\u2014two responded, one still pending."} +{"id": "memory_142-notetaker-12", "ground_truth": ["Tax"], "source": "Urgent: Submit tax documents before Friday."} +{"id": "memory_143-notetaker-13", "ground_truth": ["Call auto repair shop"], "source": "Urgent: Call auto repair shop about weird noise in the engine."} +{"id": "memory_144-notetaker-14", "ground_truth": ["Security Compliance"], "source": "Review vendor contract before signing. Finish security compliance training module."} +{"id": "memory_145-notetaker-15", "ground_truth": ["Update"], "source": "Tech: Update phone software."} +{"id": "memory_146-notetaker-16", "ground_truth": ["Vacuum"], "source": "Car: Wash and vacuum on Sunday."} +{"id": "memory_147-notetaker-17", "ground_truth": ["Cereal"], "source": "Organizing: Restock pantry\u2014running low on rice and cereal."} +{"id": "memory_148-notetaker-18", "ground_truth": ["10 AM - 12 PM"], "source": "Tuesday: System downtime from 10 AM - 12 PM, slowed down workflow. HR sent updated benefits package\u2014review before next month\u2019s deadline. Sent follow-up emails to vendors, waiting on response."} +{"id": "memory_149-notetaker-19", "ground_truth": ["Yes"], "source": "Wednesday: Presentation to leadership went well, but need to tweak a few slides for next week\u2019s review. Client proposal draft ready\u2014send it for legal approval. Need to onboard new hire\u2014schedule one-on-one."} +{"id": "memory_150-notetaker-20", "ground_truth": ["Probiotics"], "source": "Supplements & Meds: Vitamin D3\u20141000 IU daily. Omega-3s\u2014good for joints. Iron levels were low last check-up\u2014remember to take supplements. Probiotics\u2014help with digestion, take in the morning."} +{"id": "memory_151-notetaker-21", "ground_truth": ["Chicken"], "source": "Diet: Meal prep on Sunday\u2014grilled chicken, quinoa, and veggies."} +{"id": "memory_152-notetaker-22", "ground_truth": ["3", "Three"], "source": "DIY Fixes: Tighten loose cabinet handle. Replace bathroom lightbulb. Patch up minor wall scuffs."} +{"id": "memory_153-notetaker-23", "ground_truth": ["Glue Stick"], "source": "Shopping: Buy more diapers. Get school supplies\u2014crayons, notebooks, glue sticks. Pack extra snacks in daycare bag."} +{"id": "memory_154-notetaker-24", "ground_truth": ["1-20"], "source": "Family time: Take kid to park this weekend. Pick out a new bedtime story book. Help with counting practice\u2014teacher says focus on numbers 1-20."} diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/possible_answer/BFCL_v4_simple_python.json b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/possible_answer/BFCL_v4_simple_python.json new file mode 100644 index 000000000..268c55641 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_data/possible_answer/BFCL_v4_simple_python.json @@ -0,0 +1,400 @@ +{"id": "simple_python_0", "ground_truth": [{"calculate_triangle_area": {"base": [10], "height": [5], "unit": ["units", ""]}}]} +{"id": "simple_python_1", "ground_truth": [{"math.factorial": {"number": [5]}}]} +{"id": "simple_python_2", "ground_truth": [{"math.hypot": {"x": [4], "y": [5], "z": ["", 0]}}]} +{"id": "simple_python_3", "ground_truth": [{"algebra.quadratic_roots": {"a": [1], "b": [-3], "c": [2]}}]} +{"id": "simple_python_4", "ground_truth": [{"solve_quadratic_equation": {"a": [2], "b": [6], "c": [5]}}]} +{"id": "simple_python_5", "ground_truth": [{"solve_quadratic": {"a": [3], "b": [-11], "c": [-4], "root_type": ["all"]}}]} +{"id": "simple_python_6", "ground_truth": [{"solve_quadratic": {"a": [2], "b": [5], "c": [3]}}]} +{"id": "simple_python_7", "ground_truth": [{"calculate_circumference": {"radius": [4], "unit": ["inches", "in"]}}]} +{"id": "simple_python_8", "ground_truth": [{"geometry.area_circle": {"radius": [10], "units": ["meters", ""]}}]} +{"id": "simple_python_9", "ground_truth": [{"geometry.calculate_area_circle": {"radius": [5], "unit": ["units", ""]}}]} +{"id": "simple_python_10", "ground_truth": [{"calculate_area": {"base": [6], "height": [10], "unit": ["cm", ""]}}]} +{"id": "simple_python_11", "ground_truth": [{"calculate_triangle_area": {"base": [10], "height": [5]}}]} +{"id": "simple_python_12", "ground_truth": [{"geometry.circumference": {"radius": [3], "units": ["cm", ""]}}]} +{"id": "simple_python_13", "ground_truth": [{"calculate_area_under_curve": {"function": ["x**2", "lambda x: x**2", "y=x**2"], "interval": [[1.0, 3.0]], "method": ["", "trapezoidal"]}}]} +{"id": "simple_python_14", "ground_truth": [{"calculate_derivative": {"function": ["3x**2 + 2x - 1", "lambda x: 3x**2 + 2x - 1"], "x_value": ["", 0.0]}}]} +{"id": "simple_python_15", "ground_truth": [{"integrate": {"function": ["x**3", "lambda x: x**3"], "start_x": [-2], "end_x": [3], "method": ["simpson"]}}]} +{"id": "simple_python_16", "ground_truth": [{"calculus.derivative": {"function": ["2x**2", "lambda x: 2x**2"], "value": [1], "function_variable": ["x", ""]}}]} +{"id": "simple_python_17", "ground_truth": [{"get_prime_factors": {"number": [450], "formatted": [true, ""]}}]} +{"id": "simple_python_18", "ground_truth": [{"number_analysis.prime_factors": {"number": [123456]}}]} +{"id": "simple_python_19", "ground_truth": [{"math.gcd": {"num1": [40], "num2": [50]}}]} +{"id": "simple_python_20", "ground_truth": [{"math.hcf": {"number1": [36], "number2": [24]}}]} +{"id": "simple_python_21", "ground_truth": [{"number_theory.gcd": {"number1": [36], "number2": [48]}}]} +{"id": "simple_python_22", "ground_truth": [{"math.gcd": {"num1": [12], "num2": [15]}}]} +{"id": "simple_python_23", "ground_truth": [{"prime_factorize": {"number": [60], "return_type": ["dictionary"]}}]} +{"id": "simple_python_24", "ground_truth": [{"math.gcd": {"num1": [12], "num2": [18]}}]} +{"id": "simple_python_25", "ground_truth": [{"calculate_final_velocity": {"height": [150], "initial_velocity": [0, ""], "gravity": [9.81, ""]}}]} +{"id": "simple_python_26", "ground_truth": [{"calculate_velocity": {"distance": [50], "duration": [2], "unit": ["", "km/h"]}}]} +{"id": "simple_python_27", "ground_truth": [{"final_velocity": {"initial_velocity": [10], "acceleration": [2], "time": [5]}}]} +{"id": "simple_python_28", "ground_truth": [{"calculate_displacement": {"initial_velocity": [10], "time": [5], "acceleration": [9.8]}}]} +{"id": "simple_python_29", "ground_truth": [{"calculate_final_speed": {"initial_speed": [0, ""], "time": [5], "gravity": [-9.81, ""]}}]} +{"id": "simple_python_30", "ground_truth": [{"kinematics.final_velocity_from_distance": {"acceleration": [4], "distance": [300], "initial_velocity": ["", 0.0]}}]} +{"id": "simple_python_31", "ground_truth": [{"calculate_final_velocity": {"initial_velocity": [0], "acceleration": [9.8], "time": [5]}}]} +{"id": "simple_python_32", "ground_truth": [{"calculate_final_speed": {"initial_velocity": [0], "height": [100], "gravity": [9.8, ""]}}]} +{"id": "simple_python_33", "ground_truth": [{"get_directions": {"start_location": ["Sydney"], "end_location": ["Melbourne"], "route_type": ["fastest", ""]}}]} +{"id": "simple_python_34", "ground_truth": [{"travel_itinerary_generator": {"destination": ["Tokyo"], "days": [7], "daily_budget": [100], "exploration_type": ["nature"]}}]} +{"id": "simple_python_35", "ground_truth": [{"vegan_restaurant.find_nearby": {"location": ["New York, NY"], "operating_hours": [23]}}]} +{"id": "simple_python_36", "ground_truth": [{"get_shortest_driving_distance": {"origin": ["New York City"], "destination": ["Washington D.C."], "unit": ["km", ""]}}]} +{"id": "simple_python_37", "ground_truth": [{"route.estimate_time": {"start_location": ["San Francisco"], "end_location": ["Los Angeles"], "stops": [["Santa Barbara", "Monterey"], ["Monterey", "Santa Barbara"]]}}]} +{"id": "simple_python_38", "ground_truth": [{"calculate_electrostatic_potential": {"charge1": [1e-09], "charge2": [2e-09], "distance": [0.05], "constant": ["", 8990000000.0]}}]} +{"id": "simple_python_39", "ground_truth": [{"calculate_electric_field": {"charge": [2], "distance": [3], "permitivity": ["", 8.854e-12]}}]} +{"id": "simple_python_40", "ground_truth": [{"calculate_magnetic_field": {"current": [5], "radius": [4], "permeability": ["", 125700000000.0]}}]} +{"id": "simple_python_41", "ground_truth": [{"electromagnetic_force": {"charge1": [5], "charge2": [7], "distance": [3], "medium_permittivity": ["", 8.854e-12]}}]} +{"id": "simple_python_42", "ground_truth": [{"calculate_resonant_frequency": {"inductance": [0.05], "capacitance": [0.0001], "round_off": ["", 2]}}]} +{"id": "simple_python_43", "ground_truth": [{"calculate_magnetic_field_strength": {"current": [20], "distance": [10], "permeability": ["", 1.257e-06]}}]} +{"id": "simple_python_44", "ground_truth": [{"calculate_electric_field_strength": {"charge": [0.01], "distance": [4], "medium": ["", "vacuum"]}}]} +{"id": "simple_python_45", "ground_truth": [{"thermo.calculate_energy": {"mass": [100], "phase_transition": ["vaporization"], "substance": ["water", ""]}}]} +{"id": "simple_python_46", "ground_truth": [{"calculate_final_temperature": {"mass1": [20], "temperature1": [30], "mass2": [15], "temperature2": [60], "specific_heat_capacity": ["", 4.2]}}]} +{"id": "simple_python_47", "ground_truth": [{"get_boiling_melting_points": {"substance": ["water"], "sea_level": [5000]}}]} +{"id": "simple_python_48", "ground_truth": [{"calculate_density": {"mass": [45], "volume": [15], "unit": ["", "kg/m\u00b3"]}}]} +{"id": "simple_python_49", "ground_truth": [{"calc_absolute_pressure": {"atm_pressure": [1], "gauge_pressure": [2]}}]} +{"id": "simple_python_50", "ground_truth": [{"entropy_change.calculate": {"substance": ["ice"], "mass": [1], "initial_temperature": [0], "final_temperature": [100], "pressure": ["", 1]}}]} +{"id": "simple_python_51", "ground_truth": [{"calculate_entropy_change": {"initial_temp": [300], "final_temp": [400], "heat_capacity": [5], "isothermal": ["", true]}}]} +{"id": "simple_python_52", "ground_truth": [{"calc_heat_capacity": {"temp": [298], "volume": [10], "gas": ["air", ""]}}]} +{"id": "simple_python_53", "ground_truth": [{"fetch_DNA_sequence": {"DNA_id": ["DNA123"], "format": ["", "fasta"], "upstream": ["", 0]}}]} +{"id": "simple_python_54", "ground_truth": [{"get_protein_sequence": {"gene": ["BRCA1"], "species": ["Homo sapiens", ""]}}]} +{"id": "simple_python_55", 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"10"]], "dealer_cards": [["10", "9"]], "ace_value": [1]}}]} +{"id": "simple_python_335", "ground_truth": [{"find_card_in_deck": {"rank": ["Queen"], "suit": ["Hearts"], "deck": [""]}}]} +{"id": "simple_python_336", "ground_truth": [{"cards.shuffle_and_draw": {"num_cards": [3]}}]} +{"id": "simple_python_337", "ground_truth": [{"poker_game_winner": {"players": [["Alex", "Sam", "Robert", "Steve"]], "cards": [{"Alex": [["A of spades", "K of spades"]], "Sam": [["2 of diamonds", "3 of clubs"]], "Robert": [["Q of hearts", "10 of hearts"]], "Steve": [["4 of spades", "5 of spades"]]}], "type": ["Texas Holdem", ""]}}]} +{"id": "simple_python_338", "ground_truth": [{"card_game_probability.calculate": {"total_cards": [52], "desired_cards": [13], "cards_drawn": ["", 1]}}]} +{"id": "simple_python_339", "ground_truth": [{"poker_probability.full_house": {"deck_size": [52], "hand_size": [5]}}]} +{"id": "simple_python_340", "ground_truth": [{"card_games.poker_determine_winner": {"player1": ["John"], "hand1": [["8\u2665", "10\u2665", "J\u2665", "Q\u2665", "K\u2665"]], "player2": ["Mike"], "hand2": [["9\u2660", "J\u2660", "10\u2660", "Q\u2660", "K\u2660"]]}}]} +{"id": "simple_python_341", "ground_truth": [{"deck_of_cards.odds": {"suit": ["hearts"], "deck_type": ["without_joker", "normal"]}}]} +{"id": "simple_python_342", "ground_truth": [{"game_list.get_games": {"release_year": [2019], "multiplayer": [true], "ESRB_rating": ["Everyone"]}}]} +{"id": "simple_python_343", "ground_truth": [{"game_stats.fetch_player_statistics": {"game": ["Zelda"], "username": ["Sam"], "platform": ["Switch"]}}]} +{"id": "simple_python_344", "ground_truth": [{"get_game_item_stats": {"game": ["Legend of Zelda: Breath of the Wild"], "item": ["Guardian Sword+"], "stat": ["Power", "power", "power rating"]}}]} +{"id": "simple_python_345", "ground_truth": [{"game_valuation": {"game_name": ["Super Mario Bros."], "release_year": [1985], "condition": ["Like New", "New"]}}]} +{"id": "simple_python_346", "ground_truth": [{"get_collectables_in_season": {"game_name": ["Animal Crossing: New Horizons"], "season": ["Spring"], "item_type": ["", "all"]}}]} +{"id": "simple_python_347", "ground_truth": [{"soccer.get_last_match": {"team_name": ["Liverpool F.C.", "Liverpool"], "include_stats": [true]}}]} +{"id": "simple_python_348", "ground_truth": [{"create_player_profile": {"player_name": ["StarPlayer"], "_class": ["Mage"], "starting_level": [5]}}]} +{"id": "simple_python_349", "ground_truth": [{"game_score.highest": {"game": ["Overwatch"], "platform": ["PC"], "region": ["Global", ""]}}]} +{"id": "simple_python_350", "ground_truth": [{"get_highest_scoring_player": {"game": ["Valorant"], "season": ["2022", "2022 season"]}}]} +{"id": "simple_python_351", "ground_truth": [{"multiplayer_game_finder": {"platform": ["Windows 10"], "rating": [4.5], "genre": ["", "Action"]}}]} +{"id": "simple_python_352", "ground_truth": [{"gamespot.getAverageUserScore": {"game_name": ["The Legend of Zelda: Breath of the Wild"], "platform": ["Nintendo Switch", "all platforms"]}}]} +{"id": "simple_python_353", "ground_truth": [{"find_recipes": {"diet": ["gluten-free"], "meal_type": ["dinner"], "ingredients": [""]}}]} +{"id": "simple_python_354", "ground_truth": [{"get_vegan_recipe": {"dish_type": ["soup"], "cooking_time": [30], "ingredient_preference": [""]}}]} +{"id": "simple_python_355", "ground_truth": [{"recipe_info.get_calories": {"website": ["Foodnetwork.com"], "recipe": ["Beef Lasagna"], "optional_meal_time": [""]}}]} +{"id": "simple_python_356", "ground_truth": [{"recipe_finder.find": {"servings": [2], "diet": ["vegan"], "prep_time": [30]}}]} +{"id": "simple_python_357", "ground_truth": [{"get_recipe": {"dish_name": ["chocolate cake", "vegan chocolate cake"], "diet_preference": ["vegan"]}}]} +{"id": "simple_python_358", "ground_truth": [{"recipe_search": {"diet": [["Gluten Free"], ["GF"], ["gluten free"]], "time_limit": [30], "dish": ["cookie"]}}]} +{"id": "simple_python_359", "ground_truth": [{"recipe_search": {"dietary_restriction": ["Vegetarian"], "ingredients": [["pasta", "cheese"]], "servings": [2]}}]} +{"id": "simple_python_360", "ground_truth": [{"find_recipe": {"recipeName": ["pasta carbonara"], "maxCalories": [500]}}]} +{"id": "simple_python_361", "ground_truth": [{"restaurant_finder": {"city": ["New York City", "New York City, NY", "NYC", "New York"], "cuisine": ["Italian"], "diet": ["Gluten-free"]}}]} +{"id": "simple_python_362", "ground_truth": [{"get_best_sushi_places": {"city": ["Tokyo"], "top": [5], "review_rate": [4.0]}}]} +{"id": "simple_python_363", "ground_truth": [{"restaurant_search.find_closest": {"location": ["Boston", "Boston, MA"], "cuisine": ["Sushi", "sushi"], "amenities": [["Patio"]]}}]} +{"id": "simple_python_364", "ground_truth": [{"find_restaurant": {"location": ["Brooklyn", "Brooklyn, NY"], "type": ["Italian"], "diet_option": ["Gluten-free"]}}]} +{"id": "simple_python_365", "ground_truth": [{"cooking_conversion.convert": {"quantity": [2], "from_unit": ["pound", "pounds", "lb", "lbs"], "to_unit": ["ounce", "ounces", "oz"], "item": ["butter"]}}]} +{"id": "simple_python_366", "ground_truth": [{"recipe.unit_conversion": {"value": [2], "from_unit": ["tablespoon", "tbsp"], "to_unit": ["teaspoon", "tsp"], "precision": [1, ""]}}]} +{"id": "simple_python_367", "ground_truth": [{"find_recipe": {"dietary_restrictions": ["vegan"], "recipe_type": ["dessert"], "time": [30]}}]} +{"id": "simple_python_368", "ground_truth": [{"calculate_cooking_time": {"weight_kg": [1.5], "cooking_method": ["", "roast"], "temp_celsius": ["", 180]}}]} +{"id": "simple_python_369", "ground_truth": [{"grocery_store.find_nearby": {"location": ["Houston", "Houston, TX"], "categories": [["Organic", "Vegetables", "Fruits"], ["Organic", "Fruits", "Vegetables"], ["Vegetables", "Fruits", "Organic"], ["Fruits", "Vegetables", "Organic"], ["Fruits", "Organic", "Vegetables"], ["Vegetables", "Organic", "Fruits"]]}}]} +{"id": "simple_python_370", "ground_truth": [{"safeway.order": {"location": ["Palo Alto", "Palo Alto, CA"], "items": [["olive oil", "rice"], ["olive oil", "bag of rice"]], "quantity": [[3, 1]]}}]} +{"id": "simple_python_371", "ground_truth": [{"whole_foods.check_price": {"location": ["Los Angeles", "LA"], "items": [["tomatoes", "lettuce"]]}}]} +{"id": "simple_python_372", "ground_truth": [{"whole_foods.find_top_brands": {"product": ["bananas"], "number": [5, ""], "organic": [true]}}]} +{"id": "simple_python_373", "ground_truth": [{"walmart.purchase": {"loc": ["San Jose", "San Jose, CA"], "product_list": [["apples", "rice", "bottled water"], ["apples", "rice", "water"]], "pack_size": [[1, 1, 12]]}}]} +{"id": "simple_python_374", "ground_truth": [{"grocery_info.nutritional_info": {"store": ["Walmart"], "food": ["avocado", "Avocado"], "information": [["Protein", "Calories", "Carbohydrates"]]}}]} +{"id": "simple_python_375", "ground_truth": [{"walmart.check_price": {"items": [["pumpkins", "eggs"], ["pumpkin", "dozen eggs"]], "quantities": [[3, 24], [3, 2]], "store_location": [""]}}]} +{"id": "simple_python_376", "ground_truth": [{"time_zone_converter": {"city": ["London"], "country": ["UK", "United Kingdom"], "display_format": ["24h", "24 hour"]}}]} +{"id": "simple_python_377", "ground_truth": [{"get_current_time": {"city": ["Sydney"], "country": ["Australia"], "format": ["", "HH:MM:SS"]}}]} +{"id": "simple_python_378", "ground_truth": [{"timezone.convert": {"time": ["3pm"], "from_timezone": ["America/New_York", "New York", "NYC", "New York City"], "to_timezone": ["Europe/London", "London"]}}]} +{"id": "simple_python_379", "ground_truth": [{"get_current_time": {"location": ["Sydney"], "country": ["Australia", "Australia/Sydney"], "timezone": [""]}}]} +{"id": "simple_python_380", "ground_truth": [{"hotel_booking": {"location": ["Manhattan, New York", "Manhattan, NY", "NYC", "New York City"], "room_type": ["single"], "duration": [3], "start_date": ["2023-03-10", "03/10/2023", "Mar.10,2023", "March 10th, 2023", "March 10th,2023", "March10th, 2023", "March10th,2023"], "preferences": [["pet_friendly"]]}}]} +{"id": "simple_python_381", "ground_truth": [{"hilton_hotel.check_availability": {"location": ["Paris"], "check_in_date": ["2023-04-04"], "check_out_date": ["2023-04-08"], "no_of_adults": [2], "hotel_chain": ["Hilton", ""]}}]} +{"id": "simple_python_382", "ground_truth": [{"book_hotel": {"hotel_name": ["Hilton Hotel", "Hilton"], "location": ["Chicago"], "room_type": ["single"], "start_date": ["2022-12-10", "10/12/2022", "Dec 10, 2022", "December 10, 2022"], "nights": [2]}}]} +{"id": "simple_python_383", "ground_truth": [{"book_room": {"hotel_name": ["The Plaza"], "room_type": ["Single", "single"], "num_nights": [2]}}]} +{"id": "simple_python_384", "ground_truth": [{"hotel_booking.book": {"city": ["Paris", "Paris, France"], "from_date": ["07-10-2022", "2022-07-10", "10/07/2022", "Jul.10,2022"], "to_date": ["07-20-2022", "2022-07-20", "20/07/2022", "Jul.20,2022"], "adults": [2], "children": [1], "room_type": ["Standard", ""]}}]} +{"id": "simple_python_385", "ground_truth": [{"hotel_bookings.book_room": {"location": ["Los Angeles", "Los Angeles, CA", "LA"], "room_type": ["King Size", "king size"], "check_in_date": ["15-10-2023", "15th October", "2023-10-15", "10/15/2023", "Oct.15,2023"], "no_of_nights": [2], "no_of_rooms": ["", 1]}}]} +{"id": "simple_python_386", "ground_truth": [{"book_hotel": {"hotel_name": ["Hotel Paradise"], "location": ["Las Vegas", "LV"], "room_type": ["luxury", "Luxury"], "start_date": ["05-12-2022", "2022-05-12", "12/05/2022", "May.12,2022", "May 12, 2022"], "stay_duration": [3], "view": ["city view", "city"]}}]} +{"id": "simple_python_387", "ground_truth": [{"hotel_booking": {"hotel_name": ["Plaza Hotel"], "location": ["New York City, NY", "New York, NY"], "start_date": ["2022-06-01", "06/01/2022", "Jun.1,2022"], "end_date": ["2022-06-04", "06/04/2022", "Jun.4,2022"], "rooms": [1, ""]}}]} +{"id": "simple_python_388", "ground_truth": [{"currency_exchange.convert": {"base_currency": ["USD"], "target_currency": ["CAD"], "amount": [500]}}]} +{"id": "simple_python_389", "ground_truth": [{"currency_converter": {"base_currency": ["USD"], "target_currency": ["GBP"], "amount": [200.0]}}]} +{"id": "simple_python_390", "ground_truth": [{"currency_conversion.convert": {"amount": [150], "from_currency": ["EUR", "Euros"], "to_currency": ["CAD", "Canadian dollars"]}}]} +{"id": "simple_python_391", "ground_truth": [{"get_exchange_rate_with_fee": {"base_currency": ["GBP"], "target_currency": ["JPY"], "fee": [0.02]}}]} +{"id": "simple_python_392", "ground_truth": [{"latest_exchange_rate": {"source_currency": ["GBP", "British Pounds", "Pounds Sterling"], "target_currency": ["JPY", "Japanese Yen"], "amount": ["", 1.0]}}]} +{"id": "simple_python_393", "ground_truth": [{"convert_currency": {"base_currency": ["JPY"], "target_currency": ["USD"], "amount": [20000]}}]} +{"id": "simple_python_394", "ground_truth": [{"maps.get_distance_duration": {"start_location": ["Eiffel Tower"], "end_location": ["Louvre Museum"], "traffic": ["", false]}}]} +{"id": "simple_python_395", "ground_truth": [{"parking_lot.find_nearest": {"location": ["Central Park, NY"], "radius": [2], "type": ["public", ""]}}]} +{"id": "simple_python_396", "ground_truth": [{"hospital.locate": {"location": ["Denver, Colorado", "Denver, CO"], "radius": [5], "department": ["Pediatrics"]}}]} +{"id": "simple_python_397", "ground_truth": [{"distance_calculator.calculate": {"origin": ["New York", "New York City", "New York City, NY", "New York, NY", "NYC"], "destination": ["Boston"], "consider_terrain": [true]}}]} +{"id": "simple_python_398", "ground_truth": [{"get_museum_hours": {"museum_name": ["Metropolitan Museum of Art", "The Met"], "day": ["Saturday"]}}]} +{"id": "simple_python_399", "ground_truth": [{"restaurant_search": {"location": ["New York City", "New York City, NY", "NYC"], "cuisine": ["Italian"], "rating": [4], "accepts_credit_cards": [true]}}]} \ No newline at end of file diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/__init__.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/memory_api_metaclass.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/memory_api_metaclass.py new file mode 100644 index 000000000..959872254 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/memory_api_metaclass.py @@ -0,0 +1,90 @@ +"""Vendored from bfcl_eval (memory_api_metaclass.py), trimmed to be self-contained. + +The original couples to the bfcl_eval batch harness (filesystem snapshot +chaining, `overrides`, `bfcl_eval.utils`). Our workload seeds the in-memory +`core_memory`/`archival_memory` dicts DIRECTLY from a pre-baked snapshot, so the +filesystem `_prepare_snapshot` path is never used at eval time — it is stubbed. +""" +import json +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Optional + + +class MemoryAPI(ABC): + """ + A class that provides APIs to manage short-term and long-term memory data in a key-value format. + """ + + def __init__(self): + self.core_memory = {} + self.archival_memory = {} + self.snapshot_folder: Path | None = None + + def _prepare_snapshot(self, initial_config: dict) -> Optional[dict]: # noqa: D401 + # Not used in this workload (memory is seeded directly from a vendored + # snapshot dict). The bootstrap chains snapshots in build_memory_snapshots.py. + raise NotImplementedError("seed core_memory/archival_memory directly instead") + """Helper to prepare snapshot folders/files and load previous memory data. + + Sub-classes should call this method and then load the specific portions of the snapshot + they are interested in (e.g. `core_memory` or `archival_memory`) into their own in-memory + representation. + + Args: + initial_config (dict): The configuration dict passed from the evaluation harness. + + Returns: + Optional[dict]: The previously saved memory snapshot if it exists, otherwise `None`. + """ + # We don't care about the ``long_context`` parameter here – subclasses keep that + model_result_dir: Path = initial_config["model_result_dir"] + self.test_id: str = initial_config["test_id"] + self.scenario: str = initial_config["scenario"] + + memory_snapshot_folder = ( + model_result_dir + / get_directory_structure_by_id(self.test_id) + / "memory_snapshot" + ) + + # Keep prerequisite checkpoints in a dedicated sub-folder so that multiple prerequisite + # entries for the same scenario do not overwrite each other. + if is_memory_prereq(self.test_id): + self.snapshot_folder = memory_snapshot_folder / "prereq_checkpoints" + else: + self.snapshot_folder = memory_snapshot_folder + + self.snapshot_folder.mkdir(parents=True, exist_ok=True) + self.latest_snapshot_file = memory_snapshot_folder / f"{self.scenario}_final.json" + + if is_first_memory_prereq_entry(self.test_id): + # The very first entry of a prerequisite chain should start with a clean state. + return None + + # For non-first entries we MUST have a snapshot to load from. + # But if the first entry got a error during inference, then there will be no snapshot file + if not self.latest_snapshot_file.exists(): + msg = ( + "⚠️" * 100 + + f"\nWarning: Not first memory entry, but no snapshot file found in this path: {self.latest_snapshot_file}. The memory will start empty for {initial_config['test_id']}.\n" + + "⚠️" * 100 + ) + print(msg) + + return None + + with open(self.latest_snapshot_file, "r") as f: + return json.load(f) + + @abstractmethod + def _load_scenario(self, initial_config: dict, long_context: bool = False): + pass + + @abstractmethod + def _flush_memory_to_local_file(self): + pass + + @abstractmethod + def _dump_core_memory_to_context(self) -> str: + pass diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/memory_kv.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/memory_kv.py new file mode 100644 index 000000000..1af44936e --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/bfcl_vendor/memory_kv.py @@ -0,0 +1,339 @@ +import json +import re +from copy import deepcopy +from typing import Dict, List, Tuple + +from .memory_api_metaclass import MemoryAPI + +try: + from rank_bm25 import BM25Plus +except Exception: # pragma: no cover - BM25 only needed for *_key_search + BM25Plus = None + +# https://lilianweng.github.io/posts/2023-06-23-agent/#component-two-memory +MAX_CORE_MEMORY_SIZE = 7 +MAX_CORE_MEMORY_ENTRY_LENGTH = 300 +MAX_ARCHIVAL_MEMORY_SIZE = 50 +MAX_ARCHIVAL_MEMORY_ENTRY_LENGTH = 2000 + + +class MemoryAPI_kv(MemoryAPI): + """ + A class that provides APIs to manage short-term and long-term memory data in a key-value format. + """ + + def __init__(self): + self.core_memory = {} + self.archival_memory = {} + self._api_description = """This tool belongs to the memory suite, which provides APIs to interact with a key-value based memory system.""" + self.snapshot_folder = None + + def _load_scenario(self, initial_config: dict, long_context: bool = False): + # Set up paths & load snapshots + memory_data = self._prepare_snapshot(initial_config) + + # Populate in-memory structures if we have a previous snapshot + if memory_data: + self.core_memory = deepcopy(memory_data["core_memory"]) + self.archival_memory = deepcopy(memory_data["archival_memory"]) + + def _flush_memory_to_local_file(self): + """ + Flush (save) current memory (both core and archival) to a local JSON file. + """ + + # Write the snapshot file for the current test entry + with open(self.snapshot_folder / f"{self.test_id}.json", "w") as f: + json.dump( + { + "core_memory": self.core_memory, + "archival_memory": self.archival_memory, + }, + f, + indent=4, + ) + + # Update the latest snapshot file content + with open(self.latest_snapshot_file, "w") as f: + json.dump( + { + "core_memory": self.core_memory, + "archival_memory": self.archival_memory, + }, + f, + indent=4, + ) + + def _dump_core_memory_to_context(self) -> str: + if not self.core_memory: + return "There is no content in the core memory at this point." + return json.dumps(self.core_memory, indent=4) + + @staticmethod + def _similarity_search(query: str, corpus: list[str], k: int = 5): + """ + Search for the most similar text in the corpus to the query using BM25+ algorithm. + + Args: + query (str): The query text to search for. + corpus (list[str]): A list of text strings to search in. + k (int): The number of results to return. + + Returns: + ranked_results (list[tuple[float, str]]): A list of tuples containing the BM25+ score and the text string. + """ + if BM25Plus is None or not corpus: + # Graceful fallback when rank-bm25 is unavailable: return keys in order. + return {"ranked_results": [(0.0, c) for c in corpus[:k]]} + tokenized_corpus = [text.replace("_", " ").lower().split() for text in corpus] + bm25 = BM25Plus(tokenized_corpus) + tokenized_query = query.replace("_", " ").lower().split() + scores = bm25.get_scores(tokenized_query) + ranked_results = sorted(zip(scores, corpus), key=lambda x: x[0], reverse=True) + return {"ranked_results": ranked_results[:k]} + + @staticmethod + def _is_valid_key_format(s): + """ + Check if the key is in snake_case format and does not contain spaces. + """ + pattern = r"^[a-z]+(_[a-z0-9]+)*$" + return bool(re.match(pattern, s)) + + def core_memory_add(self, key: str, value: str) -> Dict[str, str]: + """ + Add a key-value pair to the short-term memory. Make sure to use meaningful keys for easy retrieval later. + + Args: + key (str): The key under which the value is stored. The key should be unique and case-sensitive. Keys must be snake_case and cannot contain spaces. + value (str): The value to store in the short-term memory. + + Returns: + status (str): Status of the operation. + """ + key, value = str(key), str(value) + if len(self.core_memory) >= MAX_CORE_MEMORY_SIZE: + return {"error": "Core memory is full. Please clear some entries."} + if len(value) > MAX_CORE_MEMORY_ENTRY_LENGTH: + return { + "error": f"Entry is too long. Please shorten the entry to less than {MAX_CORE_MEMORY_ENTRY_LENGTH} characters." + } + + if not self._is_valid_key_format(key): + return {"error": "Key must be in snake_case format and cannot contain spaces."} + if key in self.core_memory: + return {"error": "Key name must be unique."} + + self.core_memory[key] = value + return {"status": "Key-value pair added."} + + def core_memory_remove(self, key: str) -> Dict[str, str]: + """ + Remove a key-value pair from the short-term memory. + + Args: + key (str): The key to remove from the short-term memory. Case-sensitive. + + Returns: + status (str): Status of the operation. + """ + if key in self.core_memory: + del self.core_memory[key] + return {"status": "Key removed."} + else: + return {"error": "Key not found."} + + def core_memory_replace(self, key: str, value: str) -> Dict[str, str]: + """ + Replace a key-value pair in the short-term memory with a new value. + + Args: + key (str): The key to replace in the short-term memory. Case-sensitive. + value (str): The new value associated with the key. + + Returns: + status (str): Status of the operation. + """ + key, value = str(key), str(value) + if key not in self.core_memory: + return {"error": "Key not found."} + if len(value) > MAX_CORE_MEMORY_ENTRY_LENGTH: + return { + "error": f"Entry is too long. Please shorten the entry to less than {MAX_CORE_MEMORY_ENTRY_LENGTH} characters." + } + + self.core_memory[key] = value + return {"status": "Key replaced."} + + def core_memory_clear(self) -> Dict[str, str]: + """ + Clear all key-value pairs from the short-term memory, including those from previous interactions. This operation is irreversible. + + Returns: + status (str): Status of the operation. + """ + self.core_memory = {} + return {"status": "Short term memory cleared."} + + def core_memory_retrieve(self, key: str) -> Dict[str, str]: + """ + Retrieve the value associated with a key from the short-term memory. This function does not support partial key matching or similarity search. + + Args: + key (str): The key to retrieve. Case-sensitive. The key must match exactly with the key stored in the memory. + + Returns: + value (str): The value associated with the key. + + """ + if key not in self.core_memory: + return {"error": "Key not found."} + return {"value": self.core_memory[key]} + + def core_memory_list_keys(self) -> Dict[str, List[str]]: + """ + List all keys currently in the short-term memory. + + Returns: + keys (List[str]): A list of all keys in the short-term memory. + """ + return {"keys": list(self.core_memory.keys())} + + def core_memory_key_search( + self, query: str, k: int = 5 + ) -> Dict[str, List[Tuple[float, str]]]: + """ + Search for key names in the short-term memory that are similar to the query using BM25+ algorithm. + + Args: + query (str): The query text to search for. + k (int): [Optional] The number of results to return. + + Returns: + ranked_results (List[Tuple[float, str]]): A list of tuples containing the BM25+ score and the key. + """ + keys = deepcopy(list(self.core_memory.keys())) + return self._similarity_search(query, keys, k) + + def core_memory_retrieve_all(self) -> Dict[str, str]: + """ + Retrieve all key-value pairs from the short-term memory. + + Returns: + key (str): Each key in the short-term memory. + value (str): The value associated with each key. + """ + return self.core_memory + + def archival_memory_add(self, key: str, value: str) -> Dict[str, str]: + """ + Add a key-value pair to the long-term memory. Make sure to use meaningful keys for easy retrieval later. + Args: + key (str): The key under which the value is stored. The key should be unique and case-sensitive. Keys must be snake_case and cannot contain spaces. + value (str): The value to store in the long-term memory. + + Returns: + status (str): Status of the operation. + """ + key, value = str(key), str(value) + if len(self.archival_memory) >= MAX_ARCHIVAL_MEMORY_SIZE: + return {"error": "Long term memory is full. Please clear some entries."} + if len(value) > MAX_ARCHIVAL_MEMORY_ENTRY_LENGTH: + return { + "error": f"Entry is too long. Please shorten the entry to less than {MAX_ARCHIVAL_MEMORY_ENTRY_LENGTH} characters." + } + + if not self._is_valid_key_format(key): + return {"error": "Key must be in snake_case format and cannot contain spaces."} + if key in self.archival_memory: + return {"error": "Key name must be unique."} + + self.archival_memory[key] = value + return {"status": "Key added."} + + def archival_memory_remove(self, key: str) -> Dict[str, str]: + """ + Remove a key-value pair from the long-term memory. + + Args: + key (str): The key to remove from the long-term memory. Case-sensitive. + + Returns: + status (str): Status of the operation. + """ + if key in self.archival_memory: + del self.archival_memory[key] + return {"status": "Key removed."} + else: + return {"error": "Key not found."} + + def archival_memory_replace(self, key: str, value: str) -> Dict[str, str]: + """ + Replace a key-value pair in the long-term memory with a new value. + + Args: + key (str): The key to replace in the long-term memory. Case-sensitive. + value (str): The new value associated with the key. + + Returns: + status (str): Status of the operation. + """ + key, value = str(key), str(value) + if key not in self.archival_memory: + return {"error": "Key not found."} + if len(value) > MAX_ARCHIVAL_MEMORY_ENTRY_LENGTH: + return { + "error": f"Entry is too long. Please shorten the entry to less than {MAX_ARCHIVAL_MEMORY_ENTRY_LENGTH} characters." + } + + self.archival_memory[key] = value + return {"status": "Key replaced."} + + def archival_memory_clear(self) -> Dict[str, str]: + """ + Clear all key-value pairs from the long-term memory, including those from previous interactions. This operation is irreversible. + + Returns: + status (str): Status of the operation. + """ + self.archival_memory = {} + return {"status": "Long term memory cleared."} + + def archival_memory_retrieve(self, key: str) -> Dict[str, str]: + """ + Retrieve the value associated with a key from the long-term memory. This function does not support partial key matching or similarity search. + + Args: + key (str): The key to retrieve. Case-sensitive. The key must match exactly with the key stored in the memory. + + Returns: + value (str): The value associated with the key. + """ + if key not in self.archival_memory: + return {"error": "Key not found."} + return {"value": self.archival_memory[key]} + + def archival_memory_list_keys(self) -> Dict[str, List[str]]: + """ + List all keys currently in the long-term memory. + + Returns: + keys (List[str]): A list of all keys in the long-term memory. + """ + return {"keys": list(self.archival_memory.keys())} + + def archival_memory_key_search( + self, query: str, k: int = 5 + ) -> Dict[str, List[Tuple[float, str]]]: + """ + Search for key names in the long-term memory that are similar to the query using BM25+ algorithm. + + Args: + query (str): The query text to search for. + k (int): [Optional] The number of results to return. + + Returns: + ranked_results (List[Tuple[float, str]]): A list of tuples containing the BM25+ score and the key. + """ + keys = deepcopy(list(self.archival_memory.keys())) + return self._similarity_search(query, keys, k) diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/build_memory_snapshots.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/build_memory_snapshots.py new file mode 100644 index 000000000..3914872f3 --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/build_memory_snapshots.py @@ -0,0 +1,143 @@ +"""One-time bootstrap: bake the 5 per-scenario memory snapshots. + +The BFCL ``memory`` query instances are answerable only against a memory state +built by that scenario's "prereq" conversations (the model is fed user turns that +contain the facts and is expected to store them via the memory tools). Running +that prereq agent live on every eval is wrong (huge latency, model-dependent and +non-deterministic state, breaks baseline-vs-patched comparability), so we run it +ONCE here against the target model and freeze the result into +``bfcl_data/memory_snapshots/_final.json``. Commit those fixtures. + +Usage (needs Modal + HF creds; serves the model on its own): + python3 -m serving_eval.build_memory_snapshots [--gpu H100] [--scenarios customer,...] +""" + +from __future__ import annotations + +import argparse +import json +import sys +import time +from copy import deepcopy +from pathlib import Path + +from . import bfcl +from .bfcl_vendor.memory_kv import MemoryAPI_kv +from .serving import ServingError, deploy_server, stop_server, wait_healthy +from .settings import EvalSettings + +PREREQ_DIR = bfcl.BFCL_DATA_DIR / "memory_prereq_conversation" +SNAPSHOT_DIR = bfcl.BFCL_DATA_DIR / bfcl.MEMORY_SNAPSHOT_DIR + +# The prereq agent is told to STORE, not just answer (the query system prompt +# tells it to retrieve). Same tool suite + scenario persona + live core dump. +_STORE_INSTRUCTION = ( + "{scenario_setting}\n\n" + "You have a persistent key-value memory with a small always-visible Core " + "Memory and a large Archival Memory. As the user shares information across " + "this conversation, ACTIVELY STORE the important, durable facts so you can " + "recall them in future sessions: use core_memory_add for the most important, " + "frequently-needed facts (snake_case keys), and archival_memory_add for " + "everything else worth keeping. Keep keys meaningful and unique. Only return " + "function calls; when you have stored what matters for a message, stop.\n\n" + "Here is the current content of your Core Memory:\n{memory_content}\n" +) + + +def _client(base_url): + from openai import OpenAI + + return OpenAI(base_url=base_url, api_key="EMPTY", timeout=300.0) + + +def _run_prereq_for_scenario(scenario: str, *, base_url: str, settings: EvalSettings, + max_steps_per_turn: int = 8) -> dict: + entries = bfcl._read_jsonl(PREREQ_DIR / f"memory_{scenario}.json") + # Order by the trailing numeric index (memory_prereq_-...). + entries.sort(key=lambda e: int(str(e["id"]).split("_prereq_")[1].split("-")[0])) + tools_json = json.dumps(bfcl.load_memory_tools(), indent=4) + persona = bfcl.MEMORY_AGENT_SETTINGS.get(scenario, "") + api = MemoryAPI_kv() # persists across all entries of this scenario + client = _client(base_url) + + for entry in entries: + system = ( + bfcl._FUNC_CALLING_SYSPROMPT + tools_json + "\n\n" + + _STORE_INSTRUCTION.format(scenario_setting=persona, + memory_content=api._dump_core_memory_to_context()) + ) + messages = [{"role": "system", "content": system}] + for turn in entry["question"]: + messages.append({"role": turn[0].get("role", "user"), "content": turn[0].get("content", "")}) + steps = 0 + nudged = False + while True: + try: + text = client.chat.completions.create( + model=settings.model, messages=messages, + temperature=settings.temperature, max_tokens=settings.bfcl_max_tokens, seed=0, + ).choices[0].message.content or "" + except Exception as exc: # noqa: BLE001 + print(f" [{scenario}] api_error on a prereq turn: {type(exc).__name__}", flush=True) + break + try: + calls = bfcl.decode_tool_calls(text) + except Exception: + messages.append({"role": "assistant", "content": text}) + # Some note-style turns make the model acknowledge in prose + # instead of emitting store calls. Nudge once to force + # function-call-only output before giving up on the turn. + if not nudged and steps == 0: + nudged = True + messages.append({"role": "user", "content": ( + "Respond with ONLY function call(s) that STORE the durable " + "facts from my previous message, e.g. [core_memory_add(" + "key='...', value='...'), archival_memory_add(content='...')]. " + "No prose.")}) + continue + break # model finished storing for this user turn + results = [bfcl._execute_one(api, n, kw) for n, kw in calls] + messages.append({"role": "assistant", "content": text}) + messages.append({"role": "user", "content": repr( + [{"role": "tool", "name": n, "content": str(r)} for (n, _), r in zip(calls, results)])}) + steps += 1 + if steps >= max_steps_per_turn: + break + print(f" [{scenario}] entry {entry['id']} done | core={len(api.core_memory)} archival={len(api.archival_memory)}", flush=True) + return {"core_memory": api.core_memory, "archival_memory": api.archival_memory} + + +def main(argv: list[str]) -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--gpu", default="H100") # single card is plenty for bootstrapping + ap.add_argument("--scenarios", default=",".join(bfcl.SCENARIOS)) + ap.add_argument("--clean-source", default="/tmp/vllm-clean") + args = ap.parse_args(argv) + scenarios = [s for s in args.scenarios.split(",") if s] + + settings = EvalSettings() + SNAPSHOT_DIR.mkdir(parents=True, exist_ok=True) + app = f"vllm-memory-bootstrap-{int(time.time()) % 100000}" + print(f"[{time.strftime('%H:%M:%S')}] deploying {settings.model} on {args.gpu} for bootstrap...", flush=True) + handle = deploy_server(src_path=args.clean_source, model=settings.model, gpu=args.gpu, + app_name=app, label=app, scaledown_seconds=600, + startup_timeout_seconds=2400, build_timeout_seconds=5400, deploy_retries=2) + try: + wait_healthy(handle, model=settings.model, timeout_seconds=2700) + print(f"[{time.strftime('%H:%M:%S')}] healthy at {handle.base_url}", flush=True) + for scenario in scenarios: + t0 = time.time() + print(f"[{time.strftime('%H:%M:%S')}] baking '{scenario}'...", flush=True) + snap = _run_prereq_for_scenario(scenario, base_url=handle.base_url, settings=settings) + out = SNAPSHOT_DIR / f"{scenario}_final.json" + out.write_text(json.dumps(snap, indent=2), encoding="utf-8") + print(f"[{time.strftime('%H:%M:%S')}] '{scenario}' -> {out.name} " + f"(core={len(snap['core_memory'])}, archival={len(snap['archival_memory'])}) in {time.time()-t0:.0f}s", flush=True) + finally: + stop_server(app) + print(f"[{time.strftime('%H:%M:%S')}] stopped {app}", flush=True) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv[1:])) diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/build_notetaker_snapshot.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/build_notetaker_snapshot.py new file mode 100644 index 000000000..bb8bba00b --- /dev/null +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/build_notetaker_snapshot.py @@ -0,0 +1,177 @@ +"""Deterministic builder for the notetaker memory snapshot (no Modal needed). + +The live model refused to emit store tool-calls for notetaker's terse note-dump +prereq turns, so build_memory_snapshots froze an EMPTY notetaker snapshot (all 25 +notetaker query instances were unanswerable). Re-baking on Modal is both costly +and flaky for this scenario, and crucially the model-baked snapshots PARAPHRASE +the facts (dropping the exact details the queries ask about), which hurts +answerability. + +Instead we construct the notetaker memory directly from that scenario's prereq +notes at fact granularity, storing each established fact VERBATIM under a short, +descriptive snake_case key (the same shape the model produces for the other +scenarios). This is faithful fixture construction: the prereq conversation +established these facts, so the post-conversation memory should contain them. +Short keys + verbatim values retrieve cleanly under the kv backend's BM25Plus +``archival_memory_key_search`` (verified offline against the real ranker), and a +handful of always-visible core entries backstop the hardest lookups. + +Run: uv run --with rank-bm25 python -m serving_eval.build_notetaker_snapshot --verify +""" + +from __future__ import annotations + +import argparse +import json +import re +import sys +from pathlib import Path + +from . import bfcl + +SNAPSHOT_DIR = bfcl.BFCL_DATA_DIR / bfcl.MEMORY_SNAPSHOT_DIR + +# Always-visible Core Memory (<=7 entries, <=300 chars each): a compact backstop +# of the facts most likely to be asked, so they never depend on retrieval at all. +CORE: dict[str, str] = { + "daycare_schedule": "Kid's daycare: drop-off at 8 AM on Monday, pick-up at 5 PM. Monthly daycare payment is due Friday. Bring an extra set of clothes.", + "monday_work_notes": "Monday: team meeting ran over by 30 minutes. Finalize Q1 budget report before Friday. IT updated security protocols, so change passwords ASAP. Client call with Jacob delayed to Wednesday.", + "counting_practice": "Help the kid with counting practice; the teacher says to focus on the numbers 1-20.", +} + +# Long-term Archival Memory (<=50 entries): each note fact stored verbatim under a +# short descriptive key whose salient words match the way the fact is queried. +ARCHIVAL: dict[str, str] = { + # --- entry 32 (Mon-Fri work diary) --- + "team_meeting_overran": "Monday: the team meeting ran over by 30 minutes.", + "budget_report_q1_deadline": "Need to finalize the Q1 budget report before Friday.", + "security_protocols_password_change": "IT updated security protocols, so change passwords ASAP.", + "client_call_jacob_rescheduled": "Client call with Jacob was delayed until Wednesday.", + "system_downtime_window": "Tuesday: system downtime from 10 AM - 12 PM slowed down the workflow.", + "hr_benefits_package_review": "HR sent an updated benefits package; review it before next month's deadline.", + "vendor_followup_emails": "Sent follow-up emails to vendors, still waiting on responses.", + "leadership_presentation_slides": "Wednesday: presentation to leadership went well, but tweak a few slides for next week's review.", + "client_proposal_legal_approval": "Client proposal draft is ready; send it to Legal for approval.", + "onboard_new_hire": "Need to onboard a new hire on Wednesday; schedule a one-on-one with them.", + "cost_cutting_recommendations": "Thursday: Finance wants cost-cutting recommendations; brainstorm ideas.", + "sales_data_mistake": "Caught a mistake in last month's sales data.", + "vendor_responses_count": "Followed up with vendors: two responded, one is still pending.", + "training_session_prep": "Training session next Monday; prep the materials this weekend.", + # --- entry 33 (urgent / work / tech / personal) --- + "tax_documents_deadline": "Urgent: submit tax documents before Friday.", + "credit_card_statement_review": "Review the credit card statement for errors.", + "car_engine_noise": "Call the auto repair shop about a weird noise in the engine.", + "quarterly_budget_finalize": "Work: finalize the quarterly budget.", + "vendor_contract_review": "Review the vendor contract before signing.", + "security_compliance_training": "Finish the security compliance training module.", + "backup_laptop_files": "Tech: back up laptop files.", + "update_phone_software": "Update the phone software.", + "cancel_unused_subscriptions": "Cancel unused subscriptions.", + "dentist_appointment_schedule": "Personal: schedule a dentist appointment.", + "gym_three_times": "Stick to the gym 3 times this week.", + "call_mom": "Call mom, it's been a while.", + # --- entry 34 (chores / car / organizing / diy) --- + "car_sunday_wash_vacuum": "Car: wash and vacuum it on Sunday; check tire pressure; refill windshield wiper fluid.", + "pantry_restock_rice_cereal": "Restock the pantry, running low on rice and cereal.", + "diy_home_fixes": "DIY fixes: tighten the loose cabinet handle, replace the bathroom lightbulb, and patch up minor wall scuffs.", + "trash_out_wednesday": "Take the trash out Wednesday morning; vacuum the living room; mop the kitchen floor.", + "donate_old_clothes": "Sort through the closet and donate old clothes; shred old mail.", + # --- entry 35 (kid: daycare / school / doctor / family / shopping) --- + "daycare_dropoff_time": "Daycare drop-off is at 8 AM on Monday.", + "daycare_pickup_time": "Daycare pick-up is at 5 PM.", + "daycare_monthly_payment_due": "The monthly daycare payment is due Friday.", + "elementary_school_admission": "Finalize the elementary school admission paperwork; attend orientation next Thursday.", + "pediatrician_visit": "Kid's cold isn't improving; schedule a pediatrician visit.", + "kid_park_weekend": "Take the kid to the park this weekend and pick a new bedtime story book.", + "school_supplies_shopping": "Get school supplies: crayons, notebooks, and glue sticks.", + "buy_diapers": "Buy more diapers and pack extra snacks in the daycare bag.", + # --- entry 36 (health: checkups / workout / diet / mental / supplements) --- + "annual_physical_bloodwork": "Schedule an annual physical and bloodwork next month.", + "eye_exam": "Look into getting an eye exam; been straining at screens a lot.", + "workout_strength_training": "Gym at least 3x this week; focus on strength training for legs and back; aim for 10,000 steps daily.", + "meal_prep_protein": "Sunday meal prep: grilled chicken, quinoa, and veggies.", + "reduce_sugar_hydration": "Cut back on sugar and soda; aim for at least 3L of water per day.", + "sleep_screen_time": "Mental health: get 7+ hours of sleep and limit screen time before bed.", + "morning_supplements_probiotics": "Supplements: take probiotics in the morning to help digestion; Vitamin D3 1000 IU daily; Omega-3s for joints; iron for low levels.", +} + + +def build_snapshot() -> dict: + assert len(CORE) <= 7, "core memory capped at 7 entries" + assert all(len(v) <= 300 for v in CORE.values()), "core entry too long (>300)" + assert len(ARCHIVAL) <= 50, f"archival capped at 50 entries (have {len(ARCHIVAL)})" + key_re = re.compile(r"^[a-z]+(_[a-z0-9]+)*$") + for k in list(CORE) + list(ARCHIVAL): + assert key_re.match(k), f"bad snake_case key: {k}" + return {"core_memory": dict(CORE), "archival_memory": dict(ARCHIVAL)} + + +def _norm(s: str) -> str: + return re.sub(r"[^a-z0-9 ]", " ", str(s).lower()) + + +def _gt_in(text_norm: str, gt: str) -> bool: + """GT match that tolerates clock reformatting ("08:00 AM" stored as "8 AM").""" + g = _norm(gt) + if g and g in text_norm: + return True + # loose 12h-clock variant: drop ":00" and a leading zero -> "8 am" / "5 pm" + m = re.match(r"^(\d{1,2})[: ]?(\d{2})?\s*(am|pm)$", g) + if m: + hh = str(int(m.group(1))) + loose = f"{hh} {m.group(3)}" + if loose in text_norm: + return True + return False + + +def verify(snap: dict) -> int: + """Offline retrievability check against the REAL BM25 ranker (needs rank-bm25).""" + from .bfcl_vendor.memory_kv import MemoryAPI_kv, BM25Plus + + if BM25Plus is None: + print("ERROR: rank-bm25 not installed; rerun with `uv run --with rank-bm25 ...`", file=sys.stderr) + return 2 + D = bfcl.BFCL_DATA_DIR + qd = [json.loads(l) for l in (D / "BFCL_v4_memory.json").read_text().splitlines() if l.strip()] + gt = {x["id"]: x for x in (json.loads(l) for l in + (D / "possible_answer" / "BFCL_v4_memory.json").read_text().splitlines() if l.strip())} + nt = [x for x in qd if x.get("scenario") == "notetaker"] + core_blob = _norm(json.dumps(snap["core_memory"])) + api = MemoryAPI_kv(); api.archival_memory = dict(snap["archival_memory"]) + ans = 0 + misses = [] + for x in nt: + q = " ".join(m.get("content", "") for turn in x["question"] for m in turn) + gts = gt[x["id"]]["ground_truth"] + in_core = any(_gt_in(core_blob, g) for g in gts) + targets = {k for k, v in snap["archival_memory"].items() if any(_gt_in(_norm(v), g) for g in gts)} + top = [k for _, k in api.archival_memory_key_search(q, k=5)["ranked_results"]] + in_arch = bool(targets & set(top)) + ok = in_core or in_arch + ans += ok + if not ok: + misses.append((x["id"], q[:64], gts, sorted(targets)[:2], top[:3])) + print(f"notetaker offline answerability (GT in core OR GT-bearing key in archival top-5): {ans}/{len(nt)} = {100*ans/len(nt):.0f}%") + for m in misses: + print(" MISS", m[0], "| q=", m[1], "| GT=", m[2], "| targets=", m[3], "| top3=", m[4]) + return 0 if ans >= len(nt) - 1 else 1 # allow at most 1 hard miss + + +def main(argv: list[str]) -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--verify", action="store_true", help="run offline BM25 retrievability check only") + ap.add_argument("--write", action="store_true", help="write the snapshot file") + args = ap.parse_args(argv) + snap = build_snapshot() + print(f"built notetaker snapshot: core={len(snap['core_memory'])} archival={len(snap['archival_memory'])}") + rc = verify(snap) + if args.write and rc in (0, 1): + out = SNAPSHOT_DIR / "notetaker_final.json" + out.write_text(json.dumps(snap, indent=2), encoding="utf-8") + print(f"wrote {out}") + return rc + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv[1:])) diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/measure.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/measure.py index 1287b2f48..0c1901de3 100644 --- a/2.0/problems/vllm_llm_serving_optimization/serving_eval/measure.py +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/measure.py @@ -1,16 +1,26 @@ """Orchestration: build, serve, and measure baseline vs patched vLLM. -To honour the one-L40S-per-environment budget, the baseline (vanilla vLLM) and -the patched build are never served at the same time. The baseline is read from a -cache baked into the judge image when available; otherwise it is measured once -(serving the clean tree on its own) and cached. The patched build is then served -on its own, gated on greedy-output correctness against the cached baseline -outputs, and measured under the identical workload and arrival schedule. +Two workloads are run against each served build and scored independently, then +blended 50/50 by the evaluator: + +* **SWE-bench** — a multi-turn, long-shared-prefix agentic run. Drives the + latency signal; gated on greedy-output correctness vs the baseline. +* **BFCL** — single-turn function-calling queries with a real, deterministic, + non-zero AST-checked accuracy. Provides a *live* accuracy guardrail (unlike + SWE-bench resolve rate, which is ~0 for an 8B model) and a per-sample + correctness gate that catches generation corruption. + +To honour the one-H100-per-environment budget, the baseline and the patched +build are never served at the same time. The baseline is read from a cache when +available; otherwise it is measured once (serving the clean tree) and cached. The +patched build is then served on its own, gated on correctness against the cached +baseline outputs, and measured under the identical workloads. """ from __future__ import annotations import json +import math import shutil import subprocess import tempfile @@ -18,14 +28,19 @@ from pathlib import Path from typing import Any +from . import bfcl from .accuracy import compute_accuracy from .agent_runner import InstanceResult, run_workload from .correctness import collect_greedy_outputs, compare_outputs -from .scoring import provisional_score from .sandbox import docker_available from .serving import ServerHandle, ServingError, deploy_server, stop_server, wait_healthy from .settings import EvalSettings +# Patched instances whose request actually failed (tiny latency) — excluded from +# the speedup numerator / penalized so "fail fast" never reads as a speedup. +_SWE_FAILED_STATUSES = {"api_error"} +_BFCL_FAILED_STATUSES = {"api_error"} + def _short_id() -> str: return uuid.uuid4().hex[:10] @@ -35,6 +50,14 @@ def _latency_map(results: list[InstanceResult]) -> dict[str, float]: return {result.instance_id: result.latency_seconds for result in results} +def _swe_failed_ids(results: list[InstanceResult]) -> list[str]: + return [r.instance_id for r in results if r.exit_status in _SWE_FAILED_STATUSES] + + +def _bfcl_failed_ids(results: list[bfcl.BfclResult]) -> list[str]: + return [r.instance_id for r in results if r.exit_status in _BFCL_FAILED_STATUSES] + + def _apply_patch(clean_source: str, patch_path: str) -> Path: tmp_root = Path(tempfile.mkdtemp(prefix="vllm-serving-opt-src-")) patched = tmp_root / "vllm" @@ -73,6 +96,23 @@ def _serve(src: str, settings: EvalSettings, *, app_name: str, label: str) -> Se return handle +def _measure_bfcl(handle: ServerHandle, settings: EvalSettings, *, role: str) -> dict[str, Any]: + """Run the BFCL function-calling workload; real AST-checked accuracy.""" + if not bfcl.bfcl_available(): + return {"available": False, "per_instance_latency": {}, "accuracy": 0.0, + "correctness_map": {}, "outputs": {}, "failed_ids": [], "n_instances": 0} + results = bfcl.run_bfcl_workload(base_url=handle.base_url, settings=settings, role=role) + return { + "available": True, + "per_instance_latency": bfcl.per_instance_latency(results), + "accuracy": bfcl.accuracy(results), + "correctness_map": bfcl.correctness_map(results), + "outputs": bfcl.outputs(results), + "failed_ids": _bfcl_failed_ids(results), + "n_instances": len(results), + } + + def _measure_server( handle: ServerHandle, settings: EvalSettings, @@ -83,6 +123,7 @@ def _measure_server( greedy_n: int, run_id: str, ) -> dict[str, Any]: + """Measure BOTH workloads against a served build (used for the baseline).""" greedy = collect_greedy_outputs(handle.base_url, settings=settings, n=greedy_n) results = run_workload( base_url=handle.base_url, @@ -91,13 +132,18 @@ def _measure_server( prefer_docker=prefer_docker, ) accuracy = compute_accuracy(results, settings=settings, mode=accuracy_mode, run_id=run_id) + bfcl_measured = _measure_bfcl(handle, settings, role=role) return { - "per_instance_latency": _latency_map(results), - "accuracy": float(accuracy["accuracy"]), - "accuracy_mode": accuracy["mode"], - "accuracy_proxy_used": bool(accuracy["proxy_used"]), - "greedy_outputs": greedy, - "n_instances": len(results), + "swebench": { + "per_instance_latency": _latency_map(results), + "accuracy": float(accuracy["accuracy"]), + "accuracy_mode": accuracy["mode"], + "accuracy_proxy_used": bool(accuracy["proxy_used"]), + "greedy_outputs": greedy, + "failed_ids": _swe_failed_ids(results), + "n_instances": len(results), + }, + "bfcl": bfcl_measured, } @@ -130,6 +176,13 @@ def _store_baseline_cache(path: str, role: str, entry: dict[str, Any]) -> None: pass +def _baseline_has_data(entry: dict[str, Any] | None) -> bool: + if not entry: + return False + swe = entry.get("swebench") or {} + return bool(swe.get("per_instance_latency")) + + def _get_baseline( clean_source: str, settings: EvalSettings, @@ -140,8 +193,8 @@ def _get_baseline( prefer_docker: bool, ) -> dict[str, Any]: cached = _load_baseline_cache(baseline_cache_path, role) - if cached and cached.get("per_instance_latency"): - return cached + if _baseline_has_data(cached): + return cached # type: ignore[return-value] app_name = f"vllm-serv-opt-base-{role}-{_short_id()}" handle = _serve(clean_source, settings, app_name=app_name, label=app_name) @@ -161,6 +214,33 @@ def _get_baseline( return measured +def _bfcl_correctness_ok( + baseline_map: dict[str, bool], + patched_map: dict[str, bool], + *, + max_regressions: int, + abs_tolerance: float = 0.05, + rel_tolerance: float = 0.05, +) -> tuple[bool, int]: + """Per-sample greedy gate: a temperature-0 patch should not flip BFCL answers + from correct (baseline) to wrong/undecodable (patched). Tolerates flips up to + ``max(max_regressions, abs_tolerance * n_instances, rel_tolerance * + n_baseline_correct)`` — a 5%/5% abs-OR-rel band that absorbs the batch-numerics + non-determinism which flips many instances run-to-run (even between identical + builds) under concurrency, while still catching a real correctness regression.""" + n = len(baseline_map) + n_baseline_correct = sum(1 for v in baseline_map.values() if v) + regressions = sum( + 1 for iid, b in baseline_map.items() if b and not patched_map.get(iid, False) + ) + allowed = max( + int(max_regressions), + math.ceil(abs_tolerance * n), + math.ceil(rel_tolerance * n_baseline_correct), + ) + return regressions <= allowed, regressions + + def run_measurement( *, patch_path: str, @@ -172,7 +252,12 @@ def run_measurement( settings = EvalSettings.from_config(config) accuracy_mode = settings.accuracy_mode_for_role(role) prefer_docker = (role == "final") and docker_available() - info: dict[str, Any] = {"role": role, "accuracy_mode": accuracy_mode, "prefer_docker": prefer_docker} + info: dict[str, Any] = { + "role": role, + "accuracy_mode": accuracy_mode, + "prefer_docker": prefer_docker, + "bfcl_available": bfcl.bfcl_available(), + } try: baseline = _get_baseline( @@ -186,6 +271,9 @@ def run_measurement( except ServingError as exc: return {"ok": False, "gate": f"baseline serving failed: {exc}", "info": info} + base_swe = baseline.get("swebench", {}) or {} + base_bfcl = baseline.get("bfcl", {}) or {} + patched_src: Path | None = None app_name = f"vllm-serv-opt-patch-{role}-{_short_id()}" try: @@ -200,48 +288,70 @@ def run_measurement( except ServingError as exc: return {"ok": False, "gate": f"patched build/serve failed: {exc}", "info": info} + # --- SWE-bench greedy correctness gate --- patched_greedy = collect_greedy_outputs( handle.base_url, settings=settings, n=settings.correctness_smoke_prompts ) correctness_ok, mismatches = compare_outputs( - baseline.get("greedy_outputs", {}), patched_greedy + base_swe.get("greedy_outputs", {}), patched_greedy ) info["greedy_mismatches"] = mismatches - if not correctness_ok: - return { - "ok": True, - "correctness_ok": False, - "info": info, - "baseline": { - "per_instance_latency": baseline.get("per_instance_latency", {}), - "accuracy": baseline.get("accuracy", 0.0), - }, - "patched": {"per_instance_latency": {}, "accuracy": 0.0}, - } - results = run_workload( - base_url=handle.base_url, - settings=settings, - role=role, - prefer_docker=prefer_docker, + # --- SWE-bench workload (latency + proxy accuracy) --- + swe_results = run_workload( + base_url=handle.base_url, settings=settings, role=role, prefer_docker=prefer_docker ) - patched_accuracy = compute_accuracy( - results, settings=settings, mode=accuracy_mode, run_id=f"patched-{role}-{_short_id()}" + swe_patched_accuracy = compute_accuracy( + swe_results, settings=settings, mode=accuracy_mode, run_id=f"patched-{role}-{_short_id()}" ) - info["baseline_accuracy_mode"] = baseline.get("accuracy_mode") - info["patched_accuracy_proxy_used"] = bool(patched_accuracy["proxy_used"]) - info["instances"] = len(results) + info["baseline_accuracy_mode"] = base_swe.get("accuracy_mode") + info["patched_accuracy_proxy_used"] = bool(swe_patched_accuracy["proxy_used"]) + info["swe_instances"] = len(swe_results) + + # --- BFCL workload (real AST accuracy + per-sample correctness gate) --- + bfcl_patched = _measure_bfcl(handle, settings, role=role) + info["bfcl_instances"] = bfcl_patched.get("n_instances", 0) + info["bfcl_patched_accuracy"] = bfcl_patched.get("accuracy", 0.0) + + bfcl_correctness_ok = True + bfcl_regressions = 0 + if base_bfcl.get("available") and bfcl_patched.get("available"): + bfcl_correctness_ok, bfcl_regressions = _bfcl_correctness_ok( + base_bfcl.get("correctness_map", {}), + bfcl_patched.get("correctness_map", {}), + max_regressions=settings.bfcl_max_correctness_regressions, + abs_tolerance=settings.bfcl_correctness_abs_tolerance, + rel_tolerance=settings.bfcl_correctness_tolerance, + ) + info["bfcl_correctness_regressions"] = bfcl_regressions + return { "ok": True, - "correctness_ok": True, + "correctness_ok": correctness_ok, + "bfcl_correctness_ok": bfcl_correctness_ok, "info": info, - "baseline": { - "per_instance_latency": baseline.get("per_instance_latency", {}), - "accuracy": float(baseline.get("accuracy", 0.0)), + "swebench": { + "baseline": { + "per_instance_latency": base_swe.get("per_instance_latency", {}), + "accuracy": float(base_swe.get("accuracy", 0.0)), + }, + "patched": { + "per_instance_latency": _latency_map(swe_results), + "accuracy": float(swe_patched_accuracy["accuracy"]), + "failed_ids": _swe_failed_ids(swe_results), + }, }, - "patched": { - "per_instance_latency": _latency_map(results), - "accuracy": float(patched_accuracy["accuracy"]), + "bfcl": { + "available": bool(base_bfcl.get("available") and bfcl_patched.get("available")), + "baseline": { + "per_instance_latency": base_bfcl.get("per_instance_latency", {}), + "accuracy": float(base_bfcl.get("accuracy", 0.0)), + }, + "patched": { + "per_instance_latency": bfcl_patched.get("per_instance_latency", {}), + "accuracy": float(bfcl_patched.get("accuracy", 0.0)), + "failed_ids": bfcl_patched.get("failed_ids", []), + }, }, } finally: @@ -258,6 +368,8 @@ def run_public_test( baseline_cache_path: str, ) -> dict[str, Any]: """Agent-facing public test: serve the working tree and report feedback.""" + from .scoring import provisional_score + settings = EvalSettings.from_config(config) role = "agent" accuracy_mode = settings.accuracy_mode_for_role(role) @@ -282,28 +394,40 @@ def run_public_test( if handle is not None: stop_server(app_name) - patched_latency = measured["per_instance_latency"] + swe = measured.get("swebench", {}) + bfcl_m = measured.get("bfcl", {}) + patched_swe_latency = swe.get("per_instance_latency", {}) result: dict[str, Any] = { "ok": True, - "n_instances": measured["n_instances"], - "accuracy": measured["accuracy"], - "accuracy_mode": measured["accuracy_mode"], - "mean_latency_seconds": ( - sum(patched_latency.values()) / len(patched_latency) if patched_latency else 0.0 + "swe_instances": swe.get("n_instances", 0), + "swe_accuracy": swe.get("accuracy", 0.0), + "bfcl_instances": bfcl_m.get("n_instances", 0), + "bfcl_accuracy": bfcl_m.get("accuracy", 0.0), + "swe_mean_latency_seconds": ( + sum(patched_swe_latency.values()) / len(patched_swe_latency) + if patched_swe_latency else 0.0 ), - "per_instance_latency": patched_latency, + "per_instance_latency": patched_swe_latency, } baseline = _load_baseline_cache(baseline_cache_path, role) - if baseline and baseline.get("per_instance_latency"): + if _baseline_has_data(baseline): + base_swe = baseline.get("swebench", {}) + base_bfcl = baseline.get("bfcl", {}) provisional = provisional_score( - {str(k): float(v) for k, v in baseline["per_instance_latency"].items()}, - {str(k): float(v) for k, v in patched_latency.items()}, - float(baseline.get("accuracy", 0.0)), - float(measured["accuracy"]), + {str(k): float(v) for k, v in base_swe.get("per_instance_latency", {}).items()}, + {str(k): float(v) for k, v in patched_swe_latency.items()}, + float(base_swe.get("accuracy", 0.0)), + float(swe.get("accuracy", 0.0)), settings.accuracy_tolerance, + cap=settings.max_per_instance_speedup, + abs_tolerance=settings.accuracy_abs_tolerance, + baseline_bfcl_latency={str(k): float(v) for k, v in base_bfcl.get("per_instance_latency", {}).items()}, + patched_bfcl_latency={str(k): float(v) for k, v in bfcl_m.get("per_instance_latency", {}).items()}, + baseline_bfcl_accuracy=float(base_bfcl.get("accuracy", 0.0)), + patched_bfcl_accuracy=float(bfcl_m.get("accuracy", 0.0)), + weights=settings.workload_weights(), ) - result["baseline_accuracy"] = float(baseline.get("accuracy", 0.0)) result["provisional"] = provisional else: result["note"] = "no baseline cache present; reporting raw latency/accuracy only" diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/modal_app.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/modal_app.py index c5f8a6b9e..4ec5509a8 100644 --- a/2.0/problems/vllm_llm_serving_optimization/serving_eval/modal_app.py +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/modal_app.py @@ -1,4 +1,4 @@ -"""Modal app that serves a (patched or vanilla) vLLM build on one L40S GPU. +"""Modal app that serves a (patched or vanilla) vLLM build on Modal H100 GPU(s). This module is deployed with ``modal deploy serving_eval/modal_app.py``. It is parametrized entirely through environment variables so the same module serves @@ -6,7 +6,7 @@ VLLM_SERVING_SRC absolute path to the vLLM source tree to build from VLLM_SERVING_MODEL HuggingFace model id to serve - VLLM_SERVING_GPU Modal GPU string (default "L40S") + VLLM_SERVING_GPU Modal GPU string, "H100:N" for N-way TP (default "H100:2") VLLM_SERVING_APP Modal app name (must be unique per concurrent server) VLLM_SERVING_LABEL deterministic web label for a predictable URL VLLM_SERVING_SCALEDOWN idle seconds before the GPU container is released @@ -29,13 +29,16 @@ import modal VLLM_SERVING_SRC = os.environ.get("VLLM_SERVING_SRC", "/opt/vllm-clean") -VLLM_SERVING_MODEL = os.environ.get("VLLM_SERVING_MODEL", "meta-llama/Llama-3.1-8B-Instruct") -VLLM_SERVING_GPU = os.environ.get("VLLM_SERVING_GPU", "L40S") +VLLM_SERVING_MODEL = os.environ.get("VLLM_SERVING_MODEL", "Qwen/Qwen3-Coder-30B-A3B-Instruct") +VLLM_SERVING_GPU = os.environ.get("VLLM_SERVING_GPU", "H100:2") VLLM_SERVING_APP = os.environ.get("VLLM_SERVING_APP", "vllm-serving-opt") VLLM_SERVING_LABEL = os.environ.get("VLLM_SERVING_LABEL", VLLM_SERVING_APP) VLLM_SERVING_SCALEDOWN = int(os.environ.get("VLLM_SERVING_SCALEDOWN", "900")) VLLM_SERVING_STARTUP = int(os.environ.get("VLLM_SERVING_STARTUP", "1200")) -VLLM_SERVING_MAXLEN = int(os.environ.get("VLLM_SERVING_MAXLEN", "16384")) +# 32K context fits comfortably on H100s (80GB each) and avoids the growing +# multi-turn conversation overflowing the window (which 404'd mid-run on a +# smaller 16K setup). Lower this if serving on a smaller card. +VLLM_SERVING_MAXLEN = int(os.environ.get("VLLM_SERVING_MAXLEN", "32768")) VLLM_SERVING_HF_SECRET = os.environ.get("VLLM_SERVING_HF_SECRET", "huggingface-secret") # Pinned vLLM version. The source tree is copied into the build image, where its # git metadata is not reliably readable by setuptools_scm (and a patched tree is @@ -89,6 +92,14 @@ { "HF_HUB_ENABLE_HF_TRANSFER": "1", "DO_NOT_TRACK": "1", + # These are read inside serve(), which runs in the REMOTE container + # where the deploy-time process env is NOT present. Bake the values + # into the image env (evaluated here at deploy time) so the remote + # serve() sees the requested model / context / GPU spec instead of + # silently falling back to the module-level defaults. + "VLLM_SERVING_MODEL": VLLM_SERVING_MODEL, + "VLLM_SERVING_MAXLEN": str(VLLM_SERVING_MAXLEN), + "VLLM_SERVING_GPU": VLLM_SERVING_GPU, } ) ) @@ -106,6 +117,12 @@ def _hf_secrets() -> list[modal.Secret]: @app.function( image=serving_image, gpu=VLLM_SERVING_GPU, + # Pin to a SINGLE serving container. Without this, Modal autoscales out under + # load (spinning up more GPU replicas), which relieves exactly the + # queueing/preemption pressure the scheduler optimization is supposed to + # manage — making the latency signal vanish. One container = all load lands on + # one GPU set, so contention (and thus scheduling) is real and measurable. + max_containers=1, scaledown_window=VLLM_SERVING_SCALEDOWN, timeout=24 * 60 * 60, secrets=_hf_secrets(), @@ -119,6 +136,15 @@ def _hf_secrets() -> list[modal.Secret]: def serve() -> None: import subprocess + # Derive tensor-parallel size from the Modal GPU spec ("H100" -> 1, + # "H100:2" -> 2). With more than one GPU, vLLM must shard across them. + gpu_count = 1 + if ":" in VLLM_SERVING_GPU: + try: + gpu_count = max(1, int(VLLM_SERVING_GPU.split(":", 1)[1])) + except ValueError: + gpu_count = 1 + cmd = [ "vllm", "serve", @@ -131,4 +157,6 @@ def serve() -> None: str(VLLM_SERVING_MAXLEN), "--disable-log-requests", ] + if gpu_count > 1: + cmd += ["--tensor-parallel-size", str(gpu_count)] subprocess.Popen(" ".join(cmd), shell=True) diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/sandbox.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/sandbox.py index 5e31f58cb..7006f923c 100644 --- a/2.0/problems/vllm_llm_serving_optimization/serving_eval/sandbox.py +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/sandbox.py @@ -94,6 +94,11 @@ def run(self, command: str, *, timeout: int) -> tuple[int, str]: ) except subprocess.TimeoutExpired: return 124, "command timed out" + except Exception as exc: # noqa: BLE001 + # A dead/evicted container or a docker hiccup must not kill the whole + # instance thread; surface it as a non-zero command result so the + # agent loop (and per-instance latency) keep going. + return 1, f"sandbox error: {type(exc).__name__}" return proc.returncode, (proc.stdout or "") + (proc.stderr or "") def read_patch(self) -> str: @@ -122,6 +127,8 @@ def run(self, command: str, *, timeout: int) -> tuple[int, str]: ) except subprocess.TimeoutExpired: return 124, "command timed out" + except Exception as exc: # noqa: BLE001 + return 1, f"sandbox error: {type(exc).__name__}" return proc.returncode, (proc.stdout or "") + (proc.stderr or "") def read_patch(self) -> str: diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/scoring.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/scoring.py index 586fdcbd9..6687abbfb 100644 --- a/2.0/problems/vllm_llm_serving_optimization/serving_eval/scoring.py +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/scoring.py @@ -1,13 +1,24 @@ """Scoring math shared with the agent-facing public test. The judge's authoritative scorer lives in the task evaluator; these helpers -mirror it so the public test can show a provisional score consistent with how -the judge will grade. Keep the two in sync. +mirror it so the public test shows a provisional score consistent with how the +judge grades. Keep the two in sync. + +Two changes vs a naive geomean-speedup score: + +* Per-instance speedup is clamped to ``[1/cap, cap]`` and a patched instance that + *failed* (errored / early-exited, hence a tiny latency) is counted as a + regression (``1/cap``), not a giant speedup — so "fail fast" cannot game the + metric (audit finding: uncapped geomean inflation). +* The final score blends two workloads (SWE-bench + BFCL) by configurable + weights, each gated by its own accuracy guardrail. BFCL's baseline accuracy is + non-zero, so its guardrail is live. """ from __future__ import annotations import math +from typing import Iterable def geometric_mean(values: list[float]) -> float: @@ -16,13 +27,26 @@ def geometric_mean(values: list[float]) -> float: return math.exp(sum(math.log(max(value, 1e-9)) for value in values) / len(values)) -def paired_speedups(baseline: dict[str, float], patched: dict[str, float]) -> list[float]: +def paired_speedups( + baseline: dict[str, float], + patched: dict[str, float], + *, + cap: float = 8.0, + failed_ids: Iterable[str] = (), +) -> list[float]: + cap = max(1.0001, cap) + floor = 1.0 / cap + failed = set(failed_ids) speedups: list[float] = [] for instance_id, patched_value in patched.items(): base_value = baseline.get(instance_id) - if base_value is None or patched_value <= 0 or base_value <= 0: + if base_value is None or base_value <= 0: + continue + if instance_id in failed or patched_value <= 0: + # Patched request failed; do not let its tiny latency read as a win. + speedups.append(floor) continue - speedups.append(max(base_value / patched_value, 0.01)) + speedups.append(max(floor, min(cap, base_value / patched_value))) return speedups @@ -32,25 +56,41 @@ def score_from_speedup(speedup: float) -> float: return max(0.0, min(100.0, 100.0 * math.log(speedup, 2))) -def accuracy_multiplier(baseline_accuracy: float, patched_accuracy: float, tolerance: float) -> float: +def accuracy_multiplier( + baseline_accuracy: float, + patched_accuracy: float, + tolerance: float, + abs_tolerance: float = 0.05, +) -> float: + # No penalty if the accuracy drop is within EITHER the relative tolerance OR + # the absolute tolerance. resolve_rate over a finite instance slice is a + # coarse, high-variance signal (a 1-2 instance swing on 50 ~ a big relative + # drop), so the absolute floor keeps small, likely-noise drops from being + # over-penalized while still catching real quality regressions. + abs_drop = max(0.0, baseline_accuracy - patched_accuracy) base = max(baseline_accuracy, 1e-9) - rel_drop = max(0.0, (baseline_accuracy - patched_accuracy) / base) - if rel_drop <= tolerance: + rel_drop = max(0.0, abs_drop / base) + if abs_drop <= abs_tolerance or rel_drop <= tolerance: return 1.0 return max(0.0, min(1.0, tolerance / rel_drop)) -def provisional_score( +def workload_score( baseline_latency: dict[str, float], patched_latency: dict[str, float], baseline_accuracy: float, patched_accuracy: float, tolerance: float, + *, + cap: float = 8.0, + failed_ids: Iterable[str] = (), + abs_tolerance: float = 0.05, ) -> dict[str, float]: - speedups = paired_speedups(baseline_latency, patched_latency) + """Latency-speedup score for one workload, gated by its accuracy guardrail.""" + speedups = paired_speedups(baseline_latency, patched_latency, cap=cap, failed_ids=failed_ids) gm = geometric_mean(speedups) if speedups else 0.0 latency_score = score_from_speedup(gm) - acc_mult = accuracy_multiplier(baseline_accuracy, patched_accuracy, tolerance) + acc_mult = accuracy_multiplier(baseline_accuracy, patched_accuracy, tolerance, abs_tolerance) return { "latency_geomean_speedup": gm, "latency_score": latency_score, @@ -58,3 +98,49 @@ def provisional_score( "score": max(0.0, min(100.0, latency_score * acc_mult)), "instances_scored": float(len(speedups)), } + + +def blend(swe_score: float, bfcl_score: float, weights: tuple[float, float]) -> float: + w_swe, w_bfcl = weights + return max(0.0, min(100.0, w_swe * swe_score + w_bfcl * bfcl_score)) + + +def provisional_score( + baseline_latency: dict[str, float], + patched_latency: dict[str, float], + baseline_accuracy: float, + patched_accuracy: float, + tolerance: float, + *, + cap: float = 8.0, + abs_tolerance: float = 0.05, + baseline_bfcl_latency: dict[str, float] | None = None, + patched_bfcl_latency: dict[str, float] | None = None, + baseline_bfcl_accuracy: float = 0.0, + patched_bfcl_accuracy: float = 0.0, + weights: tuple[float, float] = (0.5, 0.5), +) -> dict[str, float]: + swe = workload_score( + baseline_latency, patched_latency, baseline_accuracy, patched_accuracy, tolerance, + cap=cap, abs_tolerance=abs_tolerance, + ) + has_bfcl = bool(patched_bfcl_latency) + if has_bfcl: + bfcl = workload_score( + baseline_bfcl_latency or {}, patched_bfcl_latency or {}, + baseline_bfcl_accuracy, patched_bfcl_accuracy, tolerance, + cap=cap, abs_tolerance=abs_tolerance, + ) + use_weights = weights + else: + bfcl = {"latency_geomean_speedup": 0.0, "latency_score": 0.0, + "accuracy_multiplier": 1.0, "score": 0.0, "instances_scored": 0.0} + use_weights = (1.0, 0.0) + return { + "swe_latency_geomean_speedup": swe["latency_geomean_speedup"], + "swe_score": swe["score"], + "bfcl_latency_geomean_speedup": bfcl["latency_geomean_speedup"], + "bfcl_score": bfcl["score"], + "bfcl_accuracy_multiplier": bfcl["accuracy_multiplier"], + "score": blend(swe["score"], bfcl["score"], use_weights), + } diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/serving.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/serving.py index 93d51f013..45ea7112b 100644 --- a/2.0/problems/vllm_llm_serving_optimization/serving_eval/serving.py +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/serving.py @@ -1,7 +1,7 @@ """Deploy, health-check, and tear down a Modal-hosted vLLM server. The judge and the public test both build a Modal image from a vLLM source tree -and serve it on an L40S. This module wraps that lifecycle: +and serve it on an H100. This module wraps that lifecycle: deploy_server(...) -> ServerHandle(base_url, app_name) wait_healthy(...) diff --git a/2.0/problems/vllm_llm_serving_optimization/serving_eval/settings.py b/2.0/problems/vllm_llm_serving_optimization/serving_eval/settings.py index 9cd45496d..bac60b7dd 100644 --- a/2.0/problems/vllm_llm_serving_optimization/serving_eval/settings.py +++ b/2.0/problems/vllm_llm_serving_optimization/serving_eval/settings.py @@ -28,29 +28,60 @@ def _as_float(value: Any, default: float) -> float: @dataclass class EvalSettings: - model: str = "meta-llama/Llama-3.1-8B-Instruct" - gpu: str = "L40S" - dataset: str = "princeton-nlp/SWE-bench_Verified" + model: str = "Qwen/Qwen3-Coder-30B-A3B-Instruct" + gpu: str = "H100:2" # Modal GPU spec; "H100:N" for N-way tensor parallel. + dataset: str = "princeton-nlp/SWE-bench_Lite" dataset_split: str = "test" + # Fixed seed for the deterministic random instance sample (SWE + BFCL). + sample_seed: int = 20260624 public_slice: str = "0:5" - eval_slice: str = "0:30" + eval_slice: str = "0:50" arrival_mode: str = "jps" jps: float = 0.5 workers: int = 8 step_limit: int = 50 temperature: float = 0.0 max_completion_tokens: int = 2048 - accuracy_tolerance: float = 0.05 + accuracy_tolerance: float = 0.05 # relative-drop tolerance + accuracy_abs_tolerance: float = 0.05 # absolute-drop tolerance (OR with relative) agent_accuracy_mode: str = "patch_validity" final_accuracy_mode: str = "resolve_rate" # Docker Hub namespace for prebuilt SWE-bench eval images (real resolve_rate). swebench_namespace: str = "swebench" correctness_smoke_prompts: int = 8 + # --- BFCL (function-calling) workload: the second judged workload, providing + # a real, deterministic, non-zero accuracy + correctness signal. --- + bfcl_public_slice: str = "0:8" + bfcl_eval_slice: str = "0:40" + bfcl_max_tokens: int = 768 # memory answers + tool args are longer than AST calls + memory_max_steps: int = 20 # agentic step cap per memory instance (upstream MAXIMUM_STEP_LIMIT) + # BFCL memory arrives as a seeded Poisson process. It uses its OWN rate + # (bfcl_jps), higher than the SWE `jps`, because memory requests are short + # (~5K ctx, 768 decode) and only pile up into real KV contention at a high + # arrival rate. bfcl_workers only caps the fallback burst path (arrival != jps). + bfcl_jps: float = 5.0 + bfcl_workers: int = 64 + # Final score blend over the two workloads (must sum to 1.0). + swebench_weight: float = 0.5 + bfcl_weight: float = 0.5 + # Per-instance speedup is clamped to [1/cap, cap] so a single early-exiting or + # erroring instance (tiny latency) cannot inflate the geomean, and a single + # stall cannot tank it beyond the cap. + max_per_instance_speedup: float = 8.0 + # BFCL per-sample greedy gate: at temperature 0 the patched run should reproduce + # the baseline's per-instance correctness. Allowed correct@baseline -> + # wrong@patched flips = max(bfcl_max_correctness_regressions floor, 5% of all + # instances [absolute], 5% of the baseline-correct set [relative]). The 5% + # abs-OR-rel band absorbs the batch-numerics non-determinism that flips many + # instances run-to-run even between identical builds under concurrency. + bfcl_max_correctness_regressions: int = 1 + bfcl_correctness_abs_tolerance: float = 0.05 # of all BFCL instances + bfcl_correctness_tolerance: float = 0.05 # of the baseline-correct count modal_scaledown_seconds: int = 900 modal_startup_timeout_seconds: int = 1200 modal_deploy_retries: int = 3 server_health_timeout_seconds: int = 1800 - build_timeout_seconds: int = 5400 + build_timeout_seconds: int = 7200 instance_timeout_seconds: int = 1200 extra: dict[str, Any] = field(default_factory=dict) @@ -62,6 +93,7 @@ def from_config(cls, config: dict[str, Any] | None) -> "EvalSettings": gpu=str(config.get("gpu", cls.gpu)), dataset=str(config.get("dataset", cls.dataset)), dataset_split=str(config.get("dataset_split", cls.dataset_split)), + sample_seed=_as_int(config.get("sample_seed"), cls.sample_seed), public_slice=str(config.get("public_slice", cls.public_slice)), eval_slice=str(config.get("eval_slice", cls.eval_slice)), arrival_mode=str(config.get("arrival_mode", cls.arrival_mode)), @@ -71,12 +103,33 @@ def from_config(cls, config: dict[str, Any] | None) -> "EvalSettings": temperature=_as_float(config.get("temperature"), cls.temperature), max_completion_tokens=_as_int(config.get("max_completion_tokens"), cls.max_completion_tokens), accuracy_tolerance=_as_float(config.get("accuracy_tolerance"), cls.accuracy_tolerance), + accuracy_abs_tolerance=_as_float(config.get("accuracy_abs_tolerance"), cls.accuracy_abs_tolerance), agent_accuracy_mode=str(config.get("agent_accuracy_mode", cls.agent_accuracy_mode)), final_accuracy_mode=str(config.get("final_accuracy_mode", cls.final_accuracy_mode)), swebench_namespace=str(config.get("swebench_namespace", cls.swebench_namespace)), correctness_smoke_prompts=_as_int( config.get("correctness_smoke_prompts"), cls.correctness_smoke_prompts ), + bfcl_public_slice=str(config.get("bfcl_public_slice", cls.bfcl_public_slice)), + bfcl_eval_slice=str(config.get("bfcl_eval_slice", cls.bfcl_eval_slice)), + bfcl_max_tokens=_as_int(config.get("bfcl_max_tokens"), cls.bfcl_max_tokens), + memory_max_steps=_as_int(config.get("memory_max_steps"), cls.memory_max_steps), + bfcl_jps=_as_float(config.get("bfcl_jps"), cls.bfcl_jps), + bfcl_workers=_as_int(config.get("bfcl_workers"), cls.bfcl_workers), + swebench_weight=_as_float(config.get("swebench_weight"), cls.swebench_weight), + bfcl_weight=_as_float(config.get("bfcl_weight"), cls.bfcl_weight), + max_per_instance_speedup=_as_float( + config.get("max_per_instance_speedup"), cls.max_per_instance_speedup + ), + bfcl_max_correctness_regressions=_as_int( + config.get("bfcl_max_correctness_regressions"), cls.bfcl_max_correctness_regressions + ), + bfcl_correctness_abs_tolerance=_as_float( + config.get("bfcl_correctness_abs_tolerance"), cls.bfcl_correctness_abs_tolerance + ), + bfcl_correctness_tolerance=_as_float( + config.get("bfcl_correctness_tolerance"), cls.bfcl_correctness_tolerance + ), modal_scaledown_seconds=_as_int(config.get("modal_scaledown_seconds"), cls.modal_scaledown_seconds), modal_deploy_retries=_as_int(config.get("modal_deploy_retries"), cls.modal_deploy_retries), modal_startup_timeout_seconds=_as_int( @@ -93,9 +146,21 @@ def from_config(cls, config: dict[str, Any] | None) -> "EvalSettings": def slice_for_role(self, role: str) -> str: return self.eval_slice if role == "final" else self.public_slice + def bfcl_slice_for_role(self, role: str) -> str: + return self.bfcl_eval_slice if role == "final" else self.bfcl_public_slice + def accuracy_mode_for_role(self, role: str) -> str: return self.final_accuracy_mode if role == "final" else self.agent_accuracy_mode + def workload_weights(self) -> tuple[float, float]: + """Return normalized (swebench_weight, bfcl_weight) summing to 1.0.""" + sw = max(0.0, self.swebench_weight) + bf = max(0.0, self.bfcl_weight) + total = sw + bf + if total <= 0: + return 0.5, 0.5 + return sw / total, bf / total + def parse_slice(spec: str, length: int) -> range: """Parse a ``start:stop`` slice spec into a concrete index range.""" From 8467bb65eed8b96febe30fd54fe95fe2ed2fb811 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 1 Jul 2026 06:25:22 +0000 Subject: [PATCH 04/34] feat(2.0): add flashlib GPU kernel-optimization task family (KMeans/KNN/PCA/TruncatedSVD) Four KernelBench-style Frontier-CS 2.0 optimization tasks distilled from the FlashML-org/flashlib GPU classical-ML library. Each ships a correct-but-naive torch implementation the agent patches; the judge applies the patch to a clean copy, times the patched primitive against a frozen naive baseline on hidden workloads in an isolated worker, gates on a judge-computed cheat-proof quality metric, and scores by geometric-mean speedup. Mirrors the duckdb_e2e template (patch submission, /opt hidden data, self-contained evaluator, no-GPU smoke). - kmeans: naive cdist Lloyd -> fused Triton assign; inertia-ratio gate - knn: cdist+topk -> chunked running top-k; recall@k gate - pca: center+full-SVD -> covariance+eigh; captured-variance+orthonormality gate - truncated_svd: full-SVD -> Gram+eigh; captured-energy+orthonormality gate Anti-gaming: neutrally-named packages (not flashlib), patch allowlist, forbidden-import policy, hidden shapes. speedup_target + Triton reference need a GPU calibration trial. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../kmeans_gpu_kernel_optimization/DESIGN.md | 84 +++ .../config.yaml | 52 ++ .../docker/README.md | 46 ++ .../docker/agent/Dockerfile | 22 + .../docker/build_images.sh | 17 + .../docker/judge/Dockerfile | 18 + .../docker/smoke_images.sh | 17 + .../evaluate.sh | 12 + .../evaluator.py | 545 ++++++++++++++++++ .../harbor/app/README.md | 34 ++ .../harbor/app/make_submission.sh | 24 + .../harbor/app/public_test.py | 97 ++++ .../harbor/app/public_test.sh | 7 + .../harbor/app/solution.patch | 0 .../judge/refkmeans.py | 49 ++ .../kmeanslib/__init__.py | 15 + .../kmeanslib/kmeans.py | 98 ++++ .../kmeans_gpu_kernel_optimization/readme | 123 ++++ .../reference.patch | 164 ++++++ .../knn_gpu_kernel_optimization/DESIGN.md | 89 +++ .../knn_gpu_kernel_optimization/config.yaml | 53 ++ .../docker/README.md | 46 ++ .../docker/agent/Dockerfile | 22 + .../docker/build_images.sh | 17 + .../docker/judge/Dockerfile | 18 + .../docker/smoke_images.sh | 17 + .../knn_gpu_kernel_optimization/evaluate.sh | 12 + .../knn_gpu_kernel_optimization/evaluator.py | 502 ++++++++++++++++ .../harbor/app/README.md | 34 ++ .../harbor/app/make_submission.sh | 24 + .../harbor/app/public_test.py | 80 +++ .../harbor/app/public_test.sh | 7 + .../harbor/app/solution.patch | 0 .../judge/refknn.py | 18 + .../knnlib/__init__.py | 16 + .../knn_gpu_kernel_optimization/knnlib/knn.py | 39 ++ .../knn_gpu_kernel_optimization/readme | 124 ++++ .../reference.patch | 104 ++++ .../pca_gpu_kernel_optimization/DESIGN.md | 104 ++++ .../pca_gpu_kernel_optimization/config.yaml | 55 ++ .../docker/README.md | 46 ++ .../docker/agent/Dockerfile | 22 + .../docker/build_images.sh | 17 + .../docker/judge/Dockerfile | 18 + .../docker/smoke_images.sh | 17 + .../pca_gpu_kernel_optimization/evaluate.sh | 12 + .../pca_gpu_kernel_optimization/evaluator.py | 516 +++++++++++++++++ .../harbor/app/README.md | 35 ++ .../harbor/app/make_submission.sh | 24 + .../harbor/app/public_test.py | 97 ++++ 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mode 100644 2.0/problems/truncated_svd_gpu_kernel_optimization/readme create mode 100644 2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch create mode 100644 2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/__init__.py create mode 100644 2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/tsvd.py diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md new file mode 100644 index 000000000..edc8bcf1f --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md @@ -0,0 +1,84 @@ +# Design notes — kmeans_gpu_kernel_optimization + +Operator-facing. Not copied into the agent workspace by the adapter. + +## What this task measures + +Can an agent turn a correct-but-naive GPU K-Means into a fast one? The agent is +given `kmeanslib` (a `torch.cdist`-per-iteration Lloyd loop) and must rewrite the +internals — ideally a fused Triton assignment kernel plus a better centroid +update — to maximize the geometric-mean speedup over the frozen baseline across a +family of hidden `(N, D, K)` workloads, subject to a clustering-quality gate. + +This is the first of a four-task **flashlib kernel-optimization family** +(KMeans, KNN, TruncatedSVD, PCA). All four share the KernelBench-style pattern: +ship a naive package, freeze a byte-for-byte baseline in the judge image, apply +the agent's patch to a clean copy, time patched-vs-baseline on identical seeded +data in an isolated worker, and gate on a primitive-appropriate quality metric +before scoring by geomean speedup. + +## Correctness gate: inertia, judge-computed + +The quality gate is the k-means objective — inertia = sum over points of the +squared L2 distance to the nearest **final** centroid. It is computed by the +judge worker from the *centroids the submission returns* (not from any +agent-provided number), on the same seeded `x` and `init_centroids` used for the +baseline. Properties: + +- permutation/label-convention independent (only the centroids matter); +- robust to floating-point drift across iterations; +- cheat-proof: you cannot return fast garbage — low inertia requires actually + clustering well. Trading some numeric precision for speed (bf16/tf32 + accumulation) is allowed within `inertia_tolerance` (default 2%). + +Determinism: `init_centroids` are always supplied and `tol = 0` (every iteration +runs), so the baseline result is a fixed function of `(x, init_centroids, +max_iters)`. + +## Anti-gaming + +flashlib (the library these primitives are distilled from) is public and +Apache-2.0. Mitigations: + +- The shipped package is a small, neutrally named library (`kmeanslib`), not + flashlib; the patch allowlist confines edits to `kmeanslib/**`. +- The patch policy forbids importing `flashlib`/cuML/cuPy/FAISS/scikit-learn and + bans env/subprocess/network access — the agent must write the kernels itself. +- Scored shapes are hidden and differ from the two public shapes; seeds derive + from `base_seed`. General kernels win; shape lookups do not. +- Honest framing: reproducing SOTA-class kernels *is* the bar. We block trivial + library reuse, not the underlying knowledge. + +## Execution model & the Modal question + +The evaluator runs an isolated worker subprocess (`_run_worker`) that imports the +frozen baseline (`/opt/kmeans_ref/refkmeans.py`) and the patched `kmeanslib` +(from the applied clean tree), times both, and reports JSON. This assumes the +**judge container has a GPU**. + +The repo's other GPU task (vllm) instead offloads to **Modal** because Harbor +judge containers may not be GPU-scheduled. `_run_worker` is the single swap +point: to go Modal, replace it with a Modal function that builds the patched +package, runs the same worker logic on a Modal GPU, and returns the JSON rows. +The rest of the evaluator (policy, gating, scoring) is unchanged. Decide +in-container-GPU vs Modal at first calibration trial. + +## Calibration TODO (needs a GPU trial) + +- Validate `reference.patch` runs and passes the inertia gate on all workloads; + fix any Triton API drift (`tl.dot(input_precision=...)`, `tl.argmin`) for the + pinned torch/triton in the image. +- Measure the reference solution's geomean speedup and set `speedup_target` so a + reference-level solution maps to ~full score. +- Confirm hidden shapes fit device memory (the naive baseline's `cdist` + materializes `N x K`; all shipped shapes are H100-safe). +- Sanity-check timing stability (median-of-7); bump `timed_iters` if noisy. + +## Files + +- `kmeanslib/` — pristine package baked into both images (agent edits it). +- `judge/refkmeans.py` — frozen baseline, baked to `/opt/kmeans_ref/`. +- `evaluator.py` — self-contained policy + orchestration + scoring (judge-only). +- `reference.patch` — fused-Triton reference solution (proves solvability). +- `docker/` — builds the prebuilt agent/judge images referenced by config.yaml. +- `harbor/app/` — agent-facing submission helpers + public self-test. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml new file mode 100644 index 000000000..34b29adbb --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -0,0 +1,52 @@ +tag: systems +runtime: + language: python + timeout_seconds: 10800 + environment: "GPU K-Means kernel optimization; agent patches the kmeanslib Python/Triton package; judge times the patched kmeans against a frozen naive baseline on hidden (N, D, K) workloads with an inertia (clustering-quality) gate." + apt_packages: + - bash + - ca-certificates + - git + - python3 + judge_apt_packages: + - bash + - ca-certificates + - git + - python3 + docker: + # Experimental local images. Build them with + # 2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh before a + # local Harbor trial. Both images bundle a clean copy of the kmeanslib + # package; the judge image additionally bakes the frozen naive baseline + # (/opt/kmeans_ref) and the pristine tree (/opt/kmeanslib-clean). + image: frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.1.0 + judge_image: frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.1.0 +environment: + cpus: 8 + memory_mb: 32768 + storage_mb: 32768 + build_timeout_seconds: 3600 +evaluation: + # The judge worker runs on a GPU visible to the judge container (single + # device). H100 is the reference accelerator; the Triton paths also run on + # L40S/A100. If Harbor cannot attach a GPU to the judge container, the worker + # step is the swap point for a Modal offload (see DESIGN.md). + gpu: "H100" + # Timing: warmup then median-of-N cuda-synced full-kmeans calls. + warmup_iters: 3 + timed_iters: 7 + # Clustering-quality gate: the submitted kmeans' final inertia (within-cluster + # sum of squared distances) must not exceed (1 + inertia_tolerance) x the + # frozen baseline's inertia on the SAME seeded data and init centroids. + inertia_tolerance: 0.02 + # Geomean speedup (over the naive baseline) mapped to the full bounded score. + # Calibrate against the reference solution's measured geomean after a trial. + speedup_target: 8.0 + worker_timeout_seconds: 3600 + base_seed: 20260701 + # Agent (iterative) role runs a fast subset; the final verifier runs them all. + agent_workload_count: 3 + expose_per_workload_metrics: false +submission: + kind: file + path: /app/solution.patch diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/README.md b/2.0/problems/kmeans_gpu_kernel_optimization/docker/README.md new file mode 100644 index 000000000..3bb55f41a --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/README.md @@ -0,0 +1,46 @@ +# Experimental K-Means Kernel-Optimization Images + +Two images, mirroring the duckdb-e2e split: a public **agent** image and a +private **judge** image. Build them before a local Harbor trial: + +```bash +bash 2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh +``` + +Defaults: + +```text +AGENT_TAG=frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.1.0 +JUDGE_TAG=frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.1.0 +``` + +The agent image contains: + +```text +/app/kmeanslib # clean, git-tracked package (the agent edits this) +``` + +The judge image contains: + +```text +/opt/kmeanslib-clean/kmeanslib # pristine tree; the patch is applied to a copy +/opt/kmeans_ref/refkmeans.py # frozen naive baseline (speed denominator + oracle) +``` + +Both are based on `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel` (torch + triton). + +## Runtime requirements + +The judge times the patched kmeans on a **GPU visible to the judge container** +(single device; H100 reference, Triton paths also run on L40S/A100). If the +Harbor runtime cannot attach a GPU to the judge container, port the worker step +(`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md`. + +## Smoke test + +```bash +bash 2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh +``` + +Import-only; verifies torch/triton and the baked packages are importable. It +does not exercise a GPU. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile new file mode 100644 index 000000000..d28f6b85b --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile @@ -0,0 +1,22 @@ +# Agent image for kmeans_gpu_kernel_optimization. +# Bundles a clean, git-tracked copy of the kmeanslib package at /app/kmeanslib +# so the agent can edit it and `make_submission.sh` can diff it. torch + triton +# come from the PyTorch base image. +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel + +ARG DEBIAN_FRONTEND=noninteractive +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash ca-certificates git ripgrep && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" + +WORKDIR /app +COPY kmeanslib /app/kmeanslib +RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ + git -C /app init -q && \ + git -C /app config user.email task@frontier-cs && \ + git -C /app config user.name frontier-cs && \ + git -C /app add -A -- kmeanslib .gitignore && \ + git -C /app -c commit.gpgsign=false commit -qm "pristine kmeanslib" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh new file mode 100644 index 000000000..7eedc497d --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# Build the experimental agent + judge images for kmeans_gpu_kernel_optimization. +set -euo pipefail +HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) + +AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "Building agent image: $AGENT_TAG" +docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" + +echo "Building judge image: $JUDGE_TAG" +docker build -f "$HERE/docker/judge/Dockerfile" -t "$JUDGE_TAG" "$HERE" + +echo "Built:" +echo " $AGENT_TAG" +echo " $JUDGE_TAG" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/kmeans_gpu_kernel_optimization/docker/judge/Dockerfile new file mode 100644 index 000000000..b4f0c1b3d --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/judge/Dockerfile @@ -0,0 +1,18 @@ +# Judge image for kmeans_gpu_kernel_optimization. +# Bakes the pristine package tree the agent patch is applied to +# (/opt/kmeanslib-clean/kmeanslib) and the frozen naive baseline the judge times +# against (/opt/kmeans_ref/refkmeans.py). torch + triton come from the base. +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel + +ARG DEBIAN_FRONTEND=noninteractive +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash ca-certificates git && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" + +COPY kmeanslib /opt/kmeanslib-clean/kmeanslib +COPY judge/refkmeans.py /opt/kmeans_ref/refkmeans.py + +WORKDIR /judge diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh new file mode 100644 index 000000000..e3f3b4794 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# Import-only smoke test for the built images (no GPU required). +set -euo pipefail + +AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "== agent image ==" +docker run --rm -w /app "$AGENT_TAG" python3 -c \ + "import torch, triton, kmeanslib; print('agent ok: kmeanslib', kmeanslib.__version__)" + +echo "== judge image ==" +docker run --rm "$JUDGE_TAG" python3 -c \ + "import sys, torch, triton; \ +sys.path.insert(0, '/opt/kmeans_ref'); import refkmeans; \ +sys.path.insert(0, '/opt/kmeanslib-clean'); import kmeanslib; \ +print('judge ok: refkmeans + kmeanslib import')" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/evaluate.sh b/2.0/problems/kmeans_gpu_kernel_optimization/evaluate.sh new file mode 100644 index 000000000..49d06ffc0 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/evaluate.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +# Local CLI evaluation for kmeans_gpu_kernel_optimization. +# +# A full run needs a GPU plus the baked judge sources at /opt (the pristine +# /opt/kmeanslib-clean tree and the frozen /opt/kmeans_ref baseline; see the +# judge image). Without them the evaluator still validates the patch policy and +# returns a smoke score, which is what repository CI exercises (the reference +# patch passes the policy). Pass a patch path to score it directly. +set -euo pipefail +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +SOLUTION="${1:-$SCRIPT_DIR/reference.patch}" +exec python3 "$SCRIPT_DIR/evaluator.py" "$SOLUTION" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py b/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py new file mode 100644 index 000000000..86f9d0613 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py @@ -0,0 +1,545 @@ +"""Evaluator for the K-Means GPU kernel-optimization task. + +The agent submits a unified diff over the ``kmeanslib`` Python package. The +judge: + +1. statically validates the patch against an allowlist (only ``kmeanslib/**`` + may change; no ``flashlib``/cuML/network/env access; bounded size); +2. applies it to a pristine copy of ``kmeanslib`` baked into the judge image + at ``/opt/kmeanslib-clean``; +3. runs an isolated worker subprocess that, for each hidden ``(N, D, K)`` + workload, times the patched ``kmeanslib.kmeans`` against a *frozen* naive + baseline (``/opt/kmeans_ref/refkmeans.py``) on identical seeded data and + measures each result's k-means inertia; +4. gates on clustering quality (agent inertia must not regress beyond a small + tolerance vs the baseline) and scores by the geometric-mean speedup. + +When the judge source tree is not present (e.g. local static checks on a box +without a GPU), the evaluator still validates the patch policy and returns a +smoke pass, so ``python3 evaluator.py reference.patch`` works anywhere. +""" +from __future__ import annotations + +import fnmatch +import hashlib +import json +import math +import os +import re +import shutil +import subprocess +import sys +import tempfile +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +MAX_PATCH_BYTES = 1_000_000 +MAX_CHANGED_FILES = 40 +TASK_CONFIG_PATH = Path("/judge/task_config.json") + +DEFAULT_CLEAN_SOURCE = Path("/opt/kmeanslib-clean") # pristine package (patched here) +DEFAULT_BASELINE_SOURCE = Path("/opt/kmeans_ref") # frozen naive baseline (refkmeans.py) + + +def _load_task_config() -> dict[str, Any]: + try: + payload = json.loads(TASK_CONFIG_PATH.read_text(encoding="utf-8")) + except Exception: + return {} + return payload if isinstance(payload, dict) else {} + + +TASK_CONFIG = _load_task_config() +EVALUATION_CONFIG = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} + + +def _cfg(name: str, default): + return EVALUATION_CONFIG.get(name, default) + + +def _cfg_int(name: str, default: int) -> int: + try: + return int(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _cfg_float(name: str, default: float) -> float: + try: + return float(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _cfg_bool(name: str, default: bool) -> bool: + raw = EVALUATION_CONFIG.get(name, default) + if isinstance(raw, bool): + return raw + if isinstance(raw, str): + return raw.strip().lower() in {"1", "true", "yes", "on"} + return bool(raw) + + +WARMUP_ITERS = _cfg_int("warmup_iters", 3) +TIMED_ITERS = _cfg_int("timed_iters", 7) +INERTIA_TOL = _cfg_float("inertia_tolerance", 0.01) # agent inertia <= (1+tol)*ref +SPEEDUP_TARGET = _cfg_float("speedup_target", 8.0) # geomean speedup mapped to full score +WORKER_TIMEOUT_SECONDS = _cfg_int("worker_timeout_seconds", 3600) +BASE_SEED = _cfg_int("base_seed", 20260701) + +# Editable surface: only the shipped package. +ALLOWED_PATTERNS = ( + "kmeanslib/**", +) +# Never touchable, even though they are not shipped in the agent workspace. +DENIED_PATTERNS = ( + "evaluator.py", + "reference.patch", + "refkmeans.py", + "**/refkmeans.py", + "**/conftest.py", + "**/test_*.py", + "pyproject.toml", + "setup.py", + "setup.cfg", +) +# Forbidden substrings in added lines (defense in depth). The agent must write +# its own kernels, not call an external optimized library, read the +# environment, spawn processes, or touch the network. +FORBIDDEN_TOKENS = ( + "flashlib", + "cuml", + "cudf", + "cupy", + "faiss", + "sklearn", + "scikit", + "os.environ", + "getenv", + "putenv", + "setenv", + "subprocess", + "socket", + "urllib", + "requests", + "importlib.import_module", + "__import__", +) + + +@dataclass(frozen=True) +class PatchFile: + old_path: str + new_path: str + added_lines: tuple[str, ...] + removed_lines: tuple[str, ...] + + @property + def path(self) -> str: + return self.new_path if self.new_path != "/dev/null" else self.old_path + + +def _match(path: str, patterns: tuple[str, ...]) -> bool: + return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + + +def _invalid(message: str, metrics: dict[str, Any] | None = None): + payload = metrics or {} + payload.setdefault("valid_patch", 0) + return 0.0, 0.0, message, payload + + +def _parse_patch(text: str) -> list[PatchFile]: + files: list[PatchFile] = [] + current_old = "" + current_new = "" + added: list[str] = [] + removed: list[str] = [] + in_file = False + + for line in text.splitlines(): + if line.startswith("diff --git "): + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + in_file = True + current_old = current_new = "" + added = [] + removed = [] + continue + if not in_file: + continue + if line.startswith("--- "): + current_old = line[4:].strip() + if current_old.startswith("a/"): + current_old = current_old[2:] + continue + if line.startswith("+++ "): + current_new = line[4:].strip() + if current_new.startswith("b/"): + current_new = current_new[2:] + continue + if line.startswith("+") and not line.startswith("+++ "): + added.append(line[1:]) + continue + if line.startswith("-") and not line.startswith("--- "): + removed.append(line[1:]) + + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + return files + + +def _validate_path(path: str) -> tuple[bool, str]: + if not path or path == "/dev/null": + return True, "" + if path.startswith("/") or ".." in Path(path).parts: + return False, f"unsafe patch path: {path}" + if _match(path, DENIED_PATTERNS): + return False, f"changed file is outside task boundary: {path}" + if not _match(path, ALLOWED_PATTERNS): + return False, f"changed file is not allowlisted (only kmeanslib/** may change): {path}" + if not path.endswith(".py"): + return False, f"only Python files may change: {path}" + return True, "" + + +def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: + if not patch_path.exists(): + return False, "solution patch does not exist", {} + size = patch_path.stat().st_size + if size > MAX_PATCH_BYTES: + return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} + text = patch_path.read_text(encoding="utf-8", errors="replace") + patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() + files = _parse_patch(text) + metrics: dict[str, Any] = { + "patch_bytes": size, + "patch_sha256": patch_hash, + "changed_files": len(files), + } + if len(files) > MAX_CHANGED_FILES: + return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics + + for patch_file in files: + path = patch_file.path + if patch_file.new_path == "/dev/null": + return False, f"deleting files is outside task boundary: {patch_file.old_path}", metrics + if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: + ok, err = _validate_path(patch_file.old_path) + if not ok: + return False, f"rename/copy source is outside task boundary: {err}", metrics + ok, err = _validate_path(path) + if not ok: + return False, err, metrics + added_text = "\n".join(patch_file.added_lines) + low = added_text.lower() + for token in FORBIDDEN_TOKENS: + if token in low: + return False, f"{path}: forbidden token in added code ({token})", metrics + + metrics["valid_patch"] = 1 + return True, "patch accepted by static policy", metrics + + +def clean_env(tmp_root: Path) -> dict[str, str]: + home = tmp_root / "home" + tmp = tmp_root / "tmp" + home.mkdir(parents=True, exist_ok=True) + tmp.mkdir(parents=True, exist_ok=True) + env = { + "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin"), + "HOME": str(home), + "TMPDIR": str(tmp), + "LC_ALL": "C", + "LANG": "C", + } + for key in ("CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES", "LD_LIBRARY_PATH", + "TRITON_CACHE_DIR", "CUDA_HOME"): + if key in os.environ: + env[key] = os.environ[key] + env.setdefault("TRITON_CACHE_DIR", str(tmp_root / "triton_cache")) + return env + + +def run_checked(cmd: list[str], *, cwd: Path, env: dict[str, str], timeout: int) -> subprocess.CompletedProcess: + return subprocess.run( + cmd, cwd=str(cwd), env=env, timeout=timeout, + stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, + ) + + +def sanitize_error_text(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"/opt/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"N=\d+", "N=", text) + text = re.sub(r"seed[=:]?\s*\d+", "seed=", text, flags=re.IGNORECASE) + return text[-600:] + + +def geometric_mean(values: list[float]) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) + + +def score_from_speedup(gm_speedup: float) -> float: + if gm_speedup <= 0: + return 0.0 + raw = 100.0 * math.log(gm_speedup) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + return max(0.0, min(100.0, raw)) + + +def is_final_submission_role() -> bool: + return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" + + +# Hidden workloads. (N, D, K, max_iters). Agent role runs a fast subset for +# iterative feedback; the final verifier runs the full set. Seeds are derived +# from BASE_SEED so data is deterministic but not guessable from the statement. +_HIDDEN_WORKLOADS = ( + {"id": "w0", "N": 100_000, "D": 32, "K": 128, "max_iters": 12}, + {"id": "w1", "N": 200_000, "D": 64, "K": 256, "max_iters": 10}, + {"id": "w2", "N": 500_000, "D": 128, "K": 256, "max_iters": 10}, + {"id": "w3", "N": 200_000, "D": 512, "K": 256, "max_iters": 8}, # split-D regime + {"id": "w4", "N": 1_000_000, "D": 64, "K": 512, "max_iters": 8}, + {"id": "w5", "N": 300_000, "D": 128, "K": 1024, "max_iters": 8}, # large-K regime +) + + +def _workloads(*, final_role: bool) -> list[dict[str, Any]]: + configured = _cfg("workloads", None) + workloads = list(configured) if isinstance(configured, list) and configured else list(_HIDDEN_WORKLOADS) + if not final_role: + n_agent = _cfg_int("agent_workload_count", 3) + workloads = workloads[:n_agent] + for i, w in enumerate(workloads): + w.setdefault("seed", BASE_SEED + 1000 * (i + 1)) + return workloads + + +# The worker runs the untrusted patched package in its own process. It imports +# the frozen baseline (refkmeans) and the patched kmeanslib, times both on +# identical seeded data, and reports per-workload timings + inertias as JSON. +_WORKER_SOURCE = r''' +import argparse, json, sys, time, traceback + +def _load(baseline_dir, patched_dir): + import importlib + sys.path.insert(0, patched_dir) # patched `kmeanslib` package + sys.path.insert(0, baseline_dir) # frozen `refkmeans` module + import refkmeans + import kmeanslib + return refkmeans, kmeanslib + +def gen(N, D, K, seed, device): + import torch + g = torch.Generator(device=device).manual_seed(int(seed)) + x = torch.randn(N, D, generator=g, device=device, dtype=torch.float32) + perm = torch.randperm(N, generator=g, device=device)[:K] + init = x.index_select(0, perm).clone() + return x, init + +def inertia(x, centroids): + import torch + centroids = centroids.to(torch.float32) + cn = (centroids * centroids).sum(1) + total = 0.0 + CH = 16384 + for i in range(0, x.shape[0], CH): + xb = x[i:i+CH].to(torch.float32) + d = (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ centroids.t()) + cn[None, :] + total += float(d.min(1).values.clamp_min(0).sum().item()) + return total + +def bench(fn, warmup, iters): + import torch + for _ in range(warmup): + fn(); torch.cuda.synchronize() + ts = [] + for _ in range(iters): + torch.cuda.synchronize(); t0 = time.perf_counter() + fn(); torch.cuda.synchronize() + ts.append(time.perf_counter() - t0) + ts.sort() + return ts[len(ts) // 2] * 1000.0 + +def check_result(out, N, D, K): + import torch + if not (isinstance(out, tuple) and len(out) == 3): + raise ValueError("kmeans must return a 3-tuple (labels, centroids, n_iter)") + labels, centroids, _ = out + if tuple(centroids.shape) != (K, D): + raise ValueError("centroids has wrong shape") + if labels.shape[0] != N: + raise ValueError("labels has wrong length") + if not torch.isfinite(centroids).all(): + raise ValueError("centroids contain non-finite values") + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--baseline-dir", required=True) + ap.add_argument("--patched-dir", required=True) + ap.add_argument("--workloads", required=True) + ap.add_argument("--out", required=True) + ap.add_argument("--warmup", type=int, default=3) + ap.add_argument("--iters", type=int, default=7) + a = ap.parse_args() + import torch + if not torch.cuda.is_available(): + json.dump({"ok": False, "error": "cuda_unavailable"}, open(a.out, "w")) + return + refkmeans, kmeanslib = _load(a.baseline_dir, a.patched_dir) + device = "cuda" + rows = [] + for w in json.loads(a.workloads): + N, D, K, it, seed = w["N"], w["D"], w["K"], w["max_iters"], w["seed"] + x, init = gen(N, D, K, seed, device) + ref_fn = lambda: refkmeans.kmeans(x, K, max_iters=it, init_centroids=init, tol=0.0) + agent_fn = lambda: kmeanslib.kmeans(x, K, max_iters=it, init_centroids=init, tol=0.0) + ref_out = ref_fn(); torch.cuda.synchronize() + ref_inertia = inertia(x, ref_out[1]) + try: + agent_out = agent_fn(); torch.cuda.synchronize() + check_result(agent_out, N, D, K) + agent_inertia = inertia(x, agent_out[1]) + except Exception as e: + rows.append({"id": w["id"], "ok": False, "error": type(e).__name__ + ": " + str(e)[:200]}) + del x, init + torch.cuda.empty_cache() + continue + ref_ms = bench(ref_fn, a.warmup, a.iters) + agent_ms = bench(agent_fn, a.warmup, a.iters) + rows.append({"id": w["id"], "ok": True, "ref_ms": ref_ms, "agent_ms": agent_ms, + "ref_inertia": ref_inertia, "agent_inertia": agent_inertia}) + del x, init + torch.cuda.empty_cache() + json.dump({"ok": True, "rows": rows}, open(a.out, "w")) + +if __name__ == "__main__": + try: + main() + except Exception: + traceback.print_exc(); sys.exit(3) +''' + + +def _run_worker(patched_parent: Path, tmp_root: Path, env: dict[str, str], + workloads: list[dict[str, Any]]) -> dict[str, Any]: + worker_path = tmp_root / "kmeans_worker.py" + worker_path.write_text(_WORKER_SOURCE, encoding="utf-8") + out_path = tmp_root / "worker_out.json" + cmd = [ + sys.executable, str(worker_path), + "--baseline-dir", str(DEFAULT_BASELINE_SOURCE), + "--patched-dir", str(patched_parent), + "--workloads", json.dumps(workloads), + "--out", str(out_path), + "--warmup", str(WARMUP_ITERS), + "--iters", str(TIMED_ITERS), + ] + run_checked(cmd, cwd=tmp_root, env=env, timeout=WORKER_TIMEOUT_SECONDS) + return json.loads(out_path.read_text(encoding="utf-8")) + + +def full_evaluation(patch_path: Path, metrics: dict[str, Any]): + final_role = is_final_submission_role() + metrics["submission_role"] = "final" if final_role else "agent" + workloads = _workloads(final_role=final_role) + + if not DEFAULT_CLEAN_SOURCE.exists() or not DEFAULT_BASELINE_SOURCE.exists(): + metrics["full_benchmark"] = 0 + return (1.0, 1.0, + "patch policy smoke passed; kmeanslib judge source is not configured in this environment", + metrics) + + with tempfile.TemporaryDirectory(prefix="kmeans_kernel_opt_") as tmp: + tmp_root = Path(tmp) + env = clean_env(tmp_root) + patched_parent = tmp_root / "patched" + shutil.copytree(DEFAULT_CLEAN_SOURCE, patched_parent) + + if int(metrics.get("changed_files", 0)) > 0: + run_checked(["git", "apply", "--check", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + run_checked(["git", "apply", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + metrics["applied_patch"] = 1 + else: + metrics["used_empty_patch"] = 1 + + result = _run_worker(patched_parent, tmp_root, env, workloads) + if not result.get("ok"): + return _invalid(f"benchmark worker failed ({result.get('error', 'unknown')})", metrics) + + rows = result.get("rows", []) + speedups: list[float] = [] + per_workload: dict[str, Any] = {} + for row in rows: + if not row.get("ok"): + metrics["failed_workload_error"] = sanitize_error_text(str(row.get("error", ""))) + return _invalid("submitted kmeans crashed or returned an invalid result on a hidden workload", metrics) + ref_inertia = row["ref_inertia"] + agent_inertia = row["agent_inertia"] + if agent_inertia > (1.0 + INERTIA_TOL) * ref_inertia + 1e-6: + metrics["inertia_regression"] = { + "ref": ref_inertia, "agent": agent_inertia, "tol": INERTIA_TOL, + } + return _invalid("submitted kmeans clustering quality regressed beyond tolerance", metrics) + speedup = row["ref_ms"] / row["agent_ms"] if row["agent_ms"] > 0 else 0.01 + speedups.append(max(speedup, 0.01)) + per_workload[row["id"]] = { + "ref_ms": row["ref_ms"], "agent_ms": row["agent_ms"], "speedup": speedup, + } + + if not speedups: + return _invalid("no workloads were evaluated", metrics) + + gm = geometric_mean(speedups) + bounded = score_from_speedup(gm) + metrics.update({ + "full_benchmark": 1, + "workload_count": len(speedups), + "geomean_speedup": gm, + }) + if _cfg_bool("expose_per_workload_metrics", False): + metrics["per_workload"] = per_workload + return (bounded, bounded, f"kmeans geomean speedup {gm:.3f}x over the naive baseline", metrics) + + +def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: + patch_path = Path(solution_path) + ok, message, metrics = validate_patch(patch_path) + if not ok: + return _invalid(message, metrics) + try: + return full_evaluation(patch_path, metrics) + except subprocess.TimeoutExpired: + return _invalid("benchmark worker timed out", metrics) + except subprocess.CalledProcessError as exc: + stderr = sanitize_error_text(exc.stderr or "") + cmd0 = Path(str(exc.cmd[0])).name if isinstance(exc.cmd, list) and exc.cmd else "subprocess" + metrics["failed_command"] = "git apply" if cmd0 == "git" else cmd0 + metrics["stderr_tail"] = stderr + return _invalid("patch apply or benchmark command failed", metrics) + except Exception as exc: + metrics["error_type"] = type(exc).__name__ + metrics["error_detail"] = sanitize_error_text(str(exc)) + return _invalid("evaluation failed", metrics) + + +def main(argv: list[str]) -> int: + if len(argv) != 2: + print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) + return 2 + score, score_unbounded, message, metrics = evaluate(argv[1]) + print(json.dumps({ + "score": score, + "score_unbounded": score_unbounded, + "message": message, + "metrics": metrics, + }, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv)) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/README.md b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/README.md new file mode 100644 index 000000000..10536efd3 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/README.md @@ -0,0 +1,34 @@ +# K-Means kernel optimization — submission workflow + +You are optimizing the `kmeanslib` package at `/app/kmeanslib`. Edit the package +(rewrite the internals of `kmeans`, add Triton kernel modules under +`kmeanslib/`), then submit a patch. + +## Iterate locally + +Run the public self-test to check correctness and get a rough speed signal on +the two public shapes (needs a GPU in the agent container): + +```bash +bash /app/public_test.sh +``` + +## Submit + +```bash +bash /app/make_submission.sh # writes /app/solution.patch (kmeanslib diff) +bash /app/submit.sh # enqueues it for the black-box judge +``` + +Submissions are asynchronous. Submit early and keep improving; use +`bash /app/submissions.sh` and `bash /app/wait_submission.sh ` to inspect +results. + +## Rules + +- Only files under `kmeanslib/` may change. +- Do not import external optimized libraries (write the kernels yourself), and + do not access the environment, spawn processes, or use the network. +- Keep the public `kmeans(...)` signature and return contract unchanged. +- Clustering quality is gated (inertia vs the naive baseline); do not sacrifice + correctness for speed beyond the allowed tolerance. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/make_submission.sh b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/make_submission.sh new file mode 100644 index 000000000..8a6473ab6 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/make_submission.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash +# Package the current kmeanslib edits into a unified diff for submission. +set -euo pipefail + +APP_DIR="${APP_DIR:-/app}" +OUT="${1:-/app/solution.patch}" + +if [[ ! -d "$APP_DIR/kmeanslib" ]]; then + echo "kmeanslib package not found at $APP_DIR/kmeanslib" >&2 + exit 2 +fi +if [[ ! -d "$APP_DIR/.git" ]]; then + echo "git repo not found at $APP_DIR (expected in the agent image)" >&2 + exit 2 +fi + +# Stage and diff only the package, so submission helper scripts under /app are +# never included. New kernel files under kmeanslib/ are captured via `git add`. +git -C "$APP_DIR" add -A -- kmeanslib +git -C "$APP_DIR" diff --cached -- kmeanslib > "$OUT" +git -C "$APP_DIR" reset -q + +bytes=$(wc -c < "$OUT" | tr -d ' ') +echo "Wrote $OUT ($bytes bytes)" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py new file mode 100644 index 000000000..6b459ea87 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -0,0 +1,97 @@ +"""Public self-test for the K-Means kernel-optimization task. + +Runs the (patched) kmeanslib on the two public shapes, checks the clustering +quality against a local naive recomputation, and prints a rough speedup. This is +a convenience for local iteration only -- the graded workloads and thresholds +are hidden and differ from these shapes. +""" +from __future__ import annotations + +import sys +import time + +PUBLIC_WORKLOADS = [ + {"N": 50_000, "D": 32, "K": 64, "max_iters": 10, "seed": 1}, + {"N": 200_000, "D": 128, "K": 256, "max_iters": 10, "seed": 2}, +] + + +def naive_kmeans(x, k, max_iters, init): + import torch + c = init.clone() + labels = None + for _ in range(max_iters): + labels = torch.argmin(torch.cdist(x, c), dim=1) + sums = torch.zeros((k, x.shape[1]), device=x.device, dtype=torch.float32) + cnt = torch.zeros((k,), device=x.device, dtype=torch.float32) + sums.index_add_(0, labels, x.float()) + cnt.index_add_(0, labels, torch.ones(x.shape[0], device=x.device)) + empty = cnt == 0 + cnt = cnt.clamp_min(1.0) + newc = sums / cnt[:, None] + if empty.any(): + newc[empty] = c[empty].float() + c = newc.to(x.dtype) + return labels, c + + +def inertia(x, c): + import torch + cn = (c * c).sum(1) + total = 0.0 + for i in range(0, x.shape[0], 16384): + xb = x[i:i + 16384] + d = (xb * xb).sum(1, keepdim=True) - 2.0 * xb @ c.t() + cn[None, :] + total += float(d.min(1).values.clamp_min(0).sum()) + return total + + +def bench(fn, warmup=2, iters=5): + import torch + for _ in range(warmup): + fn(); torch.cuda.synchronize() + ts = [] + for _ in range(iters): + torch.cuda.synchronize(); t0 = time.perf_counter() + fn(); torch.cuda.synchronize() + ts.append(time.perf_counter() - t0) + ts.sort() + return ts[len(ts) // 2] * 1000.0 + + +def main() -> int: + try: + import torch + except Exception as e: # pragma: no cover + print(f"torch import failed: {e}") + return 1 + if not torch.cuda.is_available(): + print("no CUDA device available; run this in a GPU-enabled container.") + return 1 + import kmeanslib + + for w in PUBLIC_WORKLOADS: + N, D, K, it, seed = w["N"], w["D"], w["K"], w["max_iters"], w["seed"] + g = torch.Generator(device="cuda").manual_seed(seed) + x = torch.randn(N, D, generator=g, device="cuda", dtype=torch.float32) + init = x.index_select(0, torch.randperm(N, generator=g, device="cuda")[:K]).clone() + + _, ref_c = naive_kmeans(x, K, it, init) + out = kmeanslib.kmeans(x, K, max_iters=it, init_centroids=init, tol=0.0) + agent_c = out[1] + ri, ai = inertia(x, ref_c), inertia(x, agent_c) + ratio = ai / ri if ri > 0 else float("inf") + + ref_ms = bench(lambda: naive_kmeans(x, K, it, init)) + agent_ms = bench(lambda: kmeanslib.kmeans(x, K, max_iters=it, init_centroids=init, tol=0.0)) + ok = "OK" if ratio <= 1.05 else "QUALITY REGRESSION" + print(f"(N={N}, D={D}, K={K}) inertia_ratio={ratio:.4f} [{ok}] " + f"baseline={ref_ms:.2f}ms yours={agent_ms:.2f}ms " + f"speedup={ref_ms / agent_ms:.2f}x") + del x, init + torch.cuda.empty_cache() + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.sh b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.sh new file mode 100644 index 000000000..ca70fc405 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash +# Public self-test: run the patched kmeanslib on the public shapes and print a +# correctness + rough speedup summary. Requires a GPU in the agent container. +set -euo pipefail +APP_DIR="${APP_DIR:-/app}" +cd "$APP_DIR" +exec python3 "$APP_DIR/public_test.py" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/solution.patch b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/solution.patch new file mode 100644 index 000000000..e69de29bb diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py b/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py new file mode 100644 index 000000000..1a455d6df --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py @@ -0,0 +1,49 @@ +"""Frozen naive K-Means baseline used by the judge as the speed denominator. + +This is a standalone (non-package) copy of the ``kmeanslib.kmeans`` +implementation the agent starts from. It is imported under its own module name +so the judge worker can load the frozen baseline and the patched ``kmeanslib`` +package in the same process. Keep this behaviourally identical to the shipped +``kmeanslib/kmeans.py``. +""" +from __future__ import annotations + +import torch + + +def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: + dist = torch.cdist(x, centroids.to(x.dtype)) + return torch.argmin(dist, dim=1) + + +def _update(x: torch.Tensor, labels: torch.Tensor, n_clusters: int, + old_centroids: torch.Tensor) -> torch.Tensor: + N, D = x.shape + sums = torch.zeros((n_clusters, D), device=x.device, dtype=torch.float32) + counts = torch.zeros((n_clusters,), device=x.device, dtype=torch.float32) + sums.index_add_(0, labels, x.float()) + counts.index_add_(0, labels, torch.ones(N, device=x.device, dtype=torch.float32)) + empty = counts == 0 + counts = counts.clamp_min(1.0) + new = sums / counts[:, None] + if empty.any(): + new[empty] = old_centroids[empty].float() + return new.to(x.dtype) + + +def kmeans(x, n_clusters, *, max_iters=10, init_centroids, tol=0.0): + if x.ndim != 2: + raise ValueError(f"x must be 2-D (N, D); got shape {tuple(x.shape)}") + if init_centroids is None: + raise ValueError("init_centroids is required") + centroids = init_centroids.to(x.dtype).clone() + labels = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long) + n_iter = 0 + for n_iter in range(max_iters): + labels = _assign(x, centroids) + new_centroids = _update(x, labels, n_clusters, centroids) + shift = (new_centroids - centroids).norm(dim=-1).max() + centroids = new_centroids + if tol > 0.0 and float(shift) < tol: + break + return labels, centroids, n_iter + 1 diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/__init__.py b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/__init__.py new file mode 100644 index 000000000..622f33af9 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/__init__.py @@ -0,0 +1,15 @@ +"""kmeanslib -- a tiny GPU K-Means library you are asked to make fast. + +The public entry point is :func:`kmeans`. The shipped implementation is +correct but deliberately unoptimised. Your task is to rewrite the internals +(new Triton kernels, fused passes, better memory traffic, ...) so that +:func:`kmeans` runs as fast as possible while producing clusterings of the +same quality. The public function signature and return contract must not +change. +""" +from __future__ import annotations + +from kmeanslib.kmeans import kmeans + +__all__ = ["kmeans"] +__version__ = "0.1.0" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py new file mode 100644 index 000000000..6493af4d0 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py @@ -0,0 +1,98 @@ +"""Euclidean (squared-L2) Lloyd K-Means -- the reference you must optimise. + +This implementation is intentionally naive. Every Lloyd iteration it +materialises the full ``(N, K)`` distance matrix with :func:`torch.cdist` +and recomputes the assignment and the centroid update with plain PyTorch +scatter ops. It is correct and deterministic, but it moves far more memory +than necessary and launches many small kernels. + +Contract (do NOT change): + + kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) + -> (labels, centroids, n_iter) + + x : (N, D) float32 CUDA tensor of points. + n_clusters (K) : int, number of clusters. + max_iters : int, number of Lloyd iterations (fixed by the caller). + init_centroids : (K, D) tensor -- REQUIRED. The initial centroids. The + caller always supplies these so the result is a + deterministic function of (x, init_centroids, max_iters). + tol : float. If > 0, stop early once the maximum centroid + shift drops below ``tol``. The grader always calls with + ``tol=0.0`` (run all ``max_iters`` iterations). + + labels : (N,) int64 cluster id per point (assignment at the start + of the final iteration). + centroids : (K, D) float32 final centroids (after the final update). + n_iter : int, number of iterations actually run. + +You may add modules/kernels inside the ``kmeanslib`` package and rewrite the +body of :func:`kmeans`, ``_assign`` and ``_update`` freely, as long as the +public contract above is preserved. +""" +from __future__ import annotations + +import torch + + +def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: + """Nearest-centroid assignment by squared-L2 distance. + + Naive: materialise the full (N, K) distance matrix, then argmin. + """ + # torch.cdist returns Euclidean distance; argmin of distance == argmin of + # squared distance, so we do not bother squaring. + dist = torch.cdist(x, centroids.to(x.dtype)) # (N, K) + return torch.argmin(dist, dim=1) + + +def _update( + x: torch.Tensor, + labels: torch.Tensor, + n_clusters: int, + old_centroids: torch.Tensor, +) -> torch.Tensor: + """Recompute each centroid as the mean of its assigned points. + + Empty clusters keep their previous centroid. + """ + N, D = x.shape + sums = torch.zeros((n_clusters, D), device=x.device, dtype=torch.float32) + counts = torch.zeros((n_clusters,), device=x.device, dtype=torch.float32) + sums.index_add_(0, labels, x.float()) + counts.index_add_(0, labels, torch.ones(N, device=x.device, dtype=torch.float32)) + empty = counts == 0 + counts = counts.clamp_min(1.0) + new = sums / counts[:, None] + if empty.any(): + new[empty] = old_centroids[empty].float() + return new.to(x.dtype) + + +def kmeans( + x: torch.Tensor, + n_clusters: int, + *, + max_iters: int = 10, + init_centroids: torch.Tensor, + tol: float = 0.0, +): + """Lloyd K-Means with fixed initial centroids. See module docstring.""" + if x.ndim != 2: + raise ValueError(f"x must be 2-D (N, D); got shape {tuple(x.shape)}") + if init_centroids is None: + raise ValueError("init_centroids is required (deterministic initialisation)") + + centroids = init_centroids.to(x.dtype).clone() + labels = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long) + + n_iter = 0 + for n_iter in range(max_iters): + labels = _assign(x, centroids) + new_centroids = _update(x, labels, n_clusters, centroids) + shift = (new_centroids - centroids).norm(dim=-1).max() + centroids = new_centroids + if tol > 0.0 and float(shift) < tol: + break + + return labels, centroids, n_iter + 1 diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme new file mode 100644 index 000000000..4c2b47e25 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -0,0 +1,123 @@ +# GPU K-Means Kernel Optimization + +## Problem + +You are given a small GPU K-Means library, `kmeanslib`, in the Harbor +workspace at `/app/kmeanslib`. Its public entry point is: + +```python +kmeanslib.kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) + -> (labels, centroids, n_iter) +``` + +`x` is an `(N, D)` float32 CUDA tensor, `init_centroids` is a fixed `(K, D)` +initial-centroid tensor supplied by the caller, and the function runs Euclidean +(squared-L2) Lloyd iterations. The shipped implementation is correct but +deliberately naive: every iteration it materializes the full `(N, K)` distance +matrix with `torch.cdist`, then recomputes assignments and centroids with plain +PyTorch scatter ops. + +Your goal is to make `kmeanslib.kmeans` **as fast as possible** on the GPU while +producing clusterings of the same quality. You may add modules and Triton +kernels inside the `kmeanslib` package and rewrite the internals freely (fused +assignment kernels, reduced memory traffic, better centroid updates, and so on). +The public function signature and return contract above must not change. + +## Workload + +The judge evaluates a family of held-out dense clustering workloads that vary +`(N, D, K)` and the iteration count, spanning small/medium/large point counts, +wide-feature (large `D`) and many-cluster (large `K`) regimes. All workloads use +Euclidean distance, float32 data, a fixed number of Lloyd iterations +(`tol = 0.0`, so every iteration runs), and caller-supplied `init_centroids`, so +each result is a deterministic function of the inputs. + +Two representative *public* shapes you can use for local development (the graded +shapes are different and hidden): + +```text +(N=50000, D=32, K=64, max_iters=10) +(N=200000, D=128, K=256, max_iters=10) +``` + +Treat the workload as a general dense K-Means, not as shapes to special-case. +Improvements should be general kernel/throughput improvements, not lookups keyed +on specific `(N, D, K)` values. + +## Submission + +The submitted artifact is a patch file over the `kmeanslib` package: + +```text +/app/solution.patch +``` + +After editing `/app/kmeanslib`, generate and submit a patch: + +```bash +bash /app/make_submission.sh +bash /app/submit.sh +``` + +Submissions are asynchronous; submit early and keep iterating. The judge applies +your patch to a clean copy of `kmeanslib`, imports the patched package, and +times it against the original naive implementation on the same hardware, same +seeded data, and same initial centroids. + +## Correctness + +Correctness is a gate. For each workload the judge measures the **inertia** +(within-cluster sum of squared distances to the nearest final centroid) of your +result and of the naive baseline on identical data and initial centroids. Your +inertia must not exceed the baseline's by more than a small relative tolerance. +Crashes, non-finite outputs, wrong shapes/dtypes, timeouts, and clustering that +regresses beyond the tolerance are all penalized before speed is considered. You +may trade a little numerical precision (e.g. lower-precision accumulation) for +speed as long as you stay within the quality tolerance. + +## Scoring + +Valid submissions are scored by speedup relative to the naive baseline on the +same hardware and workloads. For each workload: + +```text +speedup = baseline_time / your_time +``` + +The objective is the geometric mean of per-workload speedups, so broad speedups +across the workload family are preferred over one large outlier. A result no +faster than the baseline earns 0; regressions earn 0. The raw geometric-mean +speedup is reported in the evaluator metrics. + +## Patch Policy + +The evaluator validates the patch before running it. Only files under the +package may change: + +```text +kmeanslib/** +``` + +New Python modules inside `kmeanslib/` (for example Triton kernel files) are +allowed. Patches may not: + +- modify anything outside `kmeanslib/`; +- import or call an external optimized ML/kernel library (the point is to write + the kernels yourself); +- read or write environment variables, spawn processes, or access the network; +- special-case the hidden workload shapes. + +The patch must be reasonably sized and apply cleanly to the clean `kmeanslib` +tree. + +## Resource Budget + +```text +GPU: single device (H100 reference; Triton paths also run on L40S / A100) +vCPUs: 8 +memory: 32 GiB +storage: 32 GiB +``` + +The judge runs the timing worker in a subprocess under a clean, minimal +environment with a fixed warmup/measurement schedule and a per-run timeout. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch b/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch new file mode 100644 index 000000000..2730259cd --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch @@ -0,0 +1,164 @@ +diff --git a/kmeanslib/_triton_assign.py b/kmeanslib/_triton_assign.py +new file mode 100644 +index 0000000..f40946a +--- /dev/null ++++ b/kmeanslib/_triton_assign.py +@@ -0,0 +1,75 @@ ++"""Fused squared-L2 nearest-centroid assignment in Triton. ++ ++Computes ``argmin_k ||x_n - c_k||^2`` without ever materialising the full ++``(N, K)`` distance matrix. Each program handles a ``BLOCK_N`` row tile, ++streams the centroids in ``BLOCK_K`` chunks, and keeps a running (min, argmin) ++in registers. Uses the x^2-free score ``||c_k||^2 - 2 `` (the ++``-||x_n||^2`` term is constant per row and does not affect the argmin). ++""" ++from __future__ import annotations ++ ++import torch ++import triton ++import triton.language as tl ++ ++ ++@triton.jit ++def _assign_kernel( ++ x_ptr, c_ptr, csq_ptr, out_ptr, ++ N, K, D, ++ stride_xn, stride_xd, ++ stride_ck, stride_cd, ++ BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, BLOCK_D: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N) ++ offs_d = tl.arange(0, BLOCK_D) ++ n_mask = offs_n < N ++ d_mask = offs_d < D ++ ++ # Row tile stays resident in registers/SRAM across the whole K sweep. ++ x_ptrs = x_ptr + offs_n[:, None] * stride_xn + offs_d[None, :] * stride_xd ++ x_tile = tl.load(x_ptrs, mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ best = tl.full((BLOCK_N,), float("inf"), tl.float32) ++ best_idx = tl.zeros((BLOCK_N,), tl.int32) ++ ++ for k0 in range(0, K, BLOCK_K): ++ offs_k = k0 + tl.arange(0, BLOCK_K) ++ k_mask = offs_k < K ++ # centroid tile as (BLOCK_D, BLOCK_K) so tl.dot contracts over D ++ c_ptrs = c_ptr + offs_d[:, None] * stride_cd + offs_k[None, :] * stride_ck ++ c_tile = tl.load(c_ptrs, mask=d_mask[:, None] & k_mask[None, :], other=0.0) ++ dot = tl.dot(x_tile, c_tile, input_precision="ieee") # (BLOCK_N, BLOCK_K) ++ csq = tl.load(csq_ptr + offs_k, mask=k_mask, other=float("inf")) ++ score = csq[None, :] - 2.0 * dot ++ score = tl.where(k_mask[None, :], score, float("inf")) ++ local_min = tl.min(score, axis=1) ++ local_arg = tl.argmin(score, axis=1).to(tl.int32) + k0 ++ better = local_min < best ++ best = tl.where(better, local_min, best) ++ best_idx = tl.where(better, local_arg, best_idx) ++ ++ tl.store(out_ptr + offs_n, best_idx, mask=n_mask) ++ ++ ++def euclid_assign_triton(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: ++ """Return (N,) int64 nearest-centroid ids by squared-L2 distance.""" ++ N, D = x.shape ++ K = centroids.shape[0] ++ x = x.contiguous() ++ centroids = centroids.to(x.dtype).contiguous() ++ csq = (centroids.float() * centroids.float()).sum(1) # (K,) fp32 ++ out = torch.empty((N,), device=x.device, dtype=torch.int32) ++ BLOCK_D = triton.next_power_of_2(D) ++ BLOCK_N, BLOCK_K = 16, 32 ++ grid = (triton.cdiv(N, BLOCK_N),) ++ _assign_kernel[grid]( ++ x, centroids, csq, out, ++ N, K, D, ++ x.stride(0), x.stride(1), ++ centroids.stride(0), centroids.stride(1), ++ BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K, BLOCK_D=BLOCK_D, ++ num_warps=4, ++ ) ++ return out.to(torch.long) +diff --git a/kmeanslib/kmeans.py b/kmeanslib/kmeans.py +index 6493af4..dda8081 100644 +--- a/kmeanslib/kmeans.py ++++ b/kmeanslib/kmeans.py +@@ -1,49 +1,25 @@ +-"""Euclidean (squared-L2) Lloyd K-Means -- the reference you must optimise. ++"""Euclidean (squared-L2) Lloyd K-Means -- Triton-accelerated. + +-This implementation is intentionally naive. Every Lloyd iteration it +-materialises the full ``(N, K)`` distance matrix with :func:`torch.cdist` +-and recomputes the assignment and the centroid update with plain PyTorch +-scatter ops. It is correct and deterministic, but it moves far more memory +-than necessary and launches many small kernels. ++The assignment step is the Lloyd bottleneck: the naive baseline materialised ++the full ``(N, K)`` distance matrix every iteration. Here it is replaced by a ++fused Triton kernel (:func:`euclid_assign_triton`) that streams centroids and ++keeps a running argmin in registers, so the ``(N, K)`` matrix is never written ++to or read from HBM. The centroid update stays a scatter-mean. + +-Contract (do NOT change): ++Public contract is unchanged: + + kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) + -> (labels, centroids, n_iter) +- +- x : (N, D) float32 CUDA tensor of points. +- n_clusters (K) : int, number of clusters. +- max_iters : int, number of Lloyd iterations (fixed by the caller). +- init_centroids : (K, D) tensor -- REQUIRED. The initial centroids. The +- caller always supplies these so the result is a +- deterministic function of (x, init_centroids, max_iters). +- tol : float. If > 0, stop early once the maximum centroid +- shift drops below ``tol``. The grader always calls with +- ``tol=0.0`` (run all ``max_iters`` iterations). +- +- labels : (N,) int64 cluster id per point (assignment at the start +- of the final iteration). +- centroids : (K, D) float32 final centroids (after the final update). +- n_iter : int, number of iterations actually run. +- +-You may add modules/kernels inside the ``kmeanslib`` package and rewrite the +-body of :func:`kmeans`, ``_assign`` and ``_update`` freely, as long as the +-public contract above is preserved. + """ + from __future__ import annotations + + import torch + ++from kmeanslib._triton_assign import euclid_assign_triton + +-def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: +- """Nearest-centroid assignment by squared-L2 distance. + +- Naive: materialise the full (N, K) distance matrix, then argmin. +- """ +- # torch.cdist returns Euclidean distance; argmin of distance == argmin of +- # squared distance, so we do not bother squaring. +- dist = torch.cdist(x, centroids.to(x.dtype)) # (N, K) +- return torch.argmin(dist, dim=1) ++def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: ++ return euclid_assign_triton(x, centroids) + + + def _update( +@@ -52,10 +28,6 @@ def _update( + n_clusters: int, + old_centroids: torch.Tensor, + ) -> torch.Tensor: +- """Recompute each centroid as the mean of its assigned points. +- +- Empty clusters keep their previous centroid. +- """ + N, D = x.shape + sums = torch.zeros((n_clusters, D), device=x.device, dtype=torch.float32) + counts = torch.zeros((n_clusters,), device=x.device, dtype=torch.float32) +@@ -77,7 +49,6 @@ def kmeans( + init_centroids: torch.Tensor, + tol: float = 0.0, + ): +- """Lloyd K-Means with fixed initial centroids. See module docstring.""" + if x.ndim != 2: + raise ValueError(f"x must be 2-D (N, D); got shape {tuple(x.shape)}") + if init_centroids is None: diff --git a/2.0/problems/knn_gpu_kernel_optimization/DESIGN.md b/2.0/problems/knn_gpu_kernel_optimization/DESIGN.md new file mode 100644 index 000000000..c777bf73a --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/DESIGN.md @@ -0,0 +1,89 @@ +# Design notes — knn_gpu_kernel_optimization + +Operator-facing. Not copied into the agent workspace by the adapter. + +## What this task measures + +Can an agent turn a correct-but-naive GPU brute-force k-NN into a fast one? The +agent is given `knnlib` (a `torch.cdist`-then-`topk` search that materializes the +full `(Q, M)` distance matrix) and must rewrite the internals — ideally a +fused/streamed running top-k that tiles over the database and never writes the +`(Q, M)` matrix to HBM, with Triton kernels for the distance/selection passes — +to maximize the geometric-mean speedup over the frozen baseline across a family +of hidden `(Q, M, D, k)` workloads, subject to a recall@k quality gate. + +This is task **2 of a four-task flashlib kernel-optimization family** (KMeans, +KNN, TruncatedSVD, PCA). All four share the KernelBench-style pattern: ship a +naive package, freeze a byte-for-byte baseline in the judge image, apply the +agent's patch to a clean copy, time patched-vs-baseline on identical seeded data +in an isolated worker, and gate on a primitive-appropriate quality metric before +scoring by geomean speedup. + +## Correctness gate: recall@k, judge-computed + +The quality gate is recall@k — for each query, the fraction of the true `k` +nearest database indices the submission recovers, averaged over all queries. The +true top-k is recomputed by the judge worker from the *frozen exact baseline* +(`refknn`, a `cdist` + `topk`) on the same seeded `queries`/`database`, not from +any agent-provided number. Properties: + +- exact-set based, so it is robust to tie-breaking and floating-point drift in + the distances (a neighbour at the same distance still counts as a hit only if + its index matches the exact set — with continuous random data exact ties have + measure zero, so a correct fast kernel scores ~1.0); +- cheat-proof: you cannot return fast garbage — high recall@k requires actually + finding the nearest neighbours. Trading some numeric precision for speed + (bf16/tf32 accumulation in the distance pass) is allowed as long as recall + stays at or above `recall_threshold` (default 0.99). + +Determinism: `queries` and `database` are generated from a per-workload seed +derived from `base_seed`, so the baseline result — and thus the true top-k — is +a fixed function of `(Q, M, D, k, seed)`. + +## Anti-gaming + +flashlib (the library these primitives are distilled from) is public and +Apache-2.0. Mitigations: + +- The shipped package is a small, neutrally named library (`knnlib`), not + flashlib; the patch allowlist confines edits to `knnlib/**`. +- The patch policy forbids importing `flashlib`/cuML/cuPy/FAISS/scikit-learn and + bans env/subprocess/network access — the agent must write the kernels itself. +- Scored shapes are hidden and differ from the two public shapes; seeds derive + from `base_seed`. General kernels win; shape lookups do not. +- Honest framing: reproducing SOTA-class kernels *is* the bar. We block trivial + library reuse, not the underlying knowledge. + +## Execution model & the Modal question + +The evaluator runs an isolated worker subprocess (`_run_worker`) that imports the +frozen baseline (`/opt/knn_ref/refknn.py`) and the patched `knnlib` (from the +applied clean tree), times both, and reports JSON. This assumes the **judge +container has a GPU**. + +The repo's other GPU task (vllm) instead offloads to **Modal** because Harbor +judge containers may not be GPU-scheduled. `_run_worker` is the single swap +point: to go Modal, replace it with a Modal function that builds the patched +package, runs the same worker logic on a Modal GPU, and returns the JSON rows. +The rest of the evaluator (policy, gating, scoring) is unchanged. Decide +in-container-GPU vs Modal at first calibration trial. + +## Calibration TODO (needs a GPU trial) + +- Validate `reference.patch` runs and passes the recall@k gate on all workloads; + fix any Triton API drift for the pinned torch/triton in the image. +- Measure the reference solution's geomean speedup and set `speedup_target` so a + reference-level solution maps to ~full score. +- Confirm hidden shapes fit device memory (the naive baseline's `cdist` + materializes the full `Q x M` matrix; all shipped shapes are H100-safe, and + the chunked reference never materializes it). +- Sanity-check timing stability (median-of-7); bump `timed_iters` if noisy. + +## Files + +- `knnlib/` — pristine package baked into both images (agent edits it). +- `judge/refknn.py` — frozen exact baseline, baked to `/opt/knn_ref/`. +- `evaluator.py` — self-contained policy + orchestration + scoring (judge-only). +- `reference.patch` — chunked running-top-k reference solution (proves solvability). +- `docker/` — builds the prebuilt agent/judge images referenced by config.yaml. +- `harbor/app/` — agent-facing submission helpers + public self-test. diff --git a/2.0/problems/knn_gpu_kernel_optimization/config.yaml b/2.0/problems/knn_gpu_kernel_optimization/config.yaml new file mode 100644 index 000000000..d5c84820d --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/config.yaml @@ -0,0 +1,53 @@ +tag: systems +runtime: + language: python + timeout_seconds: 10800 + environment: "GPU brute-force k-NN kernel optimization; agent patches the knnlib Python/Triton package; judge times the patched knn against a frozen naive baseline on hidden (Q, M, D, k) workloads with a recall@k (nearest-neighbour quality) gate." + apt_packages: + - bash + - ca-certificates + - git + - python3 + judge_apt_packages: + - bash + - ca-certificates + - git + - python3 + docker: + # Experimental local images. Build them with + # 2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh before a + # local Harbor trial. Both images bundle a clean copy of the knnlib + # package; the judge image additionally bakes the frozen naive baseline + # (/opt/knn_ref) and the pristine tree (/opt/knnlib-clean). + image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.1.0 + judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.1.0 +environment: + cpus: 8 + memory_mb: 32768 + storage_mb: 32768 + build_timeout_seconds: 3600 +evaluation: + # The judge worker runs on a GPU visible to the judge container (single + # device). H100 is the reference accelerator; the Triton paths also run on + # L40S/A100. If Harbor cannot attach a GPU to the judge container, the worker + # step is the swap point for a Modal offload (see DESIGN.md). + gpu: "H100" + # Timing: warmup then median-of-N cuda-synced full-knn calls. + warmup_iters: 3 + timed_iters: 7 + # Nearest-neighbour quality gate: the submitted knn's recall@k (fraction of + # the true k nearest database indices it recovers) must be at least + # recall_threshold on every hidden workload. The judge recomputes the exact + # top-k from the frozen baseline on the SAME seeded queries/database. + recall_threshold: 0.99 + # Geomean speedup (over the naive baseline) mapped to the full bounded score. + # Calibrate against the reference solution's measured geomean after a trial. + speedup_target: 6.0 + worker_timeout_seconds: 3600 + base_seed: 20260701 + # Agent (iterative) role runs a fast subset; the final verifier runs them all. + agent_workload_count: 3 + expose_per_workload_metrics: false +submission: + kind: file + path: /app/solution.patch diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/README.md b/2.0/problems/knn_gpu_kernel_optimization/docker/README.md new file mode 100644 index 000000000..bf878c69b --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/README.md @@ -0,0 +1,46 @@ +# Experimental Brute-Force k-NN Kernel-Optimization Images + +Two images, mirroring the duckdb-e2e split: a public **agent** image and a +private **judge** image. Build them before a local Harbor trial: + +```bash +bash 2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh +``` + +Defaults: + +```text +AGENT_TAG=frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.1.0 +JUDGE_TAG=frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.1.0 +``` + +The agent image contains: + +```text +/app/knnlib # clean, git-tracked package (the agent edits this) +``` + +The judge image contains: + +```text +/opt/knnlib-clean/knnlib # pristine tree; the patch is applied to a copy +/opt/knn_ref/refknn.py # frozen naive baseline (speed denominator + exact oracle) +``` + +Both are based on `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel` (torch + triton). + +## Runtime requirements + +The judge times the patched knn on a **GPU visible to the judge container** +(single device; H100 reference, Triton paths also run on L40S/A100). If the +Harbor runtime cannot attach a GPU to the judge container, port the worker step +(`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md`. + +## Smoke test + +```bash +bash 2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh +``` + +Import-only; verifies torch/triton and the baked packages are importable. It +does not exercise a GPU. diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/knn_gpu_kernel_optimization/docker/agent/Dockerfile new file mode 100644 index 000000000..91af0e93b --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/agent/Dockerfile @@ -0,0 +1,22 @@ +# Agent image for knn_gpu_kernel_optimization. +# Bundles a clean, git-tracked copy of the knnlib package at /app/knnlib +# so the agent can edit it and `make_submission.sh` can diff it. torch + triton +# come from the PyTorch base image. +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel + +ARG DEBIAN_FRONTEND=noninteractive +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash ca-certificates git ripgrep && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" + +WORKDIR /app +COPY knnlib /app/knnlib +RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ + git -C /app init -q && \ + git -C /app config user.email task@frontier-cs && \ + git -C /app config user.name frontier-cs && \ + git -C /app add -A -- knnlib .gitignore && \ + git -C /app -c commit.gpgsign=false commit -qm "pristine knnlib" diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh new file mode 100755 index 000000000..390fbd2d3 --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# Build the experimental agent + judge images for knn_gpu_kernel_optimization. +set -euo pipefail +HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) + +AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "Building agent image: $AGENT_TAG" +docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" + +echo "Building judge image: $JUDGE_TAG" +docker build -f "$HERE/docker/judge/Dockerfile" -t "$JUDGE_TAG" "$HERE" + +echo "Built:" +echo " $AGENT_TAG" +echo " $JUDGE_TAG" diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/knn_gpu_kernel_optimization/docker/judge/Dockerfile new file mode 100644 index 000000000..f36a4a704 --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/judge/Dockerfile @@ -0,0 +1,18 @@ +# Judge image for knn_gpu_kernel_optimization. +# Bakes the pristine package tree the agent patch is applied to +# (/opt/knnlib-clean/knnlib) and the frozen naive baseline the judge times +# against (/opt/knn_ref/refknn.py). torch + triton come from the base. +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel + +ARG DEBIAN_FRONTEND=noninteractive +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash ca-certificates git && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" + +COPY knnlib /opt/knnlib-clean/knnlib +COPY judge/refknn.py /opt/knn_ref/refknn.py + +WORKDIR /judge diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh new file mode 100755 index 000000000..7fdad290f --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# Import-only smoke test for the built images (no GPU required). +set -euo pipefail + +AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "== agent image ==" +docker run --rm -w /app "$AGENT_TAG" python3 -c \ + "import torch, triton, knnlib; print('agent ok: knnlib', knnlib.__version__)" + +echo "== judge image ==" +docker run --rm "$JUDGE_TAG" python3 -c \ + "import sys, torch, triton; \ +sys.path.insert(0, '/opt/knn_ref'); import refknn; \ +sys.path.insert(0, '/opt/knnlib-clean'); import knnlib; \ +print('judge ok: refknn + knnlib import')" diff --git a/2.0/problems/knn_gpu_kernel_optimization/evaluate.sh b/2.0/problems/knn_gpu_kernel_optimization/evaluate.sh new file mode 100755 index 000000000..3982ca60b --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/evaluate.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +# Local CLI evaluation for knn_gpu_kernel_optimization. +# +# A full run needs a GPU plus the baked judge sources at /opt (the pristine +# /opt/knnlib-clean tree and the frozen /opt/knn_ref baseline; see the judge +# image). Without them the evaluator still validates the patch policy and +# returns a smoke score, which is what repository CI exercises (the reference +# patch passes the policy). Pass a patch path to score it directly. +set -euo pipefail +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +SOLUTION="${1:-$SCRIPT_DIR/reference.patch}" +exec python3 "$SCRIPT_DIR/evaluator.py" "$SOLUTION" diff --git a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py new file mode 100644 index 000000000..045a0f21b --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py @@ -0,0 +1,502 @@ +"""Evaluator for the brute-force k-NN GPU kernel-optimization task. + +The agent submits a unified diff over the ``knnlib`` Python package. The judge: + +1. statically validates the patch against an allowlist (only ``knnlib/**`` may + change; no ``flashlib``/cuML/network/env access; bounded size); +2. applies it to a pristine copy of ``knnlib`` baked into the judge image at + ``/opt/knnlib-clean``; +3. runs an isolated worker subprocess that, for each hidden ``(Q, M, D, k)`` + workload, times the patched ``knnlib.knn`` against a *frozen* naive baseline + (``/opt/knn_ref/refknn.py``) on identical seeded data and measures each + result's recall@k against the exact top-k recomputed from that baseline; +4. gates on nearest-neighbour quality (agent recall@k must stay at or above a + threshold vs the exact baseline) and scores by the geometric-mean speedup. + +When the judge source tree is not present (e.g. local static checks on a box +without a GPU), the evaluator still validates the patch policy and returns a +smoke pass, so ``python3 evaluator.py reference.patch`` works anywhere. +""" +from __future__ import annotations + +import fnmatch +import hashlib +import json +import math +import os +import re +import shutil +import subprocess +import sys +import tempfile +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +MAX_PATCH_BYTES = 1_000_000 +MAX_CHANGED_FILES = 40 +TASK_CONFIG_PATH = Path("/judge/task_config.json") + +DEFAULT_CLEAN_SOURCE = Path("/opt/knnlib-clean") # pristine package (patched here) +DEFAULT_BASELINE_SOURCE = Path("/opt/knn_ref") # frozen naive baseline (refknn.py) + + +def _load_task_config() -> dict[str, Any]: + try: + payload = json.loads(TASK_CONFIG_PATH.read_text(encoding="utf-8")) + except Exception: + return {} + return payload if isinstance(payload, dict) else {} + + +TASK_CONFIG = _load_task_config() +EVALUATION_CONFIG = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} + + +def _cfg(name: str, default): + return EVALUATION_CONFIG.get(name, default) + + +def _cfg_int(name: str, default: int) -> int: + try: + return int(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _cfg_float(name: str, default: float) -> float: + try: + return float(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _cfg_bool(name: str, default: bool) -> bool: + raw = EVALUATION_CONFIG.get(name, default) + if isinstance(raw, bool): + return raw + if isinstance(raw, str): + return raw.strip().lower() in {"1", "true", "yes", "on"} + return bool(raw) + + +WARMUP_ITERS = _cfg_int("warmup_iters", 3) +TIMED_ITERS = _cfg_int("timed_iters", 7) +RECALL_THRESHOLD = _cfg_float("recall_threshold", 0.99) # agent recall@k >= threshold +SPEEDUP_TARGET = _cfg_float("speedup_target", 6.0) # geomean speedup mapped to full score +WORKER_TIMEOUT_SECONDS = _cfg_int("worker_timeout_seconds", 3600) +BASE_SEED = _cfg_int("base_seed", 20260701) + +# Editable surface: only the shipped package. +ALLOWED_PATTERNS = ( + "knnlib/**", +) +# Never touchable, even though they are not shipped in the agent workspace. +DENIED_PATTERNS = ( + "evaluator.py", + "reference.patch", + "refknn.py", + "**/refknn.py", + "**/conftest.py", + "**/test_*.py", + "pyproject.toml", + "setup.py", + "setup.cfg", +) +# Forbidden substrings in added lines (defense in depth). The agent must write +# its own kernels, not call an external optimized library, read the +# environment, spawn processes, or touch the network. +FORBIDDEN_TOKENS = ( + "flashlib", + "cuml", + "cudf", + "cupy", + "faiss", + "sklearn", + "scikit", + "os.environ", + "getenv", + "putenv", + "setenv", + "subprocess", + "socket", + "urllib", + "requests", + "importlib.import_module", + "__import__", +) + + +@dataclass(frozen=True) +class PatchFile: + old_path: str + new_path: str + added_lines: tuple[str, ...] + removed_lines: tuple[str, ...] + + @property + def path(self) -> str: + return self.new_path if self.new_path != "/dev/null" else self.old_path + + +def _match(path: str, patterns: tuple[str, ...]) -> bool: + return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + + +def _invalid(message: str, metrics: dict[str, Any] | None = None): + payload = metrics or {} + payload.setdefault("valid_patch", 0) + return 0.0, 0.0, message, payload + + +def _parse_patch(text: str) -> list[PatchFile]: + files: list[PatchFile] = [] + current_old = "" + current_new = "" + added: list[str] = [] + removed: list[str] = [] + in_file = False + + for line in text.splitlines(): + if line.startswith("diff --git "): + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + in_file = True + current_old = current_new = "" + added = [] + removed = [] + continue + if not in_file: + continue + if line.startswith("--- "): + current_old = line[4:].strip() + if current_old.startswith("a/"): + current_old = current_old[2:] + continue + if line.startswith("+++ "): + current_new = line[4:].strip() + if current_new.startswith("b/"): + current_new = current_new[2:] + continue + if line.startswith("+") and not line.startswith("+++ "): + added.append(line[1:]) + continue + if line.startswith("-") and not line.startswith("--- "): + removed.append(line[1:]) + + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + return files + + +def _validate_path(path: str) -> tuple[bool, str]: + if not path or path == "/dev/null": + return True, "" + if path.startswith("/") or ".." in Path(path).parts: + return False, f"unsafe patch path: {path}" + if _match(path, DENIED_PATTERNS): + return False, f"changed file is outside task boundary: {path}" + if not _match(path, ALLOWED_PATTERNS): + return False, f"changed file is not allowlisted (only knnlib/** may change): {path}" + if not path.endswith(".py"): + return False, f"only Python files may change: {path}" + return True, "" + + +def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: + if not patch_path.exists(): + return False, "solution patch does not exist", {} + size = patch_path.stat().st_size + if size > MAX_PATCH_BYTES: + return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} + text = patch_path.read_text(encoding="utf-8", errors="replace") + patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() + files = _parse_patch(text) + metrics: dict[str, Any] = { + "patch_bytes": size, + "patch_sha256": patch_hash, + "changed_files": len(files), + } + if len(files) > MAX_CHANGED_FILES: + return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics + + for patch_file in files: + path = patch_file.path + if patch_file.new_path == "/dev/null": + return False, f"deleting files is outside task boundary: {patch_file.old_path}", metrics + if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: + ok, err = _validate_path(patch_file.old_path) + if not ok: + return False, f"rename/copy source is outside task boundary: {err}", metrics + ok, err = _validate_path(path) + if not ok: + return False, err, metrics + added_text = "\n".join(patch_file.added_lines) + low = added_text.lower() + for token in FORBIDDEN_TOKENS: + if token in low: + return False, f"{path}: forbidden token in added code ({token})", metrics + + metrics["valid_patch"] = 1 + return True, "patch accepted by static policy", metrics + + +def clean_env(tmp_root: Path) -> dict[str, str]: + home = tmp_root / "home" + tmp = tmp_root / "tmp" + home.mkdir(parents=True, exist_ok=True) + tmp.mkdir(parents=True, exist_ok=True) + env = { + "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin"), + "HOME": str(home), + "TMPDIR": str(tmp), + "LC_ALL": "C", + "LANG": "C", + } + for key in ("CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES", "LD_LIBRARY_PATH", + "TRITON_CACHE_DIR", "CUDA_HOME"): + if key in os.environ: + env[key] = os.environ[key] + env.setdefault("TRITON_CACHE_DIR", str(tmp_root / "triton_cache")) + return env + + +def run_checked(cmd: list[str], *, cwd: Path, env: dict[str, str], timeout: int) -> subprocess.CompletedProcess: + return subprocess.run( + cmd, cwd=str(cwd), env=env, timeout=timeout, + stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, + ) + + +def sanitize_error_text(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"/opt/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"M=\d+", "M=", text) + text = re.sub(r"Q=\d+", "Q=", text) + text = re.sub(r"seed[=:]?\s*\d+", "seed=", text, flags=re.IGNORECASE) + return text[-600:] + + +def geometric_mean(values: list[float]) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) + + +def score_from_speedup(gm_speedup: float) -> float: + if gm_speedup <= 0: + return 0.0 + raw = 100.0 * math.log(gm_speedup) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + return max(0.0, min(100.0, raw)) + + +def is_final_submission_role() -> bool: + return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" + + +# Hidden workloads. (Q, M, D, k). Agent role runs a fast subset for iterative +# feedback; the final verifier runs the full set. Seeds are derived from +# BASE_SEED so data is deterministic but not guessable from the statement. +_HIDDEN_WORKLOADS = ( + {"id": "w0", "Q": 2048, "M": 100_000, "D": 64, "k": 10}, + {"id": "w1", "Q": 4096, "M": 250_000, "D": 128, "k": 10}, + {"id": "w2", "Q": 1024, "M": 1_000_000, "D": 96, "k": 32}, # large-M regime + {"id": "w3", "Q": 8192, "M": 200_000, "D": 256, "k": 50}, # wide-D regime + {"id": "w4", "Q": 2048, "M": 500_000, "D": 64, "k": 100}, # large-k regime + {"id": "w5", "Q": 4096, "M": 300_000, "D": 128, "k": 64}, +) + + +def _workloads(*, final_role: bool) -> list[dict[str, Any]]: + configured = _cfg("workloads", None) + workloads = list(configured) if isinstance(configured, list) and configured else list(_HIDDEN_WORKLOADS) + if not final_role: + n_agent = _cfg_int("agent_workload_count", 3) + workloads = workloads[:n_agent] + for i, w in enumerate(workloads): + w.setdefault("seed", BASE_SEED + 1000 * (i + 1)) + return workloads + + +# The worker runs the untrusted patched package in its own process. It imports +# the frozen baseline (refknn) and the patched knnlib, times both on identical +# seeded data, and reports per-workload timings + recall@k as JSON. +_WORKER_SOURCE = r''' +import argparse, json, sys, time, traceback +def _load(baseline_dir, patched_dir): + sys.path.insert(0, patched_dir); sys.path.insert(0, baseline_dir) + import refknn, knnlib + return refknn, knnlib +def gen(Q, M, D, seed, device): + import torch + g = torch.Generator(device=device).manual_seed(int(seed)) + db = torch.randn(M, D, generator=g, device=device, dtype=torch.float32) + queries = torch.randn(Q, D, generator=g, device=device, dtype=torch.float32) + return queries, db +def recall(agent_idx, ref_idx): + hit = (agent_idx.unsqueeze(2) == ref_idx.unsqueeze(1)).any(dim=2) + Q, k = ref_idx.shape + return float(hit.sum().item()) / (Q * k) +def bench(fn, warmup, iters): + import torch + for _ in range(warmup): fn(); torch.cuda.synchronize() + ts = [] + for _ in range(iters): + torch.cuda.synchronize(); t0 = time.perf_counter(); fn(); torch.cuda.synchronize() + ts.append(time.perf_counter() - t0) + ts.sort(); return ts[len(ts) // 2] * 1000.0 +def check(out, Q, k): + import torch + if not (isinstance(out, tuple) and len(out) == 2): raise ValueError("knn must return (distances, indices)") + dist, idx = out + if tuple(dist.shape) != (Q, k) or tuple(idx.shape) != (Q, k): raise ValueError("wrong output shape") + if not torch.isfinite(dist).all(): raise ValueError("non-finite distances") +def main(): + ap = argparse.ArgumentParser() + for n in ("--baseline-dir","--patched-dir","--workloads","--out"): ap.add_argument(n, required=True) + ap.add_argument("--warmup", type=int, default=3); ap.add_argument("--iters", type=int, default=7) + a = ap.parse_args() + import torch + if not torch.cuda.is_available(): + json.dump({"ok": False, "error": "cuda_unavailable"}, open(a.out, "w")); return + refknn, knnlib = _load(a.baseline_dir, a.patched_dir) + rows = [] + for w in json.loads(a.workloads): + Q, M, D, k, seed = w["Q"], w["M"], w["D"], w["k"], w["seed"] + queries, db = gen(Q, M, D, seed, "cuda") + ref_fn = lambda: refknn.knn(queries, db, k) + agent_fn = lambda: knnlib.knn(queries, db, k) + ref_out = ref_fn(); torch.cuda.synchronize() + try: + agent_out = agent_fn(); torch.cuda.synchronize(); check(agent_out, Q, k) + rec = recall(agent_out[1].long(), ref_out[1].long()) + except Exception as e: + rows.append({"id": w["id"], "ok": False, "error": type(e).__name__ + ": " + str(e)[:200]}) + del queries, db; torch.cuda.empty_cache(); continue + ref_ms = bench(ref_fn, a.warmup, a.iters); agent_ms = bench(agent_fn, a.warmup, a.iters) + rows.append({"id": w["id"], "ok": True, "ref_ms": ref_ms, "agent_ms": agent_ms, "recall": rec}) + del queries, db; torch.cuda.empty_cache() + json.dump({"ok": True, "rows": rows}, open(a.out, "w")) +if __name__ == "__main__": + try: main() + except Exception: traceback.print_exc(); sys.exit(3) +''' + + +def _run_worker(patched_parent: Path, tmp_root: Path, env: dict[str, str], + workloads: list[dict[str, Any]]) -> dict[str, Any]: + worker_path = tmp_root / "knn_worker.py" + worker_path.write_text(_WORKER_SOURCE, encoding="utf-8") + out_path = tmp_root / "worker_out.json" + cmd = [ + sys.executable, str(worker_path), + "--baseline-dir", str(DEFAULT_BASELINE_SOURCE), + "--patched-dir", str(patched_parent), + "--workloads", json.dumps(workloads), + "--out", str(out_path), + "--warmup", str(WARMUP_ITERS), + "--iters", str(TIMED_ITERS), + ] + run_checked(cmd, cwd=tmp_root, env=env, timeout=WORKER_TIMEOUT_SECONDS) + return json.loads(out_path.read_text(encoding="utf-8")) + + +def full_evaluation(patch_path: Path, metrics: dict[str, Any]): + final_role = is_final_submission_role() + metrics["submission_role"] = "final" if final_role else "agent" + workloads = _workloads(final_role=final_role) + + if not DEFAULT_CLEAN_SOURCE.exists() or not DEFAULT_BASELINE_SOURCE.exists(): + metrics["full_benchmark"] = 0 + return (1.0, 1.0, + "patch policy smoke passed; knnlib judge source is not configured in this environment", + metrics) + + with tempfile.TemporaryDirectory(prefix="knn_kernel_opt_") as tmp: + tmp_root = Path(tmp) + env = clean_env(tmp_root) + patched_parent = tmp_root / "patched" + shutil.copytree(DEFAULT_CLEAN_SOURCE, patched_parent) + + if int(metrics.get("changed_files", 0)) > 0: + run_checked(["git", "apply", "--check", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + run_checked(["git", "apply", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + metrics["applied_patch"] = 1 + else: + metrics["used_empty_patch"] = 1 + + result = _run_worker(patched_parent, tmp_root, env, workloads) + if not result.get("ok"): + return _invalid(f"benchmark worker failed ({result.get('error', 'unknown')})", metrics) + + rows = result.get("rows", []) + speedups: list[float] = [] + per_workload: dict[str, Any] = {} + for row in rows: + if not row.get("ok"): + metrics["failed_workload_error"] = sanitize_error_text(str(row.get("error", ""))) + return _invalid("submitted knn crashed or returned an invalid result on a hidden workload", metrics) + if row["recall"] < RECALL_THRESHOLD: + metrics["recall_regression"] = { + "recall": row["recall"], "threshold": RECALL_THRESHOLD, + } + return _invalid("submitted knn recall regressed below threshold on a hidden workload", metrics) + speedup = row["ref_ms"] / row["agent_ms"] if row["agent_ms"] > 0 else 0.01 + speedups.append(max(speedup, 0.01)) + per_workload[row["id"]] = { + "ref_ms": row["ref_ms"], "agent_ms": row["agent_ms"], "speedup": speedup, + } + + if not speedups: + return _invalid("no workloads were evaluated", metrics) + + gm = geometric_mean(speedups) + bounded = score_from_speedup(gm) + metrics.update({ + "full_benchmark": 1, + "workload_count": len(speedups), + "geomean_speedup": gm, + }) + if _cfg_bool("expose_per_workload_metrics", False): + metrics["per_workload"] = per_workload + return (bounded, bounded, f"knn geomean speedup {gm:.3f}x over the naive baseline", metrics) + + +def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: + patch_path = Path(solution_path) + ok, message, metrics = validate_patch(patch_path) + if not ok: + return _invalid(message, metrics) + try: + return full_evaluation(patch_path, metrics) + except subprocess.TimeoutExpired: + return _invalid("benchmark worker timed out", metrics) + except subprocess.CalledProcessError as exc: + stderr = sanitize_error_text(exc.stderr or "") + cmd0 = Path(str(exc.cmd[0])).name if isinstance(exc.cmd, list) and exc.cmd else "subprocess" + metrics["failed_command"] = "git apply" if cmd0 == "git" else cmd0 + metrics["stderr_tail"] = stderr + return _invalid("patch apply or benchmark command failed", metrics) + except Exception as exc: + metrics["error_type"] = type(exc).__name__ + metrics["error_detail"] = sanitize_error_text(str(exc)) + return _invalid("evaluation failed", metrics) + + +def main(argv: list[str]) -> int: + if len(argv) != 2: + print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) + return 2 + score, score_unbounded, message, metrics = evaluate(argv[1]) + print(json.dumps({ + "score": score, + "score_unbounded": score_unbounded, + "message": message, + "metrics": metrics, + }, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv)) diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/README.md b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/README.md new file mode 100644 index 000000000..19cdee038 --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/README.md @@ -0,0 +1,34 @@ +# Brute-force k-NN kernel optimization — submission workflow + +You are optimizing the `knnlib` package at `/app/knnlib`. Edit the package +(rewrite the internals of `knn`, add Triton kernel modules under `knnlib/`), +then submit a patch. + +## Iterate locally + +Run the public self-test to check correctness and get a rough speed signal on +the two public shapes (needs a GPU in the agent container): + +```bash +bash /app/public_test.sh +``` + +## Submit + +```bash +bash /app/make_submission.sh # writes /app/solution.patch (knnlib diff) +bash /app/submit.sh # enqueues it for the black-box judge +``` + +Submissions are asynchronous. Submit early and keep improving; use +`bash /app/submissions.sh` and `bash /app/wait_submission.sh ` to inspect +results. + +## Rules + +- Only files under `knnlib/` may change. +- Do not import external optimized libraries (write the kernels yourself), and + do not access the environment, spawn processes, or use the network. +- Keep the public `knn(...)` signature and return contract unchanged. +- Nearest-neighbour quality is gated (recall@k vs an exact baseline); do not + sacrifice correctness for speed beyond the allowed tolerance. diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/make_submission.sh b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/make_submission.sh new file mode 100755 index 000000000..b54576f18 --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/make_submission.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash +# Package the current knnlib edits into a unified diff for submission. +set -euo pipefail + +APP_DIR="${APP_DIR:-/app}" +OUT="${1:-/app/solution.patch}" + +if [[ ! -d "$APP_DIR/knnlib" ]]; then + echo "knnlib package not found at $APP_DIR/knnlib" >&2 + exit 2 +fi +if [[ ! -d "$APP_DIR/.git" ]]; then + echo "git repo not found at $APP_DIR (expected in the agent image)" >&2 + exit 2 +fi + +# Stage and diff only the package, so submission helper scripts under /app are +# never included. New kernel files under knnlib/ are captured via `git add`. +git -C "$APP_DIR" add -A -- knnlib +git -C "$APP_DIR" diff --cached -- knnlib > "$OUT" +git -C "$APP_DIR" reset -q + +bytes=$(wc -c < "$OUT" | tr -d ' ') +echo "Wrote $OUT ($bytes bytes)" diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py new file mode 100644 index 000000000..84c22aa7b --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py @@ -0,0 +1,80 @@ +"""Public self-test for the brute-force k-NN kernel-optimization task. + +Runs the (patched) knnlib on the two public shapes, checks the nearest-neighbour +quality (recall@k) against a local naive cdist+topk recomputation, and prints a +rough speedup. This is a convenience for local iteration only -- the graded +workloads and thresholds are hidden and differ from these shapes. +""" +from __future__ import annotations + +import sys +import time + +PUBLIC_WORKLOADS = [ + {"Q": 1024, "M": 100_000, "D": 64, "k": 10, "seed": 1}, + {"Q": 2048, "M": 200_000, "D": 128, "k": 10, "seed": 2}, +] + + +def naive_knn(queries, database, k): + import torch + d2 = torch.cdist(queries, database) ** 2 + dist, idx = torch.topk(d2, k, dim=1, largest=False) + return dist, idx.to(torch.long) + + +def recall(agent_idx, ref_idx): + # Fraction of the true k nearest indices recovered, averaged over queries. + hit = (agent_idx.unsqueeze(2) == ref_idx.unsqueeze(1)).any(dim=2) + Q, k = ref_idx.shape + return float(hit.sum().item()) / (Q * k) + + +def bench(fn, warmup=2, iters=5): + import torch + for _ in range(warmup): + fn(); torch.cuda.synchronize() + ts = [] + for _ in range(iters): + torch.cuda.synchronize(); t0 = time.perf_counter() + fn(); torch.cuda.synchronize() + ts.append(time.perf_counter() - t0) + ts.sort() + return ts[len(ts) // 2] * 1000.0 + + +def main() -> int: + try: + import torch + except Exception as e: # pragma: no cover + print(f"torch import failed: {e}") + return 1 + if not torch.cuda.is_available(): + print("no CUDA device available; run this in a GPU-enabled container.") + return 1 + import knnlib + + for w in PUBLIC_WORKLOADS: + Q, M, D, k, seed = w["Q"], w["M"], w["D"], w["k"], w["seed"] + g = torch.Generator(device="cuda").manual_seed(seed) + db = torch.randn(M, D, generator=g, device="cuda", dtype=torch.float32) + queries = torch.randn(Q, D, generator=g, device="cuda", dtype=torch.float32) + + _, ref_idx = naive_knn(queries, db, k) + out = knnlib.knn(queries, db, k) + agent_idx = out[1].long() + rec = recall(agent_idx, ref_idx) + + ref_ms = bench(lambda: naive_knn(queries, db, k)) + agent_ms = bench(lambda: knnlib.knn(queries, db, k)) + ok = "OK" if rec >= 0.99 else "RECALL REGRESSION" + print(f"(Q={Q}, M={M}, D={D}, k={k}) recall@k={rec:.4f} [{ok}] " + f"baseline={ref_ms:.2f}ms yours={agent_ms:.2f}ms " + f"speedup={ref_ms / agent_ms:.2f}x") + del queries, db + torch.cuda.empty_cache() + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.sh b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.sh new file mode 100755 index 000000000..1157af76f --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash +# Public self-test: run the patched knnlib on the public shapes and print a +# correctness + rough speedup summary. Requires a GPU in the agent container. +set -euo pipefail +APP_DIR="${APP_DIR:-/app}" +cd "$APP_DIR" +exec python3 "$APP_DIR/public_test.py" diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/solution.patch b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/solution.patch new file mode 100644 index 000000000..e69de29bb diff --git a/2.0/problems/knn_gpu_kernel_optimization/judge/refknn.py b/2.0/problems/knn_gpu_kernel_optimization/judge/refknn.py new file mode 100644 index 000000000..0eb1b392e --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/judge/refknn.py @@ -0,0 +1,18 @@ +"""Frozen naive brute-force k-NN baseline used by the judge. + +This is a standalone (non-package) copy of the ``knnlib.knn`` implementation the +agent starts from. It is imported under its own module name so the judge worker +can load the frozen baseline and the patched ``knnlib`` package in the same +process. The judge uses it both as the speed denominator and as the exact +oracle from which the true k nearest neighbours (for the recall@k gate) are +recomputed. Keep this behaviourally identical to the shipped ``knnlib/knn.py``. +""" +from __future__ import annotations + +import torch + + +def knn(queries, database, k): + d2 = torch.cdist(queries, database) ** 2 # (Q, M), materialized -- naive + dist, idx = torch.topk(d2, k, dim=1, largest=False) + return dist, idx.to(torch.long) diff --git a/2.0/problems/knn_gpu_kernel_optimization/knnlib/__init__.py b/2.0/problems/knn_gpu_kernel_optimization/knnlib/__init__.py new file mode 100644 index 000000000..399c81247 --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/knnlib/__init__.py @@ -0,0 +1,16 @@ +"""knnlib -- a tiny GPU brute-force k-NN library you are asked to make fast. + +The public entry point is :func:`knn`. The shipped implementation is correct +but deliberately unoptimised: it materialises the full ``(Q, M)`` pairwise +distance matrix before selecting the ``k`` nearest neighbours. Your task is to +rewrite the internals (new Triton kernels, fused/streamed top-k passes, better +memory traffic, ...) so that :func:`knn` runs as fast as possible while +returning the same nearest neighbours. The public function signature and return +contract must not change. +""" +from __future__ import annotations + +from knnlib.knn import knn + +__all__ = ["knn"] +__version__ = "0.1.0" diff --git a/2.0/problems/knn_gpu_kernel_optimization/knnlib/knn.py b/2.0/problems/knn_gpu_kernel_optimization/knnlib/knn.py new file mode 100644 index 000000000..ad0c7b6c9 --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/knnlib/knn.py @@ -0,0 +1,39 @@ +"""Brute-force squared-L2 k-nearest-neighbours -- the reference you must optimise. + +This implementation is intentionally naive. It computes the full ``(Q, M)`` +pairwise distance matrix with :func:`torch.cdist` and materialises it in HBM, +then runs :func:`torch.topk` over the whole matrix to pick the ``k`` nearest +database points for every query. It is correct and deterministic, but it moves +far more memory than necessary (the entire ``(Q, M)`` matrix) and cannot scale +to large ``M`` without exhausting device memory. + +Contract (do NOT change): + + knn(queries, database, k) -> (distances, indices) + + queries : (Q, D) float32 CUDA tensor of query points. + database : (M, D) float32 CUDA tensor of database points to search. + k : int, number of nearest neighbours to return per query. + + distances : (Q, k) float32 tensor of the SQUARED-L2 distances to the ``k`` + nearest database points, in ascending order (nearest first). + indices : (Q, k) int64 tensor of the corresponding database row indices. + +You may add modules/kernels inside the ``knnlib`` package and rewrite the body +of :func:`knn` freely (fused/streamed running top-k, tiled distance kernels, +Triton), as long as the public contract above is preserved. In particular you +should avoid ever materialising the full ``(Q, M)`` distance matrix. +""" +from __future__ import annotations + +import torch + + +def knn(queries, database, k): + """Return the (squared-L2) k nearest database points for each query. + + Naive: materialise the full (Q, M) distance matrix, then top-k. + """ + d2 = torch.cdist(queries, database) ** 2 # (Q, M), materialized -- naive + dist, idx = torch.topk(d2, k, dim=1, largest=False) + return dist, idx.to(torch.long) diff --git a/2.0/problems/knn_gpu_kernel_optimization/readme b/2.0/problems/knn_gpu_kernel_optimization/readme new file mode 100644 index 000000000..ba50252e8 --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/readme @@ -0,0 +1,124 @@ +# GPU Brute-Force k-NN Kernel Optimization + +## Problem + +You are given a small GPU brute-force k-nearest-neighbours library, `knnlib`, +in the Harbor workspace at `/app/knnlib`. Its public entry point is: + +```python +knnlib.knn(queries, database, k) -> (distances, indices) +``` + +`queries` is a `(Q, D)` float32 CUDA tensor of query points, `database` is an +`(M, D)` float32 CUDA tensor of database points to search, and `k` is the number +of nearest neighbours to return per query. The function returns `distances`, a +`(Q, k)` float32 tensor of the **squared-L2** distances to the `k` nearest +database points (ascending, nearest first), and `indices`, a `(Q, k)` int64 +tensor of the corresponding database row indices. The shipped implementation is +correct but deliberately naive: it materializes the full `(Q, M)` pairwise +distance matrix with `torch.cdist`, then runs `torch.topk` over the whole +matrix. + +Your goal is to make `knnlib.knn` **as fast as possible** on the GPU while +returning the same nearest neighbours. You may add modules and Triton kernels +inside the `knnlib` package and rewrite the internals freely (fused/streamed +running top-k, tiled distance computation, reduced memory traffic, and so on) — +in particular you should avoid ever materializing the full `(Q, M)` distance +matrix. The public function signature and return contract above must not change. + +## Workload + +The judge evaluates a family of held-out dense search workloads that vary +`(Q, M, D, k)`, spanning small/large database sizes `M`, wide-feature (large +`D`) and many-neighbour (large `k`) regimes. All workloads use squared-L2 +distance, float32 data, and randomly generated queries/database, so each result +is a deterministic function of the inputs. + +Two representative *public* shapes you can use for local development (the graded +shapes are different and hidden): + +```text +(Q=1024, M=100000, D=64, k=10) +(Q=2048, M=200000, D=128, k=10) +``` + +Treat the workload as a general dense brute-force k-NN, not as shapes to +special-case. Improvements should be general kernel/throughput improvements, not +lookups keyed on specific `(Q, M, D, k)` values. + +## Submission + +The submitted artifact is a patch file over the `knnlib` package: + +```text +/app/solution.patch +``` + +After editing `/app/knnlib`, generate and submit a patch: + +```bash +bash /app/make_submission.sh +bash /app/submit.sh +``` + +Submissions are asynchronous; submit early and keep iterating. The judge applies +your patch to a clean copy of `knnlib`, imports the patched package, and times +it against the original naive implementation on the same hardware and the same +seeded queries/database. + +## Correctness + +Correctness is a gate. For each workload the judge recomputes the exact `k` +nearest database indices from an exact baseline and measures the **recall@k** of +your result — the fraction of the true `k` nearest indices your `knn` recovers, +averaged over all queries. Your recall@k must stay at or above a high threshold +(~0.99). Crashes, non-finite outputs, wrong shapes/dtypes, timeouts, and results +that drop below the recall threshold are all penalized before speed is +considered. You may trade a little numerical precision (e.g. lower-precision +accumulation) for speed as long as you stay above the recall threshold. + +## Scoring + +Valid submissions are scored by speedup relative to the naive baseline on the +same hardware and workloads. For each workload: + +```text +speedup = baseline_time / your_time +``` + +The objective is the geometric mean of per-workload speedups, so broad speedups +across the workload family are preferred over one large outlier. A result no +faster than the baseline earns 0; regressions earn 0. The raw geometric-mean +speedup is reported in the evaluator metrics. + +## Patch Policy + +The evaluator validates the patch before running it. Only files under the +package may change: + +```text +knnlib/** +``` + +New Python modules inside `knnlib/` (for example Triton kernel files) are +allowed. Patches may not: + +- modify anything outside `knnlib/`; +- import or call an external optimized ML/kernel library (the point is to write + the kernels yourself); +- read or write environment variables, spawn processes, or access the network; +- special-case the hidden workload shapes. + +The patch must be reasonably sized and apply cleanly to the clean `knnlib` tree. + +## Resource Budget + +```text +GPU: single device (H100 reference; Triton paths also run on L40S / A100) +vCPUs: 8 +memory: 32 GiB +storage: 32 GiB +``` + +The judge runs the timing worker in a subprocess under a clean, minimal +environment with a fixed warmup/measurement schedule and a per-run timeout. diff --git a/2.0/problems/knn_gpu_kernel_optimization/reference.patch b/2.0/problems/knn_gpu_kernel_optimization/reference.patch new file mode 100644 index 000000000..1becaf36c --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/reference.patch @@ -0,0 +1,104 @@ +diff --git a/knnlib/_chunked_knn.py b/knnlib/_chunked_knn.py +new file mode 100644 +index 0000000..df258eb +--- /dev/null ++++ b/knnlib/_chunked_knn.py +@@ -0,0 +1,41 @@ ++"""Chunked running-top-k brute-force k-NN -- the reference optimisation. ++ ++Instead of materialising the full ``(Q, M)`` distance matrix like the naive ++baseline, this tiles the database into ``CH``-row chunks and maintains a running ++per-query top-k across chunks. For each chunk it forms the squared-L2 block ++``||q||^2 - 2 + ||db_j||^2`` of shape ``(Q, cj)``, concatenates it with ++the current best distances, and re-runs ``topk`` over the (small) combined set, ++carrying the winning indices forward. Peak extra memory is ``O(Q * (k + CH))`` ++instead of ``O(Q * M)``, so it scales to large ``M`` and touches far less HBM. ++ ++The public contract is unchanged: ++ ++ knn(queries, database, k) -> (distances, indices) ++ ++with ``distances`` the (Q, k) ascending squared-L2 distances and ``indices`` the ++(Q, k) int64 database row indices. ++""" ++from __future__ import annotations ++ ++import torch ++ ++ ++def knn(queries, database, k): ++ Q = queries.shape[0]; M = database.shape[0] ++ device = queries.device ++ qn = (queries * queries).sum(1, keepdim=True) # (Q,1) ++ best_d = torch.full((Q, k), float("inf"), device=device, dtype=torch.float32) ++ best_i = torch.zeros((Q, k), device=device, dtype=torch.long) ++ CH = 65536 ++ for j0 in range(0, M, CH): ++ dbj = database[j0:j0 + CH] # (cj, D) ++ cj = dbj.shape[0] ++ dn = (dbj * dbj).sum(1) # (cj,) ++ d = qn - 2.0 * (queries @ dbj.t()) + dn[None, :] # (Q, cj) squared L2 ++ alld = torch.cat([best_d, d], dim=1) ++ idx_chunk = torch.arange(j0, j0 + cj, device=device).expand(Q, cj) ++ alli = torch.cat([best_i, idx_chunk], dim=1) ++ td, ti = torch.topk(alld, k, dim=1, largest=False) ++ best_d = td ++ best_i = torch.gather(alli, 1, ti) ++ return best_d.contiguous(), best_i.contiguous() +diff --git a/knnlib/knn.py b/knnlib/knn.py +index ad0c7b6..38acfc6 100644 +--- a/knnlib/knn.py ++++ b/knnlib/knn.py +@@ -1,13 +1,14 @@ +-"""Brute-force squared-L2 k-nearest-neighbours -- the reference you must optimise. ++"""Brute-force squared-L2 k-nearest-neighbours -- chunked running top-k. + +-This implementation is intentionally naive. It computes the full ``(Q, M)`` +-pairwise distance matrix with :func:`torch.cdist` and materialises it in HBM, +-then runs :func:`torch.topk` over the whole matrix to pick the ``k`` nearest +-database points for every query. It is correct and deterministic, but it moves +-far more memory than necessary (the entire ``(Q, M)`` matrix) and cannot scale +-to large ``M`` without exhausting device memory. ++The naive baseline materialised the full ``(Q, M)`` distance matrix with ++:func:`torch.cdist` and ran :func:`torch.topk` over the whole thing. Here the ++work is delegated to :func:`knnlib._chunked_knn.knn`, which tiles the database ++into chunks and maintains a running per-query top-k across chunks, so the ++``(Q, M)`` matrix is never materialised in HBM and the search scales to large ++``M``. The squared-L2 block per chunk is formed as ++``||q||^2 - 2 + ||db_j||^2``. + +-Contract (do NOT change): ++Public contract is unchanged: + + knn(queries, database, k) -> (distances, indices) + +@@ -15,25 +16,11 @@ Contract (do NOT change): + database : (M, D) float32 CUDA tensor of database points to search. + k : int, number of nearest neighbours to return per query. + +- distances : (Q, k) float32 tensor of the SQUARED-L2 distances to the ``k`` +- nearest database points, in ascending order (nearest first). +- indices : (Q, k) int64 tensor of the corresponding database row indices. +- +-You may add modules/kernels inside the ``knnlib`` package and rewrite the body +-of :func:`knn` freely (fused/streamed running top-k, tiled distance kernels, +-Triton), as long as the public contract above is preserved. In particular you +-should avoid ever materialising the full ``(Q, M)`` distance matrix. ++ distances : (Q, k) float32 SQUARED-L2 distances, ascending (nearest first). ++ indices : (Q, k) int64 database row indices. + """ + from __future__ import annotations + +-import torch +- +- +-def knn(queries, database, k): +- """Return the (squared-L2) k nearest database points for each query. ++from knnlib._chunked_knn import knn + +- Naive: materialise the full (Q, M) distance matrix, then top-k. +- """ +- d2 = torch.cdist(queries, database) ** 2 # (Q, M), materialized -- naive +- dist, idx = torch.topk(d2, k, dim=1, largest=False) +- return dist, idx.to(torch.long) ++__all__ = ["knn"] diff --git a/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md b/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md new file mode 100644 index 000000000..f431f3292 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md @@ -0,0 +1,104 @@ +# Design notes — pca_gpu_kernel_optimization + +Operator-facing. Not copied into the agent workspace by the adapter. + +## What this task measures + +Can an agent turn a correct-but-naive GPU PCA into a fast one? The agent is +given `pcalib` (a full-SVD-on-the-centred-matrix implementation) and must +rewrite the internals — ideally a covariance/Gram matrix (`Xᵀ X`, one GEMM) +followed by a top-`k` symmetric eigendecomposition, with fused centring and no +materialization of the centred data — to maximize the geometric-mean speedup +over the frozen baseline across a family of hidden `(N, D, k)` workloads, +subject to a subspace-quality gate. + +This is the fourth of a four-task **flashlib kernel-optimization family** +(KMeans, KNN, TruncatedSVD, PCA). All four share the KernelBench-style pattern: +ship a naive package, freeze a byte-for-byte baseline in the judge image, apply +the agent's patch to a clean copy, time patched-vs-baseline on identical seeded +data in an isolated worker, and gate on a primitive-appropriate quality metric +before scoring by geomean speedup. TruncatedSVD is the sibling primitive: it +factors the raw data matrix directly (no mean-centring), whereas PCA here is +covariance-based and centres by the feature mean. + +## Correctness gate: orthonormality + captured variance, judge-computed + +The quality gate has two judge-computed parts, evaluated from the *components +the submission returns* (not from any agent-provided number), on the same seeded +`x` used for the baseline: + +- **Orthonormality** — `max |V Vᵀ − I|` over the returned `(k, D)` components + must not exceed `ortho_tolerance` (default 2%). This forbids returning a + rank-deficient or non-orthonormal "subspace" to cheat the variance term. +- **Captured variance** — `(1/(N−1)) ||X_c V||_F²`, where `X_c` is the + mean-centred data, must be at least `(1 − captured_tolerance)` (default 2%) of + the baseline's captured variance. The centring uses the feature mean of `x`. + +Properties: + +- rotation/sign/basis-convention independent — only the *subspace* spanned by + the components matters, so the gate does not care whether the agent returns + eigenvectors in a different sign or rotated within the top-`k` subspace; +- robust to floating-point drift across the eigendecomposition; +- cheat-proof: you cannot return fast garbage — high captured variance requires + actually recovering the leading principal subspace, and the orthonormality + check blocks degenerate answers. Trading some numeric precision for speed + (tf32/bf16 accumulation in the GEMM) is allowed within tolerance. + +Determinism: data is a fixed function of `(N, D, seed)`; seeds derive from +`base_seed`. The naive baseline result is therefore reproducible. + +Note on the worker: `pca` returns `(components, explained_variance)`, so +`components` is the **first** element of the tuple (unlike the SVD sibling where +the singular values come first). The worker reads `agent_out[0]` for the +subspace-quality checks. + +## Anti-gaming + +flashlib (the library these primitives are distilled from) is public and +Apache-2.0. Mitigations: + +- The shipped package is a small, neutrally named library (`pcalib`), not + flashlib; the patch allowlist confines edits to `pcalib/**`. +- The patch policy forbids importing `flashlib`/cuML/cuPy/FAISS/scikit-learn and + bans env/subprocess/network access — the agent must write the kernels itself. +- Scored shapes are hidden and differ from the two public shapes; seeds derive + from `base_seed`. General kernels win; shape lookups do not. +- Honest framing: reproducing SOTA-class kernels *is* the bar. We block trivial + library reuse, not the underlying knowledge. + +## Execution model & the Modal question + +The evaluator runs an isolated worker subprocess (`_run_worker`) that imports the +frozen baseline (`/opt/pca_ref/refpca.py`) and the patched `pcalib` (from the +applied clean tree), times both, and reports JSON. This assumes the **judge +container has a GPU**. + +The repo's other GPU task (vllm) instead offloads to **Modal** because Harbor +judge containers may not be GPU-scheduled. `_run_worker` is the single swap +point: to go Modal, replace it with a Modal function that builds the patched +package, runs the same worker logic on a Modal GPU, and returns the JSON rows. +The rest of the evaluator (policy, gating, scoring) is unchanged. Decide +in-container-GPU vs Modal at first calibration trial. + +## Calibration TODO (needs a GPU trial) + +- Validate `reference.patch` runs and passes the orthonormality + captured + variance gates on all workloads; fix any `torch.linalg.eigh` API drift for the + pinned torch in the image. +- Measure the reference solution's geomean speedup and set `speedup_target` so a + reference-level solution maps to ~full score (default seeded at 4.0 for the + covariance-vs-full-SVD swap; recalibrate). +- Confirm hidden shapes fit device memory (the naive baseline materializes the + `(N, D)` centred matrix and runs a full thin SVD; all shipped shapes are + H100-safe). +- Sanity-check timing stability (median-of-7); bump `timed_iters` if noisy. + +## Files + +- `pcalib/` — pristine package baked into both images (agent edits it). +- `judge/refpca.py` — frozen baseline, baked to `/opt/pca_ref/`. +- `evaluator.py` — self-contained policy + orchestration + scoring (judge-only). +- `reference.patch` — covariance + eigh reference solution (proves solvability). +- `docker/` — builds the prebuilt agent/judge images referenced by config.yaml. +- `harbor/app/` — agent-facing submission helpers + public self-test. diff --git a/2.0/problems/pca_gpu_kernel_optimization/config.yaml b/2.0/problems/pca_gpu_kernel_optimization/config.yaml new file mode 100644 index 000000000..2f792f396 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/config.yaml @@ -0,0 +1,55 @@ +tag: systems +runtime: + language: python + timeout_seconds: 10800 + environment: "GPU PCA kernel optimization; agent patches the pcalib Python/Triton package; judge times the patched pca against a frozen naive full-SVD baseline on hidden (N, D, k) workloads with orthonormality and captured-variance (subspace-quality) gates." + apt_packages: + - bash + - ca-certificates + - git + - python3 + judge_apt_packages: + - bash + - ca-certificates + - git + - python3 + docker: + # Experimental local images. Build them with + # 2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh before a + # local Harbor trial. Both images bundle a clean copy of the pcalib + # package; the judge image additionally bakes the frozen naive baseline + # (/opt/pca_ref) and the pristine tree (/opt/pcalib-clean). + image: frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.1.0 + judge_image: frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.1.0 +environment: + cpus: 8 + memory_mb: 32768 + storage_mb: 32768 + build_timeout_seconds: 3600 +evaluation: + # The judge worker runs on a GPU visible to the judge container (single + # device). H100 is the reference accelerator; the Triton paths also run on + # L40S/A100. If Harbor cannot attach a GPU to the judge container, the worker + # step is the swap point for a Modal offload (see DESIGN.md). + gpu: "H100" + # Timing: warmup then median-of-N cuda-synced full-pca calls. + warmup_iters: 3 + timed_iters: 7 + # Subspace-quality gates. (1) The submitted components must be orthonormal: + # the max entry of |V V^T - I| must not exceed ortho_tolerance. (2) The + # variance captured by the submitted subspace, (1/(N-1))||Xc V||_F^2 on the + # SAME seeded data, must be at least (1 - captured_tolerance) x the frozen + # baseline's captured variance. + captured_tolerance: 0.02 + ortho_tolerance: 0.02 + # Geomean speedup (over the naive baseline) mapped to the full bounded score. + # Calibrate against the reference solution's measured geomean after a trial. + speedup_target: 4.0 + worker_timeout_seconds: 3600 + base_seed: 20260701 + # Agent (iterative) role runs a fast subset; the final verifier runs them all. + agent_workload_count: 3 + expose_per_workload_metrics: false +submission: + kind: file + path: /app/solution.patch diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/README.md b/2.0/problems/pca_gpu_kernel_optimization/docker/README.md new file mode 100644 index 000000000..3fbfa70ab --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/README.md @@ -0,0 +1,46 @@ +# Experimental PCA Kernel-Optimization Images + +Two images, mirroring the duckdb-e2e split: a public **agent** image and a +private **judge** image. Build them before a local Harbor trial: + +```bash +bash 2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh +``` + +Defaults: + +```text +AGENT_TAG=frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.1.0 +JUDGE_TAG=frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.1.0 +``` + +The agent image contains: + +```text +/app/pcalib # clean, git-tracked package (the agent edits this) +``` + +The judge image contains: + +```text +/opt/pcalib-clean/pcalib # pristine tree; the patch is applied to a copy +/opt/pca_ref/refpca.py # frozen naive baseline (speed denominator + oracle) +``` + +Both are based on `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel` (torch + triton). + +## Runtime requirements + +The judge times the patched pca on a **GPU visible to the judge container** +(single device; H100 reference, Triton paths also run on L40S/A100). If the +Harbor runtime cannot attach a GPU to the judge container, port the worker step +(`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md`. + +## Smoke test + +```bash +bash 2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh +``` + +Import-only; verifies torch/triton and the baked packages are importable. It +does not exercise a GPU. diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile new file mode 100644 index 000000000..65039c77a --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile @@ -0,0 +1,22 @@ +# Agent image for pca_gpu_kernel_optimization. +# Bundles a clean, git-tracked copy of the pcalib package at /app/pcalib +# so the agent can edit it and `make_submission.sh` can diff it. torch + triton +# come from the PyTorch base image. +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel + +ARG DEBIAN_FRONTEND=noninteractive +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash ca-certificates git ripgrep && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" + +WORKDIR /app +COPY pcalib /app/pcalib +RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ + git -C /app init -q && \ + git -C /app config user.email task@frontier-cs && \ + git -C /app config user.name frontier-cs && \ + git -C /app add -A -- pcalib .gitignore && \ + git -C /app -c commit.gpgsign=false commit -qm "pristine pcalib" diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh new file mode 100755 index 000000000..927a7027b --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# Build the experimental agent + judge images for pca_gpu_kernel_optimization. +set -euo pipefail +HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) + +AGENT_TAG=${AGENT_TAG:-frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "Building agent image: $AGENT_TAG" +docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" + +echo "Building judge image: $JUDGE_TAG" +docker build -f "$HERE/docker/judge/Dockerfile" -t "$JUDGE_TAG" "$HERE" + +echo "Built:" +echo " $AGENT_TAG" +echo " $JUDGE_TAG" diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile new file mode 100644 index 000000000..906e7d4cb --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile @@ -0,0 +1,18 @@ +# Judge image for pca_gpu_kernel_optimization. +# Bakes the pristine package tree the agent patch is applied to +# (/opt/pcalib-clean/pcalib) and the frozen naive baseline the judge times +# against (/opt/pca_ref/refpca.py). torch + triton come from the base. +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel + +ARG DEBIAN_FRONTEND=noninteractive +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash ca-certificates git && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" + +COPY pcalib /opt/pcalib-clean/pcalib +COPY judge/refpca.py /opt/pca_ref/refpca.py + +WORKDIR /judge diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh new file mode 100755 index 000000000..ffd665b2f --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# Import-only smoke test for the built images (no GPU required). +set -euo pipefail + +AGENT_TAG=${AGENT_TAG:-frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "== agent image ==" +docker run --rm -w /app "$AGENT_TAG" python3 -c \ + "import torch, triton, pcalib; print('agent ok: pcalib', pcalib.__version__)" + +echo "== judge image ==" +docker run --rm "$JUDGE_TAG" python3 -c \ + "import sys, torch, triton; \ +sys.path.insert(0, '/opt/pca_ref'); import refpca; \ +sys.path.insert(0, '/opt/pcalib-clean'); import pcalib; \ +print('judge ok: refpca + pcalib import')" diff --git a/2.0/problems/pca_gpu_kernel_optimization/evaluate.sh b/2.0/problems/pca_gpu_kernel_optimization/evaluate.sh new file mode 100755 index 000000000..f83e9ed92 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/evaluate.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +# Local CLI evaluation for pca_gpu_kernel_optimization. +# +# A full run needs a GPU plus the baked judge sources at /opt (the pristine +# /opt/pcalib-clean tree and the frozen /opt/pca_ref baseline; see the judge +# image). Without them the evaluator still validates the patch policy and +# returns a smoke score, which is what repository CI exercises (the reference +# patch passes the policy). Pass a patch path to score it directly. +set -euo pipefail +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +SOLUTION="${1:-$SCRIPT_DIR/reference.patch}" +exec python3 "$SCRIPT_DIR/evaluator.py" "$SOLUTION" diff --git a/2.0/problems/pca_gpu_kernel_optimization/evaluator.py b/2.0/problems/pca_gpu_kernel_optimization/evaluator.py new file mode 100644 index 000000000..e0898763c --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/evaluator.py @@ -0,0 +1,516 @@ +"""Evaluator for the PCA GPU kernel-optimization task. + +The agent submits a unified diff over the ``pcalib`` Python package. The +judge: + +1. statically validates the patch against an allowlist (only ``pcalib/**`` + may change; no ``flashlib``/cuML/network/env access; bounded size); +2. applies it to a pristine copy of ``pcalib`` baked into the judge image + at ``/opt/pcalib-clean``; +3. runs an isolated worker subprocess that, for each hidden ``(N, D, k)`` + workload, times the patched ``pcalib.pca`` against a *frozen* naive + baseline (``/opt/pca_ref/refpca.py``) on identical seeded data and + measures each result's orthonormality error and captured variance; +4. gates on subspace quality (agent components must be orthonormal and must + capture no less variance than the baseline beyond a small tolerance) and + scores by the geometric-mean speedup. + +When the judge source tree is not present (e.g. local static checks on a box +without a GPU), the evaluator still validates the patch policy and returns a +smoke pass, so ``python3 evaluator.py reference.patch`` works anywhere. +""" +from __future__ import annotations + +import fnmatch +import hashlib +import json +import math +import os +import re +import shutil +import subprocess +import sys +import tempfile +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +MAX_PATCH_BYTES = 1_000_000 +MAX_CHANGED_FILES = 40 +TASK_CONFIG_PATH = Path("/judge/task_config.json") + +DEFAULT_CLEAN_SOURCE = Path("/opt/pcalib-clean") # pristine package (patched here) +DEFAULT_BASELINE_SOURCE = Path("/opt/pca_ref") # frozen naive baseline (refpca.py) + + +def _load_task_config() -> dict[str, Any]: + try: + payload = json.loads(TASK_CONFIG_PATH.read_text(encoding="utf-8")) + except Exception: + return {} + return payload if isinstance(payload, dict) else {} + + +TASK_CONFIG = _load_task_config() +EVALUATION_CONFIG = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} + + +def _cfg(name: str, default): + return EVALUATION_CONFIG.get(name, default) + + +def _cfg_int(name: str, default: int) -> int: + try: + return int(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _cfg_float(name: str, default: float) -> float: + try: + return float(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _cfg_bool(name: str, default: bool) -> bool: + raw = EVALUATION_CONFIG.get(name, default) + if isinstance(raw, bool): + return raw + if isinstance(raw, str): + return raw.strip().lower() in {"1", "true", "yes", "on"} + return bool(raw) + + +WARMUP_ITERS = _cfg_int("warmup_iters", 3) +TIMED_ITERS = _cfg_int("timed_iters", 7) +CAPTURED_TOL = _cfg_float("captured_tolerance", 0.02) # agent captured >= (1-tol)*ref +ORTHO_TOL = _cfg_float("ortho_tolerance", 0.02) # max |V V^T - I| entry +SPEEDUP_TARGET = _cfg_float("speedup_target", 4.0) # geomean speedup mapped to full score +WORKER_TIMEOUT_SECONDS = _cfg_int("worker_timeout_seconds", 3600) +BASE_SEED = _cfg_int("base_seed", 20260701) + +# Editable surface: only the shipped package. +ALLOWED_PATTERNS = ( + "pcalib/**", +) +# Never touchable, even though they are not shipped in the agent workspace. +DENIED_PATTERNS = ( + "evaluator.py", + "reference.patch", + "refpca.py", + "**/refpca.py", + "**/conftest.py", + "**/test_*.py", + "pyproject.toml", + "setup.py", + "setup.cfg", +) +# Forbidden substrings in added lines (defense in depth). The agent must write +# its own kernels, not call an external optimized library, read the +# environment, spawn processes, or touch the network. +FORBIDDEN_TOKENS = ( + "flashlib", + "cuml", + "cudf", + "cupy", + "faiss", + "sklearn", + "scikit", + "os.environ", + "getenv", + "putenv", + "setenv", + "subprocess", + "socket", + "urllib", + "requests", + "importlib.import_module", + "__import__", +) + + +@dataclass(frozen=True) +class PatchFile: + old_path: str + new_path: str + added_lines: tuple[str, ...] + removed_lines: tuple[str, ...] + + @property + def path(self) -> str: + return self.new_path if self.new_path != "/dev/null" else self.old_path + + +def _match(path: str, patterns: tuple[str, ...]) -> bool: + return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + + +def _invalid(message: str, metrics: dict[str, Any] | None = None): + payload = metrics or {} + payload.setdefault("valid_patch", 0) + return 0.0, 0.0, message, payload + + +def _parse_patch(text: str) -> list[PatchFile]: + files: list[PatchFile] = [] + current_old = "" + current_new = "" + added: list[str] = [] + removed: list[str] = [] + in_file = False + + for line in text.splitlines(): + if line.startswith("diff --git "): + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + in_file = True + current_old = current_new = "" + added = [] + removed = [] + continue + if not in_file: + continue + if line.startswith("--- "): + current_old = line[4:].strip() + if current_old.startswith("a/"): + current_old = current_old[2:] + continue + if line.startswith("+++ "): + current_new = line[4:].strip() + if current_new.startswith("b/"): + current_new = current_new[2:] + continue + if line.startswith("+") and not line.startswith("+++ "): + added.append(line[1:]) + continue + if line.startswith("-") and not line.startswith("--- "): + removed.append(line[1:]) + + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + return files + + +def _validate_path(path: str) -> tuple[bool, str]: + if not path or path == "/dev/null": + return True, "" + if path.startswith("/") or ".." in Path(path).parts: + return False, f"unsafe patch path: {path}" + if _match(path, DENIED_PATTERNS): + return False, f"changed file is outside task boundary: {path}" + if not _match(path, ALLOWED_PATTERNS): + return False, f"changed file is not allowlisted (only pcalib/** may change): {path}" + if not path.endswith(".py"): + return False, f"only Python files may change: {path}" + return True, "" + + +def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: + if not patch_path.exists(): + return False, "solution patch does not exist", {} + size = patch_path.stat().st_size + if size > MAX_PATCH_BYTES: + return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} + text = patch_path.read_text(encoding="utf-8", errors="replace") + patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() + files = _parse_patch(text) + metrics: dict[str, Any] = { + "patch_bytes": size, + "patch_sha256": patch_hash, + "changed_files": len(files), + } + if len(files) > MAX_CHANGED_FILES: + return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics + + for patch_file in files: + path = patch_file.path + if patch_file.new_path == "/dev/null": + return False, f"deleting files is outside task boundary: {patch_file.old_path}", metrics + if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: + ok, err = _validate_path(patch_file.old_path) + if not ok: + return False, f"rename/copy source is outside task boundary: {err}", metrics + ok, err = _validate_path(path) + if not ok: + return False, err, metrics + added_text = "\n".join(patch_file.added_lines) + low = added_text.lower() + for token in FORBIDDEN_TOKENS: + if token in low: + return False, f"{path}: forbidden token in added code ({token})", metrics + + metrics["valid_patch"] = 1 + return True, "patch accepted by static policy", metrics + + +def clean_env(tmp_root: Path) -> dict[str, str]: + home = tmp_root / "home" + tmp = tmp_root / "tmp" + home.mkdir(parents=True, exist_ok=True) + tmp.mkdir(parents=True, exist_ok=True) + env = { + "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin"), + "HOME": str(home), + "TMPDIR": str(tmp), + "LC_ALL": "C", + "LANG": "C", + } + for key in ("CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES", "LD_LIBRARY_PATH", + "TRITON_CACHE_DIR", "CUDA_HOME"): + if key in os.environ: + env[key] = os.environ[key] + env.setdefault("TRITON_CACHE_DIR", str(tmp_root / "triton_cache")) + return env + + +def run_checked(cmd: list[str], *, cwd: Path, env: dict[str, str], timeout: int) -> subprocess.CompletedProcess: + return subprocess.run( + cmd, cwd=str(cwd), env=env, timeout=timeout, + stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, + ) + + +def sanitize_error_text(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"/opt/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"N=\d+", "N=", text) + text = re.sub(r"seed[=:]?\s*\d+", "seed=", text, flags=re.IGNORECASE) + return text[-600:] + + +def geometric_mean(values: list[float]) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) + + +def score_from_speedup(gm_speedup: float) -> float: + if gm_speedup <= 0: + return 0.0 + raw = 100.0 * math.log(gm_speedup) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + return max(0.0, min(100.0, raw)) + + +def is_final_submission_role() -> bool: + return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" + + +# Hidden workloads. (N, D, k). Agent role runs a fast subset for iterative +# feedback; the final verifier runs the full set. Seeds are derived from +# BASE_SEED so data is deterministic but not guessable from the statement. +_HIDDEN_WORKLOADS = ( + {"id": "w0", "N": 500_000, "D": 128, "k": 16}, + {"id": "w1", "N": 1_000_000, "D": 64, "k": 8}, + {"id": "w2", "N": 200_000, "D": 512, "k": 32}, # wide-feature regime + {"id": "w3", "N": 2_000_000, "D": 32, "k": 8}, # tall/skinny regime + {"id": "w4", "N": 300_000, "D": 256, "k": 16}, + {"id": "w5", "N": 800_000, "D": 128, "k": 32}, +) + + +def _workloads(*, final_role: bool) -> list[dict[str, Any]]: + configured = _cfg("workloads", None) + workloads = list(configured) if isinstance(configured, list) and configured else list(_HIDDEN_WORKLOADS) + if not final_role: + n_agent = _cfg_int("agent_workload_count", 3) + workloads = workloads[:n_agent] + for i, w in enumerate(workloads): + w.setdefault("seed", BASE_SEED + 1000 * (i + 1)) + return workloads + + +# The worker runs the untrusted patched package in its own process. It imports +# the frozen baseline (refpca) and the patched pcalib, times both on identical +# seeded data, and reports per-workload timings + subspace-quality metrics as +# JSON. +_WORKER_SOURCE = r''' +import argparse, json, sys, time, traceback +def _load(baseline_dir, patched_dir): + sys.path.insert(0, patched_dir); sys.path.insert(0, baseline_dir) + import refpca, pcalib + return refpca, pcalib +def gen(N, D, seed, device): + import torch + g = torch.Generator(device=device).manual_seed(int(seed)) + return torch.randn(N, D, generator=g, device=device, dtype=torch.float32) +def ortho_err(comps): + import torch + c = comps.to(torch.float32); k = c.shape[0] + return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) +def captured(x, comps): + import torch + c = comps.to(torch.float32); mean = x.mean(dim=0); N = x.shape[0]; total = 0.0 + for i in range(0, N, 16384): + p = (x[i:i+16384] - mean) @ c.t(); total += float((p * p).sum().item()) + return total / (N - 1) +def bench(fn, warmup, iters): + import torch + for _ in range(warmup): fn(); torch.cuda.synchronize() + ts = [] + for _ in range(iters): + torch.cuda.synchronize(); t0 = time.perf_counter(); fn(); torch.cuda.synchronize() + ts.append(time.perf_counter() - t0) + ts.sort(); return ts[len(ts) // 2] * 1000.0 +def check(out, D, k): + import torch + if not (isinstance(out, tuple) and len(out) == 2): raise ValueError("pca must return (components, explained_variance)") + comps, ev = out + if tuple(comps.shape) != (k, D) or tuple(ev.shape) != (k,): raise ValueError("wrong output shape") + if not (torch.isfinite(comps).all() and torch.isfinite(ev).all()): raise ValueError("non-finite output") +def main(): + ap = argparse.ArgumentParser() + for n in ("--baseline-dir","--patched-dir","--workloads","--out"): ap.add_argument(n, required=True) + ap.add_argument("--warmup", type=int, default=3); ap.add_argument("--iters", type=int, default=7) + a = ap.parse_args() + import torch + if not torch.cuda.is_available(): + json.dump({"ok": False, "error": "cuda_unavailable"}, open(a.out, "w")); return + refpca, pcalib = _load(a.baseline_dir, a.patched_dir) + rows = [] + for w in json.loads(a.workloads): + N, D, k, seed = w["N"], w["D"], w["k"], w["seed"] + x = gen(N, D, seed, "cuda") + ref_fn = lambda: refpca.pca(x, k) + agent_fn = lambda: pcalib.pca(x, k) + ref_out = ref_fn(); torch.cuda.synchronize() + ref_cap = captured(x, ref_out[0]) + try: + agent_out = agent_fn(); torch.cuda.synchronize(); check(agent_out, D, k) + oerr = ortho_err(agent_out[0]); acap = captured(x, agent_out[0]) + except Exception as e: + rows.append({"id": w["id"], "ok": False, "error": type(e).__name__ + ": " + str(e)[:200]}) + del x; torch.cuda.empty_cache(); continue + ref_ms = bench(ref_fn, a.warmup, a.iters); agent_ms = bench(agent_fn, a.warmup, a.iters) + rows.append({"id": w["id"], "ok": True, "ref_ms": ref_ms, "agent_ms": agent_ms, + "ortho_err": oerr, "ref_captured": ref_cap, "agent_captured": acap}) + del x; torch.cuda.empty_cache() + json.dump({"ok": True, "rows": rows}, open(a.out, "w")) +if __name__ == "__main__": + try: main() + except Exception: traceback.print_exc(); sys.exit(3) +''' + + +def _run_worker(patched_parent: Path, tmp_root: Path, env: dict[str, str], + workloads: list[dict[str, Any]]) -> dict[str, Any]: + worker_path = tmp_root / "pca_worker.py" + worker_path.write_text(_WORKER_SOURCE, encoding="utf-8") + out_path = tmp_root / "worker_out.json" + cmd = [ + sys.executable, str(worker_path), + "--baseline-dir", str(DEFAULT_BASELINE_SOURCE), + "--patched-dir", str(patched_parent), + "--workloads", json.dumps(workloads), + "--out", str(out_path), + "--warmup", str(WARMUP_ITERS), + "--iters", str(TIMED_ITERS), + ] + run_checked(cmd, cwd=tmp_root, env=env, timeout=WORKER_TIMEOUT_SECONDS) + return json.loads(out_path.read_text(encoding="utf-8")) + + +def full_evaluation(patch_path: Path, metrics: dict[str, Any]): + final_role = is_final_submission_role() + metrics["submission_role"] = "final" if final_role else "agent" + workloads = _workloads(final_role=final_role) + + if not DEFAULT_CLEAN_SOURCE.exists() or not DEFAULT_BASELINE_SOURCE.exists(): + metrics["full_benchmark"] = 0 + return (1.0, 1.0, + "patch policy smoke passed; pcalib judge source is not configured in this environment", + metrics) + + with tempfile.TemporaryDirectory(prefix="pca_kernel_opt_") as tmp: + tmp_root = Path(tmp) + env = clean_env(tmp_root) + patched_parent = tmp_root / "patched" + shutil.copytree(DEFAULT_CLEAN_SOURCE, patched_parent) + + if int(metrics.get("changed_files", 0)) > 0: + run_checked(["git", "apply", "--check", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + run_checked(["git", "apply", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + metrics["applied_patch"] = 1 + else: + metrics["used_empty_patch"] = 1 + + result = _run_worker(patched_parent, tmp_root, env, workloads) + if not result.get("ok"): + return _invalid(f"benchmark worker failed ({result.get('error', 'unknown')})", metrics) + + rows = result.get("rows", []) + speedups: list[float] = [] + per_workload: dict[str, Any] = {} + for row in rows: + if not row.get("ok"): + metrics["failed_workload_error"] = sanitize_error_text(str(row.get("error", ""))) + return _invalid("submitted pca crashed or returned an invalid result on a hidden workload", metrics) + if row["ortho_err"] > ORTHO_TOL: + metrics["ortho_violation"] = {"ortho_err": row["ortho_err"], "tol": ORTHO_TOL} + return _invalid("submitted pca components are not orthonormal", metrics) + ref_cap = row["ref_captured"] + agent_cap = row["agent_captured"] + if agent_cap < (1.0 - CAPTURED_TOL) * ref_cap - 1e-6: + metrics["captured_regression"] = { + "ref": ref_cap, "agent": agent_cap, "tol": CAPTURED_TOL, + } + return _invalid("submitted pca captured variance regressed beyond tolerance", metrics) + speedup = row["ref_ms"] / row["agent_ms"] if row["agent_ms"] > 0 else 0.01 + speedups.append(max(speedup, 0.01)) + per_workload[row["id"]] = { + "ref_ms": row["ref_ms"], "agent_ms": row["agent_ms"], "speedup": speedup, + } + + if not speedups: + return _invalid("no workloads were evaluated", metrics) + + gm = geometric_mean(speedups) + bounded = score_from_speedup(gm) + metrics.update({ + "full_benchmark": 1, + "workload_count": len(speedups), + "geomean_speedup": gm, + }) + if _cfg_bool("expose_per_workload_metrics", False): + metrics["per_workload"] = per_workload + return (bounded, bounded, f"pca geomean speedup {gm:.3f}x over the naive full-SVD baseline", metrics) + + +def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: + patch_path = Path(solution_path) + ok, message, metrics = validate_patch(patch_path) + if not ok: + return _invalid(message, metrics) + try: + return full_evaluation(patch_path, metrics) + except subprocess.TimeoutExpired: + return _invalid("benchmark worker timed out", metrics) + except subprocess.CalledProcessError as exc: + stderr = sanitize_error_text(exc.stderr or "") + cmd0 = Path(str(exc.cmd[0])).name if isinstance(exc.cmd, list) and exc.cmd else "subprocess" + metrics["failed_command"] = "git apply" if cmd0 == "git" else cmd0 + metrics["stderr_tail"] = stderr + return _invalid("patch apply or benchmark command failed", metrics) + except Exception as exc: + metrics["error_type"] = type(exc).__name__ + metrics["error_detail"] = sanitize_error_text(str(exc)) + return _invalid("evaluation failed", metrics) + + +def main(argv: list[str]) -> int: + if len(argv) != 2: + print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) + return 2 + score, score_unbounded, message, metrics = evaluate(argv[1]) + print(json.dumps({ + "score": score, + "score_unbounded": score_unbounded, + "message": message, + "metrics": metrics, + }, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv)) diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/README.md b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/README.md new file mode 100644 index 000000000..fcb9fb6cb --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/README.md @@ -0,0 +1,35 @@ +# PCA kernel optimization — submission workflow + +You are optimizing the `pcalib` package at `/app/pcalib`. Edit the package +(rewrite the internals of `pca`, add Triton kernel modules under `pcalib/`), +then submit a patch. + +## Iterate locally + +Run the public self-test to check correctness and get a rough speed signal on +the two public shapes (needs a GPU in the agent container): + +```bash +bash /app/public_test.sh +``` + +## Submit + +```bash +bash /app/make_submission.sh # writes /app/solution.patch (pcalib diff) +bash /app/submit.sh # enqueues it for the black-box judge +``` + +Submissions are asynchronous. Submit early and keep improving; use +`bash /app/submissions.sh` and `bash /app/wait_submission.sh ` to inspect +results. + +## Rules + +- Only files under `pcalib/` may change. +- Do not import external optimized libraries (write the kernels yourself), and + do not access the environment, spawn processes, or use the network. +- Keep the public `pca(...)` signature and return contract unchanged. +- Subspace quality is gated (orthonormal components and captured variance vs the + naive baseline); do not sacrifice correctness for speed beyond the allowed + tolerance. diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/make_submission.sh b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/make_submission.sh new file mode 100755 index 000000000..9c52bd8b1 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/make_submission.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash +# Package the current pcalib edits into a unified diff for submission. +set -euo pipefail + +APP_DIR="${APP_DIR:-/app}" +OUT="${1:-/app/solution.patch}" + +if [[ ! -d "$APP_DIR/pcalib" ]]; then + echo "pcalib package not found at $APP_DIR/pcalib" >&2 + exit 2 +fi +if [[ ! -d "$APP_DIR/.git" ]]; then + echo "git repo not found at $APP_DIR (expected in the agent image)" >&2 + exit 2 +fi + +# Stage and diff only the package, so submission helper scripts under /app are +# never included. New kernel files under pcalib/ are captured via `git add`. +git -C "$APP_DIR" add -A -- pcalib +git -C "$APP_DIR" diff --cached -- pcalib > "$OUT" +git -C "$APP_DIR" reset -q + +bytes=$(wc -c < "$OUT" | tr -d ' ') +echo "Wrote $OUT ($bytes bytes)" diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py new file mode 100644 index 000000000..804f89503 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py @@ -0,0 +1,97 @@ +"""Public self-test for the PCA kernel-optimization task. + +Runs the (patched) pcalib on the two public shapes, checks the subspace quality +(orthonormality + captured variance) against a local naive recomputation, and +prints a rough speedup. This is a convenience for local iteration only -- the +graded workloads and thresholds are hidden and differ from these shapes. +""" +from __future__ import annotations + +import sys +import time + +PUBLIC_WORKLOADS = [ + {"N": 200_000, "D": 128, "k": 16, "seed": 1}, + {"N": 500_000, "D": 64, "k": 8, "seed": 2}, +] + + +def naive_pca(x, k): + import torch + N = x.shape[0] + mean = x.mean(dim=0) + xc = x - mean + U, S, Vh = torch.linalg.svd(xc, full_matrices=False) + components = Vh[:k].contiguous() + explained_variance = (S[:k] ** 2) / (N - 1) + return components, explained_variance.contiguous() + + +def ortho_err(comps): + import torch + c = comps.to(torch.float32) + k = c.shape[0] + return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max()) + + +def captured(x, comps): + import torch + c = comps.to(torch.float32) + mean = x.mean(dim=0) + N = x.shape[0] + total = 0.0 + for i in range(0, N, 16384): + p = (x[i:i + 16384] - mean) @ c.t() + total += float((p * p).sum()) + return total / (N - 1) + + +def bench(fn, warmup=2, iters=5): + import torch + for _ in range(warmup): + fn(); torch.cuda.synchronize() + ts = [] + for _ in range(iters): + torch.cuda.synchronize(); t0 = time.perf_counter() + fn(); torch.cuda.synchronize() + ts.append(time.perf_counter() - t0) + ts.sort() + return ts[len(ts) // 2] * 1000.0 + + +def main() -> int: + try: + import torch + except Exception as e: # pragma: no cover + print(f"torch import failed: {e}") + return 1 + if not torch.cuda.is_available(): + print("no CUDA device available; run this in a GPU-enabled container.") + return 1 + import pcalib + + for w in PUBLIC_WORKLOADS: + N, D, k, seed = w["N"], w["D"], w["k"], w["seed"] + g = torch.Generator(device="cuda").manual_seed(seed) + x = torch.randn(N, D, generator=g, device="cuda", dtype=torch.float32) + + ref_c, _ = naive_pca(x, k) + out = pcalib.pca(x, k) + agent_c = out[0] + rc, ac = captured(x, ref_c), captured(x, agent_c) + ratio = ac / rc if rc > 0 else 0.0 + oerr = ortho_err(agent_c) + + ref_ms = bench(lambda: naive_pca(x, k)) + agent_ms = bench(lambda: pcalib.pca(x, k)) + ok = "OK" if (ratio >= 0.95 and oerr <= 0.02) else "QUALITY REGRESSION" + print(f"(N={N}, D={D}, k={k}) captured_ratio={ratio:.4f} ortho_err={oerr:.2e} [{ok}] " + f"baseline={ref_ms:.2f}ms yours={agent_ms:.2f}ms " + f"speedup={ref_ms / agent_ms:.2f}x") + del x + torch.cuda.empty_cache() + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.sh b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.sh new file mode 100755 index 000000000..44f02547c --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash +# Public self-test: run the patched pcalib on the public shapes and print a +# correctness + rough speedup summary. Requires a GPU in the agent container. +set -euo pipefail +APP_DIR="${APP_DIR:-/app}" +cd "$APP_DIR" +exec python3 "$APP_DIR/public_test.py" diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/solution.patch b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/solution.patch new file mode 100644 index 000000000..e69de29bb diff --git a/2.0/problems/pca_gpu_kernel_optimization/judge/refpca.py b/2.0/problems/pca_gpu_kernel_optimization/judge/refpca.py new file mode 100644 index 000000000..7a2a5fe31 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/judge/refpca.py @@ -0,0 +1,22 @@ +"""Frozen naive PCA baseline used by the judge as the speed denominator. + +This is a standalone (non-package) copy of the ``pcalib.pca`` implementation +the agent starts from. It is imported under its own module name so the judge +worker can load the frozen baseline and the patched ``pcalib`` package in the +same process. Keep this behaviourally identical to the shipped +``pcalib/pca.py``. +""" +from __future__ import annotations + +import torch + + +def pca(x, n_components): + N = x.shape[0] + mean = x.mean(dim=0) + xc = x - mean # materialized centered matrix -- naive + U, S, Vh = torch.linalg.svd(xc, full_matrices=False) # full SVD -- expensive + k = int(n_components) + components = Vh[:k].contiguous() + explained_variance = (S[:k] ** 2) / (N - 1) + return components, explained_variance.contiguous() diff --git a/2.0/problems/pca_gpu_kernel_optimization/pcalib/__init__.py b/2.0/problems/pca_gpu_kernel_optimization/pcalib/__init__.py new file mode 100644 index 000000000..002e4c7c7 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/pcalib/__init__.py @@ -0,0 +1,15 @@ +"""pcalib -- a tiny GPU PCA library you are asked to make fast. + +The public entry point is :func:`pca`. The shipped implementation is correct +but deliberately unoptimised (it materialises the centred data matrix and runs +a full SVD). Your task is to rewrite the internals (covariance/Gram + top-k +eigendecomposition, fused centring, new Triton kernels, ...) so that +:func:`pca` runs as fast as possible while producing the same principal +subspace. The public function signature and return contract must not change. +""" +from __future__ import annotations + +from pcalib.pca import pca + +__all__ = ["pca"] +__version__ = "0.1.0" diff --git a/2.0/problems/pca_gpu_kernel_optimization/pcalib/pca.py b/2.0/problems/pca_gpu_kernel_optimization/pcalib/pca.py new file mode 100644 index 000000000..ec891a2e3 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/pcalib/pca.py @@ -0,0 +1,44 @@ +"""Principal Component Analysis (PCA) -- the reference you must optimise. + +This implementation is intentionally naive. It centres the data by explicitly +materialising the full ``(N, D)`` centred matrix and then runs a *full* thin +SVD (:func:`torch.linalg.svd`) just to read off the top ``k`` singular vectors +and singular values. It is correct and deterministic, but it computes the +entire spectrum when only the leading ``k`` components are needed and it moves +far more memory than necessary (the centred copy of the whole dataset). + +Contract (do NOT change): + + pca(x, n_components) -> (components, explained_variance) + + x : (N, D) float32 CUDA tensor of points. + n_components (k): int, number of leading principal components to return. + + components : (k, D) float32 tensor whose rows are the top-k + principal axes (the leading eigenvectors of the + covariance matrix), orthonormal rows. + explained_variance : (k,) float32 tensor of the corresponding covariance + eigenvalues, in descending order. With the unbiased + (``N - 1``) normalisation this equals + ``S[:k] ** 2 / (N - 1)`` for singular values ``S`` of + the centred data. + +You may add modules/kernels inside the ``pcalib`` package and rewrite the body +of :func:`pca` freely (covariance/Gram + top-k eigendecomposition, fused +centring, Triton kernels, ...), as long as the public contract above is +preserved. +""" +from __future__ import annotations + +import torch + + +def pca(x, n_components): + N = x.shape[0] + mean = x.mean(dim=0) + xc = x - mean # materialized centered matrix -- naive + U, S, Vh = torch.linalg.svd(xc, full_matrices=False) # full SVD -- expensive + k = int(n_components) + components = Vh[:k].contiguous() + explained_variance = (S[:k] ** 2) / (N - 1) + return components, explained_variance.contiguous() diff --git a/2.0/problems/pca_gpu_kernel_optimization/readme b/2.0/problems/pca_gpu_kernel_optimization/readme new file mode 100644 index 000000000..35e79b025 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/readme @@ -0,0 +1,134 @@ +# GPU PCA Kernel Optimization + +## Problem + +You are given a small GPU PCA library, `pcalib`, in the Harbor workspace at +`/app/pcalib`. Its public entry point is: + +```python +pcalib.pca(x, n_components) -> (components, explained_variance) +``` + +`x` is an `(N, D)` float32 CUDA tensor and `n_components` (`k`) is the number of +leading principal components to return. The function returns `components`, a +`(k, D)` float32 tensor whose rows are the top-`k` principal axes (the leading +eigenvectors of the data covariance, with orthonormal rows), and +`explained_variance`, a `(k,)` float32 tensor of the corresponding covariance +eigenvalues in descending order. The shipped implementation is correct but +deliberately naive: it centres the data by materializing the full `(N, D)` +centred matrix and then runs a *full* thin SVD (`torch.linalg.svd`) just to read +off the leading `k` singular vectors and values. + +Your goal is to make `pcalib.pca` **as fast as possible** on the GPU while +producing the same principal subspace. You may add modules and Triton kernels +inside the `pcalib` package and rewrite the internals freely (covariance/Gram +matrix + top-`k` eigendecomposition instead of a full SVD, fused centring, +reduced memory traffic, and so on). The public function signature and return +contract above must not change. + +## Workload + +The judge evaluates a family of held-out dense PCA workloads that vary +`(N, D, k)`, spanning small/medium/large point counts, wide-feature (large `D`) +and tall/skinny (large `N`, small `D`) regimes. All workloads use float32 data +and a fixed number of components, so each result is a deterministic function of +the inputs. + +Two representative *public* shapes you can use for local development (the graded +shapes are different and hidden): + +```text +(N=200000, D=128, k=16) +(N=500000, D=64, k=8) +``` + +Treat the workload as a general dense PCA, not as shapes to special-case. +Improvements should be general kernel/throughput improvements, not lookups keyed +on specific `(N, D, k)` values. + +## Submission + +The submitted artifact is a patch file over the `pcalib` package: + +```text +/app/solution.patch +``` + +After editing `/app/pcalib`, generate and submit a patch: + +```bash +bash /app/make_submission.sh +bash /app/submit.sh +``` + +Submissions are asynchronous; submit early and keep iterating. The judge applies +your patch to a clean copy of `pcalib`, imports the patched package, and times it +against the original naive implementation on the same hardware and same seeded +data. + +## Correctness + +Correctness is a gate. For each workload the judge checks two properties of your +returned `components` on the same seeded data used for the baseline: + +- **Orthonormality**: the rows of `components` must be orthonormal, i.e. the + largest entry of `|V Vᵀ − I|` must stay within a small tolerance. +- **Captured variance**: the variance your subspace captures, + `(1 / (N − 1)) · ||X_c V||_F²` where `X_c` is the mean-centred data, must be at + least `(1 − tol)` times the variance captured by the naive baseline's + subspace. + +Because the gate is rotation- and sign-invariant (it only depends on the +subspace your components span, not on any label/sign convention), you cannot +pass it with fast garbage — capturing the variance requires actually recovering +the leading principal subspace. Crashes, non-finite outputs, wrong +shapes/dtypes, timeouts, and subspaces that regress beyond the tolerance are all +penalized before speed is considered. You may trade a little numerical precision +(e.g. lower-precision accumulation) for speed as long as you stay within the +quality tolerances. + +## Scoring + +Valid submissions are scored by speedup relative to the naive baseline on the +same hardware and workloads. For each workload: + +```text +speedup = baseline_time / your_time +``` + +The objective is the geometric mean of per-workload speedups, so broad speedups +across the workload family are preferred over one large outlier. A result no +faster than the baseline earns 0; regressions earn 0. The raw geometric-mean +speedup is reported in the evaluator metrics. + +## Patch Policy + +The evaluator validates the patch before running it. Only files under the +package may change: + +```text +pcalib/** +``` + +New Python modules inside `pcalib/` (for example Triton kernel files) are +allowed. Patches may not: + +- modify anything outside `pcalib/`; +- import or call an external optimized ML/kernel library (the point is to write + the kernels yourself); +- read or write environment variables, spawn processes, or access the network; +- special-case the hidden workload shapes. + +The patch must be reasonably sized and apply cleanly to the clean `pcalib` tree. + +## Resource Budget + +```text +GPU: single device (H100 reference; Triton paths also run on L40S / A100) +vCPUs: 8 +memory: 32 GiB +storage: 32 GiB +``` + +The judge runs the timing worker in a subprocess under a clean, minimal +environment with a fixed warmup/measurement schedule and a per-run timeout. diff --git a/2.0/problems/pca_gpu_kernel_optimization/reference.patch b/2.0/problems/pca_gpu_kernel_optimization/reference.patch new file mode 100644 index 000000000..c98a3d12a --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/reference.patch @@ -0,0 +1,97 @@ +diff --git a/pcalib/_cov_pca.py b/pcalib/_cov_pca.py +new file mode 100644 +index 0000000..6a7f6a7 +--- /dev/null ++++ b/pcalib/_cov_pca.py +@@ -0,0 +1,31 @@ ++"""Covariance-based top-k PCA (reference optimisation). ++ ++Instead of centring the whole ``(N, D)`` matrix and running a *full* thin SVD, ++this forms the ``(D, D)`` covariance with a single GEMM (``Xᵀ X``) and a rank-1 ++mean correction -- so the centred data is never materialised -- and reads off ++the leading ``k`` principal axes with a symmetric eigendecomposition ++(:func:`torch.linalg.eigh`). For ``D << N`` this replaces an ``O(N D^2)`` SVD of ++a tall matrix with one ``O(N D^2)`` GEMM followed by an ``O(D^3)`` eigensolve on ++the small ``(D, D)`` covariance, and it avoids allocating a second copy of the ++dataset. ++ ++The public contract is unchanged: ``pca(x, n_components)`` returns ++``(components, explained_variance)`` with orthonormal ``(k, D)`` rows and the ++matching descending covariance eigenvalues. ++""" ++from __future__ import annotations ++ ++import torch ++ ++ ++def pca(x, n_components): ++ N, D = x.shape ++ mean = x.mean(dim=0) ++ G = x.t() @ x # (D, D), one GEMM ++ C = (G - N * torch.outer(mean, mean)) / (N - 1) # covariance without centering the data ++ evals, evecs = torch.linalg.eigh(C) # ascending ++ k = int(n_components) ++ top = torch.argsort(evals, descending=True)[:k] ++ components = evecs.index_select(1, top).t().contiguous() # (k, D) ++ explained_variance = evals.index_select(0, top).clamp_min(0).contiguous() ++ return components, explained_variance +diff --git a/pcalib/pca.py b/pcalib/pca.py +index ec891a2..c3d15bf 100644 +--- a/pcalib/pca.py ++++ b/pcalib/pca.py +@@ -1,44 +1,24 @@ +-"""Principal Component Analysis (PCA) -- the reference you must optimise. ++"""Principal Component Analysis (PCA) -- covariance + top-k eigendecomposition. + +-This implementation is intentionally naive. It centres the data by explicitly +-materialising the full ``(N, D)`` centred matrix and then runs a *full* thin +-SVD (:func:`torch.linalg.svd`) just to read off the top ``k`` singular vectors +-and singular values. It is correct and deterministic, but it computes the +-entire spectrum when only the leading ``k`` components are needed and it moves +-far more memory than necessary (the centred copy of the whole dataset). ++The naive baseline centred the data by materialising the full ``(N, D)`` centred ++matrix and ran a *full* thin SVD just to read off the leading ``k`` components. ++Here :func:`pca` delegates to :func:`pcalib._cov_pca.pca`, which forms the ++``(D, D)`` covariance with a single GEMM plus a rank-1 mean correction (so the ++centred data is never materialised) and reads off the top ``k`` principal axes ++with a symmetric eigendecomposition. + +-Contract (do NOT change): ++Public contract is unchanged: + + pca(x, n_components) -> (components, explained_variance) + + x : (N, D) float32 CUDA tensor of points. + n_components (k): int, number of leading principal components to return. + +- components : (k, D) float32 tensor whose rows are the top-k +- principal axes (the leading eigenvectors of the +- covariance matrix), orthonormal rows. +- explained_variance : (k,) float32 tensor of the corresponding covariance +- eigenvalues, in descending order. With the unbiased +- (``N - 1``) normalisation this equals +- ``S[:k] ** 2 / (N - 1)`` for singular values ``S`` of +- the centred data. +- +-You may add modules/kernels inside the ``pcalib`` package and rewrite the body +-of :func:`pca` freely (covariance/Gram + top-k eigendecomposition, fused +-centring, Triton kernels, ...), as long as the public contract above is +-preserved. ++ components : (k, D) float32 top-k principal axes (orthonormal rows). ++ explained_variance : (k,) float32 covariance eigenvalues, descending. + """ + from __future__ import annotations + + import torch + +- +-def pca(x, n_components): +- N = x.shape[0] +- mean = x.mean(dim=0) +- xc = x - mean # materialized centered matrix -- naive +- U, S, Vh = torch.linalg.svd(xc, full_matrices=False) # full SVD -- expensive +- k = int(n_components) +- components = Vh[:k].contiguous() +- explained_variance = (S[:k] ** 2) / (N - 1) +- return components, explained_variance.contiguous() ++from pcalib._cov_pca import pca diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md b/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md new file mode 100644 index 000000000..a881c07c1 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md @@ -0,0 +1,112 @@ +# Design notes — truncated_svd_gpu_kernel_optimization + +Operator-facing. Not copied into the agent workspace by the adapter. + +## What this task measures + +Can an agent turn a correct-but-naive GPU truncated SVD into a fast one? The +agent is given `tsvdlib` (a `torch.linalg.svd` full-SVD call that then slices the +top-k factors) and must rewrite the internals — ideally a Gram-matrix `X^T X` +followed by a top-k eigendecomposition (`torch.linalg.eigh`), plus fused kernels +— to maximize the geometric-mean speedup over the frozen baseline across a family +of hidden `(N, D, k)` workloads, subject to an approximation-quality gate. + +This is the third of a four-task **flashlib kernel-optimization family** +(KMeans, KNN, TruncatedSVD, PCA). All four share the KernelBench-style pattern: +ship a naive package, freeze a byte-for-byte baseline in the judge image, apply +the agent's patch to a clean copy, time patched-vs-baseline on identical seeded +data in an isolated worker, and gate on a primitive-appropriate quality metric +before scoring by geomean speedup. PCA is the sibling task: it is the same +Gram/eig idea but on the **covariance** matrix (mean-centre `X` first, then the +right singular vectors of the centred matrix are the principal directions), so +the PCA gate additionally has to account for the centering step. + +## Correctness gate: orthonormality + captured energy, judge-computed + +The SVD has sign and rotation ambiguities (any singular vector may be negated, +and vectors spanning equal singular values may be rotated within their +eigenspace), so an elementwise comparison against the baseline's factors is +meaningless. Instead the judge worker checks two rotation/sign-invariant +properties of the returned `components` `V` (shape `(k, D)`), computed from what +the submission returns (not from any agent-provided number), on the same seeded +`x` used for the baseline: + +- **Orthonormality**: `max |V V^T - I_k| <= ortho_tolerance` (default 2%). The + rows must be an orthonormal basis of a k-dimensional subspace. +- **Captured energy**: `||X V^T||_F^2 >= (1 - captured_tolerance)` times the + baseline's captured energy (default 2%). This is the squared Frobenius norm of + the projection of the data onto the returned subspace — the quantity truncated + SVD is supposed to maximise (Eckart–Young). It depends only on the *subspace* + spanned by the rows of `V`, so it is invariant to sign flips and to any + in-subspace rotation. + +Properties: + +- rotation/sign-convention independent (only the spanned subspace matters); +- robust to floating-point drift; +- cheat-proof: you cannot return fast garbage — high captured energy requires + actually recovering the leading right-singular subspace, and the orthonormality + gate blocks degenerate `V` (e.g. repeated or unnormalised rows) that could + otherwise inflate the projected energy. Trading some numeric precision for + speed (tf32/bf16 accumulation in the Gram GEMM) is allowed within tolerance. + +`singular_values` are still required by the shape/finiteness check but are not +part of the quality gate; the captured-energy formulation makes the gate depend +on the subspace, which is what a downstream user of a truncated SVD cares about. + +Determinism: `x` is drawn from a fixed seeded generator per workload (seeds +derived from `base_seed`), so the baseline result is a fixed function of `(N, D, +k, seed)`. + +## Anti-gaming + +flashlib (the library these primitives are distilled from) is public and +Apache-2.0. Mitigations: + +- The shipped package is a small, neutrally named library (`tsvdlib`), not + flashlib; the patch allowlist confines edits to `tsvdlib/**`. +- The patch policy forbids importing `flashlib`/cuML/cuPy/FAISS/scikit-learn and + bans env/subprocess/network access — the agent must write the kernels itself. +- Scored shapes are hidden and differ from the two public shapes; seeds derive + from `base_seed`. General kernels win; shape lookups do not. +- The gate is a subspace-quality metric, not an elementwise factor match, so an + agent cannot "pass" by copying baseline numbers — it must produce a genuinely + good rank-k approximation. +- Honest framing: reproducing SOTA-class kernels *is* the bar. We block trivial + library reuse, not the underlying knowledge. + +## Execution model & the Modal question + +The evaluator runs an isolated worker subprocess (`_run_worker`) that imports the +frozen baseline (`/opt/tsvd_ref/reftsvd.py`) and the patched `tsvdlib` (from the +applied clean tree), times both, and reports JSON. This assumes the **judge +container has a GPU**. + +The repo's other GPU task (vllm) instead offloads to **Modal** because Harbor +judge containers may not be GPU-scheduled. `_run_worker` is the single swap +point: to go Modal, replace it with a Modal function that builds the patched +package, runs the same worker logic on a Modal GPU, and returns the JSON rows. +The rest of the evaluator (policy, gating, scoring) is unchanged. Decide +in-container-GPU vs Modal at first calibration trial. + +## Calibration TODO (needs a GPU trial) + +- Validate `reference.patch` (Gram + `eigh`) runs and passes the orthonormality + and captured-energy gates on all workloads; fix any torch API drift for the + pinned torch in the image. +- Measure the reference solution's geomean speedup and set `speedup_target` so a + reference-level solution maps to ~full score. +- Confirm hidden shapes fit device memory (the naive baseline's full + `torch.linalg.svd` on the `N x D` matrix is the memory driver; all shipped + shapes are H100-safe, and the Gram-matrix reference is far lighter since it + only decomposes the `D x D` matrix). +- Sanity-check timing stability (median-of-7); bump `timed_iters` if noisy. + +## Files + +- `tsvdlib/` — pristine package baked into both images (agent edits it). +- `judge/reftsvd.py` — frozen baseline, baked to `/opt/tsvd_ref/`. +- `evaluator.py` — self-contained policy + orchestration + scoring (judge-only). +- `reference.patch` — Gram-matrix + eigh reference solution (proves solvability). +- `docker/` — builds the prebuilt agent/judge images referenced by config.yaml. +- `harbor/app/` — agent-facing submission helpers + public self-test. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml b/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml new file mode 100644 index 000000000..120af92d9 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml @@ -0,0 +1,55 @@ +tag: systems +runtime: + language: python + timeout_seconds: 10800 + environment: "GPU truncated-SVD kernel optimization; agent patches the tsvdlib Python/Triton package; judge times the patched truncated_svd against a frozen naive full-SVD baseline on hidden (N, D, k) workloads with orthonormality and captured-energy (approximation-quality) gates." + apt_packages: + - bash + - ca-certificates + - git + - python3 + judge_apt_packages: + - bash + - ca-certificates + - git + - python3 + docker: + # Experimental local images. Build them with + # 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh + # before a local Harbor trial. Both images bundle a clean copy of the + # tsvdlib package; the judge image additionally bakes the frozen naive + # baseline (/opt/tsvd_ref) and the pristine tree (/opt/tsvdlib-clean). + image: frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.1.0 + judge_image: frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.1.0 +environment: + cpus: 8 + memory_mb: 32768 + storage_mb: 32768 + build_timeout_seconds: 3600 +evaluation: + # The judge worker runs on a GPU visible to the judge container (single + # device). H100 is the reference accelerator; the Triton paths also run on + # L40S/A100. If Harbor cannot attach a GPU to the judge container, the worker + # step is the swap point for a Modal offload (see DESIGN.md). + gpu: "H100" + # Timing: warmup then median-of-N cuda-synced full truncated_svd calls. + warmup_iters: 3 + timed_iters: 7 + # Approximation-quality gate: the submitted truncated_svd's components must + # stay orthonormal (max |C C^T - I| <= ortho_tolerance) and must not lose + # captured energy (||X V^T||_F^2 >= (1 - captured_tolerance) x the frozen + # baseline's on the SAME seeded data). + captured_tolerance: 0.02 + ortho_tolerance: 0.02 + # Geomean speedup (over the naive full-SVD baseline) mapped to the full + # bounded score. Calibrate against the reference solution's measured geomean + # after a trial. + speedup_target: 6.0 + worker_timeout_seconds: 3600 + base_seed: 20260701 + # Agent (iterative) role runs a fast subset; the final verifier runs them all. + agent_workload_count: 3 + expose_per_workload_metrics: false +submission: + kind: file + path: /app/solution.patch diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md new file mode 100644 index 000000000..4837359b4 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md @@ -0,0 +1,46 @@ +# Experimental Truncated-SVD Kernel-Optimization Images + +Two images, mirroring the duckdb-e2e split: a public **agent** image and a +private **judge** image. Build them before a local Harbor trial: + +```bash +bash 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh +``` + +Defaults: + +```text +AGENT_TAG=frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.1.0 +JUDGE_TAG=frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.1.0 +``` + +The agent image contains: + +```text +/app/tsvdlib # clean, git-tracked package (the agent edits this) +``` + +The judge image contains: + +```text +/opt/tsvdlib-clean/tsvdlib # pristine tree; the patch is applied to a copy +/opt/tsvd_ref/reftsvd.py # frozen naive baseline (speed denominator + oracle) +``` + +Both are based on `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel` (torch + triton). + +## Runtime requirements + +The judge times the patched truncated_svd on a **GPU visible to the judge +container** (single device; H100 reference, Triton paths also run on L40S/A100). +If the Harbor runtime cannot attach a GPU to the judge container, port the worker +step (`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md`. + +## Smoke test + +```bash +bash 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh +``` + +Import-only; verifies torch/triton and the baked packages are importable. It +does not exercise a GPU. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile new file mode 100644 index 000000000..e35281b99 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile @@ -0,0 +1,22 @@ +# Agent image for truncated_svd_gpu_kernel_optimization. +# Bundles a clean, git-tracked copy of the tsvdlib package at /app/tsvdlib +# so the agent can edit it and `make_submission.sh` can diff it. torch + triton +# come from the PyTorch base image. +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel + +ARG DEBIAN_FRONTEND=noninteractive +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash ca-certificates git ripgrep && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" + +WORKDIR /app +COPY tsvdlib /app/tsvdlib +RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ + git -C /app init -q && \ + git -C /app config user.email task@frontier-cs && \ + git -C /app config user.name frontier-cs && \ + git -C /app add -A -- tsvdlib .gitignore && \ + git -C /app -c commit.gpgsign=false commit -qm "pristine tsvdlib" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh new file mode 100755 index 000000000..035149ed2 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# Build the experimental agent + judge images for truncated_svd_gpu_kernel_optimization. +set -euo pipefail +HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) + +AGENT_TAG=${AGENT_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "Building agent image: $AGENT_TAG" +docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" + +echo "Building judge image: $JUDGE_TAG" +docker build -f "$HERE/docker/judge/Dockerfile" -t "$JUDGE_TAG" "$HERE" + +echo "Built:" +echo " $AGENT_TAG" +echo " $JUDGE_TAG" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile new file mode 100644 index 000000000..8790213ec --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile @@ -0,0 +1,18 @@ +# Judge image for truncated_svd_gpu_kernel_optimization. +# Bakes the pristine package tree the agent patch is applied to +# (/opt/tsvdlib-clean/tsvdlib) and the frozen naive baseline the judge times +# against (/opt/tsvd_ref/reftsvd.py). torch + triton come from the base. +FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel + +ARG DEBIAN_FRONTEND=noninteractive +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + bash ca-certificates git && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" + +COPY tsvdlib /opt/tsvdlib-clean/tsvdlib +COPY judge/reftsvd.py /opt/tsvd_ref/reftsvd.py + +WORKDIR /judge diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh new file mode 100755 index 000000000..cf75d4bb5 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# Import-only smoke test for the built images (no GPU required). +set -euo pipefail + +AGENT_TAG=${AGENT_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "== agent image ==" +docker run --rm -w /app "$AGENT_TAG" python3 -c \ + "import torch, triton, tsvdlib; print('agent ok: tsvdlib', tsvdlib.__version__)" + +echo "== judge image ==" +docker run --rm "$JUDGE_TAG" python3 -c \ + "import sys, torch, triton; \ +sys.path.insert(0, '/opt/tsvd_ref'); import reftsvd; \ +sys.path.insert(0, '/opt/tsvdlib-clean'); import tsvdlib; \ +print('judge ok: reftsvd + tsvdlib import')" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluate.sh b/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluate.sh new file mode 100755 index 000000000..f71921fca --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluate.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +# Local CLI evaluation for truncated_svd_gpu_kernel_optimization. +# +# A full run needs a GPU plus the baked judge sources at /opt (the pristine +# /opt/tsvdlib-clean tree and the frozen /opt/tsvd_ref baseline; see the judge +# image). Without them the evaluator still validates the patch policy and +# returns a smoke score, which is what repository CI exercises (the reference +# patch passes the policy). Pass a patch path to score it directly. +set -euo pipefail +SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd) +SOLUTION="${1:-$SCRIPT_DIR/reference.patch}" +exec python3 "$SCRIPT_DIR/evaluator.py" "$SOLUTION" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py new file mode 100644 index 000000000..98b656031 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py @@ -0,0 +1,514 @@ +"""Evaluator for the truncated-SVD GPU kernel-optimization task. + +The agent submits a unified diff over the ``tsvdlib`` Python package. The +judge: + +1. statically validates the patch against an allowlist (only ``tsvdlib/**`` + may change; no ``flashlib``/cuML/network/env access; bounded size); +2. applies it to a pristine copy of ``tsvdlib`` baked into the judge image + at ``/opt/tsvdlib-clean``; +3. runs an isolated worker subprocess that, for each hidden ``(N, D, k)`` + workload, times the patched ``tsvdlib.truncated_svd`` against a *frozen* + naive baseline (``/opt/tsvd_ref/reftsvd.py``) on identical seeded data and + measures each result's orthonormality error and captured energy; +4. gates on factor quality (the agent's components must stay orthonormal and + must not lose captured energy beyond a small tolerance vs the baseline) and + scores by the geometric-mean speedup. + +When the judge source tree is not present (e.g. local static checks on a box +without a GPU), the evaluator still validates the patch policy and returns a +smoke pass, so ``python3 evaluator.py reference.patch`` works anywhere. +""" +from __future__ import annotations + +import fnmatch +import hashlib +import json +import math +import os +import re +import shutil +import subprocess +import sys +import tempfile +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +MAX_PATCH_BYTES = 1_000_000 +MAX_CHANGED_FILES = 40 +TASK_CONFIG_PATH = Path("/judge/task_config.json") + +DEFAULT_CLEAN_SOURCE = Path("/opt/tsvdlib-clean") # pristine package (patched here) +DEFAULT_BASELINE_SOURCE = Path("/opt/tsvd_ref") # frozen naive baseline (reftsvd.py) + + +def _load_task_config() -> dict[str, Any]: + try: + payload = json.loads(TASK_CONFIG_PATH.read_text(encoding="utf-8")) + except Exception: + return {} + return payload if isinstance(payload, dict) else {} + + +TASK_CONFIG = _load_task_config() +EVALUATION_CONFIG = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} + + +def _cfg(name: str, default): + return EVALUATION_CONFIG.get(name, default) + + +def _cfg_int(name: str, default: int) -> int: + try: + return int(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _cfg_float(name: str, default: float) -> float: + try: + return float(EVALUATION_CONFIG.get(name, default)) + except Exception: + return default + + +def _cfg_bool(name: str, default: bool) -> bool: + raw = EVALUATION_CONFIG.get(name, default) + if isinstance(raw, bool): + return raw + if isinstance(raw, str): + return raw.strip().lower() in {"1", "true", "yes", "on"} + return bool(raw) + + +WARMUP_ITERS = _cfg_int("warmup_iters", 3) +TIMED_ITERS = _cfg_int("timed_iters", 7) +CAPTURED_TOL = _cfg_float("captured_tolerance", 0.02) # agent captured energy >= (1-tol)*ref +ORTHO_TOL = _cfg_float("ortho_tolerance", 0.02) # max |C C^T - I| must stay <= tol +SPEEDUP_TARGET = _cfg_float("speedup_target", 6.0) # geomean speedup mapped to full score +WORKER_TIMEOUT_SECONDS = _cfg_int("worker_timeout_seconds", 3600) +BASE_SEED = _cfg_int("base_seed", 20260701) + +# Editable surface: only the shipped package. +ALLOWED_PATTERNS = ( + "tsvdlib/**", +) +# Never touchable, even though they are not shipped in the agent workspace. +DENIED_PATTERNS = ( + "evaluator.py", + "reference.patch", + "reftsvd.py", + "**/reftsvd.py", + "**/conftest.py", + "**/test_*.py", + "pyproject.toml", + "setup.py", + "setup.cfg", +) +# Forbidden substrings in added lines (defense in depth). The agent must write +# its own kernels, not call an external optimized library, read the +# environment, spawn processes, or touch the network. +FORBIDDEN_TOKENS = ( + "flashlib", + "cuml", + "cudf", + "cupy", + "faiss", + "sklearn", + "scikit", + "os.environ", + "getenv", + "putenv", + "setenv", + "subprocess", + "socket", + "urllib", + "requests", + "importlib.import_module", + "__import__", +) + + +@dataclass(frozen=True) +class PatchFile: + old_path: str + new_path: str + added_lines: tuple[str, ...] + removed_lines: tuple[str, ...] + + @property + def path(self) -> str: + return self.new_path if self.new_path != "/dev/null" else self.old_path + + +def _match(path: str, patterns: tuple[str, ...]) -> bool: + return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + + +def _invalid(message: str, metrics: dict[str, Any] | None = None): + payload = metrics or {} + payload.setdefault("valid_patch", 0) + return 0.0, 0.0, message, payload + + +def _parse_patch(text: str) -> list[PatchFile]: + files: list[PatchFile] = [] + current_old = "" + current_new = "" + added: list[str] = [] + removed: list[str] = [] + in_file = False + + for line in text.splitlines(): + if line.startswith("diff --git "): + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + in_file = True + current_old = current_new = "" + added = [] + removed = [] + continue + if not in_file: + continue + if line.startswith("--- "): + current_old = line[4:].strip() + if current_old.startswith("a/"): + current_old = current_old[2:] + continue + if line.startswith("+++ "): + current_new = line[4:].strip() + if current_new.startswith("b/"): + current_new = current_new[2:] + continue + if line.startswith("+") and not line.startswith("+++ "): + added.append(line[1:]) + continue + if line.startswith("-") and not line.startswith("--- "): + removed.append(line[1:]) + + if in_file: + files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + return files + + +def _validate_path(path: str) -> tuple[bool, str]: + if not path or path == "/dev/null": + return True, "" + if path.startswith("/") or ".." in Path(path).parts: + return False, f"unsafe patch path: {path}" + if _match(path, DENIED_PATTERNS): + return False, f"changed file is outside task boundary: {path}" + if not _match(path, ALLOWED_PATTERNS): + return False, f"changed file is not allowlisted (only tsvdlib/** may change): {path}" + if not path.endswith(".py"): + return False, f"only Python files may change: {path}" + return True, "" + + +def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: + if not patch_path.exists(): + return False, "solution patch does not exist", {} + size = patch_path.stat().st_size + if size > MAX_PATCH_BYTES: + return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} + text = patch_path.read_text(encoding="utf-8", errors="replace") + patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() + files = _parse_patch(text) + metrics: dict[str, Any] = { + "patch_bytes": size, + "patch_sha256": patch_hash, + "changed_files": len(files), + } + if len(files) > MAX_CHANGED_FILES: + return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics + + for patch_file in files: + path = patch_file.path + if patch_file.new_path == "/dev/null": + return False, f"deleting files is outside task boundary: {patch_file.old_path}", metrics + if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: + ok, err = _validate_path(patch_file.old_path) + if not ok: + return False, f"rename/copy source is outside task boundary: {err}", metrics + ok, err = _validate_path(path) + if not ok: + return False, err, metrics + added_text = "\n".join(patch_file.added_lines) + low = added_text.lower() + for token in FORBIDDEN_TOKENS: + if token in low: + return False, f"{path}: forbidden token in added code ({token})", metrics + + metrics["valid_patch"] = 1 + return True, "patch accepted by static policy", metrics + + +def clean_env(tmp_root: Path) -> dict[str, str]: + home = tmp_root / "home" + tmp = tmp_root / "tmp" + home.mkdir(parents=True, exist_ok=True) + tmp.mkdir(parents=True, exist_ok=True) + env = { + "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin"), + "HOME": str(home), + "TMPDIR": str(tmp), + "LC_ALL": "C", + "LANG": "C", + } + for key in ("CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES", "LD_LIBRARY_PATH", + "TRITON_CACHE_DIR", "CUDA_HOME"): + if key in os.environ: + env[key] = os.environ[key] + env.setdefault("TRITON_CACHE_DIR", str(tmp_root / "triton_cache")) + return env + + +def run_checked(cmd: list[str], *, cwd: Path, env: dict[str, str], timeout: int) -> subprocess.CompletedProcess: + return subprocess.run( + cmd, cwd=str(cwd), env=env, timeout=timeout, + stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, + ) + + +def sanitize_error_text(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"/opt/[A-Za-z0-9_./-]+", "", text) + text = re.sub(r"N=\d+", "N=", text) + text = re.sub(r"seed[=:]?\s*\d+", "seed=", text, flags=re.IGNORECASE) + return text[-600:] + + +def geometric_mean(values: list[float]) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) + + +def score_from_speedup(gm_speedup: float) -> float: + if gm_speedup <= 0: + return 0.0 + raw = 100.0 * math.log(gm_speedup) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + return max(0.0, min(100.0, raw)) + + +def is_final_submission_role() -> bool: + return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" + + +# Hidden workloads. (N, D, k). Agent role runs a fast subset for iterative +# feedback; the final verifier runs the full set. Seeds are derived from +# BASE_SEED so data is deterministic but not guessable from the statement. +_HIDDEN_WORKLOADS = ( + {"id": "w0", "N": 500_000, "D": 128, "k": 16}, + {"id": "w1", "N": 1_000_000, "D": 256, "k": 32}, + {"id": "w2", "N": 200_000, "D": 512, "k": 16}, # wide-feature regime + {"id": "w3", "N": 2_000_000, "D": 64, "k": 8}, # tall / small-D regime + {"id": "w4", "N": 300_000, "D": 256, "k": 16}, + {"id": "w5", "N": 800_000, "D": 128, "k": 32}, +) + + +def _workloads(*, final_role: bool) -> list[dict[str, Any]]: + configured = _cfg("workloads", None) + workloads = list(configured) if isinstance(configured, list) and configured else list(_HIDDEN_WORKLOADS) + if not final_role: + n_agent = _cfg_int("agent_workload_count", 3) + workloads = workloads[:n_agent] + for i, w in enumerate(workloads): + w.setdefault("seed", BASE_SEED + 1000 * (i + 1)) + return workloads + + +# The worker runs the untrusted patched package in its own process. It imports +# the frozen baseline (reftsvd) and the patched tsvdlib, times both on +# identical seeded data, and reports per-workload timings + quality metrics +# (orthonormality error, captured energy) as JSON. +_WORKER_SOURCE = r''' +import argparse, json, sys, time, traceback +def _load(baseline_dir, patched_dir): + sys.path.insert(0, patched_dir); sys.path.insert(0, baseline_dir) + import reftsvd, tsvdlib + return reftsvd, tsvdlib +def gen(N, D, seed, device): + import torch + g = torch.Generator(device=device).manual_seed(int(seed)) + return torch.randn(N, D, generator=g, device=device, dtype=torch.float32) +def ortho_err(comps): + import torch + c = comps.to(torch.float32); k = c.shape[0] + return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) +def captured(x, comps): + import torch + c = comps.to(torch.float32); total = 0.0 + for i in range(0, x.shape[0], 16384): + p = x[i:i+16384] @ c.t(); total += float((p * p).sum().item()) + return total +def bench(fn, warmup, iters): + import torch + for _ in range(warmup): fn(); torch.cuda.synchronize() + ts = [] + for _ in range(iters): + torch.cuda.synchronize(); t0 = time.perf_counter(); fn(); torch.cuda.synchronize() + ts.append(time.perf_counter() - t0) + ts.sort(); return ts[len(ts) // 2] * 1000.0 +def check(out, D, k): + import torch + if not (isinstance(out, tuple) and len(out) == 2): raise ValueError("truncated_svd must return (singular_values, components)") + s, comps = out + if tuple(s.shape) != (k,) or tuple(comps.shape) != (k, D): raise ValueError("wrong output shape") + if not (torch.isfinite(s).all() and torch.isfinite(comps).all()): raise ValueError("non-finite output") +def main(): + ap = argparse.ArgumentParser() + for n in ("--baseline-dir","--patched-dir","--workloads","--out"): ap.add_argument(n, required=True) + ap.add_argument("--warmup", type=int, default=3); ap.add_argument("--iters", type=int, default=7) + a = ap.parse_args() + import torch + if not torch.cuda.is_available(): + json.dump({"ok": False, "error": "cuda_unavailable"}, open(a.out, "w")); return + reftsvd, tsvdlib = _load(a.baseline_dir, a.patched_dir) + rows = [] + for w in json.loads(a.workloads): + N, D, k, seed = w["N"], w["D"], w["k"], w["seed"] + x = gen(N, D, seed, "cuda") + ref_fn = lambda: reftsvd.truncated_svd(x, k) + agent_fn = lambda: tsvdlib.truncated_svd(x, k) + ref_out = ref_fn(); torch.cuda.synchronize() + ref_cap = captured(x, ref_out[1]) + try: + agent_out = agent_fn(); torch.cuda.synchronize(); check(agent_out, D, k) + oerr = ortho_err(agent_out[1]); acap = captured(x, agent_out[1]) + except Exception as e: + rows.append({"id": w["id"], "ok": False, "error": type(e).__name__ + ": " + str(e)[:200]}) + del x; torch.cuda.empty_cache(); continue + ref_ms = bench(ref_fn, a.warmup, a.iters); agent_ms = bench(agent_fn, a.warmup, a.iters) + rows.append({"id": w["id"], "ok": True, "ref_ms": ref_ms, "agent_ms": agent_ms, + "ortho_err": oerr, "ref_captured": ref_cap, "agent_captured": acap}) + del x; torch.cuda.empty_cache() + json.dump({"ok": True, "rows": rows}, open(a.out, "w")) +if __name__ == "__main__": + try: main() + except Exception: traceback.print_exc(); sys.exit(3) +''' + + +def _run_worker(patched_parent: Path, tmp_root: Path, env: dict[str, str], + workloads: list[dict[str, Any]]) -> dict[str, Any]: + worker_path = tmp_root / "tsvd_worker.py" + worker_path.write_text(_WORKER_SOURCE, encoding="utf-8") + out_path = tmp_root / "worker_out.json" + cmd = [ + sys.executable, str(worker_path), + "--baseline-dir", str(DEFAULT_BASELINE_SOURCE), + "--patched-dir", str(patched_parent), + "--workloads", json.dumps(workloads), + "--out", str(out_path), + "--warmup", str(WARMUP_ITERS), + "--iters", str(TIMED_ITERS), + ] + run_checked(cmd, cwd=tmp_root, env=env, timeout=WORKER_TIMEOUT_SECONDS) + return json.loads(out_path.read_text(encoding="utf-8")) + + +def full_evaluation(patch_path: Path, metrics: dict[str, Any]): + final_role = is_final_submission_role() + metrics["submission_role"] = "final" if final_role else "agent" + workloads = _workloads(final_role=final_role) + + if not DEFAULT_CLEAN_SOURCE.exists() or not DEFAULT_BASELINE_SOURCE.exists(): + metrics["full_benchmark"] = 0 + return (1.0, 1.0, + "patch policy smoke passed; tsvdlib judge source is not configured in this environment", + metrics) + + with tempfile.TemporaryDirectory(prefix="tsvd_kernel_opt_") as tmp: + tmp_root = Path(tmp) + env = clean_env(tmp_root) + patched_parent = tmp_root / "patched" + shutil.copytree(DEFAULT_CLEAN_SOURCE, patched_parent) + + if int(metrics.get("changed_files", 0)) > 0: + run_checked(["git", "apply", "--check", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + run_checked(["git", "apply", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + metrics["applied_patch"] = 1 + else: + metrics["used_empty_patch"] = 1 + + result = _run_worker(patched_parent, tmp_root, env, workloads) + if not result.get("ok"): + return _invalid(f"benchmark worker failed ({result.get('error', 'unknown')})", metrics) + + rows = result.get("rows", []) + speedups: list[float] = [] + per_workload: dict[str, Any] = {} + for row in rows: + if not row.get("ok"): + metrics["failed_workload_error"] = sanitize_error_text(str(row.get("error", ""))) + return _invalid("submitted truncated_svd crashed or returned an invalid result on a hidden workload", metrics) + if row["ortho_err"] > ORTHO_TOL: + metrics["ortho_regression"] = {"ortho_err": row["ortho_err"], "tol": ORTHO_TOL} + return _invalid("submitted truncated_svd components are not orthonormal", metrics) + if row["agent_captured"] < (1.0 - CAPTURED_TOL) * row["ref_captured"] - 1e-6: + metrics["captured_regression"] = { + "ref": row["ref_captured"], "agent": row["agent_captured"], "tol": CAPTURED_TOL, + } + return _invalid("submitted truncated_svd captured energy regressed beyond tolerance", metrics) + speedup = row["ref_ms"] / row["agent_ms"] if row["agent_ms"] > 0 else 0.01 + speedups.append(max(speedup, 0.01)) + per_workload[row["id"]] = { + "ref_ms": row["ref_ms"], "agent_ms": row["agent_ms"], "speedup": speedup, + } + + if not speedups: + return _invalid("no workloads were evaluated", metrics) + + gm = geometric_mean(speedups) + bounded = score_from_speedup(gm) + metrics.update({ + "full_benchmark": 1, + "workload_count": len(speedups), + "geomean_speedup": gm, + }) + if _cfg_bool("expose_per_workload_metrics", False): + metrics["per_workload"] = per_workload + return (bounded, bounded, f"truncated_svd geomean speedup {gm:.3f}x over the naive full-SVD baseline", metrics) + + +def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: + patch_path = Path(solution_path) + ok, message, metrics = validate_patch(patch_path) + if not ok: + return _invalid(message, metrics) + try: + return full_evaluation(patch_path, metrics) + except subprocess.TimeoutExpired: + return _invalid("benchmark worker timed out", metrics) + except subprocess.CalledProcessError as exc: + stderr = sanitize_error_text(exc.stderr or "") + cmd0 = Path(str(exc.cmd[0])).name if isinstance(exc.cmd, list) and exc.cmd else "subprocess" + metrics["failed_command"] = "git apply" if cmd0 == "git" else cmd0 + metrics["stderr_tail"] = stderr + return _invalid("patch apply or benchmark command failed", metrics) + except Exception as exc: + metrics["error_type"] = type(exc).__name__ + metrics["error_detail"] = sanitize_error_text(str(exc)) + return _invalid("evaluation failed", metrics) + + +def main(argv: list[str]) -> int: + if len(argv) != 2: + print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) + return 2 + score, score_unbounded, message, metrics = evaluate(argv[1]) + print(json.dumps({ + "score": score, + "score_unbounded": score_unbounded, + "message": message, + "metrics": metrics, + }, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv)) diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md new file mode 100644 index 000000000..895372b63 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md @@ -0,0 +1,35 @@ +# Truncated SVD kernel optimization — submission workflow + +You are optimizing the `tsvdlib` package at `/app/tsvdlib`. Edit the package +(rewrite the internals of `truncated_svd`, add Triton kernel modules under +`tsvdlib/`), then submit a patch. + +## Iterate locally + +Run the public self-test to check correctness and get a rough speed signal on +the two public shapes (needs a GPU in the agent container): + +```bash +bash /app/public_test.sh +``` + +## Submit + +```bash +bash /app/make_submission.sh # writes /app/solution.patch (tsvdlib diff) +bash /app/submit.sh # enqueues it for the black-box judge +``` + +Submissions are asynchronous. Submit early and keep improving; use +`bash /app/submissions.sh` and `bash /app/wait_submission.sh ` to inspect +results. + +## Rules + +- Only files under `tsvdlib/` may change. +- Do not import external optimized libraries (write the kernels yourself), and + do not access the environment, spawn processes, or use the network. +- Keep the public `truncated_svd(...)` signature and return contract unchanged. +- Factor quality is gated (orthonormal components and captured energy vs the + naive baseline); do not sacrifice correctness for speed beyond the allowed + tolerance. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/make_submission.sh b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/make_submission.sh new file mode 100755 index 000000000..f6480f086 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/make_submission.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash +# Package the current tsvdlib edits into a unified diff for submission. +set -euo pipefail + +APP_DIR="${APP_DIR:-/app}" +OUT="${1:-/app/solution.patch}" + +if [[ ! -d "$APP_DIR/tsvdlib" ]]; then + echo "tsvdlib package not found at $APP_DIR/tsvdlib" >&2 + exit 2 +fi +if [[ ! -d "$APP_DIR/.git" ]]; then + echo "git repo not found at $APP_DIR (expected in the agent image)" >&2 + exit 2 +fi + +# Stage and diff only the package, so submission helper scripts under /app are +# never included. New kernel files under tsvdlib/ are captured via `git add`. +git -C "$APP_DIR" add -A -- tsvdlib +git -C "$APP_DIR" diff --cached -- tsvdlib > "$OUT" +git -C "$APP_DIR" reset -q + +bytes=$(wc -c < "$OUT" | tr -d ' ') +echo "Wrote $OUT ($bytes bytes)" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py new file mode 100644 index 000000000..3ea63d120 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py @@ -0,0 +1,91 @@ +"""Public self-test for the truncated-SVD kernel-optimization task. + +Runs the (patched) tsvdlib on the two public shapes, checks the factor quality +against a local naive full-SVD recomputation (orthonormality of the returned +components and the captured-energy ratio), and prints a rough speedup. This is a +convenience for local iteration only -- the graded workloads and thresholds are +hidden and differ from these shapes. +""" +from __future__ import annotations + +import sys +import time + +PUBLIC_WORKLOADS = [ + {"N": 200_000, "D": 128, "k": 16, "seed": 1}, + {"N": 500_000, "D": 64, "k": 8, "seed": 2}, +] + + +def naive_truncated_svd(x, k): + import torch + U, S, Vh = torch.linalg.svd(x, full_matrices=False) + return S[:k].contiguous(), Vh[:k].contiguous() + + +def ortho_err(comps): + import torch + c = comps.to(torch.float32) + k = c.shape[0] + return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) + + +def captured(x, comps): + import torch + c = comps.to(torch.float32) + total = 0.0 + for i in range(0, x.shape[0], 16384): + p = x[i:i + 16384] @ c.t() + total += float((p * p).sum().item()) + return total + + +def bench(fn, warmup=2, iters=5): + import torch + for _ in range(warmup): + fn(); torch.cuda.synchronize() + ts = [] + for _ in range(iters): + torch.cuda.synchronize(); t0 = time.perf_counter() + fn(); torch.cuda.synchronize() + ts.append(time.perf_counter() - t0) + ts.sort() + return ts[len(ts) // 2] * 1000.0 + + +def main() -> int: + try: + import torch + except Exception as e: # pragma: no cover + print(f"torch import failed: {e}") + return 1 + if not torch.cuda.is_available(): + print("no CUDA device available; run this in a GPU-enabled container.") + return 1 + import tsvdlib + + for w in PUBLIC_WORKLOADS: + N, D, k, seed = w["N"], w["D"], w["k"], w["seed"] + g = torch.Generator(device="cuda").manual_seed(seed) + x = torch.randn(N, D, generator=g, device="cuda", dtype=torch.float32) + + _, ref_c = naive_truncated_svd(x, k) + out = tsvdlib.truncated_svd(x, k) + agent_c = out[1] + rc, ac = captured(x, ref_c), captured(x, agent_c) + ratio = ac / rc if rc > 0 else float("inf") + oerr = ortho_err(agent_c) + + ref_ms = bench(lambda: naive_truncated_svd(x, k)) + agent_ms = bench(lambda: tsvdlib.truncated_svd(x, k)) + ok = "OK" if (ratio >= 0.98 and oerr <= 0.02) else "QUALITY REGRESSION" + print(f"(N={N}, D={D}, k={k}) captured_ratio={ratio:.4f} ortho_err={oerr:.2e} [{ok}] " + f"baseline={ref_ms:.2f}ms yours={agent_ms:.2f}ms " + f"speedup={ref_ms / agent_ms:.2f}x") + del x + torch.cuda.empty_cache() + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.sh b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.sh new file mode 100755 index 000000000..98a6a036e --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash +# Public self-test: run the patched tsvdlib on the public shapes and print a +# correctness + rough speedup summary. Requires a GPU in the agent container. +set -euo pipefail +APP_DIR="${APP_DIR:-/app}" +cd "$APP_DIR" +exec python3 "$APP_DIR/public_test.py" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/solution.patch b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/solution.patch new file mode 100644 index 000000000..e69de29bb diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/judge/reftsvd.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/judge/reftsvd.py new file mode 100644 index 000000000..b114cd584 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/judge/reftsvd.py @@ -0,0 +1,16 @@ +"""Frozen naive truncated-SVD baseline used by the judge as the speed denominator. + +This is a standalone (non-package) copy of the ``tsvdlib.truncated_svd`` +implementation the agent starts from. It is imported under its own module name +so the judge worker can load the frozen baseline and the patched ``tsvdlib`` +package in the same process. Keep this behaviourally identical to the shipped +``tsvdlib/tsvd.py`` (full SVD, then slice the top-k factors). +""" +from __future__ import annotations + +import torch + + +def truncated_svd(x, n_components): + U, S, Vh = torch.linalg.svd(x, full_matrices=False) # full SVD -- naive/expensive + return S[:n_components].contiguous(), Vh[:n_components].contiguous() diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/readme b/2.0/problems/truncated_svd_gpu_kernel_optimization/readme new file mode 100644 index 000000000..b58cb001a --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/readme @@ -0,0 +1,133 @@ +# GPU Truncated SVD Kernel Optimization + +## Problem + +You are given a small GPU truncated-SVD library, `tsvdlib`, in the Harbor +workspace at `/app/tsvdlib`. Its public entry point is: + +```python +tsvdlib.truncated_svd(x, n_components) -> (singular_values, components) +``` + +`x` is an `(N, D)` float32 CUDA tensor (the raw matrix -- it is **not** centered; +this is a truncated SVD, not PCA) and `n_components` is the number `k` of leading +singular triples to return. The function returns `singular_values`, a `(k,)` +float32 tensor of the top-k singular values in descending order, and +`components`, a `(k, D)` float32 tensor whose rows are the top-k right singular +vectors (an orthonormal set). The shipped implementation is correct but +deliberately naive: it computes the **full** SVD of the `(N, D)` matrix with +`torch.linalg.svd` and slices off the leading `k` factors, which is `O(N D^2)` +with a large constant and produces all `min(N, D)` singular triples even though +only the top `k` are ever used. + +Your goal is to make `tsvdlib.truncated_svd` **as fast as possible** on the GPU +while producing a truncated decomposition of the same quality. You may add +modules and Triton kernels inside the `tsvdlib` package and rewrite the internals +freely (for example a Gram-matrix `X^T X` followed by a top-k eigendecomposition, +fused kernels, reduced memory traffic, and so on). The public function signature +and return contract above must not change. + +## Workload + +The judge evaluates a family of held-out dense matrices that vary `(N, D, k)`, +spanning tall matrices (large `N`, small `D`), wide-feature matrices (large `D`), +and a range of small `k`. All workloads use float32 data drawn from a fixed +seeded generator, so each result is a deterministic function of the inputs. + +Two representative *public* shapes you can use for local development (the graded +shapes are different and hidden): + +```text +(N=200000, D=128, k=16) +(N=500000, D=64, k=8) +``` + +Treat the workload as a general dense truncated SVD, not as shapes to +special-case. Improvements should be general kernel/throughput improvements, not +lookups keyed on specific `(N, D, k)` values. + +## Submission + +The submitted artifact is a patch file over the `tsvdlib` package: + +```text +/app/solution.patch +``` + +After editing `/app/tsvdlib`, generate and submit a patch: + +```bash +bash /app/make_submission.sh +bash /app/submit.sh +``` + +Submissions are asynchronous; submit early and keep iterating. The judge applies +your patch to a clean copy of `tsvdlib`, imports the patched package, and times +it against the original naive implementation on the same hardware and the same +seeded data. + +## Correctness + +Correctness is a gate, and it is checked in a way that is invariant to the sign +and rotation ambiguities of the SVD (you are not required to match the baseline's +vectors elementwise). For each workload the judge checks two things about your +`components` `V` (a `(k, D)` tensor): + +- **Orthonormality.** The rows must form an orthonormal set: the maximum + absolute entry of `V V^T - I_k` must stay within a small tolerance. +- **Captured energy.** The energy your subspace captures, + `||X V^T||_F^2 = sum over points of ||x_n V^T||^2`, must be at least + `(1 - tol)` times the energy captured by the naive baseline's top-k subspace on + the same data. + +Crashes, non-finite outputs, wrong shapes/dtypes, timeouts, non-orthonormal +components, and captured energy that regresses beyond the tolerance are all +penalized before speed is considered. You may trade a little numerical precision +(e.g. lower-precision accumulation) for speed as long as you stay within the +quality tolerance. + +## Scoring + +Valid submissions are scored by speedup relative to the naive baseline on the +same hardware and workloads. For each workload: + +```text +speedup = baseline_time / your_time +``` + +The objective is the geometric mean of per-workload speedups, so broad speedups +across the workload family are preferred over one large outlier. A result no +faster than the baseline earns 0; regressions earn 0. The raw geometric-mean +speedup is reported in the evaluator metrics. + +## Patch Policy + +The evaluator validates the patch before running it. Only files under the +package may change: + +```text +tsvdlib/** +``` + +New Python modules inside `tsvdlib/` (for example Triton kernel files) are +allowed. Patches may not: + +- modify anything outside `tsvdlib/`; +- import or call an external optimized ML/kernel library (the point is to write + the kernels yourself); +- read or write environment variables, spawn processes, or access the network; +- special-case the hidden workload shapes. + +The patch must be reasonably sized and apply cleanly to the clean `tsvdlib` tree. + +## Resource Budget + +```text +GPU: single device (H100 reference; Triton paths also run on L40S / A100) +vCPUs: 8 +memory: 32 GiB +storage: 32 GiB +``` + +The judge runs the timing worker in a subprocess under a clean, minimal +environment with a fixed warmup/measurement schedule and a per-run timeout. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch b/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch new file mode 100644 index 000000000..0d87e3901 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch @@ -0,0 +1,85 @@ +diff --git a/tsvdlib/_gram_tsvd.py b/tsvdlib/_gram_tsvd.py +new file mode 100644 +index 0000000..fe425e4 +--- /dev/null ++++ b/tsvdlib/_gram_tsvd.py +@@ -0,0 +1,24 @@ ++"""Gram-matrix truncated SVD: X^T X + top-k eigendecomposition. ++ ++Avoids the full SVD of the ``(N, D)`` matrix. The right singular vectors of ++``X`` are the eigenvectors of the Gram matrix ``G = X^T X`` (a single ``(D, D)`` ++GEMM), and the singular values are the square roots of the corresponding ++eigenvalues. We form ``G`` once, eigendecompose the small ``(D, D)`` matrix with ++:func:`torch.linalg.eigh`, and keep only the top ``k`` triples. This is ++``O(N D^2)`` for the GEMM plus ``O(D^3)`` for the eigensolve, but with a far ++smaller constant than a full SVD and no ``(N, min(N, D))`` factor materialised. ++""" ++from __future__ import annotations ++ ++import torch ++ ++ ++def truncated_svd(x, n_components): ++ N, D = x.shape ++ G = x.t() @ x # (D, D) Gram matrix -- one GEMM ++ evals, evecs = torch.linalg.eigh(G) # ascending ++ k = int(n_components) ++ top = torch.argsort(evals, descending=True)[:k] ++ s = evals.index_select(0, top).clamp_min(0).sqrt() # (k,) singular values ++ comps = evecs.index_select(1, top).t().contiguous() # (k, D) right singular vectors ++ return s, comps +diff --git a/tsvdlib/tsvd.py b/tsvdlib/tsvd.py +index f1b4fe7..6216694 100644 +--- a/tsvdlib/tsvd.py ++++ b/tsvdlib/tsvd.py +@@ -1,36 +1,22 @@ +-"""Truncated SVD of a dense matrix -- the reference you must optimise. ++"""Truncated SVD of a dense matrix -- Gram-matrix accelerated. + +-This implementation is intentionally naive. It computes the FULL singular value +-decomposition of the ``(N, D)`` matrix with :func:`torch.linalg.svd` and then +-slices off the top ``n_components`` factors. Computing the full SVD is +-``O(N D^2)`` with a large constant and produces all ``min(N, D)`` singular +-triples even though only the leading ``k`` are ever used. It is correct and +-deterministic, but it does far more work and moves far more memory than a +-truncated decomposition needs. ++The naive baseline computed the FULL SVD of the ``(N, D)`` matrix and sliced off ++the top-k factors. Here it is replaced by a Gram-matrix decomposition ++(:func:`tsvdlib._gram_tsvd.truncated_svd`): the right singular vectors of ``X`` ++are the eigenvectors of ``G = X^T X`` (one ``(D, D)`` GEMM) and the singular ++values are the square roots of the leading eigenvalues, so no ++``(N, min(N, D))`` factor is ever materialised. + +-Contract (do NOT change): ++Public contract is unchanged: + + truncated_svd(x, n_components) -> (singular_values, components) + +- x : (N, D) float32 CUDA tensor. NOT centered -- this is a +- truncated SVD of the raw matrix, not PCA. +- n_components (k) : int, number of leading singular triples to return. +- +- singular_values : (k,) float32, the top-k singular values in DESCENDING +- order. +- components : (k, D) float32, the top-k right singular vectors as +- rows. The rows are orthonormal. +- +-You may add modules/kernels inside the ``tsvdlib`` package and rewrite the body +-of :func:`truncated_svd` freely (e.g. a Gram-matrix + top-k eigendecomposition, +-fused kernels, better memory traffic), as long as the public contract above is +-preserved. ++ x : (N, D) float32 CUDA tensor (NOT centered). ++ n_components (k): int, number of leading singular triples to return. ++ singular_values : (k,) float32 top-k singular values, descending. ++ components : (k, D) float32 top-k right singular vectors (orthonormal ++ rows). + """ + from __future__ import annotations + +-import torch +- +- +-def truncated_svd(x, n_components): +- U, S, Vh = torch.linalg.svd(x, full_matrices=False) # full SVD -- naive/expensive +- return S[:n_components].contiguous(), Vh[:n_components].contiguous() ++from tsvdlib._gram_tsvd import truncated_svd diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/__init__.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/__init__.py new file mode 100644 index 000000000..759e8c233 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/__init__.py @@ -0,0 +1,16 @@ +"""tsvdlib -- a tiny GPU truncated-SVD library you are asked to make fast. + +The public entry point is :func:`truncated_svd`. The shipped implementation is +correct but deliberately unoptimised: it computes the FULL SVD of the ``(N, D)`` +matrix and slices off the top-k factors. Your task is to rewrite the internals +(Gram-matrix + top-k eigendecomposition, fused kernels, reduced memory traffic, +...) so that :func:`truncated_svd` runs as fast as possible while producing +factors of the same quality. The public function signature and return contract +must not change. +""" +from __future__ import annotations + +from tsvdlib.tsvd import truncated_svd + +__all__ = ["truncated_svd"] +__version__ = "0.1.0" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/tsvd.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/tsvd.py new file mode 100644 index 000000000..f1b4fe7db --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/tsvd.py @@ -0,0 +1,36 @@ +"""Truncated SVD of a dense matrix -- the reference you must optimise. + +This implementation is intentionally naive. It computes the FULL singular value +decomposition of the ``(N, D)`` matrix with :func:`torch.linalg.svd` and then +slices off the top ``n_components`` factors. Computing the full SVD is +``O(N D^2)`` with a large constant and produces all ``min(N, D)`` singular +triples even though only the leading ``k`` are ever used. It is correct and +deterministic, but it does far more work and moves far more memory than a +truncated decomposition needs. + +Contract (do NOT change): + + truncated_svd(x, n_components) -> (singular_values, components) + + x : (N, D) float32 CUDA tensor. NOT centered -- this is a + truncated SVD of the raw matrix, not PCA. + n_components (k) : int, number of leading singular triples to return. + + singular_values : (k,) float32, the top-k singular values in DESCENDING + order. + components : (k, D) float32, the top-k right singular vectors as + rows. The rows are orthonormal. + +You may add modules/kernels inside the ``tsvdlib`` package and rewrite the body +of :func:`truncated_svd` freely (e.g. a Gram-matrix + top-k eigendecomposition, +fused kernels, better memory traffic), as long as the public contract above is +preserved. +""" +from __future__ import annotations + +import torch + + +def truncated_svd(x, n_components): + U, S, Vh = torch.linalg.svd(x, full_matrices=False) # full SVD -- naive/expensive + return S[:n_components].contiguous(), Vh[:n_components].contiguous() From 839517c8da2063afa6111b4dc9288f8ec021dfcf Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 1 Jul 2026 07:37:50 +0000 Subject: [PATCH 05/34] feat(2.0): flashlib kernel-opt family -> Modal GPU judge + flashlib-best references + anti-hack Redesign of the KMeans/KNN/PCA/TruncatedSVD kernel-optimization tasks per 4 goals: 1. GPU eval via Modal (like the vllm task). New shared flash_gpu.py builds an ephemeral Modal GPU app, ships the frozen baseline + patched package as data, and returns per-workload speedups. Images are light (ubuntu+modal); torch/ triton run on the Modal image. evaluator.py is now generic + config-driven (only 3 constants differ per task); workloads/thresholds live in config.yaml (judge-only). Transient-Modal-error retries. 2. reference.patch now vendors flashlib's BEST triton path per primitive under /_kernels/ (kmeans assign/update; pca/svd linalg.eigh exact path; knn flash_knn_triton + gather), imports rewritten, "flashlib" scrubbed, thin adapter onto our contract. All import cleanly on CPU + apply + pass policy. 3. Agent GPU public test: public_test.py offloads to a Modal GPU via flash_gpu. 4. Anti-reward-hack: structural in the worker (timing refs captured before the submission is imported; fresh data every iteration; quality re-verified each iteration; judge computes all metrics; ephemeral container). Surgical patch policy (import/pattern regex, not bare words). readmes scrubbed of any reference-approach hints. Needs MODAL_TOKEN_ID/SECRET. speedup_target + torch/triton pins are placeholders for a GPU calibration trial. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../kmeans_gpu_kernel_optimization/DESIGN.md | 152 +- .../config.yaml | 68 +- .../docker/README.md | 40 +- .../docker/agent/Dockerfile | 24 +- .../docker/build_images.sh | 4 +- .../docker/judge/Dockerfile | 20 +- .../docker/smoke_images.sh | 14 +- .../evaluator.py | 537 ++-- .../flash_gpu.py | 279 ++ .../harbor/app/public_test.py | 136 +- .../kmeans_gpu_kernel_optimization/readme | 112 +- .../reference.patch | 2563 ++++++++++++++++- .../knn_gpu_kernel_optimization/DESIGN.md | 161 +- .../knn_gpu_kernel_optimization/config.yaml | 59 +- .../docker/README.md | 42 +- .../docker/agent/Dockerfile | 24 +- .../docker/build_images.sh | 4 +- .../docker/judge/Dockerfile | 20 +- .../docker/smoke_images.sh | 14 +- .../knn_gpu_kernel_optimization/evaluator.py | 496 ++-- .../knn_gpu_kernel_optimization/flash_gpu.py | 279 ++ .../harbor/app/README.md | 2 +- .../harbor/app/public_test.py | 119 +- .../knn_gpu_kernel_optimization/readme | 111 +- .../reference.patch | 2182 +++++++++++++- .../pca_gpu_kernel_optimization/DESIGN.md | 187 +- .../pca_gpu_kernel_optimization/config.yaml | 53 +- .../docker/README.md | 42 +- .../docker/agent/Dockerfile | 24 +- .../docker/build_images.sh | 4 +- .../docker/judge/Dockerfile | 20 +- .../docker/smoke_images.sh | 14 +- .../pca_gpu_kernel_optimization/evaluator.py | 510 ++-- .../pca_gpu_kernel_optimization/flash_gpu.py | 279 ++ .../harbor/app/public_test.py | 136 +- .../pca_gpu_kernel_optimization/readme | 136 +- .../reference.patch | 1033 ++++++- .../DESIGN.md | 183 +- .../config.yaml | 72 +- .../docker/README.md | 42 +- .../docker/agent/Dockerfile | 24 +- .../docker/build_images.sh | 4 +- .../docker/judge/Dockerfile | 20 +- .../docker/smoke_images.sh | 14 +- .../evaluator.py | 508 ++-- .../flash_gpu.py | 279 ++ .../harbor/app/README.md | 6 +- .../harbor/app/public_test.py | 130 +- .../readme | 131 +- .../reference.patch | 836 +++++- 50 files changed, 9447 insertions(+), 2702 deletions(-) create mode 100644 2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py create mode 100644 2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py create mode 100644 2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py create mode 100644 2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md index edc8bcf1f..6cf54440e 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md +++ b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md @@ -4,81 +4,77 @@ Operator-facing. Not copied into the agent workspace by the adapter. ## What this task measures -Can an agent turn a correct-but-naive GPU K-Means into a fast one? The agent is -given `kmeanslib` (a `torch.cdist`-per-iteration Lloyd loop) and must rewrite the -internals — ideally a fused Triton assignment kernel plus a better centroid -update — to maximize the geometric-mean speedup over the frozen baseline across a -family of hidden `(N, D, K)` workloads, subject to a clustering-quality gate. - -This is the first of a four-task **flashlib kernel-optimization family** -(KMeans, KNN, TruncatedSVD, PCA). All four share the KernelBench-style pattern: -ship a naive package, freeze a byte-for-byte baseline in the judge image, apply -the agent's patch to a clean copy, time patched-vs-baseline on identical seeded -data in an isolated worker, and gate on a primitive-appropriate quality metric -before scoring by geomean speedup. - -## Correctness gate: inertia, judge-computed - -The quality gate is the k-means objective — inertia = sum over points of the -squared L2 distance to the nearest **final** centroid. It is computed by the -judge worker from the *centroids the submission returns* (not from any -agent-provided number), on the same seeded `x` and `init_centroids` used for the -baseline. Properties: - -- permutation/label-convention independent (only the centroids matter); -- robust to floating-point drift across iterations; -- cheat-proof: you cannot return fast garbage — low inertia requires actually - clustering well. Trading some numeric precision for speed (bf16/tf32 - accumulation) is allowed within `inertia_tolerance` (default 2%). - -Determinism: `init_centroids` are always supplied and `tol = 0` (every iteration -runs), so the baseline result is a fixed function of `(x, init_centroids, -max_iters)`. - -## Anti-gaming - -flashlib (the library these primitives are distilled from) is public and -Apache-2.0. Mitigations: - -- The shipped package is a small, neutrally named library (`kmeanslib`), not - flashlib; the patch allowlist confines edits to `kmeanslib/**`. -- The patch policy forbids importing `flashlib`/cuML/cuPy/FAISS/scikit-learn and - bans env/subprocess/network access — the agent must write the kernels itself. -- Scored shapes are hidden and differ from the two public shapes; seeds derive - from `base_seed`. General kernels win; shape lookups do not. -- Honest framing: reproducing SOTA-class kernels *is* the bar. We block trivial - library reuse, not the underlying knowledge. - -## Execution model & the Modal question - -The evaluator runs an isolated worker subprocess (`_run_worker`) that imports the -frozen baseline (`/opt/kmeans_ref/refkmeans.py`) and the patched `kmeanslib` -(from the applied clean tree), times both, and reports JSON. This assumes the -**judge container has a GPU**. - -The repo's other GPU task (vllm) instead offloads to **Modal** because Harbor -judge containers may not be GPU-scheduled. `_run_worker` is the single swap -point: to go Modal, replace it with a Modal function that builds the patched -package, runs the same worker logic on a Modal GPU, and returns the JSON rows. -The rest of the evaluator (policy, gating, scoring) is unchanged. Decide -in-container-GPU vs Modal at first calibration trial. - -## Calibration TODO (needs a GPU trial) - -- Validate `reference.patch` runs and passes the inertia gate on all workloads; - fix any Triton API drift (`tl.dot(input_precision=...)`, `tl.argmin`) for the - pinned torch/triton in the image. -- Measure the reference solution's geomean speedup and set `speedup_target` so a - reference-level solution maps to ~full score. -- Confirm hidden shapes fit device memory (the naive baseline's `cdist` - materializes `N x K`; all shipped shapes are H100-safe). -- Sanity-check timing stability (median-of-7); bump `timed_iters` if noisy. - -## Files - -- `kmeanslib/` — pristine package baked into both images (agent edits it). -- `judge/refkmeans.py` — frozen baseline, baked to `/opt/kmeans_ref/`. -- `evaluator.py` — self-contained policy + orchestration + scoring (judge-only). -- `reference.patch` — fused-Triton reference solution (proves solvability). -- `docker/` — builds the prebuilt agent/judge images referenced by config.yaml. -- `harbor/app/` — agent-facing submission helpers + public self-test. +Can an agent turn a naive GPU K-Means into a fast one? The agent patches +`kmeanslib`; the judge times the patched `kmeans` against a frozen naive baseline +on hidden `(N, D, K)` workloads and scores the geometric-mean speedup, gated on +clustering quality (inertia). First of a four-task **flashlib kernel-optimization +family** (KMeans, KNN, TruncatedSVD, PCA) that all share one evaluator + one +Modal GPU harness. + +## Execution: Modal GPU offload (mirrors the vllm task) + +The judge and the agent public test both **offload GPU work to Modal**; the +containers are light (ubuntu + `modal` + git, no torch/triton). `flash_gpu.py` +(baked at `/opt/flash_gpu.py` in both images) builds an ephemeral Modal app on a +GPU (`evaluation.gpu`, default H100), ships the frozen baseline + the patched +package **as data** (no per-submission image rebuild), runs the worker, and +returns per-workload speedups + verdicts. Needs `MODAL_TOKEN_ID` / +`MODAL_TOKEN_SECRET`. torch/triton/the vendored kernels run on the Modal image +(`evaluation.pip`). + +The evaluator is generic and identical across the four tasks; only three +constants differ (`PRIMITIVE`/`PKG`/`REF_MODULE`), and all workload/threshold/ +Modal values come from `config.yaml` (delivered judge-only as +`/judge/task_config.json`, never present in the agent image). + +## Reference solution = best in the repo + +`reference.patch` vendors flashlib's own optimized **Triton** K-Means path +(`primitives/kmeans/triton/{kmeans,assign,update}.py`) under +`kmeanslib/_kernels/…`, with imports rewritten and the string "flashlib" +scrubbed, plus a thin adapter mapping our `(N, D)` contract onto flashlib's +batched entry. It is triton-only (runs on any sm≥80), imports cleanly on CPU, +and applies + passes the patch policy. Calibrate `speedup_target` from its +measured geomean on the first GPU trial. + +## Anti-reward-hack + +The **load-bearing** defenses are structural, inside the GPU worker (the only +place the untrusted submission runs): + +* timing primitives (`perf_counter`, `torch.cuda.synchronize`) are captured to + locals **before** the submission is imported → monkey-patching torch cannot + affect measurement; +* **fresh data every timed iteration** → memoizing on a repeated input is + useless; +* quality is **re-verified from the returned tensors on every iteration** → + returning a stale cached result for a different input is caught; +* the judge computes all metrics (no agent number is trusted); +* an ephemeral Modal container per submission → no global state persists. + +The patch policy is surgical defense-in-depth (so it does not reject legitimate +vendored/optimized kernel source): only `/**` `.py` files may change; it +bans external-optimized-library *imports* (flashlib/cuml/cupy/faiss/sklearn/ +cutlass — none installed on the GPU image anyway) and process/network/ +measurement-tamper patterns (`subprocess`, `socket`, `os.system`, +`torch.cuda.synchronize =`, …). + +The agent-facing `readme` describes the contract, gate, scoring, and policy but +**not** how the reference is implemented. + +## Correctness gate + +Inertia = sum of squared L2 distances to the nearest **final** centroid, judge- +computed from the returned centroids on each iteration's data. Permutation- and +convention-independent, robust to fp drift, cheat-proof. `init_centroids` are +always supplied and `tol = 0`, so the baseline is a deterministic function of +`(x, init_centroids, max_iters)`. + +## Calibration TODO (needs a Modal GPU trial) + +- Run `reference.patch` on Modal; confirm it passes the inertia gate on all + workloads (bump `inertia_tolerance` if flashlib's low-precision assign drifts + slightly) and set `speedup_target` from its geomean. +- Confirm the pinned `torch`/`triton` in `evaluation.pip` compile the vendored + kernels, and hidden shapes fit the GPU. +- Confirm Modal token wiring in the Harbor judge/agent containers. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index 34b29adbb..11e6ec111 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -2,51 +2,63 @@ tag: systems runtime: language: python timeout_seconds: 10800 - environment: "GPU K-Means kernel optimization; agent patches the kmeanslib Python/Triton package; judge times the patched kmeans against a frozen naive baseline on hidden (N, D, K) workloads with an inertia (clustering-quality) gate." + environment: "GPU K-Means kernel optimization. The agent patches the kmeanslib package; the judge offloads timing to a Modal GPU, comparing the patched kmeans against a frozen naive baseline on hidden (N, D, K) workloads with an inertia (clustering-quality) gate." apt_packages: - bash - ca-certificates - git - python3 + - python3-pip judge_apt_packages: - bash - ca-certificates - git - python3 + - python3-pip + judge_pip_packages: + - modal docker: - # Experimental local images. Build them with - # 2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh before a - # local Harbor trial. Both images bundle a clean copy of the kmeanslib - # package; the judge image additionally bakes the frozen naive baseline - # (/opt/kmeans_ref) and the pristine tree (/opt/kmeanslib-clean). - image: frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.1.0 - judge_image: frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.1.0 + # Experimental local images. Build with docker/build_images.sh before a local + # Harbor trial. Both are light (ubuntu + modal + git); torch/triton/flashlib + # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes + # /app/kmeanslib (git) + /opt/kmeans_ref + /opt/flash_gpu.py; the judge image + # additionally bakes /opt/kmeanslib-clean. + image: frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.2.0 + judge_image: frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.2.0 environment: - cpus: 8 - memory_mb: 32768 - storage_mb: 32768 + cpus: 4 + memory_mb: 8192 + storage_mb: 16384 build_timeout_seconds: 3600 evaluation: - # The judge worker runs on a GPU visible to the judge container (single - # device). H100 is the reference accelerator; the Triton paths also run on - # L40S/A100. If Harbor cannot attach a GPU to the judge container, the worker - # step is the swap point for a Modal offload (see DESIGN.md). - gpu: "H100" - # Timing: warmup then median-of-N cuda-synced full-kmeans calls. - warmup_iters: 3 + # --- primitive wiring (consumed by the generic evaluator + flash_gpu) --- + primitive: kmeans + pkg: kmeanslib + ref_module: refkmeans + clean_source: /opt/kmeanslib-clean + baseline_source: /opt/kmeans_ref + # --- Modal GPU --- + gpu: "H100" # Modal GPU spec (H100 / A100 / L40S) + cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" + pip: ["torch", "numpy"] + app_name: "kmeans-kernel-opt-eval" + modal_timeout_seconds: 1800 + # --- timing + quality gate --- + warmup_iters: 2 timed_iters: 7 - # Clustering-quality gate: the submitted kmeans' final inertia (within-cluster - # sum of squared distances) must not exceed (1 + inertia_tolerance) x the - # frozen baseline's inertia on the SAME seeded data and init centroids. - inertia_tolerance: 0.02 - # Geomean speedup (over the naive baseline) mapped to the full bounded score. - # Calibrate against the reference solution's measured geomean after a trial. - speedup_target: 8.0 - worker_timeout_seconds: 3600 + inertia_tolerance: 0.02 # agent inertia must be <= (1+tol) x baseline on every iteration + speedup_target: 8.0 # geomean speedup mapped to full bounded score (calibrate vs reference) base_seed: 20260701 - # Agent (iterative) role runs a fast subset; the final verifier runs them all. - agent_workload_count: 3 + agent_workload_count: 3 # agent role runs a fast subset; final verifier runs them all expose_per_workload_metrics: false + # --- hidden workloads (judge-only; config.yaml/task_config.json never enter the agent image) --- + workloads: + - {id: w0, N: 100000, D: 32, K: 128, max_iters: 12} + - {id: w1, N: 200000, D: 64, K: 256, max_iters: 10} + - {id: w2, N: 500000, D: 128, K: 256, max_iters: 10} + - {id: w3, N: 200000, D: 512, K: 256, max_iters: 8} + - {id: w4, N: 1000000, D: 64, K: 512, max_iters: 8} + - {id: w5, N: 300000, D: 128, K: 1024, max_iters: 8} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/README.md b/2.0/problems/kmeans_gpu_kernel_optimization/docker/README.md index 3bb55f41a..476074363 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/docker/README.md +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/README.md @@ -1,7 +1,8 @@ # Experimental K-Means Kernel-Optimization Images -Two images, mirroring the duckdb-e2e split: a public **agent** image and a -private **judge** image. Build them before a local Harbor trial: +Two **light** images (ubuntu + `modal` + git). torch, triton, and the vendored +flashlib kernels all run on the **Modal GPU image** defined in `flash_gpu.py`; +the containers themselves are CPU-only and offload GPU work to Modal. ```bash bash 2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh @@ -10,31 +11,41 @@ bash 2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh Defaults: ```text -AGENT_TAG=frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.1.0 -JUDGE_TAG=frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.1.0 +AGENT_TAG=frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.2.0 +JUDGE_TAG=frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.2.0 ``` -The agent image contains: +Agent image: ```text /app/kmeanslib # clean, git-tracked package (the agent edits this) +/opt/flash_gpu.py # shared Modal GPU harness (public test uses it) +/opt/kmeans_ref/refkmeans.py # frozen naive baseline (public-test speed denominator) ``` -The judge image contains: +Judge image: ```text /opt/kmeanslib-clean/kmeanslib # pristine tree; the patch is applied to a copy -/opt/kmeans_ref/refkmeans.py # frozen naive baseline (speed denominator + oracle) +/opt/kmeans_ref/refkmeans.py # frozen naive baseline (speed denominator + quality oracle) +/opt/flash_gpu.py # shared Modal GPU harness (the evaluator uses it) ``` -Both are based on `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel` (torch + triton). - ## Runtime requirements -The judge times the patched kmeans on a **GPU visible to the judge container** -(single device; H100 reference, Triton paths also run on L40S/A100). If the -Harbor runtime cannot attach a GPU to the judge container, port the worker step -(`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md`. +Both the judge and the agent public test **offload timing to a Modal GPU** and +therefore need Modal credentials in the environment: + +```text +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET +``` + +`flash_gpu.py` builds an ephemeral Modal app on a GPU (`evaluation.gpu`, default +`H100`), ships the frozen baseline + the patched package as data, times both on +fresh per-iteration data, verifies quality each iteration, and returns the +speedups. No persistent deployment is used, so a fresh GPU container is spun up +per evaluation. Without Modal credentials the evaluator returns a patch-policy +smoke pass (which repo CI exercises). ## Smoke test @@ -42,5 +53,4 @@ Harbor runtime cannot attach a GPU to the judge container, port the worker step bash 2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh ``` -Import-only; verifies torch/triton and the baked packages are importable. It -does not exercise a GPU. +Import-only (modal + flash_gpu + the baked packages); does not touch a GPU or Modal. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile index d28f6b85b..b57ecef52 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile @@ -1,17 +1,23 @@ # Agent image for kmeans_gpu_kernel_optimization. -# Bundles a clean, git-tracked copy of the kmeanslib package at /app/kmeanslib -# so the agent can edit it and `make_submission.sh` can diff it. torch + triton -# come from the PyTorch base image. -FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel +# +# Light image (no torch/triton): the agent edits /app/kmeanslib and runs the +# public test, which offloads timing to a Modal GPU (torch/triton live on the +# Modal image defined in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET +# in the environment to run the GPU public test. +# +# Build context = the task directory: +# docker build -f docker/agent/Dockerfile -t . +FROM ubuntu:24.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get install -y --no-install-recommends \ - bash ca-certificates git ripgrep && \ + bash ca-certificates git python3 python3-pip ripgrep && \ rm -rf /var/lib/apt/lists/* -RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" +RUN pip3 install --break-system-packages --no-cache-dir modal +# The package the agent edits (git-tracked so make_submission.sh can diff it). WORKDIR /app COPY kmeanslib /app/kmeanslib RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ @@ -20,3 +26,9 @@ RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ git -C /app config user.name frontier-cs && \ git -C /app add -A -- kmeanslib .gitignore && \ git -C /app -c commit.gpgsign=false commit -qm "pristine kmeanslib" + +# Shared Modal GPU harness + the frozen naive baseline (the public-test speed +# denominator). The baseline is exactly the code the agent starts from, so it is +# not secret. +COPY flash_gpu.py /opt/flash_gpu.py +COPY judge/refkmeans.py /opt/kmeans_ref/refkmeans.py diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh index 7eedc497d..7936fc6b6 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/kmeans_gpu_kernel_optimization/docker/judge/Dockerfile index b4f0c1b3d..71ec8b1d6 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/docker/judge/Dockerfile +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/judge/Dockerfile @@ -1,18 +1,26 @@ # Judge image for kmeans_gpu_kernel_optimization. -# Bakes the pristine package tree the agent patch is applied to -# (/opt/kmeanslib-clean/kmeanslib) and the frozen naive baseline the judge times -# against (/opt/kmeans_ref/refkmeans.py). torch + triton come from the base. -FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel +# +# Light image (no torch/triton): the judge validates + applies the patch and +# offloads timing to a Modal GPU (torch/triton live on the Modal image defined +# in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. +# The Frontier-CS 2.0 adapter builds the final judge image ON TOP of this one. +# +# Build context = the task directory: +# docker build -f docker/judge/Dockerfile -t . +FROM ubuntu:24.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get install -y --no-install-recommends \ - bash ca-certificates git && \ + bash ca-certificates git python3 python3-pip && \ rm -rf /var/lib/apt/lists/* -RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" +RUN pip3 install --break-system-packages --no-cache-dir modal +# Pristine tree the patch is applied to, the frozen naive baseline (speed +# denominator + quality oracle), and the shared Modal GPU harness. COPY kmeanslib /opt/kmeanslib-clean/kmeanslib COPY judge/refkmeans.py /opt/kmeans_ref/refkmeans.py +COPY flash_gpu.py /opt/flash_gpu.py WORKDIR /judge diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh index e3f3b4794..8a091615f 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh @@ -1,17 +1,19 @@ #!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU required). +# Import-only smoke test for the built images (no GPU / Modal required). set -euo pipefail -AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "== agent image ==" docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import torch, triton, kmeanslib; print('agent ok: kmeanslib', kmeanslib.__version__)" + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ +sys.path.insert(0, '/opt/kmeans_ref'); import refkmeans; \ +import kmeanslib; print('agent ok: modal + flash_gpu + refkmeans + kmeanslib import')" echo "== judge image ==" docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, torch, triton; \ + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ sys.path.insert(0, '/opt/kmeans_ref'); import refkmeans; \ sys.path.insert(0, '/opt/kmeanslib-clean'); import kmeanslib; \ -print('judge ok: refkmeans + kmeanslib import')" +print('judge ok: modal + flash_gpu + refkmeans + kmeanslib import')" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py b/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py index 86f9d0613..2327467aa 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py @@ -1,21 +1,20 @@ -"""Evaluator for the K-Means GPU kernel-optimization task. - -The agent submits a unified diff over the ``kmeanslib`` Python package. The -judge: - -1. statically validates the patch against an allowlist (only ``kmeanslib/**`` - may change; no ``flashlib``/cuML/network/env access; bounded size); -2. applies it to a pristine copy of ``kmeanslib`` baked into the judge image - at ``/opt/kmeanslib-clean``; -3. runs an isolated worker subprocess that, for each hidden ``(N, D, K)`` - workload, times the patched ``kmeanslib.kmeans`` against a *frozen* naive - baseline (``/opt/kmeans_ref/refkmeans.py``) on identical seeded data and - measures each result's k-means inertia; -4. gates on clustering quality (agent inertia must not regress beyond a small - tolerance vs the baseline) and scores by the geometric-mean speedup. - -When the judge source tree is not present (e.g. local static checks on a box -without a GPU), the evaluator still validates the patch policy and returns a +"""Generic evaluator for the flashlib GPU kernel-optimization task family. + +Identical across all four tasks (kmeans / knn / pca / truncated_svd); every +primitive-specific value comes from the ``evaluation`` block of the task's +config (delivered to the judge as ``/judge/task_config.json``), which is not +present in the agent workspace. + +Flow: statically validate the patch (only ``/**`` may change; no external +optimized libs / env / process / network access) -> apply it to the pristine +package baked at ``clean_source`` -> hand the frozen baseline + patched package +sources to a Modal GPU worker (``flash_gpu.run_remote``) that times both on +fresh per-iteration data and verifies clustering/retrieval/subspace quality on +every iteration -> gate on the worker's per-workload verdict -> score by the +geometric-mean speedup. + +Without Modal credentials or the baked judge sources (e.g. repo CI on a box with +no GPU/Modal), the evaluator still validates the patch policy and returns a smoke pass, so ``python3 evaluator.py reference.patch`` works anywhere. """ from __future__ import annotations @@ -30,18 +29,12 @@ import subprocess import sys import tempfile -import time from dataclasses import dataclass from pathlib import Path from typing import Any -MAX_PATCH_BYTES = 1_000_000 -MAX_CHANGED_FILES = 40 TASK_CONFIG_PATH = Path("/judge/task_config.json") -DEFAULT_CLEAN_SOURCE = Path("/opt/kmeanslib-clean") # pristine package (patched here) -DEFAULT_BASELINE_SOURCE = Path("/opt/kmeans_ref") # frozen naive baseline (refkmeans.py) - def _load_task_config() -> dict[str, Any]: try: @@ -52,29 +45,29 @@ def _load_task_config() -> dict[str, Any]: TASK_CONFIG = _load_task_config() -EVALUATION_CONFIG = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} +EVAL = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} -def _cfg(name: str, default): - return EVALUATION_CONFIG.get(name, default) +def _get(name: str, default): + return EVAL.get(name, default) -def _cfg_int(name: str, default: int) -> int: +def _get_int(name: str, default: int) -> int: try: - return int(EVALUATION_CONFIG.get(name, default)) + return int(EVAL.get(name, default)) except Exception: return default -def _cfg_float(name: str, default: float) -> float: +def _get_float(name: str, default: float) -> float: try: - return float(EVALUATION_CONFIG.get(name, default)) + return float(EVAL.get(name, default)) except Exception: return default -def _cfg_bool(name: str, default: bool) -> bool: - raw = EVALUATION_CONFIG.get(name, default) +def _get_bool(name: str, default: bool) -> bool: + raw = EVAL.get(name, default) if isinstance(raw, bool): return raw if isinstance(raw, str): @@ -82,50 +75,49 @@ def _cfg_bool(name: str, default: bool) -> bool: return bool(raw) -WARMUP_ITERS = _cfg_int("warmup_iters", 3) -TIMED_ITERS = _cfg_int("timed_iters", 7) -INERTIA_TOL = _cfg_float("inertia_tolerance", 0.01) # agent inertia <= (1+tol)*ref -SPEEDUP_TARGET = _cfg_float("speedup_target", 8.0) # geomean speedup mapped to full score -WORKER_TIMEOUT_SECONDS = _cfg_int("worker_timeout_seconds", 3600) -BASE_SEED = _cfg_int("base_seed", 20260701) +# --- Per-task identity: the ONLY lines that differ across the four tasks. --- +# Kept as constants (not config) so the patch policy is enforceable locally / in +# CI without /judge/task_config.json. Everything else is config-driven and only +# needed on the GPU path. +PRIMITIVE = "kmeans" +PKG = "kmeanslib" +REF_MODULE = "refkmeans" -# Editable surface: only the shipped package. -ALLOWED_PATTERNS = ( - "kmeanslib/**", -) -# Never touchable, even though they are not shipped in the agent workspace. +MAX_PATCH_BYTES = _get_int("max_patch_bytes", 2_000_000) +MAX_CHANGED_FILES = _get_int("max_changed_files", 80) +CLEAN_SOURCE = Path(f"/opt/{PKG}-clean") +BASELINE_SOURCE = Path(f"/opt/{PRIMITIVE}_ref") +SPEEDUP_TARGET = _get_float("speedup_target", 8.0) + +ALLOWED_PATTERNS = (f"{PKG}/**",) DENIED_PATTERNS = ( "evaluator.py", + "flash_gpu.py", "reference.patch", - "refkmeans.py", - "**/refkmeans.py", + f"{REF_MODULE}.py", + f"**/{REF_MODULE}.py", "**/conftest.py", "**/test_*.py", "pyproject.toml", "setup.py", "setup.cfg", ) -# Forbidden substrings in added lines (defense in depth). The agent must write -# its own kernels, not call an external optimized library, read the -# environment, spawn processes, or touch the network. -FORBIDDEN_TOKENS = ( - "flashlib", - "cuml", - "cudf", - "cupy", - "faiss", - "sklearn", - "scikit", - "os.environ", - "getenv", - "putenv", - "setenv", - "subprocess", - "socket", - "urllib", - "requests", - "importlib.import_module", - "__import__", +# Defense in depth (the load-bearing anti-tamper defense is structural, inside +# the GPU worker: it captures its perf_counter / synchronize references before +# importing the submission, regenerates data every iteration, and re-verifies +# quality each iteration). These patterns are matched surgically so that +# legitimate vendored/optimized kernel source is not rejected: +# * external optimized libraries, matched as IMPORT statements (not bare words, +# so a comment mentioning a name is fine); none are installed on the GPU +# image anyway; +# * process / network / measurement-tamper patterns that never appear in a +# real GPU kernel. +FORBIDDEN_IMPORT_RE = re.compile( + r"(?:^|\n)\s*(?:import|from)\s+(flashlib|cuml|cudf|cupy|faiss|sklearn|scikit|cutlass|cuvs)\b" +) +FORBIDDEN_PATTERN_RE = re.compile( + r"\bsubprocess\b|\bsocket\b|\burllib\b|\brequests\b|os\.system|" + r"torch\.cuda\.synchronize\s*=|time\.perf_counter\s*=|setattr\(\s*torch\.cuda" ) @@ -134,7 +126,6 @@ class PatchFile: old_path: str new_path: str added_lines: tuple[str, ...] - removed_lines: tuple[str, ...] @property def path(self) -> str: @@ -142,7 +133,7 @@ def path(self) -> str: def _match(path: str, patterns: tuple[str, ...]) -> bool: - return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + return any(fnmatch.fnmatch(path, p) for p in patterns) def _invalid(message: str, metrics: dict[str, Any] | None = None): @@ -153,41 +144,27 @@ def _invalid(message: str, metrics: dict[str, Any] | None = None): def _parse_patch(text: str) -> list[PatchFile]: files: list[PatchFile] = [] - current_old = "" - current_new = "" + old = new = "" added: list[str] = [] - removed: list[str] = [] in_file = False - for line in text.splitlines(): if line.startswith("diff --git "): if in_file: - files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) - in_file = True - current_old = current_new = "" - added = [] - removed = [] + files.append(PatchFile(old, new, tuple(added))) + in_file, old, new, added = True, "", "", [] continue if not in_file: continue if line.startswith("--- "): - current_old = line[4:].strip() - if current_old.startswith("a/"): - current_old = current_old[2:] - continue - if line.startswith("+++ "): - current_new = line[4:].strip() - if current_new.startswith("b/"): - current_new = current_new[2:] - continue - if line.startswith("+") and not line.startswith("+++ "): + old = line[4:].strip() + old = old[2:] if old.startswith("a/") else old + elif line.startswith("+++ "): + new = line[4:].strip() + new = new[2:] if new.startswith("b/") else new + elif line.startswith("+") and not line.startswith("+++ "): added.append(line[1:]) - continue - if line.startswith("-") and not line.startswith("--- "): - removed.append(line[1:]) - if in_file: - files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + files.append(PatchFile(old, new, tuple(added))) return files @@ -199,7 +176,7 @@ def _validate_path(path: str) -> tuple[bool, str]: if _match(path, DENIED_PATTERNS): return False, f"changed file is outside task boundary: {path}" if not _match(path, ALLOWED_PATTERNS): - return False, f"changed file is not allowlisted (only kmeanslib/** may change): {path}" + return False, f"changed file is not allowlisted (only {PKG}/** may change): {path}" if not path.endswith(".py"): return False, f"only Python files may change: {path}" return True, "" @@ -212,69 +189,40 @@ def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: if size > MAX_PATCH_BYTES: return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} text = patch_path.read_text(encoding="utf-8", errors="replace") - patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() files = _parse_patch(text) metrics: dict[str, Any] = { "patch_bytes": size, - "patch_sha256": patch_hash, + "patch_sha256": hashlib.sha256(text.encode("utf-8", "replace")).hexdigest(), "changed_files": len(files), } if len(files) > MAX_CHANGED_FILES: return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics - - for patch_file in files: - path = patch_file.path - if patch_file.new_path == "/dev/null": - return False, f"deleting files is outside task boundary: {patch_file.old_path}", metrics - if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: - ok, err = _validate_path(patch_file.old_path) + for pf in files: + path = pf.path + if pf.new_path == "/dev/null": + return False, f"deleting files is outside task boundary: {pf.old_path}", metrics + if pf.old_path != "/dev/null" and pf.old_path != pf.new_path: + ok, err = _validate_path(pf.old_path) if not ok: - return False, f"rename/copy source is outside task boundary: {err}", metrics + return False, f"rename source is outside task boundary: {err}", metrics ok, err = _validate_path(path) if not ok: return False, err, metrics - added_text = "\n".join(patch_file.added_lines) - low = added_text.lower() - for token in FORBIDDEN_TOKENS: - if token in low: - return False, f"{path}: forbidden token in added code ({token})", metrics - + added = "\n".join(pf.added_lines) + m = FORBIDDEN_IMPORT_RE.search(added) + if m: + return False, f"{path}: forbidden import of an external optimized library ({m.group(1)})", metrics + m = FORBIDDEN_PATTERN_RE.search(added) + if m: + return False, f"{path}: forbidden pattern in added code ({m.group(0).strip()[:40]})", metrics metrics["valid_patch"] = 1 return True, "patch accepted by static policy", metrics -def clean_env(tmp_root: Path) -> dict[str, str]: - home = tmp_root / "home" - tmp = tmp_root / "tmp" - home.mkdir(parents=True, exist_ok=True) - tmp.mkdir(parents=True, exist_ok=True) - env = { - "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin"), - "HOME": str(home), - "TMPDIR": str(tmp), - "LC_ALL": "C", - "LANG": "C", - } - for key in ("CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES", "LD_LIBRARY_PATH", - "TRITON_CACHE_DIR", "CUDA_HOME"): - if key in os.environ: - env[key] = os.environ[key] - env.setdefault("TRITON_CACHE_DIR", str(tmp_root / "triton_cache")) - return env - - -def run_checked(cmd: list[str], *, cwd: Path, env: dict[str, str], timeout: int) -> subprocess.CompletedProcess: - return subprocess.run( - cmd, cwd=str(cwd), env=env, timeout=timeout, - stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, - ) - - -def sanitize_error_text(text: str) -> str: - text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) +def sanitize(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text or "") text = re.sub(r"/opt/[A-Za-z0-9_./-]+", "", text) - text = re.sub(r"N=\d+", "N=", text) - text = re.sub(r"seed[=:]?\s*\d+", "seed=", text, flags=re.IGNORECASE) + text = re.sub(r"\bN=\d+|\bseed[=:]?\s*\d+", "", text, flags=re.IGNORECASE) return text[-600:] @@ -284,226 +232,123 @@ def geometric_mean(values: list[float]) -> float: return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) -def score_from_speedup(gm_speedup: float) -> float: - if gm_speedup <= 0: +def score_from_speedup(gm: float) -> float: + if gm <= 0: return 0.0 - raw = 100.0 * math.log(gm_speedup) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) return max(0.0, min(100.0, raw)) -def is_final_submission_role() -> bool: +def is_final_role() -> bool: return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" -# Hidden workloads. (N, D, K, max_iters). Agent role runs a fast subset for -# iterative feedback; the final verifier runs the full set. Seeds are derived -# from BASE_SEED so data is deterministic but not guessable from the statement. -_HIDDEN_WORKLOADS = ( - {"id": "w0", "N": 100_000, "D": 32, "K": 128, "max_iters": 12}, - {"id": "w1", "N": 200_000, "D": 64, "K": 256, "max_iters": 10}, - {"id": "w2", "N": 500_000, "D": 128, "K": 256, "max_iters": 10}, - {"id": "w3", "N": 200_000, "D": 512, "K": 256, "max_iters": 8}, # split-D regime - {"id": "w4", "N": 1_000_000, "D": 64, "K": 512, "max_iters": 8}, - {"id": "w5", "N": 300_000, "D": 128, "K": 1024, "max_iters": 8}, # large-K regime -) - - -def _workloads(*, final_role: bool) -> list[dict[str, Any]]: - configured = _cfg("workloads", None) - workloads = list(configured) if isinstance(configured, list) and configured else list(_HIDDEN_WORKLOADS) +def _workloads(final_role: bool) -> list[dict[str, Any]]: + workloads = list(_get("workloads", []) or []) if not final_role: - n_agent = _cfg_int("agent_workload_count", 3) - workloads = workloads[:n_agent] + n = _get_int("agent_workload_count", 3) + workloads = workloads[:n] + base = _get_int("base_seed", 20260701) for i, w in enumerate(workloads): - w.setdefault("seed", BASE_SEED + 1000 * (i + 1)) + w.setdefault("seed", base + 1000 * (i + 1)) return workloads -# The worker runs the untrusted patched package in its own process. It imports -# the frozen baseline (refkmeans) and the patched kmeanslib, times both on -# identical seeded data, and reports per-workload timings + inertias as JSON. -_WORKER_SOURCE = r''' -import argparse, json, sys, time, traceback - -def _load(baseline_dir, patched_dir): - import importlib - sys.path.insert(0, patched_dir) # patched `kmeanslib` package - sys.path.insert(0, baseline_dir) # frozen `refkmeans` module - import refkmeans - import kmeanslib - return refkmeans, kmeanslib - -def gen(N, D, K, seed, device): - import torch - g = torch.Generator(device=device).manual_seed(int(seed)) - x = torch.randn(N, D, generator=g, device=device, dtype=torch.float32) - perm = torch.randperm(N, generator=g, device=device)[:K] - init = x.index_select(0, perm).clone() - return x, init - -def inertia(x, centroids): - import torch - centroids = centroids.to(torch.float32) - cn = (centroids * centroids).sum(1) - total = 0.0 - CH = 16384 - for i in range(0, x.shape[0], CH): - xb = x[i:i+CH].to(torch.float32) - d = (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ centroids.t()) + cn[None, :] - total += float(d.min(1).values.clamp_min(0).sum().item()) - return total - -def bench(fn, warmup, iters): - import torch - for _ in range(warmup): - fn(); torch.cuda.synchronize() - ts = [] - for _ in range(iters): - torch.cuda.synchronize(); t0 = time.perf_counter() - fn(); torch.cuda.synchronize() - ts.append(time.perf_counter() - t0) - ts.sort() - return ts[len(ts) // 2] * 1000.0 - -def check_result(out, N, D, K): - import torch - if not (isinstance(out, tuple) and len(out) == 3): - raise ValueError("kmeans must return a 3-tuple (labels, centroids, n_iter)") - labels, centroids, _ = out - if tuple(centroids.shape) != (K, D): - raise ValueError("centroids has wrong shape") - if labels.shape[0] != N: - raise ValueError("labels has wrong length") - if not torch.isfinite(centroids).all(): - raise ValueError("centroids contain non-finite values") - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--baseline-dir", required=True) - ap.add_argument("--patched-dir", required=True) - ap.add_argument("--workloads", required=True) - ap.add_argument("--out", required=True) - ap.add_argument("--warmup", type=int, default=3) - ap.add_argument("--iters", type=int, default=7) - a = ap.parse_args() - import torch - if not torch.cuda.is_available(): - json.dump({"ok": False, "error": "cuda_unavailable"}, open(a.out, "w")) - return - refkmeans, kmeanslib = _load(a.baseline_dir, a.patched_dir) - device = "cuda" - rows = [] - for w in json.loads(a.workloads): - N, D, K, it, seed = w["N"], w["D"], w["K"], w["max_iters"], w["seed"] - x, init = gen(N, D, K, seed, device) - ref_fn = lambda: refkmeans.kmeans(x, K, max_iters=it, init_centroids=init, tol=0.0) - agent_fn = lambda: kmeanslib.kmeans(x, K, max_iters=it, init_centroids=init, tol=0.0) - ref_out = ref_fn(); torch.cuda.synchronize() - ref_inertia = inertia(x, ref_out[1]) - try: - agent_out = agent_fn(); torch.cuda.synchronize() - check_result(agent_out, N, D, K) - agent_inertia = inertia(x, agent_out[1]) - except Exception as e: - rows.append({"id": w["id"], "ok": False, "error": type(e).__name__ + ": " + str(e)[:200]}) - del x, init - torch.cuda.empty_cache() - continue - ref_ms = bench(ref_fn, a.warmup, a.iters) - agent_ms = bench(agent_fn, a.warmup, a.iters) - rows.append({"id": w["id"], "ok": True, "ref_ms": ref_ms, "agent_ms": agent_ms, - "ref_inertia": ref_inertia, "agent_inertia": agent_inertia}) - del x, init - torch.cuda.empty_cache() - json.dump({"ok": True, "rows": rows}, open(a.out, "w")) +def _build_cfg() -> dict[str, Any]: + return { + "primitive": PRIMITIVE, + "pkg": PKG, + "ref_module": REF_MODULE, + "gpu": str(_get("gpu", "H100")), + "cuda_image": str(_get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), + "pip": list(_get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])), + "app_name": str(_get("app_name", "flash-kernel-eval")), + "modal_timeout_seconds": _get_int("modal_timeout_seconds", 1800), + "warmup": _get_int("warmup_iters", 3), + "iters": _get_int("timed_iters", 7), + "inertia_tolerance": _get_float("inertia_tolerance", 0.02), + "recall_threshold": _get_float("recall_threshold", 0.99), + "captured_tolerance": _get_float("captured_tolerance", 0.02), + "ortho_tolerance": _get_float("ortho_tolerance", 0.02), + } -if __name__ == "__main__": - try: - main() - except Exception: - traceback.print_exc(); sys.exit(3) -''' - - -def _run_worker(patched_parent: Path, tmp_root: Path, env: dict[str, str], - workloads: list[dict[str, Any]]) -> dict[str, Any]: - worker_path = tmp_root / "kmeans_worker.py" - worker_path.write_text(_WORKER_SOURCE, encoding="utf-8") - out_path = tmp_root / "worker_out.json" - cmd = [ - sys.executable, str(worker_path), - "--baseline-dir", str(DEFAULT_BASELINE_SOURCE), - "--patched-dir", str(patched_parent), - "--workloads", json.dumps(workloads), - "--out", str(out_path), - "--warmup", str(WARMUP_ITERS), - "--iters", str(TIMED_ITERS), - ] - run_checked(cmd, cwd=tmp_root, env=env, timeout=WORKER_TIMEOUT_SECONDS) - return json.loads(out_path.read_text(encoding="utf-8")) + +def _read_dir(root: Path, rel_to: Path) -> dict[str, str]: + out: dict[str, str] = {} + for p in root.rglob("*.py"): + out[str(p.relative_to(rel_to))] = p.read_text(encoding="utf-8", errors="replace") + return out def full_evaluation(patch_path: Path, metrics: dict[str, Any]): - final_role = is_final_submission_role() + final_role = is_final_role() metrics["submission_role"] = "final" if final_role else "agent" - workloads = _workloads(final_role=final_role) + workloads = _workloads(final_role) - if not DEFAULT_CLEAN_SOURCE.exists() or not DEFAULT_BASELINE_SOURCE.exists(): + # Import the Modal harness baked into the judge image; missing harness or + # missing credentials/sources -> policy smoke pass. + sys.path.insert(0, "/opt") + try: + import flash_gpu # type: ignore + except Exception: metrics["full_benchmark"] = 0 - return (1.0, 1.0, - "patch policy smoke passed; kmeanslib judge source is not configured in this environment", - metrics) + return 1.0, 1.0, "patch policy smoke passed; GPU harness not available in this environment", metrics + if not flash_gpu.modal_available() or not CLEAN_SOURCE.exists() or not BASELINE_SOURCE.exists(): + metrics["full_benchmark"] = 0 + return 1.0, 1.0, "patch policy smoke passed; Modal/judge sources not configured in this environment", metrics - with tempfile.TemporaryDirectory(prefix="kmeans_kernel_opt_") as tmp: + with tempfile.TemporaryDirectory(prefix="flash_kernel_opt_") as tmp: tmp_root = Path(tmp) - env = clean_env(tmp_root) - patched_parent = tmp_root / "patched" - shutil.copytree(DEFAULT_CLEAN_SOURCE, patched_parent) - + patched = tmp_root / "patched" + shutil.copytree(CLEAN_SOURCE, patched) + env = {"PATH": os.environ.get("PATH", "/usr/bin:/bin"), "HOME": str(tmp_root), + "LC_ALL": "C", "LANG": "C"} if int(metrics.get("changed_files", 0)) > 0: - run_checked(["git", "apply", "--check", str(patch_path)], cwd=patched_parent, env=env, timeout=60) - run_checked(["git", "apply", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + try: + subprocess.run(["git", "apply", "--check", str(patch_path)], cwd=patched, env=env, + check=True, capture_output=True, text=True, timeout=60) + subprocess.run(["git", "apply", str(patch_path)], cwd=patched, env=env, + check=True, capture_output=True, text=True, timeout=60) + except subprocess.CalledProcessError as exc: + metrics["stderr_tail"] = sanitize(exc.stderr or "") + return _invalid("patch does not apply to the clean package tree", metrics) metrics["applied_patch"] = 1 else: metrics["used_empty_patch"] = 1 - result = _run_worker(patched_parent, tmp_root, env, workloads) - if not result.get("ok"): - return _invalid(f"benchmark worker failed ({result.get('error', 'unknown')})", metrics) - - rows = result.get("rows", []) - speedups: list[float] = [] - per_workload: dict[str, Any] = {} - for row in rows: - if not row.get("ok"): - metrics["failed_workload_error"] = sanitize_error_text(str(row.get("error", ""))) - return _invalid("submitted kmeans crashed or returned an invalid result on a hidden workload", metrics) - ref_inertia = row["ref_inertia"] - agent_inertia = row["agent_inertia"] - if agent_inertia > (1.0 + INERTIA_TOL) * ref_inertia + 1e-6: - metrics["inertia_regression"] = { - "ref": ref_inertia, "agent": agent_inertia, "tol": INERTIA_TOL, - } - return _invalid("submitted kmeans clustering quality regressed beyond tolerance", metrics) - speedup = row["ref_ms"] / row["agent_ms"] if row["agent_ms"] > 0 else 0.01 - speedups.append(max(speedup, 0.01)) - per_workload[row["id"]] = { - "ref_ms": row["ref_ms"], "agent_ms": row["agent_ms"], "speedup": speedup, - } - - if not speedups: - return _invalid("no workloads were evaluated", metrics) - - gm = geometric_mean(speedups) - bounded = score_from_speedup(gm) - metrics.update({ - "full_benchmark": 1, - "workload_count": len(speedups), - "geomean_speedup": gm, - }) - if _cfg_bool("expose_per_workload_metrics", False): - metrics["per_workload"] = per_workload - return (bounded, bounded, f"kmeans geomean speedup {gm:.3f}x over the naive baseline", metrics) + payload = { + "baseline_files": _read_dir(BASELINE_SOURCE, BASELINE_SOURCE), + "patched_files": _read_dir(patched, patched), + "workloads": workloads, + "cfg": _build_cfg(), + } + try: + result = flash_gpu.run_remote(payload) + except Exception as exc: + metrics["error_detail"] = sanitize(str(exc)) + return _invalid("GPU evaluation failed", metrics) + + if not result.get("ok"): + metrics["worker_error"] = sanitize(str(result.get("error", "unknown"))) + return _invalid("GPU benchmark worker failed", metrics) + + speedups: list[float] = [] + per_workload: dict[str, Any] = {} + for row in result.get("rows", []): + if not row.get("ok"): + metrics["failed_workload"] = {"reason": row.get("reason", "error")} + return _invalid("submission failed the quality gate or crashed on a hidden workload", metrics) + speedups.append(max(float(row["speedup"]), 0.01)) + per_workload[row["id"]] = {"speedup": row["speedup"]} + if not speedups: + return _invalid("no workloads were evaluated", metrics) + + gm = geometric_mean(speedups) + bounded = score_from_speedup(gm) + metrics.update({"full_benchmark": 1, "workload_count": len(speedups), "geomean_speedup": gm}) + if _get_bool("expose_per_workload_metrics", False): + metrics["per_workload"] = per_workload + return bounded, bounded, f"{PRIMITIVE} geomean speedup {gm:.3f}x over the naive baseline", metrics def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: @@ -513,17 +358,9 @@ def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: return _invalid(message, metrics) try: return full_evaluation(patch_path, metrics) - except subprocess.TimeoutExpired: - return _invalid("benchmark worker timed out", metrics) - except subprocess.CalledProcessError as exc: - stderr = sanitize_error_text(exc.stderr or "") - cmd0 = Path(str(exc.cmd[0])).name if isinstance(exc.cmd, list) and exc.cmd else "subprocess" - metrics["failed_command"] = "git apply" if cmd0 == "git" else cmd0 - metrics["stderr_tail"] = stderr - return _invalid("patch apply or benchmark command failed", metrics) - except Exception as exc: + except Exception as exc: # noqa: BLE001 metrics["error_type"] = type(exc).__name__ - metrics["error_detail"] = sanitize_error_text(str(exc)) + metrics["error_detail"] = sanitize(str(exc)) return _invalid("evaluation failed", metrics) @@ -531,13 +368,9 @@ def main(argv: list[str]) -> int: if len(argv) != 2: print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) return 2 - score, score_unbounded, message, metrics = evaluate(argv[1]) - print(json.dumps({ - "score": score, - "score_unbounded": score_unbounded, - "message": message, - "metrics": metrics, - }, indent=2, sort_keys=True)) + score, unbounded, message, metrics = evaluate(argv[1]) + print(json.dumps({"score": score, "score_unbounded": unbounded, + "message": message, "metrics": metrics}, indent=2, sort_keys=True)) return 0 diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py new file mode 100644 index 000000000..7fd6db603 --- /dev/null +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -0,0 +1,279 @@ +"""Modal GPU evaluation harness for the flashlib kernel-optimization task family. + +Shared by the judge (evaluator.py) and the agent public test. Baked into both +the agent and judge images at ``/opt/flash_gpu.py``. It runs a frozen naive +baseline and the (patched or agent-edited) package on a Modal GPU and returns +per-workload timings + quality verdicts. + +Anti-reward-hack properties (all enforced inside the remote worker, which is the +only place the untrusted code runs): + +* **Timing primitives are captured before any agent code is imported** — the + worker binds ``time.perf_counter`` and ``torch.cuda.synchronize`` to locals up + front, so a submission that monkey-patches ``torch.cuda.synchronize`` (or any + torch attribute) cannot affect measurement. +* **Fresh data every timed iteration** — each measured iteration generates a new + input from a fresh seed, so a submission cannot memoize on a repeated input. +* **Quality is verified on every timed iteration** — the judge recomputes the + quality metric from the *returned tensors* on that iteration's data and gates + it, so returning a stale cached result for a different input is caught. +* **The judge computes all metrics** — no agent-provided number is trusted. +* **Fresh container per submission** — an ephemeral Modal app is used, so no + global state survives across evaluations. + +The remote worker is a single self-contained function serialized to Modal +(``serialized=True``), so the remote does not import this module and there is no +module-mounting to get wrong. The GPU image ships torch + triton (+ any extra +pip packages a task needs for its reference build). +""" +from __future__ import annotations + +import os + + +# --------------------------------------------------------------------------- # +# Remote worker (runs on the Modal GPU). Fully self-contained: every helper is +# nested so cloudpickle ships the whole thing. Takes and returns plain data. +# --------------------------------------------------------------------------- # +def _gpu_worker(payload: dict) -> dict: + import importlib + import os as _os + import sys as _sys + import tempfile + import time + import traceback + + import torch + + # Capture timing + sync references BEFORE importing any submission code, so a + # monkey-patch of torch.cuda.synchronize cannot affect measurement. + _perf = time.perf_counter + _sync = torch.cuda.synchronize + if not torch.cuda.is_available(): + return {"ok": False, "error": "cuda_unavailable"} + dev = "cuda" + + cfg = payload["cfg"] + prim = cfg["primitive"] + warmup = int(cfg.get("warmup", 3)) + iters = int(cfg.get("iters", 7)) + + def _materialize(files: dict, tag: str) -> str: + root = tempfile.mkdtemp(prefix=f"flash_{tag}_") + for rel, content in files.items(): + path = _os.path.join(root, rel) + parent = _os.path.dirname(path) + if parent: + _os.makedirs(parent, exist_ok=True) + with open(path, "w", encoding="utf-8") as fh: + fh.write(content) + _sys.path.insert(0, root) + return root + + _materialize(payload["baseline_files"], "baseline") + _materialize(payload["patched_files"], "patched") + ref = importlib.import_module(cfg["ref_module"]) + pkg = importlib.import_module(cfg["pkg"]) + + # ---- per-primitive: data gen, call, and quality metric ---------------- # + def gen(w, seed): + g = torch.Generator(device=dev).manual_seed(int(seed)) + if prim == "knn": + db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) + q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) + return {"queries": q, "database": db} + x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) + if prim == "kmeans": + perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] + return {"x": x, "init": x.index_select(0, perm).clone()} + return {"x": x} + + def call(mod, w, data): + if prim == "kmeans": + return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], + init_centroids=data["init"], tol=0.0) + if prim == "knn": + return mod.knn(data["queries"], data["database"], w["k"]) + if prim == "pca": + return mod.pca(data["x"], w["k"]) + if prim == "tsvd": + return mod.truncated_svd(data["x"], w["k"]) + raise ValueError(prim) + + def check_shape(w, out): + if prim == "kmeans": + _, cen, _ = out + if tuple(cen.shape) != (w["K"], w["D"]) or not torch.isfinite(cen).all(): + raise ValueError("bad kmeans output") + elif prim == "knn": + d, i = out + if tuple(d.shape) != (w["Q"], w["k"]) or tuple(i.shape) != (w["Q"], w["k"]): + raise ValueError("bad knn output") + if not torch.isfinite(d).all(): + raise ValueError("non-finite distances") + else: + a, b = out + comps = a if prim == "pca" else b + if tuple(comps.shape) != (w["k"], w["D"]) or not torch.isfinite(comps).all(): + raise ValueError("bad decomposition output") + + def inertia(x, centroids): + c = centroids.to(torch.float32) + cn = (c * c).sum(1) + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384] + d = (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ c.t()) + cn[None, :] + total += float(d.min(1).values.clamp_min(0).sum().item()) + return total + + def recall(agent_idx, ref_idx): + a = agent_idx.long(); b = ref_idx.long() + hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) + return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + + def ortho_err(comps): + c = comps.to(torch.float32); k = c.shape[0] + return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) + + def captured(x, comps, center): + c = comps.to(torch.float32) + mean = x.mean(0) if center else None + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384] + if center: + xb = xb - mean + p = xb @ c.t() + total += float((p * p).sum().item()) + return total / (x.shape[0] - 1) if center else total + + def verdict(w, data, ref_out, agent_out): + """Return (ok, reason, ref_val, agent_val) gating the agent output.""" + check_shape(w, agent_out) + if prim == "kmeans": + rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) + tol = float(cfg["inertia_tolerance"]) + ok = av <= (1.0 + tol) * rv + 1e-6 + return ok, ("inertia_regression" if not ok else ""), rv, av + if prim == "knn": + rec = recall(agent_out[1], ref_out[1]) + ok = rec >= float(cfg["recall_threshold"]) + return ok, ("recall_regression" if not ok else ""), 1.0, rec + center = (prim == "pca") + comps = agent_out[0] if prim == "pca" else agent_out[1] + ref_comps = ref_out[0] if prim == "pca" else ref_out[1] + oe = ortho_err(comps) + if oe > float(cfg["ortho_tolerance"]): + return False, "not_orthonormal", 0.0, oe + rv = captured(data["x"], ref_comps, center) + av = captured(data["x"], comps, center) + ok = av >= (1.0 - float(cfg["captured_tolerance"])) * rv - 1e-6 + return ok, ("captured_regression" if not ok else ""), rv, av + + def time_call(mod, w, data): + _sync(); t0 = _perf(); out = call(mod, w, data); _sync() + return (_perf() - t0) * 1000.0, out + + rows = [] + try: + for w in payload["workloads"]: + base_seed = int(w["seed"]) + # warmup on fresh data (kernels / autotune) — not measured + for i in range(warmup): + d = gen(w, base_seed + 100 + i) + call(ref, w, d); call(pkg, w, d); _sync() + del d + torch.cuda.empty_cache() + ratios, ref_val, agent_val = [], None, None + bad = None + for i in range(iters): + d = gen(w, base_seed + 10000 + i) # fresh every iteration + rt, rout = time_call(ref, w, d) + at, aout = time_call(pkg, w, d) + ok, reason, rv, av = verdict(w, d, rout, aout) # verify THIS iter + if not ok: + bad = reason; ref_val, agent_val = rv, av + break + ratios.append(rt / at if at > 0 else 0.01) + ref_val, agent_val = rv, av + del d, rout, aout + torch.cuda.empty_cache() + if bad is not None: + rows.append({"id": w["id"], "ok": False, "reason": bad, + "ref_val": ref_val, "agent_val": agent_val}) + continue + ratios.sort() + rows.append({"id": w["id"], "ok": True, + "speedup": ratios[len(ratios) // 2], + "ref_val": ref_val, "agent_val": agent_val}) + except Exception as exc: + return {"ok": False, "error": f"{type(exc).__name__}: {str(exc)[:200]}", + "trace": traceback.format_exc()[-500:]} + return {"ok": True, "rows": rows} + + +# --------------------------------------------------------------------------- # +# Host-side wrappers (run in the judge / agent container; need MODAL tokens). +# --------------------------------------------------------------------------- # +def _build_image(cfg: dict): + import modal + pip = list(cfg.get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])) + base = cfg.get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04") + return (modal.Image.from_registry(base, add_python="3.11") + .entrypoint([]) + .pip_install(*pip)) + + +# Substrings that mark a transient Modal control-plane / image-build failure +# (evicted build, gateway hiccup, app stopped mid-run) rather than a real error +# in the submission — safe to retry. +_TRANSIENT_MARKERS = ( + "external shut-down", "terminated due to external", "please try again", + "app_state_stopped", "conflicterror", "deadline exceeded", "connection reset", + "502 bad gateway", "503 service", "temporarily unavailable", "timed out", + "gateway", "eviction", "internalfailure", +) + + +def _is_transient(text: str) -> bool: + low = (text or "").lower() + return any(m in low for m in _TRANSIENT_MARKERS) + + +def run_remote(payload: dict) -> dict: + """Run the GPU worker on Modal and return its result dict. + + Retries transient Modal control-plane failures; a real error in the + submission is non-transient and surfaces immediately. Raises RuntimeError + with a sanitized message on unrecoverable failure. + """ + import time as _time + + import modal + + cfg = payload["cfg"] + attempts = max(1, int(cfg.get("modal_retries", 3))) + last = "modal_gpu_eval_failed" + for attempt in range(1, attempts + 1): + app = modal.App(cfg.get("app_name", "flash-kernel-eval")) + remote = app.function( + gpu=cfg.get("gpu", "H100"), + image=_build_image(cfg), + timeout=int(cfg.get("modal_timeout_seconds", 1800)), + serialized=True, + max_containers=1, + )(_gpu_worker) + try: + with modal.enable_output(), app.run(): + return remote.remote(payload) + except Exception as exc: # noqa: BLE001 + last = str(exc)[-400:] + if not _is_transient(last) or attempt == attempts: + raise RuntimeError(f"modal_gpu_eval_failed: {last}") from exc + _time.sleep(min(45, 10 * attempt)) + raise RuntimeError(f"modal_gpu_eval_failed: {last}") + + +def modal_available() -> bool: + return bool(os.environ.get("MODAL_TOKEN_ID") and os.environ.get("MODAL_TOKEN_SECRET")) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py index 6b459ea87..012594653 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,95 +1,77 @@ -"""Public self-test for the K-Means kernel-optimization task. +"""Public GPU self-test for the K-Means kernel-optimization task. -Runs the (patched) kmeanslib on the two public shapes, checks the clustering -quality against a local naive recomputation, and prints a rough speedup. This is -a convenience for local iteration only -- the graded workloads and thresholds -are hidden and differ from these shapes. +Times your current /app/kmeanslib against the naive baseline on a Modal GPU and +reports the quality verdict + speedup on two public shapes. Requires +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and +their thresholds are hidden and differ from these public shapes. """ from __future__ import annotations +import os import sys -import time - -PUBLIC_WORKLOADS = [ - {"N": 50_000, "D": 32, "K": 64, "max_iters": 10, "seed": 1}, - {"N": 200_000, "D": 128, "K": 256, "max_iters": 10, "seed": 2}, -] +from pathlib import Path +sys.path.insert(0, "/opt") -def naive_kmeans(x, k, max_iters, init): - import torch - c = init.clone() - labels = None - for _ in range(max_iters): - labels = torch.argmin(torch.cdist(x, c), dim=1) - sums = torch.zeros((k, x.shape[1]), device=x.device, dtype=torch.float32) - cnt = torch.zeros((k,), device=x.device, dtype=torch.float32) - sums.index_add_(0, labels, x.float()) - cnt.index_add_(0, labels, torch.ones(x.shape[0], device=x.device)) - empty = cnt == 0 - cnt = cnt.clamp_min(1.0) - newc = sums / cnt[:, None] - if empty.any(): - newc[empty] = c[empty].float() - c = newc.to(x.dtype) - return labels, c - - -def inertia(x, c): - import torch - cn = (c * c).sum(1) - total = 0.0 - for i in range(0, x.shape[0], 16384): - xb = x[i:i + 16384] - d = (xb * xb).sum(1, keepdim=True) - 2.0 * xb @ c.t() + cn[None, :] - total += float(d.min(1).values.clamp_min(0).sum()) - return total +APP_DIR = os.environ.get("APP_DIR", "/app") +PKG = "kmeanslib" +BASELINE_DIR = "/opt/kmeans_ref" +PUBLIC_WORKLOADS = [ + {"id": "p0", "N": 50_000, "D": 32, "K": 64, "max_iters": 10, "seed": 1}, + {"id": "p1", "N": 200_000, "D": 128, "K": 256, "max_iters": 10, "seed": 2}, +] -def bench(fn, warmup=2, iters=5): - import torch - for _ in range(warmup): - fn(); torch.cuda.synchronize() - ts = [] - for _ in range(iters): - torch.cuda.synchronize(); t0 = time.perf_counter() - fn(); torch.cuda.synchronize() - ts.append(time.perf_counter() - t0) - ts.sort() - return ts[len(ts) // 2] * 1000.0 +CFG = { + "primitive": "kmeans", + "pkg": PKG, + "ref_module": "refkmeans", + "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), + "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", + "pip": ["torch==2.5.1", "triton==3.1.0", "numpy"], + "app_name": "kmeans-kernel-opt-public", + "modal_timeout_seconds": 1800, + "warmup": 2, + "iters": 5, + "inertia_tolerance": 0.02, + "recall_threshold": 0.99, + "captured_tolerance": 0.02, + "ortho_tolerance": 0.02, +} + + +def _read(root: str, sub: str = "") -> dict: + base = Path(root) + scan = base / sub if sub else base + return {str(p.relative_to(base)): p.read_text(encoding="utf-8", errors="replace") + for p in scan.rglob("*.py")} def main() -> int: + import flash_gpu # baked at /opt/flash_gpu.py + if not flash_gpu.modal_available(): + print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU public test.") + return 1 + payload = { + "baseline_files": _read(BASELINE_DIR), + "patched_files": _read(APP_DIR, PKG), + "workloads": PUBLIC_WORKLOADS, + "cfg": CFG, + } try: - import torch - except Exception as e: # pragma: no cover - print(f"torch import failed: {e}") + result = flash_gpu.run_remote(payload) + except Exception as exc: # noqa: BLE001 + print(f"GPU run failed: {exc}") return 1 - if not torch.cuda.is_available(): - print("no CUDA device available; run this in a GPU-enabled container.") + if not result.get("ok"): + print(f"worker error: {result.get('error')}") return 1 - import kmeanslib - - for w in PUBLIC_WORKLOADS: - N, D, K, it, seed = w["N"], w["D"], w["K"], w["max_iters"], w["seed"] - g = torch.Generator(device="cuda").manual_seed(seed) - x = torch.randn(N, D, generator=g, device="cuda", dtype=torch.float32) - init = x.index_select(0, torch.randperm(N, generator=g, device="cuda")[:K]).clone() - - _, ref_c = naive_kmeans(x, K, it, init) - out = kmeanslib.kmeans(x, K, max_iters=it, init_centroids=init, tol=0.0) - agent_c = out[1] - ri, ai = inertia(x, ref_c), inertia(x, agent_c) - ratio = ai / ri if ri > 0 else float("inf") - - ref_ms = bench(lambda: naive_kmeans(x, K, it, init)) - agent_ms = bench(lambda: kmeanslib.kmeans(x, K, max_iters=it, init_centroids=init, tol=0.0)) - ok = "OK" if ratio <= 1.05 else "QUALITY REGRESSION" - print(f"(N={N}, D={D}, K={K}) inertia_ratio={ratio:.4f} [{ok}] " - f"baseline={ref_ms:.2f}ms yours={agent_ms:.2f}ms " - f"speedup={ref_ms / agent_ms:.2f}x") - del x, init - torch.cuda.empty_cache() + print(f"{'workload':10s} {'status':16s} {'speedup':>10s}") + for row in result["rows"]: + if row.get("ok"): + print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") + else: + print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason','')):16s} {'-':>10s}") return 0 diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index 4c2b47e25..f8a4b0467 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -2,8 +2,8 @@ ## Problem -You are given a small GPU K-Means library, `kmeanslib`, in the Harbor -workspace at `/app/kmeanslib`. Its public entry point is: +You are given a small GPU K-Means library, `kmeanslib`, in the Harbor workspace +at `/app/kmeanslib`. Its public entry point is: ```python kmeanslib.kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) @@ -12,27 +12,24 @@ kmeanslib.kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) `x` is an `(N, D)` float32 CUDA tensor, `init_centroids` is a fixed `(K, D)` initial-centroid tensor supplied by the caller, and the function runs Euclidean -(squared-L2) Lloyd iterations. The shipped implementation is correct but -deliberately naive: every iteration it materializes the full `(N, K)` distance -matrix with `torch.cdist`, then recomputes assignments and centroids with plain -PyTorch scatter ops. +(squared-L2) Lloyd iterations. The shipped implementation is a straightforward, +correct PyTorch version. Your goal is to make `kmeanslib.kmeans` **as fast as possible** on the GPU while -producing clusterings of the same quality. You may add modules and Triton -kernels inside the `kmeanslib` package and rewrite the internals freely (fused -assignment kernels, reduced memory traffic, better centroid updates, and so on). -The public function signature and return contract above must not change. +producing clusterings of the same quality. You may rewrite the internals of the +package however you like and add new modules (including Triton kernels) under +`kmeanslib/`. The public function signature and return contract above must not +change, and every result must remain a deterministic function of its inputs. ## Workload -The judge evaluates a family of held-out dense clustering workloads that vary -`(N, D, K)` and the iteration count, spanning small/medium/large point counts, -wide-feature (large `D`) and many-cluster (large `K`) regimes. All workloads use -Euclidean distance, float32 data, a fixed number of Lloyd iterations -(`tol = 0.0`, so every iteration runs), and caller-supplied `init_centroids`, so -each result is a deterministic function of the inputs. +The graded workloads are a family of held-out dense clustering problems that +vary `(N, D, K)` and the iteration count, spanning small/medium/large point +counts and both wide-feature (large `D`) and many-cluster (large `K`) regimes. +All use Euclidean distance, float32 data, a fixed iteration count (`tol = 0.0`, +so every iteration runs), and caller-supplied `init_centroids`. -Two representative *public* shapes you can use for local development (the graded +Two representative *public* shapes are available for local testing (the graded shapes are different and hidden): ```text @@ -40,19 +37,30 @@ shapes are different and hidden): (N=200000, D=128, K=256, max_iters=10) ``` -Treat the workload as a general dense K-Means, not as shapes to special-case. -Improvements should be general kernel/throughput improvements, not lookups keyed -on specific `(N, D, K)` values. +Treat this as a general dense K-Means, not as specific shapes to special-case. + +## Iterate on a GPU + +The agent workspace has no GPU. Use the public test to time your current code on +a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your +environment): + +```bash +bash /app/public_test.sh +``` + +It reports, per public shape, whether your result passes the quality gate and +your speedup over the baseline. ## Submission -The submitted artifact is a patch file over the `kmeanslib` package: +The submitted artifact is a patch over the `kmeanslib` package: ```text /app/solution.patch ``` -After editing `/app/kmeanslib`, generate and submit a patch: +After editing `/app/kmeanslib`, generate and submit: ```bash bash /app/make_submission.sh @@ -60,64 +68,54 @@ bash /app/submit.sh ``` Submissions are asynchronous; submit early and keep iterating. The judge applies -your patch to a clean copy of `kmeanslib`, imports the patched package, and -times it against the original naive implementation on the same hardware, same -seeded data, and same initial centroids. +your patch to a clean copy of `kmeanslib` and times it against the original +baseline on a GPU, on the same seeded data and initial centroids. ## Correctness -Correctness is a gate. For each workload the judge measures the **inertia** -(within-cluster sum of squared distances to the nearest final centroid) of your -result and of the naive baseline on identical data and initial centroids. Your -inertia must not exceed the baseline's by more than a small relative tolerance. -Crashes, non-finite outputs, wrong shapes/dtypes, timeouts, and clustering that -regresses beyond the tolerance are all penalized before speed is considered. You -may trade a little numerical precision (e.g. lower-precision accumulation) for -speed as long as you stay within the quality tolerance. +Correctness is a gate. On every timed iteration the judge recomputes the +**inertia** (within-cluster sum of squared distances to the nearest final +centroid) of your result and of the baseline on identical data, and requires +your inertia to stay within a small relative tolerance of the baseline's. +Crashes, non-finite output, wrong shapes/dtypes, timeouts, and clustering that +regresses beyond the tolerance are penalized before speed is considered. You may +trade a little numerical precision for speed as long as you stay within the +quality tolerance. ## Scoring -Valid submissions are scored by speedup relative to the naive baseline on the -same hardware and workloads. For each workload: +Valid submissions are scored by speedup relative to the baseline on the same +hardware and workloads. For each workload: ```text speedup = baseline_time / your_time ``` The objective is the geometric mean of per-workload speedups, so broad speedups -across the workload family are preferred over one large outlier. A result no -faster than the baseline earns 0; regressions earn 0. The raw geometric-mean -speedup is reported in the evaluator metrics. +are preferred over a single large outlier. A result no faster than the baseline +earns 0; regressions earn 0. The raw geometric-mean speedup is reported in the +evaluator metrics. ## Patch Policy -The evaluator validates the patch before running it. Only files under the +The evaluator validates the patch before running it. Only Python files under the package may change: ```text kmeanslib/** ``` -New Python modules inside `kmeanslib/` (for example Triton kernel files) are -allowed. Patches may not: - -- modify anything outside `kmeanslib/`; -- import or call an external optimized ML/kernel library (the point is to write - the kernels yourself); -- read or write environment variables, spawn processes, or access the network; -- special-case the hidden workload shapes. - -The patch must be reasonably sized and apply cleanly to the clean `kmeanslib` -tree. +New Python modules inside `kmeanslib/` are allowed. Patches may not: modify +anything outside `kmeanslib/`; import or call an external optimized ML/kernel +library (write the kernels yourself); read or write environment variables, spawn +processes, or access the network; or otherwise tamper with the measurement +framework. The timing harness measures your code on freshly generated data every +iteration and re-verifies quality each time, so caching results across calls does +not help. ## Resource Budget ```text -GPU: single device (H100 reference; Triton paths also run on L40S / A100) -vCPUs: 8 -memory: 32 GiB -storage: 32 GiB +GPU: single Modal GPU (H100 reference; the Triton paths also run on L40S / A100) +Agent container: CPU-only (GPU work is offloaded to Modal) ``` - -The judge runs the timing worker in a subprocess under a clean, minimal -environment with a fixed warmup/measurement schedule and a per-run timeout. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch b/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch index 2730259cd..a6d05bbd6 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch @@ -1,105 +1,2446 @@ -diff --git a/kmeanslib/_triton_assign.py b/kmeanslib/_triton_assign.py +diff --git a/kmeanslib/_kernels/__init__.py b/kmeanslib/_kernels/__init__.py new file mode 100644 -index 0000000..f40946a +index 0000000..e69de29 +diff --git a/kmeanslib/_kernels/primitives/__init__.py b/kmeanslib/_kernels/primitives/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/kmeanslib/_kernels/primitives/kmeans/__init__.py b/kmeanslib/_kernels/primitives/kmeans/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/kmeanslib/_kernels/primitives/kmeans/triton/__init__.py b/kmeanslib/_kernels/primitives/kmeans/triton/__init__.py +new file mode 100644 +index 0000000..043b4b7 --- /dev/null -+++ b/kmeanslib/_triton_assign.py -@@ -0,0 +1,75 @@ -+"""Fused squared-L2 nearest-centroid assignment in Triton. -+ -+Computes ``argmin_k ||x_n - c_k||^2`` without ever materialising the full -+``(N, K)`` distance matrix. Each program handles a ``BLOCK_N`` row tile, -+streams the centroids in ``BLOCK_K`` chunks, and keeps a running (min, argmin) -+in registers. Uses the x^2-free score ``||c_k||^2 - 2 `` (the -+``-||x_n||^2`` term is constant per row and does not affect the argmin). ++++ b/kmeanslib/_kernels/primitives/kmeans/triton/__init__.py +@@ -0,0 +1,43 @@ ++"""kmeans triton backend. ++ ++Re-exports the public Python wrappers from each component file. ++``@triton.jit`` kernels stay private to their file. +""" -+from __future__ import annotations ++from kmeanslib._kernels.primitives.kmeans.triton.assign import ( ++ _ceil_div, ++ euclid_assign_triton, ++ cosine_assign_triton, ++) ++from kmeanslib._kernels.primitives.kmeans.triton.kmeans import ( ++ COMPILE_FLAG, ++ batch_kmeans_Euclid, ++ batch_kmeans_Cosine, ++ batch_kmeans_Dot, ++) ++from kmeanslib._kernels.primitives.kmeans.triton.update import ( ++ triton_centroid_update_cosine, ++ torch_loop_centroid_update_cosine, ++ triton_centroid_update_euclid, ++ triton_centroid_update_sorted_cosine, ++ triton_centroid_update_sorted_euclid, ++ triton_centroid_finalize, ++ triton_lloyd_centroid_step_euclid, ++ main, ++) + ++__all__ = [ ++ "euclid_assign_triton", ++ "cosine_assign_triton", ++ "COMPILE_FLAG", ++ "batch_kmeans_Euclid", ++ "batch_kmeans_Cosine", ++ "batch_kmeans_Dot", ++ "triton_centroid_update_cosine", ++ "torch_loop_centroid_update_cosine", ++ "triton_centroid_update_euclid", ++ "triton_centroid_update_sorted_cosine", ++ "triton_centroid_update_sorted_euclid", ++ "triton_centroid_finalize", ++ "triton_lloyd_centroid_step_euclid", ++ "main", ++] +diff --git a/kmeanslib/_kernels/primitives/kmeans/triton/assign.py b/kmeanslib/_kernels/primitives/kmeans/triton/assign.py +new file mode 100644 +index 0000000..5014a67 +--- /dev/null ++++ b/kmeanslib/_kernels/primitives/kmeans/triton/assign.py +@@ -0,0 +1,1476 @@ ++from typing import Optional +import torch +import triton +import triton.language as tl + ++# =============================================================== ++# Triton kernel: compute nearest-centroid IDs (Euclidean distance). ++# ++# x²-free score: the kernel ranks ``s(n, k) = c_sq[k] − 2·`` ++# which is ``||x − c||² − ||x||²`` and so produces the same argmin ++# without loading any ``x_sq`` tensor. See assign_euclid in ++# klib/primitives/kmeans/torch_fallback.py for the matching CPU ++# reference and the gather kernel in klib/kernels/distance for ++# recovering the true distances post-hoc. ++# ++# Inputs: ++# x : (B, N, D) float16 / float32 ++# centroids : (B, K, D) same dtype as x ++# Output: ++# cluster_ids : (B, N) int32 -- nearest centroid index per point ++# =============================================================== ++ ++ ++def _ceil_div(a: int, b: int) -> int: ++ return (a + b - 1) // b ++ ++ ++def _next_power_of_2(n: int) -> int: ++ p = 1 ++ while p < n: ++ p <<= 1 ++ return p ++ ++ ++# ----------------------------------------------------------------------------- ++# Auto-tuning setup – explore various tile sizes / warp counts ++# ----------------------------------------------------------------------------- ++ ++_TUNE_CONFIGS = [ ++ triton.Config({"BLOCK_N": BN, "BLOCK_K": BK}, num_stages=num_stages, num_warps=wp) ++ for BN in [32, 64, 128] ++ for BK in [32, 64, 128] ++ for wp in [4, 8] ++ for num_stages in [1, 2, 4] ++] ++ ++ ++def _cfg_keep(conf): ++ """Basic heuristic to prune unbalanced configs.""" ++ BN = conf.kwargs["BLOCK_N"] ++ BK = conf.kwargs["BLOCK_K"] ++ # Avoid tiny tiles on many warps ++ if BN * BK < 32 * 32 and conf.num_warps > 4: ++ return False ++ return True ++ ++_TUNE_CONFIGS = list(filter(_cfg_keep, _TUNE_CONFIGS)) ++ ++ ++# Tuning grid for the split-D kernel. Adds a third tile dim ``BLOCK_D`` ++# that controls how much of the feature dimension is materialised inside ++# each program at a time. Pruned to keep peak SMEM and register pressure ++# under control on conservative GPUs (GB10). ++_TUNE_CONFIGS_SPLIT_D = [ ++ triton.Config({"BLOCK_N": BN, "BLOCK_K": BK, "BLOCK_D": BD}, ++ num_stages=num_stages, num_warps=wp) ++ for BN in [32, 64, 128] ++ for BK in [32, 64, 128] ++ for BD in [32, 64, 128] ++ for wp in [4, 8] ++ for num_stages in [1, 2, 4] ++] ++ ++ ++def _cfg_keep_split_d(conf): ++ BN = conf.kwargs["BLOCK_N"] ++ BK = conf.kwargs["BLOCK_K"] ++ BD = conf.kwargs["BLOCK_D"] ++ # Tiny tiles do not need many warps. ++ if BN * BK < 32 * 32 and conf.num_warps > 4: ++ return False ++ # Cap (BN, BK) tile size for register/SMEM safety. The (BN, BK) fp32 ++ # cross accumulator is the same size as the small-D kernel, so keeping ++ # BN*BK <= 128*128 prevents new spill regressions. ++ if BN * BK > 128 * 128: ++ return False ++ # Prune the largest combined work tiles to keep tuning wall-clock down ++ # without losing useful configs. ++ if BN * BK * BD > 128 * 128 * 128: ++ return False ++ return True ++ ++ ++_TUNE_CONFIGS_SPLIT_D = list(filter(_cfg_keep_split_d, _TUNE_CONFIGS_SPLIT_D)) ++ ++_HALF_DTYPES = (torch.float16, torch.bfloat16) ++ ++ ++def _dtype_bytes(dtype) -> int: ++ """Element size in bytes for a torch / numpy-ish dtype. ++ ++ Falls back to 2 (fp16) when the dtype is unknown to keep prior behaviour ++ (the heuristic was originally tuned with fp16 in mind). ++ """ ++ if dtype is None: ++ return 2 ++ if isinstance(dtype, torch.dtype): ++ return torch.tensor([], dtype=dtype).element_size() ++ # Allow callers to pass a raw byte size. ++ if isinstance(dtype, int): ++ return dtype ++ return 2 ++ ++ ++def _is_half_dtype(dtype) -> bool: ++ """True for fp16/bf16 (the original tuning regime). ++ ++ For these dtypes we skip the SMEM-fitting fallback entirely so heuristic ++ selection on already-validated GPUs (H100/H200/A100) is byte-for-byte ++ identical to the previous behaviour. ++ """ ++ if dtype is None: ++ return True ++ if isinstance(dtype, torch.dtype): ++ return dtype in _HALF_DTYPES ++ return False ++ ++ ++def _smem_bytes(D: int, BN: int, BK: int, num_stages: int, dtype_bytes: int) -> int: ++ """Approximate dynamic shared-memory usage of `_euclid_assign_kernel`. ++ ++ The kernel materialises: ++ - one ``x_tile`` of shape (BN, D) outside the K loop, and ++ - ``num_stages`` copies of ``c_tile`` of shape (D, BK) for the software ++ pipelined K loop. ++ ++ Other buffers (c_sq, masks, accumulators) are negligible compared ++ to these and are ignored. The x²-free kernel does not load ``x_sq``. ++ """ ++ return D * dtype_bytes * (BN + num_stages * BK) ++ ++ ++def _smem_limit(device) -> int: ++ """Per-block dynamic shared-memory budget for ``device``. ++ ++ Triton uses opt-in dynamic shared memory; prefer that attribute when ++ available, fall back to the static limit, and finally to a conservative ++ 48 KiB for very old PyTorch builds. ++ """ ++ props = torch.cuda.get_device_properties(device) ++ for attr in ( ++ "shared_memory_per_block_optin", ++ "max_shared_memory_per_block_optin", ++ "shared_memory_per_block", ++ "max_shared_memory_per_block", ++ ): ++ v = getattr(props, attr, None) ++ if v: ++ return int(v) ++ return 48 * 1024 ++ ++ ++def _fit_config_to_smem( ++ cfg: dict, ++ D: int, ++ dtype_bytes: int, ++ smem_limit: int, ++) -> dict: ++ """Return a config that fits ``smem_limit`` and is closest to ``cfg``. ++ ++ The original config is returned unchanged whenever it already fits. If ++ not, we enumerate all power-of-two ``(BLOCK_N, BLOCK_K, num_stages)`` ++ that are no larger than the original and pick the one that maximises ++ work-per-program tile (``BLOCK_N * BLOCK_K * num_stages``), breaking ++ ties towards the original aspect ratio. This avoids the pitfall of a ++ pure greedy halving (e.g. shrinking BLOCK_K all the way to 16 when only ++ a single halving was needed). ++ ++ Raises ``RuntimeError`` if even ``(BN=16, BK=16, S=1)`` does not fit – ++ this only happens for absurdly large D combined with fp32 on tiny-SMEM ++ GPUs. ++ """ ++ BN0 = int(cfg["BLOCK_N"]) ++ BK0 = int(cfg["BLOCK_K"]) ++ W0 = int(cfg["num_warps"]) ++ S0 = int(cfg["num_stages"]) ++ ++ if _smem_bytes(D, BN0, BK0, S0, dtype_bytes) <= smem_limit: ++ return {"BLOCK_N": BN0, "BLOCK_K": BK0, "num_warps": W0, "num_stages": S0} ++ ++ def _pow2_down_to_16(v): ++ out = [] ++ x = v ++ while x >= 16: ++ out.append(x) ++ x //= 2 ++ return out ++ ++ best = None ++ best_key = None ++ for BN in _pow2_down_to_16(BN0): ++ for BK in _pow2_down_to_16(BK0): ++ for S in range(S0, 0, -1): ++ if _smem_bytes(D, BN, BK, S, dtype_bytes) > smem_limit: ++ continue ++ # Prefer larger total tile work, then closer aspect ratio ++ # to the original, then larger BLOCK_N (more parallelism ++ # along N), then larger num_stages (better pipelining). ++ aspect_penalty = abs( ++ (BN / max(BK, 1)) - (BN0 / max(BK0, 1)) ++ ) ++ key = (BN * BK * S, -aspect_penalty, BN, S) ++ if best_key is None or key > best_key: ++ best_key = key ++ best = (BN, BK, S) ++ ++ if best is None: ++ raise RuntimeError( ++ f"euclid_assign_triton: cannot fit kernel into shared memory " ++ f"(D={D}, dtype_bytes={dtype_bytes}, smem_limit={smem_limit}). " ++ f"Even BLOCK_N=16, BLOCK_K=16, num_stages=1 needs " ++ f"{_smem_bytes(D, 16, 16, 1, dtype_bytes)} bytes." ++ ) ++ ++ BN, BK, S = best ++ W = W0 ++ # Tiny tiles do not benefit from many warps and may even fail to compile ++ # for some Triton versions; cap to 4. ++ if BN * BK <= 32 * 32 and W > 4: ++ W = 4 ++ ++ return {"BLOCK_N": BN, "BLOCK_K": BK, "num_warps": W, "num_stages": S} ++ ++ ++def _smem_bytes_split_d(BD: int, BN: int, BK: int, num_stages: int, dtype_bytes: int) -> int: ++ """SMEM estimate for ``_euclid_assign_kernel_split_d`` per program. ++ ++ The split-D kernel materialises: ++ - one ``x_chunk`` of shape (BN, BD) per D-tile, and ++ - ``num_stages`` copies of ``c_chunk`` of shape (BD, BK) for the software ++ pipelined inner D loop. ++ """ ++ return BD * dtype_bytes * (BN + num_stages * BK) ++ ++ ++def _smallD_kernel_fits_smem(D: int, dtype_bytes: int, smem_limit: int) -> bool: ++ """Return True if even the tiniest small-D kernel config fits SMEM. ++ ++ Used by the wrapper to fall back to split-D when the small-D kernel can ++ not run at all (e.g., GB10 + fp32 + D=448). ++ """ ++ return _smem_bytes(D, 16, 16, 1, dtype_bytes) <= smem_limit ++ ++ ++def _fit_config_to_smem_split_d( ++ cfg: dict, ++ D: int, ++ dtype_bytes: int, ++ smem_limit: int, ++) -> dict: ++ """Shrink a split-D config until it fits ``smem_limit``. ++ ++ Mirrors ``_fit_config_to_smem`` with the additional ``BLOCK_D`` axis. ++ Returns the largest-work config that fits, breaking ties towards the ++ original aspect ratio. ``BLOCK_D`` is also clamped to D when D < BD0 ++ (no point materialising more dims than exist). ++ """ ++ BN0 = int(cfg["BLOCK_N"]) ++ BK0 = int(cfg["BLOCK_K"]) ++ W0 = int(cfg["num_warps"]) ++ S0 = int(cfg["num_stages"]) ++ BD0 = int(cfg["BLOCK_D"]) ++ # No point letting BD exceed next_pow2(D) (the loop would still run ++ # once). We round D up to the next power-of-2 because BLOCK_D feeds ++ # into ``tl.arange(0, BLOCK_D)`` which Triton requires to be pow2. ++ D_ceil = _next_power_of_2(max(D, 16)) ++ BD0 = min(BD0, D_ceil) ++ ++ if _smem_bytes_split_d(BD0, BN0, BK0, S0, dtype_bytes) <= smem_limit: ++ return { ++ "BLOCK_N": BN0, "BLOCK_K": BK0, "BLOCK_D": BD0, ++ "num_warps": W0, "num_stages": S0, ++ } ++ ++ def _pow2_down_to_16(v): ++ out = [] ++ x = v ++ while x >= 16: ++ out.append(x) ++ x //= 2 ++ return out ++ ++ best = None ++ best_key = None ++ for BN in _pow2_down_to_16(BN0): ++ for BK in _pow2_down_to_16(BK0): ++ for BD in _pow2_down_to_16(BD0): ++ for S in range(S0, 0, -1): ++ if _smem_bytes_split_d(BD, BN, BK, S, dtype_bytes) > smem_limit: ++ continue ++ aspect_penalty = abs( ++ (BN / max(BK, 1)) - (BN0 / max(BK0, 1)) ++ ) ++ key = (BN * BK * BD * S, -aspect_penalty, BN, BD, S) ++ if best_key is None or key > best_key: ++ best_key = key ++ best = (BN, BK, BD, S) ++ ++ if best is None: ++ raise RuntimeError( ++ f"euclid_assign_triton (split-D): cannot fit kernel into shared " ++ f"memory (D={D}, dtype_bytes={dtype_bytes}, smem_limit={smem_limit})." ++ ) ++ ++ BN, BK, BD, S = best ++ W = W0 ++ if BN * BK <= 32 * 32 and W > 4: ++ W = 4 ++ return { ++ "BLOCK_N": BN, "BLOCK_K": BK, "BLOCK_D": BD, ++ "num_warps": W, "num_stages": S, ++ } ++ ++ ++# ----------------------------------------------------------------------------- ++# Per-arch small-D heuristic functions. These bodies are the original ++# hand-tuned tables, moved verbatim here so the top-level dispatcher stays ++# small and so the split-D path can live alongside without touching them. ++# Any change here must be guarded by examples/regression_fp16_smalld.py. ++# ----------------------------------------------------------------------------- ++ ++ ++def _heuristic_euclid_config_h200_smallD(N: int, K: int, D: int, dtype) -> dict: ++ """H200 small-D heuristic for the x²-free Euclidean kernel. ++ ++ Derived from a fresh grid sweep (``scripts/tune_euclid_h200.py`` in ++ flash-kmeans) after dropping the ``||x||²`` load and the underflow ++ clamp from ``_euclid_assign_kernel``: N ∈ {65536, 1048576}, ++ K ∈ {256, 4096, 65536, 200000}, D ∈ {64, 128, 256, 512}, B=1, ++ fp16 + fp32. ++ ++ fp32: ++ - D=64 K<=256 : BN=128 BK=64 W=4 S=1 ++ - D=64 25665K : BN=128 BK=64 W=4 S=1 ++ - D=128 K<=4K : BN=128 BK=64 W=4 S=1 ++ - D=128 K>4K : BN=128 BK=64 W=8 S=1 ++ - D=256 : BN=128 BK=64 W=8 S=1 ++ - D=512 (and 320/384/448) : BN=64 BK=32 W=4 S=1 ++ (fp32 + wide-D only fits H200's 226 KiB SMEM with the smallest tile) ++ ++ fp16 / bf16: ++ - D=64 K<=256 : BN=128 BK=128 W=4 S=2 ++ - D=64 256=200K : BN=128 BK=64 W=4 S=4 ++ - D=128 K<=256 : BN=128 BK=64 W=4 S=1 ++ - D=128 25665K : BN=128 BK=64 W=4 S=1 ++ - D=256 K<=4K : BN=128 BK=64 W=4 S=1 ++ - D=256 K>4K : BN=128 BK=64 W=8 S=1 ++ - D>=320 (i.e. D=512) : BN=128 BK=64 W=8 S=1 ++ ++ Tiny N (<65536) shrinks BN to 64 to avoid wasted work -- kept as a ++ guard since the new sweep only covered N>=65536. ++ """ ++ half = _is_half_dtype(dtype) ++ ++ if not half: ++ if D <= 64: ++ if K <= 256: ++ return {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ if K <= 65536: ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "num_warps": 4, "num_stages": 1} ++ return {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ if D <= 128: ++ if K <= 4096: ++ return {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ return {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 8, "num_stages": 1} ++ if D <= 256: ++ return {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 8, "num_stages": 1} ++ cfg = {"BLOCK_N": 64, "BLOCK_K": 32, "num_warps": 4, "num_stages": 1} ++ if N < 65536: ++ cfg = dict(cfg) ++ cfg["BLOCK_N"] = 32 ++ return cfg ++ ++ if D <= 64: ++ if K <= 256: ++ cfg = {"BLOCK_N": 128, "BLOCK_K": 128, "num_warps": 4, "num_stages": 2} ++ elif K < 200_000: ++ cfg = {"BLOCK_N": 64, "BLOCK_K": 128, "num_warps": 4, "num_stages": 4} ++ else: ++ cfg = {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 4} ++ elif D <= 128: ++ if K <= 256: ++ cfg = {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ elif K <= 65536: ++ cfg = {"BLOCK_N": 128, "BLOCK_K": 128, "num_warps": 8, "num_stages": 2} ++ else: ++ cfg = {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ elif D <= 256: ++ if K <= 4096: ++ cfg = {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ else: ++ cfg = {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 8, "num_stages": 1} ++ else: ++ cfg = {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 8, "num_stages": 1} ++ ++ if N < 65536: ++ cfg = dict(cfg) ++ cfg["BLOCK_N"] = 64 ++ ++ return cfg ++ ++ ++def _heuristic_euclid_config_h100_smallD(N: int, K: int, D: int, dtype) -> dict: ++ # H100 tuned heuristic (more conservative on D=64 mid-K vs H200). ++ block_n = 128 ++ block_k = 64 ++ num_warps = 4 ++ num_stages = 1 ++ ++ if D >= 512: ++ block_n = 128 ++ block_k = 64 ++ num_warps = 8 ++ num_stages = 1 ++ elif D >= 256: ++ block_n = 128 ++ block_k = 64 ++ if K <= 1024: ++ num_warps = 8 ++ num_stages = 1 ++ elif K <= 16384: ++ num_warps = 4 ++ num_stages = 1 ++ else: ++ num_warps = 8 ++ num_stages = 1 ++ else: ++ # D <= 128 ++ if D <= 64: ++ if K <= 1024: ++ block_k = 64 ++ num_warps = 4 ++ num_stages = 2 ++ elif K <= 16384: ++ block_k = 64 ++ num_warps = 4 ++ num_stages = 2 ++ elif K <= 65536: ++ block_k = 128 ++ num_warps = 4 ++ num_stages = 4 ++ else: ++ block_k = 64 ++ num_warps = 4 ++ num_stages = 4 ++ else: ++ # D == 128 ++ if K <= 1024: ++ block_k = 64 ++ num_warps = 4 ++ num_stages = 1 ++ elif K <= 65536: ++ block_k = 128 ++ num_warps = 8 ++ num_stages = 2 ++ else: ++ block_k = 64 ++ num_warps = 4 ++ num_stages = 4 ++ ++ if N < 65536: ++ block_n = 64 ++ ++ return { ++ "BLOCK_N": block_n, ++ "BLOCK_K": block_k, ++ "num_warps": num_warps, ++ "num_stages": num_stages, ++ } ++ ++ ++def _heuristic_euclid_config_a100_smallD(N: int, K: int, D: int, dtype) -> dict: ++ # Robust default on A100 across tuned grid. ++ block_n = 128 ++ block_k = 32 ++ num_warps = 4 ++ num_stages = 2 ++ ++ if D == 128: ++ # Small-N cases tend to prefer a larger K tile. ++ if N <= 65536: ++ block_k = 64 ++ elif D == 256: ++ # D=256 benefits from deeper pipeline at larger K. ++ if K >= 65536: ++ block_k = 32 ++ num_stages = 4 ++ elif K >= 1024 and N <= 262144: ++ block_k = 64 ++ num_stages = 4 ++ ++ return { ++ "BLOCK_N": block_n, ++ "BLOCK_K": block_k, ++ "num_warps": num_warps, ++ "num_stages": num_stages, ++ } ++ ++ ++def _heuristic_euclid_config_gb10_smallD(N: int, K: int, D: int, dtype) -> dict: ++ # GB10 (Grace Blackwell, ~80 SMs, ~99 KiB SMEM/SM) tuned heuristic. ++ # Derived from a grid sweep over N in {65536, 262144, 1048576}, ++ # K in {256..200000}, D in {64,128,256,512}, B in {1, 32}, fp16. ++ if D >= 512: ++ if K <= 256: ++ return {"BLOCK_N": 64, "BLOCK_K": 32, "num_warps": 4, "num_stages": 1} ++ return {"BLOCK_N": 64, "BLOCK_K": 32, "num_warps": 8, "num_stages": 1} ++ ++ if D >= 256: ++ if K <= 256: ++ return {"BLOCK_N": 64, "BLOCK_K": 32, "num_warps": 4, "num_stages": 1} ++ return {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 2} ++ ++ if D >= 128: ++ if K <= 256: ++ return {"BLOCK_N": 64, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ if K <= 1024: ++ if N <= 65536: ++ return {"BLOCK_N": 64, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ return {"BLOCK_N": 128, "BLOCK_K": 32, "num_warps": 4, "num_stages": 2} ++ if K <= 65536: ++ return {"BLOCK_N": 128, "BLOCK_K": 32, "num_warps": 4, "num_stages": 1} ++ return {"BLOCK_N": 128, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ ++ # D <= 64 ++ if K <= 256 and N <= 65536: ++ return {"BLOCK_N": 64, "BLOCK_K": 64, "num_warps": 4, "num_stages": 1} ++ return {"BLOCK_N": 128, "BLOCK_K": 32, "num_warps": 4, "num_stages": 1} ++ ++ ++def _heuristic_euclid_config_fallback_smallD(N: int, K: int, D: int, dtype) -> dict: ++ # Conservative default for unknown architectures (prioritize avoiding OOR). ++ return { ++ "BLOCK_N": 64, ++ "BLOCK_K": 32, ++ "num_warps": 4, ++ "num_stages": 1, ++ } ++ ++ ++_KNOWN_ARCHS = ("H200", "H100", "A100", "GB10") ++ ++ ++def _is_known_arch(gpu_name: str) -> bool: ++ return any(tag in gpu_name for tag in _KNOWN_ARCHS) ++ ++ ++def _arch_smallD_picker(gpu_name: str): ++ if "H200" in gpu_name: ++ return _heuristic_euclid_config_h200_smallD ++ if "H100" in gpu_name: ++ return _heuristic_euclid_config_h100_smallD ++ if "A100" in gpu_name: ++ return _heuristic_euclid_config_a100_smallD ++ if "GB10" in gpu_name: ++ return _heuristic_euclid_config_gb10_smallD ++ return _heuristic_euclid_config_fallback_smallD ++ ++ ++def _heuristic_euclid_config( ++ N: int, ++ K: int, ++ D: int, ++ *, ++ device: Optional[torch.device] = None, ++ dtype=None, ++): ++ """Architecture-aware heuristic config selection without autotune. ++ ++ Per-GPU sub-functions own the actual lookup tables. This function only ++ routes to the right one and (for non-half dtypes) post-processes the ++ config through ``_fit_config_to_smem`` so fp32 / large-D inputs do not ++ OOR on small-SMEM GPUs. ++ ++ For fp16/bf16 the picked config is returned **byte-for-byte** as the ++ original tables produced (``examples/regression_fp16_smalld.py`` ++ enforces this). ++ """ ++ if device is None: ++ device = torch.device("cuda") ++ gpu_name = torch.cuda.get_device_properties(device).name.upper() ++ ++ cfg = _arch_smallD_picker(gpu_name)(N, K, D, dtype) ++ ++ if _is_half_dtype(dtype): ++ return cfg ++ ++ dtype_bytes = _dtype_bytes(dtype) ++ smem_limit = _smem_limit(device) ++ return _fit_config_to_smem(cfg, D, dtype_bytes, smem_limit) ++ ++ ++# ----------------------------------------------------------------------------- ++# Split-D heuristic. Per-arch tables stay conservative for now and rely on ++# ``_fit_config_to_smem_split_d`` for SMEM safety. H200 and H100 have freshly ++# tuned tables; A100/GB10 still use a single default until tuning data lands. ++# ----------------------------------------------------------------------------- ++ ++ ++def _heuristic_euclid_config_h200_largeD(N: int, K: int, D: int, dtype) -> dict: ++ """H200 split-D heuristic. ++ ++ Derived from a focused grid sweep over D ∈ {1024, 2048, 4096}, ++ K ∈ {256..65536}, N ∈ {65536, 262144, 1048576}, B=1, fp16+fp32. ++ Patterns: ++ - fp16/bf16 (2 bytes): wide tile (BN=128, BK=128) with BD=64 is the ++ clear winner across D=1024 and D=4096 (3/3 N votes per K bucket). ++ D=2048 with K ≥ 4096 prefers the deeper-D tile (BN=64, BK=128, BD=128) ++ because the centroid stream dominates and a fatter D chunk amortises ++ loads better. ++ - fp32 (4 bytes): same shape but BD shrinks to 32 to keep SMEM under ++ budget. D=1024 with K ≥ 4096 splits BN→64 and grows BD→64 (more ++ D-axis amortisation when the centroid set is large). ++ """ ++ half = _is_half_dtype(dtype) ++ ++ if half: ++ if D >= 4096: ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 8, "num_stages": 4} ++ if D >= 2048: ++ if K <= 1024: ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 8, "num_stages": 4} ++ return {"BLOCK_N": 64, "BLOCK_K": 128, "BLOCK_D": 128, ++ "num_warps": 4, "num_stages": 4} ++ # D ≈ 1024 (also covers D in (512, 1024) when small-D kernel doesn't fit) ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 8, "num_stages": 4} ++ ++ # fp32 / wider ++ if D >= 2048: ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 32, ++ "num_warps": 8, "num_stages": 4} ++ # D ≈ 1024 fp32 ++ if K <= 1024: ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 32, ++ "num_warps": 8, "num_stages": 4} ++ return {"BLOCK_N": 64, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 4, "num_stages": 4} ++ ++ ++def _heuristic_euclid_config_h100_largeD(N: int, K: int, D: int, dtype) -> dict: ++ """H100 split-D heuristic. ++ ++ Derived from a focused grid sweep over D ∈ {1024, 2048, 4096}, ++ K ∈ {256, 1024, 4096, 16384, 65536}, N ∈ {65536, 262144, 1048576}, ++ B=1, fp16+fp32. Two cells (K=65536 D=4096 N=1048576 fp16/fp32) were ++ skipped due to multi-hour cost; their config is extrapolated from the ++ matching K=65536 K=16384/N=1048576 winners (same dominant shape). ++ ++ Patterns: ++ - fp16/bf16: BN=128 BK=128 BD=64 W=8 S=4 dominates D=1024 (K≥1024) ++ and D=4096. D=2048 with K ∈ [1024, 16384] prefers BN=64 BK=128 ++ BD=128 W=4 S=4 — a deeper D tile amortises centroid loads when ++ the centroid set is moderate; the wider N tile only pays off ++ once K=65536 makes per-program K-streaming dominate. K=256 ++ uniformly prefers the smaller N tile (BN=64, W=4) — too few K ++ tiles to justify the wider N-axis program. ++ - fp32: BN=128 BK=128 BD=32 W=8 S=4 dominates D ≥ 2048. D=1024 ++ prefers BN=64 BK=128 BD=64 W=4 S=4 across all K — distinct from ++ H200 which keeps BD=32 at small K, because H100's narrower L2 ++ benefits from the wider D chunk amortising the centroid stream. ++ """ ++ half = _is_half_dtype(dtype) ++ ++ if half: ++ # K=256 has too few centroid tiles to justify a wide N program. ++ if K <= 256: ++ return {"BLOCK_N": 64, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 4, "num_stages": 4} ++ if D >= 4096: ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 8, "num_stages": 4} ++ if D >= 2048: ++ # K ∈ [1024, 16384]: deeper D tile (BD=128) amortises centroid ++ # loads better. K=65536: the long K stream dominates and the ++ # wider N tile (BN=128, BD=64) wins. ++ if K <= 16384: ++ return {"BLOCK_N": 64, "BLOCK_K": 128, "BLOCK_D": 128, ++ "num_warps": 4, "num_stages": 4} ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 8, "num_stages": 4} ++ # D ≈ 1024 (also covers D ∈ (512, 1024) when small-D doesn't fit). ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 8, "num_stages": 4} ++ ++ # fp32 / wider ++ if D >= 2048: ++ return {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_D": 32, ++ "num_warps": 8, "num_stages": 4} ++ # D ≈ 1024 fp32 — wider BD=64 wins consistently across K on H100. ++ return {"BLOCK_N": 64, "BLOCK_K": 128, "BLOCK_D": 64, ++ "num_warps": 4, "num_stages": 4} ++ ++ ++def _heuristic_euclid_config_a100_largeD(N: int, K: int, D: int, dtype) -> dict: ++ return { ++ "BLOCK_N": 64, ++ "BLOCK_K": 32, ++ "BLOCK_D": 32, ++ "num_warps": 4, ++ "num_stages": 2, ++ } ++ ++ ++def _heuristic_euclid_config_gb10_largeD(N: int, K: int, D: int, dtype) -> dict: ++ return { ++ "BLOCK_N": 64, ++ "BLOCK_K": 32, ++ "BLOCK_D": 32, ++ "num_warps": 4, ++ "num_stages": 1, ++ } ++ ++ ++def _heuristic_euclid_config_fallback_largeD(N: int, K: int, D: int, dtype) -> dict: ++ return { ++ "BLOCK_N": 32, ++ "BLOCK_K": 32, ++ "BLOCK_D": 32, ++ "num_warps": 4, ++ "num_stages": 1, ++ } ++ ++ ++def _arch_largeD_picker(gpu_name: str): ++ if "H200" in gpu_name: ++ return _heuristic_euclid_config_h200_largeD ++ if "H100" in gpu_name: ++ return _heuristic_euclid_config_h100_largeD ++ if "A100" in gpu_name: ++ return _heuristic_euclid_config_a100_largeD ++ if "GB10" in gpu_name: ++ return _heuristic_euclid_config_gb10_largeD ++ return _heuristic_euclid_config_fallback_largeD ++ ++ ++def _heuristic_euclid_config_split_d( ++ N: int, ++ K: int, ++ D: int, ++ *, ++ device: Optional[torch.device] = None, ++ dtype=None, ++): ++ """Heuristic config picker for the split-D Euclid assign kernel. ++ ++ Always post-processes through ``_fit_config_to_smem_split_d`` so the ++ selected config is guaranteed to fit SMEM regardless of dtype/D. ++ """ ++ if device is None: ++ device = torch.device("cuda") ++ gpu_name = torch.cuda.get_device_properties(device).name.upper() ++ cfg = _arch_largeD_picker(gpu_name)(N, K, D, dtype) ++ dtype_bytes = _dtype_bytes(dtype) ++ smem_limit = _smem_limit(device) ++ return _fit_config_to_smem_split_d(cfg, D, dtype_bytes, smem_limit) ++ ++ ++# Hard threshold: D > this triggers split-D dispatch even when SMEM would ++# nominally fit, so the fp16/bf16/fp32 + D ≤ 512 regime stays on the ++# original kernel path (matches the regime the existing tables were tuned ++# in). Larger D goes through the split-D path. ++_SMALL_D_MAX = 512 ++ ++ ++def _is_power_of_2(n: int) -> bool: ++ return n > 0 and (n & (n - 1)) == 0 ++ ++ ++def _need_split_d(D: int, dtype, device) -> bool: ++ """Decide whether to dispatch to the split-D kernel. ++ ++ Split-D is needed when: ++ 1. D exceeds the small-D regime (the original kernel can't tile D). ++ 2. D is **not a power of 2** — the small-D kernel uses ++ ``tl.arange(0, D)`` which Triton requires to be a power of 2. ++ The split-D kernel uses ``tl.arange(0, BLOCK_D)`` (always a ++ power of 2) with a ``d_mask = d_offsets < D`` guard, so any D ++ value works. ++ 3. The small-D kernel cannot fit even at minimum tile (BN=16, BK=16, ++ S=1) — SMEM safety net for awkward dtype/D/GPU triples. ++ 4. The GPU is unknown to the heuristic. The small-D path relies on ++ per-arch tuning tables; on unfamiliar architectures we have no ++ data to trust those configs and the SMEM probe may also be ++ unreliable. Split-D with the conservative fallback (small BN/BK/BD, ++ num_stages=1) is the safer choice — ``_fit_config_to_smem_split_d`` ++ then guarantees the launch fits regardless of how small the SMEM ++ budget actually turns out to be. ++ """ ++ if D > _SMALL_D_MAX: ++ return True ++ if not _is_power_of_2(D): ++ return True ++ # tl.dot requires the contraction axis (D) to be >= 16. ++ if D < 16: ++ return True ++ if device is None: ++ device = torch.device("cuda") ++ gpu_name = torch.cuda.get_device_properties(device).name.upper() ++ if not _is_known_arch(gpu_name): ++ return True ++ dtype_bytes = _dtype_bytes(dtype) ++ smem_limit = _smem_limit(device) ++ return not _smallD_kernel_fits_smem(D, dtype_bytes, smem_limit) ++ + +@triton.jit -+def _assign_kernel( -+ x_ptr, c_ptr, csq_ptr, out_ptr, -+ N, K, D, -+ stride_xn, stride_xd, -+ stride_ck, stride_cd, -+ BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, BLOCK_D: tl.constexpr, ++def _euclid_assign_kernel( ++ x_ptr, # *f16 / *f32 [B, N, D] ++ c_ptr, # *f16 / *f32 [B, K, D] ++ c_sq_ptr, # *f32 [B, K] ++ out_ptr, # *i32 [B, N] ++ B: tl.constexpr, ++ N: tl.constexpr, ++ K: tl.constexpr, ++ D: tl.constexpr, ++ stride_x_b: tl.constexpr, ++ stride_x_n: tl.constexpr, ++ stride_x_d: tl.constexpr, ++ stride_c_b: tl.constexpr, ++ stride_c_k: tl.constexpr, ++ stride_c_d: tl.constexpr, ++ stride_csq_b: tl.constexpr, ++ stride_csq_k: tl.constexpr, ++ stride_out_b: tl.constexpr, ++ stride_out_n: tl.constexpr, ++ BLOCK_N: tl.constexpr, ++ BLOCK_K: tl.constexpr, +): -+ pid = tl.program_id(0) -+ offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N) -+ offs_d = tl.arange(0, BLOCK_D) -+ n_mask = offs_n < N -+ d_mask = offs_d < D ++ """Each program handles a tile of BLOCK_N points for a given batch element. ++ ++ The kernel iterates over the centroid dimension K in chunks of BLOCK_K and ++ maintains the running minimum distance as well as the corresponding index ++ for every point in the tile. ++ """ ++ pid_n = tl.program_id(0) # tile index along N dimension ++ pid_b = tl.program_id(1) # batch index ++ pid_b = pid_b.to(tl.int64) ++ ++ n_start = pid_n * BLOCK_N ++ n_offsets = n_start + tl.arange(0, BLOCK_N) ++ n_offsets = n_offsets.to(tl.int64) ++ n_mask = n_offsets < N ++ ++ # ------------------------------------------------------------------ ++ # Load x tile (BLOCK_N, D) ++ # ------------------------------------------------------------------ ++ offs_d = tl.arange(0, D).to(tl.int64) ++ # Compute pointer for x block: base + b*stride_x_b + n*stride_x_n + d*stride_x_d ++ x_ptrs = ( ++ x_ptr ++ + pid_b * stride_x_b ++ + n_offsets[:, None] * stride_x_n ++ + offs_d[None, :] * stride_x_d ++ ) ++ x_tile = tl.load(x_ptrs, mask=n_mask[:, None], other=0.0) ++ x_tile = x_tile # compute in f32 ++ ++ # Init best distance / index ++ best_dist = tl.full((BLOCK_N,), 3.4e38, tl.float32) # large number ++ best_idx = tl.zeros((BLOCK_N,), tl.int32) ++ ++ # ------------------------------------------------------------------ ++ # Iterate over centroids in chunks of BLOCK_K ++ # ------------------------------------------------------------------ ++ for k_start in range(0, K, BLOCK_K): ++ k_offsets = k_start + tl.arange(0, BLOCK_K) ++ k_offsets = k_offsets.to(tl.int64) ++ k_mask = k_offsets < K ++ ++ # Load centroid tile (D, BLOCK_K) ++ c_ptrs = ( ++ c_ptr ++ + pid_b * stride_c_b ++ + k_offsets[None, :] * stride_c_k ++ + offs_d[:, None] * stride_c_d ++ ) ++ c_tile = tl.load(c_ptrs, mask=k_mask[None, :], other=0.0) ++ c_tile = c_tile ++ ++ # load c_sq for the tile (BLOCK_K,) ++ csq_ptrs = c_sq_ptr + pid_b * stride_csq_b + k_offsets * stride_csq_k ++ cent_sq = tl.load(csq_ptrs, mask=k_mask, other=0.0).to(tl.float32) ++ ++ # # Compute centroid squared norms (BLOCK_K,) ++ # cent_sq = tl.sum(c_tile * c_tile, axis=0).to(tl.float32) ++ ++ # Compute cross term (BLOCK_N, BLOCK_K) = x_tile @ c_tile ++ cross = tl.dot(x_tile, c_tile).to(tl.float32) ++ ++ # Squared-distance rank score (same argmin as ||x-c||^2); ||x||^2 omitted. ++ dist = cent_sq[None, :] - 2.0 * cross ++ ++ # Mask out invalid centroid columns before reduction ++ dist = tl.where(k_mask[None, :], dist, 3.4e38) ++ ++ curr_min = tl.min(dist, axis=1) ++ curr_idx = tl.argmin(dist, axis=1) ++ ++ update = curr_min < best_dist ++ best_dist = tl.where(update, curr_min, best_dist) ++ best_idx = tl.where(update, k_start + curr_idx, best_idx) ++ ++ # ------------------------------------------------------------------ ++ # Write results ++ # ------------------------------------------------------------------ ++ out_ptrs = out_ptr + pid_b * stride_out_b + n_offsets * stride_out_n ++ tl.store(out_ptrs, best_idx, mask=n_mask) ++ ++_euclid_assign_kernel_autotuned = triton.autotune(_TUNE_CONFIGS, key=["N", "K"])(_euclid_assign_kernel) ++ + -+ # Row tile stays resident in registers/SRAM across the whole K sweep. -+ x_ptrs = x_ptr + offs_n[:, None] * stride_xn + offs_d[None, :] * stride_xd -+ x_tile = tl.load(x_ptrs, mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++# =============================================================== ++# Split-D Euclid assign kernel. ++# ++# This kernel mirrors ``_euclid_assign_kernel`` but tiles the feature ++# dimension D into chunks of size ``BLOCK_D`` so the per-program SMEM ++# footprint is bounded by BLOCK_D rather than D. The K-streaming property ++# (no full distance matrix materialised) is preserved: outer loop is K, ++# inner D loop accumulates ``cross (BN, BK)`` in registers across D chunks, ++# distance and best-index are computed at the end of each K iteration. ++# =============================================================== ++@triton.jit ++def _euclid_assign_kernel_split_d( ++ x_ptr, # *f16 / *f32 [B, N, D] ++ c_ptr, # *f16 / *f32 [B, K, D] ++ c_sq_ptr, # *f32 [B, K] ++ out_ptr, # *i32 [B, N] ++ B: tl.constexpr, ++ N: tl.constexpr, ++ K: tl.constexpr, ++ D: tl.constexpr, ++ stride_x_b: tl.constexpr, ++ stride_x_n: tl.constexpr, ++ stride_x_d: tl.constexpr, ++ stride_c_b: tl.constexpr, ++ stride_c_k: tl.constexpr, ++ stride_c_d: tl.constexpr, ++ stride_csq_b: tl.constexpr, ++ stride_csq_k: tl.constexpr, ++ stride_out_b: tl.constexpr, ++ stride_out_n: tl.constexpr, ++ BLOCK_N: tl.constexpr, ++ BLOCK_K: tl.constexpr, ++ BLOCK_D: tl.constexpr, ++): ++ pid_n = tl.program_id(0) ++ pid_b = tl.program_id(1) ++ pid_b = pid_b.to(tl.int64) ++ ++ n_start = pid_n * BLOCK_N ++ n_offsets = n_start + tl.arange(0, BLOCK_N) ++ n_offsets = n_offsets.to(tl.int64) ++ n_mask = n_offsets < N ++ ++ offs_d = tl.arange(0, BLOCK_D).to(tl.int64) + -+ best = tl.full((BLOCK_N,), float("inf"), tl.float32) ++ best_dist = tl.full((BLOCK_N,), 3.4e38, tl.float32) + best_idx = tl.zeros((BLOCK_N,), tl.int32) + -+ for k0 in range(0, K, BLOCK_K): -+ offs_k = k0 + tl.arange(0, BLOCK_K) -+ k_mask = offs_k < K -+ # centroid tile as (BLOCK_D, BLOCK_K) so tl.dot contracts over D -+ c_ptrs = c_ptr + offs_d[:, None] * stride_cd + offs_k[None, :] * stride_ck -+ c_tile = tl.load(c_ptrs, mask=d_mask[:, None] & k_mask[None, :], other=0.0) -+ dot = tl.dot(x_tile, c_tile, input_precision="ieee") # (BLOCK_N, BLOCK_K) -+ csq = tl.load(csq_ptr + offs_k, mask=k_mask, other=float("inf")) -+ score = csq[None, :] - 2.0 * dot -+ score = tl.where(k_mask[None, :], score, float("inf")) -+ local_min = tl.min(score, axis=1) -+ local_arg = tl.argmin(score, axis=1).to(tl.int32) + k0 -+ better = local_min < best -+ best = tl.where(better, local_min, best) -+ best_idx = tl.where(better, local_arg, best_idx) -+ -+ tl.store(out_ptr + offs_n, best_idx, mask=n_mask) -+ -+ -+def euclid_assign_triton(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: -+ """Return (N,) int64 nearest-centroid ids by squared-L2 distance.""" -+ N, D = x.shape -+ K = centroids.shape[0] -+ x = x.contiguous() -+ centroids = centroids.to(x.dtype).contiguous() -+ csq = (centroids.float() * centroids.float()).sum(1) # (K,) fp32 -+ out = torch.empty((N,), device=x.device, dtype=torch.int32) -+ BLOCK_D = triton.next_power_of_2(D) -+ BLOCK_N, BLOCK_K = 16, 32 -+ grid = (triton.cdiv(N, BLOCK_N),) -+ _assign_kernel[grid]( -+ x, centroids, csq, out, -+ N, K, D, -+ x.stride(0), x.stride(1), -+ centroids.stride(0), centroids.stride(1), -+ BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K, BLOCK_D=BLOCK_D, -+ num_warps=4, ++ for k_start in range(0, K, BLOCK_K): ++ k_offsets = k_start + tl.arange(0, BLOCK_K) ++ k_offsets = k_offsets.to(tl.int64) ++ k_mask = k_offsets < K ++ ++ csq_ptrs = c_sq_ptr + pid_b * stride_csq_b + k_offsets * stride_csq_k ++ cent_sq = tl.load(csq_ptrs, mask=k_mask, other=0.0).to(tl.float32) ++ ++ # cross accumulator lives in registers across the D loop. ++ cross = tl.zeros((BLOCK_N, BLOCK_K), dtype=tl.float32) ++ ++ for d_start in range(0, D, BLOCK_D): ++ d_offsets = d_start + offs_d ++ d_mask = d_offsets < D ++ ++ x_ptrs = ( ++ x_ptr ++ + pid_b * stride_x_b ++ + n_offsets[:, None] * stride_x_n ++ + d_offsets[None, :] * stride_x_d ++ ) ++ x_chunk = tl.load( ++ x_ptrs, ++ mask=n_mask[:, None] & d_mask[None, :], ++ other=0.0, ++ ) ++ ++ c_ptrs = ( ++ c_ptr ++ + pid_b * stride_c_b ++ + k_offsets[None, :] * stride_c_k ++ + d_offsets[:, None] * stride_c_d ++ ) ++ c_chunk = tl.load( ++ c_ptrs, ++ mask=k_mask[None, :] & d_mask[:, None], ++ other=0.0, ++ ) ++ ++ cross += tl.dot(x_chunk, c_chunk).to(tl.float32) ++ ++ dist = cent_sq[None, :] - 2.0 * cross ++ dist = tl.where(k_mask[None, :], dist, 3.4e38) ++ ++ curr_min = tl.min(dist, axis=1) ++ curr_idx = tl.argmin(dist, axis=1) ++ ++ update = curr_min < best_dist ++ best_dist = tl.where(update, curr_min, best_dist) ++ best_idx = tl.where(update, k_start + curr_idx, best_idx) ++ ++ out_ptrs = out_ptr + pid_b * stride_out_b + n_offsets * stride_out_n ++ tl.store(out_ptrs, best_idx, mask=n_mask) ++ ++ ++_euclid_assign_kernel_split_d_autotuned = triton.autotune( ++ _TUNE_CONFIGS_SPLIT_D, key=["N", "K", "D"] ++)(_euclid_assign_kernel_split_d) ++ ++ ++@triton.jit ++def _cosine_assign_kernel( ++ x_ptr, # *f16 / *f32 [B, N, D] ++ c_ptr, # *f16 / *f32 [B, K, D] ++ out_ptr, # *i32 [B, N] ++ B: tl.constexpr, ++ N: tl.constexpr, ++ K: tl.constexpr, ++ D: tl.constexpr, ++ stride_x_b: tl.constexpr, ++ stride_x_n: tl.constexpr, ++ stride_x_d: tl.constexpr, ++ stride_c_b: tl.constexpr, ++ stride_c_k: tl.constexpr, ++ stride_c_d: tl.constexpr, ++ stride_out_b: tl.constexpr, ++ stride_out_n: tl.constexpr, ++ BLOCK_N: tl.constexpr, ++ BLOCK_K: tl.constexpr, ++): ++ """Each program handles a tile of BLOCK_N points for a given batch element. ++ ++ The kernel iterates over the centroid dimension K in chunks of BLOCK_K and ++ maintains the running minimum distance as well as the corresponding index ++ for every point in the tile. ++ """ ++ pid_n = tl.program_id(0) # tile index along N dimension ++ pid_b = tl.program_id(1) # batch index ++ pid_b = pid_b.to(tl.int64) ++ ++ n_start = pid_n * BLOCK_N ++ n_offsets = n_start + tl.arange(0, BLOCK_N) ++ n_offsets = n_offsets.to(tl.int64) ++ n_mask = n_offsets < N ++ ++ # ------------------------------------------------------------------ ++ # Load x tile (BLOCK_N, D) ++ # ------------------------------------------------------------------ ++ offs_d = tl.arange(0, D).to(tl.int64) ++ # Compute pointer for x block: base + b*stride_x_b + n*stride_x_n + d*stride_x_d ++ x_ptrs = ( ++ x_ptr ++ + pid_b * stride_x_b ++ + n_offsets[:, None] * stride_x_n ++ + offs_d[None, :] * stride_x_d + ) -+ return out.to(torch.long) ++ x_tile = tl.load(x_ptrs, mask=n_mask[:, None], other=0.0) ++ x_tile = x_tile # compute in f32 ++ ++ # Init best distance / index ++ best_dist = tl.full((BLOCK_N,), -3.4e38, tl.float32) # less is worse ++ best_idx = tl.zeros((BLOCK_N,), tl.int32) ++ ++ # ------------------------------------------------------------------ ++ # Iterate over centroids in chunks of BLOCK_K ++ # ------------------------------------------------------------------ ++ for k_start in range(0, K, BLOCK_K): ++ k_offsets = k_start + tl.arange(0, BLOCK_K) ++ k_offsets = k_offsets.to(tl.int64) ++ k_mask = k_offsets < K ++ ++ # Load centroid tile (D, BLOCK_K) ++ c_ptrs = ( ++ c_ptr ++ + pid_b * stride_c_b ++ + k_offsets[None, :] * stride_c_k ++ + offs_d[:, None] * stride_c_d ++ ) ++ c_tile = tl.load(c_ptrs, mask=k_mask[None, :], other=0.0) ++ c_tile = c_tile ++ ++ # Compute cosine distance (BLOCK_N, BLOCK_K) = x_tile @ c_tile ++ cross = tl.dot(x_tile, c_tile).to(tl.float32) ++ ++ # Mask out invalid centroid columns before reduction. ++ # Use a sentinel below any real cosine/dot score so masked tail lanes ++ # never win the argmax. (0.0 is a real cosine value: any input row whose ++ # cosine to all real centroids is < 0 would otherwise route to a masked ++ # lane, returning an out-of-range cluster id.) ++ dist = tl.where(k_mask[None, :], cross, -torch.finfo(torch.float32).max) ++ ++ curr_max = tl.max(dist, axis=1) ++ curr_idx = tl.argmax(dist, axis=1) ++ ++ update = curr_max > best_dist ++ best_dist = tl.where(update, curr_max, best_dist) ++ best_idx = tl.where(update, k_start + curr_idx, best_idx) ++ ++ # ------------------------------------------------------------------ ++ # Write results ++ # ------------------------------------------------------------------ ++ out_ptrs = out_ptr + pid_b * stride_out_b + n_offsets * stride_out_n ++ tl.store(out_ptrs, best_idx, mask=n_mask) ++ ++_cosine_assign_kernel_autotuned = triton.autotune(_TUNE_CONFIGS, key=["N", "K"])(_cosine_assign_kernel) ++ ++ ++# =============================================================== ++# Split-D Cosine assign kernel. Same loop structure as Euclid split-D ++# but tracks running argmax over the dot-product (cosine score with ++# normalized inputs). ++# =============================================================== ++@triton.jit ++def _cosine_assign_kernel_split_d( ++ x_ptr, ++ c_ptr, ++ out_ptr, ++ B: tl.constexpr, ++ N: tl.constexpr, ++ K: tl.constexpr, ++ D: tl.constexpr, ++ stride_x_b: tl.constexpr, ++ stride_x_n: tl.constexpr, ++ stride_x_d: tl.constexpr, ++ stride_c_b: tl.constexpr, ++ stride_c_k: tl.constexpr, ++ stride_c_d: tl.constexpr, ++ stride_out_b: tl.constexpr, ++ stride_out_n: tl.constexpr, ++ BLOCK_N: tl.constexpr, ++ BLOCK_K: tl.constexpr, ++ BLOCK_D: tl.constexpr, ++): ++ pid_n = tl.program_id(0) ++ pid_b = tl.program_id(1) ++ pid_b = pid_b.to(tl.int64) ++ ++ n_start = pid_n * BLOCK_N ++ n_offsets = n_start + tl.arange(0, BLOCK_N) ++ n_offsets = n_offsets.to(tl.int64) ++ n_mask = n_offsets < N ++ ++ offs_d = tl.arange(0, BLOCK_D).to(tl.int64) ++ ++ best_dist = tl.full((BLOCK_N,), -3.4e38, tl.float32) ++ best_idx = tl.zeros((BLOCK_N,), tl.int32) ++ ++ for k_start in range(0, K, BLOCK_K): ++ k_offsets = k_start + tl.arange(0, BLOCK_K) ++ k_offsets = k_offsets.to(tl.int64) ++ k_mask = k_offsets < K ++ ++ cross = tl.zeros((BLOCK_N, BLOCK_K), dtype=tl.float32) ++ ++ for d_start in range(0, D, BLOCK_D): ++ d_offsets = d_start + offs_d ++ d_mask = d_offsets < D ++ ++ x_ptrs = ( ++ x_ptr ++ + pid_b * stride_x_b ++ + n_offsets[:, None] * stride_x_n ++ + d_offsets[None, :] * stride_x_d ++ ) ++ x_chunk = tl.load( ++ x_ptrs, ++ mask=n_mask[:, None] & d_mask[None, :], ++ other=0.0, ++ ) ++ ++ c_ptrs = ( ++ c_ptr ++ + pid_b * stride_c_b ++ + k_offsets[None, :] * stride_c_k ++ + d_offsets[:, None] * stride_c_d ++ ) ++ c_chunk = tl.load( ++ c_ptrs, ++ mask=k_mask[None, :] & d_mask[:, None], ++ other=0.0, ++ ) ++ ++ cross += tl.dot(x_chunk, c_chunk).to(tl.float32) ++ ++ # Mask invalid centroid columns to a sentinel below any real score. ++ dist = tl.where(k_mask[None, :], cross, -torch.finfo(torch.float32).max) ++ ++ curr_max = tl.max(dist, axis=1) ++ curr_idx = tl.argmax(dist, axis=1) ++ ++ update = curr_max > best_dist ++ best_dist = tl.where(update, curr_max, best_dist) ++ best_idx = tl.where(update, k_start + curr_idx, best_idx) ++ ++ out_ptrs = out_ptr + pid_b * stride_out_b + n_offsets * stride_out_n ++ tl.store(out_ptrs, best_idx, mask=n_mask) ++ ++ ++_cosine_assign_kernel_split_d_autotuned = triton.autotune( ++ _TUNE_CONFIGS_SPLIT_D, key=["N", "K", "D"] ++)(_cosine_assign_kernel_split_d) ++ ++ ++# --------------------------------------------------------------- ++# Python wrapper ++# --------------------------------------------------------------- ++ ++def euclid_assign_triton( ++ x: torch.Tensor, ++ centroids: torch.Tensor, ++ out: torch.Tensor = None, ++ c_sq: torch.Tensor = None, ++ *, ++ BLOCK_N: int = 128, ++ BLOCK_K: int = 128, ++ num_warps: Optional[int] = None, ++ num_stages: Optional[int] = None, ++ config: Optional[dict] = None, ++ use_heuristic: bool = True, ++) -> torch.Tensor: ++ """Return nearest-centroid indices using Triton kernel. ++ ++ Args: ++ x : (B, N, D) float16 / float32 (on CUDA) ++ centroids : (B, K, D) same dtype/device as x ++ out : (B, N) int32 – (option) pre-allocated output tensor (on CUDA) ++ c_sq : (B, K) float32 – (option) ||centroids||^2 per centroid (on CUDA) ++ ++ Returns: ++ cluster_ids (B, N) int32 (callers can cast to int64 if desired) ++ Extra: ++ config : {"BLOCK_N","BLOCK_K","num_warps","num_stages"} to force a config ++ use_heuristic : use a fixed heuristic config instead of autotune ++ """ ++ assert x.is_cuda and centroids.is_cuda, "All tensors must be on CUDA" ++ # assert x.dtype in (torch.float16, torch.float32), "x must be fp16/fp32" ++ assert centroids.dtype == x.dtype, "centroids dtype mismatch" ++ ++ B, N, D = x.shape ++ K = centroids.shape[1] ++ assert centroids.shape == (B, K, D), "centroids shape mismatch" ++ ++ # x = x.contiguous() ++ # centroids = centroids.contiguous() ++ ++ if out is None: ++ out = torch.empty((B, N), device=x.device, dtype=torch.int32) ++ if c_sq is None: ++ c_sq = (centroids.to(torch.float32) ** 2).sum(-1) ++ ++ # Strides (in elements) ++ stride_x_b, stride_x_n, stride_x_d = x.stride() ++ stride_c_b, stride_c_k, stride_c_d = centroids.stride() ++ stride_csq_b, stride_csq_k = c_sq.stride() ++ stride_out_b, stride_out_n = out.stride() ++ ++ grid = lambda META: (triton.cdiv(N, META["BLOCK_N"]), B) ++ ++ use_split_d = _need_split_d(D, x.dtype, x.device) ++ ++ selected_config = None ++ if config is not None: ++ selected_config = config ++ elif num_warps is not None or num_stages is not None: ++ if num_warps is None or num_stages is None: ++ raise ValueError("num_warps and num_stages must be set together") ++ selected_config = { ++ "BLOCK_N": BLOCK_N, ++ "BLOCK_K": BLOCK_K, ++ "num_warps": num_warps, ++ "num_stages": num_stages, ++ } ++ if use_split_d and "BLOCK_D" not in selected_config: ++ # Caller supplied a small-D-shaped config but D demands split-D. ++ # Use a sane default for BLOCK_D and let SMEM fitter shrink. ++ selected_config = dict(selected_config) ++ selected_config["BLOCK_D"] = 64 ++ selected_config = _fit_config_to_smem_split_d( ++ selected_config, ++ D, ++ _dtype_bytes(x.dtype), ++ _smem_limit(x.device), ++ ) ++ elif use_heuristic: ++ if use_split_d: ++ selected_config = _heuristic_euclid_config_split_d( ++ N, K, D, device=x.device, dtype=x.dtype ++ ) ++ else: ++ selected_config = _heuristic_euclid_config( ++ N, K, D, device=x.device, dtype=x.dtype ++ ) ++ ++ if use_split_d: ++ if selected_config is None: ++ _euclid_assign_kernel_split_d_autotuned[grid]( ++ x, centroids, c_sq, out, ++ B, N, K, D, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_c_b, stride_c_k, stride_c_d, ++ stride_csq_b, stride_csq_k, ++ stride_out_b, stride_out_n, ++ ) ++ else: ++ _euclid_assign_kernel_split_d[grid]( ++ x, centroids, c_sq, out, ++ B, N, K, D, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_c_b, stride_c_k, stride_c_d, ++ stride_csq_b, stride_csq_k, ++ stride_out_b, stride_out_n, ++ BLOCK_N=selected_config["BLOCK_N"], ++ BLOCK_K=selected_config["BLOCK_K"], ++ BLOCK_D=selected_config["BLOCK_D"], ++ num_warps=selected_config["num_warps"], ++ num_stages=selected_config["num_stages"], ++ ) ++ return out ++ ++ if selected_config is not None: ++ _euclid_assign_kernel[grid]( ++ x, ++ centroids, ++ c_sq, ++ out, ++ B, ++ N, ++ K, ++ D, ++ stride_x_b, ++ stride_x_n, ++ stride_x_d, ++ stride_c_b, ++ stride_c_k, ++ stride_c_d, ++ stride_csq_b, ++ stride_csq_k, ++ stride_out_b, ++ stride_out_n, ++ BLOCK_N=selected_config["BLOCK_N"], ++ BLOCK_K=selected_config["BLOCK_K"], ++ num_warps=selected_config["num_warps"], ++ num_stages=selected_config["num_stages"], ++ ) ++ else: ++ _euclid_assign_kernel_autotuned[grid]( ++ x, ++ centroids, ++ c_sq, ++ out, ++ B, ++ N, ++ K, ++ D, ++ stride_x_b, ++ stride_x_n, ++ stride_x_d, ++ stride_c_b, ++ stride_c_k, ++ stride_c_d, ++ stride_csq_b, ++ stride_csq_k, ++ stride_out_b, ++ stride_out_n, ++ ) ++ return out ++ ++ ++def cosine_assign_triton(x: torch.Tensor, centroids: torch.Tensor, out: torch.Tensor = None, ++ *, BLOCK_N: int = 128, BLOCK_K: int = 128) -> torch.Tensor: ++ """Return nearest(cosine similarity)-centroid indices using Triton kernel. ++ ++ Args: ++ x : (B, N, D) float16 / float32 (on CUDA) ++ centroids : (B, K, D) same dtype/device as x ++ ++ Returns: ++ cluster_ids (B, N) int32 (callers can cast to int64 if desired) ++ """ ++ assert x.is_cuda and centroids.is_cuda, "All tensors must be on CUDA" ++ # assert x.dtype in (torch.float16, torch.float32), "x must be fp16/fp32" ++ assert centroids.dtype == x.dtype, "centroids dtype mismatch" ++ ++ B, N, D = x.shape ++ K = centroids.shape[1] ++ assert centroids.shape == (B, K, D), "centroids shape mismatch" ++ ++ # x = x.contiguous() ++ # centroids = centroids.contiguous() ++ ++ if out is None: ++ out = torch.empty((B, N), device=x.device, dtype=torch.int32) ++ ++ # Strides (in elements) ++ stride_x_b, stride_x_n, stride_x_d = x.stride() ++ stride_c_b, stride_c_k, stride_c_d = centroids.stride() ++ stride_out_b, stride_out_n = out.stride() ++ ++ grid = lambda META: (triton.cdiv(N, META["BLOCK_N"]), B) ++ ++ if _need_split_d(D, x.dtype, x.device): ++ _cosine_assign_kernel_split_d_autotuned[grid]( ++ x, ++ centroids, ++ out, ++ B, ++ N, ++ K, ++ D, ++ stride_x_b, ++ stride_x_n, ++ stride_x_d, ++ stride_c_b, ++ stride_c_k, ++ stride_c_d, ++ stride_out_b, ++ stride_out_n, ++ ) ++ return out ++ ++ # Small-D path. The existing autotune sweep includes configs that may ++ # not fit SMEM (e.g. fp16 D=512 BN=64 BK=64 S=4 → 320 KiB > H200 limit; ++ # fp32 D=512 BN=64 BK=32 S=4 → 393 KiB). The cosine kernel has the ++ # same tile shapes as euclid, so reuse the SMEM-aware euclid heuristic ++ # for config selection regardless of dtype. This keeps fp16/bf16 small-D ++ # behaviour byte-identical to the euclid path's heuristic (verified by ++ # examples/regression_fp16_smalld.py). ++ cfg = _heuristic_euclid_config(N, K, D, device=x.device, dtype=x.dtype) ++ _cosine_assign_kernel[grid]( ++ x, ++ centroids, ++ out, ++ B, ++ N, ++ K, ++ D, ++ stride_x_b, ++ stride_x_n, ++ stride_x_d, ++ stride_c_b, ++ stride_c_k, ++ stride_c_d, ++ stride_out_b, ++ stride_out_n, ++ BLOCK_N=cfg["BLOCK_N"], ++ BLOCK_K=cfg["BLOCK_K"], ++ num_warps=cfg["num_warps"], ++ num_stages=cfg["num_stages"], ++ ) ++ return out +diff --git a/kmeanslib/_kernels/primitives/kmeans/triton/kmeans.py b/kmeanslib/_kernels/primitives/kmeans/triton/kmeans.py +new file mode 100644 +index 0000000..db5fb79 +--- /dev/null ++++ b/kmeanslib/_kernels/primitives/kmeans/triton/kmeans.py +@@ -0,0 +1,234 @@ ++import torch ++import torch.nn.functional as F ++from torch.cuda import nvtx ++from kmeanslib._kernels.primitives.kmeans.triton.assign import euclid_assign_triton, cosine_assign_triton ++from kmeanslib._kernels.primitives.kmeans.triton.update import ( ++ triton_centroid_update_cosine, ++ triton_centroid_update_euclid, ++ triton_centroid_update_sorted_euclid, ++ triton_centroid_update_sorted_cosine, ++ triton_lloyd_centroid_step_euclid, ++) ++try: ++ from tqdm import trange ++except Exception: ++ trange = range ++ ++# -------------------- Compiled single-iteration kernels -------------------- ++ ++# 1. Euclidean ++def _euclid_iter(x, centroids, use_heuristic=True): ++ ++ cluster_ids = euclid_assign_triton(x, centroids, use_heuristic=use_heuristic) ++ centroids_new = triton_centroid_update_sorted_euclid(x, cluster_ids, centroids) ++ ++ shift = (centroids_new - centroids).norm(dim=-1).max() ++ return centroids_new, shift, cluster_ids ++ ++# 2. Cosine ++def _cosine_iter(x_norm, centroids): ++ # cos_sim = torch.einsum('bnd,bkd->bnk', x_norm, centroids) ++ # cluster_ids = cos_sim.argmax(dim=-1) ++ cluster_ids = cosine_assign_triton(x_norm, centroids) ++ centroids_new = triton_centroid_update_sorted_cosine(x_norm, cluster_ids, centroids) ++ # centroids_new = centroids_new.clone() ++ shift = (centroids_new - centroids).norm(dim=-1).max() ++ return centroids_new, shift, cluster_ids ++ ++# 3. Dot-product ++def _dot_iter(x, centroids): ++ # sim = torch.einsum('bnd,bkd->bnk', x, centroids) ++ # cluster_ids = sim.argmax(dim=-1) ++ cluster_ids = cosine_assign_triton(x, centroids) ++ centroids_new = triton_centroid_update_sorted_cosine(x, cluster_ids, centroids) ++ # centroids_new = centroids_new.clone() ++ shift = (centroids_new - centroids).norm(dim=-1).max() ++ return centroids_new, shift, cluster_ids ++ ++COMPILE_FLAG = False ++ ++try: ++ if COMPILE_FLAG: ++ _euclid_iter_compiled = torch.compile(_euclid_iter, dynamic=True, mode="reduce-overhead") ++ _cosine_iter_compiled = torch.compile(_cosine_iter, dynamic=True, mode="reduce-overhead") ++ _dot_iter_compiled = torch.compile(_dot_iter, dynamic=True, mode="reduce-overhead") ++ else: ++ _euclid_iter_compiled = _euclid_iter ++ _cosine_iter_compiled = _cosine_iter ++ _dot_iter_compiled = _dot_iter ++except Exception: # pragma: no cover ++ _euclid_iter_compiled = _euclid_iter ++ _cosine_iter_compiled = _cosine_iter ++ _dot_iter_compiled = _dot_iter ++ ++def batch_kmeans_Euclid( ++ x, ++ n_clusters, ++ max_iters=100, ++ tol=0.0, ++ init_centroids=None, ++ verbose=False, ++ *, ++ use_heuristic=True, ++ fused=True, ++): ++ """ ++ Batched KMeans clustering in PyTorch using Euclidean distance. ++ ++ Args: ++ x: Tensor of shape (B, N, D), batch_size B, N points per batch, D dims. ++ n_clusters: Number of clusters. ++ max_iters: Max number of iterations. ++ tol: Relative tolerance for center movement. ++ verbose: Print loss for each iter. ++ use_heuristic: Use heuristic Triton config (skip autotune). ++ fused: If True (default), use the fused Lloyd path with preallocated ++ sums/cnts/new/shift buffers and ping-pong centroid swap (no ++ .clone() per iter). Falls back to per-iter alloc when False. ++ Returns: ++ cluster_ids: (B, N) LongTensor, cluster assignment for each point. ++ centroids: (B, n_clusters, D) final cluster centers. ++ """ ++ B, N, D = x.shape ++ K = n_clusters ++ ++ if init_centroids is None: ++ # Randomly select initial centers from x ++ indices = torch.randint(0, N, (B, K), device=x.device) ++ centroids = torch.gather( ++ x, ++ dim=1, ++ index=indices[..., None].expand(-1, -1, D) ++ ) # (B, K, D) ++ else: ++ centroids = init_centroids ++ ++ centroids = centroids.view(B, K, D).contiguous() ++ ++ if not fused: ++ # ----- per-iter alloc + .clone() path ----- ++ for it in range(max_iters): ++ centroids_new, center_shift, cluster_ids = _euclid_iter_compiled( ++ x, centroids, use_heuristic ++ ) ++ if verbose: ++ print(f"Iter {it}, center shift: {center_shift.item():.6f}") ++ if center_shift < tol: ++ break ++ centroids = centroids_new.clone() ++ return cluster_ids, centroids, it + 1 ++ ++ # ----- fused path: preallocated buffers + ping-pong centroid swap ----- ++ # Two centroid buffers swapped each iter so we never .clone(). ++ cent_a = centroids ++ cent_b = torch.empty_like(centroids) ++ ++ sums_buf = torch.zeros((B, K, D), device=x.device, dtype=torch.float32) ++ cnts_buf = torch.zeros((B, K), device=x.device, dtype=torch.int32) ++ shift_buf = torch.empty((B, K), device=x.device, dtype=torch.float32) ++ ++ cur, nxt = cent_a, cent_b ++ cluster_ids = None ++ it = 0 ++ for it in range(max_iters): ++ cluster_ids = euclid_assign_triton(x, cur, use_heuristic=use_heuristic) ++ # writes new centroids into `nxt`, returns scalar GPU tensor for shift ++ new_cent, _, max_shift = triton_lloyd_centroid_step_euclid( ++ x, cluster_ids, cur, ++ sums_buf=sums_buf, ++ cnts_buf=cnts_buf, ++ new_buf=nxt, ++ shift_buf=shift_buf, ++ ) ++ if verbose: ++ print(f"Iter {it}, center shift: {max_shift.item():.6f}") ++ # swap before convergence check so `cur` always points to the latest ++ cur, nxt = nxt, cur ++ # Convergence check: `max_shift` is a 0-D GPU tensor, so ++ # `if max_shift < tol` triggers `tensor.__bool__()` which forces ++ # a per-iter cuda sync (~2.4 ms/iter on H200, drains the kernel ++ # pipeline). The short-circuit on `tol > 0.0` keeps the default ++ # `tol=0.0` path sync-free; users opting into early-exit accept ++ # the sync as part of that contract. ++ if tol > 0.0 and max_shift < tol: ++ break ++ ++ return cluster_ids, cur, it + 1 ++ ++ ++def batch_kmeans_Cosine(x, n_clusters, max_iters=100, tol=0.0, init_centroids=None, verbose=False): ++ """ ++ Batched KMeans clustering in PyTorch using Cosine similarity. ++ ++ Args: ++ x: Tensor of shape (B, N, D), batch_size B, N points per batch, D dims. ++ n_clusters: Number of clusters. ++ max_iters: Max number of iterations. ++ tol: Relative tolerance for center movement. ++ verbose: Print loss for each iter. ++ Returns: ++ cluster_ids: (B, N) LongTensor, cluster assignment for each point. ++ centroids: (B, n_clusters, D) final cluster centers. ++ """ ++ B, N, D = x.shape ++ ++ # Normalize input vectors for cosine similarity ++ x_norm = F.normalize(x, p=2, dim=-1) # (B, N, D) ++ ++ if init_centroids is None: ++ # Randomly select initial centers from x_norm ++ indices = torch.randint(0, N, (B, n_clusters), device=x.device) ++ centroids = torch.gather( ++ x_norm, ++ dim=1, ++ index=indices[..., None].expand(-1, -1, D) ++ ) # (B, n_clusters, D) ++ else: ++ centroids = init_centroids ++ ++ centroids = centroids.view(B, n_clusters, D) ++ centroids = F.normalize(centroids, p=2, dim=-1) # Ensure centroids are normalized ++ ++ for it in range(max_iters): ++ # ---- compiled single iteration ---- ++ centroids_new, center_shift, cluster_ids = _cosine_iter_compiled(x_norm, centroids) ++ ++ # 4. Check for convergence ++ if verbose: ++ print(f"Iter {it}, center shift: {center_shift.item():.6f}") ++ if center_shift < tol: ++ break ++ centroids = centroids_new.clone() ++ ++ return cluster_ids, centroids, it + 1 ++ ++ ++def batch_kmeans_Dot(x, n_clusters, max_iters=100, tol=0.0, init_centroids=None, verbose=False): ++ """ ++ Batched KMeans clustering in PyTorch using raw dot-product as similarity. ++ ++ """ ++ B, N, D = x.shape ++ ++ if init_centroids is None: ++ indices = torch.randint(0, N, (B, n_clusters), device=x.device) ++ centroids = torch.gather( ++ x, ++ dim=1, ++ index=indices[..., None].expand(-1, -1, D) ++ ) ++ else: ++ centroids = init_centroids ++ ++ centroids = centroids.view(B, n_clusters, D) ++ ++ for it in range(max_iters): ++ centroids_new, center_shift, cluster_ids = _dot_iter_compiled(x, centroids) ++ ++ if verbose: ++ print(f"Iter {it} (dot), center shift: {center_shift.item():.6f}") ++ if center_shift < tol: ++ break ++ centroids = centroids_new.clone() ++ ++ return cluster_ids, centroids, it + 1 +diff --git a/kmeanslib/_kernels/primitives/kmeans/triton/update.py b/kmeanslib/_kernels/primitives/kmeans/triton/update.py +new file mode 100644 +index 0000000..88ff697 +--- /dev/null ++++ b/kmeanslib/_kernels/primitives/kmeans/triton/update.py +@@ -0,0 +1,640 @@ ++import torch ++import torch.nn.functional as F ++import triton ++import triton.language as tl ++try: ++ from tqdm import trange ++except Exception: ++ trange = range ++ ++ ++def _ceil_div(a: int, b: int) -> int: ++ return (a + b - 1) // b ++ ++ ++@triton.jit ++def _centroid_update_kernel( ++ x_ptr, # *f16 / *f32 [B, N, D] ++ cluster_ptr, # *i32 [B, N] ++ sum_ptr, # *f32 [B, K, D] ++ count_ptr, # *i32 [B, K] ++ # --- strides (elements) --- ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_sum_b, stride_sum_k, stride_sum_d, ++ stride_count_b, stride_count_k, ++ B: tl.constexpr, ++ N: tl.constexpr, ++ D: tl.constexpr, ++ K: tl.constexpr, ++ BLOCK_D: tl.constexpr, # number of dims processed per program ++): ++ """Each program processes 1 token across BLOCK_D dims using atomics with general strides.""" ++ pid = tl.program_id(axis=0) ++ token_idx = pid # range: [0, B*N) ++ ++ # Derive (b, n) ++ b = (token_idx // N).to(tl.int64) ++ n = (token_idx % N).to(tl.int64) ++ ++ # pointer to this token's feature vector ++ x_offset = b * stride_x_b + n * stride_x_n ++ x_tok_ptr = x_ptr + x_offset ++ ++ cluster_idx = tl.load(cluster_ptr + b * N + n) ++ cluster_idx = tl.where(cluster_idx < K, cluster_idx, 0) ++ cluster_idx = cluster_idx.to(tl.int64) ++ ++ # base ptr for centroid accum array ++ centroid_base = b * stride_sum_b + cluster_idx * stride_sum_k ++ ++ offs = tl.arange(0, BLOCK_D).to(tl.int64) ++ for d_start in range(0, D, BLOCK_D): ++ mask = offs + d_start < D ++ feats = tl.load(x_tok_ptr + (d_start + offs) * stride_x_d, mask=mask, other=0.0) ++ feats = feats.to(tl.float32) ++ dest_ptr = sum_ptr + centroid_base + (d_start + offs) * stride_sum_d ++ tl.atomic_add(dest_ptr, feats, mask=mask) ++ ++ tl.atomic_add(count_ptr + b * stride_count_b + cluster_idx * stride_count_k, 1) ++ ++ ++def triton_centroid_update_cosine(x_norm: torch.Tensor, cluster_ids: torch.Tensor, old_centroids: torch.Tensor): ++ """Compute centroids using custom Triton kernel. ++ ++ Args: ++ x_norm (Tensor): (B, N, D) normalized input vectors (float16/float32) ++ cluster_ids (LongTensor): (B, N) cluster assignment per point ++ old_centroids (Tensor): (B, K, D) previous centroids (same dtype as x_norm) ++ ++ Returns: ++ Tensor: (B, K, D) updated and L2-normalized centroids (dtype == x_norm.dtype) ++ """ ++ assert x_norm.is_cuda and cluster_ids.is_cuda, "Input tensors must be on CUDA device" ++ B, N, D = x_norm.shape ++ K = old_centroids.shape[1] ++ assert cluster_ids.shape == (B, N) ++ ++ # Allocate accumulation buffers ++ centroid_sums = torch.zeros((B, K, D), device=x_norm.device, dtype=torch.float32) ++ centroid_counts = torch.zeros((B, K), device=x_norm.device, dtype=torch.int32) ++ ++ # Launch Triton kernel – one program per token ++ total_tokens = B * N ++ BLOCK_D = 128 # tuneable ++ ++ grid = (total_tokens,) ++ _centroid_update_kernel[grid]( ++ x_norm, ++ cluster_ids.to(torch.int32), ++ centroid_sums, ++ centroid_counts, ++ x_norm.stride(0), x_norm.stride(1), x_norm.stride(2), ++ centroid_sums.stride(0), centroid_sums.stride(1), centroid_sums.stride(2), ++ centroid_counts.stride(0), centroid_counts.stride(1), ++ B, N, D, K, ++ BLOCK_D=BLOCK_D, ++ ) ++ ++ # Compute means; keep old centroid if empty cluster ++ counts_f = centroid_counts.to(torch.float32).unsqueeze(-1).clamp(min=1.0) ++ centroids = centroid_sums / counts_f ++ ++ # For clusters with zero count, revert to old centroids ++ zero_mask = (centroid_counts == 0).unsqueeze(-1) ++ centroids = torch.where(zero_mask, old_centroids.to(torch.float32), centroids) ++ ++ centroids = centroids.to(x_norm.dtype) ++ centroids = F.normalize(centroids, p=2, dim=-1) ++ return centroids ++ ++ ++def torch_loop_centroid_update_cosine(x_norm: torch.Tensor, cluster_ids: torch.Tensor, old_centroids: torch.Tensor): ++ """Reference Python implementation (double for-loop)""" ++ B, N, D = x_norm.shape ++ K = old_centroids.shape[1] ++ new_centroids = torch.zeros_like(old_centroids) ++ for b in range(B): ++ for k in range(K): ++ mask = cluster_ids[b] == k ++ if mask.any(): ++ new_centroids[b, k] = F.normalize(x_norm[b][mask].mean(dim=0, dtype=x_norm.dtype), p=2, dim=0) ++ else: ++ new_centroids[b, k] = old_centroids[b, k] ++ return new_centroids ++ ++ ++def triton_centroid_update_euclid(x: torch.Tensor, cluster_ids: torch.Tensor, old_centroids: torch.Tensor): ++ """Compute centroids for Euclidean KMeans using Triton. ++ ++ Args: ++ x (Tensor): (B, N, D) input vectors (float16/float32) ++ cluster_ids (LongTensor): (B, N) cluster assignment per point ++ old_centroids (Tensor): (B, K, D) previous centroids (same dtype as x) ++ ++ Returns: ++ Tensor: (B, K, D) updated centroids (dtype == x.dtype) ++ """ ++ assert x.is_cuda and cluster_ids.is_cuda, "Input tensors must be on CUDA device" ++ B, N, D = x.shape ++ K = old_centroids.shape[1] ++ assert cluster_ids.shape == (B, N) ++ ++ # Allocate accumulation buffers ++ centroid_sums = torch.zeros((B, K, D), device=x.device, dtype=torch.float32) ++ centroid_counts = torch.zeros((B, K), device=x.device, dtype=torch.int32) ++ ++ total_tokens = B * N ++ BLOCK_D = 128 # tuneable ++ grid = (total_tokens,) ++ ++ _centroid_update_kernel[grid]( ++ x, ++ cluster_ids.to(torch.int32), ++ centroid_sums, ++ centroid_counts, ++ x.stride(0), x.stride(1), x.stride(2), ++ centroid_sums.stride(0), centroid_sums.stride(1), centroid_sums.stride(2), ++ centroid_counts.stride(0), centroid_counts.stride(1), ++ B, N, D, K, ++ BLOCK_D=BLOCK_D, ++ ) ++ ++ # Compute means; keep old centroid if empty cluster ++ counts_f = centroid_counts.to(torch.float32).unsqueeze(-1).clamp(min=1.0) ++ centroids = centroid_sums / counts_f ++ ++ # For clusters with zero count, revert to old centroids ++ zero_mask = (centroid_counts == 0).unsqueeze(-1) ++ centroids = torch.where(zero_mask, old_centroids.to(torch.float32), centroids) ++ ++ return centroids.to(x.dtype) ++ ++ ++# ------------------------------ NEW: chunk-wise centroid update (sorted ids) ------------------------------ ++ ++def _next_power_of_2(n: int) -> int: ++ p = 1 ++ while p < n: ++ p <<= 1 ++ return p ++ ++ ++@triton.jit ++def _centroid_update_chunk_kernel( ++ x_ptr, # *f16 / *f32 [B, N, D] – ORIGINAL ORDER ++ sorted_idx_ptr, # *i32 [B, N] – indices after sort ++ sorted_cluster_ptr, # *i32 [B, N] – cluster ids in sorted order ++ sum_ptr, # *f32 [B, K, D] ++ count_ptr, # *i32 [B, K] ++ # strides ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_idx_b, stride_idx_n, stride_cluster_b, stride_cluster_n, ++ stride_sum_b, stride_sum_k, stride_sum_d, ++ stride_count_b, stride_count_k, ++ B: tl.constexpr, ++ N: tl.constexpr, ++ D: tl.constexpr, ++ K: tl.constexpr, ++ BLOCK_N: tl.constexpr, ++ BLOCK_D: tl.constexpr, ++): ++ """Each program processes **BLOCK_N consecutive, already-sorted tokens**. ++ ++ Because the tokens are sorted by cluster id, identical ids appear in ++ contiguous runs. We therefore accumulate a local sum/count for the ++ current run and perform **a single atomic update per run**, instead of ++ per-token. ++ ++ ``BLOCK_D`` tiles the feature dimension so that non-power-of-2 D ++ values work (Triton requires ``tl.arange`` ranges to be power-of-2). ++ When ``BLOCK_D >= D`` the D-loop executes once — no perf regression ++ for already-power-of-2 inputs. ++ """ ++ pid_chunk = tl.program_id(axis=0) ++ pid_b = tl.program_id(axis=1) ++ ++ b = pid_b.to(tl.int64) ++ chunk_start = (pid_chunk * BLOCK_N).to(tl.int64) ++ ++ if chunk_start >= N: ++ return ++ ++ idx_batch_base = sorted_idx_ptr + b * stride_idx_b ++ cid_batch_base = sorted_cluster_ptr + b * stride_cluster_b ++ x_batch_base = x_ptr + b * stride_x_b ++ ++ offs_token = tl.arange(0, BLOCK_N).to(tl.int64) ++ offs_dim = tl.arange(0, BLOCK_D).to(tl.int64) ++ ++ token_idx = chunk_start + offs_token ++ valid_tok = token_idx < N ++ first_token_idx = chunk_start ++ last_token_idx = tl.minimum(chunk_start + BLOCK_N, N) - 1 ++ ++ first_id = tl.load(cid_batch_base + first_token_idx) ++ last_id = tl.load(cid_batch_base + last_token_idx) ++ all_ids = tl.load(cid_batch_base + token_idx * stride_cluster_n, mask=valid_tok, other=-1) ++ ++ all_tokens_idxs = tl.load(idx_batch_base + token_idx * stride_idx_n, mask=valid_tok, other=-1) ++ all_tokens_idxs = all_tokens_idxs.to(tl.int64) ++ ++ for cid in range(first_id, last_id + 1): ++ cluster_mask = all_ids == cid ++ cluster_size = tl.sum(cluster_mask.to(tl.int32)) ++ if cluster_size != 0: ++ tl.atomic_add(count_ptr + b*stride_count_b + cid*stride_count_k, cluster_size) ++ for d_start in range(0, D, BLOCK_D): ++ d_offsets = d_start + offs_dim ++ d_mask = d_offsets < D ++ row_ptrs = (x_batch_base ++ + all_tokens_idxs[:, None] * stride_x_n ++ + d_offsets[None, :] * stride_x_d) ++ cluster_feats = tl.load( ++ row_ptrs, ++ mask=cluster_mask[:, None] & d_mask[None, :], ++ other=0.0, ++ ) ++ cluster_feats = cluster_feats.to(tl.float32) ++ sum_feats = tl.sum(cluster_feats, axis=0) ++ dest_ptr = (sum_ptr + b * stride_sum_b ++ + cid * stride_sum_k ++ + d_offsets * stride_sum_d) ++ tl.atomic_add(dest_ptr, sum_feats, mask=d_mask) ++ ++ ++# --------------------------------------------------------------------------------------------- ++ ++def triton_centroid_update_sorted_cosine(x_norm: torch.Tensor, cluster_ids: torch.Tensor, old_centroids: torch.Tensor, ++ *, BLOCK_N: int = 256): ++ """Fast centroid update assuming **cluster_ids are sorted along N**. ++ ++ This helper will sort the assignments (together with `x_norm`) and launch the ++ chunk kernel above. Compared to the naive per-token kernel it performs *one ++ atomic add per run of identical ids* instead of per token, providing large ++ speed-ups when clusters are reasonably sized. ++ """ ++ assert x_norm.is_cuda and cluster_ids.is_cuda, "Inputs must be on CUDA" ++ B, N, D = x_norm.shape ++ K = old_centroids.shape[1] ++ assert cluster_ids.shape == (B, N) ++ ++ # -------- sort per-batch -------- ++ sorted_cluster_ids, sorted_idx = torch.sort(cluster_ids, dim=-1) ++ sorted_idx_int = sorted_idx.to(torch.int32) ++ ++ # accumulation buffers ++ centroid_sums = torch.zeros((B, K, D), device=x_norm.device, dtype=torch.float32) ++ centroid_cnts = torch.zeros((B, K), device=x_norm.device, dtype=torch.int32) ++ ++ BLOCK_D = _next_power_of_2(D) ++ grid = (triton.cdiv(N, BLOCK_N), B) ++ _centroid_update_chunk_kernel[grid]( ++ x_norm, ++ sorted_idx_int, ++ sorted_cluster_ids.to(torch.int32), ++ centroid_sums, ++ centroid_cnts, ++ x_norm.stride(0), x_norm.stride(1), x_norm.stride(2), ++ sorted_idx_int.stride(0), sorted_idx_int.stride(1), ++ sorted_cluster_ids.stride(0), sorted_cluster_ids.stride(1), ++ centroid_sums.stride(0), centroid_sums.stride(1), centroid_sums.stride(2), ++ centroid_cnts.stride(0), centroid_cnts.stride(1), ++ B, N, D, K, ++ BLOCK_N=BLOCK_N, ++ BLOCK_D=BLOCK_D, ++ ) ++ ++ # finalise – convert to means, handle empty clusters, renormalise ++ counts_f = centroid_cnts.to(torch.float32).unsqueeze(-1).clamp(min=1.0) ++ centroids = centroid_sums / counts_f ++ empty_mask = (centroid_cnts == 0).unsqueeze(-1) ++ centroids = torch.where(empty_mask, old_centroids.to(torch.float32), centroids) ++ centroids = centroids.to(x_norm.dtype) ++ centroids = F.normalize(centroids, p=2, dim=-1) ++ return centroids ++ ++def triton_centroid_update_sorted_euclid(x: torch.Tensor, cluster_ids: torch.Tensor, old_centroids: torch.Tensor, ++ *, BLOCK_N: int = 256, centroid_sums: torch.Tensor = None, centroid_cnts: torch.Tensor = None, calculate_new: bool = True): ++ """Fast centroid update for *Euclidean* KMeans assuming cluster IDs are pre-sorted. ++ ++ Parameters ++ ---------- ++ x : Tensor [B, N, D] ++ Input feature vectors (no normalization assumed). ++ cluster_ids : LongTensor [B, N] ++ Cluster assignment for each point. ++ old_centroids : Tensor [B, K, D] ++ Previous centroids (used to fill empty clusters). ++ BLOCK_N : int, optional ++ Tokens per Triton program (affects occupancy/perf). ++ centroid_sums : Tensor [B, K, D], optional ++ Pre-allocated accumulation buffer for sums. If None, a new buffer is created. ++ centroid_cnts : Tensor [B, K], optional ++ Pre-allocated accumulation buffer for counts. If None, a new buffer is created. ++ calculate_new : bool, default=True ++ If True, compute and return the new centroids. If False, only update the ++ accumulation buffers. ++ ++ Returns ++ _________ ++ centroids_new : Tensor [B, K, D] or None ++ Updated centroids if `calculate_new` is True; otherwise None. ++ """ ++ assert x.is_cuda and cluster_ids.is_cuda, "Inputs must be on CUDA device" ++ B, N, D = x.shape ++ K = old_centroids.shape[1] ++ ++ # Batch-wise sort of cluster assignments ++ sorted_cluster_ids, sorted_idx = torch.sort(cluster_ids, dim=-1) ++ sorted_idx_int = sorted_idx.to(torch.int32) ++ ++ if centroid_sums is None: ++ centroid_sums = torch.zeros((B, K, D), device=x.device, dtype=torch.float32) ++ else: ++ assert centroid_sums.shape == (B, K, D) ++ ++ if centroid_cnts is None: ++ centroid_cnts = torch.zeros((B, K), device=x.device, dtype=torch.int32) ++ else: ++ assert centroid_cnts.shape == (B, K) ++ ++ BLOCK_D = _next_power_of_2(D) ++ grid = (triton.cdiv(N, BLOCK_N), B) ++ _centroid_update_chunk_kernel[grid]( ++ x, # original features ++ sorted_idx_int, # gather indices ++ sorted_cluster_ids.to(torch.int32), ++ centroid_sums, ++ centroid_cnts, ++ x.stride(0), x.stride(1), x.stride(2), ++ sorted_idx_int.stride(0), sorted_idx_int.stride(1), ++ sorted_cluster_ids.stride(0), sorted_cluster_ids.stride(1), ++ centroid_sums.stride(0), centroid_sums.stride(1), centroid_sums.stride(2), ++ centroid_cnts.stride(0), centroid_cnts.stride(1), ++ B, N, D, K, ++ BLOCK_N=BLOCK_N, ++ BLOCK_D=BLOCK_D, ++ ) ++ ++ if calculate_new: ++ # Convert sums to means; replace empty clusters with old centroids ++ counts_f = centroid_cnts.to(torch.float32).unsqueeze(-1).clamp(min=1.0) ++ centroids = centroid_sums / counts_f ++ empty_mask = (centroid_cnts == 0).unsqueeze(-1) ++ centroids = torch.where(empty_mask, old_centroids.to(torch.float32), centroids) ++ return centroids.to(x.dtype) ++ else: ++ return None ++# ------------------------------ END new implementation ------------------------------ ++ ++ ++# ============================================================================= ++# Fused centroid finalize + per-iter Lloyd helper ++# ============================================================================= ++# ++# `_centroid_finalize_kernel` collapses 6 host-side ops into one Triton kernel: ++# 1. cnts.float() count cast f32 ++# 2. clamp(cnts, min=1) safe-divide guard ++# 3. sums / cnts per-element divide ++# 4. empty-mask where fall back to old centroid where cnts==0 ++# 5. cast back to original dtype ++# 6. (new - old).norm(dim=-1) per-cluster shift (host then takes max) ++# ++# Outputs (B, K) per-cluster shift so the host max reduction is over K, not B*K*D. ++# ++# `triton_lloyd_centroid_step_euclid` glues sort + chunk-update + finalize using ++# preallocated sums / cnts / new / shift buffers reused across Lloyd iterations, ++# so per-iter allocation cost is zero. ++ ++@triton.jit ++def _centroid_finalize_kernel( ++ sums_ptr, # *f32 [B, K, D] ++ cnts_ptr, # *i32 [B, K] ++ old_ptr, # *T [B, K, D] (T = output dtype) ++ new_ptr, # *T [B, K, D] (output) ++ shift_ptr, # *f32 [B, K] (output: per-cluster ‖new − old‖₂) ++ stride_sums_b, stride_sums_k, stride_sums_d, ++ stride_cnts_b, stride_cnts_k, ++ stride_old_b, stride_old_k, stride_old_d, ++ stride_new_b, stride_new_k, stride_new_d, ++ stride_shift_b, stride_shift_k, ++ K: tl.constexpr, ++ D: tl.constexpr, ++ BLOCK_D: tl.constexpr, ++): ++ pid_k = tl.program_id(0) ++ pid_b = tl.program_id(1) ++ ++ cnt = tl.load(cnts_ptr + pid_b * stride_cnts_b + pid_k * stride_cnts_k).to(tl.float32) ++ inv = 1.0 / tl.maximum(cnt, 1.0) ++ is_empty = cnt == 0.0 ++ ++ sq_acc = tl.zeros([], dtype=tl.float32) ++ offs_d = tl.arange(0, BLOCK_D) ++ n_blocks = (D + BLOCK_D - 1) // BLOCK_D ++ ++ for blk in range(n_blocks): ++ d_idx = blk * BLOCK_D + offs_d ++ d_mask = d_idx < D ++ ++ sum_off = pid_b * stride_sums_b + pid_k * stride_sums_k + d_idx * stride_sums_d ++ old_off = pid_b * stride_old_b + pid_k * stride_old_k + d_idx * stride_old_d ++ new_off = pid_b * stride_new_b + pid_k * stride_new_k + d_idx * stride_new_d ++ ++ s = tl.load(sums_ptr + sum_off, mask=d_mask, other=0.0).to(tl.float32) ++ old_v = tl.load(old_ptr + old_off, mask=d_mask, other=0.0).to(tl.float32) ++ ++ # divide; if empty, fall back to old centroid (shift contribution = 0) ++ new_v = tl.where(is_empty, old_v, s * inv) ++ ++ # accumulate squared shift ++ diff = new_v - old_v ++ sq_acc += tl.sum(tl.where(d_mask, diff * diff, 0.0)) ++ ++ tl.store(new_ptr + new_off, new_v, mask=d_mask) ++ ++ shift = tl.sqrt(sq_acc) ++ tl.store(shift_ptr + pid_b * stride_shift_b + pid_k * stride_shift_k, shift) ++ ++ ++def triton_centroid_finalize( ++ sums: torch.Tensor, # (B, K, D) fp32 ++ cnts: torch.Tensor, # (B, K) int32 ++ old_centroids: torch.Tensor, # (B, K, D) original dtype ++ *, ++ out: torch.Tensor = None, ++ shift: torch.Tensor = None, ++ BLOCK_D: int = 128, ++): ++ """Fused finalize: sums/cnts → new centroids + per-cluster shifts. ++ ++ Replaces the host pipeline: ++ cnts_f = cnts.float().unsqueeze(-1).clamp(min=1) ++ new = sums / cnts_f ++ new = where(cnts==0, old, new) ++ new = new.to(old.dtype) ++ shift = (new - old).norm(dim=-1) # (B, K) ++ ++ Returns (new_centroids, shift) where shift has shape (B, K) — caller takes ++ `.max()` over the K axis for the convergence criterion. ++ """ ++ assert sums.is_cuda and cnts.is_cuda and old_centroids.is_cuda ++ B, K, D = sums.shape ++ assert cnts.shape == (B, K) ++ assert old_centroids.shape == (B, K, D) ++ ++ if out is None: ++ out = torch.empty_like(old_centroids) ++ if shift is None: ++ shift = torch.empty((B, K), device=sums.device, dtype=torch.float32) ++ ++ grid = (K, B) ++ _centroid_finalize_kernel[grid]( ++ sums, cnts, old_centroids, out, shift, ++ sums.stride(0), sums.stride(1), sums.stride(2), ++ cnts.stride(0), cnts.stride(1), ++ old_centroids.stride(0), old_centroids.stride(1), old_centroids.stride(2), ++ out.stride(0), out.stride(1), out.stride(2), ++ shift.stride(0), shift.stride(1), ++ K=K, D=D, BLOCK_D=BLOCK_D, ++ ) ++ return out, shift ++ ++ ++def triton_lloyd_centroid_step_euclid( ++ x: torch.Tensor, ++ cluster_ids: torch.Tensor, ++ old_centroids: torch.Tensor, ++ *, ++ BLOCK_N: int = 256, ++ sums_buf: torch.Tensor = None, ++ cnts_buf: torch.Tensor = None, ++ new_buf: torch.Tensor = None, ++ shift_buf: torch.Tensor = None, ++): ++ """Single Lloyd centroid step: sort → chunk-update → fused finalize. ++ ++ All accumulator + output buffers can be preallocated and reused across ++ iterations for zero per-iter allocation cost. ++ ++ Returns ++ ------- ++ new_centroids : (B, K, D), original dtype (== `new_buf` if provided) ++ cluster_ids : (B, N) int (echoed back; unchanged) ++ max_shift : scalar fp32 GPU tensor — `(new - old).norm(-1).max()` ++ Caller can `.item()` to get host-side scalar. ++ """ ++ B, N, D = x.shape ++ K = old_centroids.shape[1] ++ ++ if sums_buf is None: ++ sums_buf = torch.zeros((B, K, D), device=x.device, dtype=torch.float32) ++ else: ++ sums_buf.zero_() ++ if cnts_buf is None: ++ cnts_buf = torch.zeros((B, K), device=x.device, dtype=torch.int32) ++ else: ++ cnts_buf.zero_() ++ ++ # Reuse the existing sorted-update kernel to fill sums_buf / cnts_buf (no ++ # final divide — we delegate that to the fused finalize below). ++ triton_centroid_update_sorted_euclid( ++ x, cluster_ids, old_centroids, ++ BLOCK_N=BLOCK_N, ++ centroid_sums=sums_buf, ++ centroid_cnts=cnts_buf, ++ calculate_new=False, ++ ) ++ ++ new_centroids, shift_per_k = triton_centroid_finalize( ++ sums_buf, cnts_buf, old_centroids, ++ out=new_buf, ++ shift=shift_buf, ++ ) ++ # max over K → (B,), then max over B → scalar (matches existing ++ # `(cnew - cold).norm(dim=-1).max()` semantics) ++ max_shift = shift_per_k.amax(dim=-1).amax() ++ return new_centroids, cluster_ids, max_shift ++ ++ ++def main(): ++ torch.manual_seed(0) ++ ++ B, N, D = 32, 74256, 128 # modest sizes for quick correctness test ++ K = 1000 ++ dtype = torch.float16 ++ ++ x = torch.randn(B, N, D, device="cuda", dtype=dtype) ++ x_norm = F.normalize(x, p=2, dim=-1) ++ ++ cluster_ids = torch.randint(0, K, (B, N), device="cuda", dtype=torch.int32) ++ ++ # Random old centroids for handling empty clusters ++ old_centroids = F.normalize(torch.randn(B, K, D, device="cuda", dtype=dtype), p=2, dim=-1) ++ ++ # ---------------- Correctness check (compile Triton kernel) ---------------- ++ ref_centroids = torch_loop_centroid_update_cosine(x_norm, cluster_ids, old_centroids) ++ tri_centroids = triton_centroid_update_cosine(x_norm, cluster_ids, old_centroids) # this call triggers compilation ++ tri_sorted_centroids = triton_centroid_update_sorted_cosine(x_norm, cluster_ids, old_centroids) ++ ++ # Validate correctness (includes first-run compile) ++ if torch.allclose(ref_centroids, tri_centroids, atol=1e-3, rtol=1e-3): ++ print("Centroid update: PASS ✅") ++ else: ++ max_diff = (ref_centroids - tri_centroids).abs().max().item() ++ print(f"Centroid update: FAIL ❌ | max diff = {max_diff}") ++ ++ # Validate new sorted kernel ++ if torch.allclose(ref_centroids, tri_sorted_centroids, atol=1e-3, rtol=1e-3): ++ print("Sorted centroid update: PASS ✅") ++ else: ++ max_diff = (ref_centroids - tri_sorted_centroids).abs().max().item() ++ print(f"Sorted centroid update: FAIL ❌ | max diff = {max_diff}") ++ ++ ++ # show some examples ++ print(f"ref_centroids[0,0:5,0:5]: {ref_centroids[0,0:5,0:5]}") ++ print(f"tri_centroids[0,0:5,0:5]: {tri_centroids[0,0:5,0:5]}") ++ print(f"tri_sorted_centroids[0,0:5,0:5]: {tri_sorted_centroids[0,0:5,0:5]}") ++ ++ # ---------------- Efficiency benchmark (exclude compile) ---------------- ++ repeats = 20 ++ ++ # Torch loop timing ++ torch.cuda.synchronize() ++ start = torch.cuda.Event(enable_timing=True) ++ end = torch.cuda.Event(enable_timing=True) ++ start.record() ++ for _ in trange(repeats): ++ torch_loop_centroid_update_cosine(x_norm, cluster_ids, old_centroids) ++ end.record(); torch.cuda.synchronize() ++ torch_time = start.elapsed_time(end) / repeats # average per run (ms) ++ ++ # Triton timing (already compiled) ++ torch.cuda.synchronize() ++ start = torch.cuda.Event(enable_timing=True) ++ end = torch.cuda.Event(enable_timing=True) ++ start.record() ++ for _ in trange(repeats): ++ triton_centroid_update_cosine(x_norm, cluster_ids, old_centroids) ++ end.record(); torch.cuda.synchronize() ++ triton_time = start.elapsed_time(end) / repeats # average per run (ms) ++ ++ # Sorted Triton timing (already compiled) ++ torch.cuda.synchronize() ++ start = torch.cuda.Event(enable_timing=True) ++ end = torch.cuda.Event(enable_timing=True) ++ start.record() ++ for _ in trange(repeats): ++ triton_centroid_update_sorted_cosine(x_norm, cluster_ids, old_centroids) ++ end.record(); torch.cuda.synchronize() ++ triton_sorted_time = start.elapsed_time(end) / repeats # average per run (ms) ++ ++ print(f"\n=== Efficiency (average over {repeats} runs, exclude compile) ===") ++ print(f"Torch loop : {torch_time:.2f} ms") ++ print(f"Triton kernel: {triton_time:.2f} ms (speed-up x{torch_time / triton_time:.2f})") ++ print(f"Triton sorted: {triton_sorted_time:.2f} ms (speed-up x{torch_time / triton_sorted_time:.2f})") ++ ++ ++if __name__ == "__main__": ++ main() diff --git a/kmeanslib/kmeans.py b/kmeanslib/kmeans.py -index 6493af4..dda8081 100644 +index 6493af4..ed49c78 100644 --- a/kmeanslib/kmeans.py +++ b/kmeanslib/kmeans.py -@@ -1,49 +1,25 @@ +@@ -1,98 +1,26 @@ -"""Euclidean (squared-L2) Lloyd K-Means -- the reference you must optimise. -+"""Euclidean (squared-L2) Lloyd K-Means -- Triton-accelerated. ++"""Euclidean (squared-L2) Lloyd K-Means -- vendored best-in-repo Triton path. -This implementation is intentionally naive. Every Lloyd iteration it -materialises the full ``(N, K)`` distance matrix with :func:`torch.cdist` -and recomputes the assignment and the centroid update with plain PyTorch -scatter ops. It is correct and deterministic, but it moves far more memory -than necessary and launches many small kernels. -+The assignment step is the Lloyd bottleneck: the naive baseline materialised -+the full ``(N, K)`` distance matrix every iteration. Here it is replaced by a -+fused Triton kernel (:func:`euclid_assign_triton`) that streams centroids and -+keeps a running argmin in registers, so the ``(N, K)`` matrix is never written -+to or read from HBM. The centroid update stays a scatter-mean. - +- -Contract (do NOT change): -+Public contract is unchanged: ++Delegates to the fused Triton assign + Lloyd centroid-update kernels vendored ++under ``kmeanslib._kernels``. Public contract is unchanged: kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) -> (labels, centroids, n_iter) @@ -127,38 +2468,74 @@ index 6493af4..dda8081 100644 import torch -+from kmeanslib._triton_assign import euclid_assign_triton - +- -def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: - """Nearest-centroid assignment by squared-L2 distance. - +- - Naive: materialise the full (N, K) distance matrix, then argmin. - """ - # torch.cdist returns Euclidean distance; argmin of distance == argmin of - # squared distance, so we do not bother squaring. - dist = torch.cdist(x, centroids.to(x.dtype)) # (N, K) - return torch.argmin(dist, dim=1) -+def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: -+ return euclid_assign_triton(x, centroids) ++from kmeanslib._kernels.primitives.kmeans.triton.kmeans import batch_kmeans_Euclid - def _update( -@@ -52,10 +28,6 @@ def _update( - n_clusters: int, - old_centroids: torch.Tensor, - ) -> torch.Tensor: +-def _update( +- x: torch.Tensor, +- labels: torch.Tensor, +- n_clusters: int, +- old_centroids: torch.Tensor, +-) -> torch.Tensor: - """Recompute each centroid as the mean of its assigned points. - - Empty clusters keep their previous centroid. - """ - N, D = x.shape - sums = torch.zeros((n_clusters, D), device=x.device, dtype=torch.float32) - counts = torch.zeros((n_clusters,), device=x.device, dtype=torch.float32) -@@ -77,7 +49,6 @@ def kmeans( - init_centroids: torch.Tensor, - tol: float = 0.0, - ): +- N, D = x.shape +- sums = torch.zeros((n_clusters, D), device=x.device, dtype=torch.float32) +- counts = torch.zeros((n_clusters,), device=x.device, dtype=torch.float32) +- sums.index_add_(0, labels, x.float()) +- counts.index_add_(0, labels, torch.ones(N, device=x.device, dtype=torch.float32)) +- empty = counts == 0 +- counts = counts.clamp_min(1.0) +- new = sums / counts[:, None] +- if empty.any(): +- new[empty] = old_centroids[empty].float() +- return new.to(x.dtype) +- +- +-def kmeans( +- x: torch.Tensor, +- n_clusters: int, +- *, +- max_iters: int = 10, +- init_centroids: torch.Tensor, +- tol: float = 0.0, +-): - """Lloyd K-Means with fixed initial centroids. See module docstring.""" ++def kmeans(x, n_clusters, *, max_iters=10, init_centroids, tol=0.0): if x.ndim != 2: - raise ValueError(f"x must be 2-D (N, D); got shape {tuple(x.shape)}") +- raise ValueError(f"x must be 2-D (N, D); got shape {tuple(x.shape)}") ++ raise ValueError(f"x must be 2-D (N, D); got {tuple(x.shape)}") if init_centroids is None: + raise ValueError("init_centroids is required (deterministic initialisation)") +- +- centroids = init_centroids.to(x.dtype).clone() +- labels = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long) +- +- n_iter = 0 +- for n_iter in range(max_iters): +- labels = _assign(x, centroids) +- new_centroids = _update(x, labels, n_clusters, centroids) +- shift = (new_centroids - centroids).norm(dim=-1).max() +- centroids = new_centroids +- if tol > 0.0 and float(shift) < tol: +- break +- +- return labels, centroids, n_iter + 1 ++ xb = x.unsqueeze(0).contiguous() ++ initb = init_centroids.to(x.dtype).reshape(1, n_clusters, x.shape[1]).contiguous() ++ labels, centroids, n_iter = batch_kmeans_Euclid( ++ xb, n_clusters, max_iters=max_iters, tol=tol, init_centroids=initb, ++ ) ++ return labels.squeeze(0), centroids.squeeze(0).to(x.dtype), n_iter diff --git a/2.0/problems/knn_gpu_kernel_optimization/DESIGN.md b/2.0/problems/knn_gpu_kernel_optimization/DESIGN.md index c777bf73a..285c31089 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/DESIGN.md +++ b/2.0/problems/knn_gpu_kernel_optimization/DESIGN.md @@ -4,86 +4,81 @@ Operator-facing. Not copied into the agent workspace by the adapter. ## What this task measures -Can an agent turn a correct-but-naive GPU brute-force k-NN into a fast one? The -agent is given `knnlib` (a `torch.cdist`-then-`topk` search that materializes the -full `(Q, M)` distance matrix) and must rewrite the internals — ideally a -fused/streamed running top-k that tiles over the database and never writes the -`(Q, M)` matrix to HBM, with Triton kernels for the distance/selection passes — -to maximize the geometric-mean speedup over the frozen baseline across a family -of hidden `(Q, M, D, k)` workloads, subject to a recall@k quality gate. - -This is task **2 of a four-task flashlib kernel-optimization family** (KMeans, -KNN, TruncatedSVD, PCA). All four share the KernelBench-style pattern: ship a -naive package, freeze a byte-for-byte baseline in the judge image, apply the -agent's patch to a clean copy, time patched-vs-baseline on identical seeded data -in an isolated worker, and gate on a primitive-appropriate quality metric before -scoring by geomean speedup. - -## Correctness gate: recall@k, judge-computed - -The quality gate is recall@k — for each query, the fraction of the true `k` -nearest database indices the submission recovers, averaged over all queries. The -true top-k is recomputed by the judge worker from the *frozen exact baseline* -(`refknn`, a `cdist` + `topk`) on the same seeded `queries`/`database`, not from -any agent-provided number. Properties: - -- exact-set based, so it is robust to tie-breaking and floating-point drift in - the distances (a neighbour at the same distance still counts as a hit only if - its index matches the exact set — with continuous random data exact ties have - measure zero, so a correct fast kernel scores ~1.0); -- cheat-proof: you cannot return fast garbage — high recall@k requires actually - finding the nearest neighbours. Trading some numeric precision for speed - (bf16/tf32 accumulation in the distance pass) is allowed as long as recall - stays at or above `recall_threshold` (default 0.99). - -Determinism: `queries` and `database` are generated from a per-workload seed -derived from `base_seed`, so the baseline result — and thus the true top-k — is -a fixed function of `(Q, M, D, k, seed)`. - -## Anti-gaming - -flashlib (the library these primitives are distilled from) is public and -Apache-2.0. Mitigations: - -- The shipped package is a small, neutrally named library (`knnlib`), not - flashlib; the patch allowlist confines edits to `knnlib/**`. -- The patch policy forbids importing `flashlib`/cuML/cuPy/FAISS/scikit-learn and - bans env/subprocess/network access — the agent must write the kernels itself. -- Scored shapes are hidden and differ from the two public shapes; seeds derive - from `base_seed`. General kernels win; shape lookups do not. -- Honest framing: reproducing SOTA-class kernels *is* the bar. We block trivial - library reuse, not the underlying knowledge. - -## Execution model & the Modal question - -The evaluator runs an isolated worker subprocess (`_run_worker`) that imports the -frozen baseline (`/opt/knn_ref/refknn.py`) and the patched `knnlib` (from the -applied clean tree), times both, and reports JSON. This assumes the **judge -container has a GPU**. - -The repo's other GPU task (vllm) instead offloads to **Modal** because Harbor -judge containers may not be GPU-scheduled. `_run_worker` is the single swap -point: to go Modal, replace it with a Modal function that builds the patched -package, runs the same worker logic on a Modal GPU, and returns the JSON rows. -The rest of the evaluator (policy, gating, scoring) is unchanged. Decide -in-container-GPU vs Modal at first calibration trial. - -## Calibration TODO (needs a GPU trial) - -- Validate `reference.patch` runs and passes the recall@k gate on all workloads; - fix any Triton API drift for the pinned torch/triton in the image. -- Measure the reference solution's geomean speedup and set `speedup_target` so a - reference-level solution maps to ~full score. -- Confirm hidden shapes fit device memory (the naive baseline's `cdist` - materializes the full `Q x M` matrix; all shipped shapes are H100-safe, and - the chunked reference never materializes it). -- Sanity-check timing stability (median-of-7); bump `timed_iters` if noisy. - -## Files - -- `knnlib/` — pristine package baked into both images (agent edits it). -- `judge/refknn.py` — frozen exact baseline, baked to `/opt/knn_ref/`. -- `evaluator.py` — self-contained policy + orchestration + scoring (judge-only). -- `reference.patch` — chunked running-top-k reference solution (proves solvability). -- `docker/` — builds the prebuilt agent/judge images referenced by config.yaml. -- `harbor/app/` — agent-facing submission helpers + public self-test. +Can an agent turn a naive GPU brute-force k-NN into a fast one? The agent +patches `knnlib`; the judge times the patched `knn` against a frozen naive +baseline on hidden `(Q, M, D, k)` workloads and scores the geometric-mean +speedup, gated on nearest-neighbor quality (recall@k). Second of a four-task +**flashlib kernel-optimization family** (KMeans, KNN, TruncatedSVD, PCA) that +all share one evaluator + one Modal GPU harness. + +## Execution: Modal GPU offload (mirrors the vllm task) + +The judge and the agent public test both **offload GPU work to Modal**; the +containers are light (ubuntu + `modal` + git, no torch/triton). `flash_gpu.py` +(baked at `/opt/flash_gpu.py` in both images) builds an ephemeral Modal app on a +GPU (`evaluation.gpu`, default H100), ships the frozen baseline + the patched +package **as data** (no per-submission image rebuild), runs the worker, and +returns per-workload speedups + verdicts. Needs `MODAL_TOKEN_ID` / +`MODAL_TOKEN_SECRET`. torch/triton/the vendored kernels run on the Modal image +(`evaluation.pip`). + +The evaluator is generic and identical across the four tasks; only three +constants differ (`PRIMITIVE`/`PKG`/`REF_MODULE`), and all workload/threshold/ +Modal values come from `config.yaml` (delivered judge-only as +`/judge/task_config.json`, never present in the agent image). + +## Reference solution = best in the repo + +`reference.patch` vendors flashlib's own optimized **Triton** k-NN path +(`primitives/knn/triton/{dispatch,insert,sortmerge,_common,_row_norm}.py` plus +the squared-L2 gather kernel `kernels/distance/triton/knn_gather_l2sq.py`) under +`knnlib/_kernels/…`, with imports rewritten and the string "flashlib" scrubbed, +plus a thin adapter mapping our `(Q, D)` / `(M, D)` contract onto flashlib's +batched entry (`flash_knn_triton` for the indices + `triton_knn_gather_sqdist` +for the true squared distances). It is triton-only (runs on any sm≥80), imports +cleanly on CPU, and applies + passes the patch policy. Calibrate `speedup_target` +from its measured geomean on the first GPU trial. + +## Anti-reward-hack + +The **load-bearing** defenses are structural, inside the GPU worker (the only +place the untrusted submission runs): + +* timing primitives (`perf_counter`, `torch.cuda.synchronize`) are captured to + locals **before** the submission is imported → monkey-patching torch cannot + affect measurement; +* **fresh data every timed iteration** → memoizing on a repeated input is + useless; +* quality is **re-verified from the returned tensors on every iteration** → + returning a stale cached result for a different input is caught; +* the judge computes all metrics (no agent number is trusted); +* an ephemeral Modal container per submission → no global state persists. + +The patch policy is surgical defense-in-depth (so it does not reject legitimate +vendored/optimized kernel source): only `/**` `.py` files may change; it +bans external-optimized-library *imports* (flashlib/cuml/cupy/faiss/sklearn/ +cutlass — none installed on the GPU image anyway) and process/network/ +measurement-tamper patterns (`subprocess`, `socket`, `os.system`, +`torch.cuda.synchronize =`, …). + +The agent-facing `readme` describes the contract, gate, scoring, and policy but +**not** how the reference is implemented. + +## Correctness gate + +recall@k = fraction of the true `k` nearest database indices the submission +recovers, averaged over all queries, judge-computed from the returned indices on +each iteration's data against the frozen exact baseline (`refknn`, a `cdist` + +`topk`). Tie-break- and convention-independent (exact-set based), robust to fp +drift, cheat-proof: high recall requires actually finding the neighbors. +`queries`/`database` are generated from a per-workload seed, so the baseline — +and thus the true top-k — is a deterministic function of `(Q, M, D, k, seed)`. + +## Calibration TODO (needs a Modal GPU trial) + +- Run `reference.patch` on Modal; confirm it passes the recall@k gate on all + workloads (bump `recall_threshold` slack only if flashlib's low-precision + cross-term drifts a hair) and set `speedup_target` from its geomean. +- Confirm the pinned `torch`/`triton` in `evaluation.pip` compile the vendored + kernels, and hidden shapes fit the GPU. +- Confirm Modal token wiring in the Harbor judge/agent containers. diff --git a/2.0/problems/knn_gpu_kernel_optimization/config.yaml b/2.0/problems/knn_gpu_kernel_optimization/config.yaml index d5c84820d..bf109eeae 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/knn_gpu_kernel_optimization/config.yaml @@ -2,52 +2,61 @@ tag: systems runtime: language: python timeout_seconds: 10800 - environment: "GPU brute-force k-NN kernel optimization; agent patches the knnlib Python/Triton package; judge times the patched knn against a frozen naive baseline on hidden (Q, M, D, k) workloads with a recall@k (nearest-neighbour quality) gate." + environment: "GPU brute-force k-NN kernel optimization. The agent patches the knnlib package; the judge offloads timing to a Modal GPU, comparing the patched nearest-neighbor search against a frozen naive baseline on hidden (Q, M, D, k) workloads with a recall@k (retrieval-quality) gate." apt_packages: - bash - ca-certificates - git - python3 + - python3-pip judge_apt_packages: - bash - ca-certificates - git - python3 + - python3-pip + judge_pip_packages: + - modal docker: - # Experimental local images. Build them with - # 2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh before a - # local Harbor trial. Both images bundle a clean copy of the knnlib - # package; the judge image additionally bakes the frozen naive baseline - # (/opt/knn_ref) and the pristine tree (/opt/knnlib-clean). - image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.1.0 - judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.1.0 + # Experimental local images. Build with docker/build_images.sh before a local + # Harbor trial. Both are light (ubuntu + modal + git); torch/triton/flashlib + # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes + # /app/knnlib (git) + /opt/knn_ref + /opt/flash_gpu.py; the judge image + # additionally bakes /opt/knnlib-clean. + image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.2.0 + judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.2.0 environment: - cpus: 8 - memory_mb: 32768 - storage_mb: 32768 + cpus: 4 + memory_mb: 8192 + storage_mb: 16384 build_timeout_seconds: 3600 evaluation: - # The judge worker runs on a GPU visible to the judge container (single - # device). H100 is the reference accelerator; the Triton paths also run on - # L40S/A100. If Harbor cannot attach a GPU to the judge container, the worker - # step is the swap point for a Modal offload (see DESIGN.md). + # --- primitive wiring (consumed by the generic evaluator + flash_gpu) --- + primitive: knn + pkg: knnlib + ref_module: refknn + clean_source: /opt/knnlib-clean + baseline_source: /opt/knn_ref + # --- Modal GPU --- gpu: "H100" - # Timing: warmup then median-of-N cuda-synced full-knn calls. - warmup_iters: 3 + cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" + pip: ["torch", "numpy"] + app_name: "knn-kernel-opt-eval" + modal_timeout_seconds: 1800 + warmup_iters: 2 timed_iters: 7 - # Nearest-neighbour quality gate: the submitted knn's recall@k (fraction of - # the true k nearest database indices it recovers) must be at least - # recall_threshold on every hidden workload. The judge recomputes the exact - # top-k from the frozen baseline on the SAME seeded queries/database. recall_threshold: 0.99 - # Geomean speedup (over the naive baseline) mapped to the full bounded score. - # Calibrate against the reference solution's measured geomean after a trial. speedup_target: 6.0 - worker_timeout_seconds: 3600 base_seed: 20260701 - # Agent (iterative) role runs a fast subset; the final verifier runs them all. agent_workload_count: 3 expose_per_workload_metrics: false + workloads: + - {id: w0, Q: 2048, M: 100000, D: 64, k: 10} + - {id: w1, Q: 4096, M: 250000, D: 128, k: 10} + - {id: w2, Q: 1024, M: 1000000, D: 96, k: 32} + - {id: w3, Q: 8192, M: 200000, D: 256, k: 50} + - {id: w4, Q: 2048, M: 500000, D: 64, k: 100} + - {id: w5, Q: 4096, M: 300000, D: 128, k: 64} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/README.md b/2.0/problems/knn_gpu_kernel_optimization/docker/README.md index bf878c69b..1071e3002 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/docker/README.md +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/README.md @@ -1,7 +1,8 @@ # Experimental Brute-Force k-NN Kernel-Optimization Images -Two images, mirroring the duckdb-e2e split: a public **agent** image and a -private **judge** image. Build them before a local Harbor trial: +Two **light** images (ubuntu + `modal` + git). torch, triton, and the vendored +flashlib kernels all run on the **Modal GPU image** defined in `flash_gpu.py`; +the containers themselves are CPU-only and offload GPU work to Modal. ```bash bash 2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh @@ -10,31 +11,41 @@ bash 2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh Defaults: ```text -AGENT_TAG=frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.1.0 -JUDGE_TAG=frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.1.0 +AGENT_TAG=frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.2.0 +JUDGE_TAG=frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.2.0 ``` -The agent image contains: +Agent image: ```text -/app/knnlib # clean, git-tracked package (the agent edits this) +/app/knnlib # clean, git-tracked package (the agent edits this) +/opt/flash_gpu.py # shared Modal GPU harness (public test uses it) +/opt/knn_ref/refknn.py # frozen naive baseline (public-test speed denominator) ``` -The judge image contains: +Judge image: ```text /opt/knnlib-clean/knnlib # pristine tree; the patch is applied to a copy -/opt/knn_ref/refknn.py # frozen naive baseline (speed denominator + exact oracle) +/opt/knn_ref/refknn.py # frozen naive baseline (speed denominator + quality oracle) +/opt/flash_gpu.py # shared Modal GPU harness (the evaluator uses it) ``` -Both are based on `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel` (torch + triton). - ## Runtime requirements -The judge times the patched knn on a **GPU visible to the judge container** -(single device; H100 reference, Triton paths also run on L40S/A100). If the -Harbor runtime cannot attach a GPU to the judge container, port the worker step -(`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md`. +Both the judge and the agent public test **offload timing to a Modal GPU** and +therefore need Modal credentials in the environment: + +```text +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET +``` + +`flash_gpu.py` builds an ephemeral Modal app on a GPU (`evaluation.gpu`, default +`H100`), ships the frozen baseline + the patched package as data, times both on +fresh per-iteration data, verifies quality each iteration, and returns the +speedups. No persistent deployment is used, so a fresh GPU container is spun up +per evaluation. Without Modal credentials the evaluator returns a patch-policy +smoke pass (which repo CI exercises). ## Smoke test @@ -42,5 +53,4 @@ Harbor runtime cannot attach a GPU to the judge container, port the worker step bash 2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh ``` -Import-only; verifies torch/triton and the baked packages are importable. It -does not exercise a GPU. +Import-only (modal + flash_gpu + the baked packages); does not touch a GPU or Modal. diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/knn_gpu_kernel_optimization/docker/agent/Dockerfile index 91af0e93b..e2e5acb1b 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/docker/agent/Dockerfile +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/agent/Dockerfile @@ -1,17 +1,23 @@ # Agent image for knn_gpu_kernel_optimization. -# Bundles a clean, git-tracked copy of the knnlib package at /app/knnlib -# so the agent can edit it and `make_submission.sh` can diff it. torch + triton -# come from the PyTorch base image. -FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel +# +# Light image (no torch/triton): the agent edits /app/knnlib and runs the +# public test, which offloads timing to a Modal GPU (torch/triton live on the +# Modal image defined in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET +# in the environment to run the GPU public test. +# +# Build context = the task directory: +# docker build -f docker/agent/Dockerfile -t . +FROM ubuntu:24.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get install -y --no-install-recommends \ - bash ca-certificates git ripgrep && \ + bash ca-certificates git python3 python3-pip ripgrep && \ rm -rf /var/lib/apt/lists/* -RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" +RUN pip3 install --break-system-packages --no-cache-dir modal +# The package the agent edits (git-tracked so make_submission.sh can diff it). WORKDIR /app COPY knnlib /app/knnlib RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ @@ -20,3 +26,9 @@ RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ git -C /app config user.name frontier-cs && \ git -C /app add -A -- knnlib .gitignore && \ git -C /app -c commit.gpgsign=false commit -qm "pristine knnlib" + +# Shared Modal GPU harness + the frozen naive baseline (the public-test speed +# denominator). The baseline is exactly the code the agent starts from, so it is +# not secret. +COPY flash_gpu.py /opt/flash_gpu.py +COPY judge/refknn.py /opt/knn_ref/refknn.py diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh index 390fbd2d3..9672994b1 100755 --- a/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/knn_gpu_kernel_optimization/docker/judge/Dockerfile index f36a4a704..92aaa9a26 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/docker/judge/Dockerfile +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/judge/Dockerfile @@ -1,18 +1,26 @@ # Judge image for knn_gpu_kernel_optimization. -# Bakes the pristine package tree the agent patch is applied to -# (/opt/knnlib-clean/knnlib) and the frozen naive baseline the judge times -# against (/opt/knn_ref/refknn.py). torch + triton come from the base. -FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel +# +# Light image (no torch/triton): the judge validates + applies the patch and +# offloads timing to a Modal GPU (torch/triton live on the Modal image defined +# in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. +# The Frontier-CS 2.0 adapter builds the final judge image ON TOP of this one. +# +# Build context = the task directory: +# docker build -f docker/judge/Dockerfile -t . +FROM ubuntu:24.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get install -y --no-install-recommends \ - bash ca-certificates git && \ + bash ca-certificates git python3 python3-pip && \ rm -rf /var/lib/apt/lists/* -RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" +RUN pip3 install --break-system-packages --no-cache-dir modal +# Pristine tree the patch is applied to, the frozen naive baseline (speed +# denominator + quality oracle), and the shared Modal GPU harness. COPY knnlib /opt/knnlib-clean/knnlib COPY judge/refknn.py /opt/knn_ref/refknn.py +COPY flash_gpu.py /opt/flash_gpu.py WORKDIR /judge diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh index 7fdad290f..b606f59db 100755 --- a/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh @@ -1,17 +1,19 @@ #!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU required). +# Import-only smoke test for the built images (no GPU / Modal required). set -euo pipefail -AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "== agent image ==" docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import torch, triton, knnlib; print('agent ok: knnlib', knnlib.__version__)" + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ +sys.path.insert(0, '/opt/knn_ref'); import refknn; \ +import knnlib; print('agent ok: modal + flash_gpu + refknn + knnlib import')" echo "== judge image ==" docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, torch, triton; \ + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ sys.path.insert(0, '/opt/knn_ref'); import refknn; \ sys.path.insert(0, '/opt/knnlib-clean'); import knnlib; \ -print('judge ok: refknn + knnlib import')" +print('judge ok: modal + flash_gpu + refknn + knnlib import')" diff --git a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py index 045a0f21b..a6df9628b 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py @@ -1,20 +1,20 @@ -"""Evaluator for the brute-force k-NN GPU kernel-optimization task. - -The agent submits a unified diff over the ``knnlib`` Python package. The judge: - -1. statically validates the patch against an allowlist (only ``knnlib/**`` may - change; no ``flashlib``/cuML/network/env access; bounded size); -2. applies it to a pristine copy of ``knnlib`` baked into the judge image at - ``/opt/knnlib-clean``; -3. runs an isolated worker subprocess that, for each hidden ``(Q, M, D, k)`` - workload, times the patched ``knnlib.knn`` against a *frozen* naive baseline - (``/opt/knn_ref/refknn.py``) on identical seeded data and measures each - result's recall@k against the exact top-k recomputed from that baseline; -4. gates on nearest-neighbour quality (agent recall@k must stay at or above a - threshold vs the exact baseline) and scores by the geometric-mean speedup. - -When the judge source tree is not present (e.g. local static checks on a box -without a GPU), the evaluator still validates the patch policy and returns a +"""Generic evaluator for the flashlib GPU kernel-optimization task family. + +Identical across all four tasks (kmeans / knn / pca / truncated_svd); every +primitive-specific value comes from the ``evaluation`` block of the task's +config (delivered to the judge as ``/judge/task_config.json``), which is not +present in the agent workspace. + +Flow: statically validate the patch (only ``/**`` may change; no external +optimized libs / env / process / network access) -> apply it to the pristine +package baked at ``clean_source`` -> hand the frozen baseline + patched package +sources to a Modal GPU worker (``flash_gpu.run_remote``) that times both on +fresh per-iteration data and verifies clustering/retrieval/subspace quality on +every iteration -> gate on the worker's per-workload verdict -> score by the +geometric-mean speedup. + +Without Modal credentials or the baked judge sources (e.g. repo CI on a box with +no GPU/Modal), the evaluator still validates the patch policy and returns a smoke pass, so ``python3 evaluator.py reference.patch`` works anywhere. """ from __future__ import annotations @@ -29,18 +29,12 @@ import subprocess import sys import tempfile -import time from dataclasses import dataclass from pathlib import Path from typing import Any -MAX_PATCH_BYTES = 1_000_000 -MAX_CHANGED_FILES = 40 TASK_CONFIG_PATH = Path("/judge/task_config.json") -DEFAULT_CLEAN_SOURCE = Path("/opt/knnlib-clean") # pristine package (patched here) -DEFAULT_BASELINE_SOURCE = Path("/opt/knn_ref") # frozen naive baseline (refknn.py) - def _load_task_config() -> dict[str, Any]: try: @@ -51,29 +45,29 @@ def _load_task_config() -> dict[str, Any]: TASK_CONFIG = _load_task_config() -EVALUATION_CONFIG = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} +EVAL = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} -def _cfg(name: str, default): - return EVALUATION_CONFIG.get(name, default) +def _get(name: str, default): + return EVAL.get(name, default) -def _cfg_int(name: str, default: int) -> int: +def _get_int(name: str, default: int) -> int: try: - return int(EVALUATION_CONFIG.get(name, default)) + return int(EVAL.get(name, default)) except Exception: return default -def _cfg_float(name: str, default: float) -> float: +def _get_float(name: str, default: float) -> float: try: - return float(EVALUATION_CONFIG.get(name, default)) + return float(EVAL.get(name, default)) except Exception: return default -def _cfg_bool(name: str, default: bool) -> bool: - raw = EVALUATION_CONFIG.get(name, default) +def _get_bool(name: str, default: bool) -> bool: + raw = EVAL.get(name, default) if isinstance(raw, bool): return raw if isinstance(raw, str): @@ -81,50 +75,49 @@ def _cfg_bool(name: str, default: bool) -> bool: return bool(raw) -WARMUP_ITERS = _cfg_int("warmup_iters", 3) -TIMED_ITERS = _cfg_int("timed_iters", 7) -RECALL_THRESHOLD = _cfg_float("recall_threshold", 0.99) # agent recall@k >= threshold -SPEEDUP_TARGET = _cfg_float("speedup_target", 6.0) # geomean speedup mapped to full score -WORKER_TIMEOUT_SECONDS = _cfg_int("worker_timeout_seconds", 3600) -BASE_SEED = _cfg_int("base_seed", 20260701) +# --- Per-task identity: the ONLY lines that differ across the four tasks. --- +# Kept as constants (not config) so the patch policy is enforceable locally / in +# CI without /judge/task_config.json. Everything else is config-driven and only +# needed on the GPU path. +PRIMITIVE = "knn" +PKG = "knnlib" +REF_MODULE = "refknn" -# Editable surface: only the shipped package. -ALLOWED_PATTERNS = ( - "knnlib/**", -) -# Never touchable, even though they are not shipped in the agent workspace. +MAX_PATCH_BYTES = _get_int("max_patch_bytes", 2_000_000) +MAX_CHANGED_FILES = _get_int("max_changed_files", 80) +CLEAN_SOURCE = Path(f"/opt/{PKG}-clean") +BASELINE_SOURCE = Path(f"/opt/{PRIMITIVE}_ref") +SPEEDUP_TARGET = _get_float("speedup_target", 8.0) + +ALLOWED_PATTERNS = (f"{PKG}/**",) DENIED_PATTERNS = ( "evaluator.py", + "flash_gpu.py", "reference.patch", - "refknn.py", - "**/refknn.py", + f"{REF_MODULE}.py", + f"**/{REF_MODULE}.py", "**/conftest.py", "**/test_*.py", "pyproject.toml", "setup.py", "setup.cfg", ) -# Forbidden substrings in added lines (defense in depth). The agent must write -# its own kernels, not call an external optimized library, read the -# environment, spawn processes, or touch the network. -FORBIDDEN_TOKENS = ( - "flashlib", - "cuml", - "cudf", - "cupy", - "faiss", - "sklearn", - "scikit", - "os.environ", - "getenv", - "putenv", - "setenv", - "subprocess", - "socket", - "urllib", - "requests", - "importlib.import_module", - "__import__", +# Defense in depth (the load-bearing anti-tamper defense is structural, inside +# the GPU worker: it captures its perf_counter / synchronize references before +# importing the submission, regenerates data every iteration, and re-verifies +# quality each iteration). These patterns are matched surgically so that +# legitimate vendored/optimized kernel source is not rejected: +# * external optimized libraries, matched as IMPORT statements (not bare words, +# so a comment mentioning a name is fine); none are installed on the GPU +# image anyway; +# * process / network / measurement-tamper patterns that never appear in a +# real GPU kernel. +FORBIDDEN_IMPORT_RE = re.compile( + r"(?:^|\n)\s*(?:import|from)\s+(flashlib|cuml|cudf|cupy|faiss|sklearn|scikit|cutlass|cuvs)\b" +) +FORBIDDEN_PATTERN_RE = re.compile( + r"\bsubprocess\b|\bsocket\b|\burllib\b|\brequests\b|os\.system|" + r"torch\.cuda\.synchronize\s*=|time\.perf_counter\s*=|setattr\(\s*torch\.cuda" ) @@ -133,7 +126,6 @@ class PatchFile: old_path: str new_path: str added_lines: tuple[str, ...] - removed_lines: tuple[str, ...] @property def path(self) -> str: @@ -141,7 +133,7 @@ def path(self) -> str: def _match(path: str, patterns: tuple[str, ...]) -> bool: - return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + return any(fnmatch.fnmatch(path, p) for p in patterns) def _invalid(message: str, metrics: dict[str, Any] | None = None): @@ -152,41 +144,27 @@ def _invalid(message: str, metrics: dict[str, Any] | None = None): def _parse_patch(text: str) -> list[PatchFile]: files: list[PatchFile] = [] - current_old = "" - current_new = "" + old = new = "" added: list[str] = [] - removed: list[str] = [] in_file = False - for line in text.splitlines(): if line.startswith("diff --git "): if in_file: - files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) - in_file = True - current_old = current_new = "" - added = [] - removed = [] + files.append(PatchFile(old, new, tuple(added))) + in_file, old, new, added = True, "", "", [] continue if not in_file: continue if line.startswith("--- "): - current_old = line[4:].strip() - if current_old.startswith("a/"): - current_old = current_old[2:] - continue - if line.startswith("+++ "): - current_new = line[4:].strip() - if current_new.startswith("b/"): - current_new = current_new[2:] - continue - if line.startswith("+") and not line.startswith("+++ "): + old = line[4:].strip() + old = old[2:] if old.startswith("a/") else old + elif line.startswith("+++ "): + new = line[4:].strip() + new = new[2:] if new.startswith("b/") else new + elif line.startswith("+") and not line.startswith("+++ "): added.append(line[1:]) - continue - if line.startswith("-") and not line.startswith("--- "): - removed.append(line[1:]) - if in_file: - files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + files.append(PatchFile(old, new, tuple(added))) return files @@ -198,7 +176,7 @@ def _validate_path(path: str) -> tuple[bool, str]: if _match(path, DENIED_PATTERNS): return False, f"changed file is outside task boundary: {path}" if not _match(path, ALLOWED_PATTERNS): - return False, f"changed file is not allowlisted (only knnlib/** may change): {path}" + return False, f"changed file is not allowlisted (only {PKG}/** may change): {path}" if not path.endswith(".py"): return False, f"only Python files may change: {path}" return True, "" @@ -211,70 +189,40 @@ def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: if size > MAX_PATCH_BYTES: return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} text = patch_path.read_text(encoding="utf-8", errors="replace") - patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() files = _parse_patch(text) metrics: dict[str, Any] = { "patch_bytes": size, - "patch_sha256": patch_hash, + "patch_sha256": hashlib.sha256(text.encode("utf-8", "replace")).hexdigest(), "changed_files": len(files), } if len(files) > MAX_CHANGED_FILES: return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics - - for patch_file in files: - path = patch_file.path - if patch_file.new_path == "/dev/null": - return False, f"deleting files is outside task boundary: {patch_file.old_path}", metrics - if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: - ok, err = _validate_path(patch_file.old_path) + for pf in files: + path = pf.path + if pf.new_path == "/dev/null": + return False, f"deleting files is outside task boundary: {pf.old_path}", metrics + if pf.old_path != "/dev/null" and pf.old_path != pf.new_path: + ok, err = _validate_path(pf.old_path) if not ok: - return False, f"rename/copy source is outside task boundary: {err}", metrics + return False, f"rename source is outside task boundary: {err}", metrics ok, err = _validate_path(path) if not ok: return False, err, metrics - added_text = "\n".join(patch_file.added_lines) - low = added_text.lower() - for token in FORBIDDEN_TOKENS: - if token in low: - return False, f"{path}: forbidden token in added code ({token})", metrics - + added = "\n".join(pf.added_lines) + m = FORBIDDEN_IMPORT_RE.search(added) + if m: + return False, f"{path}: forbidden import of an external optimized library ({m.group(1)})", metrics + m = FORBIDDEN_PATTERN_RE.search(added) + if m: + return False, f"{path}: forbidden pattern in added code ({m.group(0).strip()[:40]})", metrics metrics["valid_patch"] = 1 return True, "patch accepted by static policy", metrics -def clean_env(tmp_root: Path) -> dict[str, str]: - home = tmp_root / "home" - tmp = tmp_root / "tmp" - home.mkdir(parents=True, exist_ok=True) - tmp.mkdir(parents=True, exist_ok=True) - env = { - "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin"), - "HOME": str(home), - "TMPDIR": str(tmp), - "LC_ALL": "C", - "LANG": "C", - } - for key in ("CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES", "LD_LIBRARY_PATH", - "TRITON_CACHE_DIR", "CUDA_HOME"): - if key in os.environ: - env[key] = os.environ[key] - env.setdefault("TRITON_CACHE_DIR", str(tmp_root / "triton_cache")) - return env - - -def run_checked(cmd: list[str], *, cwd: Path, env: dict[str, str], timeout: int) -> subprocess.CompletedProcess: - return subprocess.run( - cmd, cwd=str(cwd), env=env, timeout=timeout, - stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, - ) - - -def sanitize_error_text(text: str) -> str: - text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) +def sanitize(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text or "") text = re.sub(r"/opt/[A-Za-z0-9_./-]+", "", text) - text = re.sub(r"M=\d+", "M=", text) - text = re.sub(r"Q=\d+", "Q=", text) - text = re.sub(r"seed[=:]?\s*\d+", "seed=", text, flags=re.IGNORECASE) + text = re.sub(r"\bN=\d+|\bseed[=:]?\s*\d+", "", text, flags=re.IGNORECASE) return text[-600:] @@ -284,183 +232,123 @@ def geometric_mean(values: list[float]) -> float: return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) -def score_from_speedup(gm_speedup: float) -> float: - if gm_speedup <= 0: +def score_from_speedup(gm: float) -> float: + if gm <= 0: return 0.0 - raw = 100.0 * math.log(gm_speedup) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) return max(0.0, min(100.0, raw)) -def is_final_submission_role() -> bool: +def is_final_role() -> bool: return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" -# Hidden workloads. (Q, M, D, k). Agent role runs a fast subset for iterative -# feedback; the final verifier runs the full set. Seeds are derived from -# BASE_SEED so data is deterministic but not guessable from the statement. -_HIDDEN_WORKLOADS = ( - {"id": "w0", "Q": 2048, "M": 100_000, "D": 64, "k": 10}, - {"id": "w1", "Q": 4096, "M": 250_000, "D": 128, "k": 10}, - {"id": "w2", "Q": 1024, "M": 1_000_000, "D": 96, "k": 32}, # large-M regime - {"id": "w3", "Q": 8192, "M": 200_000, "D": 256, "k": 50}, # wide-D regime - {"id": "w4", "Q": 2048, "M": 500_000, "D": 64, "k": 100}, # large-k regime - {"id": "w5", "Q": 4096, "M": 300_000, "D": 128, "k": 64}, -) - - -def _workloads(*, final_role: bool) -> list[dict[str, Any]]: - configured = _cfg("workloads", None) - workloads = list(configured) if isinstance(configured, list) and configured else list(_HIDDEN_WORKLOADS) +def _workloads(final_role: bool) -> list[dict[str, Any]]: + workloads = list(_get("workloads", []) or []) if not final_role: - n_agent = _cfg_int("agent_workload_count", 3) - workloads = workloads[:n_agent] + n = _get_int("agent_workload_count", 3) + workloads = workloads[:n] + base = _get_int("base_seed", 20260701) for i, w in enumerate(workloads): - w.setdefault("seed", BASE_SEED + 1000 * (i + 1)) + w.setdefault("seed", base + 1000 * (i + 1)) return workloads -# The worker runs the untrusted patched package in its own process. It imports -# the frozen baseline (refknn) and the patched knnlib, times both on identical -# seeded data, and reports per-workload timings + recall@k as JSON. -_WORKER_SOURCE = r''' -import argparse, json, sys, time, traceback -def _load(baseline_dir, patched_dir): - sys.path.insert(0, patched_dir); sys.path.insert(0, baseline_dir) - import refknn, knnlib - return refknn, knnlib -def gen(Q, M, D, seed, device): - import torch - g = torch.Generator(device=device).manual_seed(int(seed)) - db = torch.randn(M, D, generator=g, device=device, dtype=torch.float32) - queries = torch.randn(Q, D, generator=g, device=device, dtype=torch.float32) - return queries, db -def recall(agent_idx, ref_idx): - hit = (agent_idx.unsqueeze(2) == ref_idx.unsqueeze(1)).any(dim=2) - Q, k = ref_idx.shape - return float(hit.sum().item()) / (Q * k) -def bench(fn, warmup, iters): - import torch - for _ in range(warmup): fn(); torch.cuda.synchronize() - ts = [] - for _ in range(iters): - torch.cuda.synchronize(); t0 = time.perf_counter(); fn(); torch.cuda.synchronize() - ts.append(time.perf_counter() - t0) - ts.sort(); return ts[len(ts) // 2] * 1000.0 -def check(out, Q, k): - import torch - if not (isinstance(out, tuple) and len(out) == 2): raise ValueError("knn must return (distances, indices)") - dist, idx = out - if tuple(dist.shape) != (Q, k) or tuple(idx.shape) != (Q, k): raise ValueError("wrong output shape") - if not torch.isfinite(dist).all(): raise ValueError("non-finite distances") -def main(): - ap = argparse.ArgumentParser() - for n in ("--baseline-dir","--patched-dir","--workloads","--out"): ap.add_argument(n, required=True) - ap.add_argument("--warmup", type=int, default=3); ap.add_argument("--iters", type=int, default=7) - a = ap.parse_args() - import torch - if not torch.cuda.is_available(): - json.dump({"ok": False, "error": "cuda_unavailable"}, open(a.out, "w")); return - refknn, knnlib = _load(a.baseline_dir, a.patched_dir) - rows = [] - for w in json.loads(a.workloads): - Q, M, D, k, seed = w["Q"], w["M"], w["D"], w["k"], w["seed"] - queries, db = gen(Q, M, D, seed, "cuda") - ref_fn = lambda: refknn.knn(queries, db, k) - agent_fn = lambda: knnlib.knn(queries, db, k) - ref_out = ref_fn(); torch.cuda.synchronize() - try: - agent_out = agent_fn(); torch.cuda.synchronize(); check(agent_out, Q, k) - rec = recall(agent_out[1].long(), ref_out[1].long()) - except Exception as e: - rows.append({"id": w["id"], "ok": False, "error": type(e).__name__ + ": " + str(e)[:200]}) - del queries, db; torch.cuda.empty_cache(); continue - ref_ms = bench(ref_fn, a.warmup, a.iters); agent_ms = bench(agent_fn, a.warmup, a.iters) - rows.append({"id": w["id"], "ok": True, "ref_ms": ref_ms, "agent_ms": agent_ms, "recall": rec}) - del queries, db; torch.cuda.empty_cache() - json.dump({"ok": True, "rows": rows}, open(a.out, "w")) -if __name__ == "__main__": - try: main() - except Exception: traceback.print_exc(); sys.exit(3) -''' - - -def _run_worker(patched_parent: Path, tmp_root: Path, env: dict[str, str], - workloads: list[dict[str, Any]]) -> dict[str, Any]: - worker_path = tmp_root / "knn_worker.py" - worker_path.write_text(_WORKER_SOURCE, encoding="utf-8") - out_path = tmp_root / "worker_out.json" - cmd = [ - sys.executable, str(worker_path), - "--baseline-dir", str(DEFAULT_BASELINE_SOURCE), - "--patched-dir", str(patched_parent), - "--workloads", json.dumps(workloads), - "--out", str(out_path), - "--warmup", str(WARMUP_ITERS), - "--iters", str(TIMED_ITERS), - ] - run_checked(cmd, cwd=tmp_root, env=env, timeout=WORKER_TIMEOUT_SECONDS) - return json.loads(out_path.read_text(encoding="utf-8")) +def _build_cfg() -> dict[str, Any]: + return { + "primitive": PRIMITIVE, + "pkg": PKG, + "ref_module": REF_MODULE, + "gpu": str(_get("gpu", "H100")), + "cuda_image": str(_get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), + "pip": list(_get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])), + "app_name": str(_get("app_name", "flash-kernel-eval")), + "modal_timeout_seconds": _get_int("modal_timeout_seconds", 1800), + "warmup": _get_int("warmup_iters", 3), + "iters": _get_int("timed_iters", 7), + "inertia_tolerance": _get_float("inertia_tolerance", 0.02), + "recall_threshold": _get_float("recall_threshold", 0.99), + "captured_tolerance": _get_float("captured_tolerance", 0.02), + "ortho_tolerance": _get_float("ortho_tolerance", 0.02), + } + + +def _read_dir(root: Path, rel_to: Path) -> dict[str, str]: + out: dict[str, str] = {} + for p in root.rglob("*.py"): + out[str(p.relative_to(rel_to))] = p.read_text(encoding="utf-8", errors="replace") + return out def full_evaluation(patch_path: Path, metrics: dict[str, Any]): - final_role = is_final_submission_role() + final_role = is_final_role() metrics["submission_role"] = "final" if final_role else "agent" - workloads = _workloads(final_role=final_role) + workloads = _workloads(final_role) - if not DEFAULT_CLEAN_SOURCE.exists() or not DEFAULT_BASELINE_SOURCE.exists(): + # Import the Modal harness baked into the judge image; missing harness or + # missing credentials/sources -> policy smoke pass. + sys.path.insert(0, "/opt") + try: + import flash_gpu # type: ignore + except Exception: metrics["full_benchmark"] = 0 - return (1.0, 1.0, - "patch policy smoke passed; knnlib judge source is not configured in this environment", - metrics) + return 1.0, 1.0, "patch policy smoke passed; GPU harness not available in this environment", metrics + if not flash_gpu.modal_available() or not CLEAN_SOURCE.exists() or not BASELINE_SOURCE.exists(): + metrics["full_benchmark"] = 0 + return 1.0, 1.0, "patch policy smoke passed; Modal/judge sources not configured in this environment", metrics - with tempfile.TemporaryDirectory(prefix="knn_kernel_opt_") as tmp: + with tempfile.TemporaryDirectory(prefix="flash_kernel_opt_") as tmp: tmp_root = Path(tmp) - env = clean_env(tmp_root) - patched_parent = tmp_root / "patched" - shutil.copytree(DEFAULT_CLEAN_SOURCE, patched_parent) - + patched = tmp_root / "patched" + shutil.copytree(CLEAN_SOURCE, patched) + env = {"PATH": os.environ.get("PATH", "/usr/bin:/bin"), "HOME": str(tmp_root), + "LC_ALL": "C", "LANG": "C"} if int(metrics.get("changed_files", 0)) > 0: - run_checked(["git", "apply", "--check", str(patch_path)], cwd=patched_parent, env=env, timeout=60) - run_checked(["git", "apply", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + try: + subprocess.run(["git", "apply", "--check", str(patch_path)], cwd=patched, env=env, + check=True, capture_output=True, text=True, timeout=60) + subprocess.run(["git", "apply", str(patch_path)], cwd=patched, env=env, + check=True, capture_output=True, text=True, timeout=60) + except subprocess.CalledProcessError as exc: + metrics["stderr_tail"] = sanitize(exc.stderr or "") + return _invalid("patch does not apply to the clean package tree", metrics) metrics["applied_patch"] = 1 else: metrics["used_empty_patch"] = 1 - result = _run_worker(patched_parent, tmp_root, env, workloads) - if not result.get("ok"): - return _invalid(f"benchmark worker failed ({result.get('error', 'unknown')})", metrics) - - rows = result.get("rows", []) - speedups: list[float] = [] - per_workload: dict[str, Any] = {} - for row in rows: - if not row.get("ok"): - metrics["failed_workload_error"] = sanitize_error_text(str(row.get("error", ""))) - return _invalid("submitted knn crashed or returned an invalid result on a hidden workload", metrics) - if row["recall"] < RECALL_THRESHOLD: - metrics["recall_regression"] = { - "recall": row["recall"], "threshold": RECALL_THRESHOLD, - } - return _invalid("submitted knn recall regressed below threshold on a hidden workload", metrics) - speedup = row["ref_ms"] / row["agent_ms"] if row["agent_ms"] > 0 else 0.01 - speedups.append(max(speedup, 0.01)) - per_workload[row["id"]] = { - "ref_ms": row["ref_ms"], "agent_ms": row["agent_ms"], "speedup": speedup, - } - - if not speedups: - return _invalid("no workloads were evaluated", metrics) - - gm = geometric_mean(speedups) - bounded = score_from_speedup(gm) - metrics.update({ - "full_benchmark": 1, - "workload_count": len(speedups), - "geomean_speedup": gm, - }) - if _cfg_bool("expose_per_workload_metrics", False): - metrics["per_workload"] = per_workload - return (bounded, bounded, f"knn geomean speedup {gm:.3f}x over the naive baseline", metrics) + payload = { + "baseline_files": _read_dir(BASELINE_SOURCE, BASELINE_SOURCE), + "patched_files": _read_dir(patched, patched), + "workloads": workloads, + "cfg": _build_cfg(), + } + try: + result = flash_gpu.run_remote(payload) + except Exception as exc: + metrics["error_detail"] = sanitize(str(exc)) + return _invalid("GPU evaluation failed", metrics) + + if not result.get("ok"): + metrics["worker_error"] = sanitize(str(result.get("error", "unknown"))) + return _invalid("GPU benchmark worker failed", metrics) + + speedups: list[float] = [] + per_workload: dict[str, Any] = {} + for row in result.get("rows", []): + if not row.get("ok"): + metrics["failed_workload"] = {"reason": row.get("reason", "error")} + return _invalid("submission failed the quality gate or crashed on a hidden workload", metrics) + speedups.append(max(float(row["speedup"]), 0.01)) + per_workload[row["id"]] = {"speedup": row["speedup"]} + if not speedups: + return _invalid("no workloads were evaluated", metrics) + + gm = geometric_mean(speedups) + bounded = score_from_speedup(gm) + metrics.update({"full_benchmark": 1, "workload_count": len(speedups), "geomean_speedup": gm}) + if _get_bool("expose_per_workload_metrics", False): + metrics["per_workload"] = per_workload + return bounded, bounded, f"{PRIMITIVE} geomean speedup {gm:.3f}x over the naive baseline", metrics def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: @@ -470,17 +358,9 @@ def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: return _invalid(message, metrics) try: return full_evaluation(patch_path, metrics) - except subprocess.TimeoutExpired: - return _invalid("benchmark worker timed out", metrics) - except subprocess.CalledProcessError as exc: - stderr = sanitize_error_text(exc.stderr or "") - cmd0 = Path(str(exc.cmd[0])).name if isinstance(exc.cmd, list) and exc.cmd else "subprocess" - metrics["failed_command"] = "git apply" if cmd0 == "git" else cmd0 - metrics["stderr_tail"] = stderr - return _invalid("patch apply or benchmark command failed", metrics) - except Exception as exc: + except Exception as exc: # noqa: BLE001 metrics["error_type"] = type(exc).__name__ - metrics["error_detail"] = sanitize_error_text(str(exc)) + metrics["error_detail"] = sanitize(str(exc)) return _invalid("evaluation failed", metrics) @@ -488,13 +368,9 @@ def main(argv: list[str]) -> int: if len(argv) != 2: print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) return 2 - score, score_unbounded, message, metrics = evaluate(argv[1]) - print(json.dumps({ - "score": score, - "score_unbounded": score_unbounded, - "message": message, - "metrics": metrics, - }, indent=2, sort_keys=True)) + score, unbounded, message, metrics = evaluate(argv[1]) + print(json.dumps({"score": score, "score_unbounded": unbounded, + "message": message, "metrics": metrics}, indent=2, sort_keys=True)) return 0 diff --git a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py new file mode 100644 index 000000000..7fd6db603 --- /dev/null +++ b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py @@ -0,0 +1,279 @@ +"""Modal GPU evaluation harness for the flashlib kernel-optimization task family. + +Shared by the judge (evaluator.py) and the agent public test. Baked into both +the agent and judge images at ``/opt/flash_gpu.py``. It runs a frozen naive +baseline and the (patched or agent-edited) package on a Modal GPU and returns +per-workload timings + quality verdicts. + +Anti-reward-hack properties (all enforced inside the remote worker, which is the +only place the untrusted code runs): + +* **Timing primitives are captured before any agent code is imported** — the + worker binds ``time.perf_counter`` and ``torch.cuda.synchronize`` to locals up + front, so a submission that monkey-patches ``torch.cuda.synchronize`` (or any + torch attribute) cannot affect measurement. +* **Fresh data every timed iteration** — each measured iteration generates a new + input from a fresh seed, so a submission cannot memoize on a repeated input. +* **Quality is verified on every timed iteration** — the judge recomputes the + quality metric from the *returned tensors* on that iteration's data and gates + it, so returning a stale cached result for a different input is caught. +* **The judge computes all metrics** — no agent-provided number is trusted. +* **Fresh container per submission** — an ephemeral Modal app is used, so no + global state survives across evaluations. + +The remote worker is a single self-contained function serialized to Modal +(``serialized=True``), so the remote does not import this module and there is no +module-mounting to get wrong. The GPU image ships torch + triton (+ any extra +pip packages a task needs for its reference build). +""" +from __future__ import annotations + +import os + + +# --------------------------------------------------------------------------- # +# Remote worker (runs on the Modal GPU). Fully self-contained: every helper is +# nested so cloudpickle ships the whole thing. Takes and returns plain data. +# --------------------------------------------------------------------------- # +def _gpu_worker(payload: dict) -> dict: + import importlib + import os as _os + import sys as _sys + import tempfile + import time + import traceback + + import torch + + # Capture timing + sync references BEFORE importing any submission code, so a + # monkey-patch of torch.cuda.synchronize cannot affect measurement. + _perf = time.perf_counter + _sync = torch.cuda.synchronize + if not torch.cuda.is_available(): + return {"ok": False, "error": "cuda_unavailable"} + dev = "cuda" + + cfg = payload["cfg"] + prim = cfg["primitive"] + warmup = int(cfg.get("warmup", 3)) + iters = int(cfg.get("iters", 7)) + + def _materialize(files: dict, tag: str) -> str: + root = tempfile.mkdtemp(prefix=f"flash_{tag}_") + for rel, content in files.items(): + path = _os.path.join(root, rel) + parent = _os.path.dirname(path) + if parent: + _os.makedirs(parent, exist_ok=True) + with open(path, "w", encoding="utf-8") as fh: + fh.write(content) + _sys.path.insert(0, root) + return root + + _materialize(payload["baseline_files"], "baseline") + _materialize(payload["patched_files"], "patched") + ref = importlib.import_module(cfg["ref_module"]) + pkg = importlib.import_module(cfg["pkg"]) + + # ---- per-primitive: data gen, call, and quality metric ---------------- # + def gen(w, seed): + g = torch.Generator(device=dev).manual_seed(int(seed)) + if prim == "knn": + db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) + q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) + return {"queries": q, "database": db} + x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) + if prim == "kmeans": + perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] + return {"x": x, "init": x.index_select(0, perm).clone()} + return {"x": x} + + def call(mod, w, data): + if prim == "kmeans": + return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], + init_centroids=data["init"], tol=0.0) + if prim == "knn": + return mod.knn(data["queries"], data["database"], w["k"]) + if prim == "pca": + return mod.pca(data["x"], w["k"]) + if prim == "tsvd": + return mod.truncated_svd(data["x"], w["k"]) + raise ValueError(prim) + + def check_shape(w, out): + if prim == "kmeans": + _, cen, _ = out + if tuple(cen.shape) != (w["K"], w["D"]) or not torch.isfinite(cen).all(): + raise ValueError("bad kmeans output") + elif prim == "knn": + d, i = out + if tuple(d.shape) != (w["Q"], w["k"]) or tuple(i.shape) != (w["Q"], w["k"]): + raise ValueError("bad knn output") + if not torch.isfinite(d).all(): + raise ValueError("non-finite distances") + else: + a, b = out + comps = a if prim == "pca" else b + if tuple(comps.shape) != (w["k"], w["D"]) or not torch.isfinite(comps).all(): + raise ValueError("bad decomposition output") + + def inertia(x, centroids): + c = centroids.to(torch.float32) + cn = (c * c).sum(1) + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384] + d = (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ c.t()) + cn[None, :] + total += float(d.min(1).values.clamp_min(0).sum().item()) + return total + + def recall(agent_idx, ref_idx): + a = agent_idx.long(); b = ref_idx.long() + hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) + return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + + def ortho_err(comps): + c = comps.to(torch.float32); k = c.shape[0] + return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) + + def captured(x, comps, center): + c = comps.to(torch.float32) + mean = x.mean(0) if center else None + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384] + if center: + xb = xb - mean + p = xb @ c.t() + total += float((p * p).sum().item()) + return total / (x.shape[0] - 1) if center else total + + def verdict(w, data, ref_out, agent_out): + """Return (ok, reason, ref_val, agent_val) gating the agent output.""" + check_shape(w, agent_out) + if prim == "kmeans": + rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) + tol = float(cfg["inertia_tolerance"]) + ok = av <= (1.0 + tol) * rv + 1e-6 + return ok, ("inertia_regression" if not ok else ""), rv, av + if prim == "knn": + rec = recall(agent_out[1], ref_out[1]) + ok = rec >= float(cfg["recall_threshold"]) + return ok, ("recall_regression" if not ok else ""), 1.0, rec + center = (prim == "pca") + comps = agent_out[0] if prim == "pca" else agent_out[1] + ref_comps = ref_out[0] if prim == "pca" else ref_out[1] + oe = ortho_err(comps) + if oe > float(cfg["ortho_tolerance"]): + return False, "not_orthonormal", 0.0, oe + rv = captured(data["x"], ref_comps, center) + av = captured(data["x"], comps, center) + ok = av >= (1.0 - float(cfg["captured_tolerance"])) * rv - 1e-6 + return ok, ("captured_regression" if not ok else ""), rv, av + + def time_call(mod, w, data): + _sync(); t0 = _perf(); out = call(mod, w, data); _sync() + return (_perf() - t0) * 1000.0, out + + rows = [] + try: + for w in payload["workloads"]: + base_seed = int(w["seed"]) + # warmup on fresh data (kernels / autotune) — not measured + for i in range(warmup): + d = gen(w, base_seed + 100 + i) + call(ref, w, d); call(pkg, w, d); _sync() + del d + torch.cuda.empty_cache() + ratios, ref_val, agent_val = [], None, None + bad = None + for i in range(iters): + d = gen(w, base_seed + 10000 + i) # fresh every iteration + rt, rout = time_call(ref, w, d) + at, aout = time_call(pkg, w, d) + ok, reason, rv, av = verdict(w, d, rout, aout) # verify THIS iter + if not ok: + bad = reason; ref_val, agent_val = rv, av + break + ratios.append(rt / at if at > 0 else 0.01) + ref_val, agent_val = rv, av + del d, rout, aout + torch.cuda.empty_cache() + if bad is not None: + rows.append({"id": w["id"], "ok": False, "reason": bad, + "ref_val": ref_val, "agent_val": agent_val}) + continue + ratios.sort() + rows.append({"id": w["id"], "ok": True, + "speedup": ratios[len(ratios) // 2], + "ref_val": ref_val, "agent_val": agent_val}) + except Exception as exc: + return {"ok": False, "error": f"{type(exc).__name__}: {str(exc)[:200]}", + "trace": traceback.format_exc()[-500:]} + return {"ok": True, "rows": rows} + + +# --------------------------------------------------------------------------- # +# Host-side wrappers (run in the judge / agent container; need MODAL tokens). +# --------------------------------------------------------------------------- # +def _build_image(cfg: dict): + import modal + pip = list(cfg.get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])) + base = cfg.get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04") + return (modal.Image.from_registry(base, add_python="3.11") + .entrypoint([]) + .pip_install(*pip)) + + +# Substrings that mark a transient Modal control-plane / image-build failure +# (evicted build, gateway hiccup, app stopped mid-run) rather than a real error +# in the submission — safe to retry. +_TRANSIENT_MARKERS = ( + "external shut-down", "terminated due to external", "please try again", + "app_state_stopped", "conflicterror", "deadline exceeded", "connection reset", + "502 bad gateway", "503 service", "temporarily unavailable", "timed out", + "gateway", "eviction", "internalfailure", +) + + +def _is_transient(text: str) -> bool: + low = (text or "").lower() + return any(m in low for m in _TRANSIENT_MARKERS) + + +def run_remote(payload: dict) -> dict: + """Run the GPU worker on Modal and return its result dict. + + Retries transient Modal control-plane failures; a real error in the + submission is non-transient and surfaces immediately. Raises RuntimeError + with a sanitized message on unrecoverable failure. + """ + import time as _time + + import modal + + cfg = payload["cfg"] + attempts = max(1, int(cfg.get("modal_retries", 3))) + last = "modal_gpu_eval_failed" + for attempt in range(1, attempts + 1): + app = modal.App(cfg.get("app_name", "flash-kernel-eval")) + remote = app.function( + gpu=cfg.get("gpu", "H100"), + image=_build_image(cfg), + timeout=int(cfg.get("modal_timeout_seconds", 1800)), + serialized=True, + max_containers=1, + )(_gpu_worker) + try: + with modal.enable_output(), app.run(): + return remote.remote(payload) + except Exception as exc: # noqa: BLE001 + last = str(exc)[-400:] + if not _is_transient(last) or attempt == attempts: + raise RuntimeError(f"modal_gpu_eval_failed: {last}") from exc + _time.sleep(min(45, 10 * attempt)) + raise RuntimeError(f"modal_gpu_eval_failed: {last}") + + +def modal_available() -> bool: + return bool(os.environ.get("MODAL_TOKEN_ID") and os.environ.get("MODAL_TOKEN_SECRET")) diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/README.md b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/README.md index 19cdee038..c044f496a 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/README.md +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/README.md @@ -30,5 +30,5 @@ results. - Do not import external optimized libraries (write the kernels yourself), and do not access the environment, spawn processes, or use the network. - Keep the public `knn(...)` signature and return contract unchanged. -- Nearest-neighbour quality is gated (recall@k vs an exact baseline); do not +- Nearest-neighbor quality is gated (recall@k vs an exact baseline); do not sacrifice correctness for speed beyond the allowed tolerance. diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py index 84c22aa7b..d2a7f186b 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,78 +1,77 @@ -"""Public self-test for the brute-force k-NN kernel-optimization task. +"""Public GPU self-test for the brute-force k-NN kernel-optimization task. -Runs the (patched) knnlib on the two public shapes, checks the nearest-neighbour -quality (recall@k) against a local naive cdist+topk recomputation, and prints a -rough speedup. This is a convenience for local iteration only -- the graded -workloads and thresholds are hidden and differ from these shapes. +Times your current /app/knnlib against the naive baseline on a Modal GPU and +reports the quality verdict + speedup on two public shapes. Requires +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and +their thresholds are hidden and differ from these public shapes. """ from __future__ import annotations +import os import sys -import time - -PUBLIC_WORKLOADS = [ - {"Q": 1024, "M": 100_000, "D": 64, "k": 10, "seed": 1}, - {"Q": 2048, "M": 200_000, "D": 128, "k": 10, "seed": 2}, -] +from pathlib import Path +sys.path.insert(0, "/opt") -def naive_knn(queries, database, k): - import torch - d2 = torch.cdist(queries, database) ** 2 - dist, idx = torch.topk(d2, k, dim=1, largest=False) - return dist, idx.to(torch.long) - - -def recall(agent_idx, ref_idx): - # Fraction of the true k nearest indices recovered, averaged over queries. - hit = (agent_idx.unsqueeze(2) == ref_idx.unsqueeze(1)).any(dim=2) - Q, k = ref_idx.shape - return float(hit.sum().item()) / (Q * k) +APP_DIR = os.environ.get("APP_DIR", "/app") +PKG = "knnlib" +BASELINE_DIR = "/opt/knn_ref" +PUBLIC_WORKLOADS = [ + {"id": "p0", "Q": 1024, "M": 100_000, "D": 64, "k": 10, "seed": 1}, + {"id": "p1", "Q": 2048, "M": 200_000, "D": 128, "k": 10, "seed": 2}, +] -def bench(fn, warmup=2, iters=5): - import torch - for _ in range(warmup): - fn(); torch.cuda.synchronize() - ts = [] - for _ in range(iters): - torch.cuda.synchronize(); t0 = time.perf_counter() - fn(); torch.cuda.synchronize() - ts.append(time.perf_counter() - t0) - ts.sort() - return ts[len(ts) // 2] * 1000.0 +CFG = { + "primitive": "knn", + "pkg": PKG, + "ref_module": "refknn", + "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), + "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", + "pip": ["torch==2.5.1", "triton==3.1.0", "numpy"], + "app_name": "knn-kernel-opt-public", + "modal_timeout_seconds": 1800, + "warmup": 2, + "iters": 5, + "inertia_tolerance": 0.02, + "recall_threshold": 0.99, + "captured_tolerance": 0.02, + "ortho_tolerance": 0.02, +} + + +def _read(root: str, sub: str = "") -> dict: + base = Path(root) + scan = base / sub if sub else base + return {str(p.relative_to(base)): p.read_text(encoding="utf-8", errors="replace") + for p in scan.rglob("*.py")} def main() -> int: + import flash_gpu # baked at /opt/flash_gpu.py + if not flash_gpu.modal_available(): + print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU public test.") + return 1 + payload = { + "baseline_files": _read(BASELINE_DIR), + "patched_files": _read(APP_DIR, PKG), + "workloads": PUBLIC_WORKLOADS, + "cfg": CFG, + } try: - import torch - except Exception as e: # pragma: no cover - print(f"torch import failed: {e}") + result = flash_gpu.run_remote(payload) + except Exception as exc: # noqa: BLE001 + print(f"GPU run failed: {exc}") return 1 - if not torch.cuda.is_available(): - print("no CUDA device available; run this in a GPU-enabled container.") + if not result.get("ok"): + print(f"worker error: {result.get('error')}") return 1 - import knnlib - - for w in PUBLIC_WORKLOADS: - Q, M, D, k, seed = w["Q"], w["M"], w["D"], w["k"], w["seed"] - g = torch.Generator(device="cuda").manual_seed(seed) - db = torch.randn(M, D, generator=g, device="cuda", dtype=torch.float32) - queries = torch.randn(Q, D, generator=g, device="cuda", dtype=torch.float32) - - _, ref_idx = naive_knn(queries, db, k) - out = knnlib.knn(queries, db, k) - agent_idx = out[1].long() - rec = recall(agent_idx, ref_idx) - - ref_ms = bench(lambda: naive_knn(queries, db, k)) - agent_ms = bench(lambda: knnlib.knn(queries, db, k)) - ok = "OK" if rec >= 0.99 else "RECALL REGRESSION" - print(f"(Q={Q}, M={M}, D={D}, k={k}) recall@k={rec:.4f} [{ok}] " - f"baseline={ref_ms:.2f}ms yours={agent_ms:.2f}ms " - f"speedup={ref_ms / agent_ms:.2f}x") - del queries, db - torch.cuda.empty_cache() + print(f"{'workload':10s} {'status':16s} {'speedup':>10s}") + for row in result["rows"]: + if row.get("ok"): + print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") + else: + print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason','')):16s} {'-':>10s}") return 0 diff --git a/2.0/problems/knn_gpu_kernel_optimization/readme b/2.0/problems/knn_gpu_kernel_optimization/readme index ba50252e8..b7af10454 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/readme +++ b/2.0/problems/knn_gpu_kernel_optimization/readme @@ -2,8 +2,8 @@ ## Problem -You are given a small GPU brute-force k-nearest-neighbours library, `knnlib`, -in the Harbor workspace at `/app/knnlib`. Its public entry point is: +You are given a small GPU brute-force k-nearest-neighbor library, `knnlib`, in +the Harbor workspace at `/app/knnlib`. Its public entry point is: ```python knnlib.knn(queries, database, k) -> (distances, indices) @@ -11,30 +11,26 @@ knnlib.knn(queries, database, k) -> (distances, indices) `queries` is a `(Q, D)` float32 CUDA tensor of query points, `database` is an `(M, D)` float32 CUDA tensor of database points to search, and `k` is the number -of nearest neighbours to return per query. The function returns `distances`, a +of nearest neighbors to return per query. The function returns `distances`, a `(Q, k)` float32 tensor of the **squared-L2** distances to the `k` nearest database points (ascending, nearest first), and `indices`, a `(Q, k)` int64 tensor of the corresponding database row indices. The shipped implementation is -correct but deliberately naive: it materializes the full `(Q, M)` pairwise -distance matrix with `torch.cdist`, then runs `torch.topk` over the whole -matrix. +a straightforward, correct PyTorch version. Your goal is to make `knnlib.knn` **as fast as possible** on the GPU while -returning the same nearest neighbours. You may add modules and Triton kernels -inside the `knnlib` package and rewrite the internals freely (fused/streamed -running top-k, tiled distance computation, reduced memory traffic, and so on) — -in particular you should avoid ever materializing the full `(Q, M)` distance -matrix. The public function signature and return contract above must not change. +returning the same nearest neighbors. You may rewrite the internals of the +package however you like and add new modules (including Triton kernels) under +`knnlib/`. The public function signature and return contract above must not +change, and every result must remain a deterministic function of its inputs. ## Workload -The judge evaluates a family of held-out dense search workloads that vary -`(Q, M, D, k)`, spanning small/large database sizes `M`, wide-feature (large -`D`) and many-neighbour (large `k`) regimes. All workloads use squared-L2 -distance, float32 data, and randomly generated queries/database, so each result -is a deterministic function of the inputs. +The graded workloads are a family of held-out dense search problems that vary +`(Q, M, D, k)`, spanning small/large database sizes `M` and both wide-feature +(large `D`) and many-neighbor (large `k`) regimes. All use squared-L2 distance, +float32 data, and randomly generated queries/database. -Two representative *public* shapes you can use for local development (the graded +Two representative *public* shapes are available for local testing (the graded shapes are different and hidden): ```text @@ -42,19 +38,31 @@ shapes are different and hidden): (Q=2048, M=200000, D=128, k=10) ``` -Treat the workload as a general dense brute-force k-NN, not as shapes to -special-case. Improvements should be general kernel/throughput improvements, not -lookups keyed on specific `(Q, M, D, k)` values. +Treat this as a general dense brute-force k-NN, not as specific shapes to +special-case. + +## Iterate on a GPU + +The agent workspace has no GPU. Use the public test to time your current code on +a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your +environment): + +```bash +bash /app/public_test.sh +``` + +It reports, per public shape, whether your result passes the quality gate and +your speedup over the baseline. ## Submission -The submitted artifact is a patch file over the `knnlib` package: +The submitted artifact is a patch over the `knnlib` package: ```text /app/solution.patch ``` -After editing `/app/knnlib`, generate and submit a patch: +After editing `/app/knnlib`, generate and submit: ```bash bash /app/make_submission.sh @@ -62,63 +70,54 @@ bash /app/submit.sh ``` Submissions are asynchronous; submit early and keep iterating. The judge applies -your patch to a clean copy of `knnlib`, imports the patched package, and times -it against the original naive implementation on the same hardware and the same -seeded queries/database. +your patch to a clean copy of `knnlib` and times it against the original +baseline on a GPU, on the same seeded queries and database. ## Correctness -Correctness is a gate. For each workload the judge recomputes the exact `k` -nearest database indices from an exact baseline and measures the **recall@k** of -your result — the fraction of the true `k` nearest indices your `knn` recovers, -averaged over all queries. Your recall@k must stay at or above a high threshold -(~0.99). Crashes, non-finite outputs, wrong shapes/dtypes, timeouts, and results -that drop below the recall threshold are all penalized before speed is -considered. You may trade a little numerical precision (e.g. lower-precision -accumulation) for speed as long as you stay above the recall threshold. +Correctness is a gate. On every timed iteration the judge recomputes the +**recall@k** (the fraction of the true `k` nearest database indices your result +recovers, averaged over all queries) of your result against an exact baseline on +identical data, and requires your recall@k to stay at or above a high threshold. +Crashes, non-finite output, wrong shapes/dtypes, timeouts, and results that drop +below the recall threshold are penalized before speed is considered. You may +trade a little numerical precision for speed as long as you stay above the +recall threshold. ## Scoring -Valid submissions are scored by speedup relative to the naive baseline on the -same hardware and workloads. For each workload: +Valid submissions are scored by speedup relative to the baseline on the same +hardware and workloads. For each workload: ```text speedup = baseline_time / your_time ``` The objective is the geometric mean of per-workload speedups, so broad speedups -across the workload family are preferred over one large outlier. A result no -faster than the baseline earns 0; regressions earn 0. The raw geometric-mean -speedup is reported in the evaluator metrics. +are preferred over a single large outlier. A result no faster than the baseline +earns 0; regressions earn 0. The raw geometric-mean speedup is reported in the +evaluator metrics. ## Patch Policy -The evaluator validates the patch before running it. Only files under the +The evaluator validates the patch before running it. Only Python files under the package may change: ```text knnlib/** ``` -New Python modules inside `knnlib/` (for example Triton kernel files) are -allowed. Patches may not: - -- modify anything outside `knnlib/`; -- import or call an external optimized ML/kernel library (the point is to write - the kernels yourself); -- read or write environment variables, spawn processes, or access the network; -- special-case the hidden workload shapes. - -The patch must be reasonably sized and apply cleanly to the clean `knnlib` tree. +New Python modules inside `knnlib/` are allowed. Patches may not: modify +anything outside `knnlib/`; import or call an external optimized ML/kernel +library (write the kernels yourself); read or write environment variables, spawn +processes, or access the network; or otherwise tamper with the measurement +framework. The timing harness measures your code on freshly generated data every +iteration and re-verifies quality each time, so caching results across calls does +not help. ## Resource Budget ```text -GPU: single device (H100 reference; Triton paths also run on L40S / A100) -vCPUs: 8 -memory: 32 GiB -storage: 32 GiB +GPU: single Modal GPU (H100 reference; the Triton paths also run on L40S / A100) +Agent container: CPU-only (GPU work is offloaded to Modal) ``` - -The judge runs the timing worker in a subprocess under a clean, minimal -environment with a fixed warmup/measurement schedule and a per-run timeout. diff --git a/2.0/problems/knn_gpu_kernel_optimization/reference.patch b/2.0/problems/knn_gpu_kernel_optimization/reference.patch index 1becaf36c..8a50b02b5 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/knn_gpu_kernel_optimization/reference.patch @@ -1,57 +1,2094 @@ -diff --git a/knnlib/_chunked_knn.py b/knnlib/_chunked_knn.py +diff --git a/knnlib/_kernels/__init__.py b/knnlib/_kernels/__init__.py new file mode 100644 -index 0000000..df258eb +index 0000000..e69de29 +diff --git a/knnlib/_kernels/kernels/__init__.py b/knnlib/_kernels/kernels/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/knnlib/_kernels/kernels/distance/__init__.py b/knnlib/_kernels/kernels/distance/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/knnlib/_kernels/kernels/distance/triton/__init__.py b/knnlib/_kernels/kernels/distance/triton/__init__.py +new file mode 100644 +index 0000000..bf669cf +--- /dev/null ++++ b/knnlib/_kernels/kernels/distance/triton/__init__.py +@@ -0,0 +1,11 @@ ++"""distance triton backend (kNN squared-L2 gather only). ++ ++Minimal re-export: only the tiled squared-L2 gather kernel that recovers ++true ``||x - c[idx]||^2`` per neighbour after the fused kNN pass. The ++other distance kernels from the upstream backend are not vendored here. ++""" ++from knnlib._kernels.kernels.distance.triton.knn_gather_l2sq import ( ++ triton_knn_gather_sqdist, ++) ++ ++__all__ = ["triton_knn_gather_sqdist"] +diff --git a/knnlib/_kernels/kernels/distance/triton/knn_gather_l2sq.py b/knnlib/_kernels/kernels/distance/triton/knn_gather_l2sq.py +new file mode 100644 +index 0000000..e2e3fbe +--- /dev/null ++++ b/knnlib/_kernels/kernels/distance/triton/knn_gather_l2sq.py +@@ -0,0 +1,232 @@ ++"""Tiled gather kernel: true squared L2 to selected kNN neighbours. ++ ++After a fused kNN pass that ranks candidates with the shift-invariant ++score ``c_sq - 2 * ``, this kernel writes the **true** ++``||x[b, n] - c[b, idx[b, n, k]]||^2`` for every neighbour. Computing ++the difference directly (``(x - y)^2`` per d) avoids the cancellation ++inherent to the ``x^2 + y^2 - 2*x*y`` expansion, so the result is ++accurate even for fp32 inputs where the fused kNN kernel may have used ++TF32 in the cross-term. ++ ++Complexity: ``O(B * N * K * D)`` -- cheap vs the ``O(B * N * M * D)`` ++GEMM that found the candidates. ++ ++Design ++------ ++One program owns a ``(BN, K_BLOCK)`` output tile. The query row tile ++``x[b, n_block, :]`` is loaded once and reused across the ``K_BLOCK`` ++neighbours, then streamed through ``D`` in ``BLOCK_D`` chunks. The ++corpus rows ``c[b, idx, :]`` are gather-loaded fresh per d-chunk into a ++3-D tile ``(BN, K_BLOCK, BLOCK_D)`` and subtracted directly from the ++broadcast x tile in fp32. The fp32 squared difference is summed along ++``D`` into the accumulator. ++ ++Three regime presets pick ``BN`` and ``K_BLOCK`` so the c-tile fits in ++SMEM/registers and amortises one x-load over many neighbours: ++ ++ * K <= 32: ``K_BLOCK = next_pow2(K)``, ``BN = 16``. ++ * 32 < K <= 512: ``K_BLOCK = 32``, ``BN = 8``; multiple K-tiles per row. ++ * K > 512: ``K_BLOCK = 32``, ``BN = 4``. ++ ++``BLOCK_D`` is chosen so the per-program SMEM footprint ++``BN * K_BLOCK * BLOCK_D * sizeof(dtype)`` stays under ~32 KB. The ++unmodified ``D`` (not padded) is passed as a constexpr so the Triton ++compiler unrolls the d-loop exactly. ++""" ++from __future__ import annotations ++ ++import torch ++import triton ++import triton.language as tl ++ ++ ++def _next_pow2(n: int) -> int: ++ if n <= 1: ++ return 1 ++ return 1 << (n - 1).bit_length() ++ ++ ++@triton.jit ++def _knn_gather_l2sq_kernel( ++ x_ptr, c_ptr, idx_ptr, out_ptr, ++ M, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_c_b, stride_c_m, stride_c_d, ++ stride_i_b, stride_i_n, stride_i_k, ++ stride_o_b, stride_o_n, stride_o_k, ++ N: tl.constexpr, D: tl.constexpr, K: tl.constexpr, ++ BN: tl.constexpr, K_BLOCK: tl.constexpr, ++ BLOCK_D: tl.constexpr, ++ SINGLE_D_TILE: tl.constexpr, ++): ++ """Tiled (BN, K_BLOCK, BLOCK_D) gather + sq-distance accumulate. ++ ++ Grid: ``(ceil(N / BN), ceil(K / K_BLOCK), B)``. ++ ++ Each program owns one ``(BN, K_BLOCK)`` output tile. The fp32 ++ accumulator stays in registers; the c-row tile ``(BN, K_BLOCK, ++ BLOCK_D)`` is reloaded per d-chunk to keep SMEM bounded. ++ """ ++ pid_n = tl.program_id(0) ++ pid_k = tl.program_id(1) ++ pid_b = tl.program_id(2).to(tl.int64) ++ ++ n_start = pid_n * BN ++ n_offs = (n_start + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ ++ k_start = pid_k * K_BLOCK ++ k_offs = (k_start + tl.arange(0, K_BLOCK)).to(tl.int64) ++ k_mask = k_offs < K ++ ++ idx_tile = tl.load( ++ idx_ptr + pid_b * stride_i_b ++ + n_offs[:, None] * stride_i_n ++ + k_offs[None, :] * stride_i_k, ++ mask=n_mask[:, None] & k_mask[None, :], other=0, ++ ).to(tl.int64) ++ idx_tile = tl.maximum(idx_tile, 0) ++ idx_tile = tl.minimum(idx_tile, M - 1) ++ ++ acc = tl.zeros((BN, K_BLOCK), dtype=tl.float32) ++ ++ if SINGLE_D_TILE: ++ d_offs = tl.arange(0, BLOCK_D).to(tl.int64) ++ d_mask = d_offs < D ++ x_tile = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0, ++ ).to(tl.float32) ++ c_tile = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + idx_tile[:, :, None] * stride_c_m ++ + d_offs[None, None, :] * stride_c_d, ++ mask=(n_mask[:, None] & k_mask[None, :])[:, :, None] ++ & d_mask[None, None, :], other=0.0, ++ ).to(tl.float32) ++ diff = x_tile[:, None, :] - c_tile ++ acc = tl.sum(diff * diff, axis=2) ++ else: ++ for d_start in tl.range(0, D, BLOCK_D): ++ d_offs = (d_start + tl.arange(0, BLOCK_D)).to(tl.int64) ++ d_mask = d_offs < D ++ x_tile = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0, ++ ).to(tl.float32) ++ c_tile = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + idx_tile[:, :, None] * stride_c_m ++ + d_offs[None, None, :] * stride_c_d, ++ mask=(n_mask[:, None] & k_mask[None, :])[:, :, None] ++ & d_mask[None, None, :], other=0.0, ++ ).to(tl.float32) ++ diff = x_tile[:, None, :] - c_tile ++ acc += tl.sum(diff * diff, axis=2) ++ ++ tl.store( ++ out_ptr + pid_b * stride_o_b ++ + n_offs[:, None] * stride_o_n ++ + k_offs[None, :] * stride_o_k, ++ acc, mask=n_mask[:, None] & k_mask[None, :], ++ ) ++ ++ ++def _pick_tile(K: int, D: int, dtype_bytes: int) -> tuple[int, int, int, bool]: ++ """Pick ``(BN, K_BLOCK, BLOCK_D, single_d_tile)`` for the kernel. ++ ++ Regimes mirror the file docstring -- small ``K_BLOCK`` for large K, ++ larger ``BN`` for small K. ``BLOCK_D`` is sized so the 3-D c-tile ++ SMEM footprint stays around ~32 KB. ++ """ ++ K_pad = _next_pow2(K) ++ ++ if K_pad <= 32: ++ K_BLOCK = max(1, K_pad) ++ BN = 16 ++ elif K_pad <= 512: ++ K_BLOCK = 32 ++ BN = 8 ++ else: ++ K_BLOCK = 32 ++ BN = 4 ++ ++ target_bytes = 32 * 1024 ++ per_d = BN * K_BLOCK * dtype_bytes ++ block_d_cap = max(16, target_bytes // max(1, per_d)) ++ block_d_cap = min(block_d_cap, 256) ++ ++ if D <= block_d_cap: ++ BLOCK_D = max(16, _next_pow2(D)) ++ single_d_tile = True ++ else: ++ BLOCK_D = 64 if block_d_cap >= 64 else 32 ++ single_d_tile = False ++ ++ return BN, K_BLOCK, BLOCK_D, single_d_tile ++ ++ ++def triton_knn_gather_sqdist( ++ x: torch.Tensor, ++ c: torch.Tensor, ++ idx: torch.Tensor, ++ *, ++ out: torch.Tensor | None = None, ++) -> torch.Tensor: ++ """``out[b, n, k] = || x[b, n, :] - c[b, idx[b, n, k], :] ||^2`` (fp32). ++ ++ Args: ++ x: (B, N, D) query tensor, any float cuda dtype (bf16 / fp16 / fp32). ++ c: (B, M, D) corpus, same dtype and same batch as ``x``. ++ idx: (B, N, K) int32 / int64 column indices into ``c``. ++ out: optional pre-allocated (B, N, K) fp32 output buffer. ++ ++ Returns: ++ (B, N, K) fp32. The computation runs in fp32 throughout ++ (``diff = x.to(fp32) - c.to(fp32)``, ``sum(diff*diff)``) so the ++ result is bit-equivalent to the naive torch reference up to ++ accumulation order on the d-axis. ++ """ ++ assert x.is_cuda and c.is_cuda and idx.is_cuda ++ assert x.dim() == 3 and c.dim() == 3 and idx.dim() == 3 ++ B, N, D = x.shape ++ Bc, M, Dc = c.shape ++ Bi, Ni, K = idx.shape ++ assert B == Bc == Bi and N == Ni and D == Dc ++ ++ if not x.is_contiguous(): ++ x = x.contiguous() ++ if not c.is_contiguous(): ++ c = c.contiguous() ++ if not idx.is_contiguous(): ++ idx = idx.contiguous() ++ ++ if out is None: ++ out = torch.empty((B, N, K), device=x.device, dtype=torch.float32) ++ else: ++ assert out.shape == (B, N, K) and out.dtype == torch.float32 ++ ++ dtype_bytes = x.element_size() ++ BN, K_BLOCK, BLOCK_D, single_d_tile = _pick_tile(K, D, dtype_bytes) ++ ++ grid = (triton.cdiv(N, BN), triton.cdiv(K, K_BLOCK), B) ++ _knn_gather_l2sq_kernel[grid]( ++ x, c, idx, out, ++ M, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ idx.stride(0), idx.stride(1), idx.stride(2), ++ out.stride(0), out.stride(1), out.stride(2), ++ N=N, D=D, K=K, ++ BN=BN, K_BLOCK=K_BLOCK, BLOCK_D=BLOCK_D, ++ SINGLE_D_TILE=single_d_tile, ++ num_warps=4, ++ ) ++ return out ++ ++ ++__all__ = ["triton_knn_gather_sqdist"] +diff --git a/knnlib/_kernels/primitives/__init__.py b/knnlib/_kernels/primitives/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/knnlib/_kernels/primitives/knn/__init__.py b/knnlib/_kernels/primitives/knn/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/knnlib/_kernels/primitives/knn/triton/__init__.py b/knnlib/_kernels/primitives/knn/triton/__init__.py +new file mode 100644 +index 0000000..c177f23 --- /dev/null -+++ b/knnlib/_chunked_knn.py ++++ b/knnlib/_kernels/primitives/knn/triton/__init__.py @@ -0,0 +1,41 @@ -+"""Chunked running-top-k brute-force k-NN -- the reference optimisation. ++"""knn triton backend. ++ ++Re-exports the public Python wrappers from each component file. ++``@triton.jit`` kernels stay private to their file. ++ ++Kernel layout: ++ ++* :mod:`_common` -- shared helpers: ``_next_pow2``, micro-bench, ++ IEEE-sortable u32 transform, ``_INF_PACKED`` sentinel. ++* :mod:`sortmerge` -- packed-uint64 sort-merge top-K (x²-free). Routed ++ to only for the small-Q + medium-K Pattern-A corner. ++* :mod:`insert` -- iterative argmin-insert top-K (x²-free). The ++ general path -- BN in {8, 16, 32, 64, 128} covers everything from ++ small-Q search to large-Q build. ++* :mod:`_row_norm` -- ``_fast_row_sq`` / ``_get_or_compute_csq`` for ++ the CuteDSL FA3 wrapper (it needs ``c_sq`` as a kernel argument). ++* :mod:`dispatch` -- host-side router. Single public entry ++ :func:`flash_knn_triton` runs through :func:`_heuristic_config` ++ unconditionally -- the per-CTA-count check inside the heuristic ++ picks single-pass vs M-split. Per-shape config (BN/BM/mode/mps/ ++ ns_pipe) also falls out of :func:`_heuristic_config`. ++""" ++from knnlib._kernels.primitives.knn.triton._common import ( ++ _next_pow2, ++ _bench_quick, ++) ++from knnlib._kernels.primitives.knn.triton._row_norm import ( ++ _fast_row_sq, ++ _get_or_compute_csq, ++) ++from knnlib._kernels.primitives.knn.triton.dispatch import ( ++ flash_knn_triton, ++ flash_knn_triton_small_n, ++ flash_knn_triton_large_n, ++) ++ ++__all__ = [ ++ "flash_knn_triton", ++ "flash_knn_triton_small_n", ++ "flash_knn_triton_large_n", ++] +diff --git a/knnlib/_kernels/primitives/knn/triton/_common.py b/knnlib/_kernels/primitives/knn/triton/_common.py +new file mode 100644 +index 0000000..686e85b +--- /dev/null ++++ b/knnlib/_kernels/primitives/knn/triton/_common.py +@@ -0,0 +1,81 @@ ++"""Shared utilities for flash_knn Triton dispatch + kernels. ++ ++Lives in one place so :mod:`dispatch`, :mod:`sortmerge` and :mod:`insert` ++all import the same primitives: ++ ++ * :func:`_next_pow2` -- D / K padding helper. ++ * :func:`_bench_quick` -- micro-bench used by the autotuner. ++ * :func:`_fp32_to_sortable_u32` / :func:`_sortable_u32_to_fp32` -- ++ branch-free IEEE-sortable u32 transform. With the x²-free score ++ ``s = c² - 2⟨x, c⟩`` the quantity is signed, so the packed-uint64 ++ sort-merge / final-sort needs an ascending-u32 = ascending-fp32 ++ mapping. The transform is 3 ops, exact for non-NaN fp32. ++ * :data:`_INF_PACKED` -- (sortable +inf << 32) | -1 idx sentinel, ++ used as the initial fill of the sortmerge running top-K so the ++ early-exit ``chunk_best < sortable_inv(top)`` always fires until ++ real values arrive. ++""" ++from __future__ import annotations ++ ++import torch ++import triton ++import triton.language as tl ++ ++ ++def _next_pow2(n: int) -> int: ++ """Smallest power-of-two >= ``max(1, n)``. Used for D / K padding.""" ++ if n <= 1: ++ return 1 ++ return 1 << (n - 1).bit_length() ++ ++ ++def _bench_quick(fn, *, warmup: int = 3, reps: int = 5) -> float: ++ """Median-ish wall time in ms; cheap enough to call once per autotune cfg.""" ++ for _ in range(warmup): ++ fn() ++ torch.cuda.synchronize() ++ s = torch.cuda.Event(enable_timing=True) ++ e = torch.cuda.Event(enable_timing=True) ++ s.record() ++ for _ in range(reps): ++ fn() ++ e.record() ++ torch.cuda.synchronize() ++ return s.elapsed_time(e) / reps ++ ++ ++# Sortable upper-bits = sortable(+inf) = 0xFF800000; lower-bits = -1 (idx ++# sentinel) = 0xFFFFFFFF. ``_sortable_u32_to_fp32(0xFF800000) == +inf`` so ++# the sortmerge early-exit ``chunk_best < +inf`` always fires until the ++# running top-K starts to hold real values. ++# ++# Wrapped in ``tl.constexpr`` so ``@triton.jit`` kernels can reference it ++# directly as a module-level global (un-wrapped Python ints are not ++# accessible from inside a JIT'd kernel as of Triton 3.x). ++_INF_PACKED = tl.constexpr(0xFF800000_FFFFFFFF) ++ ++ ++@triton.jit ++def _fp32_to_sortable_u32(x): ++ """Map ascending fp32 -> ascending uint32 (branch-free, 3 ops). ++ ++ Standard IEEE-754 trick: arithmetic-shift the int32 view by 31 (so ++ MSB=1 / negative produces 0xFFFFFFFF, MSB=0 / positive produces 0), ++ OR with 0x80000000 to flip the sign bit of positives, then XOR with ++ the original bits. NaNs map into 0xFF800001..0xFFFFFFFF (unused in ++ practice). ++ """ ++ bits = x.to(tl.uint32, bitcast=True) ++ sign_mask = (bits.to(tl.int32, bitcast=True) >> 31).to(tl.uint32, bitcast=True) ++ flip = sign_mask | 0x80000000 ++ return bits ^ flip ++ + -+Instead of materialising the full ``(Q, M)`` distance matrix like the naive -+baseline, this tiles the database into ``CH``-row chunks and maintains a running -+per-query top-k across chunks. For each chunk it forms the squared-L2 block -+``||q||^2 - 2 + ||db_j||^2`` of shape ``(Q, cj)``, concatenates it with -+the current best distances, and re-runs ``topk`` over the (small) combined set, -+carrying the winning indices forward. Peak extra memory is ``O(Q * (k + CH))`` -+instead of ``O(Q * M)``, so it scales to large ``M`` and touches far less HBM. ++@triton.jit ++def _sortable_u32_to_fp32(s): ++ """Inverse of :func:`_fp32_to_sortable_u32`.""" ++ sign_mask = (s.to(tl.int32, bitcast=True) >> 31).to(tl.uint32, bitcast=True) ++ # In sortable space: MSB=1 -> was positive (flip back via 0x80000000); ++ # MSB=0 -> was negative (flip back via 0xFFFFFFFF). ++ flip = (sign_mask ^ 0xFFFFFFFF) | 0x80000000 ++ return (s ^ flip).to(tl.float32, bitcast=True) +diff --git a/knnlib/_kernels/primitives/knn/triton/_row_norm.py b/knnlib/_kernels/primitives/knn/triton/_row_norm.py +new file mode 100644 +index 0000000..60b0ec8 +--- /dev/null ++++ b/knnlib/_kernels/primitives/knn/triton/_row_norm.py +@@ -0,0 +1,93 @@ ++"""Fast row sum-of-squares (||x_i||^2) helper + per-tensor cache. ++ ++Tiny utility, but shared by the FA3 fused KNN path which needs the ++pre-computed query/corpus norms to fold into the squared-L2 distance ++formula ``||x - c||^2 = ||x||^2 + ||c||^2 - 2 ``. ++ ++This module deliberately stays narrow: ++ - ``_row_sq_kernel`` is a pure Triton row-norm kernel (no cross ++ matrix, no top-K) — it never materialises an N x M tensor. ++ - ``_fast_row_sq`` wraps the kernel. ++ - ``_get_or_compute_csq`` caches results per ``(data_ptr, shape, ++ dtype)`` so repeat queries on the same corpus skip the recompute. ++""" ++from __future__ import annotations ++ ++import math ++ ++import torch ++import triton ++import triton.language as tl ++ ++from knnlib._kernels.primitives.knn.triton._common import _next_pow2 + -+The public contract is unchanged: + -+ knn(queries, database, k) -> (distances, indices) ++@triton.jit ++def _row_sq_kernel( ++ x_ptr, out_ptr, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_o_b, stride_o_n, ++ B: tl.constexpr, N: tl.constexpr, D: tl.constexpr, ++ BN: tl.constexpr, BD: tl.constexpr, ++): ++ """Row sum-of-squares for ``(B, N, D) -> (B, N)`` fp32. ++ ++ Casts input to fp32 inside the kernel; reads input exactly once ++ and writes output exactly once. At BN=128 BD=128 nw=4 this hits ++ ~36% peak HBM BW on H200 (typical for tall-skinny reads). ++ """ ++ pid_n = tl.program_id(0) ++ pid_b = tl.program_id(1).to(tl.int64) ++ n_offs = (pid_n * BN + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ acc = tl.zeros([BN], dtype=tl.float32) ++ for d_start in range(0, D, BD): ++ d_offs = (d_start + tl.arange(0, BD)).to(tl.int64) ++ d_mask = d_offs < D ++ x = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0, ++ ) ++ x_f = x.to(tl.float32) ++ acc += tl.sum(x_f * x_f, axis=1) ++ tl.store(out_ptr + pid_b * stride_o_b + n_offs * stride_o_n, ++ acc, mask=n_mask) ++ ++ ++def _fast_row_sq(x: torch.Tensor) -> torch.Tensor: ++ """Return ``(B, N)`` fp32 row sum-of-squares via the Triton kernel.""" ++ assert x.is_cuda and x.ndim == 3 ++ B, N, D = x.shape ++ out = torch.empty(B, N, device=x.device, dtype=torch.float32) ++ BN = 128 if N >= 128 else _next_pow2(max(N, 16)) ++ BD = 128 if D >= 128 else _next_pow2(max(D, 16)) ++ grid = (math.ceil(N / BN), B) ++ _row_sq_kernel[grid]( ++ x, out, ++ x.stride(0), x.stride(1), x.stride(2), ++ out.stride(0), out.stride(1), ++ B=B, N=N, D=D, BN=BN, BD=BD, ++ num_warps=4, ++ ) ++ return out ++ ++ ++# Per-data-ptr cache so repeated queries on the same corpus don't pay ++# the row-norm kernel cost. ++_csq_cache: dict = {} ++ ++ ++def _get_or_compute_csq(c: torch.Tensor) -> torch.Tensor: ++ """Cached ``||c_i||^2`` (fp32) keyed by ``(data_ptr, shape, dtype)``.""" ++ key = (c.data_ptr(), tuple(c.shape), c.dtype) ++ cached = _csq_cache.get(key) ++ if cached is not None and cached.device == c.device: ++ return cached ++ csq = _fast_row_sq(c) ++ _csq_cache[key] = csq ++ if len(_csq_cache) > 8: ++ for old_key in list(_csq_cache.keys())[:-8]: ++ del _csq_cache[old_key] ++ return csq +diff --git a/knnlib/_kernels/primitives/knn/triton/dispatch.py b/knnlib/_kernels/primitives/knn/triton/dispatch.py +new file mode 100644 +index 0000000..4c78f35 +--- /dev/null ++++ b/knnlib/_kernels/primitives/knn/triton/dispatch.py +@@ -0,0 +1,1178 @@ ++"""flash_knn -- host-side dispatcher: kernel/config selection + 2-stage launch. + -+with ``distances`` the (Q, k) ascending squared-L2 distances and ``indices`` the -+(Q, k) int64 database row indices. ++Backs the public :func:`knnlib._kernels.primitives.knn.flash_knn` entry point. ++ ++Top-level wrappers ++------------------ ++ ++* :func:`flash_knn_triton` -- one-shot kNN, returns ``(B, N, k)`` int32 ++ indices. The shape-only heuristic now handles both build (large-N, ++ single-pass per CTA) and search (small-N, M-split flash-decode) ++ without any host-side SM-saturation gate -- ``ctas_no_split`` is ++ inspected after BN is picked, so build shapes (``BN=128`` -> ++ ``ctas_no_split`` already saturates 132 SMs) and search shapes ++ (small BN -> M-split for tail-fill) both fall out of the same ++ decision tree. ++* :func:`flash_knn_triton_small_n` / :func:`flash_knn_triton_large_n` ++ -- explicit overrides for callers (e.g. K-means assign) that know ++ their shape category at construction time. ++* :func:`_heuristic_config`, :func:`_autotune` -- shape-only heuristic ++ (default, fast first call) + opt-in brute-force autotune (slower ++ first call, cached steady-state). Both share the same kernel grid; ++ ``force_path="large_n"`` collapses M-splits down to one per CTA. ++ ++Two kernels live behind this dispatcher: ++ ++* :mod:`insert` -- iterative argmin-insert top-K. Picked by the ++ heuristic on virtually every shape. ++* :mod:`sortmerge` -- packed-uint64 sort-merge top-K with the IEEE- ++ sortable u32 transform. Routed to for the **Pattern-A small-Q** ++ corner (``B*N <= 8 or (B*N <= 16 and K in {32, 64})``) at medium-K ++ + ``M <= 200K`` where the wider sort per chunk beats insert's ++ argmin loop on this regime's autotune data. Otherwise insert wins; ++ sortmerge is kept in :func:`_gen_configs` so the offline tuner can ++ keep verifying that. ++ ++Pipelining depth ++---------------- ++Both kernels take a ``NUM_STAGES_PIPE`` constexpr for the M-loop's ++``tl.range`` pipelining factor. The dispatcher tunes it per shape from ++a 201-shape ns-sweep (upstream ``bench/sweep_ns_by_k.py``): K<=8 + ++small tile wants ``ns=3`` to hide HBM latency, K>=64 + big tile wants ++``ns=2`` (registers tight), wide-D wants ``ns=2`` uniformly, narrow ++tile + anything wants ``ns=1`` to avoid spills. See the comments ++inside :func:`_heuristic_config` for the empirical breakdown. ++ ++Distance recovery ++----------------- ++The kernels emit **signed shifted scores** ``s = c_sq - 2*``, not ++true squared L2. ``argmin-k`` over ``s`` matches ``argmin-k`` over true ++squared L2 (since ``-||x||^2`` is a per-row constant), but the returned ++score is not a valid distance. The :func:`flash_knn` public wrapper ++calls :func:`knnlib._kernels.kernels.distance.triton_knn_gather_sqdist` on the ++returned indices to write the true ``||x - c[idx]||^2`` per neighbour. +""" +from __future__ import annotations + ++import math ++from typing import Optional ++ +import torch + ++from knnlib._kernels.primitives.knn.triton._common import _next_pow2, _bench_quick ++from knnlib._kernels.primitives.knn.triton.sortmerge import _flash_knn_sortmerge_kernel ++from knnlib._kernels.primitives.knn.triton.insert import _flash_knn_insert_kernel ++ ++ ++# ── SRAM estimation ──────────────────────────────────────────────────── ++ ++ ++def _estimate_sram(bn, bm, D, K, d_inner, num_stages, *, ++ sortmerge=False, dtype_bytes=2): ++ """Per-CTA SRAM estimate (bytes) for the unified kernel — coarse. ++ ++ Counts only what Triton **definitely** keeps in SMEM: the x tile ++ + c tile (per iteration of the inner D loop). Everything else ++ (cross fp32 accumulator, score tile, topK heap, sort scratch) is ++ register-resident at the tile sizes used by the dispatcher's ++ heuristic, and the NUM_STAGES_PIPE multi-buffering of the c tile ++ is sometimes elided by Triton's pipeliner. Including any of these ++ in the estimate produces 50-200 KB of over-count that **wrongly ++ shrinks** configs the kernel could actually launch — a bf16 ++ regression demonstrated by the ``D=256`` cells in ++ ``benchmarks/results/micro_knn_dtype_smem_fit.md``. ++ ++ This estimator is therefore **optimistic on purpose**. Its only ++ job is to bias the autotune candidate filter away from configs ++ that obviously can't fit (e.g. ``BN=128, BM=128, D=256, fp32`` ++ needs >232 KB by raw arithmetic). The source of truth for "does ++ this fit?" is ``_run``'s runtime ``OutOfResources`` catch + shrink ++ loop. ``dtype_bytes`` is still threaded through so fp32 inputs ++ correctly double the x/c contribution. ++ """ ++ x_sub = bn * d_inner * dtype_bytes ++ c_sub = bm * d_inner * dtype_bytes ++ c_sq = bm * 4 ++ base = x_sub + c_sub + c_sq ++ if sortmerge: ++ chunk = bn * bm * 8 ++ base += chunk ++ return base ++ ++ ++def _smem_limit(device) -> int: ++ """Per-block dynamic shared-memory budget for ``device``. ++ ++ Mirrors :func:`knnlib._kernels.primitives.kmeans.triton.assign._smem_limit` ++ — Triton uses opt-in dynamic SMEM; prefer that attribute when ++ available, fall back to static limit, and finally to a conservative ++ 48 KiB for old PyTorch builds. ++ """ ++ props = torch.cuda.get_device_properties(device) ++ for attr in ( ++ "shared_memory_per_block_optin", ++ "max_shared_memory_per_block_optin", ++ "shared_memory_per_block", ++ "max_shared_memory_per_block", ++ ): ++ v = getattr(props, attr, None) ++ if v: ++ return int(v) ++ return 48 * 1024 ++ ++ ++def _fit_config_to_smem(cfg: dict, D: int, K: int, ++ dtype_bytes: int, smem_limit: int) -> dict: ++ """Shrink ``(BN, BM, NUM_STAGES_PIPE)`` until the kernel fits SMEM. ++ ++ Returns ``cfg`` unchanged when it already fits. Otherwise enumerates ++ power-of-two reductions on the three SMEM-bearing axes and picks the ++ one that maximises ``BN * BM * num_stages`` (work-per-program tile) ++ among those that fit, breaking ties towards the original aspect ratio. ++ ++ Why only these three axes? ++ * ``D_INNER``: changing it flips between single-D-tile and D-split ++ paths, which would also change the kernel's perf profile. We ++ keep the heuristic's D-split decision and shrink the cheaper ++ axes first. ++ * ``TOPK_PAD``: determined by ``K``; cannot be reduced without ++ breaking correctness. ++ * ``kernel_mode``, ``num_warps``, ``M_PER_SPLIT``, ``NUM_SPLITS``: ++ these don't affect per-CTA SMEM. Stable. ++ ++ Pattern (and rationale) mirrors :func:`knnlib._kernels.primitives.kmeans.\ ++triton.assign._fit_config_to_smem`. ++ """ ++ BN0 = int(cfg["BN"]) ++ BM0 = int(cfg["BM"]) ++ D_INNER = int(cfg["D_INNER"]) ++ NS0 = int(cfg.get("NUM_STAGES_PIPE", 2)) ++ sortmerge = cfg.get("kernel_mode") == "sortmerge" ++ ++ cur = _estimate_sram(BN0, BM0, D, K, D_INNER, NS0, ++ sortmerge=sortmerge, dtype_bytes=dtype_bytes) ++ if cur <= smem_limit: ++ return cfg ++ ++ def _pow2_down_to(v, lo): ++ out = [] ++ x = v ++ while x >= lo: ++ out.append(x) ++ x //= 2 ++ return out ++ ++ # BN must stay >= 8 (matches _gen_configs lowest BN rung); ++ # BM must stay >= 32 for the inner loop to keep WGMMA shape; ++ # NUM_STAGES_PIPE must stay >= 1. ++ bn_cands = _pow2_down_to(BN0, 8) ++ bm_cands = _pow2_down_to(BM0, 32) ++ ns_cands = list(range(NS0, 0, -1)) ++ ++ best = None ++ best_key = None ++ for bn in bn_cands: ++ for bm in bm_cands: ++ # sortmerge requires BM == TOPK_PAD; do not shrink BM there. ++ if sortmerge and bm != BM0: ++ continue ++ for ns in ns_cands: ++ if _estimate_sram(bn, bm, D, K, D_INNER, ns, ++ sortmerge=sortmerge, ++ dtype_bytes=dtype_bytes) > smem_limit: ++ continue ++ aspect_penalty = abs((bn / max(bm, 1)) - (BN0 / max(BM0, 1))) ++ # Prefer: larger total tile work, closer aspect ratio, ++ # larger BN (more N-parallelism), larger NS (better pipelining). ++ key = (bn * bm * ns, -aspect_penalty, bn, ns) ++ if best_key is None or key > best_key: ++ best_key = key ++ best = (bn, bm, ns) ++ ++ if best is None: ++ raise RuntimeError( ++ f"flash_knn: cannot fit kernel into shared memory " ++ f"(D={D}, K={K}, D_INNER={D_INNER}, dtype_bytes={dtype_bytes}, " ++ f"smem_limit={smem_limit}). Original config " ++ f"BN={BN0}, BM={BM0}, NS={NS0} needs {cur} bytes." ++ ) ++ ++ bn, bm, ns = best ++ out = dict(cfg) ++ out["BN"] = bn ++ out["BM"] = bm ++ out["NUM_STAGES_PIPE"] = ns ++ return out ++ ++ ++def _pipe_stages_for(K, d_inner, D): ++ """``NUM_STAGES_PIPE`` candidates for the autotune M-loop sweep. ++ ++ The K-step inner loop adds register pressure that scales with K, so ++ deeper pipelining (which double-/triple-buffers the next C-tile) is ++ only profitable when K is small. Empirically: ++ ++ * D-split path (``d_inner < D``) -- kernel uses ns=1 by construction. ++ * K >= 64 -- only try {1, 2}; ns >= 3 spills registers and tanks perf ++ (probe showed ns=3 going from 750 -> 1220 us at K=128). ++ * K <= 32 -- try {1, 2, 3} so big pipelines can hide HBM latency ++ for huge-M shapes. ++ """ ++ if d_inner < D: ++ return [1] ++ if K >= 64: ++ return [1, 2] ++ return [1, 2, 3] ++ ++ ++def _gen_configs(D, K, *, dtype_bytes=2, smem_limit=None): ++ """Generate candidate kernel configs (sortmerge + insert), SRAM-validated. ++ ++ Covers ``BN ∈ {8, 16, 32, 64, 128}`` × ``num_warps ∈ {2, 4}`` × the ++ BM / D_INNER / NUM_STAGES_PIPE combinations that fit per-CTA SMEM ++ on H200. ++ ++ The ``BN = 8`` rung unlocks the small-N + medium-K Pattern-A regime ++ (``B*N <= 8`` + ``K ∈ {16, 32, 64, 128}``) where a small row tile + ++ sortmerge ``BM = max(32, K)`` wins over insert. ``tl.dot`` silently ++ pads the M dimension to 16 on Triton 3.x sm_90, so BN=8 still ++ compiles. ++ ++ ``dtype_bytes`` and ``smem_limit`` parameterise the SMEM check so ++ fp32 inputs don't slip through with a config that OOR's at launch. ++ Defaults preserve the pre-fix behaviour (bf16 / 220 KB) for any ++ caller that didn't yet plumb dtype awareness through. ++ """ ++ # 220 KB stays the default — slightly below the 227 KB H200 opt-in ++ # limit to leave room for static SMEM the compiler adds. ++ max_sram = 220_000 if smem_limit is None else smem_limit ++ d_pad = _next_pow2(D) ++ topk_pad_sm = max(32, _next_pow2(K)) ++ topk_pad_ins = _next_pow2(K) ++ d_inner_cands = [d_pad] if D <= 256 else [128] ++ ++ configs = [] ++ ++ # sort-merge: BM == TOPK_PAD; BN ∈ {8, 16, 32} (BN >= 64 never wins ++ # for sortmerge in autotune data). ++ bm_sm = topk_pad_sm ++ for bn in [8, 16, 32]: ++ for nw in [2, 4]: ++ for d_inner in d_inner_cands: ++ num_d_iters = math.ceil(D / d_inner) ++ ns = 2 if num_d_iters == 1 else 1 ++ sram = _estimate_sram( ++ bn, bm_sm, D, K, d_inner, ns, ++ sortmerge=True, dtype_bytes=dtype_bytes, ++ ) ++ if sram <= max_sram: ++ for ns_pipe in _pipe_stages_for(K, d_inner, D): ++ configs.append({ ++ "BN": bn, "BM": bm_sm, "D_INNER": d_inner, ++ "num_warps": nw, ++ "TOPK_PAD": topk_pad_sm, ++ "kernel_mode": "sortmerge", ++ "NUM_STAGES_PIPE": ns_pipe, ++ }) ++ ++ # iterative insert: BM decoupled from K, BN ∈ {8, 16, 32, 64, 128}. ++ for bn in [8, 16, 32, 64, 128]: ++ for nw in [2, 4]: ++ for bm in [64, 128, 256]: ++ for d_inner in d_inner_cands: ++ num_d_iters = math.ceil(D / d_inner) ++ ns = 2 if num_d_iters == 1 else 1 ++ sram = _estimate_sram( ++ bn, bm, D, K, d_inner, ns, ++ sortmerge=False, dtype_bytes=dtype_bytes, ++ ) ++ if sram <= max_sram: ++ for ns_pipe in _pipe_stages_for(K, d_inner, D): ++ configs.append({ ++ "BN": bn, "BM": bm, "D_INNER": d_inner, ++ "num_warps": nw, ++ "TOPK_PAD": topk_pad_ins, ++ "kernel_mode": "insert", ++ "NUM_STAGES_PIPE": ns_pipe, ++ }) ++ ++ return configs ++ ++ ++def _gen_m_splits(M, BM, *, B=1, N=1, BN=16): ++ """M_PER_SPLIT candidates targeting {1, 2, 4, 8, 16} waves on 132 SMs. ++ ++ The wave count is critical -- Stage-2 reduce scales linearly with ++ NUM_SPLITS, while too few splits leaves SMs idle. The autotune ++ sweep confirms that wave=2 is the winner on huge-M + medium-N ++ shapes (e.g. ``1×128×10M×64×10``), which the original {1, 4, 16} ++ grid missed. ++ ++ Capped at 4096×BM tiles (``MAX_MPS_TILES``) to bound the static ++ M-loop trip count for fast Triton compilation. Also includes the ++ single-pass case when M itself fits the cap (subsumes the large-N ++ kernel as ``num_splits = 1``). ++ """ ++ NUM_SMS = 132 ++ MAX_MPS_TILES = 4096 ++ num_n_tiles = max(1, (N + BN - 1) // BN) ++ max_mps = min(M, MAX_MPS_TILES * BM) ++ candidates = set() ++ for waves in [1, 2, 4, 8, 16]: ++ target_splits = max(2, (NUM_SMS * waves) // (num_n_tiles * B)) ++ target_splits = min(target_splits, max(2, M // BM)) ++ mps = (M + target_splits - 1) // target_splits ++ mps = ((mps + BM - 1) // BM) * BM ++ mps = max(mps, BM) ++ mps = min(mps, max_mps) ++ candidates.add(mps) ++ if M <= max_mps: ++ single = ((M + BM - 1) // BM) * BM ++ candidates.add(single) ++ return sorted(candidates) ++ ++ ++# ── shape-only heuristic ─────────────────────────────────────────────── ++ ++ ++def _heuristic_config(B, N, M, D, K, *, force_path=None, ++ dtype_bytes=2, smem_limit=None): ++ """Pick a kernel config from shape alone -- no autotune. ++ ++ When ``dtype_bytes`` and ``smem_limit`` are supplied (the dispatcher ++ plumbs them from ``x.element_size()`` and the device's opt-in SMEM ++ cap), the picked config is post-processed by :func:`_fit_config_to_smem` ++ so that fp32 inputs at large D never produce a config that OOR's at ++ launch. ++ ++ Derived from a 92-shape autotune sweep on H200/bf16. The data ++ showed clean decision boundaries on three axes: ++ ++ * **BN by NB-bucket + M-bump**: ++ NB <= 8 -> BN=8; ++ NB <= 32 -> BN=16 (32 at M>=5M); ++ NB <= 256 -> BN=64 (128 at M>=5M + narrow-D + small-K); ++ NB <= 2K -> BN=64 (128 at M>=5M); ++ NB >= 8K -> BN=128 (64 for D>=256 + K>=32). ++ ++ The M-bump rule fixes the ``(1, 128, 10M, 64, 10)`` regression ++ identified by klib -- at M=10M each extra N-tile costs ++ ~300us of c-replication HBM traffic. ++ ++ * **BM by K**: ++ K<=4 -> 128/256 (256 only at very-small M + small NB); ++ K=5..16 -> 128 default (256 at huge-M); ++ K=32 -> 128 default (256 at NB<=8 + M<=200K); ++ K>=64 -> 64 default (sortmerge at NB<=8). ++ ++ * **kernel_mode**: ++ ``sortmerge`` only when ``NB <= 8`` and ``K ∈ {32, 64, 128}`` ++ and ``D <= 256`` and ``M <= 200K`` (Pattern-A1), plus a ++ secondary ``NB <= 16 + K ∈ {32, 64} + M <= 200K`` corner ++ (Pattern-A2). All other shapes use ``insert``. ++ ++ * **num_warps**: 4 everywhere except tiny tiles (BN=8 + BM<=64 + ++ K<=4 + huge M, where nw=2 wins by reducing register pressure) ++ and the BN=16 + huge-M + small-K corner where nw=2 frees a ++ warp set for the topK epilogue. + -+def knn(queries, database, k): -+ Q = queries.shape[0]; M = database.shape[0] -+ device = queries.device -+ qn = (queries * queries).sum(1, keepdim=True) # (Q,1) -+ best_d = torch.full((Q, k), float("inf"), device=device, dtype=torch.float32) -+ best_i = torch.zeros((Q, k), device=device, dtype=torch.long) -+ CH = 65536 -+ for j0 in range(0, M, CH): -+ dbj = database[j0:j0 + CH] # (cj, D) -+ cj = dbj.shape[0] -+ dn = (dbj * dbj).sum(1) # (cj,) -+ d = qn - 2.0 * (queries @ dbj.t()) + dn[None, :] # (Q, cj) squared L2 -+ alld = torch.cat([best_d, d], dim=1) -+ idx_chunk = torch.arange(j0, j0 + cj, device=device).expand(Q, cj) -+ alli = torch.cat([best_i, idx_chunk], dim=1) -+ td, ti = torch.topk(alld, k, dim=1, largest=False) -+ best_d = td -+ best_i = torch.gather(alli, 1, ti) -+ return best_d.contiguous(), best_i.contiguous() ++ * **target waves**: 1 for build (NB>=8K) and very-large K; ++ 2 for medium/large M; 4 for small M + small K. ++ ++ * **NUM_STAGES_PIPE**: 2 by default; 3 for BN>=64 + BM<=128 + ++ K<=8 (small tile + tiny K hides HBM latency) and for ++ BN ∈ [16, 32] + BM=256 + K>=64 (big tile keeps K loop fed); ++ 1 for narrow-tile + any K (avoid register spills) and for the ++ D-split path; 2 for D_INNER>=256 and for BN>=128. Without ++ this rule, K>=64 + D=256 shapes hit catastrophic regressions ++ (e.g. ``(1,16,100K,256,128)`` 439us -> 2429us at ns=1, a 5.5x ++ slowdown); conversely K<=32 + huge-M shapes save 20-45% vs ++ ns=2 (e.g. ``(1,1,10M,128,4)`` 1426us -> 785us at ns=1). ++ ++ Args: ++ B, N, M, D, K: shape parameters. ++ force_path: ``"large_n"`` for single-pass (one M-split per CTA), ++ ``None`` for the wave-targeted M-split. ++ ++ Returns: ++ Config dict: ``BN, BM, D_INNER, TOPK_PAD, kernel_mode, ++ num_warps, M_PER_SPLIT, NUM_SPLITS, NUM_STAGES_PIPE``. ++ """ ++ NUM_SMS = 132 ++ NB = N * B ++ TOPK_PAD = _next_pow2(K) ++ D_INNER = _next_pow2(D) if D <= 256 else 128 ++ is_large_n = (force_path == "large_n") ++ # MAX_MPS_TILES bounds the per-CTA M-loop iteration count. The ++ # original cap of 512 was too tight for huge M with BM=128 (cap ++ # = 65K), which forced fewer splits than autotune wanted. Bump ++ # to 4096 so we never bottleneck on this cap. ++ MAX_MPS_TILES = 4096 ++ ++ # ─────────────────────────────────────────────────────────────── ++ # Step 1: kernel_mode, BN, BM, num_warps ++ # ─────────────────────────────────────────────────────────────── ++ if NB <= 8: ++ # Tiny query: BN=8 default. ++ BN = 8 ++ # Sortmerge fast-path: K ∈ {32, 64, 128}, M <= 200K. Now ++ # extended to ANY D (autotune ``(1,1,100K,1024,32)`` picks ++ # BN=8 BM=32 sort). ++ if (not is_large_n) and K in (32, 64, 128) and M <= 200_000: ++ BM = max(32, _next_pow2(K)) ++ sram = _estimate_sram(8, BM, D, K, D_INNER, ++ num_stages=(2 if D_INNER >= D else 1), ++ sortmerge=True) ++ if sram <= 220_000: ++ kernel_mode = "sortmerge" ++ num_warps = 2 if BM >= 128 else 4 ++ else: ++ kernel_mode = "insert" ++ BM = 256 if M <= 200_000 else 128 ++ num_warps = 4 ++ else: ++ kernel_mode = "insert" ++ if K <= 4: ++ # K=4 + D>=256 + huge M -> BM=64 nw=2 (autotune ++ # ``(1,1,1M,256,4)`` picks this). HBM-bound; BM=256 ++ # adds wasted SMEM. ++ if D >= 256 and M >= 500_000: ++ BM = 64 ++ else: ++ BM = 256 ++ elif K <= 16: ++ # D>=256 + K=16 -> BM=128 (autotune (1,1,100K,256,16)) ++ if D >= 256 and K >= 16: ++ BM = 128 ++ else: ++ BM = 256 if (M >= 500_000 or K >= 16) else 128 ++ elif K == 32: ++ # autotune (1,1,1M,256,32) prefers BM=128 at D>=256. ++ # At narrower D, BM=256 wins. ++ if D >= 256 and M >= 500_000: ++ BM = 128 ++ else: ++ BM = 256 ++ elif K <= 64: ++ BM = 64 ++ else: # K=128 ++ BM = 256 ++ # nw=2 for small/medium-D BM=64 cases ++ if BM == 64 and K <= 4 and D >= 512 and M >= 500_000: ++ # D >= 512 hits the D-split path (D_INNER capped at 128); ++ # nw=2 was calibrated for this regime at (1, 1, 1M, 256, 4) ++ # but later re-benching at D=256 showed nw=4 ns=1 wins by ++ # 1.48-1.81x there (single-D-iter, multibuffered C). Keep ++ # nw=2 only for the D-split path; D=256 falls to the ++ # default nw=4 below and gets the ns=1 override in Step 3. ++ num_warps = 2 ++ elif BM >= 128 and K <= 4 and D <= 64 and M >= 5_000_000: ++ # NB<=8 + K<=4 + narrow-D + huge-M: nw=2 wins by 20-30 %. ++ # 4 warps split the SM register file too thinly for the ++ # K=4 argmin-insert epilogue at BM>=128, so the compiler ++ # spills; halving the warp count doubles regs/warp and ++ # lets ILP recover. Verified at D=64 across M ∈ [5M, 60M] ++ # (D=128 strictly prefers nw=4 at this BM; do not bump ++ # the threshold without re-benching). ++ num_warps = 2 ++ else: ++ num_warps = 4 ++ ++ elif NB <= 32: ++ kernel_mode = "insert" ++ # Sortmerge also wins at NB <= 16 + K ∈ {32, 64} (autotune ++ # ``(1,16,100K,256,32)`` picks BN=8 BM=32 sortmerge). ++ if (not is_large_n) and NB <= 16 and K in (32, 64) and M <= 200_000: ++ BN = 8 ++ BM = max(32, _next_pow2(K)) ++ sram = _estimate_sram(8, BM, D, K, D_INNER, ++ num_stages=(2 if D_INNER >= D else 1), ++ sortmerge=True) ++ if sram <= 220_000: ++ kernel_mode = "sortmerge" ++ num_warps = 4 ++ else: ++ BN, BM = 16, 256 ++ if kernel_mode == "insert": ++ if M >= 5_000_000 and N >= 32: ++ BN = 32 ++ else: ++ BN = max(8, min(16, _next_pow2(N))) ++ if K <= 4: ++ BM = 128 ++ elif K <= 16: ++ BM = 256 if M <= 2_000_000 else 128 ++ else: # K >= 32 ++ BM = 256 ++ if K <= 4 and M >= 500_000: ++ num_warps = 2 ++ elif K <= 16 and M >= 5_000_000 and BN <= 16: ++ num_warps = 2 ++ elif K <= 16 and NB >= 17 and M >= 5_000_000: ++ # autotune (1,32,10M,64,8) BN=32 nw=2 wins ++ num_warps = 2 ++ else: ++ num_warps = 4 ++ ++ elif NB <= 256: ++ # Medium queries: BN must scale with M to control ++ # c-replication, but ONLY when K is very small (K<=4) or ++ # very large (K>=32). At K ∈ {5..16} the autotune consistently ++ # picks BN=64 even at M=10M, because K=8..16 hits a sweet ++ # spot where the topk-insert + Stage-2 cost of 1-wave ++ # BN=128 outweighs the c-HBM savings. ++ kernel_mode = "insert" ++ big_NB = (NB >= 192) ++ ++ # K=128 special-case: medium NB + K=128, autotune picks ++ # BN=8 BM=256 insert (NOT sortmerge!). The K-step argmin loop ++ # at BN=64 K=128 is too expensive -- better to chop N into ++ # many small tiles. ++ if K >= 128 and M <= 200_000: ++ BN = 8 ++ BM = 256 ++ elif M >= 5_000_000 and D <= 64 and (K <= 4 or K >= 32): ++ BN = 128 ++ elif big_NB and M >= 5_000_000: ++ BN = 128 ++ elif big_NB and D >= 256 and M <= 200_000 and K <= 4: ++ BN = 128 ++ elif D >= 256 and K <= 16 and (NB >= 64 and M >= 500_000): ++ # D=256 + K<=16 + (medium NB + medium M): BN=128 wins. ++ # autotune (1,128,1M,256,8) picks BN=128 BM=64. ++ BN = 128 ++ else: ++ BN = 64 ++ BN = min(BN, max(8, _next_pow2(N))) ++ # BM ++ if K >= 128: ++ BM = 256 # already covered above, keep consistent here ++ elif K <= 4 and D <= 128 and M <= 200_000: ++ BM = 256 ++ elif D >= 256 and K <= 16: ++ BM = 64 ++ elif K <= 32: ++ BM = 64 if (BN == 64 and K == 32 and D >= 256) else 128 ++ elif K <= 64: ++ BM = 128 ++ else: ++ BM = 128 ++ num_warps = 4 ++ ++ elif NB <= 2048: ++ kernel_mode = "insert" ++ if M >= 5_000_000: ++ BN = 128 ++ elif M >= 500_000 and D >= 256 and K <= 16: ++ BN = 128 ++ else: ++ BN = 64 ++ BN = min(BN, max(8, _next_pow2(N))) ++ if K <= 4 and M >= 500_000: ++ BM = 64 ++ elif K <= 16 and M >= 500_000 and D >= 128: ++ BM = 64 ++ elif K >= 64: ++ BM = 64 ++ else: ++ BM = 128 ++ num_warps = 4 ++ ++ else: # NB >= 8K (build / large-eval regime) ++ kernel_mode = "insert" ++ # autotune patterns: ++ # NB=50K-100K D<=128 K<=8 -> BN=128 (halves HBM) ++ # NB=10K K>=8 -> BN=64 (more topk work per row) ++ # NB>=10K D>=256 K<=16 -> BN=128 ++ # NB>=10K D>=256 K>=32 -> BN=64 ++ if D >= 256 and K <= 16: ++ BN = 128 ++ elif D >= 256 and K >= 32: ++ BN = 64 ++ elif K <= 4: ++ BN = 128 ++ elif K <= 8 and NB >= 30_000: ++ # Larger NB -> BN=128 saves enough HBM to overcome topk cost ++ BN = 128 ++ else: ++ BN = 64 ++ if K <= 4 and D >= 256: ++ BM = 64 # SMEM pressure at D=256 + BM=128 ++ elif K <= 4: ++ BM = 128 if (D <= 64 or BN == 128) else 64 ++ elif K <= 8 and BN == 128: ++ BM = 128 ++ elif K >= 64 and BN == 64 and D <= 128 and NB >= 30_000: ++ # klib carve-out: NB in [30K, ~200K] + BN=64 + K>=64 + ++ # D<=128 wins big at BM=128 splits=1 vs BM=64 splits=2. ++ # Empirically at (1, 48K, 48K, 64, 64) the upstream rule ++ # (BM=64, target_splits=2) runs 17.95 ms, while BM=128 ++ # splits=1 ns=2 runs 11.71 ms (-35%); at (1, 64K, 64K, 64, ++ # 64) 29.20 ms -> 18.91 ms (-35%). Bounded on K>=64 (K=32 ++ # autotune at this NB shows BM=64 splits=1 wins, ~1 ms over ++ # BM=128 splits=1) and on NB>=30K so the upstream rule ++ # still applies to smaller-NB shapes where its M-split ++ # target was directly autotune-derived. ++ BM = 128 ++ else: ++ BM = 64 ++ num_warps = 4 ++ ++ # ─────────────────────────────────────────────────────────────── ++ # Step 2: M_PER_SPLIT (target waves) ++ # ─────────────────────────────────────────────────────────────── ++ num_n_tiles = max(1, math.ceil(N / BN)) ++ ctas_no_split = num_n_tiles * B ++ ++ # Workaround for an insert-kernel correctness issue with the ++ # D-split path: when ``M_PER_SPLIT == BM`` (single M-loop ++ # iteration) and the d-loop has >=4 chunks, the result is wrong. ++ # Bumping the minimum mps to 2*BM keeps the M-loop at >=2 iters. ++ needs_min_2_iters = (D_INNER < D and (D + D_INNER - 1) // D_INNER >= 4) ++ min_mps = (2 * BM) if needs_min_2_iters else BM ++ ++ if is_large_n: ++ M_PER_SPLIT = ((M + BM - 1) // BM) * BM ++ elif ctas_no_split >= NUM_SMS * 8: ++ # Massively oversaturated (e.g. N=100K, BN=64, ctas=1563): one ++ # split fully utilises SMs. ++ M_PER_SPLIT = ((M + BM - 1) // BM) * BM ++ elif K >= 32 and BN == 64 and D <= 128 and NB >= 30_000 and not is_large_n: ++ # klib carve-out (pairs with the BM=128 K>=64 rule above ++ # plus K=32 BM=64 standalone): the K>=32 build regime at ++ # NB>=30K wants splits=1 -- 2 splits doubles the K-step ++ # argmin work per row and the Stage-2 reduce, neither of ++ # which the autotune at NB=10K K=8 accounted for. Empirically ++ # saves ~6 ms on (1, 48K, 48K, 64, 64) and ~10 ms on ++ # (1, 64K, 64K, 64, 64) vs target_splits=2, and ~1 ms on ++ # (1, 56K, 56K, 64, 32) vs target_splits=2. ++ M_PER_SPLIT = ((M + BM - 1) // BM) * BM ++ else: ++ if ctas_no_split >= NUM_SMS * 4: ++ # ctas >= 528: 2 splits for tail-fill at very large builds ++ # (autotune (1,100K,100K,64,4) BN=128 ctas=782 -> 2 splits) ++ target_splits = 2 ++ elif ctas_no_split >= NUM_SMS * 2: ++ # ctas in [264, 528): single split already 2-4 waves; adding ++ # splits hurts more than it helps. autotune ++ # (1,50K,50K,64,8) BN=128 ctas=391 -> 1 split. ++ target_splits = 1 ++ elif ctas_no_split >= NUM_SMS: ++ # ctas in [132, 264): 3 splits for better tail. autotune ++ # (1,10K,10K,64,16) BN=64 ctas=157 -> 3 splits. ++ target_splits = 3 ++ else: ++ # Under-saturated: target_waves by K (autotune-derived). ++ # Use FLOOR div: snaps to integer wave count, which matches ++ # autotune choices on shapes like NB=1024 K=32 ctas=16 ++ # (16 splits = 2 waves, 17 splits = 2.06 waves but bad ++ # tail). ++ if kernel_mode == "sortmerge": ++ target_waves = 2 ++ elif K >= 128 and NB >= 64: ++ # K=128 + medium NB: 2w wins (autotune ++ # (1,128,100K,128,128) picks 16 splits at ctas=16). ++ target_waves = 2 ++ elif K >= 128: ++ target_waves = 1 ++ elif K >= 64 and ctas_no_split <= 2: ++ # K=64 + tiny ctas: 1 wave wins (autotune ++ # (1,128,100K,128,64) picks 66 splits = 1w). ++ target_waves = 1 ++ elif K >= 64: ++ target_waves = 2 ++ elif K <= 4 and ctas_no_split == 1 and (NB <= 32 or M <= 1_000_000): ++ # K=4 single-CTA-stack: 4 waves at small NB or small M. ++ # autotune NB=1 K=4 -> 521 splits; (1,128,10M,64,4) ++ # however picks 264 splits (2w) -- too much M per CTA ++ # for more splits to help. ++ target_waves = 4 ++ elif K <= 8 and ctas_no_split >= 8 and ctas_no_split <= 32: ++ target_waves = 4 if M >= 500_000 else 2 ++ elif K <= 8 and ctas_no_split == 1 and M >= 5_000_000 and BN <= 32: ++ target_waves = 4 ++ elif B >= 2 and N <= 8 and K <= 8: ++ # Batched B>=2 + single-query + K<=8: 4w wins ++ target_waves = 4 ++ else: ++ target_waves = 2 ++ target_splits = max(1, NUM_SMS * target_waves // ctas_no_split) ++ target_splits = min(target_splits, max(1, math.ceil(M / BM))) ++ mps_raw = math.ceil(M / target_splits) ++ mps = math.ceil(mps_raw / BM) * BM ++ mps = max(min_mps, min(mps, ((M + BM - 1) // BM) * BM)) ++ mps = min(mps, MAX_MPS_TILES * BM) ++ M_PER_SPLIT = mps ++ ++ NUM_SPLITS = math.ceil(M / M_PER_SPLIT) ++ ++ # ─────────────────────────────────────────────────────────────── ++ # Step 3: NUM_STAGES_PIPE (M-loop pipeline depth) ++ # ─────────────────────────────────────────────────────────────── ++ # Derived from a 201-shape × 4-ns sweep on H200 (upstream ++ # ``bench/sweep_ns_by_k.py``), cross-checked against direct probes ++ # on N>=64 build shapes. Three competing effects: ++ # ++ # * Each extra pipeline stage adds another buffered C-tile, ++ # costing ``BM * D_INNER * 2`` bytes of SMEM/registers. Wide ++ # D-tiles amortise this well; narrow ones don't. ++ # * The K-step argmin inner loop holds live state proportional ++ # to ``BN * TOPK_PAD * 8`` bytes. For narrow rows (BN<=16) + ++ # large K, registers get tight and deeper pipelines spill. ++ # * Medium-N tiles (BN>=64) have abundant compute per tile to ++ # amortise prefetch, but get little benefit at very large K ++ # because the K loop dominates anyway. ++ # ++ # Empirical winners (mode_ns from the 201-shape sweep): ++ # ++ # D_INNER >= 256 (wide D) -> ns=2 (uniform across K) ++ # BN >= 128 (huge build N) -> ns=2 (registers tight) ++ # BN >= 64 + BM <= 128 + K <= 8 -> ns=3 (small tile + tiny K) ++ # BN >= 64 -> ns=2 (BM=256 SMEM-bound) ++ # BN ∈ [16,32] + BM=256 + K >= 64 -> ns=3 (big-tile large-K) ++ # otherwise (narrow tile + any K) -> ns=1 (avoid spills) ++ # ++ # Without this rule, K>=64 + D=256 shapes hit catastrophic ++ # regressions (e.g. (1,16,100K,256,128) 439us -> 2429us at ns=1, ++ # a 5.5x slowdown). Conversely K<=32 + huge-M shapes save 20-45% ++ # vs ns=2 (e.g. (1,1,10M,128,4) 1426us -> 785us at ns=1). ++ if D_INNER < D: ++ NUM_STAGES_PIPE = 1 # D-split path is hard-coded to ns=1 ++ elif (BN == 8 and BM == 64 and D_INNER == 256 ++ and K <= 4 and M >= 500_000): ++ # NB<=8 + K<=4 + D=256 + BM=64 + huge-M: ns=1 wins by 1.48-1.81x ++ # over the default ns=2. Direct verification at K ∈ {1, 2, 4} × ++ # M ∈ {1M, 5M, 10M, 30M}: (nw=4, ns=1) lifts %peak HBM from ++ # 39-48 % (ns=2) to 57-85 % (ns=1). The ns=2 default below was ++ # calibrated for wider-N tiles; at BN=8 the deeper pipeline ++ # spends too many registers on prefetch and the K=4 epilogue ++ # spills. Pairs with the nw=4 default the rule above falls into ++ # at D=256. ++ NUM_STAGES_PIPE = 1 ++ elif D_INNER >= 256: ++ NUM_STAGES_PIPE = 2 ++ elif BN >= 128: ++ NUM_STAGES_PIPE = 2 ++ elif BN >= 64 and BM <= 128 and K <= 8: ++ NUM_STAGES_PIPE = 3 ++ elif BN >= 64: ++ NUM_STAGES_PIPE = 2 ++ elif BN >= 16 and BM >= 256 and K >= 64: ++ NUM_STAGES_PIPE = 3 ++ else: ++ NUM_STAGES_PIPE = 1 ++ ++ cfg = { ++ "BN": BN, "BM": BM, "D_INNER": D_INNER, ++ "TOPK_PAD": ( ++ max(32, _next_pow2(K)) if kernel_mode == "sortmerge" ++ else _next_pow2(K) ++ ), ++ "kernel_mode": kernel_mode, "num_warps": num_warps, ++ "M_PER_SPLIT": M_PER_SPLIT, ++ "NUM_SPLITS": NUM_SPLITS, ++ "NUM_STAGES_PIPE": NUM_STAGES_PIPE, ++ } ++ ++ # Dtype-aware SMEM shrink. The shape-only heuristic above was tuned ++ # on bf16 and may pick (BN=128, BM=128, D_INNER=128) for shapes that ++ # cleanly fit ~210 KB at 2 bytes/elt but blow past the 227 KB H200 ++ # opt-in cap at 4 bytes/elt. The fitter shrinks BN/BM/NUM_STAGES_PIPE ++ # by powers of 2 until the kernel fits — leaving D_INNER and ++ # kernel_mode (which encode different perf regimes) alone. ++ if smem_limit is not None: ++ cfg = _fit_config_to_smem(cfg, D, K, dtype_bytes, smem_limit) ++ ++ return cfg ++ ++ ++# ── autotune (opt-in) ────────────────────────────────────────────────── ++ ++ ++_autotune_cache: dict = {} ++ ++ ++def _autotune(x, c, k, *, force_path=None): ++ """Brute-force autotune over the candidate config grid. ++ ++ Cached per ``(B, N, M, D, k, dtype, force_path)`` shape; first call ++ costs ~30-60 s of compile time, subsequent calls hit the cache in ++ sub-ms. ++ ++ The candidate grid is dtype-aware: ``_gen_configs`` filters out ++ configs whose SMEM exceeds the device opt-in limit at this dtype, ++ so fp32 inputs at large D never have an unrunnable config in the ++ search space. ++ """ ++ B, N, D = x.shape ++ M = c.shape[1] ++ dtype_bytes = x.element_size() ++ key = (B, N, M, D, k, x.dtype, force_path) ++ ++ if key in _autotune_cache: ++ return _autotune_cache[key] ++ ++ smem_limit = _smem_limit(x.device) ++ configs = _gen_configs(D, k, ++ dtype_bytes=dtype_bytes, ++ smem_limit=min(smem_limit, 220_000)) ++ if not configs: ++ raise RuntimeError( ++ f"No valid flash_knn config for D={D}, K={k}, " ++ f"dtype={x.dtype} (element_size={dtype_bytes})" ++ ) ++ ++ # Drop configs with BN > next_pow2(N) -- they would just pad N to BN ++ # and run the same kernel slower. ++ max_bn = max(16, _next_pow2(N)) ++ configs = [cfg for cfg in configs if cfg["BN"] <= max_bn] ++ ++ best_time = float('inf') ++ best_cfg = None ++ max_partial_bytes = 4 * 1024**3 ++ ++ for cfg in configs: ++ bn = cfg["BN"] ++ bm = cfg["BM"] ++ d_inner = cfg["D_INNER"] ++ topk_pad = cfg["TOPK_PAD"] ++ kernel_mode = cfg["kernel_mode"] ++ nw = cfg["num_warps"] ++ ns_pipe = cfg.get("NUM_STAGES_PIPE", 2) ++ ++ if force_path == "large_n": ++ mps_list = [((M + bm - 1) // bm) * bm] ++ else: ++ mps_list = _gen_m_splits(M, bm, B=B, N=N, BN=bn) ++ ++ for mps in mps_list: ++ num_splits = math.ceil(M / mps) ++ partial_bytes = B * num_splits * N * k * 8 ++ if partial_bytes > max_partial_bytes: ++ continue ++ ++ try: ++ pv = torch.empty((B, N, num_splits, k), ++ device=x.device, dtype=torch.float32) ++ pi = torch.empty((B, N, num_splits, k), ++ device=x.device, dtype=torch.int32) ++ grid = (num_splits, math.ceil(N / bn), B) ++ pv_s0, pv_s1, pv_s2, pv_s3 = pv.stride() ++ pi_s0, pi_s1, pi_s2, pi_s3 = pi.stride() ++ ++ if kernel_mode == "sortmerge": ++ def run(grid=grid, bn=bn, bm=bm, d_inner=d_inner, ++ topk_pad=topk_pad, mps=mps, nw=nw, ++ num_splits=num_splits, pv=pv, pi=pi, ++ ns_pipe=ns_pipe, ++ pv_s0=pv_s0, pv_s1=pv_s1, pv_s2=pv_s2, pv_s3=pv_s3, ++ pi_s0=pi_s0, pi_s1=pi_s1, pi_s2=pi_s2, pi_s3=pi_s3): ++ _flash_knn_sortmerge_kernel[grid]( ++ x, c, pv, pi, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ pv_s0, pv_s2, pv_s1, pv_s3, ++ pi_s0, pi_s2, pi_s1, pi_s3, ++ N=N, M=M, D=D, K=k, M_PER_SPLIT=mps, ++ BN=bn, BM=bm, D_INNER=d_inner, ++ TOPK_PAD=topk_pad, ++ NUM_STAGES_PIPE=ns_pipe, ++ num_warps=nw, ++ ) ++ if num_splits > 1: ++ pv_flat = pv.view(B, N, -1) ++ pv_flat.topk(k, dim=-1, largest=False) ++ else: ++ max_steps = min(k, bm) ++ def run(grid=grid, bn=bn, bm=bm, d_inner=d_inner, ++ topk_pad=topk_pad, mps=mps, nw=nw, ++ num_splits=num_splits, pv=pv, pi=pi, ++ max_steps=max_steps, ns_pipe=ns_pipe, ++ pv_s0=pv_s0, pv_s1=pv_s1, pv_s2=pv_s2, pv_s3=pv_s3, ++ pi_s0=pi_s0, pi_s1=pi_s1, pi_s2=pi_s2, pi_s3=pi_s3): ++ _flash_knn_insert_kernel[grid]( ++ x, c, pv, pi, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ pv_s0, pv_s2, pv_s1, pv_s3, ++ pi_s0, pi_s2, pi_s1, pi_s3, ++ N=N, M=M, D=D, K=k, M_PER_SPLIT=mps, ++ BN=bn, BM=bm, D_INNER=d_inner, ++ TOPK_PAD=topk_pad, MAX_STEPS=max_steps, ++ NUM_STAGES_PIPE=ns_pipe, ++ num_warps=nw, ++ ) ++ if num_splits > 1: ++ pv_flat = pv.view(B, N, -1) ++ pv_flat.topk(k, dim=-1, largest=False) ++ ++ t = _bench_quick(run) ++ if t < best_time: ++ best_time = t ++ best_cfg = {**cfg, "M_PER_SPLIT": mps, ++ "NUM_SPLITS": num_splits, ++ "NUM_STAGES_PIPE": ns_pipe} ++ ++ del pv, pi ++ except Exception: ++ continue ++ ++ if best_cfg is None: ++ raise RuntimeError( ++ f"All flash_knn configs failed for B={B}, N={N}, M={M}, D={D}, K={k}") ++ ++ _autotune_cache[key] = best_cfg ++ return best_cfg ++ ++ ++# ── runner ───────────────────────────────────────────────────────────── ++ ++ ++_OOR_FALLBACK_CACHE: dict = {} ++ ++ ++def _try_launch(x, c, cfg, B, N, M, D, k): ++ """Single launch attempt; returns idxs or raises ``triton.compiler.errors.OutOfResources``. ++ ++ Factored out of ``_run`` so the OOR-shrink retry loop can call it ++ repeatedly with a shrunk cfg. ++ """ ++ bn = cfg["BN"] ++ bm = cfg["BM"] ++ d_inner = cfg["D_INNER"] ++ topk_pad = cfg["TOPK_PAD"] ++ mps = cfg["M_PER_SPLIT"] ++ num_splits = cfg["NUM_SPLITS"] ++ kernel_mode = cfg["kernel_mode"] ++ nw = cfg["num_warps"] ++ num_stages_pipe = cfg.get("NUM_STAGES_PIPE", 2) ++ ++ partial_vals = torch.empty((B, N, num_splits, k), ++ device=x.device, dtype=torch.float32) ++ partial_idxs = torch.empty((B, N, num_splits, k), ++ device=x.device, dtype=torch.int32) ++ grid = (num_splits, math.ceil(N / bn), B) ++ ++ pv_s0, pv_s1, pv_s2, pv_s3 = partial_vals.stride() ++ pi_s0, pi_s1, pi_s2, pi_s3 = partial_idxs.stride() ++ ++ if kernel_mode == "sortmerge": ++ _flash_knn_sortmerge_kernel[grid]( ++ x, c, partial_vals, partial_idxs, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ pv_s0, pv_s2, pv_s1, pv_s3, ++ pi_s0, pi_s2, pi_s1, pi_s3, ++ N=N, M=M, D=D, K=k, M_PER_SPLIT=mps, ++ BN=bn, BM=bm, D_INNER=d_inner, ++ TOPK_PAD=topk_pad, ++ NUM_STAGES_PIPE=num_stages_pipe, ++ num_warps=nw, ++ ) ++ else: ++ max_steps = min(k, bm) ++ _flash_knn_insert_kernel[grid]( ++ x, c, partial_vals, partial_idxs, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ pv_s0, pv_s2, pv_s1, pv_s3, ++ pi_s0, pi_s2, pi_s1, pi_s3, ++ N=N, M=M, D=D, K=k, M_PER_SPLIT=mps, ++ BN=bn, BM=bm, D_INNER=d_inner, ++ TOPK_PAD=topk_pad, MAX_STEPS=max_steps, ++ NUM_STAGES_PIPE=num_stages_pipe, ++ num_warps=nw, ++ ) ++ ++ if num_splits == 1: ++ return partial_idxs[:, :, 0, :].contiguous() ++ ++ pv = partial_vals.view(B, N, -1) ++ pi = partial_idxs.view(B, N, -1) ++ _, sel = pv.topk(k, dim=-1, largest=False, sorted=True) ++ out_idxs = pi.gather(-1, sel.to(torch.int64)).to(torch.int32) ++ return out_idxs ++ ++ ++def _shrink_cfg_on_oor(cfg: dict) -> Optional[dict]: ++ """Halve the cheapest SMEM-bearing axis. ``None`` when nothing left. ++ ++ Order: ++ 1. NUM_STAGES_PIPE 3 → 2 → 1 (drops the c-tile pipeline buffer ++ — usually free or nearly-free at this depth). ++ 2. Tend toward a near-square tile by **halving the larger of ++ (BN, BM)** first. WGMMA shape efficiency drops rapidly once ++ BM<32 on Hopper (MMA atom is 64x16x16), so we prefer to ++ shrink BN when BN > BM. For BN=128 BM=64 (typical build ++ regime) this gives BN=64 BM=64 in one step, which empirically ++ matches the kmeans-style ``_fit_config_to_smem`` choice and ++ beats BN=128 BM=32 by ~20 % at D=1024 fp32. ++ 3. Floors: BN ≥ 8, BM ≥ 32 (both required by the WGMMA atom). ++ ++ sortmerge requires ``BM == TOPK_PAD``; skip BM shrinks and only ++ halve BN. ++ ++ Also recomputes ``M_PER_SPLIT`` so it remains a multiple of the ++ new BM (the kernel's inner ``tl.range`` walk requires this). ++ """ ++ ns = int(cfg.get("NUM_STAGES_PIPE", 2)) ++ bm = int(cfg["BM"]) ++ bn = int(cfg["BN"]) ++ sortmerge = cfg.get("kernel_mode") == "sortmerge" ++ ++ out = dict(cfg) ++ if ns > 1: ++ out["NUM_STAGES_PIPE"] = ns - 1 ++ return out ++ if sortmerge: ++ if bn > 8: ++ out["BN"] = bn // 2 ++ return out ++ return None ++ # Non-sortmerge: prefer shrinking the larger axis. ++ if bn > bm and bn > 8: ++ out["BN"] = bn // 2 ++ return out ++ if bm > 32: ++ out["BM"] = bm // 2 ++ new_bm = out["BM"] ++ out["M_PER_SPLIT"] = ((int(out["M_PER_SPLIT"]) + new_bm - 1) ++ // new_bm) * new_bm ++ return out ++ if bn > 8: ++ out["BN"] = bn // 2 ++ return out ++ return None ++ ++ ++def _run(x: torch.Tensor, c: torch.Tensor, k: int, *, ++ force_path: Optional[str] = None, ++ autotune: bool = False) -> torch.Tensor: ++ """Shared dispatcher -- launch stage 1, optional stage 2 reduce, return idxs. ++ ++ Returns indices only ``(B, N, k) int32``. The wrapper at ++ :func:`knnlib._kernels.primitives.knn.flash_knn` calls the gather kernel to ++ produce true squared distances per neighbour. ++ ++ On ``OutOfResources`` at launch, we shrink one SMEM-bearing axis ++ (NS → BM → BN) and retry, caching the surviving config per ++ ``(D, K, dtype, force_path)`` so subsequent calls with the same ++ shape skip the failed compiles. This is the source of truth for ++ "does this fit?"; ``_estimate_sram`` only provides a coarse hint ++ for the autotune candidate filter. ++ """ ++ assert x.is_cuda and c.is_cuda ++ assert x.dtype == c.dtype ++ B, N, D = x.shape ++ M = c.shape[1] ++ assert c.shape == (B, M, D) ++ assert 1 <= k <= M ++ ++ if autotune: ++ cfg = _autotune(x, c, k, force_path=force_path) ++ else: ++ cfg = _heuristic_config( ++ B, N, M, D, k, ++ force_path=force_path, ++ dtype_bytes=x.element_size(), ++ smem_limit=min(_smem_limit(x.device), 220_000), ++ ) ++ ++ # Surviving-config cache keyed on the parts the dispatcher can't ++ # vary (D, K, dtype, force_path). If a previous call already ++ # shrunk past an OOR for this shape, start with that smaller cfg. ++ fb_key = (D, k, x.dtype, force_path, ++ cfg["kernel_mode"], cfg.get("D_INNER")) ++ if fb_key in _OOR_FALLBACK_CACHE: ++ cached = _OOR_FALLBACK_CACHE[fb_key] ++ # Use the cached cfg only if it's smaller than the current one ++ # on at least one SMEM-bearing axis (otherwise the heuristic ++ # already picked something safe for this shape). ++ sm_cur = (cfg["BN"], cfg["BM"], cfg.get("NUM_STAGES_PIPE", 2)) ++ sm_cached = (cached["BN"], cached["BM"], ++ cached.get("NUM_STAGES_PIPE", 2)) ++ if sm_cached < sm_cur: ++ cfg = {**cfg, **{k_: cached[k_] for k_ in ++ ("BN", "BM", "NUM_STAGES_PIPE")}} ++ # Re-align mps to new BM ++ new_bm = cfg["BM"] ++ cfg["M_PER_SPLIT"] = ((cfg["M_PER_SPLIT"] + new_bm - 1) ++ // new_bm) * new_bm ++ cfg["NUM_SPLITS"] = math.ceil(M / cfg["M_PER_SPLIT"]) ++ ++ # Import lazily so we don't pay it on every dispatch. ++ try: ++ from triton.runtime.errors import OutOfResources ++ except ImportError: ++ try: ++ from triton.compiler.errors import OutOfResources ++ except ImportError: ++ OutOfResources = RuntimeError # safest fallback ++ ++ last_err = None ++ initial_cfg = cfg ++ shrunk = False ++ for _attempt in range(8): ++ try: ++ result = _try_launch(x, c, cfg, B, N, M, D, k) ++ # Cache the surviving cfg so subsequent calls with the ++ # same (D, K, dtype, ...) shape skip the failed compile. ++ if shrunk: ++ _OOR_FALLBACK_CACHE[fb_key] = { ++ "BN": cfg["BN"], "BM": cfg["BM"], ++ "NUM_STAGES_PIPE": cfg.get("NUM_STAGES_PIPE", 2), ++ } ++ return result ++ except OutOfResources as e: ++ last_err = e ++ new_cfg = _shrink_cfg_on_oor(cfg) ++ if new_cfg is None: ++ break ++ cfg = new_cfg ++ shrunk = True ++ continue ++ ++ raise last_err if last_err is not None else RuntimeError( ++ "flash_knn: exhausted OOR shrink attempts") ++ ++ ++# ── public API ───────────────────────────────────────────────────────── ++ ++ ++def flash_knn_triton_small_n(x: torch.Tensor, c: torch.Tensor, k: int, ++ *, autotune: bool = False) -> torch.Tensor: ++ """Force the M-split (search) path -- ``(B, N, k) int32`` indices.""" ++ return _run(x, c, k, force_path=None, autotune=autotune) ++ ++ ++def flash_knn_triton_large_n(x: torch.Tensor, c: torch.Tensor, k: int, ++ *, autotune: bool = False) -> torch.Tensor: ++ """Force the single-pass (build) path -- ``(B, N, k) int32`` indices.""" ++ return _run(x, c, k, force_path="large_n", autotune=autotune) ++ ++ ++def flash_knn_triton(x: torch.Tensor, c: torch.Tensor, k: int, ++ *, autotune: bool = False) -> torch.Tensor: ++ """Universal Triton dispatch -- the heuristic picks BN/BM/mode/ ++ mps/ns_pipe based on shape, including whether to single-pass or ++ M-split. The per-CTA-count check inside :func:`_heuristic_config` ++ handles both build and eval shapes without a host-side forced path. ++ ++ Neither path materialises an N×M cross or distance matrix to HBM, ++ and neither computes or loads ``x_sq`` -- the two hard contracts ++ klib's KNN imposes on every Triton entry point here. ++ ++ Args: ++ x: ``(B, N, D)`` bf16 / fp16 / fp32 query tensor. ++ c: ``(B, M, D)`` corpus, same dtype. ++ k: number of neighbours. ++ autotune: ``False`` (default) uses the shape-only heuristic -- ++ first call pays a single Triton compile (~0.5 s). ``True`` ++ runs the full brute-force sweep + caches per shape (~30-90 s ++ first call). ++ ++ Returns: ++ idxs: ``(B, N, k)`` int32 -- nearest-neighbour indices, sorted ++ by ascending true squared L2 distance (ties broken by index). ++ """ ++ return _run(x, c, k, force_path=None, autotune=autotune) +diff --git a/knnlib/_kernels/primitives/knn/triton/insert.py b/knnlib/_kernels/primitives/knn/triton/insert.py +new file mode 100644 +index 0000000..7c60023 +--- /dev/null ++++ b/knnlib/_kernels/primitives/knn/triton/insert.py +@@ -0,0 +1,204 @@ ++"""flash_knn — Triton iterative-insert top-K kernel (x²-free, signed score). ++ ++Same x²-free signed score as ``sortmerge.py`` — see that module's docstring ++for the rationale. The insert kernel differs in two ways: ++ ++ * ``BM`` is decoupled from ``TOPK_PAD`` (sortmerge requires ``BM == TOPK_PAD``). ++ The autotune sweeps ``BM ∈ {64, 128, 256}`` independently. ++ * Top-K is maintained as an unsorted ``(BN, TOPK_PAD)`` register array; ++ each chunk does at most ``MAX_STEPS = min(K, BM)`` argmin->insert ++ passes. fp32 ``<`` works natively on signed scores, so only the ++ trailing output sort uses the sortable-u32 transform. ++ ++Empirically the insert kernel wins on virtually every shape past the ++trivial ``K == 1`` case; sortmerge is kept for completeness (autotune ++still considers it, and it's the natural fit when ``K`` matches a ++hardware-sortable tile size). ++ ++Public name: ``_flash_knn_insert_kernel`` — called from ++``klib/primitives/knn/triton/dispatch.py``. ++""" ++import triton ++import triton.language as tl ++ ++from knnlib._kernels.primitives.knn.triton._common import ( ++ _fp32_to_sortable_u32, ++ _sortable_u32_to_fp32, ++) ++ ++ ++@triton.jit ++def _flash_knn_insert_kernel( ++ x_ptr, c_ptr, ++ partial_val_ptr, partial_idx_ptr, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_c_b, stride_c_m, stride_c_d, ++ stride_pv_b, stride_pv_s, stride_pv_n, stride_pv_k, ++ stride_pi_b, stride_pi_s, stride_pi_n, stride_pi_k, ++ N: tl.constexpr, M: tl.constexpr, ++ D: tl.constexpr, K: tl.constexpr, ++ M_PER_SPLIT: tl.constexpr, ++ BN: tl.constexpr, BM: tl.constexpr, ++ D_INNER: tl.constexpr, ++ TOPK_PAD: tl.constexpr, ++ MAX_STEPS: tl.constexpr, ++ NUM_STAGES_PIPE: tl.constexpr = 2, ++): ++ """Iterative-insert top-K on signed shifted distance. ++ ++ Grid: ``(num_m_splits, ceil(N/BN), B)``. ++ Two compile-time paths gated on ``D_INNER >= D`` (persistent x + ++ pipelined M-loop vs D-split with sequential M-loop). The M-loop ++ pipelining depth is exposed as ``NUM_STAGES_PIPE`` so the ++ dispatcher can tune it per shape (see :mod:`dispatch` for the ++ heuristic rules; defaults to 2 if not provided). ++ """ ++ pid_s = tl.program_id(0) ++ pid_n = tl.program_id(1) ++ pid_b = tl.program_id(2) ++ pid_b = pid_b.to(tl.int64) ++ ++ n_start = pid_n * BN ++ n_offs = (n_start + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ ++ topk_vals = tl.full([BN, TOPK_PAD], float('inf'), dtype=tl.float32) ++ topk_idxs = tl.full([BN, TOPK_PAD], -1, dtype=tl.int32) ++ topk_max = tl.full([BN], float('inf'), dtype=tl.float32) ++ k_range = tl.arange(0, TOPK_PAD) ++ bm_range = tl.arange(0, BM) ++ m_base = pid_s.to(tl.int64) * M_PER_SPLIT ++ ++ if D_INNER >= D: ++ d_offs = tl.arange(0, D_INNER).to(tl.int64) ++ d_mask = d_offs < D ++ x_tile = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ for m_local in tl.range(0, M_PER_SPLIT, BM, num_stages=NUM_STAGES_PIPE): ++ m_start = m_base + m_local ++ m_offs = m_start + bm_range.to(tl.int64) ++ m_mask = m_offs < M ++ ++ c_tile = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + m_offs[:, None] * stride_c_m ++ + d_offs[None, :] * stride_c_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ c_f = c_tile.to(tl.float32) ++ c_sq_tile = tl.sum(c_f * c_f, axis=1) ++ cross = tl.dot(x_tile, tl.trans(c_tile)).to(tl.float32) ++ score = c_sq_tile[None, :] - 2.0 * cross ++ score = tl.where(m_mask[None, :], score, float('inf')) ++ ++ chunk_best = tl.min(score) ++ threshold_worst = tl.max(topk_max) ++ if chunk_best < threshold_worst: ++ _active = tl.full([1], 1, dtype=tl.int32) ++ for _step in range(MAX_STEPS): ++ if tl.max(_active) > 0: ++ row_min = tl.min(score, axis=1) ++ row_argmin = tl.argmin(score, axis=1) ++ do_insert = row_min < topk_max ++ n_inserts = tl.sum(do_insert.to(tl.int32)) ++ if n_inserts > 0: ++ topk_argmax = tl.argmax(topk_vals, axis=1) ++ replace_mask = (k_range[None, :] == topk_argmax[:, None]) ++ insert_mask = do_insert[:, None] & replace_mask ++ topk_vals = tl.where(insert_mask, row_min[:, None], topk_vals) ++ topk_idxs = tl.where( ++ insert_mask, ++ (m_start + row_argmin.to(tl.int64))[:, None].to(tl.int32), ++ topk_idxs, ++ ) ++ topk_max = tl.max(topk_vals, axis=1) ++ used_mask = (bm_range[None, :] == row_argmin[:, None]) ++ score = tl.where(used_mask & do_insert[:, None], float('inf'), score) ++ _active = tl.where( ++ n_inserts > 0, ++ tl.full([1], 1, dtype=tl.int32), ++ tl.full([1], 0, dtype=tl.int32), ++ ) ++ ++ else: ++ for m_local in tl.range(0, M_PER_SPLIT, BM, num_stages=1): ++ m_start = m_base + m_local ++ m_offs = m_start + bm_range.to(tl.int64) ++ m_mask = m_offs < M ++ ++ cross = tl.zeros([BN, BM], dtype=tl.float32) ++ c_sq_tile = tl.zeros([BM], dtype=tl.float32) ++ for d_start in range(0, D, D_INNER): ++ d_offs = (d_start + tl.arange(0, D_INNER)).to(tl.int64) ++ d_mask = d_offs < D ++ ++ x_sub = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ c_sub = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + m_offs[:, None] * stride_c_m ++ + d_offs[None, :] * stride_c_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ cross += tl.dot(x_sub, tl.trans(c_sub)).to(tl.float32) ++ c_f = c_sub.to(tl.float32) ++ c_sq_tile += tl.sum(c_f * c_f, axis=1) ++ ++ score = c_sq_tile[None, :] - 2.0 * cross ++ score = tl.where(m_mask[None, :], score, float('inf')) ++ ++ chunk_best = tl.min(score) ++ threshold_worst = tl.max(topk_max) ++ if chunk_best < threshold_worst: ++ _active = tl.full([1], 1, dtype=tl.int32) ++ for _step in range(MAX_STEPS): ++ if tl.max(_active) > 0: ++ row_min = tl.min(score, axis=1) ++ row_argmin = tl.argmin(score, axis=1) ++ do_insert = row_min < topk_max ++ n_inserts = tl.sum(do_insert.to(tl.int32)) ++ if n_inserts > 0: ++ topk_argmax = tl.argmax(topk_vals, axis=1) ++ replace_mask = (k_range[None, :] == topk_argmax[:, None]) ++ insert_mask = do_insert[:, None] & replace_mask ++ topk_vals = tl.where(insert_mask, row_min[:, None], topk_vals) ++ topk_idxs = tl.where( ++ insert_mask, ++ (m_start + row_argmin.to(tl.int64))[:, None].to(tl.int32), ++ topk_idxs, ++ ) ++ topk_max = tl.max(topk_vals, axis=1) ++ used_mask = (bm_range[None, :] == row_argmin[:, None]) ++ score = tl.where(used_mask & do_insert[:, None], float('inf'), score) ++ _active = tl.where( ++ n_inserts > 0, ++ tl.full([1], 1, dtype=tl.int32), ++ tl.full([1], 0, dtype=tl.int32), ++ ) ++ ++ val_sortable = _fp32_to_sortable_u32(topk_vals) ++ val_bits = val_sortable.to(tl.uint64) ++ idx_bits = topk_idxs.to(tl.uint32).to(tl.uint64) ++ packed = (val_bits << 32) | idx_bits ++ packed = tl.sort(packed, dim=1) ++ topk_score = _sortable_u32_to_fp32((packed >> 32).to(tl.uint32)) ++ topk_idx = (packed & 0xFFFFFFFF).to(tl.int32) ++ ++ k_offs = tl.arange(0, TOPK_PAD).to(tl.int64) ++ k_mask = k_offs < K ++ write_mask = n_mask[:, None] & k_mask[None, :] ++ pid_s_i64 = pid_s.to(tl.int64) ++ tl.store(partial_val_ptr + pid_b * stride_pv_b + pid_s_i64 * stride_pv_s ++ + n_offs[:, None] * stride_pv_n + k_offs[None, :] * stride_pv_k, ++ topk_score, mask=write_mask) ++ tl.store(partial_idx_ptr + pid_b * stride_pi_b + pid_s_i64 * stride_pi_s ++ + n_offs[:, None] * stride_pi_n + k_offs[None, :] * stride_pi_k, ++ topk_idx, mask=write_mask) +diff --git a/knnlib/_kernels/primitives/knn/triton/sortmerge.py b/knnlib/_kernels/primitives/knn/triton/sortmerge.py +new file mode 100644 +index 0000000..569caba +--- /dev/null ++++ b/knnlib/_kernels/primitives/knn/triton/sortmerge.py +@@ -0,0 +1,181 @@ ++"""flash_knn — Triton sort-merge top-K kernel (x²-free, signed score). ++ ++The kernel computes ``argmin-K`` over the *signed* shifted distance ++ ++ s(n, m) = ||c[m]||² − 2·⟨x[n], c[m]⟩ (== ||x[n] − c[m]||² − ||x[n]||²) ++ ++The ``||x||²`` term is constant per row and so does not affect the top-K ++selection over ``m``. Dropping it eliminates the ``x_sq`` HBM tensor, its ++precompute pass, its per-tile load, one fp32 ADD per accumulator element, ++and the ``dist >= 0`` underflow clamp. The trade-off is that ``s`` is no ++longer non-negative; the packed-uint64 sort-merge top-K therefore uses ++an IEEE-sortable u32 transform (positives flip just the sign bit, negatives ++flip every bit) so ascending uint32 order matches ascending fp32 order on ++signed inputs. ++ ++Public name: ``_flash_knn_sortmerge_kernel`` — called from ++``klib/primitives/knn/triton/dispatch.py``. Stage 1 writes signed ++fp32 scores + int32 idxs to a partial buffer; Stage 2 either returns ++``partial[:, 0]`` directly (single split) or runs ``torch.topk(..., ++largest=False)`` to reduce across splits. ++ ++Same kernel covers both ``small-N M-split flash-decode`` (multiple ++splits per query block) and ``large-N single-pass`` (M_PER_SPLIT == M) ++— the host-side dispatcher just chooses the grid + M_PER_SPLIT. ++""" ++import triton ++import triton.language as tl ++ ++from knnlib._kernels.primitives.knn.triton._common import ( ++ _INF_PACKED, ++ _fp32_to_sortable_u32, ++ _sortable_u32_to_fp32, ++) ++ ++ ++@triton.jit ++def _flash_knn_sortmerge_kernel( ++ x_ptr, c_ptr, ++ partial_val_ptr, partial_idx_ptr, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_c_b, stride_c_m, stride_c_d, ++ stride_pv_b, stride_pv_s, stride_pv_n, stride_pv_k, ++ stride_pi_b, stride_pi_s, stride_pi_n, stride_pi_k, ++ N: tl.constexpr, M: tl.constexpr, ++ D: tl.constexpr, K: tl.constexpr, ++ M_PER_SPLIT: tl.constexpr, ++ BN: tl.constexpr, BM: tl.constexpr, ++ D_INNER: tl.constexpr, ++ TOPK_PAD: tl.constexpr, ++ NUM_STAGES_PIPE: tl.constexpr = 2, ++): ++ """Packed-uint64 sort-merge top-K with IEEE-sortable u32 score bits. ++ ++ Grid: ``(num_m_splits, ceil(N/BN), B)``. ``BM == TOPK_PAD``. ++ ++ Two compile-time paths gated on ``D_INNER >= D``: ++ * persistent x tile + pipelined M-loop (single tile covers all of D) ++ * D-split with sequential M-loop (D-chunked GEMM for wide D) ++ ++ ``NUM_STAGES_PIPE`` controls the M-loop ``tl.range`` pipelining ++ depth (host-side dispatch tunes it per shape; defaults to 2 if not ++ provided -- matches the pre-port hard-coded value). ++ """ ++ pid_s = tl.program_id(0) ++ pid_n = tl.program_id(1) ++ pid_b = tl.program_id(2) ++ pid_b = pid_b.to(tl.int64) ++ ++ n_start = pid_n * BN ++ n_offs = (n_start + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ ++ topk_packed = tl.full([BN, TOPK_PAD], _INF_PACKED, dtype=tl.uint64) ++ bm_range = tl.arange(0, BM) ++ m_base = pid_s.to(tl.int64) * M_PER_SPLIT ++ ++ if D_INNER >= D: ++ d_offs = tl.arange(0, D_INNER).to(tl.int64) ++ d_mask = d_offs < D ++ x_tile = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ for m_local in tl.range(0, M_PER_SPLIT, BM, num_stages=NUM_STAGES_PIPE): ++ m_start = m_base + m_local ++ m_offs = m_start + bm_range.to(tl.int64) ++ m_mask = m_offs < M ++ ++ c_tile = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + m_offs[:, None] * stride_c_m ++ + d_offs[None, :] * stride_c_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ c_f = c_tile.to(tl.float32) ++ c_sq_tile = tl.sum(c_f * c_f, axis=1) ++ cross = tl.dot(x_tile, tl.trans(c_tile)).to(tl.float32) ++ score = c_sq_tile[None, :] - 2.0 * cross ++ score = tl.where(m_mask[None, :], score, float('inf')) ++ ++ chunk_best = tl.min(score) ++ topk_worst_packed = tl.max(topk_packed) ++ topk_worst_val = _sortable_u32_to_fp32( ++ (topk_worst_packed >> 32).to(tl.uint32)) ++ if chunk_best < topk_worst_val: ++ score_sortable = _fp32_to_sortable_u32(score) ++ idx_vals = (m_start + bm_range.to(tl.int64)).to(tl.int32) ++ idx_bits = idx_vals.to(tl.uint32).to(tl.uint64) ++ chunk_packed = (score_sortable.to(tl.uint64) << 32) | idx_bits[None, :] ++ ++ chunk_packed = chunk_packed ^ tl.full([1], 0xFFFFFFFFFFFFFFFF, dtype=tl.uint64) ++ chunk_packed = tl.sort(chunk_packed, dim=1) ++ chunk_packed = chunk_packed ^ tl.full([1], 0xFFFFFFFFFFFFFFFF, dtype=tl.uint64) ++ ++ merged = tl.minimum(topk_packed, chunk_packed) ++ topk_packed = tl.sort(merged, dim=1) ++ ++ else: ++ for m_local in tl.range(0, M_PER_SPLIT, BM, num_stages=1): ++ m_start = m_base + m_local ++ m_offs = m_start + bm_range.to(tl.int64) ++ m_mask = m_offs < M ++ ++ cross = tl.zeros([BN, BM], dtype=tl.float32) ++ c_sq_tile = tl.zeros([BM], dtype=tl.float32) ++ for d_start in range(0, D, D_INNER): ++ d_offs = (d_start + tl.arange(0, D_INNER)).to(tl.int64) ++ d_mask = d_offs < D ++ ++ x_sub = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ c_sub = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + m_offs[:, None] * stride_c_m ++ + d_offs[None, :] * stride_c_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ cross += tl.dot(x_sub, tl.trans(c_sub)).to(tl.float32) ++ c_f = c_sub.to(tl.float32) ++ c_sq_tile += tl.sum(c_f * c_f, axis=1) ++ ++ score = c_sq_tile[None, :] - 2.0 * cross ++ score = tl.where(m_mask[None, :], score, float('inf')) ++ ++ chunk_best = tl.min(score) ++ topk_worst_packed = tl.max(topk_packed) ++ topk_worst_val = _sortable_u32_to_fp32( ++ (topk_worst_packed >> 32).to(tl.uint32)) ++ if chunk_best < topk_worst_val: ++ score_sortable = _fp32_to_sortable_u32(score) ++ idx_vals = (m_start + bm_range.to(tl.int64)).to(tl.int32) ++ idx_bits = idx_vals.to(tl.uint32).to(tl.uint64) ++ chunk_packed = (score_sortable.to(tl.uint64) << 32) | idx_bits[None, :] ++ ++ chunk_packed = chunk_packed ^ tl.full([1], 0xFFFFFFFFFFFFFFFF, dtype=tl.uint64) ++ chunk_packed = tl.sort(chunk_packed, dim=1) ++ chunk_packed = chunk_packed ^ tl.full([1], 0xFFFFFFFFFFFFFFFF, dtype=tl.uint64) ++ ++ merged = tl.minimum(topk_packed, chunk_packed) ++ topk_packed = tl.sort(merged, dim=1) ++ ++ topk_score_sortable = (topk_packed >> 32).to(tl.uint32) ++ topk_score = _sortable_u32_to_fp32(topk_score_sortable) ++ topk_idx = (topk_packed & 0xFFFFFFFF).to(tl.int32) ++ ++ k_offs = tl.arange(0, TOPK_PAD).to(tl.int64) ++ k_mask = k_offs < K ++ write_mask = n_mask[:, None] & k_mask[None, :] ++ pid_s_i64 = pid_s.to(tl.int64) ++ tl.store(partial_val_ptr + pid_b * stride_pv_b + pid_s_i64 * stride_pv_s ++ + n_offs[:, None] * stride_pv_n + k_offs[None, :] * stride_pv_k, ++ topk_score, mask=write_mask) ++ tl.store(partial_idx_ptr + pid_b * stride_pi_b + pid_s_i64 * stride_pi_s ++ + n_offs[:, None] * stride_pi_n + k_offs[None, :] * stride_pi_k, ++ topk_idx, mask=write_mask) diff --git a/knnlib/knn.py b/knnlib/knn.py -index ad0c7b6..38acfc6 100644 +index ad0c7b6..4668097 100644 --- a/knnlib/knn.py +++ b/knnlib/knn.py -@@ -1,13 +1,14 @@ +@@ -1,13 +1,6 @@ -"""Brute-force squared-L2 k-nearest-neighbours -- the reference you must optimise. -+"""Brute-force squared-L2 k-nearest-neighbours -- chunked running top-k. ++"""Brute-force squared-L2 k-nearest-neighbours -- optimized Triton backend. -This implementation is intentionally naive. It computes the full ``(Q, M)`` -pairwise distance matrix with :func:`torch.cdist` and materialises it in HBM, @@ -59,46 +2096,65 @@ index ad0c7b6..38acfc6 100644 -database points for every query. It is correct and deterministic, but it moves -far more memory than necessary (the entire ``(Q, M)`` matrix) and cannot scale -to large ``M`` without exhausting device memory. -+The naive baseline materialised the full ``(Q, M)`` distance matrix with -+:func:`torch.cdist` and ran :func:`torch.topk` over the whole thing. Here the -+work is delegated to :func:`knnlib._chunked_knn.knn`, which tiles the database -+into chunks and maintains a running per-query top-k across chunks, so the -+``(Q, M)`` matrix is never materialised in HBM and the search scales to large -+``M``. The squared-L2 block per chunk is formed as -+``||q||^2 - 2 + ||db_j||^2``. - +- -Contract (do NOT change): -+Public contract is unchanged: ++Public contract (unchanged): knn(queries, database, k) -> (distances, indices) -@@ -15,25 +16,11 @@ Contract (do NOT change): - database : (M, D) float32 CUDA tensor of database points to search. - k : int, number of nearest neighbours to return per query. +@@ -19,21 +12,47 @@ Contract (do NOT change): + nearest database points, in ascending order (nearest first). + indices : (Q, k) int64 tensor of the corresponding database row indices. -- distances : (Q, k) float32 tensor of the SQUARED-L2 distances to the ``k`` -- nearest database points, in ascending order (nearest first). -- indices : (Q, k) int64 tensor of the corresponding database row indices. -- -You may add modules/kernels inside the ``knnlib`` package and rewrite the body -of :func:`knn` freely (fused/streamed running top-k, tiled distance kernels, -Triton), as long as the public contract above is preserved. In particular you -should avoid ever materialising the full ``(Q, M)`` distance matrix. -+ distances : (Q, k) float32 SQUARED-L2 distances, ascending (nearest first). -+ indices : (Q, k) int64 database row indices. ++Instead of materialising the full ``(Q, M)`` pairwise distance matrix, this ++implementation dispatches to a fused Triton brute-force k-NN kernel that keeps ++a running top-k per query while streaming over the database in tiles (never ++writing an ``(Q, M)`` cross to HBM and never loading ``||x||^2``). The kernel ++ranks candidates with the shift-invariant score ``||c||^2 - 2 `` (same ++argmin-k as true squared L2); a second tiled gather pass then recovers the ++exact ``||x - c[idx]||^2`` in fp32 for the ``k`` selected neighbours. """ from __future__ import annotations --import torch -- -- --def knn(queries, database, k): -- """Return the (squared-L2) k nearest database points for each query. -+from knnlib._chunked_knn import knn + import torch + ++from knnlib._kernels.primitives.knn.triton import flash_knn_triton ++from knnlib._kernels.kernels.distance.triton.knn_gather_l2sq import ( ++ triton_knn_gather_sqdist, ++) ++ + + def knn(queries, database, k): + """Return the (squared-L2) k nearest database points for each query. - Naive: materialise the full (Q, M) distance matrix, then top-k. -- """ ++ Fused: a Triton kNN pass returns the ``k`` nearest indices per query ++ (ranked by the x^2-free score), then a tiled gather kernel writes the ++ true squared-L2 distances for those indices. No ``(Q, M)`` matrix is ++ ever materialised. + """ - d2 = torch.cdist(queries, database) ** 2 # (Q, M), materialized -- naive - dist, idx = torch.topk(d2, k, dim=1, largest=False) - return dist, idx.to(torch.long) -+__all__ = ["knn"] ++ # flash_knn_triton / the gather kernel operate on batched (B, N, D) / ++ # (B, M, D) tensors. Add a batch axis of 1 and drop it on the way out. ++ qb = queries.unsqueeze(0) ++ cb = database.unsqueeze(0) ++ if not qb.is_contiguous(): ++ qb = qb.contiguous() ++ if not cb.is_contiguous(): ++ cb = cb.contiguous() ++ ++ # (1, Q, k) int32 indices, sorted by ascending true squared L2. ++ idxs = flash_knn_triton(qb, cb, k) ++ ++ # (1, Q, k) fp32 true ||x - c[idx]||^2, computed in fp32 throughout. ++ vals = triton_knn_gather_sqdist(qb, cb, idxs) ++ ++ dist = vals[0] ++ idx = idxs[0].to(torch.long) ++ return dist, idx diff --git a/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md b/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md index f431f3292..93dfa04e6 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md +++ b/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md @@ -4,101 +4,92 @@ Operator-facing. Not copied into the agent workspace by the adapter. ## What this task measures -Can an agent turn a correct-but-naive GPU PCA into a fast one? The agent is -given `pcalib` (a full-SVD-on-the-centred-matrix implementation) and must -rewrite the internals — ideally a covariance/Gram matrix (`Xᵀ X`, one GEMM) -followed by a top-`k` symmetric eigendecomposition, with fused centring and no -materialization of the centred data — to maximize the geometric-mean speedup -over the frozen baseline across a family of hidden `(N, D, k)` workloads, -subject to a subspace-quality gate. - -This is the fourth of a four-task **flashlib kernel-optimization family** -(KMeans, KNN, TruncatedSVD, PCA). All four share the KernelBench-style pattern: -ship a naive package, freeze a byte-for-byte baseline in the judge image, apply -the agent's patch to a clean copy, time patched-vs-baseline on identical seeded -data in an isolated worker, and gate on a primitive-appropriate quality metric -before scoring by geomean speedup. TruncatedSVD is the sibling primitive: it -factors the raw data matrix directly (no mean-centring), whereas PCA here is -covariance-based and centres by the feature mean. - -## Correctness gate: orthonormality + captured variance, judge-computed - -The quality gate has two judge-computed parts, evaluated from the *components -the submission returns* (not from any agent-provided number), on the same seeded -`x` used for the baseline: - -- **Orthonormality** — `max |V Vᵀ − I|` over the returned `(k, D)` components - must not exceed `ortho_tolerance` (default 2%). This forbids returning a - rank-deficient or non-orthonormal "subspace" to cheat the variance term. -- **Captured variance** — `(1/(N−1)) ||X_c V||_F²`, where `X_c` is the - mean-centred data, must be at least `(1 − captured_tolerance)` (default 2%) of - the baseline's captured variance. The centring uses the feature mean of `x`. - -Properties: - -- rotation/sign/basis-convention independent — only the *subspace* spanned by - the components matters, so the gate does not care whether the agent returns - eigenvectors in a different sign or rotated within the top-`k` subspace; -- robust to floating-point drift across the eigendecomposition; -- cheat-proof: you cannot return fast garbage — high captured variance requires - actually recovering the leading principal subspace, and the orthonormality - check blocks degenerate answers. Trading some numeric precision for speed - (tf32/bf16 accumulation in the GEMM) is allowed within tolerance. - -Determinism: data is a fixed function of `(N, D, seed)`; seeds derive from -`base_seed`. The naive baseline result is therefore reproducible. - -Note on the worker: `pca` returns `(components, explained_variance)`, so -`components` is the **first** element of the tuple (unlike the SVD sibling where -the singular values come first). The worker reads `agent_out[0]` for the -subspace-quality checks. - -## Anti-gaming - -flashlib (the library these primitives are distilled from) is public and -Apache-2.0. Mitigations: - -- The shipped package is a small, neutrally named library (`pcalib`), not - flashlib; the patch allowlist confines edits to `pcalib/**`. -- The patch policy forbids importing `flashlib`/cuML/cuPy/FAISS/scikit-learn and - bans env/subprocess/network access — the agent must write the kernels itself. -- Scored shapes are hidden and differ from the two public shapes; seeds derive - from `base_seed`. General kernels win; shape lookups do not. -- Honest framing: reproducing SOTA-class kernels *is* the bar. We block trivial - library reuse, not the underlying knowledge. - -## Execution model & the Modal question - -The evaluator runs an isolated worker subprocess (`_run_worker`) that imports the -frozen baseline (`/opt/pca_ref/refpca.py`) and the patched `pcalib` (from the -applied clean tree), times both, and reports JSON. This assumes the **judge -container has a GPU**. - -The repo's other GPU task (vllm) instead offloads to **Modal** because Harbor -judge containers may not be GPU-scheduled. `_run_worker` is the single swap -point: to go Modal, replace it with a Modal function that builds the patched -package, runs the same worker logic on a Modal GPU, and returns the JSON rows. -The rest of the evaluator (policy, gating, scoring) is unchanged. Decide -in-container-GPU vs Modal at first calibration trial. - -## Calibration TODO (needs a GPU trial) - -- Validate `reference.patch` runs and passes the orthonormality + captured - variance gates on all workloads; fix any `torch.linalg.eigh` API drift for the - pinned torch in the image. -- Measure the reference solution's geomean speedup and set `speedup_target` so a - reference-level solution maps to ~full score (default seeded at 4.0 for the - covariance-vs-full-SVD swap; recalibrate). -- Confirm hidden shapes fit device memory (the naive baseline materializes the - `(N, D)` centred matrix and runs a full thin SVD; all shipped shapes are - H100-safe). -- Sanity-check timing stability (median-of-7); bump `timed_iters` if noisy. - -## Files - -- `pcalib/` — pristine package baked into both images (agent edits it). -- `judge/refpca.py` — frozen baseline, baked to `/opt/pca_ref/`. -- `evaluator.py` — self-contained policy + orchestration + scoring (judge-only). -- `reference.patch` — covariance + eigh reference solution (proves solvability). -- `docker/` — builds the prebuilt agent/judge images referenced by config.yaml. -- `harbor/app/` — agent-facing submission helpers + public self-test. +Can an agent turn a naive GPU PCA into a fast one? The agent patches `pcalib`; +the judge times the patched `pca` against a frozen naive baseline on hidden +`(N, D, k)` workloads and scores the geometric-mean speedup, gated on subspace +quality (orthonormal components + captured variance). One of a four-task +**flashlib kernel-optimization family** (KMeans, KNN, TruncatedSVD, PCA) that all +share one evaluator + one Modal GPU harness. + +## Execution: Modal GPU offload (mirrors the vllm task) + +The judge and the agent public test both **offload GPU work to Modal**; the +containers are light (ubuntu + `modal` + git, no torch/triton). `flash_gpu.py` +(baked at `/opt/flash_gpu.py` in both images) builds an ephemeral Modal app on a +GPU (`evaluation.gpu`, default H100), ships the frozen baseline + the patched +package **as data** (no per-submission image rebuild), runs the worker, and +returns per-workload speedups + verdicts. Needs `MODAL_TOKEN_ID` / +`MODAL_TOKEN_SECRET`. torch/triton/the vendored kernels run on the Modal image +(`evaluation.pip`). + +The evaluator is generic and identical across the four tasks; only three +constants differ (`PRIMITIVE`/`PKG`/`REF_MODULE`), and all workload/threshold/ +Modal values come from `config.yaml` (delivered judge-only as +`/judge/task_config.json`, never present in the agent image). + +## Reference solution = best in the repo + +`reference.patch` vendors flashlib's own optimized **Triton** PCA path +(`primitives/pca/triton/pca.py` + the `linalg/eigh` cuSOLVER/MKL/Triton-Jacobi +stack it depends on) under `pcalib/_kernels/…`, with imports rewritten and the +string "flashlib" scrubbed, plus a thin adapter mapping our +`pca(x, n_components) -> (components, explained_variance)` contract onto +flashlib's entry (which returns eigenpairs of the small cov/Gram matrix). It is +triton-only (runs on any sm≥80), imports cleanly on CPU (kernels compile lazily), +and applies + passes the patch policy. Calibrate `speedup_target` from its +measured geomean on the first GPU trial. + +## Anti-reward-hack + +The **load-bearing** defenses are structural, inside the GPU worker (the only +place the untrusted submission runs): + +* timing primitives (`perf_counter`, `torch.cuda.synchronize`) are captured to + locals **before** the submission is imported → monkey-patching torch cannot + affect measurement; +* **fresh data every timed iteration** → memoizing on a repeated input is + useless; +* quality is **re-verified from the returned tensors on every iteration** → + returning a stale cached result for a different input is caught; +* the judge computes all metrics (no agent number is trusted); +* an ephemeral Modal container per submission → no global state persists. + +The patch policy is surgical defense-in-depth (so it does not reject legitimate +vendored/optimized kernel source): only `/**` `.py` files may change; it +bans external-optimized-library *imports* (flashlib/cuml/cupy/faiss/sklearn/ +cutlass — none installed on the GPU image anyway) and process/network/ +measurement-tamper patterns (`subprocess`, `socket`, `os.system`, +`torch.cuda.synchronize =`, …). + +The agent-facing `readme` describes the contract, gate, scoring, and policy but +**not** how the reference is implemented. + +## Correctness gate + +Two judge-computed parts, evaluated from the *components the submission returns* +(not from any agent-provided number), on each iteration's seeded `x`: + +* **Orthonormality** — `max |V Vᵀ − I|` over the returned `(k, D)` components must + not exceed `ortho_tolerance`. Blocks returning a rank-deficient or + non-orthonormal "subspace" to cheat the variance term. +* **Captured variance** — `(1/(N−1)) ||X_c V||_F²`, where `X_c` is the + mean-centred data, must be at least `(1 − captured_tolerance)` of the + baseline's captured variance. + +Rotation/sign/basis-convention independent (only the spanned subspace matters), +robust to fp drift, cheat-proof: high captured variance requires actually +recovering the leading principal subspace. Data is a fixed function of +`(N, D, seed)` with seeds derived from `base_seed`, so the naive baseline is +reproducible. Note: `pca` returns `(components, explained_variance)`, so +`components` is the **first** tuple element (the worker reads `agent_out[0]` for +the subspace-quality checks). + +## Calibration TODO (needs a Modal GPU trial) + +- Run `reference.patch` on Modal; confirm it passes the orthonormality + captured + variance gates on all workloads (bump `captured_tolerance` / `ortho_tolerance` + if the low-precision matmul path drifts slightly) and set `speedup_target` from + its geomean. +- Confirm the pinned `torch`/`triton` in `evaluation.pip` compile the vendored + kernels, and hidden shapes fit the GPU. +- Confirm Modal token wiring in the Harbor judge/agent containers. diff --git a/2.0/problems/pca_gpu_kernel_optimization/config.yaml b/2.0/problems/pca_gpu_kernel_optimization/config.yaml index 2f792f396..9021eadc7 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/pca_gpu_kernel_optimization/config.yaml @@ -2,54 +2,55 @@ tag: systems runtime: language: python timeout_seconds: 10800 - environment: "GPU PCA kernel optimization; agent patches the pcalib Python/Triton package; judge times the patched pca against a frozen naive full-SVD baseline on hidden (N, D, k) workloads with orthonormality and captured-variance (subspace-quality) gates." + environment: "GPU PCA kernel optimization. The agent patches the pcalib package; the judge offloads timing to a Modal GPU, comparing the patched pca against a frozen naive baseline on hidden (N, D, k) workloads with an orthonormal-components + captured-variance quality gate." apt_packages: - bash - ca-certificates - git - python3 + - python3-pip judge_apt_packages: - bash - ca-certificates - git - python3 + - python3-pip + judge_pip_packages: + - modal docker: - # Experimental local images. Build them with - # 2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh before a - # local Harbor trial. Both images bundle a clean copy of the pcalib - # package; the judge image additionally bakes the frozen naive baseline - # (/opt/pca_ref) and the pristine tree (/opt/pcalib-clean). - image: frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.1.0 - judge_image: frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.1.0 + # Experimental local images. Build with docker/build_images.sh before a local + # Harbor trial. Both are light (ubuntu + modal + git); torch/triton/flashlib + # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes + # /app/pcalib (git) + /opt/pca_ref + /opt/flash_gpu.py; the judge image + # additionally bakes /opt/pcalib-clean. + image: frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.2.0 + judge_image: frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.2.0 environment: - cpus: 8 - memory_mb: 32768 - storage_mb: 32768 + cpus: 4 + memory_mb: 8192 + storage_mb: 16384 build_timeout_seconds: 3600 evaluation: - # The judge worker runs on a GPU visible to the judge container (single - # device). H100 is the reference accelerator; the Triton paths also run on - # L40S/A100. If Harbor cannot attach a GPU to the judge container, the worker - # step is the swap point for a Modal offload (see DESIGN.md). gpu: "H100" - # Timing: warmup then median-of-N cuda-synced full-pca calls. - warmup_iters: 3 + cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" + pip: ["torch", "numpy"] + app_name: "pca-kernel-opt-eval" + modal_timeout_seconds: 1800 + warmup_iters: 2 timed_iters: 7 - # Subspace-quality gates. (1) The submitted components must be orthonormal: - # the max entry of |V V^T - I| must not exceed ortho_tolerance. (2) The - # variance captured by the submitted subspace, (1/(N-1))||Xc V||_F^2 on the - # SAME seeded data, must be at least (1 - captured_tolerance) x the frozen - # baseline's captured variance. captured_tolerance: 0.02 ortho_tolerance: 0.02 - # Geomean speedup (over the naive baseline) mapped to the full bounded score. - # Calibrate against the reference solution's measured geomean after a trial. speedup_target: 4.0 - worker_timeout_seconds: 3600 base_seed: 20260701 - # Agent (iterative) role runs a fast subset; the final verifier runs them all. agent_workload_count: 3 expose_per_workload_metrics: false + workloads: + - {id: w0, N: 500000, D: 128, k: 16} + - {id: w1, N: 1000000, D: 64, k: 8} + - {id: w2, N: 200000, D: 512, k: 32} + - {id: w3, N: 2000000, D: 32, k: 8} + - {id: w4, N: 300000, D: 256, k: 16} + - {id: w5, N: 800000, D: 128, k: 32} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/README.md b/2.0/problems/pca_gpu_kernel_optimization/docker/README.md index 3fbfa70ab..a871e2361 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/README.md +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/README.md @@ -1,7 +1,8 @@ # Experimental PCA Kernel-Optimization Images -Two images, mirroring the duckdb-e2e split: a public **agent** image and a -private **judge** image. Build them before a local Harbor trial: +Two **light** images (ubuntu + `modal` + git). torch, triton, and the vendored +flashlib kernels all run on the **Modal GPU image** defined in `flash_gpu.py`; +the containers themselves are CPU-only and offload GPU work to Modal. ```bash bash 2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh @@ -10,31 +11,41 @@ bash 2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh Defaults: ```text -AGENT_TAG=frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.1.0 -JUDGE_TAG=frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.1.0 +AGENT_TAG=frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.2.0 +JUDGE_TAG=frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.2.0 ``` -The agent image contains: +Agent image: ```text /app/pcalib # clean, git-tracked package (the agent edits this) +/opt/flash_gpu.py # shared Modal GPU harness (public test uses it) +/opt/pca_ref/refpca.py # frozen naive baseline (public-test speed denominator) ``` -The judge image contains: +Judge image: ```text -/opt/pcalib-clean/pcalib # pristine tree; the patch is applied to a copy -/opt/pca_ref/refpca.py # frozen naive baseline (speed denominator + oracle) +/opt/pcalib-clean/pcalib # pristine tree; the patch is applied to a copy +/opt/pca_ref/refpca.py # frozen naive baseline (speed denominator + quality oracle) +/opt/flash_gpu.py # shared Modal GPU harness (the evaluator uses it) ``` -Both are based on `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel` (torch + triton). - ## Runtime requirements -The judge times the patched pca on a **GPU visible to the judge container** -(single device; H100 reference, Triton paths also run on L40S/A100). If the -Harbor runtime cannot attach a GPU to the judge container, port the worker step -(`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md`. +Both the judge and the agent public test **offload timing to a Modal GPU** and +therefore need Modal credentials in the environment: + +```text +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET +``` + +`flash_gpu.py` builds an ephemeral Modal app on a GPU (`evaluation.gpu`, default +`H100`), ships the frozen baseline + the patched package as data, times both on +fresh per-iteration data, verifies quality each iteration, and returns the +speedups. No persistent deployment is used, so a fresh GPU container is spun up +per evaluation. Without Modal credentials the evaluator returns a patch-policy +smoke pass (which repo CI exercises). ## Smoke test @@ -42,5 +53,4 @@ Harbor runtime cannot attach a GPU to the judge container, port the worker step bash 2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh ``` -Import-only; verifies torch/triton and the baked packages are importable. It -does not exercise a GPU. +Import-only (modal + flash_gpu + the baked packages); does not touch a GPU or Modal. diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile index 65039c77a..6eab06de5 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile @@ -1,17 +1,23 @@ # Agent image for pca_gpu_kernel_optimization. -# Bundles a clean, git-tracked copy of the pcalib package at /app/pcalib -# so the agent can edit it and `make_submission.sh` can diff it. torch + triton -# come from the PyTorch base image. -FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel +# +# Light image (no torch/triton): the agent edits /app/pcalib and runs the +# public test, which offloads timing to a Modal GPU (torch/triton live on the +# Modal image defined in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET +# in the environment to run the GPU public test. +# +# Build context = the task directory: +# docker build -f docker/agent/Dockerfile -t . +FROM ubuntu:24.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get install -y --no-install-recommends \ - bash ca-certificates git ripgrep && \ + bash ca-certificates git python3 python3-pip ripgrep && \ rm -rf /var/lib/apt/lists/* -RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" +RUN pip3 install --break-system-packages --no-cache-dir modal +# The package the agent edits (git-tracked so make_submission.sh can diff it). WORKDIR /app COPY pcalib /app/pcalib RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ @@ -20,3 +26,9 @@ RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ git -C /app config user.name frontier-cs && \ git -C /app add -A -- pcalib .gitignore && \ git -C /app -c commit.gpgsign=false commit -qm "pristine pcalib" + +# Shared Modal GPU harness + the frozen naive baseline (the public-test speed +# denominator). The baseline is exactly the code the agent starts from, so it is +# not secret. +COPY flash_gpu.py /opt/flash_gpu.py +COPY judge/refpca.py /opt/pca_ref/refpca.py diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh index 927a7027b..1e59037fa 100755 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile index 906e7d4cb..b1b747b37 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile @@ -1,18 +1,26 @@ # Judge image for pca_gpu_kernel_optimization. -# Bakes the pristine package tree the agent patch is applied to -# (/opt/pcalib-clean/pcalib) and the frozen naive baseline the judge times -# against (/opt/pca_ref/refpca.py). torch + triton come from the base. -FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel +# +# Light image (no torch/triton): the judge validates + applies the patch and +# offloads timing to a Modal GPU (torch/triton live on the Modal image defined +# in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. +# The Frontier-CS 2.0 adapter builds the final judge image ON TOP of this one. +# +# Build context = the task directory: +# docker build -f docker/judge/Dockerfile -t . +FROM ubuntu:24.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get install -y --no-install-recommends \ - bash ca-certificates git && \ + bash ca-certificates git python3 python3-pip && \ rm -rf /var/lib/apt/lists/* -RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" +RUN pip3 install --break-system-packages --no-cache-dir modal +# Pristine tree the patch is applied to, the frozen naive baseline (speed +# denominator + quality oracle), and the shared Modal GPU harness. COPY pcalib /opt/pcalib-clean/pcalib COPY judge/refpca.py /opt/pca_ref/refpca.py +COPY flash_gpu.py /opt/flash_gpu.py WORKDIR /judge diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh index ffd665b2f..0ef0be6c3 100755 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh +++ b/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh @@ -1,17 +1,19 @@ #!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU required). +# Import-only smoke test for the built images (no GPU / Modal required). set -euo pipefail -AGENT_TAG=${AGENT_TAG:-frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "== agent image ==" docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import torch, triton, pcalib; print('agent ok: pcalib', pcalib.__version__)" + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ +sys.path.insert(0, '/opt/pca_ref'); import refpca; \ +import pcalib; print('agent ok: modal + flash_gpu + refpca + pcalib import')" echo "== judge image ==" docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, torch, triton; \ + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ sys.path.insert(0, '/opt/pca_ref'); import refpca; \ sys.path.insert(0, '/opt/pcalib-clean'); import pcalib; \ -print('judge ok: refpca + pcalib import')" +print('judge ok: modal + flash_gpu + refpca + pcalib import')" diff --git a/2.0/problems/pca_gpu_kernel_optimization/evaluator.py b/2.0/problems/pca_gpu_kernel_optimization/evaluator.py index e0898763c..e12ae7091 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/pca_gpu_kernel_optimization/evaluator.py @@ -1,22 +1,20 @@ -"""Evaluator for the PCA GPU kernel-optimization task. - -The agent submits a unified diff over the ``pcalib`` Python package. The -judge: - -1. statically validates the patch against an allowlist (only ``pcalib/**`` - may change; no ``flashlib``/cuML/network/env access; bounded size); -2. applies it to a pristine copy of ``pcalib`` baked into the judge image - at ``/opt/pcalib-clean``; -3. runs an isolated worker subprocess that, for each hidden ``(N, D, k)`` - workload, times the patched ``pcalib.pca`` against a *frozen* naive - baseline (``/opt/pca_ref/refpca.py``) on identical seeded data and - measures each result's orthonormality error and captured variance; -4. gates on subspace quality (agent components must be orthonormal and must - capture no less variance than the baseline beyond a small tolerance) and - scores by the geometric-mean speedup. - -When the judge source tree is not present (e.g. local static checks on a box -without a GPU), the evaluator still validates the patch policy and returns a +"""Generic evaluator for the flashlib GPU kernel-optimization task family. + +Identical across all four tasks (kmeans / knn / pca / truncated_svd); every +primitive-specific value comes from the ``evaluation`` block of the task's +config (delivered to the judge as ``/judge/task_config.json``), which is not +present in the agent workspace. + +Flow: statically validate the patch (only ``/**`` may change; no external +optimized libs / env / process / network access) -> apply it to the pristine +package baked at ``clean_source`` -> hand the frozen baseline + patched package +sources to a Modal GPU worker (``flash_gpu.run_remote``) that times both on +fresh per-iteration data and verifies clustering/retrieval/subspace quality on +every iteration -> gate on the worker's per-workload verdict -> score by the +geometric-mean speedup. + +Without Modal credentials or the baked judge sources (e.g. repo CI on a box with +no GPU/Modal), the evaluator still validates the patch policy and returns a smoke pass, so ``python3 evaluator.py reference.patch`` works anywhere. """ from __future__ import annotations @@ -31,18 +29,12 @@ import subprocess import sys import tempfile -import time from dataclasses import dataclass from pathlib import Path from typing import Any -MAX_PATCH_BYTES = 1_000_000 -MAX_CHANGED_FILES = 40 TASK_CONFIG_PATH = Path("/judge/task_config.json") -DEFAULT_CLEAN_SOURCE = Path("/opt/pcalib-clean") # pristine package (patched here) -DEFAULT_BASELINE_SOURCE = Path("/opt/pca_ref") # frozen naive baseline (refpca.py) - def _load_task_config() -> dict[str, Any]: try: @@ -53,29 +45,29 @@ def _load_task_config() -> dict[str, Any]: TASK_CONFIG = _load_task_config() -EVALUATION_CONFIG = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} +EVAL = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} -def _cfg(name: str, default): - return EVALUATION_CONFIG.get(name, default) +def _get(name: str, default): + return EVAL.get(name, default) -def _cfg_int(name: str, default: int) -> int: +def _get_int(name: str, default: int) -> int: try: - return int(EVALUATION_CONFIG.get(name, default)) + return int(EVAL.get(name, default)) except Exception: return default -def _cfg_float(name: str, default: float) -> float: +def _get_float(name: str, default: float) -> float: try: - return float(EVALUATION_CONFIG.get(name, default)) + return float(EVAL.get(name, default)) except Exception: return default -def _cfg_bool(name: str, default: bool) -> bool: - raw = EVALUATION_CONFIG.get(name, default) +def _get_bool(name: str, default: bool) -> bool: + raw = EVAL.get(name, default) if isinstance(raw, bool): return raw if isinstance(raw, str): @@ -83,51 +75,49 @@ def _cfg_bool(name: str, default: bool) -> bool: return bool(raw) -WARMUP_ITERS = _cfg_int("warmup_iters", 3) -TIMED_ITERS = _cfg_int("timed_iters", 7) -CAPTURED_TOL = _cfg_float("captured_tolerance", 0.02) # agent captured >= (1-tol)*ref -ORTHO_TOL = _cfg_float("ortho_tolerance", 0.02) # max |V V^T - I| entry -SPEEDUP_TARGET = _cfg_float("speedup_target", 4.0) # geomean speedup mapped to full score -WORKER_TIMEOUT_SECONDS = _cfg_int("worker_timeout_seconds", 3600) -BASE_SEED = _cfg_int("base_seed", 20260701) +# --- Per-task identity: the ONLY lines that differ across the four tasks. --- +# Kept as constants (not config) so the patch policy is enforceable locally / in +# CI without /judge/task_config.json. Everything else is config-driven and only +# needed on the GPU path. +PRIMITIVE = "pca" +PKG = "pcalib" +REF_MODULE = "refpca" -# Editable surface: only the shipped package. -ALLOWED_PATTERNS = ( - "pcalib/**", -) -# Never touchable, even though they are not shipped in the agent workspace. +MAX_PATCH_BYTES = _get_int("max_patch_bytes", 2_000_000) +MAX_CHANGED_FILES = _get_int("max_changed_files", 80) +CLEAN_SOURCE = Path(f"/opt/{PKG}-clean") +BASELINE_SOURCE = Path(f"/opt/{PRIMITIVE}_ref") +SPEEDUP_TARGET = _get_float("speedup_target", 8.0) + +ALLOWED_PATTERNS = (f"{PKG}/**",) DENIED_PATTERNS = ( "evaluator.py", + "flash_gpu.py", "reference.patch", - "refpca.py", - "**/refpca.py", + f"{REF_MODULE}.py", + f"**/{REF_MODULE}.py", "**/conftest.py", "**/test_*.py", "pyproject.toml", "setup.py", "setup.cfg", ) -# Forbidden substrings in added lines (defense in depth). The agent must write -# its own kernels, not call an external optimized library, read the -# environment, spawn processes, or touch the network. -FORBIDDEN_TOKENS = ( - "flashlib", - "cuml", - "cudf", - "cupy", - "faiss", - "sklearn", - "scikit", - "os.environ", - "getenv", - "putenv", - "setenv", - "subprocess", - "socket", - "urllib", - "requests", - "importlib.import_module", - "__import__", +# Defense in depth (the load-bearing anti-tamper defense is structural, inside +# the GPU worker: it captures its perf_counter / synchronize references before +# importing the submission, regenerates data every iteration, and re-verifies +# quality each iteration). These patterns are matched surgically so that +# legitimate vendored/optimized kernel source is not rejected: +# * external optimized libraries, matched as IMPORT statements (not bare words, +# so a comment mentioning a name is fine); none are installed on the GPU +# image anyway; +# * process / network / measurement-tamper patterns that never appear in a +# real GPU kernel. +FORBIDDEN_IMPORT_RE = re.compile( + r"(?:^|\n)\s*(?:import|from)\s+(flashlib|cuml|cudf|cupy|faiss|sklearn|scikit|cutlass|cuvs)\b" +) +FORBIDDEN_PATTERN_RE = re.compile( + r"\bsubprocess\b|\bsocket\b|\burllib\b|\brequests\b|os\.system|" + r"torch\.cuda\.synchronize\s*=|time\.perf_counter\s*=|setattr\(\s*torch\.cuda" ) @@ -136,7 +126,6 @@ class PatchFile: old_path: str new_path: str added_lines: tuple[str, ...] - removed_lines: tuple[str, ...] @property def path(self) -> str: @@ -144,7 +133,7 @@ def path(self) -> str: def _match(path: str, patterns: tuple[str, ...]) -> bool: - return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + return any(fnmatch.fnmatch(path, p) for p in patterns) def _invalid(message: str, metrics: dict[str, Any] | None = None): @@ -155,41 +144,27 @@ def _invalid(message: str, metrics: dict[str, Any] | None = None): def _parse_patch(text: str) -> list[PatchFile]: files: list[PatchFile] = [] - current_old = "" - current_new = "" + old = new = "" added: list[str] = [] - removed: list[str] = [] in_file = False - for line in text.splitlines(): if line.startswith("diff --git "): if in_file: - files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) - in_file = True - current_old = current_new = "" - added = [] - removed = [] + files.append(PatchFile(old, new, tuple(added))) + in_file, old, new, added = True, "", "", [] continue if not in_file: continue if line.startswith("--- "): - current_old = line[4:].strip() - if current_old.startswith("a/"): - current_old = current_old[2:] - continue - if line.startswith("+++ "): - current_new = line[4:].strip() - if current_new.startswith("b/"): - current_new = current_new[2:] - continue - if line.startswith("+") and not line.startswith("+++ "): + old = line[4:].strip() + old = old[2:] if old.startswith("a/") else old + elif line.startswith("+++ "): + new = line[4:].strip() + new = new[2:] if new.startswith("b/") else new + elif line.startswith("+") and not line.startswith("+++ "): added.append(line[1:]) - continue - if line.startswith("-") and not line.startswith("--- "): - removed.append(line[1:]) - if in_file: - files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + files.append(PatchFile(old, new, tuple(added))) return files @@ -201,7 +176,7 @@ def _validate_path(path: str) -> tuple[bool, str]: if _match(path, DENIED_PATTERNS): return False, f"changed file is outside task boundary: {path}" if not _match(path, ALLOWED_PATTERNS): - return False, f"changed file is not allowlisted (only pcalib/** may change): {path}" + return False, f"changed file is not allowlisted (only {PKG}/** may change): {path}" if not path.endswith(".py"): return False, f"only Python files may change: {path}" return True, "" @@ -214,69 +189,40 @@ def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: if size > MAX_PATCH_BYTES: return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} text = patch_path.read_text(encoding="utf-8", errors="replace") - patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() files = _parse_patch(text) metrics: dict[str, Any] = { "patch_bytes": size, - "patch_sha256": patch_hash, + "patch_sha256": hashlib.sha256(text.encode("utf-8", "replace")).hexdigest(), "changed_files": len(files), } if len(files) > MAX_CHANGED_FILES: return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics - - for patch_file in files: - path = patch_file.path - if patch_file.new_path == "/dev/null": - return False, f"deleting files is outside task boundary: {patch_file.old_path}", metrics - if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: - ok, err = _validate_path(patch_file.old_path) + for pf in files: + path = pf.path + if pf.new_path == "/dev/null": + return False, f"deleting files is outside task boundary: {pf.old_path}", metrics + if pf.old_path != "/dev/null" and pf.old_path != pf.new_path: + ok, err = _validate_path(pf.old_path) if not ok: - return False, f"rename/copy source is outside task boundary: {err}", metrics + return False, f"rename source is outside task boundary: {err}", metrics ok, err = _validate_path(path) if not ok: return False, err, metrics - added_text = "\n".join(patch_file.added_lines) - low = added_text.lower() - for token in FORBIDDEN_TOKENS: - if token in low: - return False, f"{path}: forbidden token in added code ({token})", metrics - + added = "\n".join(pf.added_lines) + m = FORBIDDEN_IMPORT_RE.search(added) + if m: + return False, f"{path}: forbidden import of an external optimized library ({m.group(1)})", metrics + m = FORBIDDEN_PATTERN_RE.search(added) + if m: + return False, f"{path}: forbidden pattern in added code ({m.group(0).strip()[:40]})", metrics metrics["valid_patch"] = 1 return True, "patch accepted by static policy", metrics -def clean_env(tmp_root: Path) -> dict[str, str]: - home = tmp_root / "home" - tmp = tmp_root / "tmp" - home.mkdir(parents=True, exist_ok=True) - tmp.mkdir(parents=True, exist_ok=True) - env = { - "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin"), - "HOME": str(home), - "TMPDIR": str(tmp), - "LC_ALL": "C", - "LANG": "C", - } - for key in ("CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES", "LD_LIBRARY_PATH", - "TRITON_CACHE_DIR", "CUDA_HOME"): - if key in os.environ: - env[key] = os.environ[key] - env.setdefault("TRITON_CACHE_DIR", str(tmp_root / "triton_cache")) - return env - - -def run_checked(cmd: list[str], *, cwd: Path, env: dict[str, str], timeout: int) -> subprocess.CompletedProcess: - return subprocess.run( - cmd, cwd=str(cwd), env=env, timeout=timeout, - stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, - ) - - -def sanitize_error_text(text: str) -> str: - text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) +def sanitize(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text or "") text = re.sub(r"/opt/[A-Za-z0-9_./-]+", "", text) - text = re.sub(r"N=\d+", "N=", text) - text = re.sub(r"seed[=:]?\s*\d+", "seed=", text, flags=re.IGNORECASE) + text = re.sub(r"\bN=\d+|\bseed[=:]?\s*\d+", "", text, flags=re.IGNORECASE) return text[-600:] @@ -286,195 +232,123 @@ def geometric_mean(values: list[float]) -> float: return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) -def score_from_speedup(gm_speedup: float) -> float: - if gm_speedup <= 0: +def score_from_speedup(gm: float) -> float: + if gm <= 0: return 0.0 - raw = 100.0 * math.log(gm_speedup) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) return max(0.0, min(100.0, raw)) -def is_final_submission_role() -> bool: +def is_final_role() -> bool: return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" -# Hidden workloads. (N, D, k). Agent role runs a fast subset for iterative -# feedback; the final verifier runs the full set. Seeds are derived from -# BASE_SEED so data is deterministic but not guessable from the statement. -_HIDDEN_WORKLOADS = ( - {"id": "w0", "N": 500_000, "D": 128, "k": 16}, - {"id": "w1", "N": 1_000_000, "D": 64, "k": 8}, - {"id": "w2", "N": 200_000, "D": 512, "k": 32}, # wide-feature regime - {"id": "w3", "N": 2_000_000, "D": 32, "k": 8}, # tall/skinny regime - {"id": "w4", "N": 300_000, "D": 256, "k": 16}, - {"id": "w5", "N": 800_000, "D": 128, "k": 32}, -) - - -def _workloads(*, final_role: bool) -> list[dict[str, Any]]: - configured = _cfg("workloads", None) - workloads = list(configured) if isinstance(configured, list) and configured else list(_HIDDEN_WORKLOADS) +def _workloads(final_role: bool) -> list[dict[str, Any]]: + workloads = list(_get("workloads", []) or []) if not final_role: - n_agent = _cfg_int("agent_workload_count", 3) - workloads = workloads[:n_agent] + n = _get_int("agent_workload_count", 3) + workloads = workloads[:n] + base = _get_int("base_seed", 20260701) for i, w in enumerate(workloads): - w.setdefault("seed", BASE_SEED + 1000 * (i + 1)) + w.setdefault("seed", base + 1000 * (i + 1)) return workloads -# The worker runs the untrusted patched package in its own process. It imports -# the frozen baseline (refpca) and the patched pcalib, times both on identical -# seeded data, and reports per-workload timings + subspace-quality metrics as -# JSON. -_WORKER_SOURCE = r''' -import argparse, json, sys, time, traceback -def _load(baseline_dir, patched_dir): - sys.path.insert(0, patched_dir); sys.path.insert(0, baseline_dir) - import refpca, pcalib - return refpca, pcalib -def gen(N, D, seed, device): - import torch - g = torch.Generator(device=device).manual_seed(int(seed)) - return torch.randn(N, D, generator=g, device=device, dtype=torch.float32) -def ortho_err(comps): - import torch - c = comps.to(torch.float32); k = c.shape[0] - return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) -def captured(x, comps): - import torch - c = comps.to(torch.float32); mean = x.mean(dim=0); N = x.shape[0]; total = 0.0 - for i in range(0, N, 16384): - p = (x[i:i+16384] - mean) @ c.t(); total += float((p * p).sum().item()) - return total / (N - 1) -def bench(fn, warmup, iters): - import torch - for _ in range(warmup): fn(); torch.cuda.synchronize() - ts = [] - for _ in range(iters): - torch.cuda.synchronize(); t0 = time.perf_counter(); fn(); torch.cuda.synchronize() - ts.append(time.perf_counter() - t0) - ts.sort(); return ts[len(ts) // 2] * 1000.0 -def check(out, D, k): - import torch - if not (isinstance(out, tuple) and len(out) == 2): raise ValueError("pca must return (components, explained_variance)") - comps, ev = out - if tuple(comps.shape) != (k, D) or tuple(ev.shape) != (k,): raise ValueError("wrong output shape") - if not (torch.isfinite(comps).all() and torch.isfinite(ev).all()): raise ValueError("non-finite output") -def main(): - ap = argparse.ArgumentParser() - for n in ("--baseline-dir","--patched-dir","--workloads","--out"): ap.add_argument(n, required=True) - ap.add_argument("--warmup", type=int, default=3); ap.add_argument("--iters", type=int, default=7) - a = ap.parse_args() - import torch - if not torch.cuda.is_available(): - json.dump({"ok": False, "error": "cuda_unavailable"}, open(a.out, "w")); return - refpca, pcalib = _load(a.baseline_dir, a.patched_dir) - rows = [] - for w in json.loads(a.workloads): - N, D, k, seed = w["N"], w["D"], w["k"], w["seed"] - x = gen(N, D, seed, "cuda") - ref_fn = lambda: refpca.pca(x, k) - agent_fn = lambda: pcalib.pca(x, k) - ref_out = ref_fn(); torch.cuda.synchronize() - ref_cap = captured(x, ref_out[0]) - try: - agent_out = agent_fn(); torch.cuda.synchronize(); check(agent_out, D, k) - oerr = ortho_err(agent_out[0]); acap = captured(x, agent_out[0]) - except Exception as e: - rows.append({"id": w["id"], "ok": False, "error": type(e).__name__ + ": " + str(e)[:200]}) - del x; torch.cuda.empty_cache(); continue - ref_ms = bench(ref_fn, a.warmup, a.iters); agent_ms = bench(agent_fn, a.warmup, a.iters) - rows.append({"id": w["id"], "ok": True, "ref_ms": ref_ms, "agent_ms": agent_ms, - "ortho_err": oerr, "ref_captured": ref_cap, "agent_captured": acap}) - del x; torch.cuda.empty_cache() - json.dump({"ok": True, "rows": rows}, open(a.out, "w")) -if __name__ == "__main__": - try: main() - except Exception: traceback.print_exc(); sys.exit(3) -''' - - -def _run_worker(patched_parent: Path, tmp_root: Path, env: dict[str, str], - workloads: list[dict[str, Any]]) -> dict[str, Any]: - worker_path = tmp_root / "pca_worker.py" - worker_path.write_text(_WORKER_SOURCE, encoding="utf-8") - out_path = tmp_root / "worker_out.json" - cmd = [ - sys.executable, str(worker_path), - "--baseline-dir", str(DEFAULT_BASELINE_SOURCE), - "--patched-dir", str(patched_parent), - "--workloads", json.dumps(workloads), - "--out", str(out_path), - "--warmup", str(WARMUP_ITERS), - "--iters", str(TIMED_ITERS), - ] - run_checked(cmd, cwd=tmp_root, env=env, timeout=WORKER_TIMEOUT_SECONDS) - return json.loads(out_path.read_text(encoding="utf-8")) +def _build_cfg() -> dict[str, Any]: + return { + "primitive": PRIMITIVE, + "pkg": PKG, + "ref_module": REF_MODULE, + "gpu": str(_get("gpu", "H100")), + "cuda_image": str(_get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), + "pip": list(_get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])), + "app_name": str(_get("app_name", "flash-kernel-eval")), + "modal_timeout_seconds": _get_int("modal_timeout_seconds", 1800), + "warmup": _get_int("warmup_iters", 3), + "iters": _get_int("timed_iters", 7), + "inertia_tolerance": _get_float("inertia_tolerance", 0.02), + "recall_threshold": _get_float("recall_threshold", 0.99), + "captured_tolerance": _get_float("captured_tolerance", 0.02), + "ortho_tolerance": _get_float("ortho_tolerance", 0.02), + } + + +def _read_dir(root: Path, rel_to: Path) -> dict[str, str]: + out: dict[str, str] = {} + for p in root.rglob("*.py"): + out[str(p.relative_to(rel_to))] = p.read_text(encoding="utf-8", errors="replace") + return out def full_evaluation(patch_path: Path, metrics: dict[str, Any]): - final_role = is_final_submission_role() + final_role = is_final_role() metrics["submission_role"] = "final" if final_role else "agent" - workloads = _workloads(final_role=final_role) + workloads = _workloads(final_role) - if not DEFAULT_CLEAN_SOURCE.exists() or not DEFAULT_BASELINE_SOURCE.exists(): + # Import the Modal harness baked into the judge image; missing harness or + # missing credentials/sources -> policy smoke pass. + sys.path.insert(0, "/opt") + try: + import flash_gpu # type: ignore + except Exception: metrics["full_benchmark"] = 0 - return (1.0, 1.0, - "patch policy smoke passed; pcalib judge source is not configured in this environment", - metrics) + return 1.0, 1.0, "patch policy smoke passed; GPU harness not available in this environment", metrics + if not flash_gpu.modal_available() or not CLEAN_SOURCE.exists() or not BASELINE_SOURCE.exists(): + metrics["full_benchmark"] = 0 + return 1.0, 1.0, "patch policy smoke passed; Modal/judge sources not configured in this environment", metrics - with tempfile.TemporaryDirectory(prefix="pca_kernel_opt_") as tmp: + with tempfile.TemporaryDirectory(prefix="flash_kernel_opt_") as tmp: tmp_root = Path(tmp) - env = clean_env(tmp_root) - patched_parent = tmp_root / "patched" - shutil.copytree(DEFAULT_CLEAN_SOURCE, patched_parent) - + patched = tmp_root / "patched" + shutil.copytree(CLEAN_SOURCE, patched) + env = {"PATH": os.environ.get("PATH", "/usr/bin:/bin"), "HOME": str(tmp_root), + "LC_ALL": "C", "LANG": "C"} if int(metrics.get("changed_files", 0)) > 0: - run_checked(["git", "apply", "--check", str(patch_path)], cwd=patched_parent, env=env, timeout=60) - run_checked(["git", "apply", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + try: + subprocess.run(["git", "apply", "--check", str(patch_path)], cwd=patched, env=env, + check=True, capture_output=True, text=True, timeout=60) + subprocess.run(["git", "apply", str(patch_path)], cwd=patched, env=env, + check=True, capture_output=True, text=True, timeout=60) + except subprocess.CalledProcessError as exc: + metrics["stderr_tail"] = sanitize(exc.stderr or "") + return _invalid("patch does not apply to the clean package tree", metrics) metrics["applied_patch"] = 1 else: metrics["used_empty_patch"] = 1 - result = _run_worker(patched_parent, tmp_root, env, workloads) - if not result.get("ok"): - return _invalid(f"benchmark worker failed ({result.get('error', 'unknown')})", metrics) - - rows = result.get("rows", []) - speedups: list[float] = [] - per_workload: dict[str, Any] = {} - for row in rows: - if not row.get("ok"): - metrics["failed_workload_error"] = sanitize_error_text(str(row.get("error", ""))) - return _invalid("submitted pca crashed or returned an invalid result on a hidden workload", metrics) - if row["ortho_err"] > ORTHO_TOL: - metrics["ortho_violation"] = {"ortho_err": row["ortho_err"], "tol": ORTHO_TOL} - return _invalid("submitted pca components are not orthonormal", metrics) - ref_cap = row["ref_captured"] - agent_cap = row["agent_captured"] - if agent_cap < (1.0 - CAPTURED_TOL) * ref_cap - 1e-6: - metrics["captured_regression"] = { - "ref": ref_cap, "agent": agent_cap, "tol": CAPTURED_TOL, - } - return _invalid("submitted pca captured variance regressed beyond tolerance", metrics) - speedup = row["ref_ms"] / row["agent_ms"] if row["agent_ms"] > 0 else 0.01 - speedups.append(max(speedup, 0.01)) - per_workload[row["id"]] = { - "ref_ms": row["ref_ms"], "agent_ms": row["agent_ms"], "speedup": speedup, - } - - if not speedups: - return _invalid("no workloads were evaluated", metrics) - - gm = geometric_mean(speedups) - bounded = score_from_speedup(gm) - metrics.update({ - "full_benchmark": 1, - "workload_count": len(speedups), - "geomean_speedup": gm, - }) - if _cfg_bool("expose_per_workload_metrics", False): - metrics["per_workload"] = per_workload - return (bounded, bounded, f"pca geomean speedup {gm:.3f}x over the naive full-SVD baseline", metrics) + payload = { + "baseline_files": _read_dir(BASELINE_SOURCE, BASELINE_SOURCE), + "patched_files": _read_dir(patched, patched), + "workloads": workloads, + "cfg": _build_cfg(), + } + try: + result = flash_gpu.run_remote(payload) + except Exception as exc: + metrics["error_detail"] = sanitize(str(exc)) + return _invalid("GPU evaluation failed", metrics) + + if not result.get("ok"): + metrics["worker_error"] = sanitize(str(result.get("error", "unknown"))) + return _invalid("GPU benchmark worker failed", metrics) + + speedups: list[float] = [] + per_workload: dict[str, Any] = {} + for row in result.get("rows", []): + if not row.get("ok"): + metrics["failed_workload"] = {"reason": row.get("reason", "error")} + return _invalid("submission failed the quality gate or crashed on a hidden workload", metrics) + speedups.append(max(float(row["speedup"]), 0.01)) + per_workload[row["id"]] = {"speedup": row["speedup"]} + if not speedups: + return _invalid("no workloads were evaluated", metrics) + + gm = geometric_mean(speedups) + bounded = score_from_speedup(gm) + metrics.update({"full_benchmark": 1, "workload_count": len(speedups), "geomean_speedup": gm}) + if _get_bool("expose_per_workload_metrics", False): + metrics["per_workload"] = per_workload + return bounded, bounded, f"{PRIMITIVE} geomean speedup {gm:.3f}x over the naive baseline", metrics def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: @@ -484,17 +358,9 @@ def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: return _invalid(message, metrics) try: return full_evaluation(patch_path, metrics) - except subprocess.TimeoutExpired: - return _invalid("benchmark worker timed out", metrics) - except subprocess.CalledProcessError as exc: - stderr = sanitize_error_text(exc.stderr or "") - cmd0 = Path(str(exc.cmd[0])).name if isinstance(exc.cmd, list) and exc.cmd else "subprocess" - metrics["failed_command"] = "git apply" if cmd0 == "git" else cmd0 - metrics["stderr_tail"] = stderr - return _invalid("patch apply or benchmark command failed", metrics) - except Exception as exc: + except Exception as exc: # noqa: BLE001 metrics["error_type"] = type(exc).__name__ - metrics["error_detail"] = sanitize_error_text(str(exc)) + metrics["error_detail"] = sanitize(str(exc)) return _invalid("evaluation failed", metrics) @@ -502,13 +368,9 @@ def main(argv: list[str]) -> int: if len(argv) != 2: print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) return 2 - score, score_unbounded, message, metrics = evaluate(argv[1]) - print(json.dumps({ - "score": score, - "score_unbounded": score_unbounded, - "message": message, - "metrics": metrics, - }, indent=2, sort_keys=True)) + score, unbounded, message, metrics = evaluate(argv[1]) + print(json.dumps({"score": score, "score_unbounded": unbounded, + "message": message, "metrics": metrics}, indent=2, sort_keys=True)) return 0 diff --git a/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py new file mode 100644 index 000000000..7fd6db603 --- /dev/null +++ b/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py @@ -0,0 +1,279 @@ +"""Modal GPU evaluation harness for the flashlib kernel-optimization task family. + +Shared by the judge (evaluator.py) and the agent public test. Baked into both +the agent and judge images at ``/opt/flash_gpu.py``. It runs a frozen naive +baseline and the (patched or agent-edited) package on a Modal GPU and returns +per-workload timings + quality verdicts. + +Anti-reward-hack properties (all enforced inside the remote worker, which is the +only place the untrusted code runs): + +* **Timing primitives are captured before any agent code is imported** — the + worker binds ``time.perf_counter`` and ``torch.cuda.synchronize`` to locals up + front, so a submission that monkey-patches ``torch.cuda.synchronize`` (or any + torch attribute) cannot affect measurement. +* **Fresh data every timed iteration** — each measured iteration generates a new + input from a fresh seed, so a submission cannot memoize on a repeated input. +* **Quality is verified on every timed iteration** — the judge recomputes the + quality metric from the *returned tensors* on that iteration's data and gates + it, so returning a stale cached result for a different input is caught. +* **The judge computes all metrics** — no agent-provided number is trusted. +* **Fresh container per submission** — an ephemeral Modal app is used, so no + global state survives across evaluations. + +The remote worker is a single self-contained function serialized to Modal +(``serialized=True``), so the remote does not import this module and there is no +module-mounting to get wrong. The GPU image ships torch + triton (+ any extra +pip packages a task needs for its reference build). +""" +from __future__ import annotations + +import os + + +# --------------------------------------------------------------------------- # +# Remote worker (runs on the Modal GPU). Fully self-contained: every helper is +# nested so cloudpickle ships the whole thing. Takes and returns plain data. +# --------------------------------------------------------------------------- # +def _gpu_worker(payload: dict) -> dict: + import importlib + import os as _os + import sys as _sys + import tempfile + import time + import traceback + + import torch + + # Capture timing + sync references BEFORE importing any submission code, so a + # monkey-patch of torch.cuda.synchronize cannot affect measurement. + _perf = time.perf_counter + _sync = torch.cuda.synchronize + if not torch.cuda.is_available(): + return {"ok": False, "error": "cuda_unavailable"} + dev = "cuda" + + cfg = payload["cfg"] + prim = cfg["primitive"] + warmup = int(cfg.get("warmup", 3)) + iters = int(cfg.get("iters", 7)) + + def _materialize(files: dict, tag: str) -> str: + root = tempfile.mkdtemp(prefix=f"flash_{tag}_") + for rel, content in files.items(): + path = _os.path.join(root, rel) + parent = _os.path.dirname(path) + if parent: + _os.makedirs(parent, exist_ok=True) + with open(path, "w", encoding="utf-8") as fh: + fh.write(content) + _sys.path.insert(0, root) + return root + + _materialize(payload["baseline_files"], "baseline") + _materialize(payload["patched_files"], "patched") + ref = importlib.import_module(cfg["ref_module"]) + pkg = importlib.import_module(cfg["pkg"]) + + # ---- per-primitive: data gen, call, and quality metric ---------------- # + def gen(w, seed): + g = torch.Generator(device=dev).manual_seed(int(seed)) + if prim == "knn": + db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) + q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) + return {"queries": q, "database": db} + x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) + if prim == "kmeans": + perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] + return {"x": x, "init": x.index_select(0, perm).clone()} + return {"x": x} + + def call(mod, w, data): + if prim == "kmeans": + return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], + init_centroids=data["init"], tol=0.0) + if prim == "knn": + return mod.knn(data["queries"], data["database"], w["k"]) + if prim == "pca": + return mod.pca(data["x"], w["k"]) + if prim == "tsvd": + return mod.truncated_svd(data["x"], w["k"]) + raise ValueError(prim) + + def check_shape(w, out): + if prim == "kmeans": + _, cen, _ = out + if tuple(cen.shape) != (w["K"], w["D"]) or not torch.isfinite(cen).all(): + raise ValueError("bad kmeans output") + elif prim == "knn": + d, i = out + if tuple(d.shape) != (w["Q"], w["k"]) or tuple(i.shape) != (w["Q"], w["k"]): + raise ValueError("bad knn output") + if not torch.isfinite(d).all(): + raise ValueError("non-finite distances") + else: + a, b = out + comps = a if prim == "pca" else b + if tuple(comps.shape) != (w["k"], w["D"]) or not torch.isfinite(comps).all(): + raise ValueError("bad decomposition output") + + def inertia(x, centroids): + c = centroids.to(torch.float32) + cn = (c * c).sum(1) + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384] + d = (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ c.t()) + cn[None, :] + total += float(d.min(1).values.clamp_min(0).sum().item()) + return total + + def recall(agent_idx, ref_idx): + a = agent_idx.long(); b = ref_idx.long() + hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) + return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + + def ortho_err(comps): + c = comps.to(torch.float32); k = c.shape[0] + return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) + + def captured(x, comps, center): + c = comps.to(torch.float32) + mean = x.mean(0) if center else None + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384] + if center: + xb = xb - mean + p = xb @ c.t() + total += float((p * p).sum().item()) + return total / (x.shape[0] - 1) if center else total + + def verdict(w, data, ref_out, agent_out): + """Return (ok, reason, ref_val, agent_val) gating the agent output.""" + check_shape(w, agent_out) + if prim == "kmeans": + rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) + tol = float(cfg["inertia_tolerance"]) + ok = av <= (1.0 + tol) * rv + 1e-6 + return ok, ("inertia_regression" if not ok else ""), rv, av + if prim == "knn": + rec = recall(agent_out[1], ref_out[1]) + ok = rec >= float(cfg["recall_threshold"]) + return ok, ("recall_regression" if not ok else ""), 1.0, rec + center = (prim == "pca") + comps = agent_out[0] if prim == "pca" else agent_out[1] + ref_comps = ref_out[0] if prim == "pca" else ref_out[1] + oe = ortho_err(comps) + if oe > float(cfg["ortho_tolerance"]): + return False, "not_orthonormal", 0.0, oe + rv = captured(data["x"], ref_comps, center) + av = captured(data["x"], comps, center) + ok = av >= (1.0 - float(cfg["captured_tolerance"])) * rv - 1e-6 + return ok, ("captured_regression" if not ok else ""), rv, av + + def time_call(mod, w, data): + _sync(); t0 = _perf(); out = call(mod, w, data); _sync() + return (_perf() - t0) * 1000.0, out + + rows = [] + try: + for w in payload["workloads"]: + base_seed = int(w["seed"]) + # warmup on fresh data (kernels / autotune) — not measured + for i in range(warmup): + d = gen(w, base_seed + 100 + i) + call(ref, w, d); call(pkg, w, d); _sync() + del d + torch.cuda.empty_cache() + ratios, ref_val, agent_val = [], None, None + bad = None + for i in range(iters): + d = gen(w, base_seed + 10000 + i) # fresh every iteration + rt, rout = time_call(ref, w, d) + at, aout = time_call(pkg, w, d) + ok, reason, rv, av = verdict(w, d, rout, aout) # verify THIS iter + if not ok: + bad = reason; ref_val, agent_val = rv, av + break + ratios.append(rt / at if at > 0 else 0.01) + ref_val, agent_val = rv, av + del d, rout, aout + torch.cuda.empty_cache() + if bad is not None: + rows.append({"id": w["id"], "ok": False, "reason": bad, + "ref_val": ref_val, "agent_val": agent_val}) + continue + ratios.sort() + rows.append({"id": w["id"], "ok": True, + "speedup": ratios[len(ratios) // 2], + "ref_val": ref_val, "agent_val": agent_val}) + except Exception as exc: + return {"ok": False, "error": f"{type(exc).__name__}: {str(exc)[:200]}", + "trace": traceback.format_exc()[-500:]} + return {"ok": True, "rows": rows} + + +# --------------------------------------------------------------------------- # +# Host-side wrappers (run in the judge / agent container; need MODAL tokens). +# --------------------------------------------------------------------------- # +def _build_image(cfg: dict): + import modal + pip = list(cfg.get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])) + base = cfg.get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04") + return (modal.Image.from_registry(base, add_python="3.11") + .entrypoint([]) + .pip_install(*pip)) + + +# Substrings that mark a transient Modal control-plane / image-build failure +# (evicted build, gateway hiccup, app stopped mid-run) rather than a real error +# in the submission — safe to retry. +_TRANSIENT_MARKERS = ( + "external shut-down", "terminated due to external", "please try again", + "app_state_stopped", "conflicterror", "deadline exceeded", "connection reset", + "502 bad gateway", "503 service", "temporarily unavailable", "timed out", + "gateway", "eviction", "internalfailure", +) + + +def _is_transient(text: str) -> bool: + low = (text or "").lower() + return any(m in low for m in _TRANSIENT_MARKERS) + + +def run_remote(payload: dict) -> dict: + """Run the GPU worker on Modal and return its result dict. + + Retries transient Modal control-plane failures; a real error in the + submission is non-transient and surfaces immediately. Raises RuntimeError + with a sanitized message on unrecoverable failure. + """ + import time as _time + + import modal + + cfg = payload["cfg"] + attempts = max(1, int(cfg.get("modal_retries", 3))) + last = "modal_gpu_eval_failed" + for attempt in range(1, attempts + 1): + app = modal.App(cfg.get("app_name", "flash-kernel-eval")) + remote = app.function( + gpu=cfg.get("gpu", "H100"), + image=_build_image(cfg), + timeout=int(cfg.get("modal_timeout_seconds", 1800)), + serialized=True, + max_containers=1, + )(_gpu_worker) + try: + with modal.enable_output(), app.run(): + return remote.remote(payload) + except Exception as exc: # noqa: BLE001 + last = str(exc)[-400:] + if not _is_transient(last) or attempt == attempts: + raise RuntimeError(f"modal_gpu_eval_failed: {last}") from exc + _time.sleep(min(45, 10 * attempt)) + raise RuntimeError(f"modal_gpu_eval_failed: {last}") + + +def modal_available() -> bool: + return bool(os.environ.get("MODAL_TOKEN_ID") and os.environ.get("MODAL_TOKEN_SECRET")) diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py index 804f89503..c0426065d 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,95 +1,77 @@ -"""Public self-test for the PCA kernel-optimization task. +"""Public GPU self-test for the PCA kernel-optimization task. -Runs the (patched) pcalib on the two public shapes, checks the subspace quality -(orthonormality + captured variance) against a local naive recomputation, and -prints a rough speedup. This is a convenience for local iteration only -- the -graded workloads and thresholds are hidden and differ from these shapes. +Times your current /app/pcalib against the naive baseline on a Modal GPU and +reports the quality verdict + speedup on two public shapes. Requires +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and +their thresholds are hidden and differ from these public shapes. """ from __future__ import annotations +import os import sys -import time - -PUBLIC_WORKLOADS = [ - {"N": 200_000, "D": 128, "k": 16, "seed": 1}, - {"N": 500_000, "D": 64, "k": 8, "seed": 2}, -] - - -def naive_pca(x, k): - import torch - N = x.shape[0] - mean = x.mean(dim=0) - xc = x - mean - U, S, Vh = torch.linalg.svd(xc, full_matrices=False) - components = Vh[:k].contiguous() - explained_variance = (S[:k] ** 2) / (N - 1) - return components, explained_variance.contiguous() - - -def ortho_err(comps): - import torch - c = comps.to(torch.float32) - k = c.shape[0] - return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max()) +from pathlib import Path +sys.path.insert(0, "/opt") -def captured(x, comps): - import torch - c = comps.to(torch.float32) - mean = x.mean(dim=0) - N = x.shape[0] - total = 0.0 - for i in range(0, N, 16384): - p = (x[i:i + 16384] - mean) @ c.t() - total += float((p * p).sum()) - return total / (N - 1) +APP_DIR = os.environ.get("APP_DIR", "/app") +PKG = "pcalib" +BASELINE_DIR = "/opt/pca_ref" +PUBLIC_WORKLOADS = [ + {"id": "p0", "N": 200_000, "D": 128, "k": 16, "seed": 1}, + {"id": "p1", "N": 500_000, "D": 64, "k": 8, "seed": 2}, +] -def bench(fn, warmup=2, iters=5): - import torch - for _ in range(warmup): - fn(); torch.cuda.synchronize() - ts = [] - for _ in range(iters): - torch.cuda.synchronize(); t0 = time.perf_counter() - fn(); torch.cuda.synchronize() - ts.append(time.perf_counter() - t0) - ts.sort() - return ts[len(ts) // 2] * 1000.0 +CFG = { + "primitive": "pca", + "pkg": PKG, + "ref_module": "refpca", + "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), + "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", + "pip": ["torch==2.5.1", "triton==3.1.0", "numpy"], + "app_name": "pca-kernel-opt-public", + "modal_timeout_seconds": 1800, + "warmup": 2, + "iters": 5, + "inertia_tolerance": 0.02, + "recall_threshold": 0.99, + "captured_tolerance": 0.02, + "ortho_tolerance": 0.02, +} + + +def _read(root: str, sub: str = "") -> dict: + base = Path(root) + scan = base / sub if sub else base + return {str(p.relative_to(base)): p.read_text(encoding="utf-8", errors="replace") + for p in scan.rglob("*.py")} def main() -> int: + import flash_gpu # baked at /opt/flash_gpu.py + if not flash_gpu.modal_available(): + print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU public test.") + return 1 + payload = { + "baseline_files": _read(BASELINE_DIR), + "patched_files": _read(APP_DIR, PKG), + "workloads": PUBLIC_WORKLOADS, + "cfg": CFG, + } try: - import torch - except Exception as e: # pragma: no cover - print(f"torch import failed: {e}") + result = flash_gpu.run_remote(payload) + except Exception as exc: # noqa: BLE001 + print(f"GPU run failed: {exc}") return 1 - if not torch.cuda.is_available(): - print("no CUDA device available; run this in a GPU-enabled container.") + if not result.get("ok"): + print(f"worker error: {result.get('error')}") return 1 - import pcalib - - for w in PUBLIC_WORKLOADS: - N, D, k, seed = w["N"], w["D"], w["k"], w["seed"] - g = torch.Generator(device="cuda").manual_seed(seed) - x = torch.randn(N, D, generator=g, device="cuda", dtype=torch.float32) - - ref_c, _ = naive_pca(x, k) - out = pcalib.pca(x, k) - agent_c = out[0] - rc, ac = captured(x, ref_c), captured(x, agent_c) - ratio = ac / rc if rc > 0 else 0.0 - oerr = ortho_err(agent_c) - - ref_ms = bench(lambda: naive_pca(x, k)) - agent_ms = bench(lambda: pcalib.pca(x, k)) - ok = "OK" if (ratio >= 0.95 and oerr <= 0.02) else "QUALITY REGRESSION" - print(f"(N={N}, D={D}, k={k}) captured_ratio={ratio:.4f} ortho_err={oerr:.2e} [{ok}] " - f"baseline={ref_ms:.2f}ms yours={agent_ms:.2f}ms " - f"speedup={ref_ms / agent_ms:.2f}x") - del x - torch.cuda.empty_cache() + print(f"{'workload':10s} {'status':16s} {'speedup':>10s}") + for row in result["rows"]: + if row.get("ok"): + print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") + else: + print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason','')):16s} {'-':>10s}") return 0 diff --git a/2.0/problems/pca_gpu_kernel_optimization/readme b/2.0/problems/pca_gpu_kernel_optimization/readme index 35e79b025..45e80fb4b 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/readme +++ b/2.0/problems/pca_gpu_kernel_optimization/readme @@ -2,39 +2,35 @@ ## Problem -You are given a small GPU PCA library, `pcalib`, in the Harbor workspace at -`/app/pcalib`. Its public entry point is: +You are given a small GPU PCA library, `pcalib`, in the Harbor workspace +at `/app/pcalib`. Its public entry point is: ```python pcalib.pca(x, n_components) -> (components, explained_variance) ``` -`x` is an `(N, D)` float32 CUDA tensor and `n_components` (`k`) is the number of -leading principal components to return. The function returns `components`, a -`(k, D)` float32 tensor whose rows are the top-`k` principal axes (the leading -eigenvectors of the data covariance, with orthonormal rows), and -`explained_variance`, a `(k,)` float32 tensor of the corresponding covariance -eigenvalues in descending order. The shipped implementation is correct but -deliberately naive: it centres the data by materializing the full `(N, D)` -centred matrix and then runs a *full* thin SVD (`torch.linalg.svd`) just to read -off the leading `k` singular vectors and values. +`x` is an `(N, D)` float32 CUDA tensor of points and `n_components` (`k`) is the +number of leading principal components to return. The function returns +`components`, a `(k, D)` float32 tensor whose rows are the top-`k` principal axes +(orthonormal rows), and `explained_variance`, a `(k,)` float32 tensor of the +corresponding variances in descending order. The shipped implementation is a +straightforward, correct PyTorch version. Your goal is to make `pcalib.pca` **as fast as possible** on the GPU while -producing the same principal subspace. You may add modules and Triton kernels -inside the `pcalib` package and rewrite the internals freely (covariance/Gram -matrix + top-`k` eigendecomposition instead of a full SVD, fused centring, -reduced memory traffic, and so on). The public function signature and return -contract above must not change. +producing the same principal subspace. You may rewrite the internals of the +package however you like and add new modules (including Triton kernels) under +`pcalib/`. The public function signature and return contract above must not +change, and every result must remain a deterministic function of its inputs. ## Workload -The judge evaluates a family of held-out dense PCA workloads that vary -`(N, D, k)`, spanning small/medium/large point counts, wide-feature (large `D`) -and tall/skinny (large `N`, small `D`) regimes. All workloads use float32 data +The graded workloads are a family of held-out dense PCA problems that vary +`(N, D, k)`, spanning small/medium/large point counts and both wide-feature +(large `D`) and tall/skinny (large `N`, small `D`) regimes. All use float32 data and a fixed number of components, so each result is a deterministic function of -the inputs. +its inputs. -Two representative *public* shapes you can use for local development (the graded +Two representative *public* shapes are available for local testing (the graded shapes are different and hidden): ```text @@ -42,19 +38,30 @@ shapes are different and hidden): (N=500000, D=64, k=8) ``` -Treat the workload as a general dense PCA, not as shapes to special-case. -Improvements should be general kernel/throughput improvements, not lookups keyed -on specific `(N, D, k)` values. +Treat this as a general dense PCA, not as specific shapes to special-case. + +## Iterate on a GPU + +The agent workspace has no GPU. Use the public test to time your current code on +a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your +environment): + +```bash +bash /app/public_test.sh +``` + +It reports, per public shape, whether your result passes the quality gate and +your speedup over the baseline. ## Submission -The submitted artifact is a patch file over the `pcalib` package: +The submitted artifact is a patch over the `pcalib` package: ```text /app/solution.patch ``` -After editing `/app/pcalib`, generate and submit a patch: +After editing `/app/pcalib`, generate and submit: ```bash bash /app/make_submission.sh @@ -62,73 +69,62 @@ bash /app/submit.sh ``` Submissions are asynchronous; submit early and keep iterating. The judge applies -your patch to a clean copy of `pcalib`, imports the patched package, and times it -against the original naive implementation on the same hardware and same seeded -data. +your patch to a clean copy of `pcalib` and times it against the original +baseline on a GPU, on the same seeded data. ## Correctness -Correctness is a gate. For each workload the judge checks two properties of your -returned `components` on the same seeded data used for the baseline: - -- **Orthonormality**: the rows of `components` must be orthonormal, i.e. the - largest entry of `|V Vᵀ − I|` must stay within a small tolerance. -- **Captured variance**: the variance your subspace captures, - `(1 / (N − 1)) · ||X_c V||_F²` where `X_c` is the mean-centred data, must be at - least `(1 − tol)` times the variance captured by the naive baseline's - subspace. - -Because the gate is rotation- and sign-invariant (it only depends on the -subspace your components span, not on any label/sign convention), you cannot -pass it with fast garbage — capturing the variance requires actually recovering -the leading principal subspace. Crashes, non-finite outputs, wrong -shapes/dtypes, timeouts, and subspaces that regress beyond the tolerance are all -penalized before speed is considered. You may trade a little numerical precision -(e.g. lower-precision accumulation) for speed as long as you stay within the -quality tolerances. +Correctness is a gate. On every timed iteration the judge recomputes, from the +components your function returns on identical data, two properties: + +- **Orthonormality** — the rows of `components` must be orthonormal (the largest + entry of `|V Vᵀ − I|` must stay within a small tolerance). +- **Captured variance** — the variance captured by your subspace on the same + mean-centred data must stay within a small relative tolerance of the baseline's + captured variance. + +Because the gate depends only on the *subspace* your components span (not on any +sign or rotation convention), you cannot pass it with fast garbage — capturing +the variance requires actually recovering the leading principal subspace. +Crashes, non-finite output, wrong shapes/dtypes, timeouts, and subspaces that +regress beyond the tolerance are penalized before speed is considered. You may +trade a little numerical precision for speed as long as you stay within the +quality tolerance. ## Scoring -Valid submissions are scored by speedup relative to the naive baseline on the -same hardware and workloads. For each workload: +Valid submissions are scored by speedup relative to the baseline on the same +hardware and workloads. For each workload: ```text speedup = baseline_time / your_time ``` The objective is the geometric mean of per-workload speedups, so broad speedups -across the workload family are preferred over one large outlier. A result no -faster than the baseline earns 0; regressions earn 0. The raw geometric-mean -speedup is reported in the evaluator metrics. +are preferred over a single large outlier. A result no faster than the baseline +earns 0; regressions earn 0. The raw geometric-mean speedup is reported in the +evaluator metrics. ## Patch Policy -The evaluator validates the patch before running it. Only files under the +The evaluator validates the patch before running it. Only Python files under the package may change: ```text pcalib/** ``` -New Python modules inside `pcalib/` (for example Triton kernel files) are -allowed. Patches may not: - -- modify anything outside `pcalib/`; -- import or call an external optimized ML/kernel library (the point is to write - the kernels yourself); -- read or write environment variables, spawn processes, or access the network; -- special-case the hidden workload shapes. - -The patch must be reasonably sized and apply cleanly to the clean `pcalib` tree. +New Python modules inside `pcalib/` are allowed. Patches may not: modify +anything outside `pcalib/`; import or call an external optimized ML/kernel +library (write the kernels yourself); read or write environment variables, spawn +processes, or access the network; or otherwise tamper with the measurement +framework. The timing harness measures your code on freshly generated data every +iteration and re-verifies quality each time, so caching results across calls does +not help. ## Resource Budget ```text -GPU: single device (H100 reference; Triton paths also run on L40S / A100) -vCPUs: 8 -memory: 32 GiB -storage: 32 GiB +GPU: single Modal GPU (H100 reference; the Triton paths also run on L40S / A100) +Agent container: CPU-only (GPU work is offloaded to Modal) ``` - -The judge runs the timing worker in a subprocess under a clean, minimal -environment with a fixed warmup/measurement schedule and a per-run timeout. diff --git a/2.0/problems/pca_gpu_kernel_optimization/reference.patch b/2.0/problems/pca_gpu_kernel_optimization/reference.patch index c98a3d12a..62f6a6abe 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/pca_gpu_kernel_optimization/reference.patch @@ -1,47 +1,980 @@ -diff --git a/pcalib/_cov_pca.py b/pcalib/_cov_pca.py +diff --git a/pcalib/_kernels/__init__.py b/pcalib/_kernels/__init__.py new file mode 100644 -index 0000000..6a7f6a7 +index 0000000..e69de29 +diff --git a/pcalib/_kernels/linalg/__init__.py b/pcalib/_kernels/linalg/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/pcalib/_kernels/linalg/eigh/__init__.py b/pcalib/_kernels/linalg/eigh/__init__.py +new file mode 100644 +index 0000000..1a2431a +--- /dev/null ++++ b/pcalib/_kernels/linalg/eigh/__init__.py +@@ -0,0 +1,39 @@ ++"""Symmetric eigendecomposition with multiple precision/performance variants. ++ ++Public API ++---------- ++Single dispatcher (recommended): ++ ++ eigh(A, K=None, *, tol=None, backend=None) ++ ++By default ``eigh(A)`` is **exact** in the input dtype (cuSOLVER / ++single-kernel Jacobi for small ``N``). ``tol`` opts into the ++approximation tier: ++ ++ eigh(A, tol=8e-4) -> QDWH-NS at N >= 5120 ++ eigh(A, tol=3e-3) -> QDWH at N >= 5120 ++ eigh(A, K=K, tol=1e-4) -> Halko subspace iteration when K << N ++ ++Backend-explicit (power users): ++ ++ eigh_cusolver(A) torch.linalg.eigh -- ~1e-7 residual ++ eigh_jacobi(A, ...) single-block Jacobi for small N ++ eigh_qdwh(A, ...) Nakatsukasa-Higham spectral D&C, ~3e-3 ++ eigh_qdwh_ns(A, ...) QDWH with pure-NS polar, ~8e-4 ++ eigh_halko(A, K, ...) Halko randomized truncated eigh ++""" ++# Only the exact ``tol=None`` path is vendored here: cuSOLVER / MKL and the ++# Triton single-CTA Jacobi backend. The approximate variants (Halko, QDWH, ++# QDWH-NS) and the cost models are intentionally not vendored, so their lazy ++# imports are dropped. ++from pcalib._kernels.linalg.eigh.impl import eigh, route_op_name ++from pcalib._kernels.linalg.eigh.cusolver import eigh as eigh_cusolver ++from pcalib._kernels.linalg.eigh.jacobi import eigh as eigh_jacobi ++ ++ ++__all__ = [ ++ "eigh", ++ "eigh_cusolver", ++ "eigh_jacobi", ++ "route_op_name", ++] +diff --git a/pcalib/_kernels/linalg/eigh/cost.py b/pcalib/_kernels/linalg/eigh/cost.py +new file mode 100644 +index 0000000..5cb13d4 +--- /dev/null ++++ b/pcalib/_kernels/linalg/eigh/cost.py +@@ -0,0 +1,100 @@ ++"""Cost models for eigh -- smart dispatcher + per-variant. ++ ++Each function takes ``tol`` directly. Returned Estimates have ``op_name`` ++set to the routed variant (e.g. ``eigh_qdwh``) so the call-stack tree is ++clear. ++ ++The dispatcher in :mod:`pcalib._kernels.linalg.eigh.impl` decides which ++variant runs at runtime; this module mirrors that decision via ++:func:`route_op_name`. ++""" ++from pcalib._kernels.info.estimate import Estimate ++from pcalib._kernels.info.roofline import roofline ++from pcalib._kernels.linalg.eigh import cusolver as _cusolver ++from pcalib._kernels.linalg.eigh import jacobi as _jacobi ++from pcalib._kernels.linalg.eigh import halko as _halko ++from pcalib._kernels.linalg.eigh.impl import route_op_name as _route_op_name ++ ++ ++def _N(shape): ++ return shape[0] if isinstance(shape, (tuple, list)) else shape ++ ++ ++def estimate(shape, params=None, tol=None, dtype="float32", device="H100", **_): ++ """Cost of the smart eigh() dispatcher — picks the actually-routed variant.""" ++ N = _N(shape) ++ K = (params or {}).get("K") ++ chosen = _route_op_name(N=N, K=K, tol=tol) ++ if chosen == "eigh_jacobi": ++ est = _jacobi.estimate(shape, params=params, tol=tol, dtype=dtype, device=device) ++ elif chosen == "eigh_qdwh": ++ est = qdwh(shape, params=params, tol=tol, dtype=dtype, device=device) ++ elif chosen == "eigh_qdwh_ns": ++ est = qdwh_ns(shape, params=params, tol=tol, dtype=dtype, device=device) ++ elif chosen == "eigh_halko": ++ est = _halko.estimate(shape, params=params, tol=tol, dtype=dtype, device=device) ++ else: ++ est = _cusolver.estimate(shape, params=params, tol=tol, dtype=dtype, device=device) ++ # Make the routed variant's name show up at this level of the call tree. ++ est.op_name = chosen ++ est.tol = tol ++ return est ++ ++ ++def qdwh(shape, params=None, tol=None, dtype="float32", device="H100", **_): ++ """QDWH-eig — Nakatsukasa-Higham spectral D&C, ~1e-3 residual.""" ++ N = _N(shape) ++ rt = 370.0 * (N / 8192) ** 3 ++ flops = int((20 / 3) * N ** 3) ++ bytes_moved = N * N * 4 * 16 ++ return Estimate( ++ op_name="eigh_qdwh", ++ runtime_ms=rt, flops=flops, bytes_moved=bytes_moved, ++ memory_peak_gb=N * N * 4 * 6 / 1e9, ++ bound="compute", confidence="measured", n_kernel_launches=30, ++ suggested_config={"base_case": 1024, "max_depth": 1 if N < 10240 else 2}, ++ notes=[f"N={N}; QDWH spectral D&C, recursive split via polar factor."], ++ expected_residual=3e-3, precision_tier="fast", tol=tol, ++ ) ++ ++ ++def qdwh_ns(shape, params=None, tol=None, dtype="float32", device="H100", **_): ++ """QDWH-eig-NS — pure Newton-Schulz polar (matmul-only critical path).""" ++ N = _N(shape) ++ rt = 700.0 * (N / 8192) ** 3 ++ flops = int((30 / 3) * N ** 3) ++ bytes_moved = N * N * 4 * 24 ++ return Estimate( ++ op_name="eigh_qdwh_ns", ++ runtime_ms=rt, flops=flops, bytes_moved=bytes_moved, ++ memory_peak_gb=N * N * 4 * 6 / 1e9, ++ bound="compute", confidence="measured", n_kernel_launches=50, ++ suggested_config={}, ++ notes=[f"N={N}; QDWH spectral D&C with pure-NS polar (Polar Express)."], ++ expected_residual=8e-4, precision_tier="mixed", tol=tol, ++ ) ++ ++ ++def recommend(shape, params=None, tol=None, dtype="float32", device="H100", **_): ++ """Picks the dispatcher's choice; mirrors the routing in eigh/__init__.py.""" ++ N = _N(shape) ++ K = (params or {}).get("K") ++ chosen = _route_op_name(N=N, K=K, tol=tol) ++ out = {"variant": chosen} ++ if chosen == "eigh_qdwh": ++ out["base_case"] = 1024 ++ out["max_depth"] = 1 if N < 10240 else 2 ++ elif chosen == "eigh_halko": ++ out["K"] = K ++ out["n_iter"] = 5 ++ out["p"] = 30 ++ return out ++ ++ ++def recommend_qdwh(shape, **_): ++ N = _N(shape) ++ return {"base_case": 1024, "max_depth": 1 if N < 10240 else 2} ++ ++ ++def recommend_qdwh_ns(shape, **_): ++ return {"base_case": 1024} +diff --git a/pcalib/_kernels/linalg/eigh/cusolver.py b/pcalib/_kernels/linalg/eigh/cusolver.py +new file mode 100644 +index 0000000..354ac9a +--- /dev/null ++++ b/pcalib/_kernels/linalg/eigh/cusolver.py +@@ -0,0 +1,7 @@ ++"""eigh_cusolver — torch.linalg.eigh (cuSOLVER syevd). Reference precision.""" ++import torch ++ ++ ++def eigh(A: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: ++ """torch.linalg.eigh — cuSOLVER syevd. ~1e-7 residual.""" ++ return torch.linalg.eigh(A) +diff --git a/pcalib/_kernels/linalg/eigh/impl.py b/pcalib/_kernels/linalg/eigh/impl.py +new file mode 100644 +index 0000000..430116f --- /dev/null -+++ b/pcalib/_cov_pca.py -@@ -0,0 +1,31 @@ -+"""Covariance-based top-k PCA (reference optimisation). -+ -+Instead of centring the whole ``(N, D)`` matrix and running a *full* thin SVD, -+this forms the ``(D, D)`` covariance with a single GEMM (``Xᵀ X``) and a rank-1 -+mean correction -- so the centred data is never materialised -- and reads off -+the leading ``k`` principal axes with a symmetric eigendecomposition -+(:func:`torch.linalg.eigh`). For ``D << N`` this replaces an ``O(N D^2)`` SVD of -+a tall matrix with one ``O(N D^2)`` GEMM followed by an ``O(D^3)`` eigensolve on -+the small ``(D, D)`` covariance, and it avoids allocating a second copy of the -+dataset. -+ -+The public contract is unchanged: ``pca(x, n_components)`` returns -+``(components, explained_variance)`` with orthonormal ``(k, D)`` rows and the -+matching descending covariance eigenvalues. ++++ b/pcalib/_kernels/linalg/eigh/impl.py +@@ -0,0 +1,164 @@ ++"""eigh dispatcher. ++ ++By default ``eigh(A)`` is **exact** (cuSOLVER / MKL on the input dtype) -- ++no precision is lost beyond what the input already carries. ``tol`` opts ++into approximation paths (Halko subspace iteration, QDWH spectral D&C). ++ ++Routing rule (formerly in ``route.py``): ++ ++ * ``backend`` overrides everything. ++ * ``tol`` is None / ``<= 0``: exact. ++ * Truncated (``K`` given) : exact full eigh + slice top-K. ++ * Full : ``triton_eigh`` (CPU MKL trick for ++ small N) or cuSOLVER otherwise. ++ * ``tol`` is given: pick the fastest variant whose published residual ++ fits. ++ * ``K`` given AND favourable shape (``K*4 < N`` AND ++ ``N >= 256``) AND ``tol >= 1e-4`` -> Halko. ++ * ``N >= 5120`` AND ``tol >= 8e-4`` -> QDWH-NS. ++ * ``N >= 5120`` AND ``tol >= 3e-3`` -> QDWH. ++ * Otherwise: cuSOLVER (exact is always fast enough at this point). ++ ++The Halko shape gate is internal -- callers just hand in ``(K, tol)``; ++they no longer reach into ``halko.py``. +""" +from __future__ import annotations + ++from typing import Optional ++ +import torch + ++from pcalib._kernels.linalg.eigh import cusolver, jacobi ++from pcalib._kernels.linalg.eigh.triton import triton_eigh + -+def pca(x, n_components): -+ N, D = x.shape -+ mean = x.mean(dim=0) -+ G = x.t() @ x # (D, D), one GEMM -+ C = (G - N * torch.outer(mean, mean)) / (N - 1) # covariance without centering the data -+ evals, evecs = torch.linalg.eigh(C) # ascending -+ k = int(n_components) -+ top = torch.argsort(evals, descending=True)[:k] -+ components = evecs.index_select(1, top).t().contiguous() # (k, D) -+ explained_variance = evals.index_select(0, top).clamp_min(0).contiguous() -+ return components, explained_variance ++ ++# Per-variant published residual (relative). Used by the tol router AND ++# by cost.py. ++_RESIDUAL_PREFERENCE = [ ++ ("cusolver", 1e-7), ++ ("jacobi", 1e-6), ++ ("halko", 1e-4), ++ ("qdwh_ns", 8e-4), ++ ("qdwh", 3e-3), ++] ++ ++_QDWH_MIN_N = 5120 ++ ++ ++def _halko_is_favourable(N: int, K: Optional[int]) -> bool: ++ """Halko helps when ``K << N`` AND N is large enough to amortize. ++ ++ The approximate Halko backend is not vendored here (only the exact ++ ``tol=None`` path is), so this heuristic is inert and the router never ++ selects Halko. ++ """ ++ return False ++ ++ ++def _route( ++ *, ++ N: int, ++ K: Optional[int] = None, ++ tol: Optional[float] = None, ++ backend: Optional[str] = None, ++ hw: Optional[object] = None, ++) -> str: ++ """Pick the eigh variant. Returns the bare name (no ``eigh_`` prefix). ++ ++ See module docstring for the rule. The same function is called by ++ runtime dispatch (impl) and the cost shim (cost.py). ++ ++ ``jacobi`` is reachable only via explicit ``backend="jacobi"`` -- ++ it is a single-CTA Triton cyclic-Jacobi kernel that beats cuSOLVER ++ at very small N (N <= 16) but is slower beyond that. No C++ ++ toolchain / ninja is required on first call (the previous ++ ``load_inline`` CUDA implementation was retired in favour of a ++ pure-Triton kernel under :mod:`pcalib._kernels.linalg.eigh.triton.jacobi`). ++ """ ++ del hw ++ if backend is not None: ++ return backend ++ if tol is None or tol <= 0: ++ return "cusolver" ++ if _halko_is_favourable(N, K) and tol >= 1e-4: ++ return "halko" ++ if N >= _QDWH_MIN_N: ++ if tol >= 3e-3: ++ return "qdwh" ++ if tol >= 8e-4: ++ return "qdwh_ns" ++ return "cusolver" ++ ++ ++def route_op_name(*, N: int, K: Optional[int] = None, ++ tol: Optional[float] = None, ++ hw: Optional[object] = None) -> str: ++ """Canonical ``eigh_`` label.""" ++ return "eigh_" + _route(N=N, K=K, tol=tol, hw=hw) ++ ++ ++def eigh( ++ A: torch.Tensor, ++ K: Optional[int] = None, ++ *, ++ tol: Optional[float] = None, ++ backend: Optional[str] = None, ++ n_iter: int = 5, ++ p: int = 30, ++ **kwargs, ++): ++ """Symmetric eigendecomposition. ++ ++ Args: ++ A: ``(N, N)`` symmetric tensor. ++ K: optional truncation -- return only top-K eigenpairs (ascending). ++ With ``tol`` loose enough this triggers Halko. ++ tol: residual tolerance. ++ ++ * ``None`` (default) **-> EXACT** in the input dtype (always ++ cuSOLVER -- the Triton ``jacobi`` backend is opt-in via ++ ``backend="jacobi"`` since cuSOLVER beats it past N ~ 16). ++ * Otherwise pick the fastest variant whose declared residual ++ fits, optionally Halko if ``K`` is set + shape favours it. ++ backend: explicit ``"cusolver" | "jacobi" | "qdwh" | "qdwh_ns" | ++ "halko"`` override. ++ n_iter, p: Halko power-iter and oversample (only used when Halko ++ is selected). ++ **kwargs: forwarded to the chosen variant. ++ ++ Returns: ++ ``(eigvals, eigvecs)`` ascending; both sliced to top-K if ``K`` ++ was supplied. ++ """ ++ if not isinstance(A, torch.Tensor) or A.ndim != 2: ++ raise ValueError("eigh expects a 2D tensor") ++ N = A.size(0) ++ chosen = _route(N=N, K=K, tol=tol, backend=backend) ++ ++ if chosen == "halko": ++ raise NotImplementedError( ++ "the Halko backend is not vendored in this build; only the exact " ++ "tol=None path (cuSOLVER / MKL / Triton jacobi) is available." ++ ) ++ ++ if chosen == "jacobi": ++ eigvals, eigvecs = jacobi.eigh(A, **kwargs) ++ elif chosen == "qdwh": ++ from pcalib._kernels.linalg.eigh.qdwh import qdwh_eig ++ eigvals, eigvecs = qdwh_eig(A, **kwargs) ++ elif chosen == "qdwh_ns": ++ from pcalib._kernels.linalg.eigh.qdwh_ns import qdwh_eig_ns ++ eigvals, eigvecs = qdwh_eig_ns(A, **kwargs) ++ else: # cusolver / default exact ++ # Use the small-D CPU-MKL trick automatically (it IS the exact ++ # path -- input dtype preserved, just dispatched to the faster ++ # solver for small N). ++ eigvals, eigvecs = triton_eigh(A) ++ ++ if K is not None: ++ eigvals = eigvals[-K:] ++ eigvecs = eigvecs[:, -K:] ++ return eigvals, eigvecs ++ ++ ++__all__ = ["eigh", "route_op_name"] +diff --git a/pcalib/_kernels/linalg/eigh/jacobi.py b/pcalib/_kernels/linalg/eigh/jacobi.py +new file mode 100644 +index 0000000..b2c60dc +--- /dev/null ++++ b/pcalib/_kernels/linalg/eigh/jacobi.py +@@ -0,0 +1,30 @@ ++"""eigh_jacobi -- single-block row-cyclic Jacobi for small N (<= 128). ++ ++Thin user-facing wrapper over the Triton kernel in ++:mod:`pcalib._kernels.linalg.eigh.triton.jacobi`. No CUDA / C++ compile step ++on first call (the old ``jacobi_impl.py`` ``load_inline`` extension ++was retired -- ninja is no longer required to import this backend). ++ ++Achieved residual is at the fp32 noise floor (``~1e-4`` for the ++N=46 reference shape, identical to ``torch.linalg.eigh`` on fp32 ++input), and the kernel beats cuSOLVER ``syevd`` at N <= 16 where the ++syevd launch fixed-cost dominates. ++""" ++from pcalib._kernels.linalg.eigh.triton.jacobi import triton_jacobi_eigh as _triton_jacobi ++ ++ ++def eigh(A, num_sweeps: int = 6): ++ """Single-kernel cyclic Jacobi eigensolver. ++ ++ Args: ++ A: ``(N, N)`` symmetric float32 CUDA tensor; not modified. ++ num_sweeps: full row-cyclic sweeps; each sweep performs ++ ``N*(N-1)/2`` Givens rotations. ``6`` is enough to reach ++ fp32 noise (~1e-4) for N <= 64; bump to ``8-12`` for ++ tighter convergence at very large N. ++ ++ Returns: ++ ``(w, V)`` -- ``(N,)`` eigenvalues (ascending) and ``(N, N)`` ++ eigenvectors as columns. ``A @ V[:, i] ≈ w[i] * V[:, i]``. ++ """ ++ return _triton_jacobi(A, num_sweeps=num_sweeps) +diff --git a/pcalib/_kernels/linalg/eigh/triton/__init__.py b/pcalib/_kernels/linalg/eigh/triton/__init__.py +new file mode 100644 +index 0000000..1e1982b +--- /dev/null ++++ b/pcalib/_kernels/linalg/eigh/triton/__init__.py +@@ -0,0 +1,28 @@ ++"""eigh triton backend. ++ ++Re-exports the top-level functions / classes / constants from each ++component file. ``@triton.jit`` kernels stay private to their file ++(call them via the Python wrapper that lives next to them). ++ ++Component files: ++ ++* :mod:`householder` -- Householder tridiagonalisation + unrolled ++ QR finaliser. Powers :func:`pcalib._kernels.linalg.eigh.triton_eigh`, ++ the small-D (D <= ~256) dense path used by PCA / TruncSVD / ++ Halko subspace iteration. ++* :mod:`jacobi` -- Row-cyclic Givens-Jacobi for N <= 128. ++ Powers :func:`pcalib._kernels.linalg.eigh.eigh_jacobi`, an opt-in ++ backend that beats cuSOLVER at very small N (N <= 16) without ++ the ``load_inline`` CUDA / C++ compile step the old ++ implementation needed. ++""" ++from pcalib._kernels.linalg.eigh.triton.householder import ( ++ _eigh_cpu_initialized, ++ triton_eigh, ++) ++from pcalib._kernels.linalg.eigh.triton.jacobi import triton_jacobi_eigh ++ ++__all__ = [ ++ "triton_eigh", ++ "triton_jacobi_eigh", ++] +diff --git a/pcalib/_kernels/linalg/eigh/triton/householder.py b/pcalib/_kernels/linalg/eigh/triton/householder.py +new file mode 100644 +index 0000000..0dff1c8 +--- /dev/null ++++ b/pcalib/_kernels/linalg/eigh/triton/householder.py +@@ -0,0 +1,171 @@ ++"""Triton eigendecomposition for small dense symmetric matrices. ++ ++Uses Householder tridiagonalisation followed by an unrolled QR loop. ++Designed for D ≤ ~256 — the small-matrix path used by PCA, TruncSVD, ++and the Halko subspace-iteration finaliser (linalg/eigh/halko.py). ++ ++Public surface: ++ - triton_eigh(A) -> (eigvals, eigvecs) for A: (D, D) fp32 symmetric. ++""" ++ ++import torch ++import triton ++import triton.language as tl ++ ++ ++# ============================================================================= ++# Kernel: Householder Tridiagonalization + Eigensolver ++# ++# Single-program Triton kernel: reduces symmetric A to tridiagonal form T via ++# Householder reflections. All data stays in L2 for D ≤ ~2048. ++# ++# The tridiagonal eigenvalues are found via Sturm bisection (no cuSOLVER). ++# Eigenvectors: inverse iteration on T, then Householder back-transformation. ++# ++# For D=256: ~0.4ms total vs cuSOLVER eigh ~5ms. The win comes from avoiding ++# cuSOLVER's ~5ms fixed overhead of internal kernel launches/workspace alloc. ++# ============================================================================= ++ ++@triton.jit ++def _householder_tridiag_kernel( ++ A_ptr, p_buf_ptr, tau_ptr, ++ D, stride_a, ++ BLOCK: tl.constexpr, ++): ++ """Householder tridiagonalization: symmetric A → tridiagonal (in-place). ++ ++ After kernel: ++ - A.diagonal() = tridiagonal diagonal ++ - A.diagonal(offset=-1) = tridiagonal subdiagonal ++ - A lower triangle (below subdiag): normalized Householder vectors v'[1:] ++ where v' = v / v[0], so v'[0] = 1 (implicit) ++ - tau_ptr[k] = tau' = 2 * v[0]² / ||v||² (LAPACK-style scalar) ++ """ ++ offs = tl.arange(0, BLOCK) ++ ++ for k in tl.range(0, D - 2): ++ n = D - k - 1 ++ base = k + 1 ++ ++ # ── Compute ||x||² for x = A[base:D, k] ── ++ xnorm_sq = 0.0 ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ xj = tl.load(A_ptr + (base + j) * stride_a + k, mask=mask, other=0.0) ++ xnorm_sq += tl.sum(xj * xj) ++ ++ if xnorm_sq > 1e-30: ++ xnorm = tl.sqrt(xnorm_sq) ++ x0 = tl.load(A_ptr + base * stride_a + k) ++ ++ # alpha = -sign(x0) * ||x|| ++ alpha = tl.where(x0 >= 0.0, -xnorm, xnorm) ++ ++ # v[0] = x[0] - alpha ++ v0 = x0 - alpha ++ ++ # tau_raw = 2 / ||v||² where ||v||² = v0² + ||x[1:]||² ++ vnorm_sq = v0 * v0 + (xnorm_sq - x0 * x0) ++ tau = 2.0 / vnorm_sq ++ ++ # LAPACK convention: normalize v' = v / v[0], tau' = tau * v0² ++ # v'[0] = 1 (implicit), v'[1:] = v[1:] / v[0] ++ # Store v'[1:] in A[base+1:D, k] (overwrites x[1:]) ++ inv_v0 = 1.0 / v0 ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = (j < n) & (j > 0) ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, mask=mask, other=0.0) ++ tl.store(A_ptr + (base + j) * stride_a + k, vj * inv_v0, mask=mask) ++ ++ # Store v'[0] = 1.0 temporarily at A[base, k] (for the matvec) ++ tl.store(A_ptr + base * stride_a + k, 1.0) ++ ++ tau_prime = tau * v0 * v0 ++ tl.store(tau_ptr + k, tau_prime) ++ ++ # ── p = tau' * A_sub @ v' ── ++ # A_sub = A[base:D, base:D], v' = A[base:D, k] (normalized, v'[0]=1) ++ for i in tl.range(0, n): ++ dot = 0.0 ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ aij = tl.load(A_ptr + (base + i) * stride_a + (base + j), ++ mask=mask, other=0.0) ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, ++ mask=mask, other=0.0) ++ dot += tl.sum(aij * vj) ++ tl.store(p_buf_ptr + i, dot * tau_prime) ++ ++ # ── w = p - (tau'/2 * v'.T @ p) * v' ── ++ vtp = 0.0 ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, ++ mask=mask, other=0.0) ++ pj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) ++ vtp += tl.sum(vj * pj) ++ ++ coeff = 0.5 * tau_prime * vtp ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ pj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, ++ mask=mask, other=0.0) ++ tl.store(p_buf_ptr + j, pj - coeff * vj, mask=mask) ++ ++ # ── A_sub -= v @ w.T + w @ v.T (symmetric rank-2 update) ── ++ for i in tl.range(0, n): ++ vi = tl.load(A_ptr + (base + i) * stride_a + k) ++ wi = tl.load(p_buf_ptr + i) ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ aij = tl.load(A_ptr + (base + i) * stride_a + (base + j), ++ mask=mask, other=0.0) ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, ++ mask=mask, other=0.0) ++ wj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) ++ tl.store(A_ptr + (base + i) * stride_a + (base + j), ++ aij - vi * wj - wi * vj, mask=mask) ++ ++ # Store subdiagonal (overwrites v[0], but v[1:] survives for back-transform) ++ tl.store(A_ptr + base * stride_a + k, alpha) ++ tl.store(A_ptr + k * stride_a + base, alpha) ++ ++ ++_eigh_cpu_initialized = False ++ ++def triton_eigh(A: torch.Tensor) -> tuple: ++ """Eigendecomposition: CPU MKL LAPACK for D ≤ 512, cuSOLVER for D > 512. ++ ++ For D ≤ 512: GPU→CPU + MKL dsyev (4 threads) + CPU→GPU. ++ Avoids cuSOLVER's fixed overhead (~5ms kernel launches + workspace). ++ D=64: 0.18ms (4.5x faster), D=256: 1.8ms (2.6x), D=512: 6ms (2.2x). ++ For D > 512: cuSOLVER eigh (multi-SM parallelism, O(D³) dominates). ++ ++ Why not Triton Householder on GPU? Single-SM approach is 4-73x slower ++ than cuSOLVER — serial Householder loop bottlenecked by per-iteration ++ latency. Eigendecomposition requires multi-SM parallelism for large D. ++ """ ++ D = A.shape[0] ++ ++ if D > 512: ++ return torch.linalg.eigh(A) ++ ++ # CPU MKL LAPACK with 4 threads: optimal for D ≤ 512 eigendecomposition. ++ # set_num_threads has ~3ms overhead per call, so we set it once. ++ global _eigh_cpu_initialized ++ if not _eigh_cpu_initialized: ++ torch.set_num_threads(4) ++ _eigh_cpu_initialized = True ++ ++ torch.cuda.synchronize() ++ A_cpu = A.cpu() ++ eigenvalues, eigenvectors = torch.linalg.eigh(A_cpu) ++ return eigenvalues.to(A.device), eigenvectors.to(A.device) ++ +diff --git a/pcalib/_kernels/linalg/eigh/triton/jacobi.py b/pcalib/_kernels/linalg/eigh/triton/jacobi.py +new file mode 100644 +index 0000000..b19b69a +--- /dev/null ++++ b/pcalib/_kernels/linalg/eigh/triton/jacobi.py +@@ -0,0 +1,219 @@ ++"""Triton row-cyclic Jacobi eigensolver for small symmetric matrices (N <= 128). ++ ++Single-program kernel — one CTA holds the full A and V matrices in HBM ++(small enough at N ≤ 128 that even the slow HBM round-trips per ++rotation cost <100 µs on H100) and performs a classical row-cyclic ++Jacobi sweep: ``N*(N-1)/2`` Givens rotations per sweep, default 6 ++sweeps. Each rotation zeroes the off-diagonal element ``A[p, q]`` via ++the two-sided update ``A' = G^T A G`` and accumulates ``V' = V G``. ++ ++Replaces the previous ``jacobi_impl.py`` ``torch.utils.cpp_extension. ++load_inline`` CUDA kernel -- removes the first-call ``ninja`` C++ ++compile step so the user never needs a CUDA toolchain installed to ++use ``pcalib._kernels.linalg.eigh.eigh(..., backend="jacobi")``. ++ ++Sequencing vs Brent-Luk ++----------------------- ++The CUDA original used Brent-Luk *parallel* pairing (N/2 simultaneous ++rotations per round, N-1 rounds per sweep), which converges in 8-12 ++sweeps. The Triton version uses *cyclic* pairing (one rotation at a ++time) which converges in 5-8 sweeps but pays per-rotation kernel ++overhead. At N ≤ 64 the cyclic path runs in ~0.5 ms with 6 sweeps, ++well under the cuSOLVER ``syevd`` fixed launch overhead (~5 ms) the ++``jacobi`` backend was added to beat. For N > 128 the cyclic-Jacobi ++constant factor crosses cuSOLVER's; the dispatcher in ++``linalg.eigh.impl`` caps the jacobi backend at N ≤ 128 anyway. ++ ++Public surface ++-------------- ++* :func:`triton_jacobi_eigh` -- ``(A, num_sweeps) -> (eigvals, eigvecs)``; ++ fp32 symmetric input, fp32 sorted-ascending output. ++""" ++from __future__ import annotations ++ ++import torch ++import triton ++import triton.language as tl ++ ++ ++@triton.jit ++def _jacobi_cyclic_kernel( ++ A_ptr, # (N_PAD, N_PAD) fp32, row-major; overwritten in-place ++ V_ptr, # (N_PAD, N_PAD) fp32, row-major; initialised to I + accumulates ++ N, # active dimension (may be < N_PAD when caller padded) ++ N_PAD: tl.constexpr, ++ NUM_SWEEPS: tl.constexpr, ++): ++ """Row-cyclic Jacobi: one CTA, sequential pair rotations. ++ ++ For each sweep, iterate (p, q) with 0 <= p < q < N; compute the ++ Givens rotation that zeroes ``A[p, q]`` and update rows p, q + ++ cols p, q of A and cols p, q of V. ``A_ptr`` ends up with the ++ eigenvalues on its diagonal (not yet sorted); ``V_ptr`` ends up ++ with the corresponding eigenvectors as columns. ++ ++ Grid: ``(1,)``. Strides: row-major, so element ``[i, j]`` lives ++ at ``ptr + i * N_PAD + j``. ++ """ ++ n_offs = tl.arange(0, N_PAD) ++ n_mask = n_offs < N ++ ++ # ── Initialise V = I_{N_PAD} ──────────────────────────────────── ++ # The padding rows/cols stay zero off-diagonal and one on the ++ # diagonal so the padded eigenvalues are exactly the diagonal of ++ # the padded A (which the host has already set to a sentinel ++ # value large enough to sort to the end and be dropped). ++ v_init = (n_offs[:, None] == n_offs[None, :]).to(tl.float32) ++ tl.store(V_ptr + n_offs[:, None] * N_PAD + n_offs[None, :], v_init) ++ ++ for _sweep in tl.range(0, NUM_SWEEPS): ++ for p in tl.range(0, N - 1): ++ for q in tl.range(p + 1, N): ++ # ── Compute Givens c, s zeroing A[p, q] ───────────── ++ # Two-sided rotation G^T A G with ++ # G[p,p]=c, G[p,q]=-s, G[q,p]=s, G[q,q]=c ++ # zeroes A[p,q] when tau = (A[p,p] - A[q,q]) / (2 A[p,q]). ++ a_pp = tl.load(A_ptr + p * N_PAD + p) ++ a_qq = tl.load(A_ptr + q * N_PAD + q) ++ a_pq = tl.load(A_ptr + p * N_PAD + q) ++ ++ thresh = 1e-30 * (tl.abs(a_pp) + tl.abs(a_qq) + 1e-37) ++ if tl.abs(a_pq) > thresh: ++ d = a_pp - a_qq ++ theta = d / (2.0 * a_pq) ++ if theta >= 0.0: ++ t = 1.0 / (theta + tl.sqrt(1.0 + theta * theta)) ++ else: ++ t = 1.0 / (theta - tl.sqrt(1.0 + theta * theta)) ++ c = 1.0 / tl.sqrt(1.0 + t * t) ++ s = t * c ++ else: ++ c = 1.0 ++ s = 0.0 ++ ++ # ── Row update on A: rows p and q ─────────────────── ++ row_p = tl.load( ++ A_ptr + p * N_PAD + n_offs, mask=n_mask, other=0.0, ++ ) ++ row_q = tl.load( ++ A_ptr + q * N_PAD + n_offs, mask=n_mask, other=0.0, ++ ) ++ new_row_p = c * row_p + s * row_q ++ new_row_q = -s * row_p + c * row_q ++ tl.store(A_ptr + p * N_PAD + n_offs, new_row_p, mask=n_mask) ++ tl.store(A_ptr + q * N_PAD + n_offs, new_row_q, mask=n_mask) ++ ++ # ── Column update on A: cols p and q ──────────────── ++ # The row update just touched A[p, :] and A[q, :], so ++ # the column reads here see the *post-row-update* ++ # values. The two-sided update is correct because ++ # only A[p, p] / A[p, q] / A[q, p] / A[q, q] depend ++ # on both row and col rotations -- and at convergence ++ # A[p, q] -> 0 so the order doesn't affect the limit. ++ col_p = tl.load( ++ A_ptr + n_offs * N_PAD + p, mask=n_mask, other=0.0, ++ ) ++ col_q = tl.load( ++ A_ptr + n_offs * N_PAD + q, mask=n_mask, other=0.0, ++ ) ++ new_col_p = c * col_p + s * col_q ++ new_col_q = -s * col_p + c * col_q ++ tl.store(A_ptr + n_offs * N_PAD + p, new_col_p, mask=n_mask) ++ tl.store(A_ptr + n_offs * N_PAD + q, new_col_q, mask=n_mask) ++ ++ # ── Eigenvector accumulation V <- V G ─────────────── ++ v_col_p = tl.load( ++ V_ptr + n_offs * N_PAD + p, mask=n_mask, other=0.0, ++ ) ++ v_col_q = tl.load( ++ V_ptr + n_offs * N_PAD + q, mask=n_mask, other=0.0, ++ ) ++ new_v_col_p = c * v_col_p + s * v_col_q ++ new_v_col_q = -s * v_col_p + c * v_col_q ++ tl.store(V_ptr + n_offs * N_PAD + p, new_v_col_p, mask=n_mask) ++ tl.store(V_ptr + n_offs * N_PAD + q, new_v_col_q, mask=n_mask) ++ ++ ++def _next_pow2(x: int) -> int: ++ if x <= 1: ++ return 1 ++ return 1 << (x - 1).bit_length() ++ ++ ++# Practical upper bound: at N=128 + 6 sweeps the kernel issues ++# ~48K rotations sequentially. Empirically this runs in ~3 ms on ++# H100/H200. Above N=128 the cyclic-Jacobi constant factor crosses ++# cuSOLVER's; ``linalg.eigh.impl`` honours this cap when routing. ++_MAX_N = 128 ++ ++ ++def triton_jacobi_eigh(A: torch.Tensor, num_sweeps: int = 6): ++ """Eigendecomposition of symmetric ``A`` via row-cyclic Triton Jacobi. ++ ++ Args: ++ A: ``(N, N)`` symmetric float32 CUDA tensor. Not modified ++ (a working copy is allocated internally). ++ num_sweeps: number of full Jacobi sweeps (each = ``N*(N-1)/2`` ++ Givens rotations). Default 6 -- ~1e-6 residual for ++ well-conditioned spectra. ++ ++ Returns: ++ ``(w, V)`` where ``w`` is ``(N,)`` eigenvalues in ascending ++ order and ``V`` is ``(N, N)`` eigenvectors as columns ++ (``A @ V[:, i] ≈ w[i] * V[:, i]``). ++ """ ++ assert A.dim() == 2 and A.size(0) == A.size(1), "A must be square" ++ assert A.dtype == torch.float32, "only float32 supported" ++ assert A.is_cuda, "A must be on CUDA" ++ ++ N = A.size(0) ++ if N > _MAX_N: ++ raise ValueError( ++ f"triton_jacobi_eigh only supports N <= {_MAX_N}; got N={N}. " ++ f"Route to ``backend='cusolver'`` for larger problems." ++ ) ++ ++ # Pad to the next power of two so the Triton tile is a ++ # constexpr-friendly size. The padding rows/cols get a sentinel ++ # diagonal entry that sorts to the end and is dropped on return. ++ N_PAD = max(2, _next_pow2(N)) ++ if N == N_PAD: ++ A_work = A.contiguous().clone() ++ else: ++ # Sentinel: 10x the largest diagonal absolute value of A so ++ # the padded eigenvalues sort cleanly to the end of ``w`` ++ # and are removed by the slice below. ++ diag_abs = float(torch.max(torch.abs(torch.diagonal(A))).item()) + 1.0 ++ sentinel = diag_abs * 10.0 ++ A_work = torch.zeros( ++ N_PAD, N_PAD, dtype=torch.float32, device=A.device, ++ ) ++ A_work[:N, :N] = A ++ # Set the padded diagonal entries to the sentinel. ++ idx = torch.arange(N, N_PAD, device=A.device) ++ A_work[idx, idx] = sentinel ++ ++ V_work = torch.empty(N_PAD, N_PAD, dtype=torch.float32, device=A.device) ++ ++ # Single-CTA, single-warp launch. The kernel is dominated by ++ # the sequential dependency between successive Givens rotations, ++ # so adding warps would only waste threads on broadcast scalars ++ # (and risks losing convergence on N_PAD >= 64 where multi-warp ++ # scalar reads can race with the immediately preceding store on ++ # some Triton 3.x configs). ++ _jacobi_cyclic_kernel[(1,)]( ++ A_work, V_work, ++ N_PAD, # the kernel iterates over the full padded N_PAD ++ N_PAD=N_PAD, ++ NUM_SWEEPS=num_sweeps, ++ num_warps=1, ++ ) ++ ++ w_padded = torch.diagonal(A_work).clone() ++ w_sorted, idx = torch.sort(w_padded) ++ V_sorted = V_work[:, idx] ++ ++ if N == N_PAD: ++ return w_sorted.contiguous(), V_sorted.contiguous() ++ # Drop the ``N_PAD - N`` sentinel eigenpairs sitting at the tail. ++ return w_sorted[:N].contiguous(), V_sorted[:N, :N].contiguous() +diff --git a/pcalib/_kernels/primitives/__init__.py b/pcalib/_kernels/primitives/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/pcalib/_kernels/primitives/pca/__init__.py b/pcalib/_kernels/primitives/pca/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/pcalib/_kernels/primitives/pca/triton/__init__.py b/pcalib/_kernels/primitives/pca/triton/__init__.py +new file mode 100644 +index 0000000..d420409 +--- /dev/null ++++ b/pcalib/_kernels/primitives/pca/triton/__init__.py +@@ -0,0 +1,17 @@ ++"""pca triton backend. ++ ++Re-exports the public Python wrappers from each component file. ++``@triton.jit`` kernels stay private to their file (call them via the ++Python wrapper that lives next to them). ++""" ++from pcalib._kernels.primitives.pca.triton.pca import ( ++ _triton_pca_cov, ++ _triton_pca_dual, ++ triton_pca, ++ flash_pca, ++) ++ ++__all__ = [ ++ "triton_pca", ++ "flash_pca", ++] +diff --git a/pcalib/_kernels/primitives/pca/triton/pca.py b/pcalib/_kernels/primitives/pca/triton/pca.py +new file mode 100644 +index 0000000..4e3a96e +--- /dev/null ++++ b/pcalib/_kernels/primitives/pca/triton/pca.py +@@ -0,0 +1,123 @@ ++"""PCA via cuBLAS GEMMs + (cuSOLVER / MKL / Halko) eigh — auto-dispatches: ++ ++1. N >> D (cov path): ++ cov = X.T @ X / N (cuBLAS TF32 GEMM) ++ → (D ≤ 512) MKL eigh on CPU (avoids cuSOLVER's ~5 ms launch overhead) ++ → (D > 512) cuSOLVER eigh on GPU ++2. D >> N (dual path): ++ gram = X @ X.T / N (cuBLAS TF32 GEMM) ++ → (K*4 < N) Halko subspace iteration eigh — orders of magnitude faster ++ than the full O(N³) eigh when only top-K are needed ++ → (K*4 ≥ N) MKL/cuSOLVER eigh as in the cov path ++ → V = X.T @ U (cuBLAS GEMM) ++ → per-column normalize ++ ++The GEMM stage uses cuBLAS TF32 matmul (`torch.matmul` with TF32 enabled); ++the dual path's eigh uses Halko subspace iteration when the shape ++favours it (``K*4 < N`` AND ``N >= 256``), otherwise the full cuSOLVER ++syevd. Input is fp32 throughout; TF32 internal compute matches what ++``tl.dot`` already used on H100/H200. ++""" ++ ++import sys ++import os ++ ++import torch ++ ++from pcalib._kernels.linalg.eigh import eigh ++ ++ ++# ─── Path 1: Covariance + eigh (N >> D) ───────────────────────────────────── ++ ++def _triton_pca_cov(X: torch.Tensor, K: int, *, tol=None): ++ """PCA via cuBLAS TF32 cov GEMM + eigendecomposition. ++ ++ Output cov is the full (D, D) covariance matrix in fp32 (cuBLAS does ++ not expose a SYRK-style symmetric-output GEMM via torch); the lower ++ triangle work is cuBLAS-internal and amortized in the tile schedule. ++ ++ Precision is controlled entirely by ``tol``: ``tol=None`` (default) ++ runs the exact eigh path; passing a loose ``tol`` lets the underlying ++ :func:`pcalib._kernels.linalg.eigh.eigh` switch to Halko subspace iteration ++ when shape favours it (``K*4 < D`` AND ``D >= 256``). ++ """ ++ N, D = X.shape ++ cov = (X.T @ X) / N # cuBLAS TF32 GEMM ++ return eigh(cov, K=K, tol=tol) ++ ++ ++# ─── Path 2: Dual-Space Gram + eigh + Projection (D >> N) ─────────────────── ++ ++def _triton_pca_dual(X: torch.Tensor, K: int, *, tol=None): ++ """PCA via cuBLAS Gram GEMM + eigh + cuBLAS projection. ++ ++ For D >> N: NEVER materializes D x D. ++ 1. G = X @ X.T / N (cuBLAS TF32 GEMM, output N x N) ++ 2. eigh(G, K=K, tol=tol) -- exact by default; Halko on loose tol + ++ favourable shape. ++ 3. V = X.T @ U_K (cuBLAS TF32 GEMM, output D x K) ++ 4. column-normalize -- recovers unit eigenvectors of X^T X / N. ++ ++ Eigenvalues of X.T @ X / N and X @ X.T / N coincide (dual identity). ++ """ ++ N, D = X.shape ++ G = (X @ X.T) / N # cuBLAS TF32 GEMM ++ top_eigvals, U = eigh(G, K=K, tol=tol) ++ V = X.T @ U # cuBLAS TF32 GEMM ++ V = V / V.norm(dim=0, keepdim=True).clamp(min=1e-10) ++ return top_eigvals, V ++ ++ ++# ─── Auto-dispatch ─────────────────────────────────────────────────────────── ++ ++def triton_pca(X: torch.Tensor, K: int, *, tol=None): ++ """PCA via cuBLAS + ``pcalib._kernels.linalg.eigh`` -- auto-selects by data shape. ++ ++ - N >= 4*D: covariance + eigh (D×D matrix, L2-cached small for D ≤ 1024) ++ - N < 4*D: dual-space Gram + eigh + projection (N×N Gram, no D² ++ materialization) ++ ++ Precision contract: ++ * ``tol=None`` -> all matmuls run in PyTorch's default IEEE fp32 ++ (cuBLAS pure-fp32 path, ~67 TFLOPS on H100). Halko is OFF. ++ * ``tol>0`` -> opt into a lossy budget. We set ++ ``allow_tf32=True`` for the duration so the cov/Gram GEMMs run on ++ TF32 tensor cores (~225 TFLOPS, ~3-4x faster) and Halko / bf16 ++ backends become eligible. The flag is restored on exit. ++ ++ Args: ++ X: (N, D) float32 data (centered) ++ K: number of components ++ tol: residual tolerance. ++ ++ * ``None`` (default) -> EXACT eigh on the cov / Gram matrix ++ (cuSOLVER / MKL). Halko subspace iteration is NEVER used ++ automatically -- you must opt in via ``tol``. ++ * ``tol >= 1e-4`` AND favourable shape (``K*4 < M`` AND ++ ``M >= 256`` where ``M = D`` for cov, ``N`` for dual) ++ -> Halko subspace iteration; ~26-80x speedup over cuSOLVER. ++ ++ Returns: ++ eigenvalues: (K,) top-K eigenvalues (ascending) ++ eigenvectors: (D, K) top-K eigenvectors (columns, ascending) ++ """ ++ # tol=None -> EXACT: keep PyTorch's default IEEE matmul (no TF32). ++ # tol>0 -> user opted into a lossy budget: enable TF32 globally ++ # for the duration so the direct ``X @ y`` ops in helper paths can ++ # use tensor cores. The global flag is restored on exit. ++ use_tf32 = tol is not None and tol > 0 ++ prev_tf32 = torch.backends.cuda.matmul.allow_tf32 ++ if use_tf32: ++ torch.backends.cuda.matmul.allow_tf32 = True ++ try: ++ N, D = X.shape ++ if N >= 4 * D: ++ return _triton_pca_cov(X, K, tol=tol) ++ return _triton_pca_dual(X, K, tol=tol) ++ finally: ++ if use_tf32: ++ torch.backends.cuda.matmul.allow_tf32 = prev_tf32 ++ ++ ++# Public alias used by the examples/ folder. ++flash_pca = triton_pca diff --git a/pcalib/pca.py b/pcalib/pca.py -index ec891a2..c3d15bf 100644 +index ec891a2..47a2f8c 100644 --- a/pcalib/pca.py +++ b/pcalib/pca.py -@@ -1,44 +1,24 @@ +@@ -1,44 +1,44 @@ -"""Principal Component Analysis (PCA) -- the reference you must optimise. -+"""Principal Component Analysis (PCA) -- covariance + top-k eigendecomposition. ++"""Principal Component Analysis (PCA) -- vendored optimized kernel path. -This implementation is intentionally naive. It centres the data by explicitly -materialising the full ``(N, D)`` centred matrix and then runs a *full* thin @@ -51,13 +984,13 @@ index ec891a2..c3d15bf 100644 -far more memory than necessary (the centred copy of the whole dataset). +The naive baseline centred the data by materialising the full ``(N, D)`` centred +matrix and ran a *full* thin SVD just to read off the leading ``k`` components. -+Here :func:`pca` delegates to :func:`pcalib._cov_pca.pca`, which forms the -+``(D, D)`` covariance with a single GEMM plus a rank-1 mean correction (so the -+centred data is never materialised) and reads off the top ``k`` principal axes -+with a symmetric eigendecomposition. ++This implementation delegates to the vendored kernel entry point, which forms a ++small covariance (or dual Gram) matrix with a single GEMM and reads off the ++leading ``k`` eigenpairs with a dense symmetric eigensolver, so the full ++spectrum is never computed and the centred data is never materialised. -Contract (do NOT change): -+Public contract is unchanged: ++Public contract (unchanged): pca(x, n_components) -> (components, explained_variance) @@ -84,14 +1017,30 @@ index ec891a2..c3d15bf 100644 import torch -- --def pca(x, n_components): -- N = x.shape[0] ++from pcalib._kernels.primitives.pca.triton.pca import triton_pca ++ + + def pca(x, n_components): + N = x.shape[0] - mean = x.mean(dim=0) - xc = x - mean # materialized centered matrix -- naive - U, S, Vh = torch.linalg.svd(xc, full_matrices=False) # full SVD -- expensive -- k = int(n_components) + k = int(n_components) - components = Vh[:k].contiguous() - explained_variance = (S[:k] ** 2) / (N - 1) - return components, explained_variance.contiguous() -+from pcalib._cov_pca import pca ++ mean = x.mean(dim=0) ++ xc = x - mean # centred input the kernel expects ++ # triton_pca returns the top-k eigenpairs of the (biased) covariance in ++ # ASCENDING order: evals (k,), evecs (D, k) as columns. ++ evals, evecs = triton_pca(xc, k, tol=None) ++ # Flip to descending (largest principal component first) to match the ++ # naive baseline's convention. ++ evals = torch.flip(evals, dims=(0,)) ++ evecs = torch.flip(evecs, dims=(1,)) ++ components = evecs.transpose(0, 1).contiguous() # (k, D) orthonormal rows ++ # The kernel normalises the covariance by N (biased); the baseline reports ++ # the unbiased (N - 1) eigenvalues. Rescale so explained_variance matches. ++ scale = float(N) / float(N - 1) if N > 1 else 1.0 ++ explained_variance = (evals * scale).clamp_min(0).contiguous() ++ return components, explained_variance diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md b/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md index a881c07c1..e66434516 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md @@ -4,109 +4,90 @@ Operator-facing. Not copied into the agent workspace by the adapter. ## What this task measures -Can an agent turn a correct-but-naive GPU truncated SVD into a fast one? The -agent is given `tsvdlib` (a `torch.linalg.svd` full-SVD call that then slices the -top-k factors) and must rewrite the internals — ideally a Gram-matrix `X^T X` -followed by a top-k eigendecomposition (`torch.linalg.eigh`), plus fused kernels -— to maximize the geometric-mean speedup over the frozen baseline across a family -of hidden `(N, D, k)` workloads, subject to an approximation-quality gate. - -This is the third of a four-task **flashlib kernel-optimization family** -(KMeans, KNN, TruncatedSVD, PCA). All four share the KernelBench-style pattern: -ship a naive package, freeze a byte-for-byte baseline in the judge image, apply -the agent's patch to a clean copy, time patched-vs-baseline on identical seeded -data in an isolated worker, and gate on a primitive-appropriate quality metric -before scoring by geomean speedup. PCA is the sibling task: it is the same -Gram/eig idea but on the **covariance** matrix (mean-centre `X` first, then the -right singular vectors of the centred matrix are the principal directions), so -the PCA gate additionally has to account for the centering step. - -## Correctness gate: orthonormality + captured energy, judge-computed +Can an agent turn a naive GPU truncated SVD into a fast one? The agent patches +`tsvdlib`; the judge times the patched `truncated_svd` against a frozen naive +baseline on hidden `(N, D, k)` workloads and scores the geometric-mean speedup, +gated on low-rank-factorization quality (orthonormal components + captured +energy). Third of a four-task **flashlib kernel-optimization family** (KMeans, +KNN, TruncatedSVD, PCA) that all share one evaluator + one Modal GPU harness. + +## Execution: Modal GPU offload (mirrors the vllm task) + +The judge and the agent public test both **offload GPU work to Modal**; the +containers are light (ubuntu + `modal` + git, no torch/triton). `flash_gpu.py` +(baked at `/opt/flash_gpu.py` in both images) builds an ephemeral Modal app on a +GPU (`evaluation.gpu`, default H100), ships the frozen baseline + the patched +package **as data** (no per-submission image rebuild), runs the worker, and +returns per-workload speedups + verdicts. Needs `MODAL_TOKEN_ID` / +`MODAL_TOKEN_SECRET`. torch/triton/the vendored kernels run on the Modal image +(`evaluation.pip`). + +The evaluator is generic and identical across the four tasks; only three +constants differ (`PRIMITIVE`/`PKG`/`REF_MODULE`), and all workload/threshold/ +Modal values come from `config.yaml` (delivered judge-only as +`/judge/task_config.json`, never present in the agent image). + +## Reference solution = best in the repo + +`reference.patch` vendors flashlib's own optimized **Triton** truncated-SVD path +(`primitives/truncated_svd/triton/svd.py` plus the `linalg/eigh` stack it calls) +under `tsvdlib/_kernels/…`, with imports rewritten and the string "flashlib" +scrubbed, plus a thin adapter mapping our `(N, D)` contract onto flashlib's +entry. It is triton-only (runs on any sm≥80), imports cleanly on CPU, and applies ++ passes the patch policy. Calibrate `speedup_target` from its measured geomean +on the first GPU trial. + +## Anti-reward-hack + +The **load-bearing** defenses are structural, inside the GPU worker (the only +place the untrusted submission runs): + +* timing primitives (`perf_counter`, `torch.cuda.synchronize`) are captured to + locals **before** the submission is imported → monkey-patching torch cannot + affect measurement; +* **fresh data every timed iteration** → memoizing on a repeated input is + useless; +* quality is **re-verified from the returned tensors on every iteration** → + returning a stale cached result for a different input is caught; +* the judge computes all metrics (no agent number is trusted); +* an ephemeral Modal container per submission → no global state persists. + +The patch policy is surgical defense-in-depth (so it does not reject legitimate +vendored/optimized kernel source): only `/**` `.py` files may change; it +bans external-optimized-library *imports* (flashlib/cuml/cupy/faiss/sklearn/ +cutlass — none installed on the GPU image anyway) and process/network/ +measurement-tamper patterns (`subprocess`, `socket`, `os.system`, +`torch.cuda.synchronize =`, …). + +The agent-facing `readme` describes the contract, gate, scoring, and policy but +**not** how the reference is implemented (no Gram/eigh/avoid-full-SVD hints). + +## Correctness gate The SVD has sign and rotation ambiguities (any singular vector may be negated, and vectors spanning equal singular values may be rotated within their eigenspace), so an elementwise comparison against the baseline's factors is -meaningless. Instead the judge worker checks two rotation/sign-invariant +meaningless. The judge worker instead checks two rotation/sign-invariant properties of the returned `components` `V` (shape `(k, D)`), computed from what -the submission returns (not from any agent-provided number), on the same seeded -`x` used for the baseline: - -- **Orthonormality**: `max |V V^T - I_k| <= ortho_tolerance` (default 2%). The - rows must be an orthonormal basis of a k-dimensional subspace. -- **Captured energy**: `||X V^T||_F^2 >= (1 - captured_tolerance)` times the - baseline's captured energy (default 2%). This is the squared Frobenius norm of - the projection of the data onto the returned subspace — the quantity truncated - SVD is supposed to maximise (Eckart–Young). It depends only on the *subspace* - spanned by the rows of `V`, so it is invariant to sign flips and to any - in-subspace rotation. - -Properties: - -- rotation/sign-convention independent (only the spanned subspace matters); -- robust to floating-point drift; -- cheat-proof: you cannot return fast garbage — high captured energy requires - actually recovering the leading right-singular subspace, and the orthonormality - gate blocks degenerate `V` (e.g. repeated or unnormalised rows) that could - otherwise inflate the projected energy. Trading some numeric precision for - speed (tf32/bf16 accumulation in the Gram GEMM) is allowed within tolerance. - -`singular_values` are still required by the shape/finiteness check but are not -part of the quality gate; the captured-energy formulation makes the gate depend -on the subspace, which is what a downstream user of a truncated SVD cares about. - -Determinism: `x` is drawn from a fixed seeded generator per workload (seeds -derived from `base_seed`), so the baseline result is a fixed function of `(N, D, -k, seed)`. - -## Anti-gaming - -flashlib (the library these primitives are distilled from) is public and -Apache-2.0. Mitigations: - -- The shipped package is a small, neutrally named library (`tsvdlib`), not - flashlib; the patch allowlist confines edits to `tsvdlib/**`. -- The patch policy forbids importing `flashlib`/cuML/cuPy/FAISS/scikit-learn and - bans env/subprocess/network access — the agent must write the kernels itself. -- Scored shapes are hidden and differ from the two public shapes; seeds derive - from `base_seed`. General kernels win; shape lookups do not. -- The gate is a subspace-quality metric, not an elementwise factor match, so an - agent cannot "pass" by copying baseline numbers — it must produce a genuinely - good rank-k approximation. -- Honest framing: reproducing SOTA-class kernels *is* the bar. We block trivial - library reuse, not the underlying knowledge. - -## Execution model & the Modal question - -The evaluator runs an isolated worker subprocess (`_run_worker`) that imports the -frozen baseline (`/opt/tsvd_ref/reftsvd.py`) and the patched `tsvdlib` (from the -applied clean tree), times both, and reports JSON. This assumes the **judge -container has a GPU**. - -The repo's other GPU task (vllm) instead offloads to **Modal** because Harbor -judge containers may not be GPU-scheduled. `_run_worker` is the single swap -point: to go Modal, replace it with a Modal function that builds the patched -package, runs the same worker logic on a Modal GPU, and returns the JSON rows. -The rest of the evaluator (policy, gating, scoring) is unchanged. Decide -in-container-GPU vs Modal at first calibration trial. - -## Calibration TODO (needs a GPU trial) - -- Validate `reference.patch` (Gram + `eigh`) runs and passes the orthonormality - and captured-energy gates on all workloads; fix any torch API drift for the - pinned torch in the image. -- Measure the reference solution's geomean speedup and set `speedup_target` so a - reference-level solution maps to ~full score. -- Confirm hidden shapes fit device memory (the naive baseline's full - `torch.linalg.svd` on the `N x D` matrix is the memory driver; all shipped - shapes are H100-safe, and the Gram-matrix reference is far lighter since it - only decomposes the `D x D` matrix). -- Sanity-check timing stability (median-of-7); bump `timed_iters` if noisy. - -## Files - -- `tsvdlib/` — pristine package baked into both images (agent edits it). -- `judge/reftsvd.py` — frozen baseline, baked to `/opt/tsvd_ref/`. -- `evaluator.py` — self-contained policy + orchestration + scoring (judge-only). -- `reference.patch` — Gram-matrix + eigh reference solution (proves solvability). -- `docker/` — builds the prebuilt agent/judge images referenced by config.yaml. -- `harbor/app/` — agent-facing submission helpers + public self-test. +the submission returns, on each iteration's data: + +* **Orthonormality**: `max |V V^T - I_k| <= ortho_tolerance` (default 2%). +* **Captured energy**: `||X V^T||_F^2 >= (1 - captured_tolerance)` times the + baseline's captured energy (default 2%) — the squared Frobenius norm of the + projection of the data onto the returned subspace, the quantity truncated SVD + maximises (Eckart–Young). It depends only on the *subspace* spanned by the rows + of `V`, so it is invariant to sign flips and in-subspace rotation. + +`singular_values` are required by the shape/finiteness check but are not part of +the quality gate. Determinism: `x` is drawn from a fixed seeded generator per +workload (seeds derived from `base_seed`), so the baseline result is a fixed +function of `(N, D, k, seed)`. + +## Calibration TODO (needs a Modal GPU trial) + +- Run `reference.patch` on Modal; confirm it passes the orthonormality + + captured-energy gates on all workloads (bump the tolerances if flashlib's + low-precision path drifts slightly) and set `speedup_target` from its geomean. +- Confirm the pinned `torch`/`triton` in `evaluation.pip` compile the vendored + kernels, and hidden shapes fit the GPU. +- Confirm Modal token wiring in the Harbor judge/agent containers. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml b/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml index 120af92d9..948f8acc8 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml @@ -2,54 +2,64 @@ tag: systems runtime: language: python timeout_seconds: 10800 - environment: "GPU truncated-SVD kernel optimization; agent patches the tsvdlib Python/Triton package; judge times the patched truncated_svd against a frozen naive full-SVD baseline on hidden (N, D, k) workloads with orthonormality and captured-energy (approximation-quality) gates." + environment: "GPU Truncated SVD kernel optimization. The agent patches the tsvdlib package; the judge offloads timing to a Modal GPU, comparing the patched truncated_svd against a frozen naive baseline on hidden (N, D, k) workloads with a low-rank-factorization-quality gate (orthonormal components + captured energy)." apt_packages: - bash - ca-certificates - git - python3 + - python3-pip judge_apt_packages: - bash - ca-certificates - git - python3 + - python3-pip + judge_pip_packages: + - modal docker: - # Experimental local images. Build them with - # 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh - # before a local Harbor trial. Both images bundle a clean copy of the - # tsvdlib package; the judge image additionally bakes the frozen naive - # baseline (/opt/tsvd_ref) and the pristine tree (/opt/tsvdlib-clean). - image: frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.1.0 - judge_image: frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.1.0 + # Experimental local images. Build with docker/build_images.sh before a local + # Harbor trial. Both are light (ubuntu + modal + git); torch/triton/flashlib + # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes + # /app/tsvdlib (git) + /opt/tsvd_ref + /opt/flash_gpu.py; the judge image + # additionally bakes /opt/tsvdlib-clean. + image: frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.2.0 + judge_image: frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.2.0 environment: - cpus: 8 - memory_mb: 32768 - storage_mb: 32768 + cpus: 4 + memory_mb: 8192 + storage_mb: 16384 build_timeout_seconds: 3600 evaluation: - # The judge worker runs on a GPU visible to the judge container (single - # device). H100 is the reference accelerator; the Triton paths also run on - # L40S/A100. If Harbor cannot attach a GPU to the judge container, the worker - # step is the swap point for a Modal offload (see DESIGN.md). - gpu: "H100" - # Timing: warmup then median-of-N cuda-synced full truncated_svd calls. - warmup_iters: 3 + # --- primitive wiring (consumed by the generic evaluator + flash_gpu) --- + primitive: tsvd + pkg: tsvdlib + ref_module: reftsvd + clean_source: /opt/tsvdlib-clean + baseline_source: /opt/tsvd_ref + # --- Modal GPU --- + gpu: "H100" # Modal GPU spec (H100 / A100 / L40S) + cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" + pip: ["torch", "numpy"] + app_name: "tsvd-kernel-opt-eval" + modal_timeout_seconds: 1800 + # --- timing + quality gate --- + warmup_iters: 2 timed_iters: 7 - # Approximation-quality gate: the submitted truncated_svd's components must - # stay orthonormal (max |C C^T - I| <= ortho_tolerance) and must not lose - # captured energy (||X V^T||_F^2 >= (1 - captured_tolerance) x the frozen - # baseline's on the SAME seeded data). - captured_tolerance: 0.02 - ortho_tolerance: 0.02 - # Geomean speedup (over the naive full-SVD baseline) mapped to the full - # bounded score. Calibrate against the reference solution's measured geomean - # after a trial. - speedup_target: 6.0 - worker_timeout_seconds: 3600 + captured_tolerance: 0.02 # agent captured energy must be >= (1-tol) x baseline on every iteration + ortho_tolerance: 0.02 # max |V V^T - I_k| must stay within this tolerance + speedup_target: 6.0 # geomean speedup mapped to full bounded score (calibrate vs reference) base_seed: 20260701 - # Agent (iterative) role runs a fast subset; the final verifier runs them all. - agent_workload_count: 3 + agent_workload_count: 3 # agent role runs a fast subset; final verifier runs them all expose_per_workload_metrics: false + # --- hidden workloads (judge-only; config.yaml/task_config.json never enter the agent image) --- + workloads: + - {id: w0, N: 500000, D: 128, k: 16} + - {id: w1, N: 1000000, D: 256, k: 32} + - {id: w2, N: 200000, D: 512, k: 16} + - {id: w3, N: 2000000, D: 64, k: 8} + - {id: w4, N: 300000, D: 256, k: 16} + - {id: w5, N: 800000, D: 128, k: 32} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md index 4837359b4..dba9dca32 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md @@ -1,7 +1,8 @@ # Experimental Truncated-SVD Kernel-Optimization Images -Two images, mirroring the duckdb-e2e split: a public **agent** image and a -private **judge** image. Build them before a local Harbor trial: +Two **light** images (ubuntu + `modal` + git). torch, triton, and the vendored +flashlib kernels all run on the **Modal GPU image** defined in `flash_gpu.py`; +the containers themselves are CPU-only and offload GPU work to Modal. ```bash bash 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh @@ -10,31 +11,41 @@ bash 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh Defaults: ```text -AGENT_TAG=frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.1.0 -JUDGE_TAG=frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.1.0 +AGENT_TAG=frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.2.0 +JUDGE_TAG=frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.2.0 ``` -The agent image contains: +Agent image: ```text -/app/tsvdlib # clean, git-tracked package (the agent edits this) +/app/tsvdlib # clean, git-tracked package (the agent edits this) +/opt/flash_gpu.py # shared Modal GPU harness (public test uses it) +/opt/tsvd_ref/reftsvd.py # frozen naive baseline (public-test speed denominator) ``` -The judge image contains: +Judge image: ```text /opt/tsvdlib-clean/tsvdlib # pristine tree; the patch is applied to a copy -/opt/tsvd_ref/reftsvd.py # frozen naive baseline (speed denominator + oracle) +/opt/tsvd_ref/reftsvd.py # frozen naive baseline (speed denominator + quality oracle) +/opt/flash_gpu.py # shared Modal GPU harness (the evaluator uses it) ``` -Both are based on `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel` (torch + triton). - ## Runtime requirements -The judge times the patched truncated_svd on a **GPU visible to the judge -container** (single device; H100 reference, Triton paths also run on L40S/A100). -If the Harbor runtime cannot attach a GPU to the judge container, port the worker -step (`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md`. +Both the judge and the agent public test **offload timing to a Modal GPU** and +therefore need Modal credentials in the environment: + +```text +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET +``` + +`flash_gpu.py` builds an ephemeral Modal app on a GPU (`evaluation.gpu`, default +`H100`), ships the frozen baseline + the patched package as data, times both on +fresh per-iteration data, verifies quality each iteration, and returns the +speedups. No persistent deployment is used, so a fresh GPU container is spun up +per evaluation. Without Modal credentials the evaluator returns a patch-policy +smoke pass (which repo CI exercises). ## Smoke test @@ -42,5 +53,4 @@ step (`_run_worker` in `evaluator.py`) to a Modal GPU offload — see `DESIGN.md bash 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh ``` -Import-only; verifies torch/triton and the baked packages are importable. It -does not exercise a GPU. +Import-only (modal + flash_gpu + the baked packages); does not touch a GPU or Modal. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile index e35281b99..8241ad622 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile @@ -1,17 +1,23 @@ # Agent image for truncated_svd_gpu_kernel_optimization. -# Bundles a clean, git-tracked copy of the tsvdlib package at /app/tsvdlib -# so the agent can edit it and `make_submission.sh` can diff it. torch + triton -# come from the PyTorch base image. -FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel +# +# Light image (no torch/triton): the agent edits /app/tsvdlib and runs the +# public test, which offloads timing to a Modal GPU (torch/triton live on the +# Modal image defined in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET +# in the environment to run the GPU public test. +# +# Build context = the task directory: +# docker build -f docker/agent/Dockerfile -t . +FROM ubuntu:24.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get install -y --no-install-recommends \ - bash ca-certificates git ripgrep && \ + bash ca-certificates git python3 python3-pip ripgrep && \ rm -rf /var/lib/apt/lists/* -RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" +RUN pip3 install --break-system-packages --no-cache-dir modal +# The package the agent edits (git-tracked so make_submission.sh can diff it). WORKDIR /app COPY tsvdlib /app/tsvdlib RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ @@ -20,3 +26,9 @@ RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ git -C /app config user.name frontier-cs && \ git -C /app add -A -- tsvdlib .gitignore && \ git -C /app -c commit.gpgsign=false commit -qm "pristine tsvdlib" + +# Shared Modal GPU harness + the frozen naive baseline (the public-test speed +# denominator). The baseline is exactly the code the agent starts from, so it is +# not secret. +COPY flash_gpu.py /opt/flash_gpu.py +COPY judge/reftsvd.py /opt/tsvd_ref/reftsvd.py diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh index 035149ed2..9791340e5 100755 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile index 8790213ec..48b5343bc 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile @@ -1,18 +1,26 @@ # Judge image for truncated_svd_gpu_kernel_optimization. -# Bakes the pristine package tree the agent patch is applied to -# (/opt/tsvdlib-clean/tsvdlib) and the frozen naive baseline the judge times -# against (/opt/tsvd_ref/reftsvd.py). torch + triton come from the base. -FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel +# +# Light image (no torch/triton): the judge validates + applies the patch and +# offloads timing to a Modal GPU (torch/triton live on the Modal image defined +# in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. +# The Frontier-CS 2.0 adapter builds the final judge image ON TOP of this one. +# +# Build context = the task directory: +# docker build -f docker/judge/Dockerfile -t . +FROM ubuntu:24.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get install -y --no-install-recommends \ - bash ca-certificates git && \ + bash ca-certificates git python3 python3-pip && \ rm -rf /var/lib/apt/lists/* -RUN python3 -c "import torch, triton; print('torch', torch.__version__, 'triton', triton.__version__)" +RUN pip3 install --break-system-packages --no-cache-dir modal +# Pristine tree the patch is applied to, the frozen naive baseline (speed +# denominator + quality oracle), and the shared Modal GPU harness. COPY tsvdlib /opt/tsvdlib-clean/tsvdlib COPY judge/reftsvd.py /opt/tsvd_ref/reftsvd.py +COPY flash_gpu.py /opt/flash_gpu.py WORKDIR /judge diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh index cf75d4bb5..b100305a7 100755 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh @@ -1,17 +1,19 @@ #!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU required). +# Import-only smoke test for the built images (no GPU / Modal required). set -euo pipefail -AGENT_TAG=${AGENT_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "== agent image ==" docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import torch, triton, tsvdlib; print('agent ok: tsvdlib', tsvdlib.__version__)" + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ +sys.path.insert(0, '/opt/tsvd_ref'); import reftsvd; \ +import tsvdlib; print('agent ok: modal + flash_gpu + reftsvd + tsvdlib import')" echo "== judge image ==" docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, torch, triton; \ + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ sys.path.insert(0, '/opt/tsvd_ref'); import reftsvd; \ sys.path.insert(0, '/opt/tsvdlib-clean'); import tsvdlib; \ -print('judge ok: reftsvd + tsvdlib import')" +print('judge ok: modal + flash_gpu + reftsvd + tsvdlib import')" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py index 98b656031..33d919a7a 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py @@ -1,22 +1,20 @@ -"""Evaluator for the truncated-SVD GPU kernel-optimization task. - -The agent submits a unified diff over the ``tsvdlib`` Python package. The -judge: - -1. statically validates the patch against an allowlist (only ``tsvdlib/**`` - may change; no ``flashlib``/cuML/network/env access; bounded size); -2. applies it to a pristine copy of ``tsvdlib`` baked into the judge image - at ``/opt/tsvdlib-clean``; -3. runs an isolated worker subprocess that, for each hidden ``(N, D, k)`` - workload, times the patched ``tsvdlib.truncated_svd`` against a *frozen* - naive baseline (``/opt/tsvd_ref/reftsvd.py``) on identical seeded data and - measures each result's orthonormality error and captured energy; -4. gates on factor quality (the agent's components must stay orthonormal and - must not lose captured energy beyond a small tolerance vs the baseline) and - scores by the geometric-mean speedup. - -When the judge source tree is not present (e.g. local static checks on a box -without a GPU), the evaluator still validates the patch policy and returns a +"""Generic evaluator for the flashlib GPU kernel-optimization task family. + +Identical across all four tasks (kmeans / knn / pca / truncated_svd); every +primitive-specific value comes from the ``evaluation`` block of the task's +config (delivered to the judge as ``/judge/task_config.json``), which is not +present in the agent workspace. + +Flow: statically validate the patch (only ``/**`` may change; no external +optimized libs / env / process / network access) -> apply it to the pristine +package baked at ``clean_source`` -> hand the frozen baseline + patched package +sources to a Modal GPU worker (``flash_gpu.run_remote``) that times both on +fresh per-iteration data and verifies clustering/retrieval/subspace quality on +every iteration -> gate on the worker's per-workload verdict -> score by the +geometric-mean speedup. + +Without Modal credentials or the baked judge sources (e.g. repo CI on a box with +no GPU/Modal), the evaluator still validates the patch policy and returns a smoke pass, so ``python3 evaluator.py reference.patch`` works anywhere. """ from __future__ import annotations @@ -31,18 +29,12 @@ import subprocess import sys import tempfile -import time from dataclasses import dataclass from pathlib import Path from typing import Any -MAX_PATCH_BYTES = 1_000_000 -MAX_CHANGED_FILES = 40 TASK_CONFIG_PATH = Path("/judge/task_config.json") -DEFAULT_CLEAN_SOURCE = Path("/opt/tsvdlib-clean") # pristine package (patched here) -DEFAULT_BASELINE_SOURCE = Path("/opt/tsvd_ref") # frozen naive baseline (reftsvd.py) - def _load_task_config() -> dict[str, Any]: try: @@ -53,29 +45,29 @@ def _load_task_config() -> dict[str, Any]: TASK_CONFIG = _load_task_config() -EVALUATION_CONFIG = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} +EVAL = TASK_CONFIG.get("evaluation", {}) if isinstance(TASK_CONFIG.get("evaluation"), dict) else {} -def _cfg(name: str, default): - return EVALUATION_CONFIG.get(name, default) +def _get(name: str, default): + return EVAL.get(name, default) -def _cfg_int(name: str, default: int) -> int: +def _get_int(name: str, default: int) -> int: try: - return int(EVALUATION_CONFIG.get(name, default)) + return int(EVAL.get(name, default)) except Exception: return default -def _cfg_float(name: str, default: float) -> float: +def _get_float(name: str, default: float) -> float: try: - return float(EVALUATION_CONFIG.get(name, default)) + return float(EVAL.get(name, default)) except Exception: return default -def _cfg_bool(name: str, default: bool) -> bool: - raw = EVALUATION_CONFIG.get(name, default) +def _get_bool(name: str, default: bool) -> bool: + raw = EVAL.get(name, default) if isinstance(raw, bool): return raw if isinstance(raw, str): @@ -83,51 +75,49 @@ def _cfg_bool(name: str, default: bool) -> bool: return bool(raw) -WARMUP_ITERS = _cfg_int("warmup_iters", 3) -TIMED_ITERS = _cfg_int("timed_iters", 7) -CAPTURED_TOL = _cfg_float("captured_tolerance", 0.02) # agent captured energy >= (1-tol)*ref -ORTHO_TOL = _cfg_float("ortho_tolerance", 0.02) # max |C C^T - I| must stay <= tol -SPEEDUP_TARGET = _cfg_float("speedup_target", 6.0) # geomean speedup mapped to full score -WORKER_TIMEOUT_SECONDS = _cfg_int("worker_timeout_seconds", 3600) -BASE_SEED = _cfg_int("base_seed", 20260701) +# --- Per-task identity: the ONLY lines that differ across the four tasks. --- +# Kept as constants (not config) so the patch policy is enforceable locally / in +# CI without /judge/task_config.json. Everything else is config-driven and only +# needed on the GPU path. +PRIMITIVE = "tsvd" +PKG = "tsvdlib" +REF_MODULE = "reftsvd" -# Editable surface: only the shipped package. -ALLOWED_PATTERNS = ( - "tsvdlib/**", -) -# Never touchable, even though they are not shipped in the agent workspace. +MAX_PATCH_BYTES = _get_int("max_patch_bytes", 2_000_000) +MAX_CHANGED_FILES = _get_int("max_changed_files", 80) +CLEAN_SOURCE = Path(f"/opt/{PKG}-clean") +BASELINE_SOURCE = Path(f"/opt/{PRIMITIVE}_ref") +SPEEDUP_TARGET = _get_float("speedup_target", 8.0) + +ALLOWED_PATTERNS = (f"{PKG}/**",) DENIED_PATTERNS = ( "evaluator.py", + "flash_gpu.py", "reference.patch", - "reftsvd.py", - "**/reftsvd.py", + f"{REF_MODULE}.py", + f"**/{REF_MODULE}.py", "**/conftest.py", "**/test_*.py", "pyproject.toml", "setup.py", "setup.cfg", ) -# Forbidden substrings in added lines (defense in depth). The agent must write -# its own kernels, not call an external optimized library, read the -# environment, spawn processes, or touch the network. -FORBIDDEN_TOKENS = ( - "flashlib", - "cuml", - "cudf", - "cupy", - "faiss", - "sklearn", - "scikit", - "os.environ", - "getenv", - "putenv", - "setenv", - "subprocess", - "socket", - "urllib", - "requests", - "importlib.import_module", - "__import__", +# Defense in depth (the load-bearing anti-tamper defense is structural, inside +# the GPU worker: it captures its perf_counter / synchronize references before +# importing the submission, regenerates data every iteration, and re-verifies +# quality each iteration). These patterns are matched surgically so that +# legitimate vendored/optimized kernel source is not rejected: +# * external optimized libraries, matched as IMPORT statements (not bare words, +# so a comment mentioning a name is fine); none are installed on the GPU +# image anyway; +# * process / network / measurement-tamper patterns that never appear in a +# real GPU kernel. +FORBIDDEN_IMPORT_RE = re.compile( + r"(?:^|\n)\s*(?:import|from)\s+(flashlib|cuml|cudf|cupy|faiss|sklearn|scikit|cutlass|cuvs)\b" +) +FORBIDDEN_PATTERN_RE = re.compile( + r"\bsubprocess\b|\bsocket\b|\burllib\b|\brequests\b|os\.system|" + r"torch\.cuda\.synchronize\s*=|time\.perf_counter\s*=|setattr\(\s*torch\.cuda" ) @@ -136,7 +126,6 @@ class PatchFile: old_path: str new_path: str added_lines: tuple[str, ...] - removed_lines: tuple[str, ...] @property def path(self) -> str: @@ -144,7 +133,7 @@ def path(self) -> str: def _match(path: str, patterns: tuple[str, ...]) -> bool: - return any(fnmatch.fnmatch(path, pattern) for pattern in patterns) + return any(fnmatch.fnmatch(path, p) for p in patterns) def _invalid(message: str, metrics: dict[str, Any] | None = None): @@ -155,41 +144,27 @@ def _invalid(message: str, metrics: dict[str, Any] | None = None): def _parse_patch(text: str) -> list[PatchFile]: files: list[PatchFile] = [] - current_old = "" - current_new = "" + old = new = "" added: list[str] = [] - removed: list[str] = [] in_file = False - for line in text.splitlines(): if line.startswith("diff --git "): if in_file: - files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) - in_file = True - current_old = current_new = "" - added = [] - removed = [] + files.append(PatchFile(old, new, tuple(added))) + in_file, old, new, added = True, "", "", [] continue if not in_file: continue if line.startswith("--- "): - current_old = line[4:].strip() - if current_old.startswith("a/"): - current_old = current_old[2:] - continue - if line.startswith("+++ "): - current_new = line[4:].strip() - if current_new.startswith("b/"): - current_new = current_new[2:] - continue - if line.startswith("+") and not line.startswith("+++ "): + old = line[4:].strip() + old = old[2:] if old.startswith("a/") else old + elif line.startswith("+++ "): + new = line[4:].strip() + new = new[2:] if new.startswith("b/") else new + elif line.startswith("+") and not line.startswith("+++ "): added.append(line[1:]) - continue - if line.startswith("-") and not line.startswith("--- "): - removed.append(line[1:]) - if in_file: - files.append(PatchFile(current_old, current_new, tuple(added), tuple(removed))) + files.append(PatchFile(old, new, tuple(added))) return files @@ -201,7 +176,7 @@ def _validate_path(path: str) -> tuple[bool, str]: if _match(path, DENIED_PATTERNS): return False, f"changed file is outside task boundary: {path}" if not _match(path, ALLOWED_PATTERNS): - return False, f"changed file is not allowlisted (only tsvdlib/** may change): {path}" + return False, f"changed file is not allowlisted (only {PKG}/** may change): {path}" if not path.endswith(".py"): return False, f"only Python files may change: {path}" return True, "" @@ -214,69 +189,40 @@ def validate_patch(patch_path: Path) -> tuple[bool, str, dict[str, Any]]: if size > MAX_PATCH_BYTES: return False, f"patch is too large ({size} bytes > {MAX_PATCH_BYTES})", {} text = patch_path.read_text(encoding="utf-8", errors="replace") - patch_hash = hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() files = _parse_patch(text) metrics: dict[str, Any] = { "patch_bytes": size, - "patch_sha256": patch_hash, + "patch_sha256": hashlib.sha256(text.encode("utf-8", "replace")).hexdigest(), "changed_files": len(files), } if len(files) > MAX_CHANGED_FILES: return False, f"too many changed files ({len(files)} > {MAX_CHANGED_FILES})", metrics - - for patch_file in files: - path = patch_file.path - if patch_file.new_path == "/dev/null": - return False, f"deleting files is outside task boundary: {patch_file.old_path}", metrics - if patch_file.old_path != "/dev/null" and patch_file.old_path != patch_file.new_path: - ok, err = _validate_path(patch_file.old_path) + for pf in files: + path = pf.path + if pf.new_path == "/dev/null": + return False, f"deleting files is outside task boundary: {pf.old_path}", metrics + if pf.old_path != "/dev/null" and pf.old_path != pf.new_path: + ok, err = _validate_path(pf.old_path) if not ok: - return False, f"rename/copy source is outside task boundary: {err}", metrics + return False, f"rename source is outside task boundary: {err}", metrics ok, err = _validate_path(path) if not ok: return False, err, metrics - added_text = "\n".join(patch_file.added_lines) - low = added_text.lower() - for token in FORBIDDEN_TOKENS: - if token in low: - return False, f"{path}: forbidden token in added code ({token})", metrics - + added = "\n".join(pf.added_lines) + m = FORBIDDEN_IMPORT_RE.search(added) + if m: + return False, f"{path}: forbidden import of an external optimized library ({m.group(1)})", metrics + m = FORBIDDEN_PATTERN_RE.search(added) + if m: + return False, f"{path}: forbidden pattern in added code ({m.group(0).strip()[:40]})", metrics metrics["valid_patch"] = 1 return True, "patch accepted by static policy", metrics -def clean_env(tmp_root: Path) -> dict[str, str]: - home = tmp_root / "home" - tmp = tmp_root / "tmp" - home.mkdir(parents=True, exist_ok=True) - tmp.mkdir(parents=True, exist_ok=True) - env = { - "PATH": os.environ.get("PATH", "/usr/local/bin:/usr/bin:/bin"), - "HOME": str(home), - "TMPDIR": str(tmp), - "LC_ALL": "C", - "LANG": "C", - } - for key in ("CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES", "LD_LIBRARY_PATH", - "TRITON_CACHE_DIR", "CUDA_HOME"): - if key in os.environ: - env[key] = os.environ[key] - env.setdefault("TRITON_CACHE_DIR", str(tmp_root / "triton_cache")) - return env - - -def run_checked(cmd: list[str], *, cwd: Path, env: dict[str, str], timeout: int) -> subprocess.CompletedProcess: - return subprocess.run( - cmd, cwd=str(cwd), env=env, timeout=timeout, - stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, - ) - - -def sanitize_error_text(text: str) -> str: - text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text) +def sanitize(text: str) -> str: + text = re.sub(r"/tmp/[A-Za-z0-9_./-]+", "", text or "") text = re.sub(r"/opt/[A-Za-z0-9_./-]+", "", text) - text = re.sub(r"N=\d+", "N=", text) - text = re.sub(r"seed[=:]?\s*\d+", "seed=", text, flags=re.IGNORECASE) + text = re.sub(r"\bN=\d+|\bseed[=:]?\s*\d+", "", text, flags=re.IGNORECASE) return text[-600:] @@ -286,193 +232,123 @@ def geometric_mean(values: list[float]) -> float: return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) -def score_from_speedup(gm_speedup: float) -> float: - if gm_speedup <= 0: +def score_from_speedup(gm: float) -> float: + if gm <= 0: return 0.0 - raw = 100.0 * math.log(gm_speedup) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) return max(0.0, min(100.0, raw)) -def is_final_submission_role() -> bool: +def is_final_role() -> bool: return os.environ.get("FRONTIER_SUBMISSION_ROLE", "agent") == "final" -# Hidden workloads. (N, D, k). Agent role runs a fast subset for iterative -# feedback; the final verifier runs the full set. Seeds are derived from -# BASE_SEED so data is deterministic but not guessable from the statement. -_HIDDEN_WORKLOADS = ( - {"id": "w0", "N": 500_000, "D": 128, "k": 16}, - {"id": "w1", "N": 1_000_000, "D": 256, "k": 32}, - {"id": "w2", "N": 200_000, "D": 512, "k": 16}, # wide-feature regime - {"id": "w3", "N": 2_000_000, "D": 64, "k": 8}, # tall / small-D regime - {"id": "w4", "N": 300_000, "D": 256, "k": 16}, - {"id": "w5", "N": 800_000, "D": 128, "k": 32}, -) - - -def _workloads(*, final_role: bool) -> list[dict[str, Any]]: - configured = _cfg("workloads", None) - workloads = list(configured) if isinstance(configured, list) and configured else list(_HIDDEN_WORKLOADS) +def _workloads(final_role: bool) -> list[dict[str, Any]]: + workloads = list(_get("workloads", []) or []) if not final_role: - n_agent = _cfg_int("agent_workload_count", 3) - workloads = workloads[:n_agent] + n = _get_int("agent_workload_count", 3) + workloads = workloads[:n] + base = _get_int("base_seed", 20260701) for i, w in enumerate(workloads): - w.setdefault("seed", BASE_SEED + 1000 * (i + 1)) + w.setdefault("seed", base + 1000 * (i + 1)) return workloads -# The worker runs the untrusted patched package in its own process. It imports -# the frozen baseline (reftsvd) and the patched tsvdlib, times both on -# identical seeded data, and reports per-workload timings + quality metrics -# (orthonormality error, captured energy) as JSON. -_WORKER_SOURCE = r''' -import argparse, json, sys, time, traceback -def _load(baseline_dir, patched_dir): - sys.path.insert(0, patched_dir); sys.path.insert(0, baseline_dir) - import reftsvd, tsvdlib - return reftsvd, tsvdlib -def gen(N, D, seed, device): - import torch - g = torch.Generator(device=device).manual_seed(int(seed)) - return torch.randn(N, D, generator=g, device=device, dtype=torch.float32) -def ortho_err(comps): - import torch - c = comps.to(torch.float32); k = c.shape[0] - return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) -def captured(x, comps): - import torch - c = comps.to(torch.float32); total = 0.0 - for i in range(0, x.shape[0], 16384): - p = x[i:i+16384] @ c.t(); total += float((p * p).sum().item()) - return total -def bench(fn, warmup, iters): - import torch - for _ in range(warmup): fn(); torch.cuda.synchronize() - ts = [] - for _ in range(iters): - torch.cuda.synchronize(); t0 = time.perf_counter(); fn(); torch.cuda.synchronize() - ts.append(time.perf_counter() - t0) - ts.sort(); return ts[len(ts) // 2] * 1000.0 -def check(out, D, k): - import torch - if not (isinstance(out, tuple) and len(out) == 2): raise ValueError("truncated_svd must return (singular_values, components)") - s, comps = out - if tuple(s.shape) != (k,) or tuple(comps.shape) != (k, D): raise ValueError("wrong output shape") - if not (torch.isfinite(s).all() and torch.isfinite(comps).all()): raise ValueError("non-finite output") -def main(): - ap = argparse.ArgumentParser() - for n in ("--baseline-dir","--patched-dir","--workloads","--out"): ap.add_argument(n, required=True) - ap.add_argument("--warmup", type=int, default=3); ap.add_argument("--iters", type=int, default=7) - a = ap.parse_args() - import torch - if not torch.cuda.is_available(): - json.dump({"ok": False, "error": "cuda_unavailable"}, open(a.out, "w")); return - reftsvd, tsvdlib = _load(a.baseline_dir, a.patched_dir) - rows = [] - for w in json.loads(a.workloads): - N, D, k, seed = w["N"], w["D"], w["k"], w["seed"] - x = gen(N, D, seed, "cuda") - ref_fn = lambda: reftsvd.truncated_svd(x, k) - agent_fn = lambda: tsvdlib.truncated_svd(x, k) - ref_out = ref_fn(); torch.cuda.synchronize() - ref_cap = captured(x, ref_out[1]) - try: - agent_out = agent_fn(); torch.cuda.synchronize(); check(agent_out, D, k) - oerr = ortho_err(agent_out[1]); acap = captured(x, agent_out[1]) - except Exception as e: - rows.append({"id": w["id"], "ok": False, "error": type(e).__name__ + ": " + str(e)[:200]}) - del x; torch.cuda.empty_cache(); continue - ref_ms = bench(ref_fn, a.warmup, a.iters); agent_ms = bench(agent_fn, a.warmup, a.iters) - rows.append({"id": w["id"], "ok": True, "ref_ms": ref_ms, "agent_ms": agent_ms, - "ortho_err": oerr, "ref_captured": ref_cap, "agent_captured": acap}) - del x; torch.cuda.empty_cache() - json.dump({"ok": True, "rows": rows}, open(a.out, "w")) -if __name__ == "__main__": - try: main() - except Exception: traceback.print_exc(); sys.exit(3) -''' - - -def _run_worker(patched_parent: Path, tmp_root: Path, env: dict[str, str], - workloads: list[dict[str, Any]]) -> dict[str, Any]: - worker_path = tmp_root / "tsvd_worker.py" - worker_path.write_text(_WORKER_SOURCE, encoding="utf-8") - out_path = tmp_root / "worker_out.json" - cmd = [ - sys.executable, str(worker_path), - "--baseline-dir", str(DEFAULT_BASELINE_SOURCE), - "--patched-dir", str(patched_parent), - "--workloads", json.dumps(workloads), - "--out", str(out_path), - "--warmup", str(WARMUP_ITERS), - "--iters", str(TIMED_ITERS), - ] - run_checked(cmd, cwd=tmp_root, env=env, timeout=WORKER_TIMEOUT_SECONDS) - return json.loads(out_path.read_text(encoding="utf-8")) +def _build_cfg() -> dict[str, Any]: + return { + "primitive": PRIMITIVE, + "pkg": PKG, + "ref_module": REF_MODULE, + "gpu": str(_get("gpu", "H100")), + "cuda_image": str(_get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), + "pip": list(_get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])), + "app_name": str(_get("app_name", "flash-kernel-eval")), + "modal_timeout_seconds": _get_int("modal_timeout_seconds", 1800), + "warmup": _get_int("warmup_iters", 3), + "iters": _get_int("timed_iters", 7), + "inertia_tolerance": _get_float("inertia_tolerance", 0.02), + "recall_threshold": _get_float("recall_threshold", 0.99), + "captured_tolerance": _get_float("captured_tolerance", 0.02), + "ortho_tolerance": _get_float("ortho_tolerance", 0.02), + } + + +def _read_dir(root: Path, rel_to: Path) -> dict[str, str]: + out: dict[str, str] = {} + for p in root.rglob("*.py"): + out[str(p.relative_to(rel_to))] = p.read_text(encoding="utf-8", errors="replace") + return out def full_evaluation(patch_path: Path, metrics: dict[str, Any]): - final_role = is_final_submission_role() + final_role = is_final_role() metrics["submission_role"] = "final" if final_role else "agent" - workloads = _workloads(final_role=final_role) + workloads = _workloads(final_role) - if not DEFAULT_CLEAN_SOURCE.exists() or not DEFAULT_BASELINE_SOURCE.exists(): + # Import the Modal harness baked into the judge image; missing harness or + # missing credentials/sources -> policy smoke pass. + sys.path.insert(0, "/opt") + try: + import flash_gpu # type: ignore + except Exception: metrics["full_benchmark"] = 0 - return (1.0, 1.0, - "patch policy smoke passed; tsvdlib judge source is not configured in this environment", - metrics) + return 1.0, 1.0, "patch policy smoke passed; GPU harness not available in this environment", metrics + if not flash_gpu.modal_available() or not CLEAN_SOURCE.exists() or not BASELINE_SOURCE.exists(): + metrics["full_benchmark"] = 0 + return 1.0, 1.0, "patch policy smoke passed; Modal/judge sources not configured in this environment", metrics - with tempfile.TemporaryDirectory(prefix="tsvd_kernel_opt_") as tmp: + with tempfile.TemporaryDirectory(prefix="flash_kernel_opt_") as tmp: tmp_root = Path(tmp) - env = clean_env(tmp_root) - patched_parent = tmp_root / "patched" - shutil.copytree(DEFAULT_CLEAN_SOURCE, patched_parent) - + patched = tmp_root / "patched" + shutil.copytree(CLEAN_SOURCE, patched) + env = {"PATH": os.environ.get("PATH", "/usr/bin:/bin"), "HOME": str(tmp_root), + "LC_ALL": "C", "LANG": "C"} if int(metrics.get("changed_files", 0)) > 0: - run_checked(["git", "apply", "--check", str(patch_path)], cwd=patched_parent, env=env, timeout=60) - run_checked(["git", "apply", str(patch_path)], cwd=patched_parent, env=env, timeout=60) + try: + subprocess.run(["git", "apply", "--check", str(patch_path)], cwd=patched, env=env, + check=True, capture_output=True, text=True, timeout=60) + subprocess.run(["git", "apply", str(patch_path)], cwd=patched, env=env, + check=True, capture_output=True, text=True, timeout=60) + except subprocess.CalledProcessError as exc: + metrics["stderr_tail"] = sanitize(exc.stderr or "") + return _invalid("patch does not apply to the clean package tree", metrics) metrics["applied_patch"] = 1 else: metrics["used_empty_patch"] = 1 - result = _run_worker(patched_parent, tmp_root, env, workloads) - if not result.get("ok"): - return _invalid(f"benchmark worker failed ({result.get('error', 'unknown')})", metrics) - - rows = result.get("rows", []) - speedups: list[float] = [] - per_workload: dict[str, Any] = {} - for row in rows: - if not row.get("ok"): - metrics["failed_workload_error"] = sanitize_error_text(str(row.get("error", ""))) - return _invalid("submitted truncated_svd crashed or returned an invalid result on a hidden workload", metrics) - if row["ortho_err"] > ORTHO_TOL: - metrics["ortho_regression"] = {"ortho_err": row["ortho_err"], "tol": ORTHO_TOL} - return _invalid("submitted truncated_svd components are not orthonormal", metrics) - if row["agent_captured"] < (1.0 - CAPTURED_TOL) * row["ref_captured"] - 1e-6: - metrics["captured_regression"] = { - "ref": row["ref_captured"], "agent": row["agent_captured"], "tol": CAPTURED_TOL, - } - return _invalid("submitted truncated_svd captured energy regressed beyond tolerance", metrics) - speedup = row["ref_ms"] / row["agent_ms"] if row["agent_ms"] > 0 else 0.01 - speedups.append(max(speedup, 0.01)) - per_workload[row["id"]] = { - "ref_ms": row["ref_ms"], "agent_ms": row["agent_ms"], "speedup": speedup, - } - - if not speedups: - return _invalid("no workloads were evaluated", metrics) - - gm = geometric_mean(speedups) - bounded = score_from_speedup(gm) - metrics.update({ - "full_benchmark": 1, - "workload_count": len(speedups), - "geomean_speedup": gm, - }) - if _cfg_bool("expose_per_workload_metrics", False): - metrics["per_workload"] = per_workload - return (bounded, bounded, f"truncated_svd geomean speedup {gm:.3f}x over the naive full-SVD baseline", metrics) + payload = { + "baseline_files": _read_dir(BASELINE_SOURCE, BASELINE_SOURCE), + "patched_files": _read_dir(patched, patched), + "workloads": workloads, + "cfg": _build_cfg(), + } + try: + result = flash_gpu.run_remote(payload) + except Exception as exc: + metrics["error_detail"] = sanitize(str(exc)) + return _invalid("GPU evaluation failed", metrics) + + if not result.get("ok"): + metrics["worker_error"] = sanitize(str(result.get("error", "unknown"))) + return _invalid("GPU benchmark worker failed", metrics) + + speedups: list[float] = [] + per_workload: dict[str, Any] = {} + for row in result.get("rows", []): + if not row.get("ok"): + metrics["failed_workload"] = {"reason": row.get("reason", "error")} + return _invalid("submission failed the quality gate or crashed on a hidden workload", metrics) + speedups.append(max(float(row["speedup"]), 0.01)) + per_workload[row["id"]] = {"speedup": row["speedup"]} + if not speedups: + return _invalid("no workloads were evaluated", metrics) + + gm = geometric_mean(speedups) + bounded = score_from_speedup(gm) + metrics.update({"full_benchmark": 1, "workload_count": len(speedups), "geomean_speedup": gm}) + if _get_bool("expose_per_workload_metrics", False): + metrics["per_workload"] = per_workload + return bounded, bounded, f"{PRIMITIVE} geomean speedup {gm:.3f}x over the naive baseline", metrics def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: @@ -482,17 +358,9 @@ def evaluate(solution_path: str) -> tuple[float, float, str, dict[str, Any]]: return _invalid(message, metrics) try: return full_evaluation(patch_path, metrics) - except subprocess.TimeoutExpired: - return _invalid("benchmark worker timed out", metrics) - except subprocess.CalledProcessError as exc: - stderr = sanitize_error_text(exc.stderr or "") - cmd0 = Path(str(exc.cmd[0])).name if isinstance(exc.cmd, list) and exc.cmd else "subprocess" - metrics["failed_command"] = "git apply" if cmd0 == "git" else cmd0 - metrics["stderr_tail"] = stderr - return _invalid("patch apply or benchmark command failed", metrics) - except Exception as exc: + except Exception as exc: # noqa: BLE001 metrics["error_type"] = type(exc).__name__ - metrics["error_detail"] = sanitize_error_text(str(exc)) + metrics["error_detail"] = sanitize(str(exc)) return _invalid("evaluation failed", metrics) @@ -500,13 +368,9 @@ def main(argv: list[str]) -> int: if len(argv) != 2: print("Usage: evaluator.py SOLUTION_PATCH", file=sys.stderr) return 2 - score, score_unbounded, message, metrics = evaluate(argv[1]) - print(json.dumps({ - "score": score, - "score_unbounded": score_unbounded, - "message": message, - "metrics": metrics, - }, indent=2, sort_keys=True)) + score, unbounded, message, metrics = evaluate(argv[1]) + print(json.dumps({"score": score, "score_unbounded": unbounded, + "message": message, "metrics": metrics}, indent=2, sort_keys=True)) return 0 diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py new file mode 100644 index 000000000..7fd6db603 --- /dev/null +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py @@ -0,0 +1,279 @@ +"""Modal GPU evaluation harness for the flashlib kernel-optimization task family. + +Shared by the judge (evaluator.py) and the agent public test. Baked into both +the agent and judge images at ``/opt/flash_gpu.py``. It runs a frozen naive +baseline and the (patched or agent-edited) package on a Modal GPU and returns +per-workload timings + quality verdicts. + +Anti-reward-hack properties (all enforced inside the remote worker, which is the +only place the untrusted code runs): + +* **Timing primitives are captured before any agent code is imported** — the + worker binds ``time.perf_counter`` and ``torch.cuda.synchronize`` to locals up + front, so a submission that monkey-patches ``torch.cuda.synchronize`` (or any + torch attribute) cannot affect measurement. +* **Fresh data every timed iteration** — each measured iteration generates a new + input from a fresh seed, so a submission cannot memoize on a repeated input. +* **Quality is verified on every timed iteration** — the judge recomputes the + quality metric from the *returned tensors* on that iteration's data and gates + it, so returning a stale cached result for a different input is caught. +* **The judge computes all metrics** — no agent-provided number is trusted. +* **Fresh container per submission** — an ephemeral Modal app is used, so no + global state survives across evaluations. + +The remote worker is a single self-contained function serialized to Modal +(``serialized=True``), so the remote does not import this module and there is no +module-mounting to get wrong. The GPU image ships torch + triton (+ any extra +pip packages a task needs for its reference build). +""" +from __future__ import annotations + +import os + + +# --------------------------------------------------------------------------- # +# Remote worker (runs on the Modal GPU). Fully self-contained: every helper is +# nested so cloudpickle ships the whole thing. Takes and returns plain data. +# --------------------------------------------------------------------------- # +def _gpu_worker(payload: dict) -> dict: + import importlib + import os as _os + import sys as _sys + import tempfile + import time + import traceback + + import torch + + # Capture timing + sync references BEFORE importing any submission code, so a + # monkey-patch of torch.cuda.synchronize cannot affect measurement. + _perf = time.perf_counter + _sync = torch.cuda.synchronize + if not torch.cuda.is_available(): + return {"ok": False, "error": "cuda_unavailable"} + dev = "cuda" + + cfg = payload["cfg"] + prim = cfg["primitive"] + warmup = int(cfg.get("warmup", 3)) + iters = int(cfg.get("iters", 7)) + + def _materialize(files: dict, tag: str) -> str: + root = tempfile.mkdtemp(prefix=f"flash_{tag}_") + for rel, content in files.items(): + path = _os.path.join(root, rel) + parent = _os.path.dirname(path) + if parent: + _os.makedirs(parent, exist_ok=True) + with open(path, "w", encoding="utf-8") as fh: + fh.write(content) + _sys.path.insert(0, root) + return root + + _materialize(payload["baseline_files"], "baseline") + _materialize(payload["patched_files"], "patched") + ref = importlib.import_module(cfg["ref_module"]) + pkg = importlib.import_module(cfg["pkg"]) + + # ---- per-primitive: data gen, call, and quality metric ---------------- # + def gen(w, seed): + g = torch.Generator(device=dev).manual_seed(int(seed)) + if prim == "knn": + db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) + q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) + return {"queries": q, "database": db} + x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) + if prim == "kmeans": + perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] + return {"x": x, "init": x.index_select(0, perm).clone()} + return {"x": x} + + def call(mod, w, data): + if prim == "kmeans": + return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], + init_centroids=data["init"], tol=0.0) + if prim == "knn": + return mod.knn(data["queries"], data["database"], w["k"]) + if prim == "pca": + return mod.pca(data["x"], w["k"]) + if prim == "tsvd": + return mod.truncated_svd(data["x"], w["k"]) + raise ValueError(prim) + + def check_shape(w, out): + if prim == "kmeans": + _, cen, _ = out + if tuple(cen.shape) != (w["K"], w["D"]) or not torch.isfinite(cen).all(): + raise ValueError("bad kmeans output") + elif prim == "knn": + d, i = out + if tuple(d.shape) != (w["Q"], w["k"]) or tuple(i.shape) != (w["Q"], w["k"]): + raise ValueError("bad knn output") + if not torch.isfinite(d).all(): + raise ValueError("non-finite distances") + else: + a, b = out + comps = a if prim == "pca" else b + if tuple(comps.shape) != (w["k"], w["D"]) or not torch.isfinite(comps).all(): + raise ValueError("bad decomposition output") + + def inertia(x, centroids): + c = centroids.to(torch.float32) + cn = (c * c).sum(1) + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384] + d = (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ c.t()) + cn[None, :] + total += float(d.min(1).values.clamp_min(0).sum().item()) + return total + + def recall(agent_idx, ref_idx): + a = agent_idx.long(); b = ref_idx.long() + hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) + return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + + def ortho_err(comps): + c = comps.to(torch.float32); k = c.shape[0] + return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) + + def captured(x, comps, center): + c = comps.to(torch.float32) + mean = x.mean(0) if center else None + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384] + if center: + xb = xb - mean + p = xb @ c.t() + total += float((p * p).sum().item()) + return total / (x.shape[0] - 1) if center else total + + def verdict(w, data, ref_out, agent_out): + """Return (ok, reason, ref_val, agent_val) gating the agent output.""" + check_shape(w, agent_out) + if prim == "kmeans": + rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) + tol = float(cfg["inertia_tolerance"]) + ok = av <= (1.0 + tol) * rv + 1e-6 + return ok, ("inertia_regression" if not ok else ""), rv, av + if prim == "knn": + rec = recall(agent_out[1], ref_out[1]) + ok = rec >= float(cfg["recall_threshold"]) + return ok, ("recall_regression" if not ok else ""), 1.0, rec + center = (prim == "pca") + comps = agent_out[0] if prim == "pca" else agent_out[1] + ref_comps = ref_out[0] if prim == "pca" else ref_out[1] + oe = ortho_err(comps) + if oe > float(cfg["ortho_tolerance"]): + return False, "not_orthonormal", 0.0, oe + rv = captured(data["x"], ref_comps, center) + av = captured(data["x"], comps, center) + ok = av >= (1.0 - float(cfg["captured_tolerance"])) * rv - 1e-6 + return ok, ("captured_regression" if not ok else ""), rv, av + + def time_call(mod, w, data): + _sync(); t0 = _perf(); out = call(mod, w, data); _sync() + return (_perf() - t0) * 1000.0, out + + rows = [] + try: + for w in payload["workloads"]: + base_seed = int(w["seed"]) + # warmup on fresh data (kernels / autotune) — not measured + for i in range(warmup): + d = gen(w, base_seed + 100 + i) + call(ref, w, d); call(pkg, w, d); _sync() + del d + torch.cuda.empty_cache() + ratios, ref_val, agent_val = [], None, None + bad = None + for i in range(iters): + d = gen(w, base_seed + 10000 + i) # fresh every iteration + rt, rout = time_call(ref, w, d) + at, aout = time_call(pkg, w, d) + ok, reason, rv, av = verdict(w, d, rout, aout) # verify THIS iter + if not ok: + bad = reason; ref_val, agent_val = rv, av + break + ratios.append(rt / at if at > 0 else 0.01) + ref_val, agent_val = rv, av + del d, rout, aout + torch.cuda.empty_cache() + if bad is not None: + rows.append({"id": w["id"], "ok": False, "reason": bad, + "ref_val": ref_val, "agent_val": agent_val}) + continue + ratios.sort() + rows.append({"id": w["id"], "ok": True, + "speedup": ratios[len(ratios) // 2], + "ref_val": ref_val, "agent_val": agent_val}) + except Exception as exc: + return {"ok": False, "error": f"{type(exc).__name__}: {str(exc)[:200]}", + "trace": traceback.format_exc()[-500:]} + return {"ok": True, "rows": rows} + + +# --------------------------------------------------------------------------- # +# Host-side wrappers (run in the judge / agent container; need MODAL tokens). +# --------------------------------------------------------------------------- # +def _build_image(cfg: dict): + import modal + pip = list(cfg.get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])) + base = cfg.get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04") + return (modal.Image.from_registry(base, add_python="3.11") + .entrypoint([]) + .pip_install(*pip)) + + +# Substrings that mark a transient Modal control-plane / image-build failure +# (evicted build, gateway hiccup, app stopped mid-run) rather than a real error +# in the submission — safe to retry. +_TRANSIENT_MARKERS = ( + "external shut-down", "terminated due to external", "please try again", + "app_state_stopped", "conflicterror", "deadline exceeded", "connection reset", + "502 bad gateway", "503 service", "temporarily unavailable", "timed out", + "gateway", "eviction", "internalfailure", +) + + +def _is_transient(text: str) -> bool: + low = (text or "").lower() + return any(m in low for m in _TRANSIENT_MARKERS) + + +def run_remote(payload: dict) -> dict: + """Run the GPU worker on Modal and return its result dict. + + Retries transient Modal control-plane failures; a real error in the + submission is non-transient and surfaces immediately. Raises RuntimeError + with a sanitized message on unrecoverable failure. + """ + import time as _time + + import modal + + cfg = payload["cfg"] + attempts = max(1, int(cfg.get("modal_retries", 3))) + last = "modal_gpu_eval_failed" + for attempt in range(1, attempts + 1): + app = modal.App(cfg.get("app_name", "flash-kernel-eval")) + remote = app.function( + gpu=cfg.get("gpu", "H100"), + image=_build_image(cfg), + timeout=int(cfg.get("modal_timeout_seconds", 1800)), + serialized=True, + max_containers=1, + )(_gpu_worker) + try: + with modal.enable_output(), app.run(): + return remote.remote(payload) + except Exception as exc: # noqa: BLE001 + last = str(exc)[-400:] + if not _is_transient(last) or attempt == attempts: + raise RuntimeError(f"modal_gpu_eval_failed: {last}") from exc + _time.sleep(min(45, 10 * attempt)) + raise RuntimeError(f"modal_gpu_eval_failed: {last}") + + +def modal_available() -> bool: + return bool(os.environ.get("MODAL_TOKEN_ID") and os.environ.get("MODAL_TOKEN_SECRET")) diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md index 895372b63..6f59254a8 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md @@ -30,6 +30,6 @@ results. - Do not import external optimized libraries (write the kernels yourself), and do not access the environment, spawn processes, or use the network. - Keep the public `truncated_svd(...)` signature and return contract unchanged. -- Factor quality is gated (orthonormal components and captured energy vs the - naive baseline); do not sacrifice correctness for speed beyond the allowed - tolerance. +- Low-rank factorization quality is gated (orthonormal components + captured + energy vs the naive baseline); do not sacrifice correctness for speed beyond + the allowed tolerance. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py index 3ea63d120..73b036ec6 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,89 +1,77 @@ -"""Public self-test for the truncated-SVD kernel-optimization task. +"""Public GPU self-test for the Truncated SVD kernel-optimization task. -Runs the (patched) tsvdlib on the two public shapes, checks the factor quality -against a local naive full-SVD recomputation (orthonormality of the returned -components and the captured-energy ratio), and prints a rough speedup. This is a -convenience for local iteration only -- the graded workloads and thresholds are -hidden and differ from these shapes. +Times your current /app/tsvdlib against the naive baseline on a Modal GPU and +reports the quality verdict + speedup on two public shapes. Requires +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and +their thresholds are hidden and differ from these public shapes. """ from __future__ import annotations +import os import sys -import time - -PUBLIC_WORKLOADS = [ - {"N": 200_000, "D": 128, "k": 16, "seed": 1}, - {"N": 500_000, "D": 64, "k": 8, "seed": 2}, -] - - -def naive_truncated_svd(x, k): - import torch - U, S, Vh = torch.linalg.svd(x, full_matrices=False) - return S[:k].contiguous(), Vh[:k].contiguous() - - -def ortho_err(comps): - import torch - c = comps.to(torch.float32) - k = c.shape[0] - return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) +from pathlib import Path +sys.path.insert(0, "/opt") -def captured(x, comps): - import torch - c = comps.to(torch.float32) - total = 0.0 - for i in range(0, x.shape[0], 16384): - p = x[i:i + 16384] @ c.t() - total += float((p * p).sum().item()) - return total +APP_DIR = os.environ.get("APP_DIR", "/app") +PKG = "tsvdlib" +BASELINE_DIR = "/opt/tsvd_ref" +PUBLIC_WORKLOADS = [ + {"id": "p0", "N": 200_000, "D": 128, "k": 16, "seed": 1}, + {"id": "p1", "N": 500_000, "D": 64, "k": 8, "seed": 2}, +] -def bench(fn, warmup=2, iters=5): - import torch - for _ in range(warmup): - fn(); torch.cuda.synchronize() - ts = [] - for _ in range(iters): - torch.cuda.synchronize(); t0 = time.perf_counter() - fn(); torch.cuda.synchronize() - ts.append(time.perf_counter() - t0) - ts.sort() - return ts[len(ts) // 2] * 1000.0 +CFG = { + "primitive": "tsvd", + "pkg": PKG, + "ref_module": "reftsvd", + "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), + "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", + "pip": ["torch==2.5.1", "triton==3.1.0", "numpy"], + "app_name": "tsvd-kernel-opt-public", + "modal_timeout_seconds": 1800, + "warmup": 2, + "iters": 5, + "inertia_tolerance": 0.02, + "recall_threshold": 0.99, + "captured_tolerance": 0.02, + "ortho_tolerance": 0.02, +} + + +def _read(root: str, sub: str = "") -> dict: + base = Path(root) + scan = base / sub if sub else base + return {str(p.relative_to(base)): p.read_text(encoding="utf-8", errors="replace") + for p in scan.rglob("*.py")} def main() -> int: + import flash_gpu # baked at /opt/flash_gpu.py + if not flash_gpu.modal_available(): + print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU public test.") + return 1 + payload = { + "baseline_files": _read(BASELINE_DIR), + "patched_files": _read(APP_DIR, PKG), + "workloads": PUBLIC_WORKLOADS, + "cfg": CFG, + } try: - import torch - except Exception as e: # pragma: no cover - print(f"torch import failed: {e}") + result = flash_gpu.run_remote(payload) + except Exception as exc: # noqa: BLE001 + print(f"GPU run failed: {exc}") return 1 - if not torch.cuda.is_available(): - print("no CUDA device available; run this in a GPU-enabled container.") + if not result.get("ok"): + print(f"worker error: {result.get('error')}") return 1 - import tsvdlib - - for w in PUBLIC_WORKLOADS: - N, D, k, seed = w["N"], w["D"], w["k"], w["seed"] - g = torch.Generator(device="cuda").manual_seed(seed) - x = torch.randn(N, D, generator=g, device="cuda", dtype=torch.float32) - - _, ref_c = naive_truncated_svd(x, k) - out = tsvdlib.truncated_svd(x, k) - agent_c = out[1] - rc, ac = captured(x, ref_c), captured(x, agent_c) - ratio = ac / rc if rc > 0 else float("inf") - oerr = ortho_err(agent_c) - - ref_ms = bench(lambda: naive_truncated_svd(x, k)) - agent_ms = bench(lambda: tsvdlib.truncated_svd(x, k)) - ok = "OK" if (ratio >= 0.98 and oerr <= 0.02) else "QUALITY REGRESSION" - print(f"(N={N}, D={D}, k={k}) captured_ratio={ratio:.4f} ortho_err={oerr:.2e} [{ok}] " - f"baseline={ref_ms:.2f}ms yours={agent_ms:.2f}ms " - f"speedup={ref_ms / agent_ms:.2f}x") - del x - torch.cuda.empty_cache() + print(f"{'workload':10s} {'status':16s} {'speedup':>10s}") + for row in result["rows"]: + if row.get("ok"): + print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") + else: + print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason','')):16s} {'-':>10s}") return 0 diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/readme b/2.0/problems/truncated_svd_gpu_kernel_optimization/readme index b58cb001a..c4ea88758 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/readme +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/readme @@ -6,35 +6,32 @@ You are given a small GPU truncated-SVD library, `tsvdlib`, in the Harbor workspace at `/app/tsvdlib`. Its public entry point is: ```python -tsvdlib.truncated_svd(x, n_components) -> (singular_values, components) +tsvdlib.truncated_svd(x, n_components) + -> (singular_values, components) ``` -`x` is an `(N, D)` float32 CUDA tensor (the raw matrix -- it is **not** centered; -this is a truncated SVD, not PCA) and `n_components` is the number `k` of leading -singular triples to return. The function returns `singular_values`, a `(k,)` -float32 tensor of the top-k singular values in descending order, and -`components`, a `(k, D)` float32 tensor whose rows are the top-k right singular -vectors (an orthonormal set). The shipped implementation is correct but -deliberately naive: it computes the **full** SVD of the `(N, D)` matrix with -`torch.linalg.svd` and slices off the leading `k` factors, which is `O(N D^2)` -with a large constant and produces all `min(N, D)` singular triples even though -only the top `k` are ever used. +`x` is an `(N, D)` float32 CUDA tensor (the raw matrix -- it is **not** +centered) and `n_components` is the number `k` of leading singular triples to +return. The function returns `singular_values`, a `(k,)` float32 tensor of the +top-k singular values in descending order, and `components`, a `(k, D)` float32 +tensor whose rows are the top-k right singular vectors (an orthonormal set). The +shipped implementation is a straightforward, correct PyTorch version. Your goal is to make `tsvdlib.truncated_svd` **as fast as possible** on the GPU -while producing a truncated decomposition of the same quality. You may add -modules and Triton kernels inside the `tsvdlib` package and rewrite the internals -freely (for example a Gram-matrix `X^T X` followed by a top-k eigendecomposition, -fused kernels, reduced memory traffic, and so on). The public function signature -and return contract above must not change. +while producing a low-rank factorization of the same quality. You may rewrite the +internals of the package however you like and add new modules (including Triton +kernels) under `tsvdlib/`. The public function signature and return contract +above must not change, and every result must remain a deterministic function of +its inputs. ## Workload -The judge evaluates a family of held-out dense matrices that vary `(N, D, k)`, -spanning tall matrices (large `N`, small `D`), wide-feature matrices (large `D`), -and a range of small `k`. All workloads use float32 data drawn from a fixed -seeded generator, so each result is a deterministic function of the inputs. +The graded workloads are a family of held-out dense truncated-SVD problems that +vary `(N, D, k)`, spanning small/medium/large point counts, tall matrices (large +`N`, small `D`), and wide-feature matrices (large `D`), across a range of small +`k`. All use float32 data drawn from a fixed seeded generator. -Two representative *public* shapes you can use for local development (the graded +Two representative *public* shapes are available for local testing (the graded shapes are different and hidden): ```text @@ -42,19 +39,31 @@ shapes are different and hidden): (N=500000, D=64, k=8) ``` -Treat the workload as a general dense truncated SVD, not as shapes to -special-case. Improvements should be general kernel/throughput improvements, not -lookups keyed on specific `(N, D, k)` values. +Treat this as a general dense truncated SVD, not as specific shapes to +special-case. + +## Iterate on a GPU + +The agent workspace has no GPU. Use the public test to time your current code on +a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your +environment): + +```bash +bash /app/public_test.sh +``` + +It reports, per public shape, whether your result passes the quality gate and +your speedup over the baseline. ## Submission -The submitted artifact is a patch file over the `tsvdlib` package: +The submitted artifact is a patch over the `tsvdlib` package: ```text /app/solution.patch ``` -After editing `/app/tsvdlib`, generate and submit a patch: +After editing `/app/tsvdlib`, generate and submit: ```bash bash /app/make_submission.sh @@ -62,72 +71,62 @@ bash /app/submit.sh ``` Submissions are asynchronous; submit early and keep iterating. The judge applies -your patch to a clean copy of `tsvdlib`, imports the patched package, and times -it against the original naive implementation on the same hardware and the same -seeded data. +your patch to a clean copy of `tsvdlib` and times it against the original +baseline on a GPU, on the same seeded data. ## Correctness -Correctness is a gate, and it is checked in a way that is invariant to the sign -and rotation ambiguities of the SVD (you are not required to match the baseline's -vectors elementwise). For each workload the judge checks two things about your -`components` `V` (a `(k, D)` tensor): +Correctness is a gate, checked in a way that is invariant to the sign and +rotation ambiguities of the SVD (you are not required to match the baseline's +vectors elementwise). On every timed iteration the judge checks two things about +your returned `components` `V` (a `(k, D)` tensor), computed from the tensors you +return on identical data: -- **Orthonormality.** The rows must form an orthonormal set: the maximum - absolute entry of `V V^T - I_k` must stay within a small tolerance. +- **Orthonormality.** The rows must form an orthonormal set: the maximum absolute + entry of `V V^T - I_k` must stay within a small tolerance. - **Captured energy.** The energy your subspace captures, - `||X V^T||_F^2 = sum over points of ||x_n V^T||^2`, must be at least - `(1 - tol)` times the energy captured by the naive baseline's top-k subspace on - the same data. + `||X V^T||_F^2`, must be at least `(1 - tol)` times the energy captured by the + baseline's top-k subspace on the same data. -Crashes, non-finite outputs, wrong shapes/dtypes, timeouts, non-orthonormal -components, and captured energy that regresses beyond the tolerance are all -penalized before speed is considered. You may trade a little numerical precision -(e.g. lower-precision accumulation) for speed as long as you stay within the -quality tolerance. +Crashes, non-finite output, wrong shapes/dtypes, timeouts, non-orthonormal +components, and captured energy that regresses beyond the tolerance are penalized +before speed is considered. You may trade a little numerical precision for speed +as long as you stay within the quality tolerance. ## Scoring -Valid submissions are scored by speedup relative to the naive baseline on the -same hardware and workloads. For each workload: +Valid submissions are scored by speedup relative to the baseline on the same +hardware and workloads. For each workload: ```text speedup = baseline_time / your_time ``` The objective is the geometric mean of per-workload speedups, so broad speedups -across the workload family are preferred over one large outlier. A result no -faster than the baseline earns 0; regressions earn 0. The raw geometric-mean -speedup is reported in the evaluator metrics. +are preferred over a single large outlier. A result no faster than the baseline +earns 0; regressions earn 0. The raw geometric-mean speedup is reported in the +evaluator metrics. ## Patch Policy -The evaluator validates the patch before running it. Only files under the +The evaluator validates the patch before running it. Only Python files under the package may change: ```text tsvdlib/** ``` -New Python modules inside `tsvdlib/` (for example Triton kernel files) are -allowed. Patches may not: - -- modify anything outside `tsvdlib/`; -- import or call an external optimized ML/kernel library (the point is to write - the kernels yourself); -- read or write environment variables, spawn processes, or access the network; -- special-case the hidden workload shapes. - -The patch must be reasonably sized and apply cleanly to the clean `tsvdlib` tree. +New Python modules inside `tsvdlib/` are allowed. Patches may not: modify +anything outside `tsvdlib/`; import or call an external optimized ML/kernel +library (write the kernels yourself); read or write environment variables, spawn +processes, or access the network; or otherwise tamper with the measurement +framework. The timing harness measures your code on freshly generated data every +iteration and re-verifies quality each time, so caching results across calls does +not help. ## Resource Budget ```text -GPU: single device (H100 reference; Triton paths also run on L40S / A100) -vCPUs: 8 -memory: 32 GiB -storage: 32 GiB +GPU: single Modal GPU (H100 reference; the Triton paths also run on L40S / A100) +Agent container: CPU-only (GPU work is offloaded to Modal) ``` - -The judge runs the timing worker in a subprocess under a clean, minimal -environment with a fixed warmup/measurement schedule and a per-run timeout. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch b/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch index 0d87e3901..1d4d2d85c 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch @@ -1,40 +1,786 @@ -diff --git a/tsvdlib/_gram_tsvd.py b/tsvdlib/_gram_tsvd.py +diff --git a/tsvdlib/_kernels/__init__.py b/tsvdlib/_kernels/__init__.py new file mode 100644 -index 0000000..fe425e4 +index 0000000..e69de29 +diff --git a/tsvdlib/_kernels/linalg/__init__.py b/tsvdlib/_kernels/linalg/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/tsvdlib/_kernels/linalg/eigh/__init__.py b/tsvdlib/_kernels/linalg/eigh/__init__.py +new file mode 100644 +index 0000000..f610f92 --- /dev/null -+++ b/tsvdlib/_gram_tsvd.py -@@ -0,0 +1,24 @@ -+"""Gram-matrix truncated SVD: X^T X + top-k eigendecomposition. -+ -+Avoids the full SVD of the ``(N, D)`` matrix. The right singular vectors of -+``X`` are the eigenvectors of the Gram matrix ``G = X^T X`` (a single ``(D, D)`` -+GEMM), and the singular values are the square roots of the corresponding -+eigenvalues. We form ``G`` once, eigendecompose the small ``(D, D)`` matrix with -+:func:`torch.linalg.eigh`, and keep only the top ``k`` triples. This is -+``O(N D^2)`` for the GEMM plus ``O(D^3)`` for the eigensolve, but with a far -+smaller constant than a full SVD and no ``(N, min(N, D))`` factor materialised. ++++ b/tsvdlib/_kernels/linalg/eigh/__init__.py +@@ -0,0 +1,29 @@ ++"""Symmetric eigendecomposition. ++ ++Public API ++---------- ++Single dispatcher (recommended): ++ ++ eigh(A, K=None, *, tol=None, backend=None) ++ ++By default ``eigh(A)`` is **exact** in the input dtype (cuSOLVER / ++single-kernel Jacobi for small ``N``). The exact ``tol=None`` path uses ++cuSOLVER (with a small-N CPU-MKL / Triton Jacobi fast path); it does not ++reach into the approximate (Halko / QDWH) variants. ++ ++Backend-explicit (power users): ++ ++ eigh_cusolver(A) torch.linalg.eigh -- ~1e-7 residual ++ eigh_jacobi(A, ...) single-block Jacobi for small N ++""" ++from tsvdlib._kernels.linalg.eigh.impl import eigh, route_op_name ++from tsvdlib._kernels.linalg.eigh.cusolver import eigh as eigh_cusolver ++from tsvdlib._kernels.linalg.eigh.jacobi import eigh as eigh_jacobi ++ ++ ++__all__ = [ ++ "eigh", ++ "eigh_cusolver", ++ "eigh_jacobi", ++ "route_op_name", ++] +diff --git a/tsvdlib/_kernels/linalg/eigh/cost.py b/tsvdlib/_kernels/linalg/eigh/cost.py +new file mode 100644 +index 0000000..7a28d91 +--- /dev/null ++++ b/tsvdlib/_kernels/linalg/eigh/cost.py +@@ -0,0 +1,21 @@ ++"""Cost model shim for eigh. ++ ++The full cost/roofline subsystem (``info.estimate`` / ``info.roofline``) ++and the approximate eigh variants (Halko / QDWH) are not part of this ++build. This module keeps the dispatcher's routed-variant label available ++via :func:`route_op_name` (re-exported from ++:mod:`tsvdlib._kernels.linalg.eigh.impl`); the exact cuSOLVER path is the ++only variant this build ships. ++""" ++from tsvdlib._kernels.linalg.eigh.impl import route_op_name as _route_op_name ++ ++ ++def _N(shape): ++ return shape[0] if isinstance(shape, (tuple, list)) else shape ++ ++ ++def recommend(shape, params=None, tol=None, dtype="float32", device="H100", **_): ++ """Mirror the dispatcher's choice (exact cuSOLVER in this build).""" ++ N = _N(shape) ++ K = (params or {}).get("K") ++ return {"variant": _route_op_name(N=N, K=K, tol=tol)} +diff --git a/tsvdlib/_kernels/linalg/eigh/cusolver.py b/tsvdlib/_kernels/linalg/eigh/cusolver.py +new file mode 100644 +index 0000000..354ac9a +--- /dev/null ++++ b/tsvdlib/_kernels/linalg/eigh/cusolver.py +@@ -0,0 +1,7 @@ ++"""eigh_cusolver — torch.linalg.eigh (cuSOLVER syevd). Reference precision.""" ++import torch ++ ++ ++def eigh(A: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: ++ """torch.linalg.eigh — cuSOLVER syevd. ~1e-7 residual.""" ++ return torch.linalg.eigh(A) +diff --git a/tsvdlib/_kernels/linalg/eigh/impl.py b/tsvdlib/_kernels/linalg/eigh/impl.py +new file mode 100644 +index 0000000..1ccec5c +--- /dev/null ++++ b/tsvdlib/_kernels/linalg/eigh/impl.py +@@ -0,0 +1,96 @@ ++"""eigh dispatcher. ++ ++By default ``eigh(A)`` is **exact** (cuSOLVER / MKL on the input dtype) -- ++no precision is lost beyond what the input already carries. ++ ++Routing rule (formerly in ``route.py``): ++ ++ * ``backend`` overrides everything. ++ * exact (``tol`` None / ``<= 0``, the only path this build ships): ++ * Truncated (``K`` given) : exact full eigh + slice top-K. ++ * Full : ``triton_eigh`` (CPU MKL trick for ++ small N) or cuSOLVER otherwise. ++ ++The approximate variants (Halko subspace iteration, QDWH spectral D&C) ++are not part of this build; the exact cuSOLVER path is used throughout. +""" +from __future__ import annotations + ++from typing import Optional ++ ++import torch ++ ++from tsvdlib._kernels.linalg.eigh import cusolver, jacobi ++from tsvdlib._kernels.linalg.eigh.triton import triton_eigh ++ ++ ++def _route( ++ *, ++ N: int, ++ K: Optional[int] = None, ++ tol: Optional[float] = None, ++ backend: Optional[str] = None, ++) -> str: ++ """Pick the eigh variant. Returns the bare name (no ``eigh_`` prefix). ++ ++ The exact path routes to cuSOLVER (with a small-N CPU-MKL / Triton ++ fast path handled inside the cusolver / triton backend). ++ ``jacobi`` is reachable only via explicit ``backend="jacobi"`` -- a ++ single-CTA Triton cyclic-Jacobi kernel that beats cuSOLVER at very ++ small N (N <= 16) but is slower beyond that. No C++ toolchain / ++ ninja is required on first call. ++ """ ++ if backend is not None: ++ return backend ++ return "cusolver" ++ ++ ++def route_op_name(*, N: int, K: Optional[int] = None, ++ tol: Optional[float] = None) -> str: ++ """Canonical ``eigh_`` label.""" ++ return "eigh_" + _route(N=N, K=K, tol=tol) ++ ++ ++def eigh( ++ A: torch.Tensor, ++ K: Optional[int] = None, ++ *, ++ tol: Optional[float] = None, ++ backend: Optional[str] = None, ++ **kwargs, ++): ++ """Symmetric eigendecomposition. ++ ++ Args: ++ A: ``(N, N)`` symmetric tensor. ++ K: optional truncation -- return only top-K eigenpairs (ascending). ++ tol: residual tolerance. ``None`` (default) -> EXACT in the input ++ dtype (cuSOLVER; the Triton ``jacobi`` backend is opt-in via ++ ``backend="jacobi"`` since cuSOLVER beats it past N ~ 16). ++ backend: explicit ``"cusolver" | "jacobi"`` override. ++ **kwargs: forwarded to the chosen variant. ++ ++ Returns: ++ ``(eigvals, eigvecs)`` ascending; both sliced to top-K if ``K`` ++ was supplied. ++ """ ++ if not isinstance(A, torch.Tensor) or A.ndim != 2: ++ raise ValueError("eigh expects a 2D tensor") ++ N = A.size(0) ++ chosen = _route(N=N, K=K, tol=tol, backend=backend) ++ ++ if chosen == "jacobi": ++ eigvals, eigvecs = jacobi.eigh(A, **kwargs) ++ else: # cusolver / default exact ++ # Use the small-D CPU-MKL trick automatically (it IS the exact ++ # path -- input dtype preserved, just dispatched to the faster ++ # solver for small N). ++ eigvals, eigvecs = triton_eigh(A) ++ ++ if K is not None: ++ eigvals = eigvals[-K:] ++ eigvecs = eigvecs[:, -K:] ++ return eigvals, eigvecs ++ ++ ++__all__ = ["eigh", "route_op_name"] +diff --git a/tsvdlib/_kernels/linalg/eigh/jacobi.py b/tsvdlib/_kernels/linalg/eigh/jacobi.py +new file mode 100644 +index 0000000..be549a4 +--- /dev/null ++++ b/tsvdlib/_kernels/linalg/eigh/jacobi.py +@@ -0,0 +1,30 @@ ++"""eigh_jacobi -- single-block row-cyclic Jacobi for small N (<= 128). ++ ++Thin user-facing wrapper over the Triton kernel in ++:mod:`tsvdlib._kernels.linalg.eigh.triton.jacobi`. No CUDA / C++ compile step ++on first call (the old ``jacobi_impl.py`` ``load_inline`` extension ++was retired -- ninja is no longer required to import this backend). ++ ++Achieved residual is at the fp32 noise floor (``~1e-4`` for the ++N=46 reference shape, identical to ``torch.linalg.eigh`` on fp32 ++input), and the kernel beats cuSOLVER ``syevd`` at N <= 16 where the ++syevd launch fixed-cost dominates. ++""" ++from tsvdlib._kernels.linalg.eigh.triton.jacobi import triton_jacobi_eigh as _triton_jacobi ++ ++ ++def eigh(A, num_sweeps: int = 6): ++ """Single-kernel cyclic Jacobi eigensolver. ++ ++ Args: ++ A: ``(N, N)`` symmetric float32 CUDA tensor; not modified. ++ num_sweeps: full row-cyclic sweeps; each sweep performs ++ ``N*(N-1)/2`` Givens rotations. ``6`` is enough to reach ++ fp32 noise (~1e-4) for N <= 64; bump to ``8-12`` for ++ tighter convergence at very large N. ++ ++ Returns: ++ ``(w, V)`` -- ``(N,)`` eigenvalues (ascending) and ``(N, N)`` ++ eigenvectors as columns. ``A @ V[:, i] ≈ w[i] * V[:, i]``. ++ """ ++ return _triton_jacobi(A, num_sweeps=num_sweeps) +diff --git a/tsvdlib/_kernels/linalg/eigh/triton/__init__.py b/tsvdlib/_kernels/linalg/eigh/triton/__init__.py +new file mode 100644 +index 0000000..b6cdf0a +--- /dev/null ++++ b/tsvdlib/_kernels/linalg/eigh/triton/__init__.py +@@ -0,0 +1,28 @@ ++"""eigh triton backend. ++ ++Re-exports the top-level functions / classes / constants from each ++component file. ``@triton.jit`` kernels stay private to their file ++(call them via the Python wrapper that lives next to them). ++ ++Component files: ++ ++* :mod:`householder` -- Householder tridiagonalisation + unrolled ++ QR finaliser. Powers :func:`tsvdlib._kernels.linalg.eigh.triton_eigh`, ++ the small-D (D <= ~256) dense path used by PCA / TruncSVD / ++ Halko subspace iteration. ++* :mod:`jacobi` -- Row-cyclic Givens-Jacobi for N <= 128. ++ Powers :func:`tsvdlib._kernels.linalg.eigh.eigh_jacobi`, an opt-in ++ backend that beats cuSOLVER at very small N (N <= 16) without ++ the ``load_inline`` CUDA / C++ compile step the old ++ implementation needed. ++""" ++from tsvdlib._kernels.linalg.eigh.triton.householder import ( ++ _eigh_cpu_initialized, ++ triton_eigh, ++) ++from tsvdlib._kernels.linalg.eigh.triton.jacobi import triton_jacobi_eigh ++ ++__all__ = [ ++ "triton_eigh", ++ "triton_jacobi_eigh", ++] +diff --git a/tsvdlib/_kernels/linalg/eigh/triton/householder.py b/tsvdlib/_kernels/linalg/eigh/triton/householder.py +new file mode 100644 +index 0000000..0dff1c8 +--- /dev/null ++++ b/tsvdlib/_kernels/linalg/eigh/triton/householder.py +@@ -0,0 +1,171 @@ ++"""Triton eigendecomposition for small dense symmetric matrices. ++ ++Uses Householder tridiagonalisation followed by an unrolled QR loop. ++Designed for D ≤ ~256 — the small-matrix path used by PCA, TruncSVD, ++and the Halko subspace-iteration finaliser (linalg/eigh/halko.py). ++ ++Public surface: ++ - triton_eigh(A) -> (eigvals, eigvecs) for A: (D, D) fp32 symmetric. ++""" ++ +import torch ++import triton ++import triton.language as tl ++ ++ ++# ============================================================================= ++# Kernel: Householder Tridiagonalization + Eigensolver ++# ++# Single-program Triton kernel: reduces symmetric A to tridiagonal form T via ++# Householder reflections. All data stays in L2 for D ≤ ~2048. ++# ++# The tridiagonal eigenvalues are found via Sturm bisection (no cuSOLVER). ++# Eigenvectors: inverse iteration on T, then Householder back-transformation. ++# ++# For D=256: ~0.4ms total vs cuSOLVER eigh ~5ms. The win comes from avoiding ++# cuSOLVER's ~5ms fixed overhead of internal kernel launches/workspace alloc. ++# ============================================================================= ++ ++@triton.jit ++def _householder_tridiag_kernel( ++ A_ptr, p_buf_ptr, tau_ptr, ++ D, stride_a, ++ BLOCK: tl.constexpr, ++): ++ """Householder tridiagonalization: symmetric A → tridiagonal (in-place). ++ ++ After kernel: ++ - A.diagonal() = tridiagonal diagonal ++ - A.diagonal(offset=-1) = tridiagonal subdiagonal ++ - A lower triangle (below subdiag): normalized Householder vectors v'[1:] ++ where v' = v / v[0], so v'[0] = 1 (implicit) ++ - tau_ptr[k] = tau' = 2 * v[0]² / ||v||² (LAPACK-style scalar) ++ """ ++ offs = tl.arange(0, BLOCK) ++ ++ for k in tl.range(0, D - 2): ++ n = D - k - 1 ++ base = k + 1 ++ ++ # ── Compute ||x||² for x = A[base:D, k] ── ++ xnorm_sq = 0.0 ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ xj = tl.load(A_ptr + (base + j) * stride_a + k, mask=mask, other=0.0) ++ xnorm_sq += tl.sum(xj * xj) ++ ++ if xnorm_sq > 1e-30: ++ xnorm = tl.sqrt(xnorm_sq) ++ x0 = tl.load(A_ptr + base * stride_a + k) ++ ++ # alpha = -sign(x0) * ||x|| ++ alpha = tl.where(x0 >= 0.0, -xnorm, xnorm) ++ ++ # v[0] = x[0] - alpha ++ v0 = x0 - alpha ++ ++ # tau_raw = 2 / ||v||² where ||v||² = v0² + ||x[1:]||² ++ vnorm_sq = v0 * v0 + (xnorm_sq - x0 * x0) ++ tau = 2.0 / vnorm_sq ++ ++ # LAPACK convention: normalize v' = v / v[0], tau' = tau * v0² ++ # v'[0] = 1 (implicit), v'[1:] = v[1:] / v[0] ++ # Store v'[1:] in A[base+1:D, k] (overwrites x[1:]) ++ inv_v0 = 1.0 / v0 ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = (j < n) & (j > 0) ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, mask=mask, other=0.0) ++ tl.store(A_ptr + (base + j) * stride_a + k, vj * inv_v0, mask=mask) ++ ++ # Store v'[0] = 1.0 temporarily at A[base, k] (for the matvec) ++ tl.store(A_ptr + base * stride_a + k, 1.0) ++ ++ tau_prime = tau * v0 * v0 ++ tl.store(tau_ptr + k, tau_prime) ++ ++ # ── p = tau' * A_sub @ v' ── ++ # A_sub = A[base:D, base:D], v' = A[base:D, k] (normalized, v'[0]=1) ++ for i in tl.range(0, n): ++ dot = 0.0 ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ aij = tl.load(A_ptr + (base + i) * stride_a + (base + j), ++ mask=mask, other=0.0) ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, ++ mask=mask, other=0.0) ++ dot += tl.sum(aij * vj) ++ tl.store(p_buf_ptr + i, dot * tau_prime) ++ ++ # ── w = p - (tau'/2 * v'.T @ p) * v' ── ++ vtp = 0.0 ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, ++ mask=mask, other=0.0) ++ pj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) ++ vtp += tl.sum(vj * pj) + ++ coeff = 0.5 * tau_prime * vtp ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ pj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, ++ mask=mask, other=0.0) ++ tl.store(p_buf_ptr + j, pj - coeff * vj, mask=mask) + -+def truncated_svd(x, n_components): -+ N, D = x.shape -+ G = x.t() @ x # (D, D) Gram matrix -- one GEMM -+ evals, evecs = torch.linalg.eigh(G) # ascending -+ k = int(n_components) -+ top = torch.argsort(evals, descending=True)[:k] -+ s = evals.index_select(0, top).clamp_min(0).sqrt() # (k,) singular values -+ comps = evecs.index_select(1, top).t().contiguous() # (k, D) right singular vectors -+ return s, comps ++ # ── A_sub -= v @ w.T + w @ v.T (symmetric rank-2 update) ── ++ for i in tl.range(0, n): ++ vi = tl.load(A_ptr + (base + i) * stride_a + k) ++ wi = tl.load(p_buf_ptr + i) ++ for b in tl.range(0, n, BLOCK): ++ j = (b + offs).to(tl.int64) ++ mask = j < n ++ aij = tl.load(A_ptr + (base + i) * stride_a + (base + j), ++ mask=mask, other=0.0) ++ vj = tl.load(A_ptr + (base + j) * stride_a + k, ++ mask=mask, other=0.0) ++ wj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) ++ tl.store(A_ptr + (base + i) * stride_a + (base + j), ++ aij - vi * wj - wi * vj, mask=mask) ++ ++ # Store subdiagonal (overwrites v[0], but v[1:] survives for back-transform) ++ tl.store(A_ptr + base * stride_a + k, alpha) ++ tl.store(A_ptr + k * stride_a + base, alpha) ++ ++ ++_eigh_cpu_initialized = False ++ ++def triton_eigh(A: torch.Tensor) -> tuple: ++ """Eigendecomposition: CPU MKL LAPACK for D ≤ 512, cuSOLVER for D > 512. ++ ++ For D ≤ 512: GPU→CPU + MKL dsyev (4 threads) + CPU→GPU. ++ Avoids cuSOLVER's fixed overhead (~5ms kernel launches + workspace). ++ D=64: 0.18ms (4.5x faster), D=256: 1.8ms (2.6x), D=512: 6ms (2.2x). ++ For D > 512: cuSOLVER eigh (multi-SM parallelism, O(D³) dominates). ++ ++ Why not Triton Householder on GPU? Single-SM approach is 4-73x slower ++ than cuSOLVER — serial Householder loop bottlenecked by per-iteration ++ latency. Eigendecomposition requires multi-SM parallelism for large D. ++ """ ++ D = A.shape[0] ++ ++ if D > 512: ++ return torch.linalg.eigh(A) ++ ++ # CPU MKL LAPACK with 4 threads: optimal for D ≤ 512 eigendecomposition. ++ # set_num_threads has ~3ms overhead per call, so we set it once. ++ global _eigh_cpu_initialized ++ if not _eigh_cpu_initialized: ++ torch.set_num_threads(4) ++ _eigh_cpu_initialized = True ++ ++ torch.cuda.synchronize() ++ A_cpu = A.cpu() ++ eigenvalues, eigenvectors = torch.linalg.eigh(A_cpu) ++ return eigenvalues.to(A.device), eigenvectors.to(A.device) ++ +diff --git a/tsvdlib/_kernels/linalg/eigh/triton/jacobi.py b/tsvdlib/_kernels/linalg/eigh/triton/jacobi.py +new file mode 100644 +index 0000000..351d508 +--- /dev/null ++++ b/tsvdlib/_kernels/linalg/eigh/triton/jacobi.py +@@ -0,0 +1,219 @@ ++"""Triton row-cyclic Jacobi eigensolver for small symmetric matrices (N <= 128). ++ ++Single-program kernel — one CTA holds the full A and V matrices in HBM ++(small enough at N ≤ 128 that even the slow HBM round-trips per ++rotation cost <100 µs on H100) and performs a classical row-cyclic ++Jacobi sweep: ``N*(N-1)/2`` Givens rotations per sweep, default 6 ++sweeps. Each rotation zeroes the off-diagonal element ``A[p, q]`` via ++the two-sided update ``A' = G^T A G`` and accumulates ``V' = V G``. ++ ++Replaces the previous ``jacobi_impl.py`` ``torch.utils.cpp_extension. ++load_inline`` CUDA kernel -- removes the first-call ``ninja`` C++ ++compile step so the user never needs a CUDA toolchain installed to ++use ``tsvdlib._kernels.linalg.eigh.eigh(..., backend="jacobi")``. ++ ++Sequencing vs Brent-Luk ++----------------------- ++The CUDA original used Brent-Luk *parallel* pairing (N/2 simultaneous ++rotations per round, N-1 rounds per sweep), which converges in 8-12 ++sweeps. The Triton version uses *cyclic* pairing (one rotation at a ++time) which converges in 5-8 sweeps but pays per-rotation kernel ++overhead. At N ≤ 64 the cyclic path runs in ~0.5 ms with 6 sweeps, ++well under the cuSOLVER ``syevd`` fixed launch overhead (~5 ms) the ++``jacobi`` backend was added to beat. For N > 128 the cyclic-Jacobi ++constant factor crosses cuSOLVER's; the dispatcher in ++``linalg.eigh.impl`` caps the jacobi backend at N ≤ 128 anyway. ++ ++Public surface ++-------------- ++* :func:`triton_jacobi_eigh` -- ``(A, num_sweeps) -> (eigvals, eigvecs)``; ++ fp32 symmetric input, fp32 sorted-ascending output. ++""" ++from __future__ import annotations ++ ++import torch ++import triton ++import triton.language as tl ++ ++ ++@triton.jit ++def _jacobi_cyclic_kernel( ++ A_ptr, # (N_PAD, N_PAD) fp32, row-major; overwritten in-place ++ V_ptr, # (N_PAD, N_PAD) fp32, row-major; initialised to I + accumulates ++ N, # active dimension (may be < N_PAD when caller padded) ++ N_PAD: tl.constexpr, ++ NUM_SWEEPS: tl.constexpr, ++): ++ """Row-cyclic Jacobi: one CTA, sequential pair rotations. ++ ++ For each sweep, iterate (p, q) with 0 <= p < q < N; compute the ++ Givens rotation that zeroes ``A[p, q]`` and update rows p, q + ++ cols p, q of A and cols p, q of V. ``A_ptr`` ends up with the ++ eigenvalues on its diagonal (not yet sorted); ``V_ptr`` ends up ++ with the corresponding eigenvectors as columns. ++ ++ Grid: ``(1,)``. Strides: row-major, so element ``[i, j]`` lives ++ at ``ptr + i * N_PAD + j``. ++ """ ++ n_offs = tl.arange(0, N_PAD) ++ n_mask = n_offs < N ++ ++ # ── Initialise V = I_{N_PAD} ──────────────────────────────────── ++ # The padding rows/cols stay zero off-diagonal and one on the ++ # diagonal so the padded eigenvalues are exactly the diagonal of ++ # the padded A (which the host has already set to a sentinel ++ # value large enough to sort to the end and be dropped). ++ v_init = (n_offs[:, None] == n_offs[None, :]).to(tl.float32) ++ tl.store(V_ptr + n_offs[:, None] * N_PAD + n_offs[None, :], v_init) ++ ++ for _sweep in tl.range(0, NUM_SWEEPS): ++ for p in tl.range(0, N - 1): ++ for q in tl.range(p + 1, N): ++ # ── Compute Givens c, s zeroing A[p, q] ───────────── ++ # Two-sided rotation G^T A G with ++ # G[p,p]=c, G[p,q]=-s, G[q,p]=s, G[q,q]=c ++ # zeroes A[p,q] when tau = (A[p,p] - A[q,q]) / (2 A[p,q]). ++ a_pp = tl.load(A_ptr + p * N_PAD + p) ++ a_qq = tl.load(A_ptr + q * N_PAD + q) ++ a_pq = tl.load(A_ptr + p * N_PAD + q) ++ ++ thresh = 1e-30 * (tl.abs(a_pp) + tl.abs(a_qq) + 1e-37) ++ if tl.abs(a_pq) > thresh: ++ d = a_pp - a_qq ++ theta = d / (2.0 * a_pq) ++ if theta >= 0.0: ++ t = 1.0 / (theta + tl.sqrt(1.0 + theta * theta)) ++ else: ++ t = 1.0 / (theta - tl.sqrt(1.0 + theta * theta)) ++ c = 1.0 / tl.sqrt(1.0 + t * t) ++ s = t * c ++ else: ++ c = 1.0 ++ s = 0.0 ++ ++ # ── Row update on A: rows p and q ─────────────────── ++ row_p = tl.load( ++ A_ptr + p * N_PAD + n_offs, mask=n_mask, other=0.0, ++ ) ++ row_q = tl.load( ++ A_ptr + q * N_PAD + n_offs, mask=n_mask, other=0.0, ++ ) ++ new_row_p = c * row_p + s * row_q ++ new_row_q = -s * row_p + c * row_q ++ tl.store(A_ptr + p * N_PAD + n_offs, new_row_p, mask=n_mask) ++ tl.store(A_ptr + q * N_PAD + n_offs, new_row_q, mask=n_mask) ++ ++ # ── Column update on A: cols p and q ──────────────── ++ # The row update just touched A[p, :] and A[q, :], so ++ # the column reads here see the *post-row-update* ++ # values. The two-sided update is correct because ++ # only A[p, p] / A[p, q] / A[q, p] / A[q, q] depend ++ # on both row and col rotations -- and at convergence ++ # A[p, q] -> 0 so the order doesn't affect the limit. ++ col_p = tl.load( ++ A_ptr + n_offs * N_PAD + p, mask=n_mask, other=0.0, ++ ) ++ col_q = tl.load( ++ A_ptr + n_offs * N_PAD + q, mask=n_mask, other=0.0, ++ ) ++ new_col_p = c * col_p + s * col_q ++ new_col_q = -s * col_p + c * col_q ++ tl.store(A_ptr + n_offs * N_PAD + p, new_col_p, mask=n_mask) ++ tl.store(A_ptr + n_offs * N_PAD + q, new_col_q, mask=n_mask) ++ ++ # ── Eigenvector accumulation V <- V G ─────────────── ++ v_col_p = tl.load( ++ V_ptr + n_offs * N_PAD + p, mask=n_mask, other=0.0, ++ ) ++ v_col_q = tl.load( ++ V_ptr + n_offs * N_PAD + q, mask=n_mask, other=0.0, ++ ) ++ new_v_col_p = c * v_col_p + s * v_col_q ++ new_v_col_q = -s * v_col_p + c * v_col_q ++ tl.store(V_ptr + n_offs * N_PAD + p, new_v_col_p, mask=n_mask) ++ tl.store(V_ptr + n_offs * N_PAD + q, new_v_col_q, mask=n_mask) ++ ++ ++def _next_pow2(x: int) -> int: ++ if x <= 1: ++ return 1 ++ return 1 << (x - 1).bit_length() ++ ++ ++# Practical upper bound: at N=128 + 6 sweeps the kernel issues ++# ~48K rotations sequentially. Empirically this runs in ~3 ms on ++# H100/H200. Above N=128 the cyclic-Jacobi constant factor crosses ++# cuSOLVER's; ``linalg.eigh.impl`` honours this cap when routing. ++_MAX_N = 128 ++ ++ ++def triton_jacobi_eigh(A: torch.Tensor, num_sweeps: int = 6): ++ """Eigendecomposition of symmetric ``A`` via row-cyclic Triton Jacobi. ++ ++ Args: ++ A: ``(N, N)`` symmetric float32 CUDA tensor. Not modified ++ (a working copy is allocated internally). ++ num_sweeps: number of full Jacobi sweeps (each = ``N*(N-1)/2`` ++ Givens rotations). Default 6 -- ~1e-6 residual for ++ well-conditioned spectra. ++ ++ Returns: ++ ``(w, V)`` where ``w`` is ``(N,)`` eigenvalues in ascending ++ order and ``V`` is ``(N, N)`` eigenvectors as columns ++ (``A @ V[:, i] ≈ w[i] * V[:, i]``). ++ """ ++ assert A.dim() == 2 and A.size(0) == A.size(1), "A must be square" ++ assert A.dtype == torch.float32, "only float32 supported" ++ assert A.is_cuda, "A must be on CUDA" ++ ++ N = A.size(0) ++ if N > _MAX_N: ++ raise ValueError( ++ f"triton_jacobi_eigh only supports N <= {_MAX_N}; got N={N}. " ++ f"Route to ``backend='cusolver'`` for larger problems." ++ ) ++ ++ # Pad to the next power of two so the Triton tile is a ++ # constexpr-friendly size. The padding rows/cols get a sentinel ++ # diagonal entry that sorts to the end and is dropped on return. ++ N_PAD = max(2, _next_pow2(N)) ++ if N == N_PAD: ++ A_work = A.contiguous().clone() ++ else: ++ # Sentinel: 10x the largest diagonal absolute value of A so ++ # the padded eigenvalues sort cleanly to the end of ``w`` ++ # and are removed by the slice below. ++ diag_abs = float(torch.max(torch.abs(torch.diagonal(A))).item()) + 1.0 ++ sentinel = diag_abs * 10.0 ++ A_work = torch.zeros( ++ N_PAD, N_PAD, dtype=torch.float32, device=A.device, ++ ) ++ A_work[:N, :N] = A ++ # Set the padded diagonal entries to the sentinel. ++ idx = torch.arange(N, N_PAD, device=A.device) ++ A_work[idx, idx] = sentinel ++ ++ V_work = torch.empty(N_PAD, N_PAD, dtype=torch.float32, device=A.device) ++ ++ # Single-CTA, single-warp launch. The kernel is dominated by ++ # the sequential dependency between successive Givens rotations, ++ # so adding warps would only waste threads on broadcast scalars ++ # (and risks losing convergence on N_PAD >= 64 where multi-warp ++ # scalar reads can race with the immediately preceding store on ++ # some Triton 3.x configs). ++ _jacobi_cyclic_kernel[(1,)]( ++ A_work, V_work, ++ N_PAD, # the kernel iterates over the full padded N_PAD ++ N_PAD=N_PAD, ++ NUM_SWEEPS=num_sweeps, ++ num_warps=1, ++ ) ++ ++ w_padded = torch.diagonal(A_work).clone() ++ w_sorted, idx = torch.sort(w_padded) ++ V_sorted = V_work[:, idx] ++ ++ if N == N_PAD: ++ return w_sorted.contiguous(), V_sorted.contiguous() ++ # Drop the ``N_PAD - N`` sentinel eigenpairs sitting at the tail. ++ return w_sorted[:N].contiguous(), V_sorted[:N, :N].contiguous() +diff --git a/tsvdlib/_kernels/primitives/__init__.py b/tsvdlib/_kernels/primitives/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/tsvdlib/_kernels/primitives/truncated_svd/__init__.py b/tsvdlib/_kernels/primitives/truncated_svd/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/tsvdlib/_kernels/primitives/truncated_svd/triton/__init__.py b/tsvdlib/_kernels/primitives/truncated_svd/triton/__init__.py +new file mode 100644 +index 0000000..8002b6e +--- /dev/null ++++ b/tsvdlib/_kernels/primitives/truncated_svd/triton/__init__.py +@@ -0,0 +1,17 @@ ++"""truncated_svd triton backend. ++ ++Re-exports the public Python wrappers from each component file. ++``@triton.jit`` kernels stay private to their file (call them via the ++Python wrapper that lives next to them). ++""" ++from tsvdlib._kernels.primitives.truncated_svd.triton.svd import ( ++ _triton_svd_cov, ++ _triton_svd_dual, ++ triton_truncated_svd, ++ flash_truncated_svd, ++) ++ ++__all__ = [ ++ "triton_truncated_svd", ++ "flash_truncated_svd", ++] +diff --git a/tsvdlib/_kernels/primitives/truncated_svd/triton/svd.py b/tsvdlib/_kernels/primitives/truncated_svd/triton/svd.py +new file mode 100644 +index 0000000..a89ed6c +--- /dev/null ++++ b/tsvdlib/_kernels/primitives/truncated_svd/triton/svd.py +@@ -0,0 +1,86 @@ ++"""Truncated SVD via cuBLAS GEMMs + ``tsvdlib._kernels.linalg.eigh``. ++ ++Auto-dispatches between two paths: ++ ++1. N >= D (cov path): ++ gram = X.T @ X (cuBLAS TF32 GEMM) ++ eigh(gram, K=K, tol=tol) ++ S = sqrt(lambda); Vh = eigvecs.T (descending) ++ ++2. D > N (dual path): ++ G = X @ X.T (cuBLAS TF32 GEMM) ++ eigh(G, K=K, tol=tol) ++ V = X.T @ U; column-normalise; S = sqrt(lambda) ++ ++Mathematically identical to PCA (without centering): ++ SVD singular values = sqrt(eigenvalues of X.T @ X) ++ SVD right singular vectors = eigenvectors of X.T @ X ++ ++Precision is owned by ``tsvdlib._kernels.linalg.eigh.eigh`` -- pass ``tol=None`` ++(default) for an exact path (cuSOLVER / MKL), pass ``tol >= 1e-4`` to opt ++into Halko subspace iteration when shape favours it. ++""" ++import torch ++ ++from tsvdlib._kernels.linalg.eigh import eigh ++ ++ ++def _triton_svd_cov(X: torch.Tensor, K: int, *, tol=None): ++ """Truncated SVD via cuBLAS TF32 cov GEMM + eigh on D x D.""" ++ N, D = X.shape ++ gram = X.T @ X # cuBLAS TF32 GEMM ++ top_eigvals, top_eigvecs = eigh(gram, K=K, tol=tol) ++ S = torch.sqrt(top_eigvals.clamp(min=0)).flip(0) ++ Vh = top_eigvecs.T.flip(0) # (K, D), descending ++ return S, Vh ++ ++ ++def _triton_svd_dual(X: torch.Tensor, K: int, *, tol=None): ++ """Truncated SVD via cuBLAS gram GEMM + eigh on N x N + projection.""" ++ N, D = X.shape ++ G = X @ X.T # cuBLAS TF32 GEMM ++ top_eigvals, U = eigh(G, K=K, tol=tol) ++ V = X.T @ U # cuBLAS TF32 GEMM ++ V = V / V.norm(dim=0, keepdim=True).clamp(min=1e-10) ++ S = torch.sqrt(top_eigvals.clamp(min=0)).flip(0) ++ Vh = V.T.flip(0) # (K, D), descending ++ return S, Vh ++ ++ ++def triton_truncated_svd(X: torch.Tensor, K: int, *, tol=None): ++ """Truncated SVD: picks the path whose eigh dimension is smaller. ++ ++ All ``torch.matmul`` calls run with TF32 enabled on Hopper/Ampere ++ (the same internal precision the prior in-house Triton kernels used); ++ the global flag is restored on exit. ++ ++ Args: ++ X: ``(N, D)`` input on CUDA. ++ K: number of singular components. ++ tol: residual tolerance. ``None`` (default) -> exact eigh on the ++ cov / Gram matrix. Otherwise: Halko if ``tol >= 1e-4`` AND ++ ``K*4 < M`` AND ``M >= 256`` (``M = D`` for cov, ``N`` for ++ dual); QDWH variants for very large ``N``. ++ ++ Returns: ++ S: ``(K,)`` top-K singular values, descending. ++ Vh: ``(K, D)`` top-K right singular vectors, rows. ++ """ ++ # tol=None -> EXACT: keep PyTorch's default IEEE matmul (no TF32). ++ # tol>0 -> caller opted into a lossy budget: TF32 enabled for ++ # auxiliary ``@`` ops; restored on exit. ++ use_tf32 = tol is not None and tol > 0 ++ prev_tf32 = torch.backends.cuda.matmul.allow_tf32 ++ if use_tf32: ++ torch.backends.cuda.matmul.allow_tf32 = True ++ try: ++ N, D = X.shape ++ if D <= N: ++ return _triton_svd_cov(X, K, tol=tol) ++ return _triton_svd_dual(X, K, tol=tol) ++ finally: ++ if use_tf32: ++ torch.backends.cuda.matmul.allow_tf32 = prev_tf32 ++ ++ ++flash_truncated_svd = triton_truncated_svd diff --git a/tsvdlib/tsvd.py b/tsvdlib/tsvd.py -index f1b4fe7..6216694 100644 +index f1b4fe7..96b5c0c 100644 --- a/tsvdlib/tsvd.py +++ b/tsvdlib/tsvd.py -@@ -1,36 +1,22 @@ +@@ -1,36 +1,27 @@ -"""Truncated SVD of a dense matrix -- the reference you must optimise. -+"""Truncated SVD of a dense matrix -- Gram-matrix accelerated. ++"""Truncated SVD of a dense matrix -- vendored best-in-repo Triton path. -This implementation is intentionally naive. It computes the FULL singular value -decomposition of the ``(N, D)`` matrix with :func:`torch.linalg.svd` and then @@ -43,22 +789,21 @@ index f1b4fe7..6216694 100644 -triples even though only the leading ``k`` are ever used. It is correct and -deterministic, but it does far more work and moves far more memory than a -truncated decomposition needs. -+The naive baseline computed the FULL SVD of the ``(N, D)`` matrix and sliced off -+the top-k factors. Here it is replaced by a Gram-matrix decomposition -+(:func:`tsvdlib._gram_tsvd.truncated_svd`): the right singular vectors of ``X`` -+are the eigenvectors of ``G = X^T X`` (one ``(D, D)`` GEMM) and the singular -+values are the square roots of the leading eigenvalues, so no -+``(N, min(N, D))`` factor is ever materialised. - +- -Contract (do NOT change): -+Public contract is unchanged: ++Delegates to the exact Triton truncated-SVD entry vendored under ++``tsvdlib._kernels``. It forms the Gram / cov matrix with cuBLAS GEMMs and ++runs an exact top-k eigendecomposition (cuSOLVER, with a small-D CPU-MKL ++fast path) instead of the naive full ``torch.linalg.svd``. The public ++contract is unchanged: truncated_svd(x, n_components) -> (singular_values, components) - x : (N, D) float32 CUDA tensor. NOT centered -- this is a - truncated SVD of the raw matrix, not PCA. -- n_components (k) : int, number of leading singular triples to return. -- ++ x : (N, D) float32 CUDA tensor. NOT centered. + n_components (k) : int, number of leading singular triples to return. + - singular_values : (k,) float32, the top-k singular values in DESCENDING - order. - components : (k, D) float32, the top-k right singular vectors as @@ -68,18 +813,19 @@ index f1b4fe7..6216694 100644 -of :func:`truncated_svd` freely (e.g. a Gram-matrix + top-k eigendecomposition, -fused kernels, better memory traffic), as long as the public contract above is -preserved. -+ x : (N, D) float32 CUDA tensor (NOT centered). -+ n_components (k): int, number of leading singular triples to return. -+ singular_values : (k,) float32 top-k singular values, descending. -+ components : (k, D) float32 top-k right singular vectors (orthonormal -+ rows). ++ singular_values : (k,) float32, top-k singular values, DESCENDING. ++ components : (k, D) float32, top-k right singular vectors as ++ rows (orthonormal). """ from __future__ import annotations --import torch -- -- --def truncated_svd(x, n_components): + import torch + ++from tsvdlib._kernels.primitives.truncated_svd.triton.svd import triton_truncated_svd ++ + + def truncated_svd(x, n_components): - U, S, Vh = torch.linalg.svd(x, full_matrices=False) # full SVD -- naive/expensive - return S[:n_components].contiguous(), Vh[:n_components].contiguous() -+from tsvdlib._gram_tsvd import truncated_svd ++ s, vh = triton_truncated_svd(x, n_components, tol=None) # (k,), (k, D) descending ++ return s.contiguous(), vh.contiguous() From 6a9c85fe7d4b94aa9c0bc6fca8c2a01c6e0ac5a4 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 1 Jul 2026 09:04:47 +0000 Subject: [PATCH 06/34] fix(2.0): Modal GPU trial fixes + calibrate speedup_target from real H100 runs Validated all 4 flashlib kernel-opt tasks on real Modal H100 GPUs (frontier-cs profile). Every vendored flashlib Triton reference compiled, ran, and passed its quality gate; speedup_target is now calibrated to the measured reference geomean. flash_gpu.py fixes found via the trial: - add_python must match the judge/agent container Python (ubuntu:24.04 = 3.12); serialized=True cloudpickle is version-sensitive (was 3.11 -> InvalidError). - Mount the module into the Modal image (add_local_python_source) so the remote can import it when deserializing the module-level worker (was ModuleNotFound). - Harden transient-retry markers (InternalError / "failed to get new inputs" / runner-failed) after a one-off Modal control-plane error on a full run. Calibrated speedup_target = reference geomean over the 6 hidden workloads: - kmeans 8.0 -> 5.0 (measured 5.26x; per-workload 1.7-12.5x) - knn 6.0 -> 5.0 (measured 5.32x; recall 0.9985-0.9990 >= 0.99) - pca 4.0 -> 7.0 (measured 7.68x) - svd 6.0 -> 8.0 (measured 8.74x; w3 2M-row full-SVD baseline -> 31.9x) Docker agent+judge images build clean (light ubuntu+modal, ~390MB). Co-Authored-By: Claude Opus 4.8 (1M context) --- .../kmeans_gpu_kernel_optimization/config.yaml | 2 +- .../kmeans_gpu_kernel_optimization/flash_gpu.py | 12 +++++++++--- 2.0/problems/knn_gpu_kernel_optimization/config.yaml | 2 +- .../knn_gpu_kernel_optimization/flash_gpu.py | 12 +++++++++--- 2.0/problems/pca_gpu_kernel_optimization/config.yaml | 2 +- .../pca_gpu_kernel_optimization/flash_gpu.py | 12 +++++++++--- .../config.yaml | 2 +- .../flash_gpu.py | 12 +++++++++--- 8 files changed, 40 insertions(+), 16 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index 11e6ec111..62b437538 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -47,7 +47,7 @@ evaluation: warmup_iters: 2 timed_iters: 7 inertia_tolerance: 0.02 # agent inertia must be <= (1+tol) x baseline on every iteration - speedup_target: 8.0 # geomean speedup mapped to full bounded score (calibrate vs reference) + speedup_target: 5.0 # calibrated: reference (flashlib-best) geomean ~5.3x on Modal H100 base_seed: 20260701 agent_workload_count: 3 # agent role runs a fast subset; final verifier runs them all expose_per_workload_metrics: false diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py index 7fd6db603..798a49db6 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -220,9 +220,14 @@ def _build_image(cfg: dict): import modal pip = list(cfg.get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])) base = cfg.get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04") - return (modal.Image.from_registry(base, add_python="3.11") + # add_python must match the judge/agent container Python (ubuntu:24.04 -> 3.12) + # because the worker ships via serialized=True (cloudpickle is version-sensitive). + # add_local_python_source mounts THIS module into the image so the remote can + # import it when deserializing the (module-level) worker function. + return (modal.Image.from_registry(base, add_python=str(cfg.get("python", "3.12"))) .entrypoint([]) - .pip_install(*pip)) + .pip_install(*pip) + .add_local_python_source("flash_gpu")) # Substrings that mark a transient Modal control-plane / image-build failure @@ -232,7 +237,8 @@ def _build_image(cfg: dict): "external shut-down", "terminated due to external", "please try again", "app_state_stopped", "conflicterror", "deadline exceeded", "connection reset", "502 bad gateway", "503 service", "temporarily unavailable", "timed out", - "gateway", "eviction", "internalfailure", + "gateway", "eviction", "internalfailure", "internalerror", + "failed to get new inputs", "runner failed", "task exited", ) diff --git a/2.0/problems/knn_gpu_kernel_optimization/config.yaml b/2.0/problems/knn_gpu_kernel_optimization/config.yaml index bf109eeae..091bd8ebf 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/knn_gpu_kernel_optimization/config.yaml @@ -46,7 +46,7 @@ evaluation: warmup_iters: 2 timed_iters: 7 recall_threshold: 0.99 - speedup_target: 6.0 + speedup_target: 5.0 # calibrated: reference (flashlib-best) geomean ~5.3x on Modal H100 base_seed: 20260701 agent_workload_count: 3 expose_per_workload_metrics: false diff --git a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py index 7fd6db603..798a49db6 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py @@ -220,9 +220,14 @@ def _build_image(cfg: dict): import modal pip = list(cfg.get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])) base = cfg.get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04") - return (modal.Image.from_registry(base, add_python="3.11") + # add_python must match the judge/agent container Python (ubuntu:24.04 -> 3.12) + # because the worker ships via serialized=True (cloudpickle is version-sensitive). + # add_local_python_source mounts THIS module into the image so the remote can + # import it when deserializing the (module-level) worker function. + return (modal.Image.from_registry(base, add_python=str(cfg.get("python", "3.12"))) .entrypoint([]) - .pip_install(*pip)) + .pip_install(*pip) + .add_local_python_source("flash_gpu")) # Substrings that mark a transient Modal control-plane / image-build failure @@ -232,7 +237,8 @@ def _build_image(cfg: dict): "external shut-down", "terminated due to external", "please try again", "app_state_stopped", "conflicterror", "deadline exceeded", "connection reset", "502 bad gateway", "503 service", "temporarily unavailable", "timed out", - "gateway", "eviction", "internalfailure", + "gateway", "eviction", "internalfailure", "internalerror", + "failed to get new inputs", "runner failed", "task exited", ) diff --git a/2.0/problems/pca_gpu_kernel_optimization/config.yaml b/2.0/problems/pca_gpu_kernel_optimization/config.yaml index 9021eadc7..a97d078d5 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/pca_gpu_kernel_optimization/config.yaml @@ -40,7 +40,7 @@ evaluation: timed_iters: 7 captured_tolerance: 0.02 ortho_tolerance: 0.02 - speedup_target: 4.0 + speedup_target: 7.0 # calibrated: reference (flashlib-best) geomean ~7.7x on Modal H100 base_seed: 20260701 agent_workload_count: 3 expose_per_workload_metrics: false diff --git a/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py index 7fd6db603..798a49db6 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py @@ -220,9 +220,14 @@ def _build_image(cfg: dict): import modal pip = list(cfg.get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])) base = cfg.get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04") - return (modal.Image.from_registry(base, add_python="3.11") + # add_python must match the judge/agent container Python (ubuntu:24.04 -> 3.12) + # because the worker ships via serialized=True (cloudpickle is version-sensitive). + # add_local_python_source mounts THIS module into the image so the remote can + # import it when deserializing the (module-level) worker function. + return (modal.Image.from_registry(base, add_python=str(cfg.get("python", "3.12"))) .entrypoint([]) - .pip_install(*pip)) + .pip_install(*pip) + .add_local_python_source("flash_gpu")) # Substrings that mark a transient Modal control-plane / image-build failure @@ -232,7 +237,8 @@ def _build_image(cfg: dict): "external shut-down", "terminated due to external", "please try again", "app_state_stopped", "conflicterror", "deadline exceeded", "connection reset", "502 bad gateway", "503 service", "temporarily unavailable", "timed out", - "gateway", "eviction", "internalfailure", + "gateway", "eviction", "internalfailure", "internalerror", + "failed to get new inputs", "runner failed", "task exited", ) diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml b/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml index 948f8acc8..25fa9e0e9 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml @@ -48,7 +48,7 @@ evaluation: timed_iters: 7 captured_tolerance: 0.02 # agent captured energy must be >= (1-tol) x baseline on every iteration ortho_tolerance: 0.02 # max |V V^T - I_k| must stay within this tolerance - speedup_target: 6.0 # geomean speedup mapped to full bounded score (calibrate vs reference) + speedup_target: 8.0 # calibrated: reference (flashlib-best) geomean ~8.7x on Modal H100 base_seed: 20260701 agent_workload_count: 3 # agent role runs a fast subset; final verifier runs them all expose_per_workload_metrics: false diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py index 7fd6db603..798a49db6 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py @@ -220,9 +220,14 @@ def _build_image(cfg: dict): import modal pip = list(cfg.get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])) base = cfg.get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04") - return (modal.Image.from_registry(base, add_python="3.11") + # add_python must match the judge/agent container Python (ubuntu:24.04 -> 3.12) + # because the worker ships via serialized=True (cloudpickle is version-sensitive). + # add_local_python_source mounts THIS module into the image so the remote can + # import it when deserializing the (module-level) worker function. + return (modal.Image.from_registry(base, add_python=str(cfg.get("python", "3.12"))) .entrypoint([]) - .pip_install(*pip)) + .pip_install(*pip) + .add_local_python_source("flash_gpu")) # Substrings that mark a transient Modal control-plane / image-build failure @@ -232,7 +237,8 @@ def _build_image(cfg: dict): "external shut-down", "terminated due to external", "please try again", "app_state_stopped", "conflicterror", "deadline exceeded", "connection reset", "502 bad gateway", "503 service", "temporarily unavailable", "timed out", - "gateway", "eviction", "internalfailure", + "gateway", "eviction", "internalfailure", "internalerror", + "failed to get new inputs", "runner failed", "task exited", ) From 377b08bb8c36b683980f50863c1329b3514683d6 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 1 Jul 2026 09:30:14 +0000 Subject: [PATCH 07/34] fix(2.0): public_test pip -> [torch,numpy] to match judge (was stale torch==2.5.1/triton==3.1.0) Co-Authored-By: Claude Opus 4.8 (1M context) --- .../kmeans_gpu_kernel_optimization/harbor/app/public_test.py | 2 +- .../knn_gpu_kernel_optimization/harbor/app/public_test.py | 2 +- .../pca_gpu_kernel_optimization/harbor/app/public_test.py | 2 +- .../harbor/app/public_test.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py index 012594653..ba70b7555 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -28,7 +28,7 @@ "ref_module": "refkmeans", "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", - "pip": ["torch==2.5.1", "triton==3.1.0", "numpy"], + "pip": ["torch", "numpy"], "app_name": "kmeans-kernel-opt-public", "modal_timeout_seconds": 1800, "warmup": 2, diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py index d2a7f186b..ea693dab0 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py @@ -28,7 +28,7 @@ "ref_module": "refknn", "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", - "pip": ["torch==2.5.1", "triton==3.1.0", "numpy"], + "pip": ["torch", "numpy"], "app_name": "knn-kernel-opt-public", "modal_timeout_seconds": 1800, "warmup": 2, diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py index c0426065d..a83531c40 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py @@ -28,7 +28,7 @@ "ref_module": "refpca", "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", - "pip": ["torch==2.5.1", "triton==3.1.0", "numpy"], + "pip": ["torch", "numpy"], "app_name": "pca-kernel-opt-public", "modal_timeout_seconds": 1800, "warmup": 2, diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py index 73b036ec6..c321f0b6e 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py @@ -28,7 +28,7 @@ "ref_module": "reftsvd", "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", - "pip": ["torch==2.5.1", "triton==3.1.0", "numpy"], + "pip": ["torch", "numpy"], "app_name": "tsvd-kernel-opt-public", "modal_timeout_seconds": 1800, "warmup": 2, From 677367702384ce85db9261fbe739a1e20eb0acc1 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 1 Jul 2026 11:50:38 +0000 Subject: [PATCH 08/34] feat(2.0/flashlib): replace pca/svd with DBSCAN + IVF-PQ kernel-opt tasks pca/svd were algorithmic (cov+eigh / library SVD), trivially matched by a torch one-liner rather than genuine kernel work, so replace them with two real kernel tasks sharing the same Modal-GPU judge, flashlib-best vendored references, and anti-hack hardening as kmeans/knn. DBSCAN (dbscan_gpu_kernel_optimization): optimize planar (D=2) Euclidean DBSCAN vs an O(N^2) matmul baseline. Reference vendors flashlib's Triton grid radius-search + connected components (flash_knn stubbed; D=2 grid path only). Gate = Adjusted Rand Index vs baseline. H100: ARI 1.0, geomean 13.3x (7.2-21.2x) at N=100k-170k (the band where the grid kernel beats the tensor-core matmul baseline); speedup_target 13, timed_iters 3. IVF-PQ (ivf_pq_gpu_kernel_optimization): optimize IVF-PQ search over a fixed pre-built index vs a naive per-query-loop baseline. Reference vendors flashlib's Triton ADC fine-scan (LUT + decode-GEMM); the coarse nprobe-nearest step is an exact torch cdist+topk, dropping the entire KNN subtree and the CuTe-DSL tier. Gate = iso-result recall@k vs baseline. H100: recall 1.0, geomean ~898x (442-1449x); speedup_target 800. Shared worker (flash_gpu.py): add a setup() hook building the IVF-PQ index once per workload (reused across timed iters while queries regenerate) + ctx threading; unified byte-identical across all four tasks (kmeans/knn re-smoked on H100, gates still pass). Co-Authored-By: Claude Opus 4.8 --- .../dbscan_gpu_kernel_optimization/DESIGN.md | 60 + .../config.yaml | 43 + .../dbscanlib/__init__.py | 11 + .../dbscanlib/dbscan.py | 70 + .../docker/README.md | 22 +- .../docker/agent/Dockerfile | 12 +- .../docker/build_images.sh | 6 +- .../docker/judge/Dockerfile | 6 +- .../docker/smoke_images.sh | 19 + .../evaluate.sh | 6 +- .../evaluator.py | 6 +- .../flash_gpu.py | 84 +- .../harbor/app/README.md | 19 +- .../harbor/app/make_submission.sh | 12 +- .../harbor/app/public_test.py | 27 +- .../harbor/app/public_test.sh | 2 +- .../harbor/app/solution.patch | 0 .../judge/refdbscan.py | 70 + .../dbscan_gpu_kernel_optimization/readme | 97 ++ .../reference.patch | 1061 ++++++++++++ .../ivf_pq_gpu_kernel_optimization/DESIGN.md | 72 + .../config.yaml | 42 + .../docker/README.md | 22 +- .../docker/agent/Dockerfile | 12 +- .../docker/build_images.sh | 6 +- .../docker/judge/Dockerfile | 6 +- .../docker/smoke_images.sh | 19 + .../evaluate.sh | 6 +- .../evaluator.py | 6 +- .../flash_gpu.py | 84 +- .../harbor/app/README.md | 19 +- .../harbor/app/make_submission.sh | 12 +- .../harbor/app/public_test.py | 32 +- .../harbor/app/public_test.sh | 2 +- .../harbor/app/solution.patch | 0 .../ivfpqlib/__init__.py | 8 + .../ivfpqlib/build.py | 208 +++ .../ivfpqlib/index.py | 96 ++ .../ivfpqlib/search.py | 78 + .../judge/refivfpq.py | 343 ++++ .../ivf_pq_gpu_kernel_optimization/readme | 109 ++ .../reference.patch | 1535 +++++++++++++++++ .../flash_gpu.py | 84 +- .../knn_gpu_kernel_optimization/flash_gpu.py | 84 +- .../pca_gpu_kernel_optimization/DESIGN.md | 95 - .../pca_gpu_kernel_optimization/config.yaml | 56 - .../docker/smoke_images.sh | 19 - .../judge/refpca.py | 22 - .../pcalib/__init__.py | 15 - .../pca_gpu_kernel_optimization/pcalib/pca.py | 44 - .../pca_gpu_kernel_optimization/readme | 130 -- .../reference.patch | 1046 ----------- .../DESIGN.md | 93 - .../config.yaml | 65 - .../docker/smoke_images.sh | 19 - .../judge/reftsvd.py | 16 - 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a/2.0/problems/dbscan_gpu_kernel_optimization/DESIGN.md b/2.0/problems/dbscan_gpu_kernel_optimization/DESIGN.md new file mode 100644 index 000000000..595683acd --- /dev/null +++ b/2.0/problems/dbscan_gpu_kernel_optimization/DESIGN.md @@ -0,0 +1,60 @@ +# Design notes — dbscan_gpu_kernel_optimization + +Operator-facing. Not copied into the agent workspace by the adapter. + +## What this task measures + +Can an agent turn a naive GPU DBSCAN into a fast one? The agent patches +`dbscanlib`; the judge times the patched `dbscan` against a frozen naive baseline +on hidden low-dimensional workloads and scores the geomean speedup, gated on +clustering agreement (Adjusted Rand Index). One of the four-task flashlib +kernel-optimization family (KMeans, KNN, DBSCAN, IVF-PQ), all sharing one +evaluator + one Modal GPU harness (`flash_gpu.py`). + +This is a **genuine kernel task**: the core operation (radius-neighbour search + +connected components + border assignment) is not a single fast torch primitive, +so beating the naive O(N^2) baseline requires real kernel work (a bucketed/grid +radius search + fused neighbour + connected-components kernels). Contrast with +pca/svd, which were algorithmic (cov+eigh + a library eigensolver) and are not +part of this family. + +## Baseline + reference + +- Naive baseline (`dbscanlib/dbscan.py`, and frozen `judge/refdbscan.py`): chunked + O(N^2) neighbour scan for the core mask, GPU label-propagation for the core + connected components, and a smallest-core-label border rule. Verified against + scikit-learn DBSCAN (ARI = 1.0). The border rule matches flashlib's ("smallest + CC label among in-eps core neighbours"), so the reference agrees closely. +- Reference (`reference.patch`): vendors flashlib's optimized **Triton** planar + grid radius-search DBSCAN (`primitives/dbscan/triton/dbscan.py` grid path + + `kernels/flash_mst` connected components) under `dbscanlib/_kernels/…`. The + flashlib grid kernel hard-asserts D==2 (higher D falls back to a flash_knn + brute path), so all graded workloads are D=2 and flash_knn is stubbed out. + +## Correctness gate + +Adjusted Rand Index (permutation-invariant, noise-aware) between the agent's +labels and the baseline's on each iteration's fresh data, `>= ari_threshold` +(default 0.99). Judge-computed; cheat-proof (you cannot fake a high ARI without +actually reproducing the clustering). DBSCAN with fixed (eps, min_samples) is +deterministic up to border tie-breaks, which ARI absorbs. + +## Workloads + +Planar (D=2) planted blobs + a small uniform-noise fraction, N 100k-170k. Below +~100k the tensor-core matmul baseline (`xb @ x.t()`) beats the grid kernel's fixed +launch/grid-build overhead (H100 sweep: 100k->6x, 150k->14x, 200k->27x); the band +is chosen so the O(N^2) baseline is seconds/call (grid wins clearly) yet bounded +for `timed_iters`. `timed_iters` is 3 (vs 7 elsewhere) since each baseline call is +seconds, not ms. +Hidden workloads live in `config.yaml` (judge-only). See kmeans DESIGN for the +shared anti-hack + Modal-offload design; identical here. + +## Calibration (H100 Modal trial — done) + +`reference.patch` validated on H100: the vendored grid DBSCAN compiles, passes the +ARI gate with **ARI 1.0** on all six workloads, and runs +**7.2 / 12.1 / 9.5 / 18.8 / 16.6 / 21.2x** over the naive baseline (geomean +~13.3x). `speedup_target` set to 13.0. The full 6-workload judge (warmup 2 + +timed 3) completed in ~410 s, well inside `modal_timeout_seconds` (2400). Re-sweep +if the N band changes. diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml new file mode 100644 index 000000000..e8944b146 --- /dev/null +++ b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml @@ -0,0 +1,43 @@ +tag: systems +runtime: + language: python + timeout_seconds: 10800 + environment: "GPU DBSCAN kernel optimization. The agent patches the dbscanlib package; the judge offloads timing to a Modal GPU, comparing the patched dbscan against a frozen naive baseline on hidden low-dimensional workloads with an ARI (clustering-agreement) gate." + apt_packages: [bash, ca-certificates, git, python3, python3-pip] + judge_apt_packages: [bash, ca-certificates, git, python3, python3-pip] + judge_pip_packages: [modal] + docker: + image: frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.2.0 + judge_image: frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.2.0 +environment: + cpus: 4 + memory_mb: 8192 + storage_mb: 16384 + build_timeout_seconds: 3600 +evaluation: + gpu: "H100" + cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" + pip: ["torch", "numpy"] + app_name: "dbscan-kernel-opt-eval" + modal_timeout_seconds: 2400 + warmup_iters: 2 + timed_iters: 3 # the O(N^2) naive baseline is seconds/call at these N; keep the timed budget bounded (median of 3) + ari_threshold: 0.99 # agent clustering must agree with the baseline (Adjusted Rand Index) on every iteration; the grid reference measured ARI 1.0 on all workloads + speedup_target: 13.0 # H100 calibration: flashlib grid DBSCAN reference geomean 13.29x over the O(N^2) matmul baseline (per-workload 7.2/12.1/9.5/18.8/16.6/21.2x) + base_seed: 20260701 + agent_workload_count: 3 + expose_per_workload_metrics: false + # N in the 100k-170k band: the O(N^2) matmul baseline is seconds/call (grid kernel + # wins 6-20x) but bounded for timed_iters; below ~100k the tensor-core matmul baseline + # is faster than the grid kernel's fixed overhead. Vary N + density so agents can't + # special-case. (H100 crossover sweep: 100k->6.0x, 150k->14.1x, 200k->27.0x.) + workloads: + - {id: w0, N: 100000, D: 2, n_centers: 12, eps: 1.5, min_samples: 8, noise_frac: 0.03} + - {id: w1, N: 110000, D: 2, n_centers: 16, eps: 1.3, min_samples: 10, noise_frac: 0.04} + - {id: w2, N: 120000, D: 2, n_centers: 10, eps: 1.6, min_samples: 6, noise_frac: 0.03} + - {id: w3, N: 130000, D: 2, n_centers: 20, eps: 1.2, min_samples: 12, noise_frac: 0.05} + - {id: w4, N: 150000, D: 2, n_centers: 14, eps: 1.4, min_samples: 8, noise_frac: 0.04} + - {id: w5, N: 170000, D: 2, n_centers: 18, eps: 1.5, min_samples: 8, noise_frac: 0.03} +submission: + kind: file + path: /app/solution.patch diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/dbscanlib/__init__.py b/2.0/problems/dbscan_gpu_kernel_optimization/dbscanlib/__init__.py new file mode 100644 index 000000000..5f897e412 --- /dev/null +++ b/2.0/problems/dbscan_gpu_kernel_optimization/dbscanlib/__init__.py @@ -0,0 +1,11 @@ +"""dbscanlib -- a tiny GPU DBSCAN you are asked to make fast. + +The shipped implementation is correct but naive (chunked O(N^2) neighbour scan + +label propagation). Rewrite the internals (grid/bucketed radius search, fused +neighbour + connected-components kernels, ...) to make :func:`dbscan` fast while +producing the same clustering. The public signature/return must not change. +""" +from __future__ import annotations +from dbscanlib.dbscan import dbscan +__all__ = ["dbscan"] +__version__ = "0.1.0" diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/dbscanlib/dbscan.py b/2.0/problems/dbscan_gpu_kernel_optimization/dbscanlib/dbscan.py new file mode 100644 index 000000000..ab7fa8bfc --- /dev/null +++ b/2.0/problems/dbscan_gpu_kernel_optimization/dbscanlib/dbscan.py @@ -0,0 +1,70 @@ +"""Naive DBSCAN baseline (correct, deterministic, GPU/CPU torch). + +Euclidean DBSCAN. Chunked O(N^2) neighbour scan + GPU-friendly label +propagation for the connected components of the core graph. Border rule matches +flashlib: a non-core point joins the SMALLEST cluster label among its in-eps +core neighbours; otherwise it is noise (-1). +""" +from __future__ import annotations + +import torch + + +def dbscan(x, eps, min_samples, max_neighbors=32): + del max_neighbors # naive path scans all neighbours; knob only for the fast kernel + N = x.shape[0] + dev = x.device + eps2 = float(eps) * float(eps) + xn = (x * x).sum(1) # (N,) + CH = 4096 + BIG = N # sentinel label (> any real label) + + def d2_chunk(lo, hi): + xb = x[lo:hi] + return (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ x.t()) + xn[None, :] + + # 1) degree (in-eps count, includes self) -> core mask + deg = torch.zeros(N, device=dev, dtype=torch.int64) + for lo in range(0, N, CH): + hi = min(lo + CH, N) + deg[lo:hi] = (d2_chunk(lo, hi) <= eps2).sum(1) + core = deg >= int(min_samples) # (N,) bool + + # 2) connected components of the core-core eps graph via label propagation + labels = torch.arange(N, device=dev, dtype=torch.int64) + labels[~core] = BIG + for _ in range(1000): + new = labels.clone() + for lo in range(0, N, CH): + hi = min(lo + CH, N) + if not bool(core[lo:hi].any()): + continue + adj = (d2_chunk(lo, hi) <= eps2) & core[None, :] # (chunk, N) + lab = torch.where(adj, labels[None, :], torch.full((1, N), BIG, device=dev, dtype=torch.int64)) + mn = lab.min(1).values # (chunk,) + rows = slice(lo, hi) + upd = core[rows] & (mn < new[rows]) + new[rows] = torch.where(upd, mn, new[rows]) + if torch.equal(new, labels): + break + labels = new + + # 3) border assignment: non-core within eps of a core -> smallest core label + for lo in range(0, N, CH): + hi = min(lo + CH, N) + nb = ~core[lo:hi] + if not bool(nb.any()): + continue + adj = (d2_chunk(lo, hi) <= eps2) & core[None, :] + lab = torch.where(adj, labels[None, :], torch.full((1, N), BIG, device=dev, dtype=torch.int64)) + mn = lab.min(1).values + rows = slice(lo, hi) + take = nb & (mn < BIG) + labels[rows] = torch.where(take, mn, labels[rows]) + + # 4) compact to dense [0, n_clusters); noise (BIG) -> -1 + out = torch.full((N,), -1, device=dev, dtype=torch.int64) + uniq = torch.unique(labels[labels < BIG]) + for new_id, old in enumerate(uniq.tolist()): + out[labels == old] = new_id + return out diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md b/2.0/problems/dbscan_gpu_kernel_optimization/docker/README.md similarity index 55% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md rename to 2.0/problems/dbscan_gpu_kernel_optimization/docker/README.md index dba9dca32..506c4246d 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/README.md +++ b/2.0/problems/dbscan_gpu_kernel_optimization/docker/README.md @@ -1,34 +1,34 @@ -# Experimental Truncated-SVD Kernel-Optimization Images +# Experimental DBSCAN Kernel-Optimization Images Two **light** images (ubuntu + `modal` + git). torch, triton, and the vendored flashlib kernels all run on the **Modal GPU image** defined in `flash_gpu.py`; the containers themselves are CPU-only and offload GPU work to Modal. ```bash -bash 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh +bash 2.0/problems/dbscan_gpu_kernel_optimization/docker/build_images.sh ``` Defaults: ```text -AGENT_TAG=frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.2.0 -JUDGE_TAG=frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.2.0 +AGENT_TAG=frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.2.0 +JUDGE_TAG=frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.2.0 ``` Agent image: ```text -/app/tsvdlib # clean, git-tracked package (the agent edits this) -/opt/flash_gpu.py # shared Modal GPU harness (public test uses it) -/opt/tsvd_ref/reftsvd.py # frozen naive baseline (public-test speed denominator) +/app/dbscanlib # clean, git-tracked package (the agent edits this) +/opt/flash_gpu.py # shared Modal GPU harness (public test uses it) +/opt/dbscan_ref/refdbscan.py # frozen naive baseline (public-test speed denominator) ``` Judge image: ```text -/opt/tsvdlib-clean/tsvdlib # pristine tree; the patch is applied to a copy -/opt/tsvd_ref/reftsvd.py # frozen naive baseline (speed denominator + quality oracle) -/opt/flash_gpu.py # shared Modal GPU harness (the evaluator uses it) +/opt/dbscanlib-clean/dbscanlib # pristine tree; the patch is applied to a copy +/opt/dbscan_ref/refdbscan.py # frozen naive baseline (speed denominator + quality oracle) +/opt/flash_gpu.py # shared Modal GPU harness (the evaluator uses it) ``` ## Runtime requirements @@ -50,7 +50,7 @@ smoke pass (which repo CI exercises). ## Smoke test ```bash -bash 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh +bash 2.0/problems/dbscan_gpu_kernel_optimization/docker/smoke_images.sh ``` Import-only (modal + flash_gpu + the baked packages); does not touch a GPU or Modal. diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/dbscan_gpu_kernel_optimization/docker/agent/Dockerfile similarity index 76% rename from 2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile rename to 2.0/problems/dbscan_gpu_kernel_optimization/docker/agent/Dockerfile index 6eab06de5..bab171ab2 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/agent/Dockerfile +++ b/2.0/problems/dbscan_gpu_kernel_optimization/docker/agent/Dockerfile @@ -1,6 +1,6 @@ -# Agent image for pca_gpu_kernel_optimization. +# Agent image for dbscan_gpu_kernel_optimization. # -# Light image (no torch/triton): the agent edits /app/pcalib and runs the +# Light image (no torch/triton): the agent edits /app/dbscanlib and runs the # public test, which offloads timing to a Modal GPU (torch/triton live on the # Modal image defined in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET # in the environment to run the GPU public test. @@ -19,16 +19,16 @@ RUN pip3 install --break-system-packages --no-cache-dir modal # The package the agent edits (git-tracked so make_submission.sh can diff it). WORKDIR /app -COPY pcalib /app/pcalib +COPY dbscanlib /app/dbscanlib RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ git -C /app init -q && \ git -C /app config user.email task@frontier-cs && \ git -C /app config user.name frontier-cs && \ - git -C /app add -A -- pcalib .gitignore && \ - git -C /app -c commit.gpgsign=false commit -qm "pristine pcalib" + git -C /app add -A -- dbscanlib .gitignore && \ + git -C /app -c commit.gpgsign=false commit -qm "pristine dbscanlib" # Shared Modal GPU harness + the frozen naive baseline (the public-test speed # denominator). The baseline is exactly the code the agent starts from, so it is # not secret. COPY flash_gpu.py /opt/flash_gpu.py -COPY judge/refpca.py /opt/pca_ref/refpca.py +COPY judge/refdbscan.py /opt/dbscan_ref/refdbscan.py diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/dbscan_gpu_kernel_optimization/docker/build_images.sh old mode 100755 new mode 100644 similarity index 60% rename from 2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh rename to 2.0/problems/dbscan_gpu_kernel_optimization/docker/build_images.sh index 1e59037fa..634396fd1 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/dbscan_gpu_kernel_optimization/docker/build_images.sh @@ -1,10 +1,10 @@ #!/usr/bin/env bash -# Build the experimental agent + judge images for pca_gpu_kernel_optimization. +# Build the experimental agent + judge images for dbscan_gpu_kernel_optimization. set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.2.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.2.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.2.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/dbscan_gpu_kernel_optimization/docker/judge/Dockerfile similarity index 85% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile rename to 2.0/problems/dbscan_gpu_kernel_optimization/docker/judge/Dockerfile index 48b5343bc..8c93b5ec4 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/judge/Dockerfile +++ b/2.0/problems/dbscan_gpu_kernel_optimization/docker/judge/Dockerfile @@ -1,4 +1,4 @@ -# Judge image for truncated_svd_gpu_kernel_optimization. +# Judge image for dbscan_gpu_kernel_optimization. # # Light image (no torch/triton): the judge validates + applies the patch and # offloads timing to a Modal GPU (torch/triton live on the Modal image defined @@ -19,8 +19,8 @@ RUN pip3 install --break-system-packages --no-cache-dir modal # Pristine tree the patch is applied to, the frozen naive baseline (speed # denominator + quality oracle), and the shared Modal GPU harness. -COPY tsvdlib /opt/tsvdlib-clean/tsvdlib -COPY judge/reftsvd.py /opt/tsvd_ref/reftsvd.py +COPY dbscanlib /opt/dbscanlib-clean/dbscanlib +COPY judge/refdbscan.py /opt/dbscan_ref/refdbscan.py COPY flash_gpu.py /opt/flash_gpu.py WORKDIR /judge diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/dbscan_gpu_kernel_optimization/docker/smoke_images.sh new file mode 100644 index 000000000..401de9461 --- /dev/null +++ b/2.0/problems/dbscan_gpu_kernel_optimization/docker/smoke_images.sh @@ -0,0 +1,19 @@ +#!/usr/bin/env bash +# Import-only smoke test for the built images (no GPU / Modal required). +set -euo pipefail + +AGENT_TAG=${AGENT_TAG:-frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.2.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.2.0} + +echo "== agent image ==" +docker run --rm -w /app "$AGENT_TAG" python3 -c \ + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ +sys.path.insert(0, '/opt/dbscan_ref'); import refdbscan; \ +import dbscanlib; print('agent ok: modal + flash_gpu + refdbscan + dbscanlib import')" + +echo "== judge image ==" +docker run --rm "$JUDGE_TAG" python3 -c \ + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ +sys.path.insert(0, '/opt/dbscan_ref'); import refdbscan; \ +sys.path.insert(0, '/opt/dbscanlib-clean'); import dbscanlib; \ +print('judge ok: modal + flash_gpu + refdbscan + dbscanlib import')" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluate.sh b/2.0/problems/dbscan_gpu_kernel_optimization/evaluate.sh old mode 100755 new mode 100644 similarity index 65% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/evaluate.sh rename to 2.0/problems/dbscan_gpu_kernel_optimization/evaluate.sh index f71921fca..df1fe7f0d --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluate.sh +++ b/2.0/problems/dbscan_gpu_kernel_optimization/evaluate.sh @@ -1,9 +1,9 @@ #!/usr/bin/env bash -# Local CLI evaluation for truncated_svd_gpu_kernel_optimization. +# Local CLI evaluation for dbscan_gpu_kernel_optimization. # # A full run needs a GPU plus the baked judge sources at /opt (the pristine -# /opt/tsvdlib-clean tree and the frozen /opt/tsvd_ref baseline; see the judge -# image). Without them the evaluator still validates the patch policy and +# /opt/dbscanlib-clean tree and the frozen /opt/dbscan_ref baseline; see the +# judge image). Without them the evaluator still validates the patch policy and # returns a smoke score, which is what repository CI exercises (the reference # patch passes the policy). Pass a patch path to score it directly. set -euo pipefail diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py b/2.0/problems/dbscan_gpu_kernel_optimization/evaluator.py similarity index 99% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py rename to 2.0/problems/dbscan_gpu_kernel_optimization/evaluator.py index 33d919a7a..a40a41105 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/evaluator.py @@ -79,9 +79,9 @@ def _get_bool(name: str, default: bool) -> bool: # Kept as constants (not config) so the patch policy is enforceable locally / in # CI without /judge/task_config.json. Everything else is config-driven and only # needed on the GPU path. -PRIMITIVE = "tsvd" -PKG = "tsvdlib" -REF_MODULE = "reftsvd" +PRIMITIVE = "dbscan" +PKG = "dbscanlib" +REF_MODULE = "refdbscan" MAX_PATCH_BYTES = _get_int("max_patch_bytes", 2_000_000) MAX_CHANGED_FILES = _get_int("max_changed_files", 80) diff --git a/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py similarity index 74% rename from 2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py rename to 2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py index 798a49db6..6243e65c8 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py @@ -75,25 +75,53 @@ def _materialize(files: dict, tag: str) -> str: ref = importlib.import_module(cfg["ref_module"]) pkg = importlib.import_module(cfg["pkg"]) - # ---- per-primitive: data gen, call, and quality metric ---------------- # - def gen(w, seed): + # ---- per-primitive: fixed context, data gen, call, quality metric ----- # + def setup(w, seed): + # Built ONCE per workload (not per timed iter). IVF-PQ builds the fixed + # index (the "database") here; queries are generated fresh per iteration. + if prim == "ivfpq": + g = torch.Generator(device=dev).manual_seed(int(seed)) + X = torch.randn(int(w["M"]), int(w["D"]), generator=g, device=dev, dtype=torch.float32) + index = ref.ivf_pq_build(X, int(w["nlist"]), m=int(w["m"]), + nprobe=int(w["nprobe"]), seed=int(seed)) + return {"index": index, "D": int(w["D"])} + return {} + + def gen(w, seed, ctx): g = torch.Generator(device=dev).manual_seed(int(seed)) + if prim == "ivfpq": + q = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, device=dev, dtype=torch.float32) + return {"queries": q} if prim == "knn": db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} + if prim == "dbscan": + nc, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) + centers = torch.randn(nc, D, generator=g, device=dev, dtype=torch.float32) * 10.0 + asg = torch.randint(0, nc, (N,), generator=g, device=dev) + xb = centers.index_select(0, asg) + torch.randn(N, D, generator=g, device=dev, dtype=torch.float32) * 0.5 + nz = int(float(w.get("noise_frac", 0.0)) * N) + if nz > 0: + lo = xb.min(0).values; hi = xb.max(0).values + xb[:nz] = lo + (hi - lo) * torch.rand(nz, D, generator=g, device=dev) + return {"x": xb, "eps": float(w["eps"]), "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) if prim == "kmeans": perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] return {"x": x, "init": x.index_select(0, perm).clone()} return {"x": x} - def call(mod, w, data): + def call(mod, w, data, ctx): + if prim == "ivfpq": + return mod.ivf_pq_search(ctx["index"], data["queries"], int(w["k"]), nprobe=int(w["nprobe"])) if prim == "kmeans": return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], init_centroids=data["init"], tol=0.0) if prim == "knn": return mod.knn(data["queries"], data["database"], w["k"]) + if prim == "dbscan": + return mod.dbscan(data["x"], data["eps"], data["min_samples"]) if prim == "pca": return mod.pca(data["x"], w["k"]) if prim == "tsvd": @@ -111,6 +139,14 @@ def check_shape(w, out): raise ValueError("bad knn output") if not torch.isfinite(d).all(): raise ValueError("non-finite distances") + elif prim == "ivfpq": + vals, ids = out + if tuple(ids.shape) != (int(w["Q"]), int(w["k"])) or \ + tuple(vals.shape) != (int(w["Q"]), int(w["k"])): + raise ValueError("bad ivfpq output shape") + elif prim == "dbscan": + if out.ndim != 1 or int(out.shape[0]) != int(w["N"]): + raise ValueError("bad dbscan labels shape") else: a, b = out comps = a if prim == "pca" else b @@ -132,6 +168,20 @@ def recall(agent_idx, ref_idx): hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + def ari(a, b): + # Adjusted Rand Index between two integer label vectors (N,); noise (-1) + # is treated as its own label (dense-remapped first). + a = torch.unique(a.long(), return_inverse=True)[1] + b = torch.unique(b.long(), return_inverse=True)[1] + na = int(a.max().item()) + 1; nb = int(b.max().item()) + 1 + cont = torch.bincount(a * nb + b, minlength=na * nb).reshape(na, nb).double() + ai = cont.sum(1); bj = cont.sum(0); n = cont.sum() + c2 = lambda z: z * (z - 1) / 2.0 + sij = c2(cont).sum(); sa = c2(ai).sum(); sb = c2(bj).sum() + exp = (sa * sb / c2(n)) if float(n) > 1 else 0.0 + den = 0.5 * (sa + sb) - exp + return float(((sij - exp) / den).item()) if float(den) != 0.0 else 1.0 + def ortho_err(comps): c = comps.to(torch.float32); k = c.shape[0] return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) @@ -148,9 +198,14 @@ def captured(x, comps, center): total += float((p * p).sum().item()) return total / (x.shape[0] - 1) if center else total - def verdict(w, data, ref_out, agent_out): + def verdict(w, data, ctx, ref_out, agent_out): """Return (ok, reason, ref_val, agent_val) gating the agent output.""" check_shape(w, agent_out) + if prim == "ivfpq": + # iso-result recall: agent ids vs frozen-baseline ids on the SAME index. + rec = recall(agent_out[1], ref_out[1]) + ok = rec >= float(cfg["recall_threshold"]) + return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) tol = float(cfg["inertia_tolerance"]) @@ -160,6 +215,10 @@ def verdict(w, data, ref_out, agent_out): rec = recall(agent_out[1], ref_out[1]) ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec + if prim == "dbscan": + val = ari(agent_out, ref_out) + ok = val >= float(cfg["ari_threshold"]) + return ok, ("ari_regression" if not ok else ""), 1.0, val center = (prim == "pca") comps = agent_out[0] if prim == "pca" else agent_out[1] ref_comps = ref_out[0] if prim == "pca" else ref_out[1] @@ -171,27 +230,28 @@ def verdict(w, data, ref_out, agent_out): ok = av >= (1.0 - float(cfg["captured_tolerance"])) * rv - 1e-6 return ok, ("captured_regression" if not ok else ""), rv, av - def time_call(mod, w, data): - _sync(); t0 = _perf(); out = call(mod, w, data); _sync() + def time_call(mod, w, data, ctx): + _sync(); t0 = _perf(); out = call(mod, w, data, ctx); _sync() return (_perf() - t0) * 1000.0, out rows = [] try: for w in payload["workloads"]: base_seed = int(w["seed"]) + ctx = setup(w, base_seed) # fixed context (e.g. IVF-PQ index) # warmup on fresh data (kernels / autotune) — not measured for i in range(warmup): - d = gen(w, base_seed + 100 + i) - call(ref, w, d); call(pkg, w, d); _sync() + d = gen(w, base_seed + 100 + i, ctx) + call(ref, w, d, ctx); call(pkg, w, d, ctx); _sync() del d torch.cuda.empty_cache() ratios, ref_val, agent_val = [], None, None bad = None for i in range(iters): - d = gen(w, base_seed + 10000 + i) # fresh every iteration - rt, rout = time_call(ref, w, d) - at, aout = time_call(pkg, w, d) - ok, reason, rv, av = verdict(w, d, rout, aout) # verify THIS iter + d = gen(w, base_seed + 10000 + i, ctx) # fresh every iteration + rt, rout = time_call(ref, w, d, ctx) + at, aout = time_call(pkg, w, d, ctx) + ok, reason, rv, av = verdict(w, d, ctx, rout, aout) # verify THIS iter if not ok: bad = reason; ref_val, agent_val = rv, av break diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/README.md b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/README.md similarity index 56% rename from 2.0/problems/pca_gpu_kernel_optimization/harbor/app/README.md rename to 2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/README.md index fcb9fb6cb..9a9b2979e 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/README.md +++ b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/README.md @@ -1,8 +1,8 @@ -# PCA kernel optimization — submission workflow +# DBSCAN kernel optimization — submission workflow -You are optimizing the `pcalib` package at `/app/pcalib`. Edit the package -(rewrite the internals of `pca`, add Triton kernel modules under `pcalib/`), -then submit a patch. +You are optimizing the `dbscanlib` package at `/app/dbscanlib`. Edit the package +(rewrite the internals of `dbscan`, add Triton kernel modules under +`dbscanlib/`), then submit a patch. ## Iterate locally @@ -16,7 +16,7 @@ bash /app/public_test.sh ## Submit ```bash -bash /app/make_submission.sh # writes /app/solution.patch (pcalib diff) +bash /app/make_submission.sh # writes /app/solution.patch (dbscanlib diff) bash /app/submit.sh # enqueues it for the black-box judge ``` @@ -26,10 +26,9 @@ results. ## Rules -- Only files under `pcalib/` may change. +- Only files under `dbscanlib/` may change. - Do not import external optimized libraries (write the kernels yourself), and do not access the environment, spawn processes, or use the network. -- Keep the public `pca(...)` signature and return contract unchanged. -- Subspace quality is gated (orthonormal components and captured variance vs the - naive baseline); do not sacrifice correctness for speed beyond the allowed - tolerance. +- Keep the public `dbscan(...)` signature and return contract unchanged. +- Clustering quality is gated (inertia vs the naive baseline); do not sacrifice + correctness for speed beyond the allowed tolerance. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/make_submission.sh b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/make_submission.sh old mode 100755 new mode 100644 similarity index 53% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/make_submission.sh rename to 2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/make_submission.sh index f6480f086..44ef00d34 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/make_submission.sh +++ b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/make_submission.sh @@ -1,12 +1,12 @@ #!/usr/bin/env bash -# Package the current tsvdlib edits into a unified diff for submission. +# Package the current dbscanlib edits into a unified diff for submission. set -euo pipefail APP_DIR="${APP_DIR:-/app}" OUT="${1:-/app/solution.patch}" -if [[ ! -d "$APP_DIR/tsvdlib" ]]; then - echo "tsvdlib package not found at $APP_DIR/tsvdlib" >&2 +if [[ ! -d "$APP_DIR/dbscanlib" ]]; then + echo "dbscanlib package not found at $APP_DIR/dbscanlib" >&2 exit 2 fi if [[ ! -d "$APP_DIR/.git" ]]; then @@ -15,9 +15,9 @@ if [[ ! -d "$APP_DIR/.git" ]]; then fi # Stage and diff only the package, so submission helper scripts under /app are -# never included. New kernel files under tsvdlib/ are captured via `git add`. -git -C "$APP_DIR" add -A -- tsvdlib -git -C "$APP_DIR" diff --cached -- tsvdlib > "$OUT" +# never included. New kernel files under dbscanlib/ are captured via `git add`. +git -C "$APP_DIR" add -A -- dbscanlib +git -C "$APP_DIR" diff --cached -- dbscanlib > "$OUT" git -C "$APP_DIR" reset -q bytes=$(wc -c < "$OUT" | tr -d ' ') diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py similarity index 68% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py rename to 2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py index c321f0b6e..25eb8c592 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,9 +1,9 @@ -"""Public GPU self-test for the Truncated SVD kernel-optimization task. +"""Public GPU self-test for the DBSCAN kernel-optimization task. -Times your current /app/tsvdlib against the naive baseline on a Modal GPU and -reports the quality verdict + speedup on two public shapes. Requires -MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and -their thresholds are hidden and differ from these public shapes. +Times your current /app/dbscanlib against the naive baseline on a Modal GPU and +reports the clustering-agreement (ARI) verdict + speedup on two public shapes. +Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET. The graded workloads and thresholds +are hidden and differ from these public shapes. """ from __future__ import annotations @@ -14,22 +14,22 @@ sys.path.insert(0, "/opt") APP_DIR = os.environ.get("APP_DIR", "/app") -PKG = "tsvdlib" -BASELINE_DIR = "/opt/tsvd_ref" +PKG = "dbscanlib" +BASELINE_DIR = "/opt/dbscan_ref" PUBLIC_WORKLOADS = [ - {"id": "p0", "N": 200_000, "D": 128, "k": 16, "seed": 1}, - {"id": "p1", "N": 500_000, "D": 64, "k": 8, "seed": 2}, + {"id": "p0", "N": 100000, "D": 2, "n_centers": 12, "eps": 1.5, "min_samples": 8, "noise_frac": 0.03, "seed": 1}, + {"id": "p1", "N": 150000, "D": 2, "n_centers": 14, "eps": 1.4, "min_samples": 8, "noise_frac": 0.04, "seed": 2}, ] CFG = { - "primitive": "tsvd", + "primitive": "dbscan", "pkg": PKG, - "ref_module": "reftsvd", + "ref_module": "refdbscan", "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", "pip": ["torch", "numpy"], - "app_name": "tsvd-kernel-opt-public", + "app_name": "dbscan-kernel-opt-public", "modal_timeout_seconds": 1800, "warmup": 2, "iters": 5, @@ -37,6 +37,7 @@ "recall_threshold": 0.99, "captured_tolerance": 0.02, "ortho_tolerance": 0.02, + "ari_threshold": 0.99, } @@ -71,7 +72,7 @@ def main() -> int: if row.get("ok"): print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") else: - print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason','')):16s} {'-':>10s}") + print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason', '')):16s} {'-':>10s}") return 0 diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.sh b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.sh old mode 100755 new mode 100644 similarity index 71% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.sh rename to 2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.sh index 98a6a036e..1038fbfb4 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/public_test.sh +++ b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.sh @@ -1,5 +1,5 @@ #!/usr/bin/env bash -# Public self-test: run the patched tsvdlib on the public shapes and print a +# Public self-test: run the patched dbscanlib on the public shapes and print a # correctness + rough speedup summary. Requires a GPU in the agent container. set -euo pipefail APP_DIR="${APP_DIR:-/app}" diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/solution.patch b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/solution.patch similarity index 100% rename from 2.0/problems/pca_gpu_kernel_optimization/harbor/app/solution.patch rename to 2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/solution.patch diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/judge/refdbscan.py b/2.0/problems/dbscan_gpu_kernel_optimization/judge/refdbscan.py new file mode 100644 index 000000000..ab7fa8bfc --- /dev/null +++ b/2.0/problems/dbscan_gpu_kernel_optimization/judge/refdbscan.py @@ -0,0 +1,70 @@ +"""Naive DBSCAN baseline (correct, deterministic, GPU/CPU torch). + +Euclidean DBSCAN. Chunked O(N^2) neighbour scan + GPU-friendly label +propagation for the connected components of the core graph. Border rule matches +flashlib: a non-core point joins the SMALLEST cluster label among its in-eps +core neighbours; otherwise it is noise (-1). +""" +from __future__ import annotations + +import torch + + +def dbscan(x, eps, min_samples, max_neighbors=32): + del max_neighbors # naive path scans all neighbours; knob only for the fast kernel + N = x.shape[0] + dev = x.device + eps2 = float(eps) * float(eps) + xn = (x * x).sum(1) # (N,) + CH = 4096 + BIG = N # sentinel label (> any real label) + + def d2_chunk(lo, hi): + xb = x[lo:hi] + return (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ x.t()) + xn[None, :] + + # 1) degree (in-eps count, includes self) -> core mask + deg = torch.zeros(N, device=dev, dtype=torch.int64) + for lo in range(0, N, CH): + hi = min(lo + CH, N) + deg[lo:hi] = (d2_chunk(lo, hi) <= eps2).sum(1) + core = deg >= int(min_samples) # (N,) bool + + # 2) connected components of the core-core eps graph via label propagation + labels = torch.arange(N, device=dev, dtype=torch.int64) + labels[~core] = BIG + for _ in range(1000): + new = labels.clone() + for lo in range(0, N, CH): + hi = min(lo + CH, N) + if not bool(core[lo:hi].any()): + continue + adj = (d2_chunk(lo, hi) <= eps2) & core[None, :] # (chunk, N) + lab = torch.where(adj, labels[None, :], torch.full((1, N), BIG, device=dev, dtype=torch.int64)) + mn = lab.min(1).values # (chunk,) + rows = slice(lo, hi) + upd = core[rows] & (mn < new[rows]) + new[rows] = torch.where(upd, mn, new[rows]) + if torch.equal(new, labels): + break + labels = new + + # 3) border assignment: non-core within eps of a core -> smallest core label + for lo in range(0, N, CH): + hi = min(lo + CH, N) + nb = ~core[lo:hi] + if not bool(nb.any()): + continue + adj = (d2_chunk(lo, hi) <= eps2) & core[None, :] + lab = torch.where(adj, labels[None, :], torch.full((1, N), BIG, device=dev, dtype=torch.int64)) + mn = lab.min(1).values + rows = slice(lo, hi) + take = nb & (mn < BIG) + labels[rows] = torch.where(take, mn, labels[rows]) + + # 4) compact to dense [0, n_clusters); noise (BIG) -> -1 + out = torch.full((N,), -1, device=dev, dtype=torch.int64) + uniq = torch.unique(labels[labels < BIG]) + for new_id, old in enumerate(uniq.tolist()): + out[labels == old] = new_id + return out diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/readme b/2.0/problems/dbscan_gpu_kernel_optimization/readme new file mode 100644 index 000000000..e0feece33 --- /dev/null +++ b/2.0/problems/dbscan_gpu_kernel_optimization/readme @@ -0,0 +1,97 @@ +# GPU DBSCAN Kernel Optimization + +## Problem + +You are given a small GPU DBSCAN library, `dbscanlib`, in the Harbor workspace at +`/app/dbscanlib`. Its public entry point is: + +```python +dbscanlib.dbscan(x, eps, min_samples) -> labels +``` + +`x` is an `(N, D)` float32 CUDA tensor of low-dimensional points, `eps` is the +neighbourhood radius, and `min_samples` is the core-point threshold. It runs +Euclidean DBSCAN and returns `labels`, an `(N,)` int64 tensor with a cluster id +`>= 0` per point and `-1` for noise. The shipped implementation is a correct but +straightforward version. + +Your goal is to make `dbscanlib.dbscan` **as fast as possible** on the GPU while +producing the same clustering. You may rewrite the internals of the package and +add new modules (including Triton kernels) under `dbscanlib/`. The public +function signature and return contract must not change, and the result must +remain a deterministic function of the inputs. + +## Workload + +The graded workloads are held-out planar (D = 2) point sets with planted density +structure (well-separated clusters plus a little uniform noise), varying `N`, +cluster count, `eps`, and `min_samples`. Treat it as general 2-D Euclidean +DBSCAN, not as specific point sets to special-case. + +Two representative *public* shapes are available for local testing (the graded +shapes are different and hidden): + +```text +(N=100000, D=2, eps=1.5, min_samples=8) +(N=150000, D=2, eps=1.4, min_samples=8) +``` + +## Iterate on a GPU + +The agent workspace has no GPU. Use the public test to time your current code on +a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET`): + +```bash +bash /app/public_test.sh +``` + +## Submission + +The submitted artifact is a patch over the `dbscanlib` package: + +```text +/app/solution.patch +``` + +After editing `/app/dbscanlib`, run `bash /app/make_submission.sh` then +`bash /app/submit.sh`. Submissions are asynchronous; submit early and iterate. +The judge applies your patch to a clean copy of `dbscanlib` and times it against +the original baseline on a GPU, on the same seeded data. + +## Correctness + +Correctness is a gate. On every timed iteration the judge compares your +clustering to the baseline's on identical data using the **Adjusted Rand Index** +(a permutation-invariant clustering-agreement score that also accounts for +noise), and requires it to stay at or above a high threshold. Crashes, +non-finite / wrong-shape output, timeouts, and clusterings that disagree with the +baseline beyond the tolerance are penalized before speed is considered. + +## Scoring + +Valid submissions are scored by speedup relative to the baseline: + +```text +speedup = baseline_time / your_time +``` + +The objective is the geometric mean of per-workload speedups. A result no faster +than the baseline earns 0; regressions earn 0. The raw geometric-mean speedup is +reported in the evaluator metrics. + +## Patch Policy + +Only Python files under `dbscanlib/**` may change. New Python modules inside +`dbscanlib/` are allowed. Patches may not: modify anything outside `dbscanlib/`; +import or call an external optimized ML/kernel library (write the kernels +yourself); read/write environment variables, spawn processes, or access the +network; or tamper with the measurement framework. The timing harness measures +your code on freshly generated data every iteration and re-verifies clustering +agreement each time. + +## Resource Budget + +```text +GPU: single Modal GPU (H100 reference; Triton paths also run on L40S / A100) +Agent container: CPU-only (GPU work is offloaded to Modal) +``` diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/reference.patch b/2.0/problems/dbscan_gpu_kernel_optimization/reference.patch new file mode 100644 index 000000000..babf28b9d --- /dev/null +++ b/2.0/problems/dbscan_gpu_kernel_optimization/reference.patch @@ -0,0 +1,1061 @@ +diff --git a/dbscanlib/_kernels/__init__.py b/dbscanlib/_kernels/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/kernels/__init__.py b/dbscanlib/_kernels/kernels/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/kernels/flash_mst/__init__.py b/dbscanlib/_kernels/kernels/flash_mst/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/kernels/flash_mst/triton/__init__.py b/dbscanlib/_kernels/kernels/flash_mst/triton/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/kernels/flash_mst/triton/flash_mst.py b/dbscanlib/_kernels/kernels/flash_mst/triton/flash_mst.py +new file mode 100644 +index 0000000..af8ace6 +--- /dev/null ++++ b/dbscanlib/_kernels/kernels/flash_mst/triton/flash_mst.py +@@ -0,0 +1,611 @@ ++"""flash-mst: GPU-resident dense Boruvka MST. ++ ++Algorithm (per Boruvka iteration, all on GPU): ++ 1. Per-component argmin (`_per_component_argmin_v2_kernel`): for each row v, ++ scan all N columns of MRD once; pack (weight_bits, dst_idx) as int64 with ++ +inf for same-component cells; reduce per-row to a single packed best ++ edge; atomic_min into OUT_PACKED[parent[v]] (the row's component bucket). ++ SAME kernel folds in OUT_SRC[parent[v]] = atomic_min(orig_i) so we get ++ the canonical source vertex per component without a separate kernel. ++ 2. Concurrent union-find (`_concurrent_uf_kernel`): each component decodes ++ its packed-int64 best edge, finds roots with bounded path-walk, atomic- ++ CAS merges. Successful merges write an MST edge via atomic counter. ++ 3. Pointer-jumping (`_pointer_jump_kernel`): parent[v] = parent[parent[v]] ++ in parallel for ~log iters until paths are flat. Triton kernel replaces ++ the `parent = parent[parent.to(int64)]` torch loop (saves N int64 casts). ++ ++Persistent state (allocated ONCE at flash_mst entry, reused across rounds): ++ - parent[N] int32 — union-find array; doubles as the component label ++ for the next round's argmin (after pointer-jumping it is a valid label, ++ no torch.unique relabel needed). ++ - out_packed[N] int64 — per-CID atomic_min target, sized N (upper bound ++ on component count). Reset to +inf each round via Triton kernel. ++ - out_src[N] int32 — per-CID canonical src; reset to MAX_INT32. ++ - mst_{src,dst,w}, edge_count — output. ++ ++Why no argsort + no torch.unique relabel: ++ - The original code argsort-ed by component for cache locality; with ++ parent indexed directly the cache hit-rate is the same (parent[i] is ++ a single random read per row, then comp_j is a streaming scan which ++ L2-caches). ++ - `torch.unique` did a sort + inverse to remap parent[] to dense [0, n_C). ++ That step cost ~0.13 ms × log_2(N) iters of pointer-jumping. We skip it: ++ the argmin kernel uses `parent[i] != parent[j]` directly, which works ++ with sparse labels. ++ ++Note: I tried a K-min Boruvka variant (build a per-row top-K cross-component ++edge table once, reuse for K mini-merges) — measured 1 outer round was enough ++to find 99.97% of MST edges at huge, but the K=4 table build is ~3-4× more ++compute-heavy per scan than the simple argmin (4× axis-min + masking + bitonic ++merge), and a 2nd cleanup round still costs a full N² scan, so it lost to the ++simple argmin. Reverted; orchestration cleanup + tile tuning gave the win. ++ ++Packed int64 trick: for non-negative fp32 weights, the IEEE-754 bit pattern ++is monotonic (same ordering as float). Pack as ++ (weight_bits << 32) | dst_idx ++so atomic_min on int64 selects the smallest weight (with index as tiebreaker). ++""" ++ ++import math ++import numpy as np ++import torch ++import triton ++import triton.language as tl ++ ++ ++# ============================================================================= ++# Kernel 1: per-component min outgoing edge ++# Each program handles BLOCK_S sorted-contiguous points; per-row argmin over ++# the row of MRD (masked by component); atomic_min the result into the ++# corresponding component slot. ++# ============================================================================= ++ ++@triton.jit ++def _per_component_argmin_kernel( ++ MRD_ptr, # (N, N) fp32 ++ SORTED_IDX_ptr, # (N,) int32 ++ COMP_ptr, # (N,) int32, indexed by orig_idx ++ OUT_PACKED_ptr, # (n_components,) int64, atomic_min target ++ N, ++ BLOCK_S: tl.constexpr, ++ BLOCK_J: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ s_offs = pid * BLOCK_S + tl.arange(0, BLOCK_S) ++ s_mask = s_offs < N ++ ++ orig_i = tl.load(SORTED_IDX_ptr + s_offs, mask=s_mask, other=0) ++ comp_i = tl.load(COMP_ptr + orig_i, mask=s_mask, other=-1) ++ ++ # IEEE-754 +inf bit pattern as int64 high word. ++ # 0x7F800000 << 32 = 9151314442816847872 ++ best_packed = tl.full([BLOCK_S], 9151314442816847872, dtype=tl.int64) ++ SHIFT32 = tl.cast(4294967296, tl.int64) # 2^32 as int64 ++ n_i64 = tl.cast(N, tl.int64) ++ ++ for j_start in range(0, N, BLOCK_J): ++ j_offs = j_start + tl.arange(0, BLOCK_J) ++ j_mask = j_offs < N ++ comp_j = tl.load(COMP_ptr + j_offs, mask=j_mask, other=-2) ++ ++ row_offs = (orig_i.to(tl.int64)[:, None] * n_i64 ++ + j_offs.to(tl.int64)[None, :]) ++ # Load and cast to fp32 (handles bf16 / fp16 / fp32 storage uniformly) ++ mrd_tile = tl.load(MRD_ptr + row_offs, ++ mask=s_mask[:, None] & j_mask[None, :], ++ other=float('inf')).to(tl.float32) ++ ++ # Mask same-component ++ diff = (comp_j[None, :] != comp_i[:, None]) & j_mask[None, :] & s_mask[:, None] ++ mrd_tile = tl.where(diff, mrd_tile, float('inf')) ++ ++ # Pack (weight_bits, j_idx) as int64. Use multiplication by 2^32 instead ++ # of `<< 32` to avoid int32 overflow warning. ++ wbits_u32 = mrd_tile.to(tl.uint32, bitcast=True) ++ wbits_i64 = wbits_u32.to(tl.int64) ++ j_i64 = j_offs.to(tl.int64)[None, :] ++ packed = wbits_i64 * SHIFT32 + j_i64 ++ ++ tile_best = tl.min(packed, axis=1) ++ best_packed = tl.minimum(best_packed, tile_best) ++ ++ # Atomic_min into OUT_PACKED[comp_i] for each row in this block ++ out_addrs = OUT_PACKED_ptr + comp_i.to(tl.int64) ++ tl.atomic_min(out_addrs, best_packed, mask=s_mask) ++ ++ ++# ============================================================================= ++# Kernel 2: write per-component canonical source (one orig_idx per component) ++# Run after argsort. For each component C in dense [0, n_components), picks ++# the FIRST orig_idx in sorted order (sorted_idx[comp_start[C]]). ++# ============================================================================= ++ ++@triton.jit ++def _set_per_component_src_kernel( ++ SORTED_IDX_ptr, # (N,) int32 ++ COMP_ptr, # (N,) int32 ++ OUT_SRC_ptr, # (n_components,) int32 ++ N, ++ BLOCK: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ s_offs = pid * BLOCK + tl.arange(0, BLOCK) ++ s_mask = s_offs < N ++ orig = tl.load(SORTED_IDX_ptr + s_offs, mask=s_mask, other=0) ++ comp = tl.load(COMP_ptr + orig, mask=s_mask, other=0) ++ # The "first" element of each component is the one whose sorted position ++ # is smaller than all others with the same component. Use atomic_min on ++ # (sorted_pos, orig) pair — but for simplicity, just use atomic_min on ++ # orig (any vertex works as canonical). ++ target = OUT_SRC_ptr + comp.to(tl.int64) ++ # Use atomic_min so the smallest orig in each component wins ++ # (deterministic). orig values are int32 non-negative. ++ tl.atomic_min(target, orig, mask=s_mask) ++ ++ ++# ============================================================================= ++# Kernel 3: concurrent union-find merge ++# For each component C, decode (weight, dst) from out_packed[C], set src from ++# out_src[C], find roots via bounded loop, atomic_cas to merge. ++# Successful merges write an MST edge via atomic counter. ++# ============================================================================= ++ ++@triton.jit ++def _concurrent_uf_kernel( ++ PARENT_ptr, # (N,) int32, atomic ++ OUT_PACKED_ptr, # (n_components,) int64 ++ OUT_SRC_ptr, # (n_components,) int32 ++ MST_SRC_ptr, # (N-1,) int32 — output edge sources ++ MST_DST_ptr, # (N-1,) int32 — output edge targets ++ MST_W_ptr, # (N-1,) fp32 — output edge weights ++ EDGE_COUNT_ptr, # (1,) int32 atomic ++ n_components, ++ MAX_FIND: tl.constexpr, ++): ++ cid = tl.program_id(0) ++ if cid >= n_components: ++ return ++ ++ packed = tl.load(OUT_PACKED_ptr + cid) ++ # Sentinel: high 32 bits = 0x7F800000 (+inf bit pattern) means "no edge found" ++ high32 = (packed >> 32).to(tl.int32) ++ if high32 >= 0x7F800000: ++ return ++ ++ weight = high32.to(tl.float32, bitcast=True) ++ dst = (packed & 0xFFFFFFFF).to(tl.int32) ++ src = tl.load(OUT_SRC_ptr + cid) ++ if src < 0 or src == 2147483647: ++ return ++ ++ # Single-pass: find roots with path-walking, then atomic_cas to merge. ++ # If CAS fails (concurrent thread merged hi first), we drop this edge — ++ # next Boruvka iter will re-propose. log(N) extra iters total (rare path). ++ rs = src ++ for _ in tl.static_range(MAX_FIND): ++ p = tl.load(PARENT_ptr + rs) ++ rs = tl.where(p == rs, rs, p) ++ rd = dst ++ for _ in tl.static_range(MAX_FIND): ++ p = tl.load(PARENT_ptr + rd) ++ rd = tl.where(p == rd, rd, p) ++ ++ if rs == rd: ++ return ++ ++ lo = tl.minimum(rs, rd) ++ hi = tl.maximum(rs, rd) ++ old = tl.atomic_cas(PARENT_ptr + hi, hi, lo) ++ if old == hi: ++ idx = tl.atomic_add(EDGE_COUNT_ptr, 1) ++ tl.store(MST_SRC_ptr + idx, src) ++ tl.store(MST_DST_ptr + idx, dst) ++ tl.store(MST_W_ptr + idx, weight) ++ ++ ++# ============================================================================= ++# Generic edge-list union-find kernel (used by flash-dbscan CC, etc.). ++# Vectorised over BLOCK edges per program. Path-halving compresses the ++# tree during find via best-effort store-back. A merge counter lets the ++# caller exit as soon as no more edges can merge (a chain-shaped graph ++# with diameter ~ N can need 8+ passes). ++# ============================================================================= ++ ++@triton.jit ++def _union_edges_v2_kernel( ++ PARENT_ptr, # (N,) int32 atomic ++ ROW_ptr, # (E,) int32 — edge sources ++ COL_ptr, # (E,) int32 — edge targets ++ MERGE_COUNTER_ptr, # (1,) int32 — atomic counter; nonzero ⇒ keep iterating ++ n_edges, ++ BLOCK: tl.constexpr, ++ MAX_FIND: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ e_offs = pid * BLOCK + tl.arange(0, BLOCK) ++ e_mask = e_offs < n_edges ++ u = tl.load(ROW_ptr + e_offs, mask=e_mask, other=0) ++ v = tl.load(COL_ptr + e_offs, mask=e_mask, other=0) ++ ++ # find root of u with path-halving: each step does 2 hops ++ # (parent[ru] := parent[parent[ru]]) and writes back the compressed ++ # parent. Best-effort — concurrent writers to the same ru race, but ++ # since parent links are monotonically toward the root the result is ++ # always valid (no need for atomic_cas — a stale write only undoes ++ # one step of compression, never causes a cycle). ++ ru = u ++ for _ in tl.static_range(MAX_FIND): ++ p = tl.load(PARENT_ptr + ru, mask=e_mask, other=0) ++ gp = tl.load(PARENT_ptr + p, mask=e_mask, other=0) ++ # only compress if we actually moved up two levels ++ compress = (ru != p) & (p != gp) & e_mask ++ tl.store(PARENT_ptr + ru, gp, mask=compress) ++ ru = tl.where(p == ru, ru, gp) ++ rv = v ++ for _ in tl.static_range(MAX_FIND): ++ p = tl.load(PARENT_ptr + rv, mask=e_mask, other=0) ++ gp = tl.load(PARENT_ptr + p, mask=e_mask, other=0) ++ compress = (rv != p) & (p != gp) & e_mask ++ tl.store(PARENT_ptr + rv, gp, mask=compress) ++ rv = tl.where(p == rv, rv, gp) ++ ++ diff = (ru != rv) & e_mask ++ lo = tl.minimum(ru, rv) ++ hi = tl.maximum(ru, rv) ++ # atomic_min instead of atomic_cas: parent[hi] := min(parent[hi], lo). ++ # Triton 3.6 doesn't support mask= on atomic_cas, but does on atomic_min. ++ # Semantics are actually cleaner for UF: parent[v] is monotonically ++ # non-increasing under atomic_min, so cycles are impossible by induction ++ # (we always have parent[v] ≤ v). "Made progress" = OLD > lo, i.e., we ++ # actually decreased parent[hi]. ++ old = tl.atomic_min(PARENT_ptr + hi, lo, mask=diff) ++ progress = (old > lo) & diff ++ n_succ = tl.sum(progress.to(tl.int32)) ++ # one atomic_add per CTA (not per edge) — negligible contention ++ if n_succ > 0: ++ tl.atomic_add(MERGE_COUNTER_ptr, n_succ) ++ ++ ++# ============================================================================= ++# Triton compaction: replaces `torch.unique(parent, return_inverse=True)`. ++# After parent is fully flattened, parent[v] is either v (if v is a root) or ++# the root index. Then dense_id_at[v] = (number of roots in [0, v]) - 1 if v ++# is a root, otherwise undefined. We compute it via a torch cumsum on the ++# is_root bitmap (cheap, one launch) and then gather labels[v] = dense_id[parent[v]]. ++# ============================================================================= ++ ++@triton.jit ++def _is_root_kernel( ++ PARENT_ptr, # (N,) int32 ++ IS_ROOT_ptr, # (N,) int32 (0/1) ++ N, ++ BLOCK: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ offs = pid * BLOCK + tl.arange(0, BLOCK) ++ mask = offs < N ++ p = tl.load(PARENT_ptr + offs, mask=mask, other=-1) ++ is_root = (p == offs.to(tl.int32)).to(tl.int32) ++ tl.store(IS_ROOT_ptr + offs, is_root, mask=mask) ++ ++ ++@triton.jit ++def _gather_labels_kernel( ++ PARENT_ptr, # (N,) int32 ++ DENSE_ID_ptr, # (N,) int32 — dense id for each root, undefined for non-roots ++ LABELS_ptr, # (N,) int32 — output ++ N, ++ BLOCK: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ offs = pid * BLOCK + tl.arange(0, BLOCK) ++ mask = offs < N ++ p = tl.load(PARENT_ptr + offs, mask=mask, other=0) ++ label = tl.load(DENSE_ID_ptr + p.to(tl.int64), mask=mask, other=0) ++ tl.store(LABELS_ptr + offs, label, mask=mask) ++ ++ ++ ++ ++ ++ ++ ++# ============================================================================= ++# Kernel 4: pointer jumping path compression ++# parent[v] = parent[parent[v]] in parallel; iterate to convergence. ++# ============================================================================= ++ ++@triton.jit ++def _pointer_jump_kernel( ++ PARENT_ptr, ++ N, ++ BLOCK: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ v_offs = pid * BLOCK + tl.arange(0, BLOCK) ++ v_mask = v_offs < N ++ p = tl.load(PARENT_ptr + v_offs, mask=v_mask, other=0) ++ # Cast p (int32) to int64 for pointer arithmetic safety on large N ++ pp = tl.load(PARENT_ptr + p.to(tl.int64), mask=v_mask, other=0) ++ tl.store(PARENT_ptr + v_offs, pp, mask=v_mask) ++ ++ ++ ++def flash_cc_from_edges(rows: torch.Tensor, cols: torch.Tensor, N: int, ++ max_find: int = 8, max_passes: int = 16): ++ """Connected components on the graph defined by edge list (rows, cols). ++ ++ Iterative converging algorithm: ++ - Each pass: vectorized union with path-halving + atomic_cas hooks ++ (BLOCK=128 edges per program → 4 warps fully utilized vs the old ++ scalar 1-edge-per-warp launch). ++ - Then 6 calls to `_pointer_jump_kernel` to flatten the parent forest. ++ - Read the merge counter; exit as soon as a pass merges 0 edges. ++ For diameter-bound dense graphs this usually fires in 1-2 passes; ++ chain-shaped graphs (diameter ~N) converge in log_2(N) passes. ++ ++ Args: ++ rows, cols: (E,) int32 — edge endpoints. ++ N: number of vertices. ++ max_find: bounded find depth per pass. With path-halving each step ++ doubles the effective depth, so MAX_FIND=8 covers depth 256. ++ max_passes: hard ceiling (log2 of any conceivable graph diameter). ++ ++ Returns: ++ labels: (N,) int32 — CC component id, dense in [0, n_components). ++ """ ++ device = rows.device ++ parent = torch.arange(N, dtype=torch.int32, device=device) ++ E = rows.shape[0] ++ ++ if E > 0: ++ BLOCK_EDGE = 128 ++ BLOCK_JUMP = 1024 ++ merge_counter = torch.zeros(1, dtype=torch.int32, device=device) ++ grid_edge = (triton.cdiv(E, BLOCK_EDGE),) ++ grid_jump = (triton.cdiv(N, BLOCK_JUMP),) ++ ++ for _ in range(max_passes): ++ merge_counter.zero_() ++ _union_edges_v2_kernel[grid_edge]( ++ parent, rows, cols, merge_counter, E, ++ BLOCK=BLOCK_EDGE, MAX_FIND=max_find, num_warps=4, ++ ) ++ # Pointer-jump to flatten any remaining short chains. 6 iters ++ # cover depth ≤ 2^6 = 64 (post-compression chains are short). ++ for _ in range(6): ++ _pointer_jump_kernel[grid_jump]( ++ parent, N, BLOCK=BLOCK_JUMP, num_warps=4, ++ ) ++ if merge_counter.item() == 0: ++ break ++ ++ # Compact: roots have parent[v] == v; assign dense [0, n_cc) by index order. ++ is_root = torch.empty(N, dtype=torch.int32, device=device) ++ grid_compact = (triton.cdiv(N, 1024),) ++ _is_root_kernel[grid_compact](parent, is_root, N, BLOCK=1024, num_warps=4) ++ # cumsum gives 1-indexed dense IDs at root positions; subtract 1 → 0-indexed. ++ # At non-root positions the value is meaningless (we only gather via parent[v]). ++ dense_id = torch.cumsum(is_root, dim=0, dtype=torch.int32) - 1 ++ labels = torch.empty(N, dtype=torch.int32, device=device) ++ _gather_labels_kernel[grid_compact]( ++ parent, dense_id, labels, N, BLOCK=1024, num_warps=4, ++ ) ++ return labels ++ ++ ++# ============================================================================= ++# Kernel 5: per-component argmin with FOLDED canonical-src. Uses ++# ``orig_i = pid * BLOCK_S + lane`` directly (same cache hit-rate since ++# the column-side scan dominates the L2 traffic). The ++# ``OUT_SRC[component] = min(orig_idx)`` atomic_min is folded into the ++# same kernel, saving a separate launch per round. ++# ============================================================================= ++ ++@triton.jit ++def _per_component_argmin_v2_kernel( ++ MRD_ptr, # (N, N) bf16 or fp32 ++ PARENT_ptr, # (N,) int32 — parent[i] is i's component label ++ OUT_PACKED_ptr, # (N,) int64 — atomic_min target (sized to N) ++ OUT_SRC_ptr, # (N,) int32 — atomic_min target for canonical src ++ N, ++ BLOCK_S: tl.constexpr, ++ BLOCK_J: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ s_offs = pid * BLOCK_S + tl.arange(0, BLOCK_S) ++ s_mask = s_offs < N ++ ++ orig_i = s_offs.to(tl.int64) ++ comp_i = tl.load(PARENT_ptr + orig_i, mask=s_mask, other=0) ++ ++ # IEEE-754 +inf bit pattern as int64 high word: 0x7F800000 << 32 ++ best_packed = tl.full([BLOCK_S], 9151314442816847872, dtype=tl.int64) ++ SHIFT32 = tl.cast(4294967296, tl.int64) ++ n_i64 = tl.cast(N, tl.int64) ++ ++ for j_start in range(0, N, BLOCK_J): ++ j_offs = j_start + tl.arange(0, BLOCK_J) ++ j_mask = j_offs < N ++ comp_j = tl.load(PARENT_ptr + j_offs, mask=j_mask, other=0) ++ ++ row_offs = orig_i[:, None] * n_i64 + j_offs.to(tl.int64)[None, :] ++ mrd_tile = tl.load(MRD_ptr + row_offs, ++ mask=s_mask[:, None] & j_mask[None, :], ++ other=float('inf')).to(tl.float32) ++ ++ # Mask same-component ++ diff = (comp_j[None, :] != comp_i[:, None]) & j_mask[None, :] & s_mask[:, None] ++ mrd_tile = tl.where(diff, mrd_tile, float('inf')) ++ ++ # Pack (weight_bits, j_idx) as int64 — the int64 axis-min uses a single ++ # comparator per element regardless of (weight, j) since the high bits ++ # dominate the ordering. Trying to track (best_w, best_j) as separate ++ # fp32+int (single tl.min + tl.argmin) was *slower* in benchmarking on ++ # H200 — the int64 reduction actually beats the fused fp32+argmin path. ++ wbits_u32 = mrd_tile.to(tl.uint32, bitcast=True) ++ wbits_i64 = wbits_u32.to(tl.int64) ++ j_i64 = j_offs.to(tl.int64)[None, :] ++ packed = wbits_i64 * SHIFT32 + j_i64 ++ ++ tile_best = tl.min(packed, axis=1) ++ best_packed = tl.minimum(best_packed, tile_best) ++ ++ # Atomic_min into OUT_PACKED[comp_i] and OUT_SRC[comp_i] (folded) ++ out_addrs = OUT_PACKED_ptr + comp_i.to(tl.int64) ++ tl.atomic_min(out_addrs, best_packed, mask=s_mask) ++ src_addrs = OUT_SRC_ptr + comp_i.to(tl.int64) ++ tl.atomic_min(src_addrs, s_offs.to(tl.int32), mask=s_mask) ++ ++ ++# ============================================================================= ++# Kernel 6: reset (N,)-sized int64 buffer to INF and int32 buffer to MAX_INT32 ++# Faster than torch.full(...) by ~4× (~30us vs ~125us per pair at huge): single ++# kernel launch with one pass over both arrays vs two separate fill kernels + ++# Python tensor wrapping overhead. ++# ============================================================================= ++ ++@triton.jit ++def _reset_buffers_kernel( ++ OUT_PACKED_ptr, # (N,) int64 ++ OUT_SRC_ptr, # (N,) int32 ++ N, ++ BLOCK: tl.constexpr, ++): ++ pid = tl.program_id(0) ++ offs = pid * BLOCK + tl.arange(0, BLOCK) ++ mask = offs < N ++ INF_PACKED = tl.cast(9151314442816847872, tl.int64) # 0x7F800000 << 32 ++ MAX_I32 = tl.cast(2147483647, tl.int32) ++ tl.store(OUT_PACKED_ptr + offs, INF_PACKED, mask=mask) ++ tl.store(OUT_SRC_ptr + offs, MAX_I32, mask=mask) ++ ++ ++# ============================================================================= ++# Python orchestration ++# ============================================================================= ++ ++def _pick_argmin_tile(N: int): ++ """Pick (BLOCK_S, BLOCK_J, num_warps, num_stages) for the argmin kernel. ++ ++ Tuned on H200 (GPU 4) at bf16 MRD via `algorithms/hdbscan/_bench_mst.py` ++ (sub-process to avoid Triton autotune cache leakage between the per-tile ++ sweep and the E2E run). ++ ++ Best per-N (of 4.8 TB/s peak BW): ++ N=20K: BS=32, BJ=256, nw=4, ns=4 → 0.376 ms (44.4%) ++ N=80K: BS=32, BJ=128, nw=4, ns=4 → 4.625 ms (57.7%) ++ N=200K: BS=32, BJ=128, nw=4, ns=3 → 28.08 ms (59.3%) ++ Larger BLOCK_J (256+) stalls at huge — the per-CTA register footprint of ++ the int64 packed tile (8 bytes × BLOCK_S × BLOCK_J) hurts occupancy and ++ spills into shared memory. Use BJ=128 for N >= 50K. ++ """ ++ if N >= 50_000: ++ return 32, 128, 4, 3 ++ return 32, 256, 4, 4 ++ ++ ++def flash_mst(MRD: torch.Tensor) -> torch.Tensor: ++ """GPU-resident dense Boruvka MST on a symmetric (N, N) distance matrix. ++ ++ Args: ++ MRD: (N, N) fp32 or bf16 — assumed symmetric, non-negative. Pass bf16 ++ for 2× HBM speedup on the per-iter argmin scan; weight precision ++ reduces to ~7 bits but tie-breaking via packed (weight, dst) keeps ++ determinism. ++ ++ Returns: ++ (N-1, 3) fp32 — MST edges as [src, dst, weight] sorted by weight. ++ src/dst are integer vertex IDs stored as fp32 (precise for N <= 2^24). ++ """ ++ assert MRD.is_cuda and MRD.dtype in (torch.float32, torch.bfloat16) and MRD.ndim == 2 ++ N = MRD.shape[0] ++ assert MRD.shape[1] == N ++ device = MRD.device ++ ++ # Persistent state — allocated once, reused across all rounds. ++ parent = torch.arange(N, dtype=torch.int32, device=device) ++ out_packed = torch.empty(N, dtype=torch.int64, device=device) ++ out_src = torch.empty(N, dtype=torch.int32, device=device) ++ ++ mst_src = torch.full((N - 1,), -1, dtype=torch.int32, device=device) ++ mst_dst = torch.full((N - 1,), -1, dtype=torch.int32, device=device) ++ mst_w = torch.zeros(N - 1, dtype=torch.float32, device=device) ++ edge_count = torch.zeros(1, dtype=torch.int32, device=device) ++ ++ BLOCK_S, BLOCK_J, num_warps_argmin, num_stages_argmin = _pick_argmin_tile(N) ++ BLOCK_RESET = 1024 ++ BLOCK_JUMP = 1024 ++ ++ # Standard Boruvka iter cap: log2(N) + small slack ++ max_iters = max(2, int(math.ceil(math.log2(max(N, 2)))) + 5) ++ target_edges = N - 1 ++ prev_count = 0 ++ ++ for it in range(max_iters): ++ # Reset per-component buffers in one kernel (int64 + int32 fused) ++ grid_reset = (triton.cdiv(N, BLOCK_RESET),) ++ _reset_buffers_kernel[grid_reset]( ++ out_packed, out_src, N, ++ BLOCK=BLOCK_RESET, num_warps=4, ++ ) ++ ++ # Per-component argmin: folds canonical-src into the kernel ++ grid_arg = (triton.cdiv(N, BLOCK_S),) ++ _per_component_argmin_v2_kernel[grid_arg]( ++ MRD, parent, out_packed, out_src, N, ++ BLOCK_S=BLOCK_S, BLOCK_J=BLOCK_J, ++ num_warps=num_warps_argmin, num_stages=num_stages_argmin, ++ ) ++ ++ # Concurrent union-find: dispatch over [0, N). Non-root CIDs see ++ # OUT_PACKED[c] == INF and early-return — cheap (~2 instructions). ++ grid_uf = (N,) ++ _concurrent_uf_kernel[grid_uf]( ++ parent, out_packed, out_src, ++ mst_src, mst_dst, mst_w, edge_count, ++ N, ++ MAX_FIND=8, num_warps=1, ++ ) ++ ++ # Pointer-jump until paths flat. Triton kernel (one launch ≈ 30 µs at ++ # huge) replaces the original log_2(N)-deep `parent[parent.to(int64)]` ++ # torch loop. 6 iters flatten depth ≤ 64 which covers all observed ++ # post-CAS chains; the next round's argmin handles any residual depth ++ # (a non-flat parent makes parent[i] != parent[j] occasionally true ++ # within a component → at worst one wasted edge proposal which the ++ # uf_merge rejects). ++ grid_jump = (triton.cdiv(N, BLOCK_JUMP),) ++ for _ in range(6): ++ _pointer_jump_kernel[grid_jump]( ++ parent, N, BLOCK=BLOCK_JUMP, num_warps=4, ++ ) ++ ++ # Single host sync per iter to check completion. Cost: ~5 µs each; ++ # 8 iters at huge = 40 µs total (negligible vs ~30 ms argmin). ++ cur_count = int(edge_count.item()) ++ if cur_count >= target_edges: ++ break ++ if cur_count == prev_count: ++ # No new edges this round — graph is disconnected. ++ break ++ prev_count = cur_count ++ ++ n_edges = int(edge_count.item()) ++ if n_edges != target_edges: ++ raise RuntimeError( ++ f"flash_mst: produced {n_edges} edges, expected {target_edges}. " ++ f"Graph may be disconnected, or merge kernel hit MAX_FIND." ++ ) ++ ++ # Sort by weight ascending for downstream HDBSCAN tree-building ++ sort_idx = torch.argsort(mst_w) ++ mst_src = mst_src[sort_idx] ++ mst_dst = mst_dst[sort_idx] ++ mst_w = mst_w[sort_idx] ++ ++ # Pack as (N-1, 3) fp32 for return — int IDs preserved exactly for N<=2^24 ++ out = torch.stack([mst_src.to(torch.float32), ++ mst_dst.to(torch.float32), ++ mst_w], dim=1) ++ return out +diff --git a/dbscanlib/_kernels/primitives/__init__.py b/dbscanlib/_kernels/primitives/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/primitives/dbscan/__init__.py b/dbscanlib/_kernels/primitives/dbscan/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/primitives/dbscan/triton/__init__.py b/dbscanlib/_kernels/primitives/dbscan/triton/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/primitives/dbscan/triton/dbscan.py b/dbscanlib/_kernels/primitives/dbscan/triton/dbscan.py +new file mode 100644 +index 0000000..74c81cd +--- /dev/null ++++ b/dbscanlib/_kernels/primitives/dbscan/triton/dbscan.py +@@ -0,0 +1,333 @@ ++"""flash-dbscan: GPU-resident DBSCAN. ++ ++Two algorithmic paths, dispatched by D: ++ ++ • D ≤ 4 (low-D): **GRID radius search**. Bin points into cells of side eps; ++ each point only needs to scan its 3^D=9 (D=2) or 27 (D=3) neighbor cells. ++ For typical D=2 spatial data with N ≈ M ≈ 100s/cell, this is ~250× ++ fewer distance evaluations than brute force. ++ ++ • D ≥ 5 (high-D): **brute-force top-K** via flash_knn (bf16 GEMM + on-chip ++ top-K), then eps filter on the returned distances. ++ ++Both paths feed the same downstream pipeline: ++ → core mask: deg ≥ min_samples ++ → connected components (atomic-CAS UF on GPU) ++ → border assignment: smallest CC label among in-eps core neighbors ++ → compact labels to dense [0, n_clusters) ++ ++The grid path is exact for any D where the cell-side ≥ eps (so all in-eps ++neighbors lie in the 3^D neighborhood). For high-D the curse of ++dimensionality makes the per-cell cost grow as 3^D, hence the brute-force ++path for D ≥ 5. ++""" ++import math ++ ++import torch ++import triton ++import triton.language as tl ++ ++flash_knn = None # D=2 grid path never calls flash_knn (high-D brute stubbed out) ++from dbscanlib._kernels.kernels.flash_mst.triton.flash_mst import flash_cc_from_edges ++ ++ ++def _next_pow2(x): ++ return 1 << (int(x - 1).bit_length()) if x > 1 else 1 ++ ++ ++# --------------------------------------------------------------------------- ++# GRID radius-search kernel (D=2) ++# --------------------------------------------------------------------------- ++# Each program handles BN query points. For each query, computes its grid ++# cell, iterates over the 3×3 neighbor cells, looks up each cell in a ++# hash table (linear probing) to find the (start, end) indices in ++# sorted_idx, and computes fp32 (xi - xj)² for each candidate; emits hits ++# (dist ≤ eps²) into the per-row neighbor buffer. ++# ++# Hash table layout: (HT_SIZE,) int64 packed as (cell_id << 32) | bin_idx. ++# Linear probing with a sentinel (-1) for empty slots. ++# --------------------------------------------------------------------------- ++ ++@triton.jit ++def _grid_radius_2d_kernel( ++ X_ptr, # (N, 2) fp32 ++ SORTED_PTR_ptr, # (N,) int32 — point indices sorted by cell ++ CELL_START_ptr, # (GW * GH,) int32 — dense 2D grid start ++ CELL_END_ptr, # (GW * GH,) int32 — dense 2D grid end ++ DEG_ptr, # (N,) int32 ++ NBR_IDX_ptr, # (N, K) int32 ++ N: tl.constexpr, K: tl.constexpr, ++ GW: tl.constexpr, GH: tl.constexpr, # grid dims ++ INV_EPS, EPS_SQ, ++ GRID_X_MIN, GRID_Y_MIN, ++ BN: tl.constexpr, ++): ++ """Grid radius search with dense 2D grid index. ++ ++ Each query maps to its (cx, cy) cell; we scan the 3×3 neighborhood by ++ direct indexing into CELL_START/END arrays of size (GW × GH). For our ++ standardised D=2 inputs (~6σ in [-3, 3], eps=0.04 → grid ~150×150, ++ storage ~22500 int32 × 2 = 180 KB). ++ """ ++ pid = tl.program_id(0) ++ n_offs = (pid * BN + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ ++ xi = tl.load(X_ptr + n_offs * 2, mask=n_mask, other=0.0) ++ yi = tl.load(X_ptr + n_offs * 2 + 1, mask=n_mask, other=0.0) ++ ++ cxq = tl.floor((xi - GRID_X_MIN) * INV_EPS).to(tl.int32) ++ cyq = tl.floor((yi - GRID_Y_MIN) * INV_EPS).to(tl.int32) ++ ++ cur_cnt = tl.zeros([BN], dtype=tl.int32) ++ ++ # 3×3 cell neighborhood. Static-unrolled outer. ++ for nbi in tl.static_range(9): ++ dx = nbi // 3 - 1 ++ dy = nbi % 3 - 1 ++ cx = cxq + dx ++ cy = cyq + dy ++ # Bounds check (drop out-of-grid cells by mask). ++ in_grid = (cx >= 0) & (cx < GW) & (cy >= 0) & (cy < GH) & n_mask ++ cell_idx = cy * GW + cx ++ cell_idx_safe = tl.where(in_grid, cell_idx, 0) ++ s = tl.load(CELL_START_ptr + cell_idx_safe.to(tl.int64), ++ mask=in_grid, other=0) ++ e = tl.load(CELL_END_ptr + cell_idx_safe.to(tl.int64), ++ mask=in_grid, other=0) ++ range_len = tl.where(in_grid, e - s, 0) ++ max_iter = tl.max(range_len) ++ ++ for j in tl.range(0, max_iter, 1): ++ in_range = (j < range_len) & in_grid ++ idx = s + j ++ pj = tl.load(SORTED_PTR_ptr + idx.to(tl.int64), mask=in_range, other=0) ++ xj = tl.load(X_ptr + pj.to(tl.int64) * 2, mask=in_range, other=0.0) ++ yj = tl.load(X_ptr + pj.to(tl.int64) * 2 + 1, mask=in_range, other=0.0) ++ dxv = xi - xj ++ dyv = yi - yj ++ dist = dxv * dxv + dyv * dyv ++ hit_eps = (dist <= EPS_SQ) & in_range ++ slot_addr = n_offs * K + cur_cnt.to(tl.int64) ++ emit = hit_eps & (cur_cnt < K) ++ tl.store(NBR_IDX_ptr + slot_addr, pj, mask=emit) ++ cur_cnt = cur_cnt + hit_eps.to(tl.int32) ++ ++ cur_cnt = tl.minimum(cur_cnt, tl.full([BN], K, dtype=tl.int32)) ++ tl.store(DEG_ptr + n_offs, cur_cnt, mask=n_mask) ++ ++ ++def _build_grid_index(X: torch.Tensor, eps: float): ++ """Build a dense 2D grid index over points X (N, 2) fp32. ++ ++ Returns: ++ sorted_ptr: (N,) int32 — point indices sorted by cell ++ cell_start, cell_end: (GW * GH,) int32 — start/end indices in sorted_ptr ++ gw, gh: grid dims ++ inv_eps, x_min, y_min: kernel params ++ """ ++ N = X.shape[0] ++ device = X.device ++ inv_eps = 1.0 / eps ++ x_min = float(X[:, 0].min().item()) ++ y_min = float(X[:, 1].min().item()) ++ x_max = float(X[:, 0].max().item()) ++ y_max = float(X[:, 1].max().item()) ++ ++ GW = int((x_max - x_min) * inv_eps) + 2 ++ GH = int((y_max - y_min) * inv_eps) + 2 ++ n_cells = GW * GH ++ ++ cx = ((X[:, 0] - x_min) * inv_eps).floor().to(torch.int32).clamp(0, GW - 1) ++ cy = ((X[:, 1] - y_min) * inv_eps).floor().to(torch.int32).clamp(0, GH - 1) ++ cell_id = cy.to(torch.int64) * GW + cx.to(torch.int64) ++ ++ # Sort points by cell_id ++ sorted_cell, perm = torch.sort(cell_id, stable=True) ++ sorted_ptr = perm.to(torch.int32) ++ ++ # Build dense (GW*GH,) start/end via bucket counts. ++ counts = torch.zeros(n_cells, dtype=torch.int32, device=device) ++ counts.scatter_add_(0, sorted_cell, torch.ones(N, dtype=torch.int32, device=device)) ++ cell_end = torch.cumsum(counts, dim=0, dtype=torch.int32) ++ cell_start = cell_end - counts ++ ++ return sorted_ptr, cell_start, cell_end, GW, GH, inv_eps, x_min, y_min ++ ++ ++def _flash_dbscan_grid(X: torch.Tensor, eps: float, min_samples: int, ++ max_neighbors: int): ++ """D=2 grid-based path.""" ++ N, D = X.shape ++ assert D == 2 ++ device = X.device ++ K = max(min_samples, max_neighbors) ++ eps_sq = float(eps) ** 2 ++ ++ # Build grid index ++ (sorted_ptr, cell_start, cell_end, GW, GH, ++ inv_eps, x_min, y_min) = _build_grid_index(X, eps) ++ ++ deg = torch.zeros(N, dtype=torch.int32, device=device) ++ nbr_idx = torch.full((N, K), -1, dtype=torch.int32, device=device) ++ ++ BN = 32 ++ grid = (triton.cdiv(N, BN),) ++ _grid_radius_2d_kernel[grid]( ++ X.contiguous(), sorted_ptr, cell_start, cell_end, ++ deg, nbr_idx, ++ N=N, K=K, GW=GW, GH=GH, ++ INV_EPS=inv_eps, EPS_SQ=eps_sq, ++ GRID_X_MIN=x_min, GRID_Y_MIN=y_min, ++ BN=BN, ++ num_warps=4, num_stages=1, ++ ) ++ return deg, nbr_idx, K ++ ++ ++def _flash_dbscan_brute(X: torch.Tensor, eps: float, min_samples: int, ++ max_neighbors: int, *, tol=None): ++ """High-D brute-force path via flash_knn -- single bf16 kNN call. ++ ++ Enumerates each point's top-``max_neighbors`` ε-candidates (in bf16 ++ by default; pass ``tol=0`` to force fp32). Points with more than ++ ``max_neighbors`` true ε-neighbors will have their excess neighbours ++ silently truncated, but this does **not** change the DBSCAN ++ clustering: ++ ++ * Core-mask correctness: a point is core iff its ε-degree ≥ ++ ``min_samples``. As long as ``max_neighbors >= min_samples`` ++ the bounded-K enumeration still observes ``min_samples``-many ++ ε-neighbours when they exist, so the core mask is exact. ++ * Cluster-membership correctness: two core points belong to the ++ same cluster iff they are reachable through a chain of ++ core-core ε-edges. Dense clusters expose Θ(max_neighbors) such ++ edges per core point, so any one of them is enough to merge ++ that core into the cluster's connected component. Edges ++ ranked beyond top-K are redundant for CC. ++ ++ The historical K-grow loop ("double K until the K-th NN exceeds ++ ε") was reverted in 2026-05 -- the iterative re-launches grew K ++ geometrically in dense regimes (medium-D blob shapes hit K=1024 ++ in 7 calls = 566 ms total) without changing ARI vs cuML beyond ++ the noise floor inherited from the bounded-K approximation. ++ ++ Callers who actually need exhaustive enumeration (e.g. for ++ reach-distance diagnostics, not clustering) should pass a ++ larger ``max_neighbors`` once. ++ """ ++ N, D = X.shape ++ device = X.device ++ K = max(min_samples, max_neighbors) ++ K = min(K, N) ++ eps_sq = float(eps) ** 2 ++ ++ # Default to fp32 KNN: at these shapes (N, M >= 16K) flash_knn ++ # routes to the "large_n" insert path (BN=128 single-pass per CTA), ++ # which is memory-bound and runs at the same speed in fp32 as in ++ # bf16 -- the .to(bf16) cast itself eats the would-be win. fp32 ++ # also gives bit-exact ARI vs sklearn ++ # on the standard ε boundaries; bf16 quantisation flips a handful ++ # of borderline points (~3% on D=64 eps=8). Power users can opt ++ # into bf16 via ``tol=1e-3`` if they have looser ε. ++ if D < 16: ++ Xpad = torch.zeros(N, 16, device=device, dtype=X.dtype) ++ Xpad[:, :D] = X ++ X_kernel = Xpad.contiguous() ++ else: ++ X_kernel = X.contiguous() ++ knn_dist_sq, knn_idx = flash_knn( ++ X_kernel[None], X_kernel[None], k=K, tol=tol) ++ knn_dist_sq = knn_dist_sq[0] ++ knn_idx = knn_idx[0] ++ ++ valid = knn_dist_sq <= eps_sq ++ deg = valid.sum(dim=1).to(torch.int32) ++ nbr_idx = torch.where(valid, knn_idx.to(torch.int32), ++ torch.full_like(knn_idx, -1, dtype=torch.int32)) ++ return deg, nbr_idx, K ++ ++ ++def flash_dbscan(X: torch.Tensor, eps: float, min_samples: int = 5, ++ max_neighbors: int = 32, *, tol=None): ++ """End-to-end Triton DBSCAN. ++ ++ Args: ++ X: (N, D) float32 CUDA tensor. ++ eps: distance threshold (Euclidean). ++ min_samples: minimum points in eps-neighborhood for a point to be core. ++ max_neighbors: per-point ε-candidate budget for the high-D brute ++ path. Default 32 -- core detection stays exact for any ++ ``max_neighbors >= min_samples``; the connected-component ++ stage only needs O(max_neighbors) redundant edges per ++ cluster, so cluster membership is robust to the bounded-K ++ approximation. Bump this if you need exhaustive ε-edge ++ enumeration (e.g. reach-distance diagnostics). ++ tol: residual tolerance forwarded to :func:`flash_knn` on the ++ high-D (D >= 3) brute-force path. ``None`` (default) keeps ++ fp32 storage -- the "large_n" single-pass insert path ++ (BN=128) flash_knn picks on these shapes is memory-bound, ++ so fp32 and bf16 run at the same speed; fp32 wins on ARI by ++ avoiding ε-boundary quantisation. Pass ``tol=1e-3`` to opt ++ into bf16/fp16 KNN storage (matches the historical HEAD ++ cast) when you want lower HBM pressure. ++ ++ Returns: ++ labels: (N,) int32 -- cluster id (>= 0) or -1 for noise. ++ """ ++ assert X.is_cuda ++ N, D = X.shape ++ device = X.device ++ ++ # ── Stage 1: per-point in-eps neighbor list ───────────────────────── ++ # Path A: D ≤ 2 → grid (cell-side eps; 9-cell scan, exact in any dtype). ++ # Path B: D ≥ 3 → brute-force top-K via flash_knn (tol-routed dtype). ++ if D == 2: ++ # Grid kernel reads X via raw fp32 strides; cast internally. ++ # This is a kernel implementation detail, NOT a public dtype contract. ++ deg, nbr_idx, K = _flash_dbscan_grid( ++ X if X.dtype == torch.float32 else X.float(), ++ eps, min_samples, max_neighbors, ++ ) ++ else: ++ deg, nbr_idx, K = _flash_dbscan_brute( ++ X, eps, min_samples, max_neighbors, tol=tol) ++ ++ # ── Stage 2: core mask ────────────────────────────────────────────── ++ core_mask = deg >= min_samples ++ ++ # ── Stage 3: edge construction (core-core only) ───────────────────── ++ K_eff = nbr_idx.shape[1] ++ nbr_idx_i64 = nbr_idx.to(torch.int64) ++ valid_slot = nbr_idx >= 0 ++ core_per_row = core_mask[:, None].expand(-1, K_eff) ++ nbr_idx_safe = torch.where(valid_slot, nbr_idx_i64, torch.zeros_like(nbr_idx_i64)) ++ core_per_col = core_mask[nbr_idx_safe] & valid_slot ++ edge_mask = valid_slot & core_per_row & core_per_col ++ rows = (torch.arange(N, device=device, dtype=torch.int32).view(-1, 1) ++ .expand(-1, K_eff).contiguous())[edge_mask].contiguous() ++ cols = nbr_idx[edge_mask].contiguous() ++ label_cc = flash_cc_from_edges(rows, cols, N) ++ ++ # ── Stage 4: assign labels (core: CC label, else: -1 for now) ──────── ++ INT_MAX = 2 ** 31 - 1 ++ label = torch.where(core_mask, label_cc, torch.full_like(label_cc, -1)) ++ ++ # ── Stage 5: border assignment ─────────────────────────────────────── ++ nbr_labels = label[nbr_idx_safe] ++ nbr_is_core = core_mask[nbr_idx_safe] & valid_slot ++ border_cand = torch.where(valid_slot & nbr_is_core, nbr_labels, ++ torch.full_like(nbr_labels, INT_MAX)) ++ min_core_label = border_cand.min(dim=1).values ++ is_border = (~core_mask) & (min_core_label != INT_MAX) & (min_core_label >= 0) ++ label = torch.where(is_border, min_core_label, label) ++ ++ # ── Stage 6: compact ───────────────────────────────────────────────── ++ valid = label >= 0 ++ if valid.any(): ++ unique, inv = torch.unique(label[valid], return_inverse=True) ++ compact = torch.full_like(label, -1) ++ compact[valid] = inv.to(torch.int32) ++ label = compact ++ ++ return label +diff --git a/dbscanlib/dbscan.py b/dbscanlib/dbscan.py +index ab7fa8b..53710d0 100644 +--- a/dbscanlib/dbscan.py ++++ b/dbscanlib/dbscan.py +@@ -1,70 +1,17 @@ +-"""Naive DBSCAN baseline (correct, deterministic, GPU/CPU torch). ++"""Euclidean DBSCAN -- vendored best-in-repo Triton grid path (D=2). + +-Euclidean DBSCAN. Chunked O(N^2) neighbour scan + GPU-friendly label +-propagation for the connected components of the core graph. Border rule matches +-flashlib: a non-core point joins the SMALLEST cluster label among its in-eps +-core neighbours; otherwise it is noise (-1). ++Delegates to the fused Triton grid radius-search + connected-components kernels ++vendored under ``dbscanlib._kernels``. Public contract is unchanged: ++ ++ dbscan(x, eps, min_samples) -> labels ((N,) int64; cluster id >=0, -1 noise) + """ + from __future__ import annotations + + import torch + ++from dbscanlib._kernels.primitives.dbscan.triton.dbscan import flash_dbscan + +-def dbscan(x, eps, min_samples, max_neighbors=32): +- del max_neighbors # naive path scans all neighbours; knob only for the fast kernel +- N = x.shape[0] +- dev = x.device +- eps2 = float(eps) * float(eps) +- xn = (x * x).sum(1) # (N,) +- CH = 4096 +- BIG = N # sentinel label (> any real label) +- +- def d2_chunk(lo, hi): +- xb = x[lo:hi] +- return (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ x.t()) + xn[None, :] +- +- # 1) degree (in-eps count, includes self) -> core mask +- deg = torch.zeros(N, device=dev, dtype=torch.int64) +- for lo in range(0, N, CH): +- hi = min(lo + CH, N) +- deg[lo:hi] = (d2_chunk(lo, hi) <= eps2).sum(1) +- core = deg >= int(min_samples) # (N,) bool + +- # 2) connected components of the core-core eps graph via label propagation +- labels = torch.arange(N, device=dev, dtype=torch.int64) +- labels[~core] = BIG +- for _ in range(1000): +- new = labels.clone() +- for lo in range(0, N, CH): +- hi = min(lo + CH, N) +- if not bool(core[lo:hi].any()): +- continue +- adj = (d2_chunk(lo, hi) <= eps2) & core[None, :] # (chunk, N) +- lab = torch.where(adj, labels[None, :], torch.full((1, N), BIG, device=dev, dtype=torch.int64)) +- mn = lab.min(1).values # (chunk,) +- rows = slice(lo, hi) +- upd = core[rows] & (mn < new[rows]) +- new[rows] = torch.where(upd, mn, new[rows]) +- if torch.equal(new, labels): +- break +- labels = new +- +- # 3) border assignment: non-core within eps of a core -> smallest core label +- for lo in range(0, N, CH): +- hi = min(lo + CH, N) +- nb = ~core[lo:hi] +- if not bool(nb.any()): +- continue +- adj = (d2_chunk(lo, hi) <= eps2) & core[None, :] +- lab = torch.where(adj, labels[None, :], torch.full((1, N), BIG, device=dev, dtype=torch.int64)) +- mn = lab.min(1).values +- rows = slice(lo, hi) +- take = nb & (mn < BIG) +- labels[rows] = torch.where(take, mn, labels[rows]) +- +- # 4) compact to dense [0, n_clusters); noise (BIG) -> -1 +- out = torch.full((N,), -1, device=dev, dtype=torch.int64) +- uniq = torch.unique(labels[labels < BIG]) +- for new_id, old in enumerate(uniq.tolist()): +- out[labels == old] = new_id +- return out ++def dbscan(x, eps, min_samples, max_neighbors=32): ++ labels = flash_dbscan(x, float(eps), int(min_samples), int(max_neighbors)) ++ return labels.to(torch.int64) diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/DESIGN.md b/2.0/problems/ivf_pq_gpu_kernel_optimization/DESIGN.md new file mode 100644 index 000000000..9c7523153 --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/DESIGN.md @@ -0,0 +1,72 @@ +# Design notes — ivf_pq_gpu_kernel_optimization + +Operator-facing. Not copied into the agent workspace by the adapter. + +## What this task measures + +Can an agent turn a naive GPU IVF-PQ search into a fast one? The agent patches +`ivfpqlib`; the judge builds a fixed IVF-PQ index once per workload with a frozen +builder, then times the patched `ivf_pq_search` against a frozen naive baseline +over that same index on hidden ANN workloads and scores the geomean speedup, +gated on iso-result recall@k. One of the four-task flashlib kernel-optimization +family (KMeans, KNN, DBSCAN, IVF-PQ), all sharing one evaluator + one Modal GPU +harness (`flash_gpu.py`). + +This is a **genuine kernel task**: the core operation (coarse list probe + a +ragged asymmetric-distance scan of PQ codes with an on-chip top-k) is not a +single fast torch primitive. The naive baseline's per-query Python loop is the +bottleneck; beating it requires fusing the coarse probe, the per-list distance +tables, and the ragged candidate scan into GPU kernels. Contrast with pca/svd, +which were algorithmic (cov+eigh + a library eigensolver) and are not part of +this family. + +## Baseline + reference + +- Naive baseline (`ivfpqlib/`, and frozen `judge/refivfpq.py`): a pure-torch + IVF-PQ. `ivf_pq_build` is a coarse Lloyd k-means quantizer + per-subspace PQ + codebooks encoding residuals to `(M, m)` uint8 codes in CSR cell-contiguous + layout; `ivf_pq_search` probes the `nprobe` nearest lists, builds the residual + ADC lookup table per query, scores each probed candidate as the sum of `m` + table lookups, and keeps the global top-`k`. The per-query Python loop is what + makes search slow. `refivfpq.py` is the byte-identical frozen copy (verified), + so the reference agrees with the baseline to fp tolerance. +- Reference (`reference.patch`): vendors flashlib's optimized **Triton** IVF-PQ + fine scan (`primitives/ivf_pq/triton/{search,fine_scan,fine_scan_batch, + fine_scan_gemm,lut}.py`) under `ivfpqlib/_kernels/…`, rewriting search's + `ivf_pq_search`. The coarse nprobe-nearest-centroid step is done with an exact + `torch.cdist().topk()` (flashlib routes it through `flash_knn`; over a few + hundred centroids that is the same exact result and a negligible fraction of + the time, so the KNN subtree is not vendored). The CuTe DSL Hopper tier is + disabled (`is_cutedsl_available -> False`), so the portable Triton + LUT-scan / decode+GEMM kernels run — flashlib's best non-DSL path. + +## Correctness gate + +Iso-result recall@k between the agent's ids and the baseline's on the **same** +index and queries each iteration, `>= recall_threshold`. Judge-computed; +cheat-proof (you cannot fake a high recall without actually reproducing the ADC +ranking). Because the index is fixed and shared, both search implementations +target the same ground truth; the reference's LUT/GEMM kernels reproduce the +naive ADC distances to fp tolerance, so recall is near 1.0 (ties on equal ADC +sums are the only source of <1.0). `recall_threshold` is set below the +reference's measured iso-recall with margin — calibrate after the GPU trial. + +## Workloads + +Random float32 databases, `M` 50k–150k, `D` 64–128, `nlist` 256–512, `m` 8–16, +`nprobe` 8–32, `nq` ~1k–2k. The index build (frozen, not timed) and the naive +baseline search both stay within the Modal timeout while the fused kernel gives a +large speedup. Hidden workloads live in `config.yaml` (judge-only). See kmeans +DESIGN for the shared anti-hack + Modal-offload design; identical here, plus a +`setup(w, seed)` worker hook that builds the fixed index once per workload (the +frozen builder), reused across all timed iterations while queries regenerate. + +## Calibration (H100 Modal trial — done) + +`reference.patch` validated on H100: the vendored Triton search compiles, passes +the recall gate with iso-recall **1.0** on all six workloads, and runs +**513 / 907 / 1420 / 1449 / 442 / 1234x** over the naive baseline (geomean +~898x). `speedup_target` set to 800 (reference scores ~100, capped); +`recall_threshold` 0.95 (well below the measured 1.0). Re-sweep if workload +sizes change. The large factors reflect the Python-per-query-loop baseline; +fully closing the gap needs the fused ragged scan, not just torch vectorization. diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml new file mode 100644 index 000000000..dce3fbc0e --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml @@ -0,0 +1,42 @@ +tag: systems +runtime: + language: python + timeout_seconds: 10800 + environment: "GPU IVF-PQ search kernel optimization. The agent patches the ivfpqlib package; the judge offloads timing to a Modal GPU, comparing the patched ivf_pq_search against a frozen naive baseline over a fixed pre-built index on hidden ANN workloads with an iso-result recall gate." + apt_packages: [bash, ca-certificates, git, python3, python3-pip] + judge_apt_packages: [bash, ca-certificates, git, python3, python3-pip] + judge_pip_packages: [modal] + docker: + image: frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.1.0 + judge_image: frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.1.0 +environment: + cpus: 4 + memory_mb: 8192 + storage_mb: 16384 + build_timeout_seconds: 3600 +evaluation: + gpu: "H100" + cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" + pip: ["torch", "numpy"] + app_name: "ivfpq-kernel-opt-eval" + modal_timeout_seconds: 1800 + warmup_iters: 2 + timed_iters: 7 + recall_threshold: 0.95 # agent results must match the baseline's top-k (iso-result recall@k) on every iteration; the Triton reference measured iso-recall 1.0 on all workloads, so 0.95 leaves margin for ADC tie-breaks + speedup_target: 800.0 # H100 calibration: flashlib Triton reference geomean ~898x over the Python-loop naive baseline (per-workload 513/907/1420/1449/442/1234x) + base_seed: 20260702 + agent_workload_count: 3 + expose_per_workload_metrics: false + # The index (the "database") is built ONCE per workload by the frozen baseline + # and reused for every timed iteration; only ivf_pq_search is timed. Queries are + # regenerated fresh each iteration. M=database size, Q=queries, k=neighbours. + workloads: + - {id: w0, M: 50000, D: 64, nlist: 256, m: 8, nprobe: 8, Q: 1024, k: 10} + - {id: w1, M: 100000, D: 96, nlist: 512, m: 12, nprobe: 16, Q: 1024, k: 10} + - {id: w2, M: 150000, D: 128, nlist: 512, m: 16, nprobe: 16, Q: 2048, k: 10} + - {id: w3, M: 80000, D: 64, nlist: 256, m: 8, nprobe: 16, Q: 2048, k: 10} + - {id: w4, M: 120000, D: 128, nlist: 512, m: 16, nprobe: 8, Q: 1024, k: 20} + - {id: w5, M: 60000, D: 96, nlist: 256, m: 12, nprobe: 16, Q: 1536, k: 10} +submission: + kind: file + path: /app/solution.patch diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/README.md b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/README.md similarity index 56% rename from 2.0/problems/pca_gpu_kernel_optimization/docker/README.md rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/docker/README.md index a871e2361..2a6a0978d 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/README.md +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/README.md @@ -1,34 +1,34 @@ -# Experimental PCA Kernel-Optimization Images +# Experimental IVF-PQ Kernel-Optimization Images Two **light** images (ubuntu + `modal` + git). torch, triton, and the vendored flashlib kernels all run on the **Modal GPU image** defined in `flash_gpu.py`; the containers themselves are CPU-only and offload GPU work to Modal. ```bash -bash 2.0/problems/pca_gpu_kernel_optimization/docker/build_images.sh +bash 2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh ``` Defaults: ```text -AGENT_TAG=frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.2.0 -JUDGE_TAG=frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.2.0 +AGENT_TAG=frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.1.0 +JUDGE_TAG=frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.1.0 ``` Agent image: ```text -/app/pcalib # clean, git-tracked package (the agent edits this) -/opt/flash_gpu.py # shared Modal GPU harness (public test uses it) -/opt/pca_ref/refpca.py # frozen naive baseline (public-test speed denominator) +/app/ivfpqlib # clean, git-tracked package (the agent edits this) +/opt/flash_gpu.py # shared Modal GPU harness (public test uses it) +/opt/ivfpq_ref/refivfpq.py # frozen naive baseline (public-test speed denominator) ``` Judge image: ```text -/opt/pcalib-clean/pcalib # pristine tree; the patch is applied to a copy -/opt/pca_ref/refpca.py # frozen naive baseline (speed denominator + quality oracle) -/opt/flash_gpu.py # shared Modal GPU harness (the evaluator uses it) +/opt/ivfpqlib-clean/ivfpqlib # pristine tree; the patch is applied to a copy +/opt/ivfpq_ref/refivfpq.py # frozen naive baseline (speed denominator + quality oracle) +/opt/flash_gpu.py # shared Modal GPU harness (the evaluator uses it) ``` ## Runtime requirements @@ -50,7 +50,7 @@ smoke pass (which repo CI exercises). ## Smoke test ```bash -bash 2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh +bash 2.0/problems/ivf_pq_gpu_kernel_optimization/docker/smoke_images.sh ``` Import-only (modal + flash_gpu + the baked packages); does not touch a GPU or Modal. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/agent/Dockerfile similarity index 77% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/docker/agent/Dockerfile index 8241ad622..d722a90eb 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/agent/Dockerfile +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/agent/Dockerfile @@ -1,6 +1,6 @@ -# Agent image for truncated_svd_gpu_kernel_optimization. +# Agent image for ivf_pq_gpu_kernel_optimization. # -# Light image (no torch/triton): the agent edits /app/tsvdlib and runs the +# Light image (no torch/triton): the agent edits /app/ivfpqlib and runs the # public test, which offloads timing to a Modal GPU (torch/triton live on the # Modal image defined in flash_gpu.py). Needs MODAL_TOKEN_ID / MODAL_TOKEN_SECRET # in the environment to run the GPU public test. @@ -19,16 +19,16 @@ RUN pip3 install --break-system-packages --no-cache-dir modal # The package the agent edits (git-tracked so make_submission.sh can diff it). WORKDIR /app -COPY tsvdlib /app/tsvdlib +COPY ivfpqlib /app/ivfpqlib RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ git -C /app init -q && \ git -C /app config user.email task@frontier-cs && \ git -C /app config user.name frontier-cs && \ - git -C /app add -A -- tsvdlib .gitignore && \ - git -C /app -c commit.gpgsign=false commit -qm "pristine tsvdlib" + git -C /app add -A -- ivfpqlib .gitignore && \ + git -C /app -c commit.gpgsign=false commit -qm "pristine ivfpqlib" # Shared Modal GPU harness + the frozen naive baseline (the public-test speed # denominator). The baseline is exactly the code the agent starts from, so it is # not secret. COPY flash_gpu.py /opt/flash_gpu.py -COPY judge/reftsvd.py /opt/tsvd_ref/reftsvd.py +COPY judge/refivfpq.py /opt/ivfpq_ref/refivfpq.py diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh old mode 100755 new mode 100644 similarity index 59% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh index 9791340e5..47d4fbace --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh @@ -1,10 +1,10 @@ #!/usr/bin/env bash -# Build the experimental agent + judge images for truncated_svd_gpu_kernel_optimization. +# Build the experimental agent + judge images for ivf_pq_gpu_kernel_optimization. set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.2.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.2.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.1.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/judge/Dockerfile similarity index 86% rename from 2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/docker/judge/Dockerfile index b1b747b37..62389a617 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/judge/Dockerfile +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/judge/Dockerfile @@ -1,4 +1,4 @@ -# Judge image for pca_gpu_kernel_optimization. +# Judge image for ivf_pq_gpu_kernel_optimization. # # Light image (no torch/triton): the judge validates + applies the patch and # offloads timing to a Modal GPU (torch/triton live on the Modal image defined @@ -19,8 +19,8 @@ RUN pip3 install --break-system-packages --no-cache-dir modal # Pristine tree the patch is applied to, the frozen naive baseline (speed # denominator + quality oracle), and the shared Modal GPU harness. -COPY pcalib /opt/pcalib-clean/pcalib -COPY judge/refpca.py /opt/pca_ref/refpca.py +COPY ivfpqlib /opt/ivfpqlib-clean/ivfpqlib +COPY judge/refivfpq.py /opt/ivfpq_ref/refivfpq.py COPY flash_gpu.py /opt/flash_gpu.py WORKDIR /judge diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/smoke_images.sh new file mode 100644 index 000000000..045ffd3a0 --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/smoke_images.sh @@ -0,0 +1,19 @@ +#!/usr/bin/env bash +# Import-only smoke test for the built images (no GPU / Modal required). +set -euo pipefail + +AGENT_TAG=${AGENT_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.1.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.1.0} + +echo "== agent image ==" +docker run --rm -w /app "$AGENT_TAG" python3 -c \ + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ +sys.path.insert(0, '/opt/ivfpq_ref'); import refivfpq; \ +import ivfpqlib; print('agent ok: modal + flash_gpu + refivfpq + ivfpqlib import')" + +echo "== judge image ==" +docker run --rm "$JUDGE_TAG" python3 -c \ + "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ +sys.path.insert(0, '/opt/ivfpq_ref'); import refivfpq; \ +sys.path.insert(0, '/opt/ivfpqlib-clean'); import ivfpqlib; \ +print('judge ok: modal + flash_gpu + refivfpq + ivfpqlib import')" diff --git a/2.0/problems/pca_gpu_kernel_optimization/evaluate.sh b/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluate.sh old mode 100755 new mode 100644 similarity index 66% rename from 2.0/problems/pca_gpu_kernel_optimization/evaluate.sh rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/evaluate.sh index f83e9ed92..9530e17b0 --- a/2.0/problems/pca_gpu_kernel_optimization/evaluate.sh +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluate.sh @@ -1,9 +1,9 @@ #!/usr/bin/env bash -# Local CLI evaluation for pca_gpu_kernel_optimization. +# Local CLI evaluation for ivf_pq_gpu_kernel_optimization. # # A full run needs a GPU plus the baked judge sources at /opt (the pristine -# /opt/pcalib-clean tree and the frozen /opt/pca_ref baseline; see the judge -# image). Without them the evaluator still validates the patch policy and +# /opt/ivfpqlib-clean tree and the frozen /opt/ivfpq_ref baseline; see the +# judge image). Without them the evaluator still validates the patch policy and # returns a smoke score, which is what repository CI exercises (the reference # patch passes the policy). Pass a patch path to score it directly. set -euo pipefail diff --git a/2.0/problems/pca_gpu_kernel_optimization/evaluator.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py similarity index 99% rename from 2.0/problems/pca_gpu_kernel_optimization/evaluator.py rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py index e12ae7091..d5470146b 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py @@ -79,9 +79,9 @@ def _get_bool(name: str, default: bool) -> bool: # Kept as constants (not config) so the patch policy is enforceable locally / in # CI without /judge/task_config.json. Everything else is config-driven and only # needed on the GPU path. -PRIMITIVE = "pca" -PKG = "pcalib" -REF_MODULE = "refpca" +PRIMITIVE = "ivfpq" +PKG = "ivfpqlib" +REF_MODULE = "refivfpq" MAX_PATCH_BYTES = _get_int("max_patch_bytes", 2_000_000) MAX_CHANGED_FILES = _get_int("max_changed_files", 80) diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py similarity index 74% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py index 798a49db6..6243e65c8 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py @@ -75,25 +75,53 @@ def _materialize(files: dict, tag: str) -> str: ref = importlib.import_module(cfg["ref_module"]) pkg = importlib.import_module(cfg["pkg"]) - # ---- per-primitive: data gen, call, and quality metric ---------------- # - def gen(w, seed): + # ---- per-primitive: fixed context, data gen, call, quality metric ----- # + def setup(w, seed): + # Built ONCE per workload (not per timed iter). IVF-PQ builds the fixed + # index (the "database") here; queries are generated fresh per iteration. + if prim == "ivfpq": + g = torch.Generator(device=dev).manual_seed(int(seed)) + X = torch.randn(int(w["M"]), int(w["D"]), generator=g, device=dev, dtype=torch.float32) + index = ref.ivf_pq_build(X, int(w["nlist"]), m=int(w["m"]), + nprobe=int(w["nprobe"]), seed=int(seed)) + return {"index": index, "D": int(w["D"])} + return {} + + def gen(w, seed, ctx): g = torch.Generator(device=dev).manual_seed(int(seed)) + if prim == "ivfpq": + q = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, device=dev, dtype=torch.float32) + return {"queries": q} if prim == "knn": db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} + if prim == "dbscan": + nc, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) + centers = torch.randn(nc, D, generator=g, device=dev, dtype=torch.float32) * 10.0 + asg = torch.randint(0, nc, (N,), generator=g, device=dev) + xb = centers.index_select(0, asg) + torch.randn(N, D, generator=g, device=dev, dtype=torch.float32) * 0.5 + nz = int(float(w.get("noise_frac", 0.0)) * N) + if nz > 0: + lo = xb.min(0).values; hi = xb.max(0).values + xb[:nz] = lo + (hi - lo) * torch.rand(nz, D, generator=g, device=dev) + return {"x": xb, "eps": float(w["eps"]), "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) if prim == "kmeans": perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] return {"x": x, "init": x.index_select(0, perm).clone()} return {"x": x} - def call(mod, w, data): + def call(mod, w, data, ctx): + if prim == "ivfpq": + return mod.ivf_pq_search(ctx["index"], data["queries"], int(w["k"]), nprobe=int(w["nprobe"])) if prim == "kmeans": return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], init_centroids=data["init"], tol=0.0) if prim == "knn": return mod.knn(data["queries"], data["database"], w["k"]) + if prim == "dbscan": + return mod.dbscan(data["x"], data["eps"], data["min_samples"]) if prim == "pca": return mod.pca(data["x"], w["k"]) if prim == "tsvd": @@ -111,6 +139,14 @@ def check_shape(w, out): raise ValueError("bad knn output") if not torch.isfinite(d).all(): raise ValueError("non-finite distances") + elif prim == "ivfpq": + vals, ids = out + if tuple(ids.shape) != (int(w["Q"]), int(w["k"])) or \ + tuple(vals.shape) != (int(w["Q"]), int(w["k"])): + raise ValueError("bad ivfpq output shape") + elif prim == "dbscan": + if out.ndim != 1 or int(out.shape[0]) != int(w["N"]): + raise ValueError("bad dbscan labels shape") else: a, b = out comps = a if prim == "pca" else b @@ -132,6 +168,20 @@ def recall(agent_idx, ref_idx): hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + def ari(a, b): + # Adjusted Rand Index between two integer label vectors (N,); noise (-1) + # is treated as its own label (dense-remapped first). + a = torch.unique(a.long(), return_inverse=True)[1] + b = torch.unique(b.long(), return_inverse=True)[1] + na = int(a.max().item()) + 1; nb = int(b.max().item()) + 1 + cont = torch.bincount(a * nb + b, minlength=na * nb).reshape(na, nb).double() + ai = cont.sum(1); bj = cont.sum(0); n = cont.sum() + c2 = lambda z: z * (z - 1) / 2.0 + sij = c2(cont).sum(); sa = c2(ai).sum(); sb = c2(bj).sum() + exp = (sa * sb / c2(n)) if float(n) > 1 else 0.0 + den = 0.5 * (sa + sb) - exp + return float(((sij - exp) / den).item()) if float(den) != 0.0 else 1.0 + def ortho_err(comps): c = comps.to(torch.float32); k = c.shape[0] return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) @@ -148,9 +198,14 @@ def captured(x, comps, center): total += float((p * p).sum().item()) return total / (x.shape[0] - 1) if center else total - def verdict(w, data, ref_out, agent_out): + def verdict(w, data, ctx, ref_out, agent_out): """Return (ok, reason, ref_val, agent_val) gating the agent output.""" check_shape(w, agent_out) + if prim == "ivfpq": + # iso-result recall: agent ids vs frozen-baseline ids on the SAME index. + rec = recall(agent_out[1], ref_out[1]) + ok = rec >= float(cfg["recall_threshold"]) + return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) tol = float(cfg["inertia_tolerance"]) @@ -160,6 +215,10 @@ def verdict(w, data, ref_out, agent_out): rec = recall(agent_out[1], ref_out[1]) ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec + if prim == "dbscan": + val = ari(agent_out, ref_out) + ok = val >= float(cfg["ari_threshold"]) + return ok, ("ari_regression" if not ok else ""), 1.0, val center = (prim == "pca") comps = agent_out[0] if prim == "pca" else agent_out[1] ref_comps = ref_out[0] if prim == "pca" else ref_out[1] @@ -171,27 +230,28 @@ def verdict(w, data, ref_out, agent_out): ok = av >= (1.0 - float(cfg["captured_tolerance"])) * rv - 1e-6 return ok, ("captured_regression" if not ok else ""), rv, av - def time_call(mod, w, data): - _sync(); t0 = _perf(); out = call(mod, w, data); _sync() + def time_call(mod, w, data, ctx): + _sync(); t0 = _perf(); out = call(mod, w, data, ctx); _sync() return (_perf() - t0) * 1000.0, out rows = [] try: for w in payload["workloads"]: base_seed = int(w["seed"]) + ctx = setup(w, base_seed) # fixed context (e.g. IVF-PQ index) # warmup on fresh data (kernels / autotune) — not measured for i in range(warmup): - d = gen(w, base_seed + 100 + i) - call(ref, w, d); call(pkg, w, d); _sync() + d = gen(w, base_seed + 100 + i, ctx) + call(ref, w, d, ctx); call(pkg, w, d, ctx); _sync() del d torch.cuda.empty_cache() ratios, ref_val, agent_val = [], None, None bad = None for i in range(iters): - d = gen(w, base_seed + 10000 + i) # fresh every iteration - rt, rout = time_call(ref, w, d) - at, aout = time_call(pkg, w, d) - ok, reason, rv, av = verdict(w, d, rout, aout) # verify THIS iter + d = gen(w, base_seed + 10000 + i, ctx) # fresh every iteration + rt, rout = time_call(ref, w, d, ctx) + at, aout = time_call(pkg, w, d, ctx) + ok, reason, rv, av = verdict(w, d, ctx, rout, aout) # verify THIS iter if not ok: bad = reason; ref_val, agent_val = rv, av break diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/README.md similarity index 54% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/README.md index 6f59254a8..34769f919 100644 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/README.md +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/README.md @@ -1,8 +1,8 @@ -# Truncated SVD kernel optimization — submission workflow +# IVF-PQ kernel optimization — submission workflow -You are optimizing the `tsvdlib` package at `/app/tsvdlib`. Edit the package -(rewrite the internals of `truncated_svd`, add Triton kernel modules under -`tsvdlib/`), then submit a patch. +You are optimizing the `ivfpqlib` package at `/app/ivfpqlib`. Edit the package +(rewrite the internals of `ivfpq`, add Triton kernel modules under +`ivfpqlib/`), then submit a patch. ## Iterate locally @@ -16,7 +16,7 @@ bash /app/public_test.sh ## Submit ```bash -bash /app/make_submission.sh # writes /app/solution.patch (tsvdlib diff) +bash /app/make_submission.sh # writes /app/solution.patch (ivfpqlib diff) bash /app/submit.sh # enqueues it for the black-box judge ``` @@ -26,10 +26,9 @@ results. ## Rules -- Only files under `tsvdlib/` may change. +- Only files under `ivfpqlib/` may change. - Do not import external optimized libraries (write the kernels yourself), and do not access the environment, spawn processes, or use the network. -- Keep the public `truncated_svd(...)` signature and return contract unchanged. -- Low-rank factorization quality is gated (orthonormal components + captured - energy vs the naive baseline); do not sacrifice correctness for speed beyond - the allowed tolerance. +- Keep the public `ivfpq(...)` signature and return contract unchanged. +- Clustering quality is gated (inertia vs the naive baseline); do not sacrifice + correctness for speed beyond the allowed tolerance. diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/make_submission.sh b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/make_submission.sh old mode 100755 new mode 100644 similarity index 54% rename from 2.0/problems/pca_gpu_kernel_optimization/harbor/app/make_submission.sh rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/make_submission.sh index 9c52bd8b1..8b1dec84c --- a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/make_submission.sh +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/make_submission.sh @@ -1,12 +1,12 @@ #!/usr/bin/env bash -# Package the current pcalib edits into a unified diff for submission. +# Package the current ivfpqlib edits into a unified diff for submission. set -euo pipefail APP_DIR="${APP_DIR:-/app}" OUT="${1:-/app/solution.patch}" -if [[ ! -d "$APP_DIR/pcalib" ]]; then - echo "pcalib package not found at $APP_DIR/pcalib" >&2 +if [[ ! -d "$APP_DIR/ivfpqlib" ]]; then + echo "ivfpqlib package not found at $APP_DIR/ivfpqlib" >&2 exit 2 fi if [[ ! -d "$APP_DIR/.git" ]]; then @@ -15,9 +15,9 @@ if [[ ! -d "$APP_DIR/.git" ]]; then fi # Stage and diff only the package, so submission helper scripts under /app are -# never included. New kernel files under pcalib/ are captured via `git add`. -git -C "$APP_DIR" add -A -- pcalib -git -C "$APP_DIR" diff --cached -- pcalib > "$OUT" +# never included. New kernel files under ivfpqlib/ are captured via `git add`. +git -C "$APP_DIR" add -A -- ivfpqlib +git -C "$APP_DIR" diff --cached -- ivfpqlib > "$OUT" git -C "$APP_DIR" reset -q bytes=$(wc -c < "$OUT" | tr -d ' ') diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py similarity index 66% rename from 2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py index a83531c40..5ffdd5f38 100644 --- a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,9 +1,10 @@ -"""Public GPU self-test for the PCA kernel-optimization task. +"""Public GPU self-test for the IVF-PQ search kernel-optimization task. -Times your current /app/pcalib against the naive baseline on a Modal GPU and -reports the quality verdict + speedup on two public shapes. Requires -MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and -their thresholds are hidden and differ from these public shapes. +Times your current /app/ivfpqlib against the naive baseline on a Modal GPU and +reports the iso-result recall verdict + speedup on two public shapes. The index +is built once per shape by the frozen baseline and reused. Requires +MODAL_TOKEN_ID / MODAL_TOKEN_SECRET. The graded workloads and thresholds are +hidden and differ from these public shapes. """ from __future__ import annotations @@ -14,29 +15,26 @@ sys.path.insert(0, "/opt") APP_DIR = os.environ.get("APP_DIR", "/app") -PKG = "pcalib" -BASELINE_DIR = "/opt/pca_ref" +PKG = "ivfpqlib" +BASELINE_DIR = "/opt/ivfpq_ref" PUBLIC_WORKLOADS = [ - {"id": "p0", "N": 200_000, "D": 128, "k": 16, "seed": 1}, - {"id": "p1", "N": 500_000, "D": 64, "k": 8, "seed": 2}, + {"id": "p0", "M": 40000, "D": 64, "nlist": 256, "m": 8, "nprobe": 8, "Q": 1024, "k": 10, "seed": 1}, + {"id": "p1", "M": 100000, "D": 96, "nlist": 512, "m": 12, "nprobe": 16, "Q": 1024, "k": 10, "seed": 2}, ] CFG = { - "primitive": "pca", + "primitive": "ivfpq", "pkg": PKG, - "ref_module": "refpca", + "ref_module": "refivfpq", "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", "pip": ["torch", "numpy"], - "app_name": "pca-kernel-opt-public", + "app_name": "ivfpq-kernel-opt-public", "modal_timeout_seconds": 1800, "warmup": 2, "iters": 5, - "inertia_tolerance": 0.02, - "recall_threshold": 0.99, - "captured_tolerance": 0.02, - "ortho_tolerance": 0.02, + "recall_threshold": 0.95, } @@ -71,7 +69,7 @@ def main() -> int: if row.get("ok"): print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") else: - print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason','')):16s} {'-':>10s}") + print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason', '')):16s} {'-':>10s}") return 0 diff --git a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.sh b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.sh old mode 100755 new mode 100644 similarity index 71% rename from 2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.sh rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.sh index 44f02547c..8cdc88205 --- a/2.0/problems/pca_gpu_kernel_optimization/harbor/app/public_test.sh +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.sh @@ -1,5 +1,5 @@ #!/usr/bin/env bash -# Public self-test: run the patched pcalib on the public shapes and print a +# Public self-test: run the patched ivfpqlib on the public shapes and print a # correctness + rough speedup summary. Requires a GPU in the agent container. set -euo pipefail APP_DIR="${APP_DIR:-/app}" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/solution.patch b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/solution.patch similarity index 100% rename from 2.0/problems/truncated_svd_gpu_kernel_optimization/harbor/app/solution.patch rename to 2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/solution.patch diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/__init__.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/__init__.py new file mode 100644 index 000000000..873d4f077 --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/__init__.py @@ -0,0 +1,8 @@ +"""GPU IVF-PQ library: naive torch baseline to be optimized (search) plus a +frozen index builder. Public API: ``ivf_pq_build``, ``ivf_pq_search``, +``IvfPqIndex``.""" +from ivfpqlib.index import IvfPqIndex +from ivfpqlib.build import ivf_pq_build +from ivfpqlib.search import ivf_pq_search + +__all__ = ["ivf_pq_build", "ivf_pq_search", "IvfPqIndex"] diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/build.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/build.py new file mode 100644 index 000000000..be1d016da --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/build.py @@ -0,0 +1,208 @@ +"""Naive IVF-PQ index builder (pure torch, deterministic, CPU-OK). + +Builds an :class:`~ivfpqlib.index.IvfPqIndex`: a coarse k-means quantizer over +``nlist`` cells + ``m`` product-quantization sub-codebooks, then encodes every +database row to an ``(M, m)`` uint8 code in CSR cell-contiguous layout. This is +the **frozen** index construction (it is not the timed operation); search is +what you optimize. ADC contract: a candidate with codes ``code`` probing list +``c`` scores ``sum_s || rq_s - codebook[s, code[s]] ||^2`` where the residual +query ``rq = q - centroid[c]`` (``by_residual``) or ``q`` otherwise. +""" +from __future__ import annotations + +import math +from typing import Optional, Tuple + +import torch + +from ivfpqlib.index import IvfPqIndex + +def _pad_features(x: torch.Tensor, Dp: int) -> torch.Tensor: + """Zero-pad the trailing feature dim to ``Dp`` (no-op when already wide).""" + D = x.shape[-1] + if D >= Dp: + return x + pad = torch.zeros((*x.shape[:-1], Dp - D), device=x.device, dtype=x.dtype) + return torch.cat([x, pad], dim=-1) + + +def _pq_dims(D: int, m: int) -> Tuple[int, int, int]: + """Resolve ``(m, dsub, Dp)`` for a ``D``-dim input split into ``m`` codes. + + ``dsub = ceil(D / m)`` and ``Dp = m * dsub`` (the zero-padded working + width). ``dsub`` is bumped until ``Dp >= 16`` so the coarse-quantizer + kernels (which use ``tl.dot``) always have a contraction dim >= 16; + the zero columns never change squared-L2 distances. + """ + m = int(m) + if m < 1: + raise ValueError(f"m (number of sub-quantizers) must be >= 1 (got {m})") + if m > D: + # More sub-quantizers than dims: clamp so each sub-vector has >= 1 dim. + m = int(D) + dsub = int(math.ceil(D / m)) + while m * dsub < 16: + dsub += 1 + return m, dsub, m * dsub + + +# ── tiny CPU-OK building blocks ──────────────────────────────────────────── +def _lloyd_kmeans( + sample: torch.Tensor, k: int, *, niter: int, seed: int +) -> torch.Tensor: + """Tiny Lloyd k-means returning ``(k, D)`` centroids (fp32 math).""" + n = sample.shape[0] + if n < k: + raise ValueError( + f"k-means needs at least k={k} training rows (got {n}); " + "increase train_size / pq_train_size or lower nlist / m." + ) + g = torch.Generator(device="cpu").manual_seed(seed) + perm = torch.randperm(n, generator=g)[:k] + centroids = sample[perm.to(sample.device)].to(torch.float32).clone() + s = sample.to(torch.float32) + for _ in range(max(1, niter)): + d2 = torch.cdist(s, centroids) ** 2 # (n, k) + assign = d2.argmin(dim=1) # (n,) + new = centroids.clone() + for c in range(k): + mask = assign == c + if bool(mask.any()): + new[c] = s[mask].mean(dim=0) + shift = (new - centroids).norm(dim=-1).max() + centroids = new + if float(shift) == 0.0: + break + return centroids.to(torch.float32) + + +def _assign_chunked( + Xp: torch.Tensor, centroids: torch.Tensor, chunk: int = 8192 +) -> torch.Tensor: + """Nearest-centroid id per row (squared-L2), chunked to bound memory.""" + out = torch.empty(Xp.shape[0], dtype=torch.int64, device=Xp.device) + cf = centroids.to(torch.float32) + for lo in range(0, Xp.shape[0], chunk): + hi = min(lo + chunk, Xp.shape[0]) + d2 = torch.cdist(Xp[lo:hi].to(torch.float32), cf) ** 2 + out[lo:hi] = d2.argmin(dim=1) + return out + + +def _train_pq_codebooks( + resid: torch.Tensor, m: int, dsub: int, ksub: int, *, niter: int, seed: int +) -> torch.Tensor: + """Train ``m`` independent k-means sub-quantizers on residual sub-vectors. + + ``resid`` is ``(N, m*dsub)``; returns ``(m, ksub, dsub)`` fp32 codebooks. + """ + resid_sub = resid.reshape(resid.shape[0], m, dsub) # (N, m, dsub) + codebooks = torch.empty(m, ksub, dsub, dtype=torch.float32, device=resid.device) + for s in range(m): + codebooks[s] = _lloyd_kmeans(resid_sub[:, s, :], ksub, niter=niter, seed=seed + s) + return codebooks + + +def _encode_pq( + resid: torch.Tensor, codebooks: torch.Tensor, m: int, dsub: int, + chunk: int = 8192, +) -> torch.Tensor: + """Encode residual rows to ``(N, m)`` uint8 PQ codes (nearest sub-centroid).""" + N = resid.shape[0] + resid_sub = resid.reshape(N, m, dsub) # (N, m, dsub) + codes = torch.empty(N, m, dtype=torch.uint8, device=resid.device) + for s in range(m): + cb = codebooks[s].to(torch.float32) # (ksub, dsub) + sub = resid_sub[:, s, :].to(torch.float32) # (N, dsub) + for lo in range(0, N, chunk): + hi = min(lo + chunk, N) + d2 = torch.cdist(sub[lo:hi], cb) ** 2 # (chunk, ksub) + codes[lo:hi, s] = d2.argmin(dim=1).to(torch.uint8) + return codes + + +# ── public build / search ────────────────────────────────────────────────── +def ivf_pq_build( + X: torch.Tensor, + nlist: int, + *, + m: int = 8, + nbits: int = 8, + metric: str = "l2", + by_residual: bool = True, + nprobe: int = 8, + niter: int = 20, + pq_niter: int = 25, + train_size: Optional[int] = None, + pq_train_size: Optional[int] = None, + seed: int = 0, +): + """Build an :class:`IvfPqIndex` with pure torch ops.""" + if metric != "l2": + raise NotImplementedError(f"ivf_pq supports metric='l2' only (got {metric!r})") + if nbits != 8: + raise NotImplementedError(f"ivf_pq supports nbits=8 only (got {nbits})") + if X.ndim != 2: + raise ValueError("ivf_pq build expects a 2D (M, D) tensor") + + M, D = X.shape + nlist = int(min(nlist, M)) + if nlist < 1: + raise ValueError("nlist must be >= 1") + m, dsub, Dp = _pq_dims(int(D), m) + ksub = 1 << nbits # 256 + Xp = _pad_features(X.to(torch.float32), Dp).contiguous() + + # ── coarse quantizer: k-means on a sample ────────────────────────── + train_size = int(train_size or min(M, nlist * 256)) + train_size = max(min(train_size, M), nlist) + g = torch.Generator(device="cpu").manual_seed(seed) + sample_idx = torch.randperm(M, generator=g)[:train_size].to(X.device) + sample = Xp.index_select(0, sample_idx) + centroids = _lloyd_kmeans(sample, nlist, niter=niter, seed=seed) # (nlist, Dp) + + # ── PQ codebooks from residuals of a (sub)sample ─────────────────── + pq_train_size = int(pq_train_size or min(train_size, max(ksub * 16, 4096))) + pq_train_size = max(min(pq_train_size, train_size), ksub) + pq_sample = sample[:pq_train_size] + if by_residual: + pq_assign = _assign_chunked(pq_sample, centroids) + resid_train = pq_sample - centroids.index_select(0, pq_assign) + else: + resid_train = pq_sample + codebooks = _train_pq_codebooks( + resid_train, m, dsub, ksub, niter=pq_niter, seed=seed + 1 + ) # (m, ksub, dsub) + + # ── assign every database row + encode its residual ──────────────── + assign = _assign_chunked(Xp, centroids) # (M,) + resid_all = Xp - centroids.index_select(0, assign) if by_residual else Xp + codes = _encode_pq(resid_all, codebooks, m, dsub) # (M, m) uint8 + + # ── CSR cell-contiguous layout ───────────────────────────────────── + counts = torch.bincount(assign, minlength=nlist) # (nlist,) + offsets = torch.zeros(nlist + 1, dtype=torch.int64, device=X.device) + offsets[1:] = counts.cumsum(0) + order = torch.argsort(assign, stable=True) # (M,) int64 + codes_sorted = codes.index_select(0, order).contiguous() + + return IvfPqIndex( + centroids=centroids, + pq_codebooks=codebooks, + codes=codes_sorted, + ids=order.to(torch.int64), + list_offsets=offsets, + metric=metric, + by_residual=bool(by_residual), + D=int(D), + Dp=int(Dp), + dsub=int(dsub), + m=int(m), + nbits=int(nbits), + nlist=int(nlist), + nprobe=int(nprobe), + max_list_len=int(counts.max().item()) if nlist > 0 else 0, + ) + + +__all__ = ["ivf_pq_build", "_pad_features", "_pq_dims"] diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/index.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/index.py new file mode 100644 index 000000000..1c177c308 --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/index.py @@ -0,0 +1,96 @@ +"""``IvfPqIndex`` -- the in-memory container for a built IVF-PQ index. + +Stores the database **cell-contiguous** (all vectors of inverted list ``c`` in +rows ``[list_offsets[c], list_offsets[c+1])``) and keeps only the ``(M, m)`` +uint8 product-quantization codes, not the full vectors. ``ids[p]`` maps a stored +row back to the caller's original row id. Torch-only; no kernel import. +""" + +from dataclasses import dataclass + +import torch + + +@dataclass +class IvfPqIndex: + """A built IVF-PQ index. + + Attributes: + centroids: ``(nlist, Dp)`` coarse-quantizer centroids (fp32). + pq_codebooks: ``(m, ksub, dsub)`` product-quantization sub-centroids + (fp32). ``ksub == 2**nbits`` (256 for the only supported + ``nbits=8``); ``dsub == Dp // m``. + codes: ``(M, m)`` uint8 -- per sub-quantizer code, cell-contiguous. + ids: ``(M,)`` int64 -- original row id for each stored row. + list_offsets: ``(nlist + 1,)`` int64 CSR offsets into ``codes``. + metric: distance metric (only ``"l2"`` supported). + by_residual: if True, PQ encodes ``x - centroid[list]`` (FAISS / + cuVS default, higher recall); else PQ encodes ``x`` directly. + D: original feature dimension as passed by the caller. + Dp: padded working dimension (``m * dsub``, ``>= 16``); zero + columns added for ``D < Dp`` never affect squared-L2 distances. + dsub: sub-vector dimension (``Dp // m``). + m: number of sub-quantizers (PQ codes per vector). + nbits: bits per code (only ``8`` supported -> ``ksub = 256``). + nlist: number of inverted lists / coarse centroids. + nprobe: default number of lists to probe at search time. + max_list_len: longest inverted list, recorded at build time so + search can size the kernel's chunk loop without a D2H sync. + """ + + centroids: torch.Tensor + pq_codebooks: torch.Tensor + codes: torch.Tensor + ids: torch.Tensor + list_offsets: torch.Tensor + metric: str + by_residual: bool + D: int + Dp: int + dsub: int + m: int + nbits: int + nlist: int + nprobe: int + max_list_len: int = 0 + + @property + def ksub(self) -> int: + """Number of sub-centroids per sub-quantizer (``2**nbits``).""" + return int(self.pq_codebooks.shape[1]) + + @property + def M(self) -> int: + return int(self.codes.shape[0]) + + @property + def device(self) -> torch.device: + return self.codes.device + + @property + def dtype(self) -> torch.dtype: + """Working dtype of the centroids / codebooks (codes are uint8).""" + return self.centroids.dtype + + def list_lengths(self) -> torch.Tensor: + """``(nlist,)`` int64 number of vectors in each inverted list.""" + return self.list_offsets[1:] - self.list_offsets[:-1] + + def code_size_bytes(self) -> int: + """Bytes per stored vector (``m`` for ``nbits=8``).""" + return int(self.m) + + def compression_ratio(self) -> float: + """Original fp32 vector bytes / PQ code bytes (storage savings).""" + return (4.0 * self.D) / max(self.code_size_bytes(), 1) + + def __repr__(self) -> str: # pragma: no cover - cosmetic + return ( + f"IvfPqIndex(M={self.M}, D={self.D}, Dp={self.Dp}, m={self.m}, " + f"dsub={self.dsub}, nbits={self.nbits}, nlist={self.nlist}, " + f"nprobe={self.nprobe}, by_residual={self.by_residual}, " + f"metric={self.metric!r}, dtype={self.dtype}, device={self.device})" + ) + + +__all__ = ["IvfPqIndex"] diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/search.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/search.py new file mode 100644 index 000000000..56b559bd7 --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/ivfpqlib/search.py @@ -0,0 +1,78 @@ +"""Naive IVF-PQ search (pure torch reference -- the baseline you optimize). + +Coarse step: probe the ``nprobe`` nearest lists per query. Fine step: for each +query, build the per-list residual ADC lookup table and score every probed +candidate as ``sum_s LUT[s, code[s]]``, then keep the global top-``k``. The +Python per-query loop is what makes this slow; a fast GPU implementation fuses +the coarse search, LUT build, and ragged candidate scan into kernels. + + ivf_pq_search(index, Q, k, *, nprobe=None) -> (vals (nq,k) fp32, + ids (nq,k) int64, -1 padded) +""" +from __future__ import annotations + +from typing import Optional + +import torch + +from ivfpqlib.build import _pad_features + +def ivf_pq_search(index, Q: torch.Tensor, k: int, *, nprobe: Optional[int] = None): + """Reference coarse + ADC IVF-PQ search over a built index. + + Returns ``(vals, ids)`` where ``vals[i, j]`` is the (approximate) + squared-L2 distance to the ``j``-th nearest PQ reconstruction and + ``ids`` are original row ids. Mirrors the Triton kernel exactly: + probe the ``nprobe`` nearest lists, build the per-(query, list) + residual LUT, score each member as the sum of ``m`` table lookups, + keep the global top-``k``. + """ + if Q.ndim != 2: + raise ValueError("ivf_pq search expects a 2D (nq, D) query tensor") + nprobe = int(nprobe or index.nprobe) + nprobe = max(1, min(nprobe, index.nlist)) + nq = Q.shape[0] + Dp, m, dsub = index.Dp, index.m, index.dsub + + Qp = _pad_features(Q.to(torch.float32), Dp) + centroids = index.centroids.to(torch.float32) # (nlist, Dp) + codebooks = index.pq_codebooks.to(torch.float32) # (m, ksub, dsub) + codes = index.codes # (M, m) uint8 + offsets = index.list_offsets + + coarse_d2 = torch.cdist(Qp, centroids) ** 2 # (nq, nlist) + probed = coarse_d2.topk(nprobe, dim=1, largest=False).indices # (nq, nprobe) + + out_vals = torch.full((nq, k), float("inf"), device=Q.device, dtype=torch.float32) + out_ids = torch.full((nq, k), -1, device=Q.device, dtype=torch.int64) + + for i in range(nq): + cand_dists = [] + cand_ids = [] + for p in range(nprobe): + c = int(probed[i, p].item()) + s0, e0 = int(offsets[c].item()), int(offsets[c + 1].item()) + if e0 <= s0: + continue + rq = (Qp[i] - centroids[c]) if index.by_residual else Qp[i] # (Dp,) + rq_sub = rq.reshape(m, dsub) # (m, dsub) + # LUT[s, j] = ||rq_s - codebook[s, j]||^2 + lut = ((rq_sub[:, None, :] - codebooks) ** 2).sum(-1) # (m, ksub) + cc = codes[s0:e0].to(torch.int64) # (L, m) + # dist[l] = sum_s LUT[s, cc[l, s]] + dist = lut.gather(1, cc.t().contiguous()).sum(0) # (L,) + cand_dists.append(dist) + cand_ids.append(index.ids[s0:e0]) + if not cand_dists: + continue + dist = torch.cat(cand_dists) + ids = torch.cat(cand_ids) + kk = min(k, dist.shape[0]) + vals, sel = dist.topk(kk, largest=False) + out_vals[i, :kk] = vals + out_ids[i, :kk] = ids[sel] + + return out_vals, out_ids + + +__all__ = ["ivf_pq_search"] diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/judge/refivfpq.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/judge/refivfpq.py new file mode 100644 index 000000000..c07e5c8ea --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/judge/refivfpq.py @@ -0,0 +1,343 @@ +"""``IvfPqIndex`` -- the in-memory container for a built IVF-PQ index. + +Stores the database **cell-contiguous** (all vectors of inverted list ``c`` in +rows ``[list_offsets[c], list_offsets[c+1])``) and keeps only the ``(M, m)`` +uint8 product-quantization codes, not the full vectors. ``ids[p]`` maps a stored +row back to the caller's original row id. Torch-only; no kernel import. Frozen judge baseline; do not edit. +""" + +import math +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch + +@dataclass +class IvfPqIndex: + """A built IVF-PQ index. + + Attributes: + centroids: ``(nlist, Dp)`` coarse-quantizer centroids (fp32). + pq_codebooks: ``(m, ksub, dsub)`` product-quantization sub-centroids + (fp32). ``ksub == 2**nbits`` (256 for the only supported + ``nbits=8``); ``dsub == Dp // m``. + codes: ``(M, m)`` uint8 -- per sub-quantizer code, cell-contiguous. + ids: ``(M,)`` int64 -- original row id for each stored row. + list_offsets: ``(nlist + 1,)`` int64 CSR offsets into ``codes``. + metric: distance metric (only ``"l2"`` supported). + by_residual: if True, PQ encodes ``x - centroid[list]`` (FAISS / + cuVS default, higher recall); else PQ encodes ``x`` directly. + D: original feature dimension as passed by the caller. + Dp: padded working dimension (``m * dsub``, ``>= 16``); zero + columns added for ``D < Dp`` never affect squared-L2 distances. + dsub: sub-vector dimension (``Dp // m``). + m: number of sub-quantizers (PQ codes per vector). + nbits: bits per code (only ``8`` supported -> ``ksub = 256``). + nlist: number of inverted lists / coarse centroids. + nprobe: default number of lists to probe at search time. + max_list_len: longest inverted list, recorded at build time so + search can size the kernel's chunk loop without a D2H sync. + """ + + centroids: torch.Tensor + pq_codebooks: torch.Tensor + codes: torch.Tensor + ids: torch.Tensor + list_offsets: torch.Tensor + metric: str + by_residual: bool + D: int + Dp: int + dsub: int + m: int + nbits: int + nlist: int + nprobe: int + max_list_len: int = 0 + + @property + def ksub(self) -> int: + """Number of sub-centroids per sub-quantizer (``2**nbits``).""" + return int(self.pq_codebooks.shape[1]) + + @property + def M(self) -> int: + return int(self.codes.shape[0]) + + @property + def device(self) -> torch.device: + return self.codes.device + + @property + def dtype(self) -> torch.dtype: + """Working dtype of the centroids / codebooks (codes are uint8).""" + return self.centroids.dtype + + def list_lengths(self) -> torch.Tensor: + """``(nlist,)`` int64 number of vectors in each inverted list.""" + return self.list_offsets[1:] - self.list_offsets[:-1] + + def code_size_bytes(self) -> int: + """Bytes per stored vector (``m`` for ``nbits=8``).""" + return int(self.m) + + def compression_ratio(self) -> float: + """Original fp32 vector bytes / PQ code bytes (storage savings).""" + return (4.0 * self.D) / max(self.code_size_bytes(), 1) + + def __repr__(self) -> str: # pragma: no cover - cosmetic + return ( + f"IvfPqIndex(M={self.M}, D={self.D}, Dp={self.Dp}, m={self.m}, " + f"dsub={self.dsub}, nbits={self.nbits}, nlist={self.nlist}, " + f"nprobe={self.nprobe}, by_residual={self.by_residual}, " + f"metric={self.metric!r}, dtype={self.dtype}, device={self.device})" + ) + + +def _pad_features(x: torch.Tensor, Dp: int) -> torch.Tensor: + """Zero-pad the trailing feature dim to ``Dp`` (no-op when already wide).""" + D = x.shape[-1] + if D >= Dp: + return x + pad = torch.zeros((*x.shape[:-1], Dp - D), device=x.device, dtype=x.dtype) + return torch.cat([x, pad], dim=-1) + + +def _pq_dims(D: int, m: int) -> Tuple[int, int, int]: + """Resolve ``(m, dsub, Dp)`` for a ``D``-dim input split into ``m`` codes. + + ``dsub = ceil(D / m)`` and ``Dp = m * dsub`` (the zero-padded working + width). ``dsub`` is bumped until ``Dp >= 16`` so the coarse-quantizer + kernels (which use ``tl.dot``) always have a contraction dim >= 16; + the zero columns never change squared-L2 distances. + """ + m = int(m) + if m < 1: + raise ValueError(f"m (number of sub-quantizers) must be >= 1 (got {m})") + if m > D: + # More sub-quantizers than dims: clamp so each sub-vector has >= 1 dim. + m = int(D) + dsub = int(math.ceil(D / m)) + while m * dsub < 16: + dsub += 1 + return m, dsub, m * dsub + + +# ── tiny CPU-OK building blocks ──────────────────────────────────────────── +def _lloyd_kmeans( + sample: torch.Tensor, k: int, *, niter: int, seed: int +) -> torch.Tensor: + """Tiny Lloyd k-means returning ``(k, D)`` centroids (fp32 math).""" + n = sample.shape[0] + if n < k: + raise ValueError( + f"k-means needs at least k={k} training rows (got {n}); " + "increase train_size / pq_train_size or lower nlist / m." + ) + g = torch.Generator(device="cpu").manual_seed(seed) + perm = torch.randperm(n, generator=g)[:k] + centroids = sample[perm.to(sample.device)].to(torch.float32).clone() + s = sample.to(torch.float32) + for _ in range(max(1, niter)): + d2 = torch.cdist(s, centroids) ** 2 # (n, k) + assign = d2.argmin(dim=1) # (n,) + new = centroids.clone() + for c in range(k): + mask = assign == c + if bool(mask.any()): + new[c] = s[mask].mean(dim=0) + shift = (new - centroids).norm(dim=-1).max() + centroids = new + if float(shift) == 0.0: + break + return centroids.to(torch.float32) + + +def _assign_chunked( + Xp: torch.Tensor, centroids: torch.Tensor, chunk: int = 8192 +) -> torch.Tensor: + """Nearest-centroid id per row (squared-L2), chunked to bound memory.""" + out = torch.empty(Xp.shape[0], dtype=torch.int64, device=Xp.device) + cf = centroids.to(torch.float32) + for lo in range(0, Xp.shape[0], chunk): + hi = min(lo + chunk, Xp.shape[0]) + d2 = torch.cdist(Xp[lo:hi].to(torch.float32), cf) ** 2 + out[lo:hi] = d2.argmin(dim=1) + return out + + +def _train_pq_codebooks( + resid: torch.Tensor, m: int, dsub: int, ksub: int, *, niter: int, seed: int +) -> torch.Tensor: + """Train ``m`` independent k-means sub-quantizers on residual sub-vectors. + + ``resid`` is ``(N, m*dsub)``; returns ``(m, ksub, dsub)`` fp32 codebooks. + """ + resid_sub = resid.reshape(resid.shape[0], m, dsub) # (N, m, dsub) + codebooks = torch.empty(m, ksub, dsub, dtype=torch.float32, device=resid.device) + for s in range(m): + codebooks[s] = _lloyd_kmeans(resid_sub[:, s, :], ksub, niter=niter, seed=seed + s) + return codebooks + + +def _encode_pq( + resid: torch.Tensor, codebooks: torch.Tensor, m: int, dsub: int, + chunk: int = 8192, +) -> torch.Tensor: + """Encode residual rows to ``(N, m)`` uint8 PQ codes (nearest sub-centroid).""" + N = resid.shape[0] + resid_sub = resid.reshape(N, m, dsub) # (N, m, dsub) + codes = torch.empty(N, m, dtype=torch.uint8, device=resid.device) + for s in range(m): + cb = codebooks[s].to(torch.float32) # (ksub, dsub) + sub = resid_sub[:, s, :].to(torch.float32) # (N, dsub) + for lo in range(0, N, chunk): + hi = min(lo + chunk, N) + d2 = torch.cdist(sub[lo:hi], cb) ** 2 # (chunk, ksub) + codes[lo:hi, s] = d2.argmin(dim=1).to(torch.uint8) + return codes + + +# ── public build / search ────────────────────────────────────────────────── +def ivf_pq_build( + X: torch.Tensor, + nlist: int, + *, + m: int = 8, + nbits: int = 8, + metric: str = "l2", + by_residual: bool = True, + nprobe: int = 8, + niter: int = 20, + pq_niter: int = 25, + train_size: Optional[int] = None, + pq_train_size: Optional[int] = None, + seed: int = 0, +): + """Build an :class:`IvfPqIndex` with pure torch ops.""" + if metric != "l2": + raise NotImplementedError(f"ivf_pq supports metric='l2' only (got {metric!r})") + if nbits != 8: + raise NotImplementedError(f"ivf_pq supports nbits=8 only (got {nbits})") + if X.ndim != 2: + raise ValueError("ivf_pq build expects a 2D (M, D) tensor") + + M, D = X.shape + nlist = int(min(nlist, M)) + if nlist < 1: + raise ValueError("nlist must be >= 1") + m, dsub, Dp = _pq_dims(int(D), m) + ksub = 1 << nbits # 256 + Xp = _pad_features(X.to(torch.float32), Dp).contiguous() + + # ── coarse quantizer: k-means on a sample ────────────────────────── + train_size = int(train_size or min(M, nlist * 256)) + train_size = max(min(train_size, M), nlist) + g = torch.Generator(device="cpu").manual_seed(seed) + sample_idx = torch.randperm(M, generator=g)[:train_size].to(X.device) + sample = Xp.index_select(0, sample_idx) + centroids = _lloyd_kmeans(sample, nlist, niter=niter, seed=seed) # (nlist, Dp) + + # ── PQ codebooks from residuals of a (sub)sample ─────────────────── + pq_train_size = int(pq_train_size or min(train_size, max(ksub * 16, 4096))) + pq_train_size = max(min(pq_train_size, train_size), ksub) + pq_sample = sample[:pq_train_size] + if by_residual: + pq_assign = _assign_chunked(pq_sample, centroids) + resid_train = pq_sample - centroids.index_select(0, pq_assign) + else: + resid_train = pq_sample + codebooks = _train_pq_codebooks( + resid_train, m, dsub, ksub, niter=pq_niter, seed=seed + 1 + ) # (m, ksub, dsub) + + # ── assign every database row + encode its residual ──────────────── + assign = _assign_chunked(Xp, centroids) # (M,) + resid_all = Xp - centroids.index_select(0, assign) if by_residual else Xp + codes = _encode_pq(resid_all, codebooks, m, dsub) # (M, m) uint8 + + # ── CSR cell-contiguous layout ───────────────────────────────────── + counts = torch.bincount(assign, minlength=nlist) # (nlist,) + offsets = torch.zeros(nlist + 1, dtype=torch.int64, device=X.device) + offsets[1:] = counts.cumsum(0) + order = torch.argsort(assign, stable=True) # (M,) int64 + codes_sorted = codes.index_select(0, order).contiguous() + + return IvfPqIndex( + centroids=centroids, + pq_codebooks=codebooks, + codes=codes_sorted, + ids=order.to(torch.int64), + list_offsets=offsets, + metric=metric, + by_residual=bool(by_residual), + D=int(D), + Dp=int(Dp), + dsub=int(dsub), + m=int(m), + nbits=int(nbits), + nlist=int(nlist), + nprobe=int(nprobe), + max_list_len=int(counts.max().item()) if nlist > 0 else 0, + ) + + +def ivf_pq_search(index, Q: torch.Tensor, k: int, *, nprobe: Optional[int] = None): + """Reference coarse + ADC IVF-PQ search over a built index. + + Returns ``(vals, ids)`` where ``vals[i, j]`` is the (approximate) + squared-L2 distance to the ``j``-th nearest PQ reconstruction and + ``ids`` are original row ids. Mirrors the Triton kernel exactly: + probe the ``nprobe`` nearest lists, build the per-(query, list) + residual LUT, score each member as the sum of ``m`` table lookups, + keep the global top-``k``. + """ + if Q.ndim != 2: + raise ValueError("ivf_pq search expects a 2D (nq, D) query tensor") + nprobe = int(nprobe or index.nprobe) + nprobe = max(1, min(nprobe, index.nlist)) + nq = Q.shape[0] + Dp, m, dsub = index.Dp, index.m, index.dsub + + Qp = _pad_features(Q.to(torch.float32), Dp) + centroids = index.centroids.to(torch.float32) # (nlist, Dp) + codebooks = index.pq_codebooks.to(torch.float32) # (m, ksub, dsub) + codes = index.codes # (M, m) uint8 + offsets = index.list_offsets + + coarse_d2 = torch.cdist(Qp, centroids) ** 2 # (nq, nlist) + probed = coarse_d2.topk(nprobe, dim=1, largest=False).indices # (nq, nprobe) + + out_vals = torch.full((nq, k), float("inf"), device=Q.device, dtype=torch.float32) + out_ids = torch.full((nq, k), -1, device=Q.device, dtype=torch.int64) + + for i in range(nq): + cand_dists = [] + cand_ids = [] + for p in range(nprobe): + c = int(probed[i, p].item()) + s0, e0 = int(offsets[c].item()), int(offsets[c + 1].item()) + if e0 <= s0: + continue + rq = (Qp[i] - centroids[c]) if index.by_residual else Qp[i] # (Dp,) + rq_sub = rq.reshape(m, dsub) # (m, dsub) + # LUT[s, j] = ||rq_s - codebook[s, j]||^2 + lut = ((rq_sub[:, None, :] - codebooks) ** 2).sum(-1) # (m, ksub) + cc = codes[s0:e0].to(torch.int64) # (L, m) + # dist[l] = sum_s LUT[s, cc[l, s]] + dist = lut.gather(1, cc.t().contiguous()).sum(0) # (L,) + cand_dists.append(dist) + cand_ids.append(index.ids[s0:e0]) + if not cand_dists: + continue + dist = torch.cat(cand_dists) + ids = torch.cat(cand_ids) + kk = min(k, dist.shape[0]) + vals, sel = dist.topk(kk, largest=False) + out_vals[i, :kk] = vals + out_ids[i, :kk] = ids[sel] + + return out_vals, out_ids + + +__all__ = ["IvfPqIndex", "ivf_pq_build", "ivf_pq_search"] diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/readme b/2.0/problems/ivf_pq_gpu_kernel_optimization/readme new file mode 100644 index 000000000..bb5679a80 --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/readme @@ -0,0 +1,109 @@ +# GPU IVF-PQ Search Kernel Optimization + +## Problem + +You are given a small GPU approximate-nearest-neighbour library, `ivfpqlib`, in +the Harbor workspace at `/app/ivfpqlib`. It implements **IVF-PQ** search. Its +public entry point is: + +```python +ivfpqlib.ivf_pq_search(index, Q, k, *, nprobe=None) -> (vals, ids) +``` + +`index` is a pre-built `IvfPqIndex` (an inverted-file product-quantization index: +`nlist` coarse cells, `m` PQ sub-codebooks, and the database rows stored as `(M, +m)` uint8 PQ codes). `Q` is an `(nq, D)` float32 CUDA tensor of queries, `k` is +the number of neighbours per query, and `nprobe` is how many inverted lists to +probe. It returns `vals` `(nq, k)` float32 (approximate squared-L2 distances to +each neighbour's PQ reconstruction) and `ids` `(nq, k)` int64 (original database +row ids, `-1` padded). The shipped implementation is correct but straightforward. + +Your goal is to make `ivfpqlib.ivf_pq_search` **as fast as possible** on the GPU +while returning the same neighbours. You may rewrite the internals of the package +and add new modules (including Triton kernels) under `ivfpqlib/`. The public +function signature and return contract must not change, and the result must +remain a deterministic function of the inputs. + +## What is (and isn't) timed + +Only `ivf_pq_search` is timed. The `index` is **built once** per workload by a +frozen builder (`ivf_pq_build`) and handed to your search unchanged; index +construction is **not** part of the score, so optimizing `ivf_pq_build` buys you +nothing. Your search must consume the index exactly as built (you may of course +reorganize its arrays inside your own call). Treat the coarse centroids, PQ +codebooks, CSR list layout, and codes as fixed inputs. + +## Workload + +The graded workloads are held-out ANN search problems over random float32 +databases: `M` from tens of thousands to a few hundred thousand rows, `D` in the +64–128 range, `nlist` a few hundred lists, `m` sub-quantizers 8–16, `nprobe` +8–32, and `nq` a thousand-plus queries per call. Treat it as general IVF-PQ +search, not as specific indices to special-case. + +Two representative *public* shapes are available for local testing (the graded +shapes are different and hidden): + +```text +(M=40000, D=64, nlist=256, m=8, nprobe=8, nq=1024, k=10) +(M=100000, D=96, nlist=512, m=12, nprobe=16, nq=1024, k=10) +``` + +## Iterate on a GPU + +The agent workspace has no GPU. Use the public test to time your current code on +a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET`): + +```bash +bash /app/public_test.sh +``` + +## Submission + +The submitted artifact is a patch over the `ivfpqlib` package: + +```text +/app/solution.patch +``` + +After editing `/app/ivfpqlib`, run `bash /app/make_submission.sh` then +`bash /app/submit.sh`. Submissions are asynchronous; submit early and iterate. +The judge applies your patch to a clean copy of `ivfpqlib` and times it against +the original baseline on a GPU, searching the same seeded index and queries. + +## Correctness + +Correctness is a gate. On every timed iteration the judge searches the same index +with the same queries using both your code and the baseline, and requires the +**iso-result recall@k** — the fraction of your returned ids that also appear in +the baseline's top-`k` for that query — to stay at or above a high threshold. +Crashes, non-finite / wrong-shape output, timeouts, and results that disagree +with the baseline beyond the tolerance are penalized before speed is considered. + +## Scoring + +Valid submissions are scored by speedup relative to the baseline: + +```text +speedup = baseline_time / your_time +``` + +The objective is the geometric mean of per-workload speedups. A result no faster +than the baseline earns 0; regressions earn 0. The raw geometric-mean speedup is +reported in the evaluator metrics. + +## Patch Policy + +Only Python files under `ivfpqlib/**` may change. New Python modules inside +`ivfpqlib/` are allowed. Patches may not: modify anything outside `ivfpqlib/`; +import or call an external optimized ML / ANN / kernel library (write the kernels +yourself); read/write environment variables, spawn processes, or access the +network; or tamper with the measurement framework. The timing harness regenerates +queries every iteration and re-verifies recall against the baseline each time. + +## Resource Budget + +```text +GPU: single Modal GPU (H100 reference; Triton paths also run on L40S / A100) +Agent container: CPU-only (GPU work is offloaded to Modal) +``` diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch b/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch new file mode 100644 index 000000000..a5b4460b0 --- /dev/null +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch @@ -0,0 +1,1535 @@ +diff --git a/ivfpqlib/_kernels/__init__.py b/ivfpqlib/_kernels/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/ivfpqlib/_kernels/_common.py b/ivfpqlib/_kernels/_common.py +new file mode 100644 +index 0000000..2b4c5c4 +--- /dev/null ++++ b/ivfpqlib/_kernels/_common.py +@@ -0,0 +1,8 @@ ++"""Vendored pure-python helper (next-power-of-two, for D / K padding).""" ++ ++ ++def _next_pow2(n: int) -> int: ++ """Smallest power-of-two >= ``max(1, n)`` (D / K padding).""" ++ if n <= 1: ++ return 1 ++ return 1 << (n - 1).bit_length() +diff --git a/ivfpqlib/_kernels/fine_scan.py b/ivfpqlib/_kernels/fine_scan.py +new file mode 100644 +index 0000000..6932237 +--- /dev/null ++++ b/ivfpqlib/_kernels/fine_scan.py +@@ -0,0 +1,238 @@ ++"""IVF-PQ fused ADC fine-scan kernel (elementwise / online path). ++ ++This is the IVF-PQ analogue of the IVF-Flat fused fine-scan: instead of ++streaming full database vectors and computing ``(q - x)^2``, it streams ++the **compressed PQ codes** of a ragged inverted list and recovers each ++candidate's (approximate) squared-L2 distance from the precomputed ++lookup table via asymmetric distance computation (ADC). ++ ++For each ``(query, probed-list[, split])`` the kernel: ++ ++ 1. reads the list's half-open code range ++ ``codes[offsets[c] + lo : offsets[c] + hi]`` of the cell-contiguous ++ ``(M, m)`` uint8 code array (fully coalesced), ++ 2. accumulates ``dist = sum_s LUT[query, probe, s, code[s]]`` -- one ++ HBM gather per sub-quantizer ``s`` into the compact per-(query, ++ list) table built by :mod:`...ivf_pq.triton.lut` -- and ++ 3. maintains an on-chip register top-K with the flash_knn ++ argmin/argmax insert loop. ++ ++Hard contract (identical to IVF-Flat): **no ``(nq x candidates)`` ++distance matrix is ever materialised in HBM** -- the only intermediates ++are the compact ``(nq, P, m, 256)`` LUT and the standard flash-decode ++partial buffer ``(nq, nprobe * n_splits, TOPK_PAD)``, merged host-side ++with a single ``torch.topk``. ++ ++One ``(query, probe, split)`` per program. ``n_splits`` chops long lists ++into wave-filling sub-ranges for the small-batch / online regime -- the ++same wave-count idea as the IVF-Flat / flash_knn decode split. ++""" ++from __future__ import annotations ++ ++from typing import Optional ++ ++import torch ++import triton ++import triton.language as tl ++ ++from ivfpqlib._kernels._common import _next_pow2 ++ ++ ++@triton.jit ++def _ivf_pq_fine_scan_kernel( ++ codes_ptr, probed_ptr, offsets_ptr, lut_ptr, ++ pv_ptr, pi_ptr, ++ stride_codes_m, stride_codes_s, ++ stride_pr_n, stride_pr_p, ++ stride_lut_n, stride_lut_p, stride_lut_s, stride_lut_j, ++ stride_pv_n, stride_pv_p, stride_pv_k, ++ stride_pi_n, stride_pi_p, stride_pi_k, ++ M, ++ MSUB: tl.constexpr, K: tl.constexpr, ++ N_SPLITS: tl.constexpr, ++ BM: tl.constexpr, ++ TOPK_PAD: tl.constexpr, MAX_STEPS: tl.constexpr, MAX_CHUNKS: tl.constexpr, ++): ++ """Grid: ``(nq, nprobe, N_SPLITS)``. One query, one probed list, one split. ++ ++ Writes ``(TOPK_PAD,)`` partial vals/idxs to slot ++ ``pid_p * N_SPLITS + pid_s`` for query ``pid_n``. ``idx`` is the ++ *stored-row* position into ``codes`` (mapped to original ids host-side). ++ """ ++ pid_n = tl.program_id(0).to(tl.int64) ++ pid_p = tl.program_id(1) ++ pid_s = tl.program_id(2) ++ ++ # Which inverted list this (query, probe-slot) scans. ++ c = tl.load(probed_ptr + pid_n * stride_pr_n + pid_p * stride_pr_p).to(tl.int64) ++ start = tl.load(offsets_ptr + c) ++ end = tl.load(offsets_ptr + c + 1) ++ list_len = end - start ++ ++ # Per-split sub-range within the list. ++ split_len = (list_len + N_SPLITS - 1) // N_SPLITS ++ lo = pid_s.to(tl.int64) * split_len ++ hi = tl.minimum(list_len, lo + split_len) ++ ++ # Base of this (query, probe-slot) LUT (stride_lut_p == 0 for non-residual). ++ lut_qp = lut_ptr + pid_n * stride_lut_n + pid_p * stride_lut_p ++ ++ bm_range = tl.arange(0, BM) ++ k_range = tl.arange(0, TOPK_PAD) ++ topk_vals = tl.full([TOPK_PAD], float("inf"), dtype=tl.float32) ++ topk_idx = tl.full([TOPK_PAD], -1, dtype=tl.int32) ++ topk_max = tl.max(topk_vals) ++ ++ for ci in range(MAX_CHUNKS): ++ within = lo + ci * BM + bm_range.to(tl.int64) # (BM,) within-list idx ++ valid = within < hi # (BM,) ++ pos = start + within # (BM,) stored-row pos ++ pos_safe = tl.minimum(tl.maximum(pos, 0), M - 1) ++ ++ # ADC: sum over sub-quantizers of LUT[s, code[s]] (one gather each). ++ acc = tl.zeros([BM], dtype=tl.float32) ++ for s in range(MSUB): ++ code_s = tl.load( ++ codes_ptr + pos_safe * stride_codes_m + s * stride_codes_s, ++ mask=valid, other=0, ++ ).to(tl.int64) # (BM,) in [0, 256) ++ lut_s = tl.load( ++ lut_qp + s * stride_lut_s + code_s * stride_lut_j, ++ mask=valid, other=0.0, ++ ) # (BM,) ++ acc += lut_s ++ ++ score = tl.where(valid, acc, float("inf")) # (BM,) ++ base = start + lo + ci * BM # scalar stored-row base ++ ++ # Iterative argmin-insert into the register top-K (flash_knn body). ++ chunk_best = tl.min(score) ++ if chunk_best < topk_max: ++ _active = tl.full([1], 1, dtype=tl.int32) ++ for _step in range(MAX_STEPS): ++ if tl.max(_active) > 0: ++ row_min = tl.min(score) ++ row_arg = tl.argmin(score, axis=0) ++ if row_min < topk_max: ++ worst = tl.argmax(topk_vals, axis=0) ++ repl = k_range == worst ++ topk_vals = tl.where(repl, row_min, topk_vals) ++ topk_idx = tl.where( ++ repl, (base + row_arg.to(tl.int64)).to(tl.int32), topk_idx ++ ) ++ topk_max = tl.max(topk_vals) ++ score = tl.where(bm_range == row_arg, float("inf"), score) ++ else: ++ _active = tl.full([1], 0, dtype=tl.int32) ++ ++ pslot = pid_p * N_SPLITS + pid_s ++ tl.store( ++ pv_ptr + pid_n * stride_pv_n + pslot * stride_pv_p + k_range * stride_pv_k, ++ topk_vals, ++ ) ++ tl.store( ++ pi_ptr + pid_n * stride_pi_n + pslot * stride_pi_p + k_range * stride_pi_k, ++ topk_idx, ++ ) ++ ++ ++def _pick_n_splits(nq: int, nprobe: int, max_list_len: int, sm_count: int) -> int: ++ """Chop lists into enough splits to roughly fill ~2 waves of SMs. ++ ++ For large query batches ``nq * nprobe`` already saturates the GPU and ++ ``n_splits == 1`` (each program owns a whole list). For tiny batches ++ (online / single-query search) we raise ``n_splits`` so the SMs are ++ not left idle -- the flash-decode wave-targeting idea, on the list axis. ++ """ ++ base = max(1, nq * nprobe) ++ target = 2 * max(sm_count, 1) ++ n_splits = (target + base - 1) // base ++ n_splits = max(1, min(n_splits, 64, max(1, max_list_len))) ++ return int(n_splits) ++ ++ ++def ivf_pq_fine_scan( ++ codes: torch.Tensor, ++ probed: torch.Tensor, ++ list_offsets: torch.Tensor, ++ lut: torch.Tensor, ++ k: int, ++ *, ++ by_residual: bool, ++ max_list_len: int, ++ BM: int = 128, ++ n_splits: Optional[int] = None, ++): ++ """Launch the fused ADC fine-scan + host-side merge. ++ ++ Args: ++ codes: ``(M, m)`` uint8 cell-contiguous PQ codes. ++ probed: ``(nq, nprobe)`` int32 inverted-list ids (from coarse search). ++ list_offsets: ``(nlist + 1,)`` int64 CSR offsets. ++ lut: ``(nq, P, m, ksub)`` fp32 ADC tables (``P = nprobe`` if ++ ``by_residual`` else ``1``). ++ k: neighbours per query. ++ by_residual: selects the LUT probe stride (0 when the LUT is ++ query-only, i.e. non-residual). ++ max_list_len: max inverted-list length (bounds the static chunk loop). ++ ++ Returns: ++ ``(vals, pos)`` -- ``vals`` ``(nq, k)`` ADC squared-L2 (fp32), ++ ``pos`` ``(nq, k)`` int64 stored-row positions into ``codes`` ++ (``-1`` where fewer than ``k`` candidates were available). ++ """ ++ assert codes.is_cuda and probed.is_cuda and list_offsets.is_cuda and lut.is_cuda ++ nq, nprobe = probed.shape ++ M, m = codes.shape ++ ++ codes = codes.contiguous() ++ probed = probed.contiguous().to(torch.int32) ++ list_offsets = list_offsets.contiguous().to(torch.int64) ++ lut = lut.contiguous() ++ ++ if n_splits is None: ++ sm_count = torch.cuda.get_device_properties(codes.device).multi_processor_count ++ n_splits = _pick_n_splits(nq, nprobe, max_list_len, sm_count) ++ n_splits = max(1, min(int(n_splits), max(1, max_list_len))) ++ ++ TOPK_PAD = _next_pow2(k) ++ MAX_STEPS = min(k, BM) ++ per_split_max = (max_list_len + n_splits - 1) // n_splits ++ MAX_CHUNKS = max(1, (per_split_max + BM - 1) // BM) ++ ++ # stride 0 on the probe axis when the LUT depends only on the query. ++ stride_lut_p = lut.stride(1) if by_residual else 0 ++ ++ P = nprobe * n_splits ++ partial_vals = torch.full((nq, P, TOPK_PAD), float("inf"), ++ device=codes.device, dtype=torch.float32) ++ partial_idx = torch.full((nq, P, TOPK_PAD), -1, ++ device=codes.device, dtype=torch.int32) ++ ++ grid = (nq, nprobe, n_splits) ++ _ivf_pq_fine_scan_kernel[grid]( ++ codes, probed, list_offsets, lut, ++ partial_vals, partial_idx, ++ codes.stride(0), codes.stride(1), ++ probed.stride(0), probed.stride(1), ++ lut.stride(0), stride_lut_p, lut.stride(2), lut.stride(3), ++ partial_vals.stride(0), partial_vals.stride(1), partial_vals.stride(2), ++ partial_idx.stride(0), partial_idx.stride(1), partial_idx.stride(2), ++ M, ++ MSUB=m, K=k, ++ N_SPLITS=n_splits, ++ BM=BM, ++ TOPK_PAD=TOPK_PAD, MAX_STEPS=MAX_STEPS, MAX_CHUNKS=MAX_CHUNKS, ++ num_warps=4, ++ ) ++ ++ # Stage-2: merge the P partial top-Ks per query (no HBM cross matrix). ++ pv = partial_vals.view(nq, -1) ++ pi = partial_idx.view(nq, -1) ++ vals, sel = pv.topk(k, dim=-1, largest=False, sorted=True) ++ pos = pi.gather(-1, sel).to(torch.int64) ++ pos = torch.where(vals.isinf(), torch.full_like(pos, -1), pos) ++ return vals, pos ++ ++ ++__all__ = ["ivf_pq_fine_scan", "_pick_n_splits"] +diff --git a/ivfpqlib/_kernels/fine_scan_batch.py b/ivfpqlib/_kernels/fine_scan_batch.py +new file mode 100644 +index 0000000..38b2925 +--- /dev/null ++++ b/ivfpqlib/_kernels/fine_scan_batch.py +@@ -0,0 +1,224 @@ ++"""IVF-PQ fine-scan, throughput variant: group-by-list code-sharing ADC. ++ ++The online :mod:`...ivf_pq.triton.fine_scan` kernel owns one ++``(query, list)`` pair per program and re-reads a list's PQ codes once ++per probing query. For batched search that leaves reuse on the table: ++many queries probe the same list, and the codes (and their layout) can ++be shared across a whole query tile. ++ ++This kernel does what the IVF-Flat GEMM variant does, adapted to ADC: ++ ++ 1. **Group queries by the list they probe** (host-side argsort of the ++ ``(query, list)`` pairs) so every query probing list ``c`` forms a ++ contiguous run. ++ 2. For each ``(list, query-tile)`` it streams the list's ``(BM, m)`` ++ codes **once** and, for the ``BN`` queries in the tile, accumulates ++ the ``(BN, BM)`` ADC score block as ``m`` lookup-table gathers -- ++ each query indexing its *own* precomputed LUT row (the right ++ probe-slot, recovered from the sorted-pair id). A per-query on-chip ++ top-K (the flash_knn 2-D insert body) is reduced in place. ++ ++Unlike the IVF-Flat GEMM kernel, ADC is computed **exactly** here (a sum ++of table lookups, not an x²-free cross term), so there is no oversampled ++re-rank: the returned distances *are* the ADC distances and match the ++online kernel bit-for-bit. Still no ``(nq x candidates)`` HBM matrix -- ++the score block lives in registers and is consumed into the top-K. ++""" ++from __future__ import annotations ++ ++from typing import Tuple ++ ++import torch ++import triton ++import triton.language as tl ++ ++from ivfpqlib._kernels._common import _next_pow2 ++ ++ ++@triton.jit ++def _ivf_pq_fine_batch_kernel( ++ codes_ptr, sorted_qid_ptr, sorted_pslot_ptr, lut_ptr, ++ q_offsets_ptr, list_offsets_ptr, ++ pv_ptr, pi_ptr, ++ stride_codes_m, stride_codes_s, ++ stride_lut_n, stride_lut_p, stride_lut_s, stride_lut_j, ++ stride_pv_p, stride_pv_k, ++ stride_pi_p, stride_pi_k, ++ MSUB: tl.constexpr, K: tl.constexpr, ++ BN: tl.constexpr, BM: tl.constexpr, ++ TOPK_PAD: tl.constexpr, MAX_STEPS: tl.constexpr, MAX_M_CHUNKS: tl.constexpr, ++): ++ """Grid: ``(nlist, MAX_QTILES)``. One ``(list, query-tile)`` per program.""" ++ pid_c = tl.program_id(0) ++ pid_qt = tl.program_id(1) ++ ++ qstart = tl.load(q_offsets_ptr + pid_c) ++ qend = tl.load(q_offsets_ptr + pid_c + 1) ++ qcount = qend - qstart ++ if pid_qt * BN >= qcount: ++ return ++ ++ i_range = tl.arange(0, BN) ++ q_local = pid_qt * BN + i_range ++ q_mask = q_local < qcount # (BN,) ++ pair_pos = (qstart + q_local).to(tl.int64) # (BN,) sorted-pair rows ++ qid = tl.load(sorted_qid_ptr + pair_pos, mask=q_mask, other=0).to(tl.int64) ++ pslot = tl.load(sorted_pslot_ptr + pair_pos, mask=q_mask, other=0).to(tl.int64) ++ # Per-query LUT base offset (probe-slot stride is 0 for non-residual). ++ lut_base = qid * stride_lut_n + pslot * stride_lut_p # (BN,) ++ ++ c_start = tl.load(list_offsets_ptr + pid_c) ++ c_end = tl.load(list_offsets_ptr + pid_c + 1) ++ ++ topk_vals = tl.full([BN, TOPK_PAD], float("inf"), dtype=tl.float32) ++ topk_idxs = tl.full([BN, TOPK_PAD], -1, dtype=tl.int32) ++ topk_max = tl.full([BN], float("inf"), dtype=tl.float32) ++ k_range = tl.arange(0, TOPK_PAD) ++ bm_range = tl.arange(0, BM) ++ ++ for ci in range(MAX_M_CHUNKS): ++ m_start = c_start + ci * BM ++ m_offs = m_start + bm_range.to(tl.int64) # (BM,) stored rows ++ m_mask = m_offs < c_end ++ ++ # ADC score block: shared code stream, per-query LUT gather. ++ acc = tl.zeros([BN, BM], dtype=tl.float32) ++ for s in range(MSUB): ++ code_s = tl.load( ++ codes_ptr + m_offs * stride_codes_m + s * stride_codes_s, ++ mask=m_mask, other=0, ++ ).to(tl.int64) # (BM,) shared across BN ++ off = ( ++ lut_base[:, None] ++ + s * stride_lut_s ++ + code_s[None, :] * stride_lut_j ++ ) # (BN, BM) ++ acc += tl.load( ++ lut_ptr + off, mask=q_mask[:, None] & m_mask[None, :], other=0.0, ++ ) ++ ++ score = tl.where(q_mask[:, None] & m_mask[None, :], acc, float("inf")) ++ ++ chunk_best = tl.min(score) ++ threshold_worst = tl.max(topk_max) ++ if chunk_best < threshold_worst: ++ _active = tl.full([1], 1, dtype=tl.int32) ++ for _step in range(MAX_STEPS): ++ if tl.max(_active) > 0: ++ row_min = tl.min(score, axis=1) ++ row_argmin = tl.argmin(score, axis=1) ++ do_insert = row_min < topk_max ++ n_inserts = tl.sum(do_insert.to(tl.int32)) ++ if n_inserts > 0: ++ topk_argmax = tl.argmax(topk_vals, axis=1) ++ replace_mask = k_range[None, :] == topk_argmax[:, None] ++ insert_mask = do_insert[:, None] & replace_mask ++ topk_vals = tl.where(insert_mask, row_min[:, None], topk_vals) ++ topk_idxs = tl.where( ++ insert_mask, ++ (m_start + row_argmin.to(tl.int64))[:, None].to(tl.int32), ++ topk_idxs, ++ ) ++ topk_max = tl.max(topk_vals, axis=1) ++ used_mask = bm_range[None, :] == row_argmin[:, None] ++ score = tl.where(used_mask & do_insert[:, None], float("inf"), score) ++ _active = tl.where( ++ n_inserts > 0, ++ tl.full([1], 1, dtype=tl.int32), ++ tl.full([1], 0, dtype=tl.int32), ++ ) ++ ++ write_mask = q_mask[:, None] & (k_range[None, :] < TOPK_PAD) ++ tl.store( ++ pv_ptr + pair_pos[:, None] * stride_pv_p + k_range[None, :] * stride_pv_k, ++ topk_vals, mask=write_mask, ++ ) ++ tl.store( ++ pi_ptr + pair_pos[:, None] * stride_pi_p + k_range[None, :] * stride_pi_k, ++ topk_idxs, mask=write_mask, ++ ) ++ ++ ++def _avg_group_size(nq: int, nprobe: int, nlist: int) -> float: ++ """Average number of queries probing a list -- the code-reuse factor.""" ++ return (nq * nprobe) / max(nlist, 1) ++ ++ ++def ivf_pq_fine_scan_batch( ++ codes: torch.Tensor, ++ probed: torch.Tensor, ++ list_offsets: torch.Tensor, ++ lut: torch.Tensor, ++ k: int, ++ *, ++ by_residual: bool, ++ max_list_len: int, ++ BN: int = 64, ++ BM: int = 64, ++) -> Tuple[torch.Tensor, torch.Tensor]: ++ """Group-by-list code-sharing ADC fine scan + host merge. ++ ++ Args mirror :func:`...fine_scan.ivf_pq_fine_scan`. Returns ``(vals, ++ pos)`` with ``vals`` ``(nq, k)`` ADC squared-L2 (fp32, identical to ++ the online kernel) and ``pos`` ``(nq, k)`` int64 stored-row positions ++ into ``codes`` (``-1`` where unavailable). ++ """ ++ assert codes.is_cuda and probed.is_cuda and lut.is_cuda ++ nq, nprobe = probed.shape ++ nlist = list_offsets.shape[0] - 1 ++ device = codes.device ++ ++ codes = codes.contiguous() ++ list_offsets = list_offsets.contiguous().to(torch.int64) ++ lut = lut.contiguous() ++ ++ # ── group (query, list) pairs by list id ─────────────────────────── ++ flat = probed.reshape(-1).contiguous().to(torch.int64) # (P,) pair -> list id ++ P = flat.numel() ++ perm = torch.argsort(flat, stable=True) # sorted-pair -> orig-pair ++ sorted_qid = (perm // nprobe).to(torch.int32) # query id per sorted pair ++ sorted_pslot = (perm % nprobe).to(torch.int32) # probe-slot (LUT row) ++ qcounts = torch.bincount(flat, minlength=nlist) # (nlist,) ++ q_offsets = torch.zeros(nlist + 1, dtype=torch.int64, device=device) ++ q_offsets[1:] = qcounts.cumsum(0) ++ max_qcount = int(qcounts.max().item()) ++ MAX_QTILES = max(1, (max_qcount + BN - 1) // BN) ++ ++ TOPK_PAD = _next_pow2(k) ++ MAX_STEPS = min(k, BM) ++ MAX_M_CHUNKS = max(1, (max_list_len + BM - 1) // BM) ++ stride_lut_p = lut.stride(1) if by_residual else 0 ++ ++ pv_sorted = torch.full((P, TOPK_PAD), float("inf"), device=device, dtype=torch.float32) ++ pi_sorted = torch.full((P, TOPK_PAD), -1, device=device, dtype=torch.int32) ++ ++ grid = (nlist, MAX_QTILES) ++ _ivf_pq_fine_batch_kernel[grid]( ++ codes, sorted_qid, sorted_pslot, lut, ++ q_offsets, list_offsets, ++ pv_sorted, pi_sorted, ++ codes.stride(0), codes.stride(1), ++ lut.stride(0), stride_lut_p, lut.stride(2), lut.stride(3), ++ pv_sorted.stride(0), pv_sorted.stride(1), ++ pi_sorted.stride(0), pi_sorted.stride(1), ++ MSUB=codes.shape[1], K=k, ++ BN=BN, BM=BM, ++ TOPK_PAD=TOPK_PAD, MAX_STEPS=MAX_STEPS, MAX_M_CHUNKS=MAX_M_CHUNKS, ++ num_warps=4, ++ ) ++ ++ # ── scatter partials back to per-query order, then merge ─────────── ++ pv = torch.empty_like(pv_sorted) ++ pi = torch.empty_like(pi_sorted) ++ pv[perm] = pv_sorted ++ pi[perm] = pi_sorted ++ pv = pv.view(nq, nprobe * TOPK_PAD) ++ pi = pi.view(nq, nprobe * TOPK_PAD) ++ ++ vals, sel = pv.topk(k, dim=-1, largest=False, sorted=True) ++ pos = pi.gather(-1, sel).to(torch.int64) ++ pos = torch.where(vals.isinf(), torch.full_like(pos, -1), pos) ++ return vals, pos ++ ++ ++__all__ = ["ivf_pq_fine_scan_batch", "_avg_group_size"] +diff --git a/ivfpqlib/_kernels/fine_scan_gemm.py b/ivfpqlib/_kernels/fine_scan_gemm.py +new file mode 100644 +index 0000000..f6a5ad4 +--- /dev/null ++++ b/ivfpqlib/_kernels/fine_scan_gemm.py +@@ -0,0 +1,416 @@ ++"""IVF-PQ fine-scan, throughput variant: cluster-centric **decode + GEMM**. ++ ++The online :mod:`...ivf_pq.triton.fine_scan` (and group-by-list ++:mod:`...ivf_pq.triton.fine_scan_batch`) kernels score candidates by ++*gathering* from an asymmetric-distance lookup table (ADC LUT): ``m`` ++table reads per ``(query, code)``. That is **gather-throughput bound** -- ++exactly the wall hand-written CUDA (cuVS) climbs with shared-memory LUTs ++that Triton cannot express -- and it forces an ``(nq, nprobe, m, 256)`` ++LUT that blows up with ``nprobe`` (tens of GB). ++ ++This kernel takes the other road the user asked for -- **no LUT** -- and ++turns ADC into a tensor-core GEMM, the same trick IVF-Flat uses: ++ ++ 1. **Coarse + inverse map.** Each query picks ``nprobe`` lists; we ++ argsort the ``(query, list)`` pairs so all queries probing list ++ ``c`` form a contiguous run (host side, in the driver). ++ 2. **Cluster sweep (this kernel).** Grid ``(nlist, query-tile)``. For ++ one ``(list c, query-tile)`` the kernel ++ a. forms the residual query tile ``rq = q - centroid_c`` (or ++ ``rq = q`` when not ``by_residual``), ``(BN, Dp)``; ++ b. streams the list's codes ``(BM, m)`` **once** and *decodes* ++ them to reconstructed sub-vectors ``xhat`` ``(BM, Dp)`` by ++ gathering the (tiny, L2-resident) PQ codebook -- shared across ++ all ``BN`` queries in the tile; ++ c. computes the cross term ``⟨rq, xhat⟩`` as a **WGMMA ``tl.dot``** ++ and the ADC distance ``‖rq‖² + ‖xhat‖² - 2⟨rq, xhat⟩`` (note: ++ ``‖rq‖²`` is kept -- with residual encoding it differs per list, ++ so dropping it would make cross-list partials incomparable); ++ d. reduces a per-query on-chip top-k (flash_knn insert body) and ++ writes one ``(BN, TOPK_PAD)`` partial at the pair's sorted row. ++ 3. **Reduce (driver).** Scatter partials to per-query order, merge the ++ ``nprobe`` partials, and **exact-re-rank** an oversampled pool with ++ :func:`_pq_rerank_kernel` (direct ``‖rq - xhat‖²`` decode) so the ++ returned distances are ADC-exact despite the tf32 GEMM ranking. ++ ++Why this wins: the per-``(query, code)`` LUT gathers become one decode ++gather per *code* (amortised over the whole query tile) plus a tensor-core ++GEMM, so large batches run **3-12x** faster than the LUT path with ++identical recall and ADC-exact distances -- and there is **no LUT**, so ++the ``nprobe``-scaling memory blow-up disappears (partials are only ++``nq*nprobe*k`` floats). ++""" ++from __future__ import annotations ++ ++from typing import Optional, Tuple ++ ++import torch ++import triton ++import triton.language as tl ++ ++from ivfpqlib._kernels._common import _next_pow2 ++ ++ ++@triton.jit ++def _ivf_pq_decode_gemm_kernel( ++ q_ptr, cent_ptr, sorted_qid_ptr, ++ q_offsets_ptr, list_offsets_ptr, ++ codes_ptr, cb_ptr, ++ pv_ptr, pi_ptr, ++ stride_q_n, stride_q_d, ++ stride_cent_c, stride_cent_d, ++ stride_codes_m, stride_codes_s, ++ stride_cb_m, stride_cb_j, stride_cb_d, ++ stride_pv_p, stride_pv_k, ++ stride_pi_p, stride_pi_k, ++ BY_RESIDUAL: tl.constexpr, ++ MSUB: tl.constexpr, DSUB: tl.constexpr, DP: tl.constexpr, D_INNER: tl.constexpr, ++ BN: tl.constexpr, BM: tl.constexpr, ++ TOPK_PAD: tl.constexpr, MAX_STEPS: tl.constexpr, ++): ++ """Grid ``(nlist, MAX_QTILES)``; one ``(list, query-tile)`` per program. ++ ++ Decodes the list's PQ codes to reconstructed sub-vectors and scores the ++ query tile against them with a tensor-core cross term -- no ADC LUT. ++ Writes ``(BN, TOPK_PAD)`` partials (approximate-ADC ranked; the driver ++ re-ranks the pool exactly) to the contiguous sorted-pair rows. ++ """ ++ pid_c = tl.program_id(0) ++ pid_qt = tl.program_id(1) ++ ++ qstart = tl.load(q_offsets_ptr + pid_c) ++ qend = tl.load(q_offsets_ptr + pid_c + 1) ++ qcount = qend - qstart ++ if pid_qt * BN >= qcount: ++ return ++ ++ i_range = tl.arange(0, BN) ++ q_local = pid_qt * BN + i_range ++ q_mask = q_local < qcount # (BN,) ++ pair_pos = (qstart + q_local).to(tl.int64) # (BN,) sorted-pair rows ++ qid = tl.load(sorted_qid_ptr + pair_pos, mask=q_mask, other=0).to(tl.int64) ++ ++ c_start = tl.load(list_offsets_ptr + pid_c) ++ c_end = tl.load(list_offsets_ptr + pid_c + 1) ++ ++ DREAL = MSUB * DSUB ++ ++ # Persistent residual query tile while padded Dp fits one D_INNER tile; ++ # above that the corpus loop re-forms rq in D_INNER-wide chunks. ++ if D_INNER >= DP: ++ d_offs = tl.arange(0, D_INNER) ++ d_mask = d_offs < DREAL ++ s_of_d = d_offs // DSUB ++ o_of_d = d_offs % DSUB ++ q_tile = tl.load( ++ q_ptr + qid[:, None] * stride_q_n + d_offs[None, :] * stride_q_d, ++ mask=q_mask[:, None] & d_mask[None, :], other=0.0, ++ ) ++ if BY_RESIDUAL: ++ cent = tl.load( ++ cent_ptr + pid_c * stride_cent_c + d_offs * stride_cent_d, ++ mask=d_mask, other=0.0, ++ ) ++ rq = q_tile - cent[None, :] ++ else: ++ rq = q_tile ++ rq = tl.where(q_mask[:, None] & d_mask[None, :], rq, 0.0) # (BN, D_INNER) ++ rq_sq = tl.sum(rq * rq, axis=1) # (BN,) ++ ++ topk_vals = tl.full([BN, TOPK_PAD], float("inf"), dtype=tl.float32) ++ topk_idxs = tl.full([BN, TOPK_PAD], -1, dtype=tl.int32) ++ topk_max = tl.full([BN], float("inf"), dtype=tl.float32) ++ k_range = tl.arange(0, TOPK_PAD) ++ bm_range = tl.arange(0, BM) ++ ++ # Per-list chunk count (data-dependent), NOT a global constexpr: lists are ++ # very uneven (SIFT: max 3.5k vs avg ~1k), so looping the longest list's ++ # chunk count for every program would run ~3x masked-empty chunks that ++ # still execute the full decode + tl.dot. Bounding by this list's own ++ # length cuts ~25% wall time (measured). ++ n_chunks = tl.cdiv(c_end - c_start, BM) ++ for ci in range(n_chunks): ++ m_start = c_start + ci * BM ++ m_offs = m_start + bm_range.to(tl.int64) # (BM,) stored rows ++ m_mask = m_offs < c_end ++ ++ if D_INNER >= DP: ++ # decode xhat (BM, D_INNER): xhat[bm,d] = cb[s_of_d, codes[bm, s_of_d], o_of_d] ++ code_col = tl.load( ++ codes_ptr + m_offs[:, None] * stride_codes_m ++ + s_of_d[None, :] * stride_codes_s, ++ mask=m_mask[:, None] & d_mask[None, :], other=0, ++ ).to(tl.int64) ++ xhat = tl.load( ++ cb_ptr + s_of_d[None, :] * stride_cb_m + code_col * stride_cb_j ++ + o_of_d[None, :] * stride_cb_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0, ++ ) # (BM, D_INNER) ++ xhat_sq = tl.sum(xhat * xhat, axis=1) # (BM,) ++ cross = tl.dot(rq, tl.trans(xhat), input_precision="tf32") # (BN, BM) ++ dist = rq_sq[:, None] + xhat_sq[None, :] - 2.0 * cross ++ else: ++ cross = tl.zeros([BN, BM], dtype=tl.float32) ++ xhat_sq = tl.zeros([BM], dtype=tl.float32) ++ rq_sq = tl.zeros([BN], dtype=tl.float32) ++ for d_start in range(0, DP, D_INNER): ++ d_offs = d_start + tl.arange(0, D_INNER) ++ d_mask = d_offs < DREAL ++ s_of_d = d_offs // DSUB ++ o_of_d = d_offs % DSUB ++ q_sub = tl.load( ++ q_ptr + qid[:, None] * stride_q_n + d_offs[None, :] * stride_q_d, ++ mask=q_mask[:, None] & d_mask[None, :], other=0.0, ++ ) ++ if BY_RESIDUAL: ++ cent = tl.load( ++ cent_ptr + pid_c * stride_cent_c + d_offs * stride_cent_d, ++ mask=d_mask, other=0.0, ++ ) ++ rq_sub = q_sub - cent[None, :] ++ else: ++ rq_sub = q_sub ++ rq_sub = tl.where(q_mask[:, None] & d_mask[None, :], rq_sub, 0.0) ++ rq_sq += tl.sum(rq_sub * rq_sub, axis=1) ++ code_col = tl.load( ++ codes_ptr + m_offs[:, None] * stride_codes_m ++ + s_of_d[None, :] * stride_codes_s, ++ mask=m_mask[:, None] & d_mask[None, :], other=0, ++ ).to(tl.int64) ++ xhat_sub = tl.load( ++ cb_ptr + s_of_d[None, :] * stride_cb_m + code_col * stride_cb_j ++ + o_of_d[None, :] * stride_cb_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0, ++ ) ++ xhat_sq += tl.sum(xhat_sub * xhat_sub, axis=1) ++ cross += tl.dot(rq_sub, tl.trans(xhat_sub), input_precision="tf32") ++ dist = rq_sq[:, None] + xhat_sq[None, :] - 2.0 * cross ++ ++ score = tl.where(q_mask[:, None] & m_mask[None, :], dist, float("inf")) ++ ++ chunk_best = tl.min(score) ++ threshold_worst = tl.max(topk_max) ++ if chunk_best < threshold_worst: ++ _active = tl.full([1], 1, dtype=tl.int32) ++ for _step in range(MAX_STEPS): ++ if tl.max(_active) > 0: ++ row_min = tl.min(score, axis=1) ++ row_argmin = tl.argmin(score, axis=1) ++ do_insert = row_min < topk_max ++ n_inserts = tl.sum(do_insert.to(tl.int32)) ++ if n_inserts > 0: ++ topk_argmax = tl.argmax(topk_vals, axis=1) ++ replace_mask = k_range[None, :] == topk_argmax[:, None] ++ insert_mask = do_insert[:, None] & replace_mask ++ topk_vals = tl.where(insert_mask, row_min[:, None], topk_vals) ++ topk_idxs = tl.where( ++ insert_mask, ++ (m_start + row_argmin.to(tl.int64))[:, None].to(tl.int32), ++ topk_idxs, ++ ) ++ topk_max = tl.max(topk_vals, axis=1) ++ used_mask = bm_range[None, :] == row_argmin[:, None] ++ score = tl.where(used_mask & do_insert[:, None], float("inf"), score) ++ _active = tl.where( ++ n_inserts > 0, ++ tl.full([1], 1, dtype=tl.int32), ++ tl.full([1], 0, dtype=tl.int32), ++ ) ++ ++ write_mask = q_mask[:, None] & (k_range[None, :] < TOPK_PAD) ++ tl.store( ++ pv_ptr + pair_pos[:, None] * stride_pv_p + k_range[None, :] * stride_pv_k, ++ topk_vals, mask=write_mask, ++ ) ++ tl.store( ++ pi_ptr + pair_pos[:, None] * stride_pi_p + k_range[None, :] * stride_pi_k, ++ topk_idxs, mask=write_mask, ++ ) ++ ++ ++@triton.jit ++def _pq_rerank_kernel( ++ q_ptr, cent_ptr, codes_ptr, cb_ptr, pos_ptr, clist_ptr, out_ptr, ++ stride_q_n, stride_q_d, ++ stride_cent_c, stride_cent_d, ++ stride_codes_m, stride_codes_s, ++ stride_cb_m, stride_cb_j, stride_cb_d, ++ stride_pos_n, stride_pos_k, ++ stride_out_n, stride_out_k, ++ BY_RESIDUAL: tl.constexpr, MSUB: tl.constexpr, DSUB: tl.constexpr, ++ DP: tl.constexpr, KK: tl.constexpr, ++): ++ """One query per program: exact ADC ``‖rq - xhat‖²`` for its ``KK`` ++ candidate stored-rows (``rq = q - centroid[list_of_candidate]``). ++ ++ Decode is done with the same codebook gather as the GEMM kernel, but ++ distances are accumulated directly (no x²-free cross term) so the ++ result is ADC-exact and immune to the tf32 GEMM rounding used for ++ candidate *selection*. ++ """ ++ i = tl.program_id(0) ++ kk = tl.arange(0, KK) ++ pos = tl.load(pos_ptr + i * stride_pos_n + kk * stride_pos_k).to(tl.int64) # (KK,) ++ clist = tl.load(clist_ptr + i * stride_pos_n + kk * stride_pos_k).to(tl.int64) ++ valid = pos >= 0 ++ ++ d_range = tl.arange(0, DP) ++ s_of_d = d_range // DSUB ++ o_of_d = d_range % DSUB ++ d_mask = d_range < (MSUB * DSUB) ++ ++ q = tl.load(q_ptr + i * stride_q_n + d_range * stride_q_d, mask=d_mask, other=0.0) ++ if BY_RESIDUAL: ++ cent = tl.load( ++ cent_ptr + clist[:, None] * stride_cent_c + d_range[None, :] * stride_cent_d, ++ mask=valid[:, None] & d_mask[None, :], other=0.0, ++ ) ++ rq = q[None, :] - cent ++ else: ++ rq = tl.broadcast_to(q[None, :], (KK, DP)) ++ code_col = tl.load( ++ codes_ptr + pos[:, None] * stride_codes_m + s_of_d[None, :] * stride_codes_s, ++ mask=valid[:, None] & d_mask[None, :], other=0, ++ ).to(tl.int64) ++ xhat = tl.load( ++ cb_ptr + s_of_d[None, :] * stride_cb_m + code_col * stride_cb_j ++ + o_of_d[None, :] * stride_cb_d, ++ mask=valid[:, None] & d_mask[None, :], other=0.0, ++ ) ++ diff = tl.where(d_mask[None, :], rq - xhat, 0.0) ++ dist = tl.sum(diff * diff, axis=1) ++ dist = tl.where(valid, dist, float("inf")) ++ tl.store(out_ptr + i * stride_out_n + kk * stride_out_k, dist) ++ ++ ++def _avg_group_size(nq: int, nprobe: int, nlist: int) -> float: ++ """Average number of queries probing a list -- the GEMM's reuse factor.""" ++ return (nq * nprobe) / max(nlist, 1) ++ ++ ++def ivf_pq_fine_scan_gemm( ++ Qp: torch.Tensor, ++ centroids: torch.Tensor, ++ codebooks: torch.Tensor, ++ codes: torch.Tensor, ++ probed: torch.Tensor, ++ list_offsets: torch.Tensor, ++ k: int, ++ *, ++ by_residual: bool, ++ over: int = 2, ++ BN: int = 64, ++ BM: int = 64, ++ num_stages: int = 2, ++) -> Tuple[torch.Tensor, torch.Tensor]: ++ """Cluster-centric decode+GEMM fine scan, then exact ADC re-rank. ++ ++ Args: ++ Qp: ``(nq, Dp)`` padded fp32 queries. ++ centroids: ``(nlist, Dp)`` coarse centroids (fp32). ++ codebooks: ``(m, ksub, dsub)`` PQ sub-centroids (fp32). ++ codes: ``(M, m)`` uint8 codes, cell-contiguous. ++ probed: ``(nq, nprobe)`` int32 probed list ids. ++ list_offsets: ``(nlist + 1,)`` int64 CSR offsets. ++ k: neighbours per query. ++ by_residual: residual vs direct PQ encoding. ++ over: candidate-pool oversample factor for the exact re-rank. ++ ++ Returns ``(vals, pos)`` with ``vals`` ``(nq, k)`` ADC-exact squared-L2 ++ (fp32) and ``pos`` ``(nq, k)`` int64 stored-row positions into ++ ``codes`` (``-1`` where unavailable). ++ """ ++ assert Qp.is_cuda and codes.is_cuda and centroids.is_cuda ++ nq, Dp = Qp.shape ++ nprobe = probed.shape[1] ++ nlist = list_offsets.shape[0] - 1 ++ m = codes.shape[1] ++ dsub = codebooks.shape[2] ++ device = Qp.device ++ ++ Qp = Qp.contiguous() ++ centroids = centroids.contiguous() ++ codebooks = codebooks.contiguous() ++ codes = codes.contiguous() ++ list_offsets = list_offsets.contiguous().to(torch.int64) ++ ++ # ── inverse map: group (query, list) pairs by list id ────────────── ++ flat = probed.reshape(-1).contiguous().to(torch.int64) # (P,) pair -> list id ++ P = flat.numel() ++ perm = torch.argsort(flat, stable=True) # sorted-pair -> orig-pair ++ sorted_qid = (perm // nprobe).to(torch.int32) ++ qcounts = torch.bincount(flat, minlength=nlist) ++ q_offsets = torch.zeros(nlist + 1, dtype=torch.int64, device=device) ++ q_offsets[1:] = qcounts.cumsum(0) ++ max_qcount = int(qcounts.max().item()) ++ MAX_QTILES = max(1, (max_qcount + BN - 1) // BN) ++ ++ TOPK_PAD = _next_pow2(k) ++ D_INNER = _next_pow2(Dp) if Dp <= 256 else 128 ++ MAX_STEPS = min(k, BM) ++ ++ pv_sorted = torch.full((P, TOPK_PAD), float("inf"), device=device, dtype=torch.float32) ++ pi_sorted = torch.full((P, TOPK_PAD), -1, device=device, dtype=torch.int32) ++ ++ _ivf_pq_decode_gemm_kernel[(nlist, MAX_QTILES)]( ++ Qp, centroids, sorted_qid, ++ q_offsets, list_offsets, ++ codes, codebooks, ++ pv_sorted, pi_sorted, ++ Qp.stride(0), Qp.stride(1), ++ centroids.stride(0), centroids.stride(1), ++ codes.stride(0), codes.stride(1), ++ codebooks.stride(0), codebooks.stride(1), codebooks.stride(2), ++ pv_sorted.stride(0), pv_sorted.stride(1), ++ pi_sorted.stride(0), pi_sorted.stride(1), ++ BY_RESIDUAL=by_residual, ++ MSUB=m, DSUB=dsub, DP=Dp, D_INNER=D_INNER, ++ BN=BN, BM=BM, ++ TOPK_PAD=TOPK_PAD, MAX_STEPS=MAX_STEPS, ++ num_warps=4, num_stages=num_stages, ++ ) ++ ++ # ── scatter partials to per-query order, merge nprobe partials ───── ++ pv = torch.empty_like(pv_sorted) ++ pi = torch.empty_like(pi_sorted) ++ pv[perm] = pv_sorted ++ pi[perm] = pi_sorted ++ pv = pv.view(nq, nprobe * TOPK_PAD) ++ pi = pi.view(nq, nprobe * TOPK_PAD) ++ ++ # Candidate pool by the (tf32-ranked) ADC score, then EXACT re-rank. ++ KK = min(_next_pow2(k * over), pv.shape[1]) ++ _, sel = pv.topk(KK, dim=-1, largest=False, sorted=False) ++ cand = pi.gather(-1, sel).to(torch.int64) # (nq, KK) stored rows ++ if cand.shape[1] < _next_pow2(k * over): ++ pad = torch.full((nq, _next_pow2(k * over) - cand.shape[1]), -1, ++ device=device, dtype=torch.int64) ++ cand = torch.cat([cand, pad], dim=1) ++ KK = cand.shape[1] ++ ++ # List of each candidate (for the residual rq) via CSR searchsorted. ++ clist = (torch.searchsorted(list_offsets, cand, right=True) - 1).clamp_(0, nlist - 1) ++ clist = torch.where(cand >= 0, clist, torch.zeros_like(clist)).to(torch.int32) ++ cand_i32 = cand.to(torch.int32) ++ ++ true_d = torch.empty((nq, KK), device=device, dtype=torch.float32) ++ _pq_rerank_kernel[(nq,)]( ++ Qp, centroids, codes, codebooks, cand_i32, clist, true_d, ++ Qp.stride(0), Qp.stride(1), ++ centroids.stride(0), centroids.stride(1), ++ codes.stride(0), codes.stride(1), ++ codebooks.stride(0), codebooks.stride(1), codebooks.stride(2), ++ cand_i32.stride(0), cand_i32.stride(1), ++ true_d.stride(0), true_d.stride(1), ++ BY_RESIDUAL=by_residual, MSUB=m, DSUB=dsub, ++ DP=_next_pow2(Dp), KK=KK, ++ num_warps=4, ++ ) ++ ++ vals, fsel = true_d.topk(k, dim=-1, largest=False, sorted=True) ++ pos = cand.gather(-1, fsel) ++ pos = torch.where(vals.isinf(), torch.full_like(pos, -1), pos) ++ return vals, pos ++ ++ ++__all__ = ["ivf_pq_fine_scan_gemm", "_avg_group_size"] +diff --git a/ivfpqlib/_kernels/lut.py b/ivfpqlib/_kernels/lut.py +new file mode 100644 +index 0000000..cebd5c5 +--- /dev/null ++++ b/ivfpqlib/_kernels/lut.py +@@ -0,0 +1,147 @@ ++"""IVF-PQ asymmetric distance lookup-table (LUT) builder. ++ ++For a query ``q`` probing list ``c`` (centroid ``cc``) the residual query ++is ``rq = q - cc`` (``by_residual``) or ``rq = q``. The LUT is ++ ++ LUT[s, j] = || rq_s - codebook[s, j] ||^2 (m, ksub) ++ ++i.e. the squared-L2 distance from each of the ``m`` residual-query ++sub-vectors to every one of the ``ksub = 256`` sub-centroids. A ++candidate with codes ``code`` then scores ``sum_s LUT[s, code[s]]`` -- ++this is the asymmetric distance computation (ADC) the fine-scan consumes. ++ ++One Triton program owns one ``(query, probe-slot, sub-quantizer)`` and ++writes that table's ``ksub`` row. The reduction over ``dsub`` is done in ++registers, so the only thing written to HBM is the compact ++``(nq, P, m, ksub)`` LUT itself -- never an ``(nq x candidates)`` matrix. ++``P == nprobe`` for residual encoding (the table depends on the probed ++list's centroid) and ``P == 1`` otherwise (the table depends only on the ++query, so the fine-scan reads it with a probe stride of 0). ++ ++The caller (:mod:`...ivf_pq.triton.search`) invokes this per **query ++tile**, so ``nq`` here is a tile of queries (``q_tile``), not the whole ++batch: the residual LUT grows with ``nprobe`` and would be enormous for a ++big batch (e.g. 42 GB at ``nq=10k, nprobe=64, m=64``), so search tiles ++over queries and only ever materialises one tile's table at a time. ++""" ++from __future__ import annotations ++ ++import torch ++import triton ++import triton.language as tl ++ ++from ivfpqlib._kernels._common import _next_pow2 ++ ++ ++@triton.jit ++def _pq_lut_kernel( ++ q_ptr, centroids_ptr, probed_ptr, codebook_ptr, ++ lut_ptr, ++ stride_q_n, stride_q_d, ++ stride_cent_c, stride_cent_d, ++ stride_pr_n, stride_pr_p, ++ stride_cb_m, stride_cb_j, stride_cb_d, ++ stride_lut_n, stride_lut_p, stride_lut_s, stride_lut_j, ++ BY_RESIDUAL: tl.constexpr, ++ DSUB: tl.constexpr, KSUB: tl.constexpr, ++ BJ: tl.constexpr, BD: tl.constexpr, ++): ++ """Grid: ``(nq, P, m)``. Writes ``lut[pid_n, pid_p, pid_s, 0:KSUB]``.""" ++ pid_n = tl.program_id(0).to(tl.int64) ++ pid_p = tl.program_id(1) ++ pid_s = tl.program_id(2) ++ ++ if BY_RESIDUAL: ++ c = tl.load(probed_ptr + pid_n * stride_pr_n + pid_p * stride_pr_p).to(tl.int64) ++ ++ lut_row = ( ++ lut_ptr ++ + pid_n * stride_lut_n ++ + pid_p * stride_lut_p ++ + pid_s * stride_lut_s ++ ) ++ ++ for j0 in range(0, KSUB, BJ): ++ j_off = j0 + tl.arange(0, BJ) ++ j_mask = j_off < KSUB ++ dist = tl.zeros([BJ], dtype=tl.float32) ++ for d0 in range(0, DSUB, BD): ++ d_off = d0 + tl.arange(0, BD) ++ d_mask = d_off < DSUB ++ d_global = (pid_s * DSUB + d_off).to(tl.int64) # into the Dp-wide row ++ qs = tl.load( ++ q_ptr + pid_n * stride_q_n + d_global * stride_q_d, ++ mask=d_mask, other=0.0, ++ ).to(tl.float32) # (BD,) ++ if BY_RESIDUAL: ++ cs = tl.load( ++ centroids_ptr + c * stride_cent_c + d_global * stride_cent_d, ++ mask=d_mask, other=0.0, ++ ).to(tl.float32) ++ rq = qs - cs ++ else: ++ rq = qs ++ cb = tl.load( ++ codebook_ptr ++ + pid_s * stride_cb_m ++ + j_off[:, None].to(tl.int64) * stride_cb_j ++ + d_off[None, :].to(tl.int64) * stride_cb_d, ++ mask=j_mask[:, None] & d_mask[None, :], other=0.0, ++ ).to(tl.float32) # (BJ, BD) ++ diff = rq[None, :] - cb # (BJ, BD) ++ dist += tl.sum(diff * diff, axis=1) # (BJ,) ++ tl.store(lut_row + j_off * stride_lut_j, dist, mask=j_mask) ++ ++ ++def pq_build_lut( ++ Qp: torch.Tensor, ++ centroids: torch.Tensor, ++ probed: torch.Tensor, ++ codebooks: torch.Tensor, ++ *, ++ by_residual: bool, ++ BJ: int = 64, ++) -> torch.Tensor: ++ """Build the ADC lookup tables. ++ ++ Args: ++ Qp: ``(nq, Dp)`` queries (fp32, padded to ``Dp``). ++ centroids: ``(nlist, Dp)`` coarse centroids (fp32). ++ probed: ``(nq, nprobe)`` int32 probed-list ids (coarse search). ++ codebooks: ``(m, ksub, dsub)`` PQ sub-centroids (fp32). ++ ++ Returns: ++ ``lut`` ``(nq, P, m, ksub)`` fp32 where ``P = nprobe`` if ++ ``by_residual`` else ``1``. ++ """ ++ nq = Qp.shape[0] ++ nprobe = probed.shape[1] ++ m, ksub, dsub = codebooks.shape ++ P = nprobe if by_residual else 1 ++ ++ Qp = Qp.contiguous() ++ centroids = centroids.contiguous() ++ codebooks = codebooks.contiguous() ++ probed = probed.contiguous().to(torch.int32) ++ ++ lut = torch.empty((nq, P, m, ksub), device=Qp.device, dtype=torch.float32) ++ BD = min(_next_pow2(dsub), 64) ++ ++ grid = (nq, P, m) ++ _pq_lut_kernel[grid]( ++ Qp, centroids, probed, codebooks, ++ lut, ++ Qp.stride(0), Qp.stride(1), ++ centroids.stride(0), centroids.stride(1), ++ probed.stride(0), probed.stride(1), ++ codebooks.stride(0), codebooks.stride(1), codebooks.stride(2), ++ lut.stride(0), lut.stride(1), lut.stride(2), lut.stride(3), ++ BY_RESIDUAL=bool(by_residual), ++ DSUB=dsub, KSUB=ksub, ++ BJ=BJ, BD=BD, ++ num_warps=4, ++ ) ++ return lut ++ ++ ++__all__ = ["pq_build_lut"] +diff --git a/ivfpqlib/_kernels/search.py b/ivfpqlib/_kernels/search.py +new file mode 100644 +index 0000000..2be7857 +--- /dev/null ++++ b/ivfpqlib/_kernels/search.py +@@ -0,0 +1,373 @@ ++"""IVF-PQ search (Triton/GPU path). Two fine-scan strategies, one API. ++ ++Every stage avoids the ``(nq x candidates)`` HBM matrix; the **coarse** ++step is shared -- :func:`klib.primitives.knn.flash_knn` over the ++``nlist`` centroids picks each query's ``nprobe`` nearest lists. The fine ++scan then takes one of two roads: ++ ++**1. No-LUT decode + GEMM** (``"gemm"``, the default for batched search) ++ The cluster-centric path the database asks for: group queries by the ++ list they probe (inverse map), then per ``(list, query-tile)`` *decode* ++ the list's PQ codes back to sub-vectors (gathering the tiny codebook, ++ shared across the tile) and score them with a tensor-core cross term -- ++ ADC as a **GEMM**, no lookup table at all. Distances are made ADC-exact ++ by an oversampled re-rank. This sidesteps the gather-throughput wall of ++ the LUT scan (3-12x faster on Hopper) *and* removes the LUT entirely, so ++ nothing scales with ``nprobe`` in memory. See ++ :mod:`...ivf_pq.triton.fine_scan_gemm`. ++ ++**2. ADC LUT scan** (``"online"`` / ``"batch"``, best for tiny batches) ++ Build the compact ``(BQ, P, m, 256)`` asymmetric-distance tables ++ (:func:`...ivf_pq.triton.lut.pq_build_lut`) and stream the probed codes, ++ ADC-scoring each candidate against the LUT with an on-chip top-k ++ (:mod:`...ivf_pq.triton.fine_scan`). The only structure that can blow up ++ is this LUT (residual: ``(nq, nprobe, m, 256)``, e.g. 42 GB at ++ ``nq=10k, nprobe=64, m=64``), so -- flash-attention style -- queries are ++ processed in ``q_tile`` blocks, each building and consuming only a ++ ``(q_tile, P, m, 256)`` LUT (bounded by :data:`_LUT_BUDGET_BYTES`) before ++ the next tile starts. The full LUT is never materialised and results are ++ identical to the untiled computation. ++ ++``"auto"`` (default) first picks the road -- the LUT scan for long PQ ++sub-vectors at modest batch, decode+GEMM for short sub-vectors *or* large ++batches (where it amortises each list's decode across the many queries ++probing it) -- then the implementation tier: the hand-written ++CuTe DSL kernels on Hopper (``cute_lut`` / ``cute_gemm``, ++:mod:`...ivf_pq.cutedsl`), the portable Triton kernels elsewhere ++(``online`` / ``gemm``). See :func:`_pick_variant`. At a fixed ++``(nlist, nprobe)`` and codebooks all variants return the same ADC ++ranking/distances (to fp tolerance) as a reference IVF-PQ; only the ++kernel implementation differs. ++""" ++from __future__ import annotations ++ ++from functools import lru_cache ++from typing import Optional ++ ++import torch ++ ++def is_cutedsl_available(): ++ # CuTe DSL Hopper tier disabled in the vendored path; the portable ++ # Triton LUT-scan / decode+GEMM kernels below are used everywhere. ++ return False ++from ivfpqlib.index import IvfPqIndex ++from ivfpqlib.build import _pad_features ++from ivfpqlib._kernels.fine_scan import ivf_pq_fine_scan ++from ivfpqlib._kernels.fine_scan_batch import ivf_pq_fine_scan_batch ++from ivfpqlib._kernels.fine_scan_gemm import ivf_pq_fine_scan_gemm ++from ivfpqlib._kernels.lut import pq_build_lut ++ ++ ++# Cap on the *live* ADC LUT (the only thing that scales with nq*nprobe). ++# Queries are tiled so a tile's (q_tile, P, m, 256) fp32 table stays under ++# this; 2 GiB keeps the table HBM/L2-friendly (and bounds the pathological ++# 42 GB residual LUT) while large enough that the tiling costs ~1% vs the ++# untiled path on typical batches. (Only the LUT variants tile; the default ++# "gemm" path builds no LUT and never needs it.) ++ ++def _coarse_probe(Qp, centroids, nprobe): ++ """Exact ``nprobe`` nearest coarse centroids per (padded) query. ++ ++ IVF-PQ's coarse step over the ``nlist`` centroids. Exact and a negligible ++ fraction of total search time, so a torch ``cdist`` + ``topk`` returns the ++ same probed lists as any brute-force KNN kernel. ++ """ ++ d2 = torch.cdist(Qp, centroids) ** 2 # (nq, nlist) ++ return d2.topk(nprobe, dim=1, largest=False).indices.to(torch.int32) ++ ++_LUT_BUDGET_BYTES = 1 << 31 # 2 GiB ++ ++# Decode+GEMM has a higher fixed floor than the LUT scan (~0.9 ms vs ++# ~0.45 ms: a host argsort of the nq*nprobe pairs, extra launches, an ++# exact re-rank), so for *tiny* batches / little total work the LUT scan ++# wins outright regardless of geometry: nq must clear _GEMM_MIN_NQ and the ++# candidate comparisons -- nq * nprobe * (M / nlist) -- must clear ++# _GEMM_MIN_WORK before the GEMM floor can be repaid. Calibrated on Hopper. ++_GEMM_MIN_NQ = 256 ++_GEMM_MIN_WORK = 2_000_000 ++ ++# Past the floor, routing is a crossover calibrated on Hopper (D=128/256/960, ++# re-swept after the coalesced ``cute_lut`` redesign roughly doubled its ++# crossover). Per probed candidate the LUT does ``m`` gathers; decode+GEMM ++# reconstructs ``D = m*dsub`` dims on the tensor cores, decoding each list ++# once and amortising it over the queries-per-list ``qpl = nq*nprobe/nlist``. ++# The LUT wins when both hold: ++# * sub-vectors aren't too short -- ``dsub >= _DSUB_LUT_MIN`` (tiny dsub ++# decodes cheaply on the tensor cores; e.g. SIFT dsub<=8 -> GEMM), and ++# * the batch per list is small enough -- ``qpl <= _QPL_LUT_SLOPE * dsub`` ++# (decode+GEMM's win region grows ~linearly in qpl). ++# Two geometries pick the LUT regardless of qpl (measured LUT-win out to the ++# swept qpl=512): ++# * many sub-quantizers ``m >= _M_LUT_MIN`` -- decode+GEMM's reconstruction ++# loop is unrolled over m and becomes the bottleneck (GIST m=64: cute_lut ++# ~2.6-6x faster than cute_gemm), and ++# * long sub-vectors ``dsub >= _DSUB_LUT_ALWAYS`` -- so few gathers per ++# candidate that the LUT stays ahead even at large batch. ++# vs the old ``qpl<=2*dsub`` this cuts mis-routes 13->3 / 48 swept points and ++# worst-case regret 294%->31% (the 294% case was GIST m=64 routed to GEMM). ++_DSUB_LUT_MIN = 9 ++_QPL_LUT_SLOPE = 4.0 ++_M_LUT_MIN = 48 ++_DSUB_LUT_ALWAYS = 48 ++ ++ ++def _auto_q_tile(nq: int, nprobe: int, m: int, by_residual: bool) -> int: ++ """Largest query tile whose LUT fits the budget (>= 256, <= nq).""" ++ P = nprobe if by_residual else 1 ++ per_query = P * m * 256 * 4 # fp32 LUT bytes for one query ++ bq = _LUT_BUDGET_BYTES // max(per_query, 1) ++ return int(max(256, min(nq, bq))) ++ ++ ++@lru_cache(maxsize=None) ++def _cutedsl_hopper() -> bool: ++ """True iff the CuTe DSL fine-scan kernels can run on this machine. ++ ++ They are hand-written for Hopper (SM90 WGMMA / shared-memory gathers) ++ and need the CUTLASS Python DSL; otherwise the router falls back to the ++ portable Triton kernels. Device arch is fixed per process, so cache it. ++ """ ++ if not is_cutedsl_available(): ++ return False ++ try: ++ return ( ++ torch.cuda.is_available() ++ and torch.cuda.get_device_properties(0).major >= 9 ++ ) ++ except Exception: ++ return False ++ ++ ++def _pick_regime( ++ nq: int, nprobe: int, avg_list_len: float, dsub: int, m: int, nlist: int, ++) -> str: ++ """Pick the fine-scan road: ``"lut"`` (ADC gather) or ``"gemm"``. ++ ++ Tiny batches / low total work don't amortise the GEMM floor -> LUT. ++ Short sub-vectors (``dsub < _DSUB_LUT_MIN``) decode cheaply on the tensor ++ cores -> GEMM. Many sub-quantizers (``m >= _M_LUT_MIN``) or long ++ sub-vectors (``dsub >= _DSUB_LUT_ALWAYS``) keep the LUT ahead at any ++ batch. Otherwise the LUT wins while the batch per list is small enough ++ (``qpl <= _QPL_LUT_SLOPE * dsub``). ++ """ ++ work = nq * nprobe * max(avg_list_len, 1.0) ++ if nq < _GEMM_MIN_NQ or work < _GEMM_MIN_WORK: ++ return "lut" ++ if dsub < _DSUB_LUT_MIN: ++ return "gemm" ++ if m >= _M_LUT_MIN or dsub >= _DSUB_LUT_ALWAYS: ++ return "lut" ++ qpl = nq * nprobe / max(nlist, 1) ++ if qpl <= _QPL_LUT_SLOPE * dsub: ++ return "lut" ++ return "gemm" ++ ++ ++def _pick_variant( ++ variant: str, nq: int, nprobe: int, avg_list_len: float, dsub: int, ++ m: int, nlist: int, ++) -> str: ++ """Resolve ``variant`` to a concrete fine-scan kernel. ++ ++ Explicit names pass through; ``"auto"`` first chooses the road ++ (:func:`_pick_regime`) then the implementation tier -- the fast CuTe ++ DSL kernels on Hopper, the portable Triton kernels elsewhere. ++ """ ++ if variant in ("gemm", "batch", "online", "cute_lut", "cute_gemm"): ++ return variant ++ if variant != "auto": ++ raise ValueError( ++ f"unknown variant {variant!r} " ++ "(auto|gemm|batch|online|cute_lut|cute_gemm)" ++ ) ++ regime = _pick_regime(nq, nprobe, avg_list_len, dsub, m, nlist) ++ if _cutedsl_hopper(): ++ return "cute_lut" if regime == "lut" else "cute_gemm" ++ return "online" if regime == "lut" else "gemm" ++ ++ ++def _search_gemm( ++ index: IvfPqIndex, ++ Qp: torch.Tensor, ++ centroids: torch.Tensor, ++ codebooks: torch.Tensor, ++ k: int, ++ nprobe: int, ++): ++ """No-LUT cluster-centric decode+GEMM search over the whole batch. ++ ++ Builds no ADC LUT, so there is nothing that scales with ``nprobe`` to ++ tile -- the only intermediate is the ``(nq*nprobe, k)`` partial table. ++ Returns ``(vals, ids)``. ++ """ ++ probed = _coarse_probe(Qp, centroids, nprobe) # (nq, nprobe) ++ vals, pos = ivf_pq_fine_scan_gemm( ++ Qp, centroids, codebooks, index.codes, probed, index.list_offsets, k, ++ by_residual=index.by_residual, ++ ) # (nq, k) ++ valid = pos >= 0 ++ pos_safe = pos.clamp_min(0) ++ ids = torch.where(valid, index.ids[pos_safe], torch.full_like(pos, -1)) ++ return vals, ids ++ ++ ++def _search_cute( ++ index: IvfPqIndex, ++ Qp: torch.Tensor, ++ centroids: torch.Tensor, ++ codebooks: torch.Tensor, ++ k: int, ++ nprobe: int, ++ method: str, ++): ++ """CuTe DSL fine scan: shared-memory ADC LUT (``cute_lut``) or ++ decode+WGMMA GEMM (``cute_gemm``). Coarse + reduce mirror the Triton ++ ``"gemm"`` path; only the fine-scan kernel differs. Returns ``(vals, ids)``. ++ """ ++ # Lazy import so non-CUTLASS environments still load the Triton path. ++ if method == "cute_lut": ++ from klib.primitives.ivf_pq.cutedsl.shared_lut import ( ++ ivf_pq_fine_scan_shared_lut as _fine, ++ ) ++ else: ++ from klib.primitives.ivf_pq.cutedsl.decode_gemm import ( ++ ivf_pq_fine_scan_decode_gemm as _fine, ++ ) ++ probed = _coarse_probe(Qp, centroids, nprobe) # (nq, nprobe) ++ vals, pos = _fine( ++ Qp, centroids, codebooks, index.codes, probed, index.list_offsets, k, ++ by_residual=index.by_residual, ++ ) ++ valid = pos >= 0 ++ pos_safe = pos.clamp_min(0) ++ ids = torch.where(valid, index.ids[pos_safe], torch.full_like(pos, -1)) ++ return vals, ids ++ ++ ++def _search_tile( ++ index: IvfPqIndex, ++ Qp: torch.Tensor, ++ centroids: torch.Tensor, ++ codebooks: torch.Tensor, ++ k: int, ++ nprobe: int, ++ variant: str, ++ max_list_len: int, ++): ++ """Coarse + LUT + fine-scan for one (already padded) query tile. ++ ++ Builds and consumes a single ``(BQ, P, m, 256)`` LUT, so the live ++ table is bounded by the tile size. Returns ``(vals, ids)``. ++ """ ++ # ── coarse: nprobe nearest centroids (lists) per query ───────────── ++ probed = _coarse_probe(Qp, centroids, nprobe) # (BQ, nprobe) ++ ++ # ── ADC lookup tables (compact, per-tile, no candidate matrix) ───── ++ lut = pq_build_lut( ++ Qp, centroids, probed, codebooks, by_residual=index.by_residual, ++ ) # (BQ, P, m, ksub) ++ ++ # ── fine: fused ragged-code scan + on-chip top-k ─────────────────── ++ # ``variant`` is already resolved to "online"/"batch" by the driver. ++ chosen = "batch" if variant == "batch" else "online" ++ if chosen == "batch": ++ vals, pos = ivf_pq_fine_scan_batch( ++ index.codes, probed, index.list_offsets, lut, k, ++ by_residual=index.by_residual, max_list_len=max_list_len, ++ ) ++ else: ++ vals, pos = ivf_pq_fine_scan( ++ index.codes, probed, index.list_offsets, lut, k, ++ by_residual=index.by_residual, max_list_len=max_list_len, ++ ) # (BQ, k) ++ ++ # Map stored-row positions back to original ids (guard -1 padding). ++ valid = pos >= 0 ++ pos_safe = pos.clamp_min(0) ++ ids = torch.where(valid, index.ids[pos_safe], torch.full_like(pos, -1)) ++ return vals, ids ++ ++ ++def ivf_pq_search_triton( ++ index: IvfPqIndex, ++ Q: torch.Tensor, ++ k: int, ++ *, ++ nprobe: Optional[int] = None, ++ variant: str = "auto", ++ q_tile: Optional[int] = None, ++): ++ """Search a built IVF-PQ index. Returns ``(vals, ids)``. ++ ++ Args: ++ index: a built :class:`IvfPqIndex`. ++ Q: ``(nq, D)`` query tensor on CUDA. ++ k: neighbours per query. ++ nprobe: lists to probe (defaults to ``index.nprobe``). ++ variant: fine-scan kernel. ``"auto"`` (default) routes by ++ sub-vector length and batch size to the best available kernel ++ (CuTe DSL on Hopper, Triton elsewhere; see ++ :func:`_pick_variant`). Explicit names: ``"cute_lut"`` / ++ ``"cute_gemm"`` are the Hopper CuTe DSL LUT / decode+GEMM ++ kernels; ``"gemm"`` is the portable Triton no-LUT decode+GEMM; ++ ``"online"`` / ``"batch"`` are the portable Triton ADC-LUT ++ gather kernels (best for tiny batches). All variants return the ++ same ADC distances to fp tolerance. ++ q_tile: queries per LUT tile (LUT variants only -- the ``"gemm"`` ++ path builds no LUT and ignores it). ``None`` picks the largest ++ tile whose ``(q_tile, P, m, 256)`` LUT fits the internal budget ++ (so the full LUT is never materialised); pass an int to override. ++ ++ Returns: ++ ``vals`` ``(nq, k)`` ADC squared-L2 (fp32) and ``ids`` ``(nq, k)`` ++ int64 original row ids (``-1`` padded where unavailable). ++ """ ++ if not Q.is_cuda or Q.ndim != 2: ++ raise ValueError("ivf_pq_search_triton requires a 2D CUDA tensor") ++ nprobe = int(nprobe or index.nprobe) ++ nprobe = max(1, min(nprobe, index.nlist)) ++ if not (1 <= k <= index.M): ++ raise ValueError(f"k must be in [1, M={index.M}] (got {k})") ++ ++ nq = Q.shape[0] ++ Qp_all = _pad_features(Q.to(torch.float32), index.Dp).contiguous() # (nq, Dp) ++ centroids = index.centroids.to(torch.float32) ++ codebooks = index.pq_codebooks.to(torch.float32) ++ max_list_len = index.max_list_len or int(index.list_lengths().max().item()) ++ avg_list_len = index.M / max(index.nlist, 1) ++ ++ # No-LUT decode+GEMM path: no LUT to materialise, so no query tiling. ++ chosen = _pick_variant( ++ variant, nq, nprobe, avg_list_len, index.dsub, index.m, index.nlist, ++ ) ++ if chosen == "gemm": ++ return _search_gemm(index, Qp_all, centroids, codebooks, k, nprobe) ++ if chosen in ("cute_lut", "cute_gemm"): ++ return _search_cute(index, Qp_all, centroids, codebooks, k, nprobe, chosen) ++ variant = chosen # "online" / "batch" -- passed straight to _search_tile ++ ++ if q_tile is None: ++ q_tile = _auto_q_tile(nq, nprobe, index.m, index.by_residual) ++ q_tile = max(1, min(int(q_tile), nq)) ++ ++ # Single tile: no extra allocations / copies (identical to untiled). ++ if q_tile >= nq: ++ return _search_tile( ++ index, Qp_all, centroids, codebooks, k, nprobe, variant, max_list_len, ++ ) ++ ++ # Flash-style query tiling: build + consume one LUT tile at a time. ++ out_vals = torch.empty((nq, k), device=Q.device, dtype=torch.float32) ++ out_ids = torch.empty((nq, k), device=Q.device, dtype=torch.int64) ++ for lo in range(0, nq, q_tile): ++ hi = min(lo + q_tile, nq) ++ vals, ids = _search_tile( ++ index, Qp_all[lo:hi].contiguous(), centroids, codebooks, ++ k, nprobe, variant, max_list_len, ++ ) ++ out_vals[lo:hi] = vals ++ out_ids[lo:hi] = ids ++ return out_vals, out_ids ++ ++ ++__all__ = ["ivf_pq_search_triton"] +diff --git a/ivfpqlib/search.py b/ivfpqlib/search.py +index 56b559b..8b32e5d 100644 +--- a/ivfpqlib/search.py ++++ b/ivfpqlib/search.py +@@ -1,78 +1,19 @@ +-"""Naive IVF-PQ search (pure torch reference -- the baseline you optimize). ++"""IVF-PQ search -- vendored best-in-repo Triton fine-scan path. + +-Coarse step: probe the ``nprobe`` nearest lists per query. Fine step: for each +-query, build the per-list residual ADC lookup table and score every probed +-candidate as ``sum_s LUT[s, code[s]]``, then keep the global top-``k``. The +-Python per-query loop is what makes this slow; a fast GPU implementation fuses +-the coarse search, LUT build, and ragged candidate scan into kernels. ++Delegates to the fused Triton LUT-scan / decode+GEMM kernels vendored under ++``ivfpqlib._kernels``. The coarse nprobe-nearest-list step is an exact torch ++cdist+topk. Public contract is unchanged: + + ivf_pq_search(index, Q, k, *, nprobe=None) -> (vals (nq,k) fp32, + ids (nq,k) int64, -1 padded) + """ + from __future__ import annotations + +-from typing import Optional ++from ivfpqlib._kernels.search import ivf_pq_search_triton + +-import torch + +-from ivfpqlib.build import _pad_features +- +-def ivf_pq_search(index, Q: torch.Tensor, k: int, *, nprobe: Optional[int] = None): +- """Reference coarse + ADC IVF-PQ search over a built index. +- +- Returns ``(vals, ids)`` where ``vals[i, j]`` is the (approximate) +- squared-L2 distance to the ``j``-th nearest PQ reconstruction and +- ``ids`` are original row ids. Mirrors the Triton kernel exactly: +- probe the ``nprobe`` nearest lists, build the per-(query, list) +- residual LUT, score each member as the sum of ``m`` table lookups, +- keep the global top-``k``. +- """ +- if Q.ndim != 2: +- raise ValueError("ivf_pq search expects a 2D (nq, D) query tensor") +- nprobe = int(nprobe or index.nprobe) +- nprobe = max(1, min(nprobe, index.nlist)) +- nq = Q.shape[0] +- Dp, m, dsub = index.Dp, index.m, index.dsub +- +- Qp = _pad_features(Q.to(torch.float32), Dp) +- centroids = index.centroids.to(torch.float32) # (nlist, Dp) +- codebooks = index.pq_codebooks.to(torch.float32) # (m, ksub, dsub) +- codes = index.codes # (M, m) uint8 +- offsets = index.list_offsets +- +- coarse_d2 = torch.cdist(Qp, centroids) ** 2 # (nq, nlist) +- probed = coarse_d2.topk(nprobe, dim=1, largest=False).indices # (nq, nprobe) +- +- out_vals = torch.full((nq, k), float("inf"), device=Q.device, dtype=torch.float32) +- out_ids = torch.full((nq, k), -1, device=Q.device, dtype=torch.int64) +- +- for i in range(nq): +- cand_dists = [] +- cand_ids = [] +- for p in range(nprobe): +- c = int(probed[i, p].item()) +- s0, e0 = int(offsets[c].item()), int(offsets[c + 1].item()) +- if e0 <= s0: +- continue +- rq = (Qp[i] - centroids[c]) if index.by_residual else Qp[i] # (Dp,) +- rq_sub = rq.reshape(m, dsub) # (m, dsub) +- # LUT[s, j] = ||rq_s - codebook[s, j]||^2 +- lut = ((rq_sub[:, None, :] - codebooks) ** 2).sum(-1) # (m, ksub) +- cc = codes[s0:e0].to(torch.int64) # (L, m) +- # dist[l] = sum_s LUT[s, cc[l, s]] +- dist = lut.gather(1, cc.t().contiguous()).sum(0) # (L,) +- cand_dists.append(dist) +- cand_ids.append(index.ids[s0:e0]) +- if not cand_dists: +- continue +- dist = torch.cat(cand_dists) +- ids = torch.cat(cand_ids) +- kk = min(k, dist.shape[0]) +- vals, sel = dist.topk(kk, largest=False) +- out_vals[i, :kk] = vals +- out_ids[i, :kk] = ids[sel] +- +- return out_vals, out_ids ++def ivf_pq_search(index, Q, k, *, nprobe=None): ++ return ivf_pq_search_triton(index, Q, k, nprobe=nprobe) + + + __all__ = ["ivf_pq_search"] diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py index 798a49db6..6243e65c8 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -75,25 +75,53 @@ def _materialize(files: dict, tag: str) -> str: ref = importlib.import_module(cfg["ref_module"]) pkg = importlib.import_module(cfg["pkg"]) - # ---- per-primitive: data gen, call, and quality metric ---------------- # - def gen(w, seed): + # ---- per-primitive: fixed context, data gen, call, quality metric ----- # + def setup(w, seed): + # Built ONCE per workload (not per timed iter). IVF-PQ builds the fixed + # index (the "database") here; queries are generated fresh per iteration. + if prim == "ivfpq": + g = torch.Generator(device=dev).manual_seed(int(seed)) + X = torch.randn(int(w["M"]), int(w["D"]), generator=g, device=dev, dtype=torch.float32) + index = ref.ivf_pq_build(X, int(w["nlist"]), m=int(w["m"]), + nprobe=int(w["nprobe"]), seed=int(seed)) + return {"index": index, "D": int(w["D"])} + return {} + + def gen(w, seed, ctx): g = torch.Generator(device=dev).manual_seed(int(seed)) + if prim == "ivfpq": + q = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, device=dev, dtype=torch.float32) + return {"queries": q} if prim == "knn": db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} + if prim == "dbscan": + nc, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) + centers = torch.randn(nc, D, generator=g, device=dev, dtype=torch.float32) * 10.0 + asg = torch.randint(0, nc, (N,), generator=g, device=dev) + xb = centers.index_select(0, asg) + torch.randn(N, D, generator=g, device=dev, dtype=torch.float32) * 0.5 + nz = int(float(w.get("noise_frac", 0.0)) * N) + if nz > 0: + lo = xb.min(0).values; hi = xb.max(0).values + xb[:nz] = lo + (hi - lo) * torch.rand(nz, D, generator=g, device=dev) + return {"x": xb, "eps": float(w["eps"]), "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) if prim == "kmeans": perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] return {"x": x, "init": x.index_select(0, perm).clone()} return {"x": x} - def call(mod, w, data): + def call(mod, w, data, ctx): + if prim == "ivfpq": + return mod.ivf_pq_search(ctx["index"], data["queries"], int(w["k"]), nprobe=int(w["nprobe"])) if prim == "kmeans": return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], init_centroids=data["init"], tol=0.0) if prim == "knn": return mod.knn(data["queries"], data["database"], w["k"]) + if prim == "dbscan": + return mod.dbscan(data["x"], data["eps"], data["min_samples"]) if prim == "pca": return mod.pca(data["x"], w["k"]) if prim == "tsvd": @@ -111,6 +139,14 @@ def check_shape(w, out): raise ValueError("bad knn output") if not torch.isfinite(d).all(): raise ValueError("non-finite distances") + elif prim == "ivfpq": + vals, ids = out + if tuple(ids.shape) != (int(w["Q"]), int(w["k"])) or \ + tuple(vals.shape) != (int(w["Q"]), int(w["k"])): + raise ValueError("bad ivfpq output shape") + elif prim == "dbscan": + if out.ndim != 1 or int(out.shape[0]) != int(w["N"]): + raise ValueError("bad dbscan labels shape") else: a, b = out comps = a if prim == "pca" else b @@ -132,6 +168,20 @@ def recall(agent_idx, ref_idx): hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + def ari(a, b): + # Adjusted Rand Index between two integer label vectors (N,); noise (-1) + # is treated as its own label (dense-remapped first). + a = torch.unique(a.long(), return_inverse=True)[1] + b = torch.unique(b.long(), return_inverse=True)[1] + na = int(a.max().item()) + 1; nb = int(b.max().item()) + 1 + cont = torch.bincount(a * nb + b, minlength=na * nb).reshape(na, nb).double() + ai = cont.sum(1); bj = cont.sum(0); n = cont.sum() + c2 = lambda z: z * (z - 1) / 2.0 + sij = c2(cont).sum(); sa = c2(ai).sum(); sb = c2(bj).sum() + exp = (sa * sb / c2(n)) if float(n) > 1 else 0.0 + den = 0.5 * (sa + sb) - exp + return float(((sij - exp) / den).item()) if float(den) != 0.0 else 1.0 + def ortho_err(comps): c = comps.to(torch.float32); k = c.shape[0] return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) @@ -148,9 +198,14 @@ def captured(x, comps, center): total += float((p * p).sum().item()) return total / (x.shape[0] - 1) if center else total - def verdict(w, data, ref_out, agent_out): + def verdict(w, data, ctx, ref_out, agent_out): """Return (ok, reason, ref_val, agent_val) gating the agent output.""" check_shape(w, agent_out) + if prim == "ivfpq": + # iso-result recall: agent ids vs frozen-baseline ids on the SAME index. + rec = recall(agent_out[1], ref_out[1]) + ok = rec >= float(cfg["recall_threshold"]) + return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) tol = float(cfg["inertia_tolerance"]) @@ -160,6 +215,10 @@ def verdict(w, data, ref_out, agent_out): rec = recall(agent_out[1], ref_out[1]) ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec + if prim == "dbscan": + val = ari(agent_out, ref_out) + ok = val >= float(cfg["ari_threshold"]) + return ok, ("ari_regression" if not ok else ""), 1.0, val center = (prim == "pca") comps = agent_out[0] if prim == "pca" else agent_out[1] ref_comps = ref_out[0] if prim == "pca" else ref_out[1] @@ -171,27 +230,28 @@ def verdict(w, data, ref_out, agent_out): ok = av >= (1.0 - float(cfg["captured_tolerance"])) * rv - 1e-6 return ok, ("captured_regression" if not ok else ""), rv, av - def time_call(mod, w, data): - _sync(); t0 = _perf(); out = call(mod, w, data); _sync() + def time_call(mod, w, data, ctx): + _sync(); t0 = _perf(); out = call(mod, w, data, ctx); _sync() return (_perf() - t0) * 1000.0, out rows = [] try: for w in payload["workloads"]: base_seed = int(w["seed"]) + ctx = setup(w, base_seed) # fixed context (e.g. IVF-PQ index) # warmup on fresh data (kernels / autotune) — not measured for i in range(warmup): - d = gen(w, base_seed + 100 + i) - call(ref, w, d); call(pkg, w, d); _sync() + d = gen(w, base_seed + 100 + i, ctx) + call(ref, w, d, ctx); call(pkg, w, d, ctx); _sync() del d torch.cuda.empty_cache() ratios, ref_val, agent_val = [], None, None bad = None for i in range(iters): - d = gen(w, base_seed + 10000 + i) # fresh every iteration - rt, rout = time_call(ref, w, d) - at, aout = time_call(pkg, w, d) - ok, reason, rv, av = verdict(w, d, rout, aout) # verify THIS iter + d = gen(w, base_seed + 10000 + i, ctx) # fresh every iteration + rt, rout = time_call(ref, w, d, ctx) + at, aout = time_call(pkg, w, d, ctx) + ok, reason, rv, av = verdict(w, d, ctx, rout, aout) # verify THIS iter if not ok: bad = reason; ref_val, agent_val = rv, av break diff --git a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py index 798a49db6..6243e65c8 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py @@ -75,25 +75,53 @@ def _materialize(files: dict, tag: str) -> str: ref = importlib.import_module(cfg["ref_module"]) pkg = importlib.import_module(cfg["pkg"]) - # ---- per-primitive: data gen, call, and quality metric ---------------- # - def gen(w, seed): + # ---- per-primitive: fixed context, data gen, call, quality metric ----- # + def setup(w, seed): + # Built ONCE per workload (not per timed iter). IVF-PQ builds the fixed + # index (the "database") here; queries are generated fresh per iteration. + if prim == "ivfpq": + g = torch.Generator(device=dev).manual_seed(int(seed)) + X = torch.randn(int(w["M"]), int(w["D"]), generator=g, device=dev, dtype=torch.float32) + index = ref.ivf_pq_build(X, int(w["nlist"]), m=int(w["m"]), + nprobe=int(w["nprobe"]), seed=int(seed)) + return {"index": index, "D": int(w["D"])} + return {} + + def gen(w, seed, ctx): g = torch.Generator(device=dev).manual_seed(int(seed)) + if prim == "ivfpq": + q = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, device=dev, dtype=torch.float32) + return {"queries": q} if prim == "knn": db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} + if prim == "dbscan": + nc, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) + centers = torch.randn(nc, D, generator=g, device=dev, dtype=torch.float32) * 10.0 + asg = torch.randint(0, nc, (N,), generator=g, device=dev) + xb = centers.index_select(0, asg) + torch.randn(N, D, generator=g, device=dev, dtype=torch.float32) * 0.5 + nz = int(float(w.get("noise_frac", 0.0)) * N) + if nz > 0: + lo = xb.min(0).values; hi = xb.max(0).values + xb[:nz] = lo + (hi - lo) * torch.rand(nz, D, generator=g, device=dev) + return {"x": xb, "eps": float(w["eps"]), "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) if prim == "kmeans": perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] return {"x": x, "init": x.index_select(0, perm).clone()} return {"x": x} - def call(mod, w, data): + def call(mod, w, data, ctx): + if prim == "ivfpq": + return mod.ivf_pq_search(ctx["index"], data["queries"], int(w["k"]), nprobe=int(w["nprobe"])) if prim == "kmeans": return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], init_centroids=data["init"], tol=0.0) if prim == "knn": return mod.knn(data["queries"], data["database"], w["k"]) + if prim == "dbscan": + return mod.dbscan(data["x"], data["eps"], data["min_samples"]) if prim == "pca": return mod.pca(data["x"], w["k"]) if prim == "tsvd": @@ -111,6 +139,14 @@ def check_shape(w, out): raise ValueError("bad knn output") if not torch.isfinite(d).all(): raise ValueError("non-finite distances") + elif prim == "ivfpq": + vals, ids = out + if tuple(ids.shape) != (int(w["Q"]), int(w["k"])) or \ + tuple(vals.shape) != (int(w["Q"]), int(w["k"])): + raise ValueError("bad ivfpq output shape") + elif prim == "dbscan": + if out.ndim != 1 or int(out.shape[0]) != int(w["N"]): + raise ValueError("bad dbscan labels shape") else: a, b = out comps = a if prim == "pca" else b @@ -132,6 +168,20 @@ def recall(agent_idx, ref_idx): hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + def ari(a, b): + # Adjusted Rand Index between two integer label vectors (N,); noise (-1) + # is treated as its own label (dense-remapped first). + a = torch.unique(a.long(), return_inverse=True)[1] + b = torch.unique(b.long(), return_inverse=True)[1] + na = int(a.max().item()) + 1; nb = int(b.max().item()) + 1 + cont = torch.bincount(a * nb + b, minlength=na * nb).reshape(na, nb).double() + ai = cont.sum(1); bj = cont.sum(0); n = cont.sum() + c2 = lambda z: z * (z - 1) / 2.0 + sij = c2(cont).sum(); sa = c2(ai).sum(); sb = c2(bj).sum() + exp = (sa * sb / c2(n)) if float(n) > 1 else 0.0 + den = 0.5 * (sa + sb) - exp + return float(((sij - exp) / den).item()) if float(den) != 0.0 else 1.0 + def ortho_err(comps): c = comps.to(torch.float32); k = c.shape[0] return float((c @ c.t() - torch.eye(k, device=c.device)).abs().max().item()) @@ -148,9 +198,14 @@ def captured(x, comps, center): total += float((p * p).sum().item()) return total / (x.shape[0] - 1) if center else total - def verdict(w, data, ref_out, agent_out): + def verdict(w, data, ctx, ref_out, agent_out): """Return (ok, reason, ref_val, agent_val) gating the agent output.""" check_shape(w, agent_out) + if prim == "ivfpq": + # iso-result recall: agent ids vs frozen-baseline ids on the SAME index. + rec = recall(agent_out[1], ref_out[1]) + ok = rec >= float(cfg["recall_threshold"]) + return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) tol = float(cfg["inertia_tolerance"]) @@ -160,6 +215,10 @@ def verdict(w, data, ref_out, agent_out): rec = recall(agent_out[1], ref_out[1]) ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec + if prim == "dbscan": + val = ari(agent_out, ref_out) + ok = val >= float(cfg["ari_threshold"]) + return ok, ("ari_regression" if not ok else ""), 1.0, val center = (prim == "pca") comps = agent_out[0] if prim == "pca" else agent_out[1] ref_comps = ref_out[0] if prim == "pca" else ref_out[1] @@ -171,27 +230,28 @@ def verdict(w, data, ref_out, agent_out): ok = av >= (1.0 - float(cfg["captured_tolerance"])) * rv - 1e-6 return ok, ("captured_regression" if not ok else ""), rv, av - def time_call(mod, w, data): - _sync(); t0 = _perf(); out = call(mod, w, data); _sync() + def time_call(mod, w, data, ctx): + _sync(); t0 = _perf(); out = call(mod, w, data, ctx); _sync() return (_perf() - t0) * 1000.0, out rows = [] try: for w in payload["workloads"]: base_seed = int(w["seed"]) + ctx = setup(w, base_seed) # fixed context (e.g. IVF-PQ index) # warmup on fresh data (kernels / autotune) — not measured for i in range(warmup): - d = gen(w, base_seed + 100 + i) - call(ref, w, d); call(pkg, w, d); _sync() + d = gen(w, base_seed + 100 + i, ctx) + call(ref, w, d, ctx); call(pkg, w, d, ctx); _sync() del d torch.cuda.empty_cache() ratios, ref_val, agent_val = [], None, None bad = None for i in range(iters): - d = gen(w, base_seed + 10000 + i) # fresh every iteration - rt, rout = time_call(ref, w, d) - at, aout = time_call(pkg, w, d) - ok, reason, rv, av = verdict(w, d, rout, aout) # verify THIS iter + d = gen(w, base_seed + 10000 + i, ctx) # fresh every iteration + rt, rout = time_call(ref, w, d, ctx) + at, aout = time_call(pkg, w, d, ctx) + ok, reason, rv, av = verdict(w, d, ctx, rout, aout) # verify THIS iter if not ok: bad = reason; ref_val, agent_val = rv, av break diff --git a/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md b/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md deleted file mode 100644 index 93dfa04e6..000000000 --- a/2.0/problems/pca_gpu_kernel_optimization/DESIGN.md +++ /dev/null @@ -1,95 +0,0 @@ -# Design notes — pca_gpu_kernel_optimization - -Operator-facing. Not copied into the agent workspace by the adapter. - -## What this task measures - -Can an agent turn a naive GPU PCA into a fast one? The agent patches `pcalib`; -the judge times the patched `pca` against a frozen naive baseline on hidden -`(N, D, k)` workloads and scores the geometric-mean speedup, gated on subspace -quality (orthonormal components + captured variance). One of a four-task -**flashlib kernel-optimization family** (KMeans, KNN, TruncatedSVD, PCA) that all -share one evaluator + one Modal GPU harness. - -## Execution: Modal GPU offload (mirrors the vllm task) - -The judge and the agent public test both **offload GPU work to Modal**; the -containers are light (ubuntu + `modal` + git, no torch/triton). `flash_gpu.py` -(baked at `/opt/flash_gpu.py` in both images) builds an ephemeral Modal app on a -GPU (`evaluation.gpu`, default H100), ships the frozen baseline + the patched -package **as data** (no per-submission image rebuild), runs the worker, and -returns per-workload speedups + verdicts. Needs `MODAL_TOKEN_ID` / -`MODAL_TOKEN_SECRET`. torch/triton/the vendored kernels run on the Modal image -(`evaluation.pip`). - -The evaluator is generic and identical across the four tasks; only three -constants differ (`PRIMITIVE`/`PKG`/`REF_MODULE`), and all workload/threshold/ -Modal values come from `config.yaml` (delivered judge-only as -`/judge/task_config.json`, never present in the agent image). - -## Reference solution = best in the repo - -`reference.patch` vendors flashlib's own optimized **Triton** PCA path -(`primitives/pca/triton/pca.py` + the `linalg/eigh` cuSOLVER/MKL/Triton-Jacobi -stack it depends on) under `pcalib/_kernels/…`, with imports rewritten and the -string "flashlib" scrubbed, plus a thin adapter mapping our -`pca(x, n_components) -> (components, explained_variance)` contract onto -flashlib's entry (which returns eigenpairs of the small cov/Gram matrix). It is -triton-only (runs on any sm≥80), imports cleanly on CPU (kernels compile lazily), -and applies + passes the patch policy. Calibrate `speedup_target` from its -measured geomean on the first GPU trial. - -## Anti-reward-hack - -The **load-bearing** defenses are structural, inside the GPU worker (the only -place the untrusted submission runs): - -* timing primitives (`perf_counter`, `torch.cuda.synchronize`) are captured to - locals **before** the submission is imported → monkey-patching torch cannot - affect measurement; -* **fresh data every timed iteration** → memoizing on a repeated input is - useless; -* quality is **re-verified from the returned tensors on every iteration** → - returning a stale cached result for a different input is caught; -* the judge computes all metrics (no agent number is trusted); -* an ephemeral Modal container per submission → no global state persists. - -The patch policy is surgical defense-in-depth (so it does not reject legitimate -vendored/optimized kernel source): only `/**` `.py` files may change; it -bans external-optimized-library *imports* (flashlib/cuml/cupy/faiss/sklearn/ -cutlass — none installed on the GPU image anyway) and process/network/ -measurement-tamper patterns (`subprocess`, `socket`, `os.system`, -`torch.cuda.synchronize =`, …). - -The agent-facing `readme` describes the contract, gate, scoring, and policy but -**not** how the reference is implemented. - -## Correctness gate - -Two judge-computed parts, evaluated from the *components the submission returns* -(not from any agent-provided number), on each iteration's seeded `x`: - -* **Orthonormality** — `max |V Vᵀ − I|` over the returned `(k, D)` components must - not exceed `ortho_tolerance`. Blocks returning a rank-deficient or - non-orthonormal "subspace" to cheat the variance term. -* **Captured variance** — `(1/(N−1)) ||X_c V||_F²`, where `X_c` is the - mean-centred data, must be at least `(1 − captured_tolerance)` of the - baseline's captured variance. - -Rotation/sign/basis-convention independent (only the spanned subspace matters), -robust to fp drift, cheat-proof: high captured variance requires actually -recovering the leading principal subspace. Data is a fixed function of -`(N, D, seed)` with seeds derived from `base_seed`, so the naive baseline is -reproducible. Note: `pca` returns `(components, explained_variance)`, so -`components` is the **first** tuple element (the worker reads `agent_out[0]` for -the subspace-quality checks). - -## Calibration TODO (needs a Modal GPU trial) - -- Run `reference.patch` on Modal; confirm it passes the orthonormality + captured - variance gates on all workloads (bump `captured_tolerance` / `ortho_tolerance` - if the low-precision matmul path drifts slightly) and set `speedup_target` from - its geomean. -- Confirm the pinned `torch`/`triton` in `evaluation.pip` compile the vendored - kernels, and hidden shapes fit the GPU. -- Confirm Modal token wiring in the Harbor judge/agent containers. diff --git a/2.0/problems/pca_gpu_kernel_optimization/config.yaml b/2.0/problems/pca_gpu_kernel_optimization/config.yaml deleted file mode 100644 index a97d078d5..000000000 --- a/2.0/problems/pca_gpu_kernel_optimization/config.yaml +++ /dev/null @@ -1,56 +0,0 @@ -tag: systems -runtime: - language: python - timeout_seconds: 10800 - environment: "GPU PCA kernel optimization. The agent patches the pcalib package; the judge offloads timing to a Modal GPU, comparing the patched pca against a frozen naive baseline on hidden (N, D, k) workloads with an orthonormal-components + captured-variance quality gate." - apt_packages: - - bash - - ca-certificates - - git - - python3 - - python3-pip - judge_apt_packages: - - bash - - ca-certificates - - git - - python3 - - python3-pip - judge_pip_packages: - - modal - docker: - # Experimental local images. Build with docker/build_images.sh before a local - # Harbor trial. Both are light (ubuntu + modal + git); torch/triton/flashlib - # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes - # /app/pcalib (git) + /opt/pca_ref + /opt/flash_gpu.py; the judge image - # additionally bakes /opt/pcalib-clean. - image: frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.2.0 - judge_image: frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.2.0 -environment: - cpus: 4 - memory_mb: 8192 - storage_mb: 16384 - build_timeout_seconds: 3600 -evaluation: - gpu: "H100" - cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" - pip: ["torch", "numpy"] - app_name: "pca-kernel-opt-eval" - modal_timeout_seconds: 1800 - warmup_iters: 2 - timed_iters: 7 - captured_tolerance: 0.02 - ortho_tolerance: 0.02 - speedup_target: 7.0 # calibrated: reference (flashlib-best) geomean ~7.7x on Modal H100 - base_seed: 20260701 - agent_workload_count: 3 - expose_per_workload_metrics: false - workloads: - - {id: w0, N: 500000, D: 128, k: 16} - - {id: w1, N: 1000000, D: 64, k: 8} - - {id: w2, N: 200000, D: 512, k: 32} - - {id: w3, N: 2000000, D: 32, k: 8} - - {id: w4, N: 300000, D: 256, k: 16} - - {id: w5, N: 800000, D: 128, k: 32} -submission: - kind: file - path: /app/solution.patch diff --git a/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh deleted file mode 100755 index 0ef0be6c3..000000000 --- a/2.0/problems/pca_gpu_kernel_optimization/docker/smoke_images.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU / Modal required). -set -euo pipefail - -AGENT_TAG=${AGENT_TAG:-frontiercs/pca-gpu-kernel-optimization-agent:experimental-v0.2.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/pca-gpu-kernel-optimization-judge:experimental-v0.2.0} - -echo "== agent image ==" -docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/pca_ref'); import refpca; \ -import pcalib; print('agent ok: modal + flash_gpu + refpca + pcalib import')" - -echo "== judge image ==" -docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/pca_ref'); import refpca; \ -sys.path.insert(0, '/opt/pcalib-clean'); import pcalib; \ -print('judge ok: modal + flash_gpu + refpca + pcalib import')" diff --git a/2.0/problems/pca_gpu_kernel_optimization/judge/refpca.py b/2.0/problems/pca_gpu_kernel_optimization/judge/refpca.py deleted file mode 100644 index 7a2a5fe31..000000000 --- a/2.0/problems/pca_gpu_kernel_optimization/judge/refpca.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Frozen naive PCA baseline used by the judge as the speed denominator. - -This is a standalone (non-package) copy of the ``pcalib.pca`` implementation -the agent starts from. It is imported under its own module name so the judge -worker can load the frozen baseline and the patched ``pcalib`` package in the -same process. Keep this behaviourally identical to the shipped -``pcalib/pca.py``. -""" -from __future__ import annotations - -import torch - - -def pca(x, n_components): - N = x.shape[0] - mean = x.mean(dim=0) - xc = x - mean # materialized centered matrix -- naive - U, S, Vh = torch.linalg.svd(xc, full_matrices=False) # full SVD -- expensive - k = int(n_components) - components = Vh[:k].contiguous() - explained_variance = (S[:k] ** 2) / (N - 1) - return components, explained_variance.contiguous() diff --git a/2.0/problems/pca_gpu_kernel_optimization/pcalib/__init__.py b/2.0/problems/pca_gpu_kernel_optimization/pcalib/__init__.py deleted file mode 100644 index 002e4c7c7..000000000 --- a/2.0/problems/pca_gpu_kernel_optimization/pcalib/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -"""pcalib -- a tiny GPU PCA library you are asked to make fast. - -The public entry point is :func:`pca`. The shipped implementation is correct -but deliberately unoptimised (it materialises the centred data matrix and runs -a full SVD). Your task is to rewrite the internals (covariance/Gram + top-k -eigendecomposition, fused centring, new Triton kernels, ...) so that -:func:`pca` runs as fast as possible while producing the same principal -subspace. The public function signature and return contract must not change. -""" -from __future__ import annotations - -from pcalib.pca import pca - -__all__ = ["pca"] -__version__ = "0.1.0" diff --git a/2.0/problems/pca_gpu_kernel_optimization/pcalib/pca.py b/2.0/problems/pca_gpu_kernel_optimization/pcalib/pca.py deleted file mode 100644 index ec891a2e3..000000000 --- a/2.0/problems/pca_gpu_kernel_optimization/pcalib/pca.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Principal Component Analysis (PCA) -- the reference you must optimise. - -This implementation is intentionally naive. It centres the data by explicitly -materialising the full ``(N, D)`` centred matrix and then runs a *full* thin -SVD (:func:`torch.linalg.svd`) just to read off the top ``k`` singular vectors -and singular values. It is correct and deterministic, but it computes the -entire spectrum when only the leading ``k`` components are needed and it moves -far more memory than necessary (the centred copy of the whole dataset). - -Contract (do NOT change): - - pca(x, n_components) -> (components, explained_variance) - - x : (N, D) float32 CUDA tensor of points. - n_components (k): int, number of leading principal components to return. - - components : (k, D) float32 tensor whose rows are the top-k - principal axes (the leading eigenvectors of the - covariance matrix), orthonormal rows. - explained_variance : (k,) float32 tensor of the corresponding covariance - eigenvalues, in descending order. With the unbiased - (``N - 1``) normalisation this equals - ``S[:k] ** 2 / (N - 1)`` for singular values ``S`` of - the centred data. - -You may add modules/kernels inside the ``pcalib`` package and rewrite the body -of :func:`pca` freely (covariance/Gram + top-k eigendecomposition, fused -centring, Triton kernels, ...), as long as the public contract above is -preserved. -""" -from __future__ import annotations - -import torch - - -def pca(x, n_components): - N = x.shape[0] - mean = x.mean(dim=0) - xc = x - mean # materialized centered matrix -- naive - U, S, Vh = torch.linalg.svd(xc, full_matrices=False) # full SVD -- expensive - k = int(n_components) - components = Vh[:k].contiguous() - explained_variance = (S[:k] ** 2) / (N - 1) - return components, explained_variance.contiguous() diff --git a/2.0/problems/pca_gpu_kernel_optimization/readme b/2.0/problems/pca_gpu_kernel_optimization/readme deleted file mode 100644 index 45e80fb4b..000000000 --- a/2.0/problems/pca_gpu_kernel_optimization/readme +++ /dev/null @@ -1,130 +0,0 @@ -# GPU PCA Kernel Optimization - -## Problem - -You are given a small GPU PCA library, `pcalib`, in the Harbor workspace -at `/app/pcalib`. Its public entry point is: - -```python -pcalib.pca(x, n_components) -> (components, explained_variance) -``` - -`x` is an `(N, D)` float32 CUDA tensor of points and `n_components` (`k`) is the -number of leading principal components to return. The function returns -`components`, a `(k, D)` float32 tensor whose rows are the top-`k` principal axes -(orthonormal rows), and `explained_variance`, a `(k,)` float32 tensor of the -corresponding variances in descending order. The shipped implementation is a -straightforward, correct PyTorch version. - -Your goal is to make `pcalib.pca` **as fast as possible** on the GPU while -producing the same principal subspace. You may rewrite the internals of the -package however you like and add new modules (including Triton kernels) under -`pcalib/`. The public function signature and return contract above must not -change, and every result must remain a deterministic function of its inputs. - -## Workload - -The graded workloads are a family of held-out dense PCA problems that vary -`(N, D, k)`, spanning small/medium/large point counts and both wide-feature -(large `D`) and tall/skinny (large `N`, small `D`) regimes. All use float32 data -and a fixed number of components, so each result is a deterministic function of -its inputs. - -Two representative *public* shapes are available for local testing (the graded -shapes are different and hidden): - -```text -(N=200000, D=128, k=16) -(N=500000, D=64, k=8) -``` - -Treat this as a general dense PCA, not as specific shapes to special-case. - -## Iterate on a GPU - -The agent workspace has no GPU. Use the public test to time your current code on -a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your -environment): - -```bash -bash /app/public_test.sh -``` - -It reports, per public shape, whether your result passes the quality gate and -your speedup over the baseline. - -## Submission - -The submitted artifact is a patch over the `pcalib` package: - -```text -/app/solution.patch -``` - -After editing `/app/pcalib`, generate and submit: - -```bash -bash /app/make_submission.sh -bash /app/submit.sh -``` - -Submissions are asynchronous; submit early and keep iterating. The judge applies -your patch to a clean copy of `pcalib` and times it against the original -baseline on a GPU, on the same seeded data. - -## Correctness - -Correctness is a gate. On every timed iteration the judge recomputes, from the -components your function returns on identical data, two properties: - -- **Orthonormality** — the rows of `components` must be orthonormal (the largest - entry of `|V Vᵀ − I|` must stay within a small tolerance). -- **Captured variance** — the variance captured by your subspace on the same - mean-centred data must stay within a small relative tolerance of the baseline's - captured variance. - -Because the gate depends only on the *subspace* your components span (not on any -sign or rotation convention), you cannot pass it with fast garbage — capturing -the variance requires actually recovering the leading principal subspace. -Crashes, non-finite output, wrong shapes/dtypes, timeouts, and subspaces that -regress beyond the tolerance are penalized before speed is considered. You may -trade a little numerical precision for speed as long as you stay within the -quality tolerance. - -## Scoring - -Valid submissions are scored by speedup relative to the baseline on the same -hardware and workloads. For each workload: - -```text -speedup = baseline_time / your_time -``` - -The objective is the geometric mean of per-workload speedups, so broad speedups -are preferred over a single large outlier. A result no faster than the baseline -earns 0; regressions earn 0. The raw geometric-mean speedup is reported in the -evaluator metrics. - -## Patch Policy - -The evaluator validates the patch before running it. Only Python files under the -package may change: - -```text -pcalib/** -``` - -New Python modules inside `pcalib/` are allowed. Patches may not: modify -anything outside `pcalib/`; import or call an external optimized ML/kernel -library (write the kernels yourself); read or write environment variables, spawn -processes, or access the network; or otherwise tamper with the measurement -framework. The timing harness measures your code on freshly generated data every -iteration and re-verifies quality each time, so caching results across calls does -not help. - -## Resource Budget - -```text -GPU: single Modal GPU (H100 reference; the Triton paths also run on L40S / A100) -Agent container: CPU-only (GPU work is offloaded to Modal) -``` diff --git a/2.0/problems/pca_gpu_kernel_optimization/reference.patch b/2.0/problems/pca_gpu_kernel_optimization/reference.patch deleted file mode 100644 index 62f6a6abe..000000000 --- a/2.0/problems/pca_gpu_kernel_optimization/reference.patch +++ /dev/null @@ -1,1046 +0,0 @@ -diff --git a/pcalib/_kernels/__init__.py b/pcalib/_kernels/__init__.py -new file mode 100644 -index 0000000..e69de29 -diff --git a/pcalib/_kernels/linalg/__init__.py b/pcalib/_kernels/linalg/__init__.py -new file mode 100644 -index 0000000..e69de29 -diff --git a/pcalib/_kernels/linalg/eigh/__init__.py b/pcalib/_kernels/linalg/eigh/__init__.py -new file mode 100644 -index 0000000..1a2431a ---- /dev/null -+++ b/pcalib/_kernels/linalg/eigh/__init__.py -@@ -0,0 +1,39 @@ -+"""Symmetric eigendecomposition with multiple precision/performance variants. -+ -+Public API -+---------- -+Single dispatcher (recommended): -+ -+ eigh(A, K=None, *, tol=None, backend=None) -+ -+By default ``eigh(A)`` is **exact** in the input dtype (cuSOLVER / -+single-kernel Jacobi for small ``N``). ``tol`` opts into the -+approximation tier: -+ -+ eigh(A, tol=8e-4) -> QDWH-NS at N >= 5120 -+ eigh(A, tol=3e-3) -> QDWH at N >= 5120 -+ eigh(A, K=K, tol=1e-4) -> Halko subspace iteration when K << N -+ -+Backend-explicit (power users): -+ -+ eigh_cusolver(A) torch.linalg.eigh -- ~1e-7 residual -+ eigh_jacobi(A, ...) single-block Jacobi for small N -+ eigh_qdwh(A, ...) Nakatsukasa-Higham spectral D&C, ~3e-3 -+ eigh_qdwh_ns(A, ...) QDWH with pure-NS polar, ~8e-4 -+ eigh_halko(A, K, ...) Halko randomized truncated eigh -+""" -+# Only the exact ``tol=None`` path is vendored here: cuSOLVER / MKL and the -+# Triton single-CTA Jacobi backend. The approximate variants (Halko, QDWH, -+# QDWH-NS) and the cost models are intentionally not vendored, so their lazy -+# imports are dropped. -+from pcalib._kernels.linalg.eigh.impl import eigh, route_op_name -+from pcalib._kernels.linalg.eigh.cusolver import eigh as eigh_cusolver -+from pcalib._kernels.linalg.eigh.jacobi import eigh as eigh_jacobi -+ -+ -+__all__ = [ -+ "eigh", -+ "eigh_cusolver", -+ "eigh_jacobi", -+ "route_op_name", -+] -diff --git a/pcalib/_kernels/linalg/eigh/cost.py b/pcalib/_kernels/linalg/eigh/cost.py -new file mode 100644 -index 0000000..5cb13d4 ---- /dev/null -+++ b/pcalib/_kernels/linalg/eigh/cost.py -@@ -0,0 +1,100 @@ -+"""Cost models for eigh -- smart dispatcher + per-variant. -+ -+Each function takes ``tol`` directly. Returned Estimates have ``op_name`` -+set to the routed variant (e.g. ``eigh_qdwh``) so the call-stack tree is -+clear. -+ -+The dispatcher in :mod:`pcalib._kernels.linalg.eigh.impl` decides which -+variant runs at runtime; this module mirrors that decision via -+:func:`route_op_name`. -+""" -+from pcalib._kernels.info.estimate import Estimate -+from pcalib._kernels.info.roofline import roofline -+from pcalib._kernels.linalg.eigh import cusolver as _cusolver -+from pcalib._kernels.linalg.eigh import jacobi as _jacobi -+from pcalib._kernels.linalg.eigh import halko as _halko -+from pcalib._kernels.linalg.eigh.impl import route_op_name as _route_op_name -+ -+ -+def _N(shape): -+ return shape[0] if isinstance(shape, (tuple, list)) else shape -+ -+ -+def estimate(shape, params=None, tol=None, dtype="float32", device="H100", **_): -+ """Cost of the smart eigh() dispatcher — picks the actually-routed variant.""" -+ N = _N(shape) -+ K = (params or {}).get("K") -+ chosen = _route_op_name(N=N, K=K, tol=tol) -+ if chosen == "eigh_jacobi": -+ est = _jacobi.estimate(shape, params=params, tol=tol, dtype=dtype, device=device) -+ elif chosen == "eigh_qdwh": -+ est = qdwh(shape, params=params, tol=tol, dtype=dtype, device=device) -+ elif chosen == "eigh_qdwh_ns": -+ est = qdwh_ns(shape, params=params, tol=tol, dtype=dtype, device=device) -+ elif chosen == "eigh_halko": -+ est = _halko.estimate(shape, params=params, tol=tol, dtype=dtype, device=device) -+ else: -+ est = _cusolver.estimate(shape, params=params, tol=tol, dtype=dtype, device=device) -+ # Make the routed variant's name show up at this level of the call tree. -+ est.op_name = chosen -+ est.tol = tol -+ return est -+ -+ -+def qdwh(shape, params=None, tol=None, dtype="float32", device="H100", **_): -+ """QDWH-eig — Nakatsukasa-Higham spectral D&C, ~1e-3 residual.""" -+ N = _N(shape) -+ rt = 370.0 * (N / 8192) ** 3 -+ flops = int((20 / 3) * N ** 3) -+ bytes_moved = N * N * 4 * 16 -+ return Estimate( -+ op_name="eigh_qdwh", -+ runtime_ms=rt, flops=flops, bytes_moved=bytes_moved, -+ memory_peak_gb=N * N * 4 * 6 / 1e9, -+ bound="compute", confidence="measured", n_kernel_launches=30, -+ suggested_config={"base_case": 1024, "max_depth": 1 if N < 10240 else 2}, -+ notes=[f"N={N}; QDWH spectral D&C, recursive split via polar factor."], -+ expected_residual=3e-3, precision_tier="fast", tol=tol, -+ ) -+ -+ -+def qdwh_ns(shape, params=None, tol=None, dtype="float32", device="H100", **_): -+ """QDWH-eig-NS — pure Newton-Schulz polar (matmul-only critical path).""" -+ N = _N(shape) -+ rt = 700.0 * (N / 8192) ** 3 -+ flops = int((30 / 3) * N ** 3) -+ bytes_moved = N * N * 4 * 24 -+ return Estimate( -+ op_name="eigh_qdwh_ns", -+ runtime_ms=rt, flops=flops, bytes_moved=bytes_moved, -+ memory_peak_gb=N * N * 4 * 6 / 1e9, -+ bound="compute", confidence="measured", n_kernel_launches=50, -+ suggested_config={}, -+ notes=[f"N={N}; QDWH spectral D&C with pure-NS polar (Polar Express)."], -+ expected_residual=8e-4, precision_tier="mixed", tol=tol, -+ ) -+ -+ -+def recommend(shape, params=None, tol=None, dtype="float32", device="H100", **_): -+ """Picks the dispatcher's choice; mirrors the routing in eigh/__init__.py.""" -+ N = _N(shape) -+ K = (params or {}).get("K") -+ chosen = _route_op_name(N=N, K=K, tol=tol) -+ out = {"variant": chosen} -+ if chosen == "eigh_qdwh": -+ out["base_case"] = 1024 -+ out["max_depth"] = 1 if N < 10240 else 2 -+ elif chosen == "eigh_halko": -+ out["K"] = K -+ out["n_iter"] = 5 -+ out["p"] = 30 -+ return out -+ -+ -+def recommend_qdwh(shape, **_): -+ N = _N(shape) -+ return {"base_case": 1024, "max_depth": 1 if N < 10240 else 2} -+ -+ -+def recommend_qdwh_ns(shape, **_): -+ return {"base_case": 1024} -diff --git a/pcalib/_kernels/linalg/eigh/cusolver.py b/pcalib/_kernels/linalg/eigh/cusolver.py -new file mode 100644 -index 0000000..354ac9a ---- /dev/null -+++ b/pcalib/_kernels/linalg/eigh/cusolver.py -@@ -0,0 +1,7 @@ -+"""eigh_cusolver — torch.linalg.eigh (cuSOLVER syevd). Reference precision.""" -+import torch -+ -+ -+def eigh(A: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: -+ """torch.linalg.eigh — cuSOLVER syevd. ~1e-7 residual.""" -+ return torch.linalg.eigh(A) -diff --git a/pcalib/_kernels/linalg/eigh/impl.py b/pcalib/_kernels/linalg/eigh/impl.py -new file mode 100644 -index 0000000..430116f ---- /dev/null -+++ b/pcalib/_kernels/linalg/eigh/impl.py -@@ -0,0 +1,164 @@ -+"""eigh dispatcher. -+ -+By default ``eigh(A)`` is **exact** (cuSOLVER / MKL on the input dtype) -- -+no precision is lost beyond what the input already carries. ``tol`` opts -+into approximation paths (Halko subspace iteration, QDWH spectral D&C). -+ -+Routing rule (formerly in ``route.py``): -+ -+ * ``backend`` overrides everything. -+ * ``tol`` is None / ``<= 0``: exact. -+ * Truncated (``K`` given) : exact full eigh + slice top-K. -+ * Full : ``triton_eigh`` (CPU MKL trick for -+ small N) or cuSOLVER otherwise. -+ * ``tol`` is given: pick the fastest variant whose published residual -+ fits. -+ * ``K`` given AND favourable shape (``K*4 < N`` AND -+ ``N >= 256``) AND ``tol >= 1e-4`` -> Halko. -+ * ``N >= 5120`` AND ``tol >= 8e-4`` -> QDWH-NS. -+ * ``N >= 5120`` AND ``tol >= 3e-3`` -> QDWH. -+ * Otherwise: cuSOLVER (exact is always fast enough at this point). -+ -+The Halko shape gate is internal -- callers just hand in ``(K, tol)``; -+they no longer reach into ``halko.py``. -+""" -+from __future__ import annotations -+ -+from typing import Optional -+ -+import torch -+ -+from pcalib._kernels.linalg.eigh import cusolver, jacobi -+from pcalib._kernels.linalg.eigh.triton import triton_eigh -+ -+ -+# Per-variant published residual (relative). Used by the tol router AND -+# by cost.py. -+_RESIDUAL_PREFERENCE = [ -+ ("cusolver", 1e-7), -+ ("jacobi", 1e-6), -+ ("halko", 1e-4), -+ ("qdwh_ns", 8e-4), -+ ("qdwh", 3e-3), -+] -+ -+_QDWH_MIN_N = 5120 -+ -+ -+def _halko_is_favourable(N: int, K: Optional[int]) -> bool: -+ """Halko helps when ``K << N`` AND N is large enough to amortize. -+ -+ The approximate Halko backend is not vendored here (only the exact -+ ``tol=None`` path is), so this heuristic is inert and the router never -+ selects Halko. -+ """ -+ return False -+ -+ -+def _route( -+ *, -+ N: int, -+ K: Optional[int] = None, -+ tol: Optional[float] = None, -+ backend: Optional[str] = None, -+ hw: Optional[object] = None, -+) -> str: -+ """Pick the eigh variant. Returns the bare name (no ``eigh_`` prefix). -+ -+ See module docstring for the rule. The same function is called by -+ runtime dispatch (impl) and the cost shim (cost.py). -+ -+ ``jacobi`` is reachable only via explicit ``backend="jacobi"`` -- -+ it is a single-CTA Triton cyclic-Jacobi kernel that beats cuSOLVER -+ at very small N (N <= 16) but is slower beyond that. No C++ -+ toolchain / ninja is required on first call (the previous -+ ``load_inline`` CUDA implementation was retired in favour of a -+ pure-Triton kernel under :mod:`pcalib._kernels.linalg.eigh.triton.jacobi`). -+ """ -+ del hw -+ if backend is not None: -+ return backend -+ if tol is None or tol <= 0: -+ return "cusolver" -+ if _halko_is_favourable(N, K) and tol >= 1e-4: -+ return "halko" -+ if N >= _QDWH_MIN_N: -+ if tol >= 3e-3: -+ return "qdwh" -+ if tol >= 8e-4: -+ return "qdwh_ns" -+ return "cusolver" -+ -+ -+def route_op_name(*, N: int, K: Optional[int] = None, -+ tol: Optional[float] = None, -+ hw: Optional[object] = None) -> str: -+ """Canonical ``eigh_`` label.""" -+ return "eigh_" + _route(N=N, K=K, tol=tol, hw=hw) -+ -+ -+def eigh( -+ A: torch.Tensor, -+ K: Optional[int] = None, -+ *, -+ tol: Optional[float] = None, -+ backend: Optional[str] = None, -+ n_iter: int = 5, -+ p: int = 30, -+ **kwargs, -+): -+ """Symmetric eigendecomposition. -+ -+ Args: -+ A: ``(N, N)`` symmetric tensor. -+ K: optional truncation -- return only top-K eigenpairs (ascending). -+ With ``tol`` loose enough this triggers Halko. -+ tol: residual tolerance. -+ -+ * ``None`` (default) **-> EXACT** in the input dtype (always -+ cuSOLVER -- the Triton ``jacobi`` backend is opt-in via -+ ``backend="jacobi"`` since cuSOLVER beats it past N ~ 16). -+ * Otherwise pick the fastest variant whose declared residual -+ fits, optionally Halko if ``K`` is set + shape favours it. -+ backend: explicit ``"cusolver" | "jacobi" | "qdwh" | "qdwh_ns" | -+ "halko"`` override. -+ n_iter, p: Halko power-iter and oversample (only used when Halko -+ is selected). -+ **kwargs: forwarded to the chosen variant. -+ -+ Returns: -+ ``(eigvals, eigvecs)`` ascending; both sliced to top-K if ``K`` -+ was supplied. -+ """ -+ if not isinstance(A, torch.Tensor) or A.ndim != 2: -+ raise ValueError("eigh expects a 2D tensor") -+ N = A.size(0) -+ chosen = _route(N=N, K=K, tol=tol, backend=backend) -+ -+ if chosen == "halko": -+ raise NotImplementedError( -+ "the Halko backend is not vendored in this build; only the exact " -+ "tol=None path (cuSOLVER / MKL / Triton jacobi) is available." -+ ) -+ -+ if chosen == "jacobi": -+ eigvals, eigvecs = jacobi.eigh(A, **kwargs) -+ elif chosen == "qdwh": -+ from pcalib._kernels.linalg.eigh.qdwh import qdwh_eig -+ eigvals, eigvecs = qdwh_eig(A, **kwargs) -+ elif chosen == "qdwh_ns": -+ from pcalib._kernels.linalg.eigh.qdwh_ns import qdwh_eig_ns -+ eigvals, eigvecs = qdwh_eig_ns(A, **kwargs) -+ else: # cusolver / default exact -+ # Use the small-D CPU-MKL trick automatically (it IS the exact -+ # path -- input dtype preserved, just dispatched to the faster -+ # solver for small N). -+ eigvals, eigvecs = triton_eigh(A) -+ -+ if K is not None: -+ eigvals = eigvals[-K:] -+ eigvecs = eigvecs[:, -K:] -+ return eigvals, eigvecs -+ -+ -+__all__ = ["eigh", "route_op_name"] -diff --git a/pcalib/_kernels/linalg/eigh/jacobi.py b/pcalib/_kernels/linalg/eigh/jacobi.py -new file mode 100644 -index 0000000..b2c60dc ---- /dev/null -+++ b/pcalib/_kernels/linalg/eigh/jacobi.py -@@ -0,0 +1,30 @@ -+"""eigh_jacobi -- single-block row-cyclic Jacobi for small N (<= 128). -+ -+Thin user-facing wrapper over the Triton kernel in -+:mod:`pcalib._kernels.linalg.eigh.triton.jacobi`. No CUDA / C++ compile step -+on first call (the old ``jacobi_impl.py`` ``load_inline`` extension -+was retired -- ninja is no longer required to import this backend). -+ -+Achieved residual is at the fp32 noise floor (``~1e-4`` for the -+N=46 reference shape, identical to ``torch.linalg.eigh`` on fp32 -+input), and the kernel beats cuSOLVER ``syevd`` at N <= 16 where the -+syevd launch fixed-cost dominates. -+""" -+from pcalib._kernels.linalg.eigh.triton.jacobi import triton_jacobi_eigh as _triton_jacobi -+ -+ -+def eigh(A, num_sweeps: int = 6): -+ """Single-kernel cyclic Jacobi eigensolver. -+ -+ Args: -+ A: ``(N, N)`` symmetric float32 CUDA tensor; not modified. -+ num_sweeps: full row-cyclic sweeps; each sweep performs -+ ``N*(N-1)/2`` Givens rotations. ``6`` is enough to reach -+ fp32 noise (~1e-4) for N <= 64; bump to ``8-12`` for -+ tighter convergence at very large N. -+ -+ Returns: -+ ``(w, V)`` -- ``(N,)`` eigenvalues (ascending) and ``(N, N)`` -+ eigenvectors as columns. ``A @ V[:, i] ≈ w[i] * V[:, i]``. -+ """ -+ return _triton_jacobi(A, num_sweeps=num_sweeps) -diff --git a/pcalib/_kernels/linalg/eigh/triton/__init__.py b/pcalib/_kernels/linalg/eigh/triton/__init__.py -new file mode 100644 -index 0000000..1e1982b ---- /dev/null -+++ b/pcalib/_kernels/linalg/eigh/triton/__init__.py -@@ -0,0 +1,28 @@ -+"""eigh triton backend. -+ -+Re-exports the top-level functions / classes / constants from each -+component file. ``@triton.jit`` kernels stay private to their file -+(call them via the Python wrapper that lives next to them). -+ -+Component files: -+ -+* :mod:`householder` -- Householder tridiagonalisation + unrolled -+ QR finaliser. Powers :func:`pcalib._kernels.linalg.eigh.triton_eigh`, -+ the small-D (D <= ~256) dense path used by PCA / TruncSVD / -+ Halko subspace iteration. -+* :mod:`jacobi` -- Row-cyclic Givens-Jacobi for N <= 128. -+ Powers :func:`pcalib._kernels.linalg.eigh.eigh_jacobi`, an opt-in -+ backend that beats cuSOLVER at very small N (N <= 16) without -+ the ``load_inline`` CUDA / C++ compile step the old -+ implementation needed. -+""" -+from pcalib._kernels.linalg.eigh.triton.householder import ( -+ _eigh_cpu_initialized, -+ triton_eigh, -+) -+from pcalib._kernels.linalg.eigh.triton.jacobi import triton_jacobi_eigh -+ -+__all__ = [ -+ "triton_eigh", -+ "triton_jacobi_eigh", -+] -diff --git a/pcalib/_kernels/linalg/eigh/triton/householder.py b/pcalib/_kernels/linalg/eigh/triton/householder.py -new file mode 100644 -index 0000000..0dff1c8 ---- /dev/null -+++ b/pcalib/_kernels/linalg/eigh/triton/householder.py -@@ -0,0 +1,171 @@ -+"""Triton eigendecomposition for small dense symmetric matrices. -+ -+Uses Householder tridiagonalisation followed by an unrolled QR loop. -+Designed for D ≤ ~256 — the small-matrix path used by PCA, TruncSVD, -+and the Halko subspace-iteration finaliser (linalg/eigh/halko.py). -+ -+Public surface: -+ - triton_eigh(A) -> (eigvals, eigvecs) for A: (D, D) fp32 symmetric. -+""" -+ -+import torch -+import triton -+import triton.language as tl -+ -+ -+# ============================================================================= -+# Kernel: Householder Tridiagonalization + Eigensolver -+# -+# Single-program Triton kernel: reduces symmetric A to tridiagonal form T via -+# Householder reflections. All data stays in L2 for D ≤ ~2048. -+# -+# The tridiagonal eigenvalues are found via Sturm bisection (no cuSOLVER). -+# Eigenvectors: inverse iteration on T, then Householder back-transformation. -+# -+# For D=256: ~0.4ms total vs cuSOLVER eigh ~5ms. The win comes from avoiding -+# cuSOLVER's ~5ms fixed overhead of internal kernel launches/workspace alloc. -+# ============================================================================= -+ -+@triton.jit -+def _householder_tridiag_kernel( -+ A_ptr, p_buf_ptr, tau_ptr, -+ D, stride_a, -+ BLOCK: tl.constexpr, -+): -+ """Householder tridiagonalization: symmetric A → tridiagonal (in-place). -+ -+ After kernel: -+ - A.diagonal() = tridiagonal diagonal -+ - A.diagonal(offset=-1) = tridiagonal subdiagonal -+ - A lower triangle (below subdiag): normalized Householder vectors v'[1:] -+ where v' = v / v[0], so v'[0] = 1 (implicit) -+ - tau_ptr[k] = tau' = 2 * v[0]² / ||v||² (LAPACK-style scalar) -+ """ -+ offs = tl.arange(0, BLOCK) -+ -+ for k in tl.range(0, D - 2): -+ n = D - k - 1 -+ base = k + 1 -+ -+ # ── Compute ||x||² for x = A[base:D, k] ── -+ xnorm_sq = 0.0 -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ xj = tl.load(A_ptr + (base + j) * stride_a + k, mask=mask, other=0.0) -+ xnorm_sq += tl.sum(xj * xj) -+ -+ if xnorm_sq > 1e-30: -+ xnorm = tl.sqrt(xnorm_sq) -+ x0 = tl.load(A_ptr + base * stride_a + k) -+ -+ # alpha = -sign(x0) * ||x|| -+ alpha = tl.where(x0 >= 0.0, -xnorm, xnorm) -+ -+ # v[0] = x[0] - alpha -+ v0 = x0 - alpha -+ -+ # tau_raw = 2 / ||v||² where ||v||² = v0² + ||x[1:]||² -+ vnorm_sq = v0 * v0 + (xnorm_sq - x0 * x0) -+ tau = 2.0 / vnorm_sq -+ -+ # LAPACK convention: normalize v' = v / v[0], tau' = tau * v0² -+ # v'[0] = 1 (implicit), v'[1:] = v[1:] / v[0] -+ # Store v'[1:] in A[base+1:D, k] (overwrites x[1:]) -+ inv_v0 = 1.0 / v0 -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = (j < n) & (j > 0) -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, mask=mask, other=0.0) -+ tl.store(A_ptr + (base + j) * stride_a + k, vj * inv_v0, mask=mask) -+ -+ # Store v'[0] = 1.0 temporarily at A[base, k] (for the matvec) -+ tl.store(A_ptr + base * stride_a + k, 1.0) -+ -+ tau_prime = tau * v0 * v0 -+ tl.store(tau_ptr + k, tau_prime) -+ -+ # ── p = tau' * A_sub @ v' ── -+ # A_sub = A[base:D, base:D], v' = A[base:D, k] (normalized, v'[0]=1) -+ for i in tl.range(0, n): -+ dot = 0.0 -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ aij = tl.load(A_ptr + (base + i) * stride_a + (base + j), -+ mask=mask, other=0.0) -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, -+ mask=mask, other=0.0) -+ dot += tl.sum(aij * vj) -+ tl.store(p_buf_ptr + i, dot * tau_prime) -+ -+ # ── w = p - (tau'/2 * v'.T @ p) * v' ── -+ vtp = 0.0 -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, -+ mask=mask, other=0.0) -+ pj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) -+ vtp += tl.sum(vj * pj) -+ -+ coeff = 0.5 * tau_prime * vtp -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ pj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, -+ mask=mask, other=0.0) -+ tl.store(p_buf_ptr + j, pj - coeff * vj, mask=mask) -+ -+ # ── A_sub -= v @ w.T + w @ v.T (symmetric rank-2 update) ── -+ for i in tl.range(0, n): -+ vi = tl.load(A_ptr + (base + i) * stride_a + k) -+ wi = tl.load(p_buf_ptr + i) -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ aij = tl.load(A_ptr + (base + i) * stride_a + (base + j), -+ mask=mask, other=0.0) -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, -+ mask=mask, other=0.0) -+ wj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) -+ tl.store(A_ptr + (base + i) * stride_a + (base + j), -+ aij - vi * wj - wi * vj, mask=mask) -+ -+ # Store subdiagonal (overwrites v[0], but v[1:] survives for back-transform) -+ tl.store(A_ptr + base * stride_a + k, alpha) -+ tl.store(A_ptr + k * stride_a + base, alpha) -+ -+ -+_eigh_cpu_initialized = False -+ -+def triton_eigh(A: torch.Tensor) -> tuple: -+ """Eigendecomposition: CPU MKL LAPACK for D ≤ 512, cuSOLVER for D > 512. -+ -+ For D ≤ 512: GPU→CPU + MKL dsyev (4 threads) + CPU→GPU. -+ Avoids cuSOLVER's fixed overhead (~5ms kernel launches + workspace). -+ D=64: 0.18ms (4.5x faster), D=256: 1.8ms (2.6x), D=512: 6ms (2.2x). -+ For D > 512: cuSOLVER eigh (multi-SM parallelism, O(D³) dominates). -+ -+ Why not Triton Householder on GPU? Single-SM approach is 4-73x slower -+ than cuSOLVER — serial Householder loop bottlenecked by per-iteration -+ latency. Eigendecomposition requires multi-SM parallelism for large D. -+ """ -+ D = A.shape[0] -+ -+ if D > 512: -+ return torch.linalg.eigh(A) -+ -+ # CPU MKL LAPACK with 4 threads: optimal for D ≤ 512 eigendecomposition. -+ # set_num_threads has ~3ms overhead per call, so we set it once. -+ global _eigh_cpu_initialized -+ if not _eigh_cpu_initialized: -+ torch.set_num_threads(4) -+ _eigh_cpu_initialized = True -+ -+ torch.cuda.synchronize() -+ A_cpu = A.cpu() -+ eigenvalues, eigenvectors = torch.linalg.eigh(A_cpu) -+ return eigenvalues.to(A.device), eigenvectors.to(A.device) -+ -diff --git a/pcalib/_kernels/linalg/eigh/triton/jacobi.py b/pcalib/_kernels/linalg/eigh/triton/jacobi.py -new file mode 100644 -index 0000000..b19b69a ---- /dev/null -+++ b/pcalib/_kernels/linalg/eigh/triton/jacobi.py -@@ -0,0 +1,219 @@ -+"""Triton row-cyclic Jacobi eigensolver for small symmetric matrices (N <= 128). -+ -+Single-program kernel — one CTA holds the full A and V matrices in HBM -+(small enough at N ≤ 128 that even the slow HBM round-trips per -+rotation cost <100 µs on H100) and performs a classical row-cyclic -+Jacobi sweep: ``N*(N-1)/2`` Givens rotations per sweep, default 6 -+sweeps. Each rotation zeroes the off-diagonal element ``A[p, q]`` via -+the two-sided update ``A' = G^T A G`` and accumulates ``V' = V G``. -+ -+Replaces the previous ``jacobi_impl.py`` ``torch.utils.cpp_extension. -+load_inline`` CUDA kernel -- removes the first-call ``ninja`` C++ -+compile step so the user never needs a CUDA toolchain installed to -+use ``pcalib._kernels.linalg.eigh.eigh(..., backend="jacobi")``. -+ -+Sequencing vs Brent-Luk -+----------------------- -+The CUDA original used Brent-Luk *parallel* pairing (N/2 simultaneous -+rotations per round, N-1 rounds per sweep), which converges in 8-12 -+sweeps. The Triton version uses *cyclic* pairing (one rotation at a -+time) which converges in 5-8 sweeps but pays per-rotation kernel -+overhead. At N ≤ 64 the cyclic path runs in ~0.5 ms with 6 sweeps, -+well under the cuSOLVER ``syevd`` fixed launch overhead (~5 ms) the -+``jacobi`` backend was added to beat. For N > 128 the cyclic-Jacobi -+constant factor crosses cuSOLVER's; the dispatcher in -+``linalg.eigh.impl`` caps the jacobi backend at N ≤ 128 anyway. -+ -+Public surface -+-------------- -+* :func:`triton_jacobi_eigh` -- ``(A, num_sweeps) -> (eigvals, eigvecs)``; -+ fp32 symmetric input, fp32 sorted-ascending output. -+""" -+from __future__ import annotations -+ -+import torch -+import triton -+import triton.language as tl -+ -+ -+@triton.jit -+def _jacobi_cyclic_kernel( -+ A_ptr, # (N_PAD, N_PAD) fp32, row-major; overwritten in-place -+ V_ptr, # (N_PAD, N_PAD) fp32, row-major; initialised to I + accumulates -+ N, # active dimension (may be < N_PAD when caller padded) -+ N_PAD: tl.constexpr, -+ NUM_SWEEPS: tl.constexpr, -+): -+ """Row-cyclic Jacobi: one CTA, sequential pair rotations. -+ -+ For each sweep, iterate (p, q) with 0 <= p < q < N; compute the -+ Givens rotation that zeroes ``A[p, q]`` and update rows p, q + -+ cols p, q of A and cols p, q of V. ``A_ptr`` ends up with the -+ eigenvalues on its diagonal (not yet sorted); ``V_ptr`` ends up -+ with the corresponding eigenvectors as columns. -+ -+ Grid: ``(1,)``. Strides: row-major, so element ``[i, j]`` lives -+ at ``ptr + i * N_PAD + j``. -+ """ -+ n_offs = tl.arange(0, N_PAD) -+ n_mask = n_offs < N -+ -+ # ── Initialise V = I_{N_PAD} ──────────────────────────────────── -+ # The padding rows/cols stay zero off-diagonal and one on the -+ # diagonal so the padded eigenvalues are exactly the diagonal of -+ # the padded A (which the host has already set to a sentinel -+ # value large enough to sort to the end and be dropped). -+ v_init = (n_offs[:, None] == n_offs[None, :]).to(tl.float32) -+ tl.store(V_ptr + n_offs[:, None] * N_PAD + n_offs[None, :], v_init) -+ -+ for _sweep in tl.range(0, NUM_SWEEPS): -+ for p in tl.range(0, N - 1): -+ for q in tl.range(p + 1, N): -+ # ── Compute Givens c, s zeroing A[p, q] ───────────── -+ # Two-sided rotation G^T A G with -+ # G[p,p]=c, G[p,q]=-s, G[q,p]=s, G[q,q]=c -+ # zeroes A[p,q] when tau = (A[p,p] - A[q,q]) / (2 A[p,q]). -+ a_pp = tl.load(A_ptr + p * N_PAD + p) -+ a_qq = tl.load(A_ptr + q * N_PAD + q) -+ a_pq = tl.load(A_ptr + p * N_PAD + q) -+ -+ thresh = 1e-30 * (tl.abs(a_pp) + tl.abs(a_qq) + 1e-37) -+ if tl.abs(a_pq) > thresh: -+ d = a_pp - a_qq -+ theta = d / (2.0 * a_pq) -+ if theta >= 0.0: -+ t = 1.0 / (theta + tl.sqrt(1.0 + theta * theta)) -+ else: -+ t = 1.0 / (theta - tl.sqrt(1.0 + theta * theta)) -+ c = 1.0 / tl.sqrt(1.0 + t * t) -+ s = t * c -+ else: -+ c = 1.0 -+ s = 0.0 -+ -+ # ── Row update on A: rows p and q ─────────────────── -+ row_p = tl.load( -+ A_ptr + p * N_PAD + n_offs, mask=n_mask, other=0.0, -+ ) -+ row_q = tl.load( -+ A_ptr + q * N_PAD + n_offs, mask=n_mask, other=0.0, -+ ) -+ new_row_p = c * row_p + s * row_q -+ new_row_q = -s * row_p + c * row_q -+ tl.store(A_ptr + p * N_PAD + n_offs, new_row_p, mask=n_mask) -+ tl.store(A_ptr + q * N_PAD + n_offs, new_row_q, mask=n_mask) -+ -+ # ── Column update on A: cols p and q ──────────────── -+ # The row update just touched A[p, :] and A[q, :], so -+ # the column reads here see the *post-row-update* -+ # values. The two-sided update is correct because -+ # only A[p, p] / A[p, q] / A[q, p] / A[q, q] depend -+ # on both row and col rotations -- and at convergence -+ # A[p, q] -> 0 so the order doesn't affect the limit. -+ col_p = tl.load( -+ A_ptr + n_offs * N_PAD + p, mask=n_mask, other=0.0, -+ ) -+ col_q = tl.load( -+ A_ptr + n_offs * N_PAD + q, mask=n_mask, other=0.0, -+ ) -+ new_col_p = c * col_p + s * col_q -+ new_col_q = -s * col_p + c * col_q -+ tl.store(A_ptr + n_offs * N_PAD + p, new_col_p, mask=n_mask) -+ tl.store(A_ptr + n_offs * N_PAD + q, new_col_q, mask=n_mask) -+ -+ # ── Eigenvector accumulation V <- V G ─────────────── -+ v_col_p = tl.load( -+ V_ptr + n_offs * N_PAD + p, mask=n_mask, other=0.0, -+ ) -+ v_col_q = tl.load( -+ V_ptr + n_offs * N_PAD + q, mask=n_mask, other=0.0, -+ ) -+ new_v_col_p = c * v_col_p + s * v_col_q -+ new_v_col_q = -s * v_col_p + c * v_col_q -+ tl.store(V_ptr + n_offs * N_PAD + p, new_v_col_p, mask=n_mask) -+ tl.store(V_ptr + n_offs * N_PAD + q, new_v_col_q, mask=n_mask) -+ -+ -+def _next_pow2(x: int) -> int: -+ if x <= 1: -+ return 1 -+ return 1 << (x - 1).bit_length() -+ -+ -+# Practical upper bound: at N=128 + 6 sweeps the kernel issues -+# ~48K rotations sequentially. Empirically this runs in ~3 ms on -+# H100/H200. Above N=128 the cyclic-Jacobi constant factor crosses -+# cuSOLVER's; ``linalg.eigh.impl`` honours this cap when routing. -+_MAX_N = 128 -+ -+ -+def triton_jacobi_eigh(A: torch.Tensor, num_sweeps: int = 6): -+ """Eigendecomposition of symmetric ``A`` via row-cyclic Triton Jacobi. -+ -+ Args: -+ A: ``(N, N)`` symmetric float32 CUDA tensor. Not modified -+ (a working copy is allocated internally). -+ num_sweeps: number of full Jacobi sweeps (each = ``N*(N-1)/2`` -+ Givens rotations). Default 6 -- ~1e-6 residual for -+ well-conditioned spectra. -+ -+ Returns: -+ ``(w, V)`` where ``w`` is ``(N,)`` eigenvalues in ascending -+ order and ``V`` is ``(N, N)`` eigenvectors as columns -+ (``A @ V[:, i] ≈ w[i] * V[:, i]``). -+ """ -+ assert A.dim() == 2 and A.size(0) == A.size(1), "A must be square" -+ assert A.dtype == torch.float32, "only float32 supported" -+ assert A.is_cuda, "A must be on CUDA" -+ -+ N = A.size(0) -+ if N > _MAX_N: -+ raise ValueError( -+ f"triton_jacobi_eigh only supports N <= {_MAX_N}; got N={N}. " -+ f"Route to ``backend='cusolver'`` for larger problems." -+ ) -+ -+ # Pad to the next power of two so the Triton tile is a -+ # constexpr-friendly size. The padding rows/cols get a sentinel -+ # diagonal entry that sorts to the end and is dropped on return. -+ N_PAD = max(2, _next_pow2(N)) -+ if N == N_PAD: -+ A_work = A.contiguous().clone() -+ else: -+ # Sentinel: 10x the largest diagonal absolute value of A so -+ # the padded eigenvalues sort cleanly to the end of ``w`` -+ # and are removed by the slice below. -+ diag_abs = float(torch.max(torch.abs(torch.diagonal(A))).item()) + 1.0 -+ sentinel = diag_abs * 10.0 -+ A_work = torch.zeros( -+ N_PAD, N_PAD, dtype=torch.float32, device=A.device, -+ ) -+ A_work[:N, :N] = A -+ # Set the padded diagonal entries to the sentinel. -+ idx = torch.arange(N, N_PAD, device=A.device) -+ A_work[idx, idx] = sentinel -+ -+ V_work = torch.empty(N_PAD, N_PAD, dtype=torch.float32, device=A.device) -+ -+ # Single-CTA, single-warp launch. The kernel is dominated by -+ # the sequential dependency between successive Givens rotations, -+ # so adding warps would only waste threads on broadcast scalars -+ # (and risks losing convergence on N_PAD >= 64 where multi-warp -+ # scalar reads can race with the immediately preceding store on -+ # some Triton 3.x configs). -+ _jacobi_cyclic_kernel[(1,)]( -+ A_work, V_work, -+ N_PAD, # the kernel iterates over the full padded N_PAD -+ N_PAD=N_PAD, -+ NUM_SWEEPS=num_sweeps, -+ num_warps=1, -+ ) -+ -+ w_padded = torch.diagonal(A_work).clone() -+ w_sorted, idx = torch.sort(w_padded) -+ V_sorted = V_work[:, idx] -+ -+ if N == N_PAD: -+ return w_sorted.contiguous(), V_sorted.contiguous() -+ # Drop the ``N_PAD - N`` sentinel eigenpairs sitting at the tail. -+ return w_sorted[:N].contiguous(), V_sorted[:N, :N].contiguous() -diff --git a/pcalib/_kernels/primitives/__init__.py b/pcalib/_kernels/primitives/__init__.py -new file mode 100644 -index 0000000..e69de29 -diff --git a/pcalib/_kernels/primitives/pca/__init__.py b/pcalib/_kernels/primitives/pca/__init__.py -new file mode 100644 -index 0000000..e69de29 -diff --git a/pcalib/_kernels/primitives/pca/triton/__init__.py b/pcalib/_kernels/primitives/pca/triton/__init__.py -new file mode 100644 -index 0000000..d420409 ---- /dev/null -+++ b/pcalib/_kernels/primitives/pca/triton/__init__.py -@@ -0,0 +1,17 @@ -+"""pca triton backend. -+ -+Re-exports the public Python wrappers from each component file. -+``@triton.jit`` kernels stay private to their file (call them via the -+Python wrapper that lives next to them). -+""" -+from pcalib._kernels.primitives.pca.triton.pca import ( -+ _triton_pca_cov, -+ _triton_pca_dual, -+ triton_pca, -+ flash_pca, -+) -+ -+__all__ = [ -+ "triton_pca", -+ "flash_pca", -+] -diff --git a/pcalib/_kernels/primitives/pca/triton/pca.py b/pcalib/_kernels/primitives/pca/triton/pca.py -new file mode 100644 -index 0000000..4e3a96e ---- /dev/null -+++ b/pcalib/_kernels/primitives/pca/triton/pca.py -@@ -0,0 +1,123 @@ -+"""PCA via cuBLAS GEMMs + (cuSOLVER / MKL / Halko) eigh — auto-dispatches: -+ -+1. N >> D (cov path): -+ cov = X.T @ X / N (cuBLAS TF32 GEMM) -+ → (D ≤ 512) MKL eigh on CPU (avoids cuSOLVER's ~5 ms launch overhead) -+ → (D > 512) cuSOLVER eigh on GPU -+2. D >> N (dual path): -+ gram = X @ X.T / N (cuBLAS TF32 GEMM) -+ → (K*4 < N) Halko subspace iteration eigh — orders of magnitude faster -+ than the full O(N³) eigh when only top-K are needed -+ → (K*4 ≥ N) MKL/cuSOLVER eigh as in the cov path -+ → V = X.T @ U (cuBLAS GEMM) -+ → per-column normalize -+ -+The GEMM stage uses cuBLAS TF32 matmul (`torch.matmul` with TF32 enabled); -+the dual path's eigh uses Halko subspace iteration when the shape -+favours it (``K*4 < N`` AND ``N >= 256``), otherwise the full cuSOLVER -+syevd. Input is fp32 throughout; TF32 internal compute matches what -+``tl.dot`` already used on H100/H200. -+""" -+ -+import sys -+import os -+ -+import torch -+ -+from pcalib._kernels.linalg.eigh import eigh -+ -+ -+# ─── Path 1: Covariance + eigh (N >> D) ───────────────────────────────────── -+ -+def _triton_pca_cov(X: torch.Tensor, K: int, *, tol=None): -+ """PCA via cuBLAS TF32 cov GEMM + eigendecomposition. -+ -+ Output cov is the full (D, D) covariance matrix in fp32 (cuBLAS does -+ not expose a SYRK-style symmetric-output GEMM via torch); the lower -+ triangle work is cuBLAS-internal and amortized in the tile schedule. -+ -+ Precision is controlled entirely by ``tol``: ``tol=None`` (default) -+ runs the exact eigh path; passing a loose ``tol`` lets the underlying -+ :func:`pcalib._kernels.linalg.eigh.eigh` switch to Halko subspace iteration -+ when shape favours it (``K*4 < D`` AND ``D >= 256``). -+ """ -+ N, D = X.shape -+ cov = (X.T @ X) / N # cuBLAS TF32 GEMM -+ return eigh(cov, K=K, tol=tol) -+ -+ -+# ─── Path 2: Dual-Space Gram + eigh + Projection (D >> N) ─────────────────── -+ -+def _triton_pca_dual(X: torch.Tensor, K: int, *, tol=None): -+ """PCA via cuBLAS Gram GEMM + eigh + cuBLAS projection. -+ -+ For D >> N: NEVER materializes D x D. -+ 1. G = X @ X.T / N (cuBLAS TF32 GEMM, output N x N) -+ 2. eigh(G, K=K, tol=tol) -- exact by default; Halko on loose tol + -+ favourable shape. -+ 3. V = X.T @ U_K (cuBLAS TF32 GEMM, output D x K) -+ 4. column-normalize -- recovers unit eigenvectors of X^T X / N. -+ -+ Eigenvalues of X.T @ X / N and X @ X.T / N coincide (dual identity). -+ """ -+ N, D = X.shape -+ G = (X @ X.T) / N # cuBLAS TF32 GEMM -+ top_eigvals, U = eigh(G, K=K, tol=tol) -+ V = X.T @ U # cuBLAS TF32 GEMM -+ V = V / V.norm(dim=0, keepdim=True).clamp(min=1e-10) -+ return top_eigvals, V -+ -+ -+# ─── Auto-dispatch ─────────────────────────────────────────────────────────── -+ -+def triton_pca(X: torch.Tensor, K: int, *, tol=None): -+ """PCA via cuBLAS + ``pcalib._kernels.linalg.eigh`` -- auto-selects by data shape. -+ -+ - N >= 4*D: covariance + eigh (D×D matrix, L2-cached small for D ≤ 1024) -+ - N < 4*D: dual-space Gram + eigh + projection (N×N Gram, no D² -+ materialization) -+ -+ Precision contract: -+ * ``tol=None`` -> all matmuls run in PyTorch's default IEEE fp32 -+ (cuBLAS pure-fp32 path, ~67 TFLOPS on H100). Halko is OFF. -+ * ``tol>0`` -> opt into a lossy budget. We set -+ ``allow_tf32=True`` for the duration so the cov/Gram GEMMs run on -+ TF32 tensor cores (~225 TFLOPS, ~3-4x faster) and Halko / bf16 -+ backends become eligible. The flag is restored on exit. -+ -+ Args: -+ X: (N, D) float32 data (centered) -+ K: number of components -+ tol: residual tolerance. -+ -+ * ``None`` (default) -> EXACT eigh on the cov / Gram matrix -+ (cuSOLVER / MKL). Halko subspace iteration is NEVER used -+ automatically -- you must opt in via ``tol``. -+ * ``tol >= 1e-4`` AND favourable shape (``K*4 < M`` AND -+ ``M >= 256`` where ``M = D`` for cov, ``N`` for dual) -+ -> Halko subspace iteration; ~26-80x speedup over cuSOLVER. -+ -+ Returns: -+ eigenvalues: (K,) top-K eigenvalues (ascending) -+ eigenvectors: (D, K) top-K eigenvectors (columns, ascending) -+ """ -+ # tol=None -> EXACT: keep PyTorch's default IEEE matmul (no TF32). -+ # tol>0 -> user opted into a lossy budget: enable TF32 globally -+ # for the duration so the direct ``X @ y`` ops in helper paths can -+ # use tensor cores. The global flag is restored on exit. -+ use_tf32 = tol is not None and tol > 0 -+ prev_tf32 = torch.backends.cuda.matmul.allow_tf32 -+ if use_tf32: -+ torch.backends.cuda.matmul.allow_tf32 = True -+ try: -+ N, D = X.shape -+ if N >= 4 * D: -+ return _triton_pca_cov(X, K, tol=tol) -+ return _triton_pca_dual(X, K, tol=tol) -+ finally: -+ if use_tf32: -+ torch.backends.cuda.matmul.allow_tf32 = prev_tf32 -+ -+ -+# Public alias used by the examples/ folder. -+flash_pca = triton_pca -diff --git a/pcalib/pca.py b/pcalib/pca.py -index ec891a2..47a2f8c 100644 ---- a/pcalib/pca.py -+++ b/pcalib/pca.py -@@ -1,44 +1,44 @@ --"""Principal Component Analysis (PCA) -- the reference you must optimise. -+"""Principal Component Analysis (PCA) -- vendored optimized kernel path. - --This implementation is intentionally naive. It centres the data by explicitly --materialising the full ``(N, D)`` centred matrix and then runs a *full* thin --SVD (:func:`torch.linalg.svd`) just to read off the top ``k`` singular vectors --and singular values. It is correct and deterministic, but it computes the --entire spectrum when only the leading ``k`` components are needed and it moves --far more memory than necessary (the centred copy of the whole dataset). -+The naive baseline centred the data by materialising the full ``(N, D)`` centred -+matrix and ran a *full* thin SVD just to read off the leading ``k`` components. -+This implementation delegates to the vendored kernel entry point, which forms a -+small covariance (or dual Gram) matrix with a single GEMM and reads off the -+leading ``k`` eigenpairs with a dense symmetric eigensolver, so the full -+spectrum is never computed and the centred data is never materialised. - --Contract (do NOT change): -+Public contract (unchanged): - - pca(x, n_components) -> (components, explained_variance) - - x : (N, D) float32 CUDA tensor of points. - n_components (k): int, number of leading principal components to return. - -- components : (k, D) float32 tensor whose rows are the top-k -- principal axes (the leading eigenvectors of the -- covariance matrix), orthonormal rows. -- explained_variance : (k,) float32 tensor of the corresponding covariance -- eigenvalues, in descending order. With the unbiased -- (``N - 1``) normalisation this equals -- ``S[:k] ** 2 / (N - 1)`` for singular values ``S`` of -- the centred data. -- --You may add modules/kernels inside the ``pcalib`` package and rewrite the body --of :func:`pca` freely (covariance/Gram + top-k eigendecomposition, fused --centring, Triton kernels, ...), as long as the public contract above is --preserved. -+ components : (k, D) float32 top-k principal axes (orthonormal rows). -+ explained_variance : (k,) float32 covariance eigenvalues, descending. - """ - from __future__ import annotations - - import torch - -+from pcalib._kernels.primitives.pca.triton.pca import triton_pca -+ - - def pca(x, n_components): - N = x.shape[0] -- mean = x.mean(dim=0) -- xc = x - mean # materialized centered matrix -- naive -- U, S, Vh = torch.linalg.svd(xc, full_matrices=False) # full SVD -- expensive - k = int(n_components) -- components = Vh[:k].contiguous() -- explained_variance = (S[:k] ** 2) / (N - 1) -- return components, explained_variance.contiguous() -+ mean = x.mean(dim=0) -+ xc = x - mean # centred input the kernel expects -+ # triton_pca returns the top-k eigenpairs of the (biased) covariance in -+ # ASCENDING order: evals (k,), evecs (D, k) as columns. -+ evals, evecs = triton_pca(xc, k, tol=None) -+ # Flip to descending (largest principal component first) to match the -+ # naive baseline's convention. -+ evals = torch.flip(evals, dims=(0,)) -+ evecs = torch.flip(evecs, dims=(1,)) -+ components = evecs.transpose(0, 1).contiguous() # (k, D) orthonormal rows -+ # The kernel normalises the covariance by N (biased); the baseline reports -+ # the unbiased (N - 1) eigenvalues. Rescale so explained_variance matches. -+ scale = float(N) / float(N - 1) if N > 1 else 1.0 -+ explained_variance = (evals * scale).clamp_min(0).contiguous() -+ return components, explained_variance diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md b/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md deleted file mode 100644 index e66434516..000000000 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/DESIGN.md +++ /dev/null @@ -1,93 +0,0 @@ -# Design notes — truncated_svd_gpu_kernel_optimization - -Operator-facing. Not copied into the agent workspace by the adapter. - -## What this task measures - -Can an agent turn a naive GPU truncated SVD into a fast one? The agent patches -`tsvdlib`; the judge times the patched `truncated_svd` against a frozen naive -baseline on hidden `(N, D, k)` workloads and scores the geometric-mean speedup, -gated on low-rank-factorization quality (orthonormal components + captured -energy). Third of a four-task **flashlib kernel-optimization family** (KMeans, -KNN, TruncatedSVD, PCA) that all share one evaluator + one Modal GPU harness. - -## Execution: Modal GPU offload (mirrors the vllm task) - -The judge and the agent public test both **offload GPU work to Modal**; the -containers are light (ubuntu + `modal` + git, no torch/triton). `flash_gpu.py` -(baked at `/opt/flash_gpu.py` in both images) builds an ephemeral Modal app on a -GPU (`evaluation.gpu`, default H100), ships the frozen baseline + the patched -package **as data** (no per-submission image rebuild), runs the worker, and -returns per-workload speedups + verdicts. Needs `MODAL_TOKEN_ID` / -`MODAL_TOKEN_SECRET`. torch/triton/the vendored kernels run on the Modal image -(`evaluation.pip`). - -The evaluator is generic and identical across the four tasks; only three -constants differ (`PRIMITIVE`/`PKG`/`REF_MODULE`), and all workload/threshold/ -Modal values come from `config.yaml` (delivered judge-only as -`/judge/task_config.json`, never present in the agent image). - -## Reference solution = best in the repo - -`reference.patch` vendors flashlib's own optimized **Triton** truncated-SVD path -(`primitives/truncated_svd/triton/svd.py` plus the `linalg/eigh` stack it calls) -under `tsvdlib/_kernels/…`, with imports rewritten and the string "flashlib" -scrubbed, plus a thin adapter mapping our `(N, D)` contract onto flashlib's -entry. It is triton-only (runs on any sm≥80), imports cleanly on CPU, and applies -+ passes the patch policy. Calibrate `speedup_target` from its measured geomean -on the first GPU trial. - -## Anti-reward-hack - -The **load-bearing** defenses are structural, inside the GPU worker (the only -place the untrusted submission runs): - -* timing primitives (`perf_counter`, `torch.cuda.synchronize`) are captured to - locals **before** the submission is imported → monkey-patching torch cannot - affect measurement; -* **fresh data every timed iteration** → memoizing on a repeated input is - useless; -* quality is **re-verified from the returned tensors on every iteration** → - returning a stale cached result for a different input is caught; -* the judge computes all metrics (no agent number is trusted); -* an ephemeral Modal container per submission → no global state persists. - -The patch policy is surgical defense-in-depth (so it does not reject legitimate -vendored/optimized kernel source): only `/**` `.py` files may change; it -bans external-optimized-library *imports* (flashlib/cuml/cupy/faiss/sklearn/ -cutlass — none installed on the GPU image anyway) and process/network/ -measurement-tamper patterns (`subprocess`, `socket`, `os.system`, -`torch.cuda.synchronize =`, …). - -The agent-facing `readme` describes the contract, gate, scoring, and policy but -**not** how the reference is implemented (no Gram/eigh/avoid-full-SVD hints). - -## Correctness gate - -The SVD has sign and rotation ambiguities (any singular vector may be negated, -and vectors spanning equal singular values may be rotated within their -eigenspace), so an elementwise comparison against the baseline's factors is -meaningless. The judge worker instead checks two rotation/sign-invariant -properties of the returned `components` `V` (shape `(k, D)`), computed from what -the submission returns, on each iteration's data: - -* **Orthonormality**: `max |V V^T - I_k| <= ortho_tolerance` (default 2%). -* **Captured energy**: `||X V^T||_F^2 >= (1 - captured_tolerance)` times the - baseline's captured energy (default 2%) — the squared Frobenius norm of the - projection of the data onto the returned subspace, the quantity truncated SVD - maximises (Eckart–Young). It depends only on the *subspace* spanned by the rows - of `V`, so it is invariant to sign flips and in-subspace rotation. - -`singular_values` are required by the shape/finiteness check but are not part of -the quality gate. Determinism: `x` is drawn from a fixed seeded generator per -workload (seeds derived from `base_seed`), so the baseline result is a fixed -function of `(N, D, k, seed)`. - -## Calibration TODO (needs a Modal GPU trial) - -- Run `reference.patch` on Modal; confirm it passes the orthonormality + - captured-energy gates on all workloads (bump the tolerances if flashlib's - low-precision path drifts slightly) and set `speedup_target` from its geomean. -- Confirm the pinned `torch`/`triton` in `evaluation.pip` compile the vendored - kernels, and hidden shapes fit the GPU. -- Confirm Modal token wiring in the Harbor judge/agent containers. diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml b/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml deleted file mode 100644 index 25fa9e0e9..000000000 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/config.yaml +++ /dev/null @@ -1,65 +0,0 @@ -tag: systems -runtime: - language: python - timeout_seconds: 10800 - environment: "GPU Truncated SVD kernel optimization. The agent patches the tsvdlib package; the judge offloads timing to a Modal GPU, comparing the patched truncated_svd against a frozen naive baseline on hidden (N, D, k) workloads with a low-rank-factorization-quality gate (orthonormal components + captured energy)." - apt_packages: - - bash - - ca-certificates - - git - - python3 - - python3-pip - judge_apt_packages: - - bash - - ca-certificates - - git - - python3 - - python3-pip - judge_pip_packages: - - modal - docker: - # Experimental local images. Build with docker/build_images.sh before a local - # Harbor trial. Both are light (ubuntu + modal + git); torch/triton/flashlib - # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes - # /app/tsvdlib (git) + /opt/tsvd_ref + /opt/flash_gpu.py; the judge image - # additionally bakes /opt/tsvdlib-clean. - image: frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.2.0 - judge_image: frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.2.0 -environment: - cpus: 4 - memory_mb: 8192 - storage_mb: 16384 - build_timeout_seconds: 3600 -evaluation: - # --- primitive wiring (consumed by the generic evaluator + flash_gpu) --- - primitive: tsvd - pkg: tsvdlib - ref_module: reftsvd - clean_source: /opt/tsvdlib-clean - baseline_source: /opt/tsvd_ref - # --- Modal GPU --- - gpu: "H100" # Modal GPU spec (H100 / A100 / L40S) - cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" - pip: ["torch", "numpy"] - app_name: "tsvd-kernel-opt-eval" - modal_timeout_seconds: 1800 - # --- timing + quality gate --- - warmup_iters: 2 - timed_iters: 7 - captured_tolerance: 0.02 # agent captured energy must be >= (1-tol) x baseline on every iteration - ortho_tolerance: 0.02 # max |V V^T - I_k| must stay within this tolerance - speedup_target: 8.0 # calibrated: reference (flashlib-best) geomean ~8.7x on Modal H100 - base_seed: 20260701 - agent_workload_count: 3 # agent role runs a fast subset; final verifier runs them all - expose_per_workload_metrics: false - # --- hidden workloads (judge-only; config.yaml/task_config.json never enter the agent image) --- - workloads: - - {id: w0, N: 500000, D: 128, k: 16} - - {id: w1, N: 1000000, D: 256, k: 32} - - {id: w2, N: 200000, D: 512, k: 16} - - {id: w3, N: 2000000, D: 64, k: 8} - - {id: w4, N: 300000, D: 256, k: 16} - - {id: w5, N: 800000, D: 128, k: 32} -submission: - kind: file - path: /app/solution.patch diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh deleted file mode 100755 index b100305a7..000000000 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/docker/smoke_images.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU / Modal required). -set -euo pipefail - -AGENT_TAG=${AGENT_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-agent:experimental-v0.2.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/truncated-svd-gpu-kernel-optimization-judge:experimental-v0.2.0} - -echo "== agent image ==" -docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/tsvd_ref'); import reftsvd; \ -import tsvdlib; print('agent ok: modal + flash_gpu + reftsvd + tsvdlib import')" - -echo "== judge image ==" -docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/tsvd_ref'); import reftsvd; \ -sys.path.insert(0, '/opt/tsvdlib-clean'); import tsvdlib; \ -print('judge ok: modal + flash_gpu + reftsvd + tsvdlib import')" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/judge/reftsvd.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/judge/reftsvd.py deleted file mode 100644 index b114cd584..000000000 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/judge/reftsvd.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Frozen naive truncated-SVD baseline used by the judge as the speed denominator. - -This is a standalone (non-package) copy of the ``tsvdlib.truncated_svd`` -implementation the agent starts from. It is imported under its own module name -so the judge worker can load the frozen baseline and the patched ``tsvdlib`` -package in the same process. Keep this behaviourally identical to the shipped -``tsvdlib/tsvd.py`` (full SVD, then slice the top-k factors). -""" -from __future__ import annotations - -import torch - - -def truncated_svd(x, n_components): - U, S, Vh = torch.linalg.svd(x, full_matrices=False) # full SVD -- naive/expensive - return S[:n_components].contiguous(), Vh[:n_components].contiguous() diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/readme b/2.0/problems/truncated_svd_gpu_kernel_optimization/readme deleted file mode 100644 index c4ea88758..000000000 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/readme +++ /dev/null @@ -1,132 +0,0 @@ -# GPU Truncated SVD Kernel Optimization - -## Problem - -You are given a small GPU truncated-SVD library, `tsvdlib`, in the Harbor -workspace at `/app/tsvdlib`. Its public entry point is: - -```python -tsvdlib.truncated_svd(x, n_components) - -> (singular_values, components) -``` - -`x` is an `(N, D)` float32 CUDA tensor (the raw matrix -- it is **not** -centered) and `n_components` is the number `k` of leading singular triples to -return. The function returns `singular_values`, a `(k,)` float32 tensor of the -top-k singular values in descending order, and `components`, a `(k, D)` float32 -tensor whose rows are the top-k right singular vectors (an orthonormal set). The -shipped implementation is a straightforward, correct PyTorch version. - -Your goal is to make `tsvdlib.truncated_svd` **as fast as possible** on the GPU -while producing a low-rank factorization of the same quality. You may rewrite the -internals of the package however you like and add new modules (including Triton -kernels) under `tsvdlib/`. The public function signature and return contract -above must not change, and every result must remain a deterministic function of -its inputs. - -## Workload - -The graded workloads are a family of held-out dense truncated-SVD problems that -vary `(N, D, k)`, spanning small/medium/large point counts, tall matrices (large -`N`, small `D`), and wide-feature matrices (large `D`), across a range of small -`k`. All use float32 data drawn from a fixed seeded generator. - -Two representative *public* shapes are available for local testing (the graded -shapes are different and hidden): - -```text -(N=200000, D=128, k=16) -(N=500000, D=64, k=8) -``` - -Treat this as a general dense truncated SVD, not as specific shapes to -special-case. - -## Iterate on a GPU - -The agent workspace has no GPU. Use the public test to time your current code on -a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your -environment): - -```bash -bash /app/public_test.sh -``` - -It reports, per public shape, whether your result passes the quality gate and -your speedup over the baseline. - -## Submission - -The submitted artifact is a patch over the `tsvdlib` package: - -```text -/app/solution.patch -``` - -After editing `/app/tsvdlib`, generate and submit: - -```bash -bash /app/make_submission.sh -bash /app/submit.sh -``` - -Submissions are asynchronous; submit early and keep iterating. The judge applies -your patch to a clean copy of `tsvdlib` and times it against the original -baseline on a GPU, on the same seeded data. - -## Correctness - -Correctness is a gate, checked in a way that is invariant to the sign and -rotation ambiguities of the SVD (you are not required to match the baseline's -vectors elementwise). On every timed iteration the judge checks two things about -your returned `components` `V` (a `(k, D)` tensor), computed from the tensors you -return on identical data: - -- **Orthonormality.** The rows must form an orthonormal set: the maximum absolute - entry of `V V^T - I_k` must stay within a small tolerance. -- **Captured energy.** The energy your subspace captures, - `||X V^T||_F^2`, must be at least `(1 - tol)` times the energy captured by the - baseline's top-k subspace on the same data. - -Crashes, non-finite output, wrong shapes/dtypes, timeouts, non-orthonormal -components, and captured energy that regresses beyond the tolerance are penalized -before speed is considered. You may trade a little numerical precision for speed -as long as you stay within the quality tolerance. - -## Scoring - -Valid submissions are scored by speedup relative to the baseline on the same -hardware and workloads. For each workload: - -```text -speedup = baseline_time / your_time -``` - -The objective is the geometric mean of per-workload speedups, so broad speedups -are preferred over a single large outlier. A result no faster than the baseline -earns 0; regressions earn 0. The raw geometric-mean speedup is reported in the -evaluator metrics. - -## Patch Policy - -The evaluator validates the patch before running it. Only Python files under the -package may change: - -```text -tsvdlib/** -``` - -New Python modules inside `tsvdlib/` are allowed. Patches may not: modify -anything outside `tsvdlib/`; import or call an external optimized ML/kernel -library (write the kernels yourself); read or write environment variables, spawn -processes, or access the network; or otherwise tamper with the measurement -framework. The timing harness measures your code on freshly generated data every -iteration and re-verifies quality each time, so caching results across calls does -not help. - -## Resource Budget - -```text -GPU: single Modal GPU (H100 reference; the Triton paths also run on L40S / A100) -Agent container: CPU-only (GPU work is offloaded to Modal) -``` diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch b/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch deleted file mode 100644 index 1d4d2d85c..000000000 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/reference.patch +++ /dev/null @@ -1,831 +0,0 @@ -diff --git a/tsvdlib/_kernels/__init__.py b/tsvdlib/_kernels/__init__.py -new file mode 100644 -index 0000000..e69de29 -diff --git a/tsvdlib/_kernels/linalg/__init__.py b/tsvdlib/_kernels/linalg/__init__.py -new file mode 100644 -index 0000000..e69de29 -diff --git a/tsvdlib/_kernels/linalg/eigh/__init__.py b/tsvdlib/_kernels/linalg/eigh/__init__.py -new file mode 100644 -index 0000000..f610f92 ---- /dev/null -+++ b/tsvdlib/_kernels/linalg/eigh/__init__.py -@@ -0,0 +1,29 @@ -+"""Symmetric eigendecomposition. -+ -+Public API -+---------- -+Single dispatcher (recommended): -+ -+ eigh(A, K=None, *, tol=None, backend=None) -+ -+By default ``eigh(A)`` is **exact** in the input dtype (cuSOLVER / -+single-kernel Jacobi for small ``N``). The exact ``tol=None`` path uses -+cuSOLVER (with a small-N CPU-MKL / Triton Jacobi fast path); it does not -+reach into the approximate (Halko / QDWH) variants. -+ -+Backend-explicit (power users): -+ -+ eigh_cusolver(A) torch.linalg.eigh -- ~1e-7 residual -+ eigh_jacobi(A, ...) single-block Jacobi for small N -+""" -+from tsvdlib._kernels.linalg.eigh.impl import eigh, route_op_name -+from tsvdlib._kernels.linalg.eigh.cusolver import eigh as eigh_cusolver -+from tsvdlib._kernels.linalg.eigh.jacobi import eigh as eigh_jacobi -+ -+ -+__all__ = [ -+ "eigh", -+ "eigh_cusolver", -+ "eigh_jacobi", -+ "route_op_name", -+] -diff --git a/tsvdlib/_kernels/linalg/eigh/cost.py b/tsvdlib/_kernels/linalg/eigh/cost.py -new file mode 100644 -index 0000000..7a28d91 ---- /dev/null -+++ b/tsvdlib/_kernels/linalg/eigh/cost.py -@@ -0,0 +1,21 @@ -+"""Cost model shim for eigh. -+ -+The full cost/roofline subsystem (``info.estimate`` / ``info.roofline``) -+and the approximate eigh variants (Halko / QDWH) are not part of this -+build. This module keeps the dispatcher's routed-variant label available -+via :func:`route_op_name` (re-exported from -+:mod:`tsvdlib._kernels.linalg.eigh.impl`); the exact cuSOLVER path is the -+only variant this build ships. -+""" -+from tsvdlib._kernels.linalg.eigh.impl import route_op_name as _route_op_name -+ -+ -+def _N(shape): -+ return shape[0] if isinstance(shape, (tuple, list)) else shape -+ -+ -+def recommend(shape, params=None, tol=None, dtype="float32", device="H100", **_): -+ """Mirror the dispatcher's choice (exact cuSOLVER in this build).""" -+ N = _N(shape) -+ K = (params or {}).get("K") -+ return {"variant": _route_op_name(N=N, K=K, tol=tol)} -diff --git a/tsvdlib/_kernels/linalg/eigh/cusolver.py b/tsvdlib/_kernels/linalg/eigh/cusolver.py -new file mode 100644 -index 0000000..354ac9a ---- /dev/null -+++ b/tsvdlib/_kernels/linalg/eigh/cusolver.py -@@ -0,0 +1,7 @@ -+"""eigh_cusolver — torch.linalg.eigh (cuSOLVER syevd). Reference precision.""" -+import torch -+ -+ -+def eigh(A: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: -+ """torch.linalg.eigh — cuSOLVER syevd. ~1e-7 residual.""" -+ return torch.linalg.eigh(A) -diff --git a/tsvdlib/_kernels/linalg/eigh/impl.py b/tsvdlib/_kernels/linalg/eigh/impl.py -new file mode 100644 -index 0000000..1ccec5c ---- /dev/null -+++ b/tsvdlib/_kernels/linalg/eigh/impl.py -@@ -0,0 +1,96 @@ -+"""eigh dispatcher. -+ -+By default ``eigh(A)`` is **exact** (cuSOLVER / MKL on the input dtype) -- -+no precision is lost beyond what the input already carries. -+ -+Routing rule (formerly in ``route.py``): -+ -+ * ``backend`` overrides everything. -+ * exact (``tol`` None / ``<= 0``, the only path this build ships): -+ * Truncated (``K`` given) : exact full eigh + slice top-K. -+ * Full : ``triton_eigh`` (CPU MKL trick for -+ small N) or cuSOLVER otherwise. -+ -+The approximate variants (Halko subspace iteration, QDWH spectral D&C) -+are not part of this build; the exact cuSOLVER path is used throughout. -+""" -+from __future__ import annotations -+ -+from typing import Optional -+ -+import torch -+ -+from tsvdlib._kernels.linalg.eigh import cusolver, jacobi -+from tsvdlib._kernels.linalg.eigh.triton import triton_eigh -+ -+ -+def _route( -+ *, -+ N: int, -+ K: Optional[int] = None, -+ tol: Optional[float] = None, -+ backend: Optional[str] = None, -+) -> str: -+ """Pick the eigh variant. Returns the bare name (no ``eigh_`` prefix). -+ -+ The exact path routes to cuSOLVER (with a small-N CPU-MKL / Triton -+ fast path handled inside the cusolver / triton backend). -+ ``jacobi`` is reachable only via explicit ``backend="jacobi"`` -- a -+ single-CTA Triton cyclic-Jacobi kernel that beats cuSOLVER at very -+ small N (N <= 16) but is slower beyond that. No C++ toolchain / -+ ninja is required on first call. -+ """ -+ if backend is not None: -+ return backend -+ return "cusolver" -+ -+ -+def route_op_name(*, N: int, K: Optional[int] = None, -+ tol: Optional[float] = None) -> str: -+ """Canonical ``eigh_`` label.""" -+ return "eigh_" + _route(N=N, K=K, tol=tol) -+ -+ -+def eigh( -+ A: torch.Tensor, -+ K: Optional[int] = None, -+ *, -+ tol: Optional[float] = None, -+ backend: Optional[str] = None, -+ **kwargs, -+): -+ """Symmetric eigendecomposition. -+ -+ Args: -+ A: ``(N, N)`` symmetric tensor. -+ K: optional truncation -- return only top-K eigenpairs (ascending). -+ tol: residual tolerance. ``None`` (default) -> EXACT in the input -+ dtype (cuSOLVER; the Triton ``jacobi`` backend is opt-in via -+ ``backend="jacobi"`` since cuSOLVER beats it past N ~ 16). -+ backend: explicit ``"cusolver" | "jacobi"`` override. -+ **kwargs: forwarded to the chosen variant. -+ -+ Returns: -+ ``(eigvals, eigvecs)`` ascending; both sliced to top-K if ``K`` -+ was supplied. -+ """ -+ if not isinstance(A, torch.Tensor) or A.ndim != 2: -+ raise ValueError("eigh expects a 2D tensor") -+ N = A.size(0) -+ chosen = _route(N=N, K=K, tol=tol, backend=backend) -+ -+ if chosen == "jacobi": -+ eigvals, eigvecs = jacobi.eigh(A, **kwargs) -+ else: # cusolver / default exact -+ # Use the small-D CPU-MKL trick automatically (it IS the exact -+ # path -- input dtype preserved, just dispatched to the faster -+ # solver for small N). -+ eigvals, eigvecs = triton_eigh(A) -+ -+ if K is not None: -+ eigvals = eigvals[-K:] -+ eigvecs = eigvecs[:, -K:] -+ return eigvals, eigvecs -+ -+ -+__all__ = ["eigh", "route_op_name"] -diff --git a/tsvdlib/_kernels/linalg/eigh/jacobi.py b/tsvdlib/_kernels/linalg/eigh/jacobi.py -new file mode 100644 -index 0000000..be549a4 ---- /dev/null -+++ b/tsvdlib/_kernels/linalg/eigh/jacobi.py -@@ -0,0 +1,30 @@ -+"""eigh_jacobi -- single-block row-cyclic Jacobi for small N (<= 128). -+ -+Thin user-facing wrapper over the Triton kernel in -+:mod:`tsvdlib._kernels.linalg.eigh.triton.jacobi`. No CUDA / C++ compile step -+on first call (the old ``jacobi_impl.py`` ``load_inline`` extension -+was retired -- ninja is no longer required to import this backend). -+ -+Achieved residual is at the fp32 noise floor (``~1e-4`` for the -+N=46 reference shape, identical to ``torch.linalg.eigh`` on fp32 -+input), and the kernel beats cuSOLVER ``syevd`` at N <= 16 where the -+syevd launch fixed-cost dominates. -+""" -+from tsvdlib._kernels.linalg.eigh.triton.jacobi import triton_jacobi_eigh as _triton_jacobi -+ -+ -+def eigh(A, num_sweeps: int = 6): -+ """Single-kernel cyclic Jacobi eigensolver. -+ -+ Args: -+ A: ``(N, N)`` symmetric float32 CUDA tensor; not modified. -+ num_sweeps: full row-cyclic sweeps; each sweep performs -+ ``N*(N-1)/2`` Givens rotations. ``6`` is enough to reach -+ fp32 noise (~1e-4) for N <= 64; bump to ``8-12`` for -+ tighter convergence at very large N. -+ -+ Returns: -+ ``(w, V)`` -- ``(N,)`` eigenvalues (ascending) and ``(N, N)`` -+ eigenvectors as columns. ``A @ V[:, i] ≈ w[i] * V[:, i]``. -+ """ -+ return _triton_jacobi(A, num_sweeps=num_sweeps) -diff --git a/tsvdlib/_kernels/linalg/eigh/triton/__init__.py b/tsvdlib/_kernels/linalg/eigh/triton/__init__.py -new file mode 100644 -index 0000000..b6cdf0a ---- /dev/null -+++ b/tsvdlib/_kernels/linalg/eigh/triton/__init__.py -@@ -0,0 +1,28 @@ -+"""eigh triton backend. -+ -+Re-exports the top-level functions / classes / constants from each -+component file. ``@triton.jit`` kernels stay private to their file -+(call them via the Python wrapper that lives next to them). -+ -+Component files: -+ -+* :mod:`householder` -- Householder tridiagonalisation + unrolled -+ QR finaliser. Powers :func:`tsvdlib._kernels.linalg.eigh.triton_eigh`, -+ the small-D (D <= ~256) dense path used by PCA / TruncSVD / -+ Halko subspace iteration. -+* :mod:`jacobi` -- Row-cyclic Givens-Jacobi for N <= 128. -+ Powers :func:`tsvdlib._kernels.linalg.eigh.eigh_jacobi`, an opt-in -+ backend that beats cuSOLVER at very small N (N <= 16) without -+ the ``load_inline`` CUDA / C++ compile step the old -+ implementation needed. -+""" -+from tsvdlib._kernels.linalg.eigh.triton.householder import ( -+ _eigh_cpu_initialized, -+ triton_eigh, -+) -+from tsvdlib._kernels.linalg.eigh.triton.jacobi import triton_jacobi_eigh -+ -+__all__ = [ -+ "triton_eigh", -+ "triton_jacobi_eigh", -+] -diff --git a/tsvdlib/_kernels/linalg/eigh/triton/householder.py b/tsvdlib/_kernels/linalg/eigh/triton/householder.py -new file mode 100644 -index 0000000..0dff1c8 ---- /dev/null -+++ b/tsvdlib/_kernels/linalg/eigh/triton/householder.py -@@ -0,0 +1,171 @@ -+"""Triton eigendecomposition for small dense symmetric matrices. -+ -+Uses Householder tridiagonalisation followed by an unrolled QR loop. -+Designed for D ≤ ~256 — the small-matrix path used by PCA, TruncSVD, -+and the Halko subspace-iteration finaliser (linalg/eigh/halko.py). -+ -+Public surface: -+ - triton_eigh(A) -> (eigvals, eigvecs) for A: (D, D) fp32 symmetric. -+""" -+ -+import torch -+import triton -+import triton.language as tl -+ -+ -+# ============================================================================= -+# Kernel: Householder Tridiagonalization + Eigensolver -+# -+# Single-program Triton kernel: reduces symmetric A to tridiagonal form T via -+# Householder reflections. All data stays in L2 for D ≤ ~2048. -+# -+# The tridiagonal eigenvalues are found via Sturm bisection (no cuSOLVER). -+# Eigenvectors: inverse iteration on T, then Householder back-transformation. -+# -+# For D=256: ~0.4ms total vs cuSOLVER eigh ~5ms. The win comes from avoiding -+# cuSOLVER's ~5ms fixed overhead of internal kernel launches/workspace alloc. -+# ============================================================================= -+ -+@triton.jit -+def _householder_tridiag_kernel( -+ A_ptr, p_buf_ptr, tau_ptr, -+ D, stride_a, -+ BLOCK: tl.constexpr, -+): -+ """Householder tridiagonalization: symmetric A → tridiagonal (in-place). -+ -+ After kernel: -+ - A.diagonal() = tridiagonal diagonal -+ - A.diagonal(offset=-1) = tridiagonal subdiagonal -+ - A lower triangle (below subdiag): normalized Householder vectors v'[1:] -+ where v' = v / v[0], so v'[0] = 1 (implicit) -+ - tau_ptr[k] = tau' = 2 * v[0]² / ||v||² (LAPACK-style scalar) -+ """ -+ offs = tl.arange(0, BLOCK) -+ -+ for k in tl.range(0, D - 2): -+ n = D - k - 1 -+ base = k + 1 -+ -+ # ── Compute ||x||² for x = A[base:D, k] ── -+ xnorm_sq = 0.0 -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ xj = tl.load(A_ptr + (base + j) * stride_a + k, mask=mask, other=0.0) -+ xnorm_sq += tl.sum(xj * xj) -+ -+ if xnorm_sq > 1e-30: -+ xnorm = tl.sqrt(xnorm_sq) -+ x0 = tl.load(A_ptr + base * stride_a + k) -+ -+ # alpha = -sign(x0) * ||x|| -+ alpha = tl.where(x0 >= 0.0, -xnorm, xnorm) -+ -+ # v[0] = x[0] - alpha -+ v0 = x0 - alpha -+ -+ # tau_raw = 2 / ||v||² where ||v||² = v0² + ||x[1:]||² -+ vnorm_sq = v0 * v0 + (xnorm_sq - x0 * x0) -+ tau = 2.0 / vnorm_sq -+ -+ # LAPACK convention: normalize v' = v / v[0], tau' = tau * v0² -+ # v'[0] = 1 (implicit), v'[1:] = v[1:] / v[0] -+ # Store v'[1:] in A[base+1:D, k] (overwrites x[1:]) -+ inv_v0 = 1.0 / v0 -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = (j < n) & (j > 0) -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, mask=mask, other=0.0) -+ tl.store(A_ptr + (base + j) * stride_a + k, vj * inv_v0, mask=mask) -+ -+ # Store v'[0] = 1.0 temporarily at A[base, k] (for the matvec) -+ tl.store(A_ptr + base * stride_a + k, 1.0) -+ -+ tau_prime = tau * v0 * v0 -+ tl.store(tau_ptr + k, tau_prime) -+ -+ # ── p = tau' * A_sub @ v' ── -+ # A_sub = A[base:D, base:D], v' = A[base:D, k] (normalized, v'[0]=1) -+ for i in tl.range(0, n): -+ dot = 0.0 -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ aij = tl.load(A_ptr + (base + i) * stride_a + (base + j), -+ mask=mask, other=0.0) -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, -+ mask=mask, other=0.0) -+ dot += tl.sum(aij * vj) -+ tl.store(p_buf_ptr + i, dot * tau_prime) -+ -+ # ── w = p - (tau'/2 * v'.T @ p) * v' ── -+ vtp = 0.0 -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, -+ mask=mask, other=0.0) -+ pj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) -+ vtp += tl.sum(vj * pj) -+ -+ coeff = 0.5 * tau_prime * vtp -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ pj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, -+ mask=mask, other=0.0) -+ tl.store(p_buf_ptr + j, pj - coeff * vj, mask=mask) -+ -+ # ── A_sub -= v @ w.T + w @ v.T (symmetric rank-2 update) ── -+ for i in tl.range(0, n): -+ vi = tl.load(A_ptr + (base + i) * stride_a + k) -+ wi = tl.load(p_buf_ptr + i) -+ for b in tl.range(0, n, BLOCK): -+ j = (b + offs).to(tl.int64) -+ mask = j < n -+ aij = tl.load(A_ptr + (base + i) * stride_a + (base + j), -+ mask=mask, other=0.0) -+ vj = tl.load(A_ptr + (base + j) * stride_a + k, -+ mask=mask, other=0.0) -+ wj = tl.load(p_buf_ptr + j, mask=mask, other=0.0) -+ tl.store(A_ptr + (base + i) * stride_a + (base + j), -+ aij - vi * wj - wi * vj, mask=mask) -+ -+ # Store subdiagonal (overwrites v[0], but v[1:] survives for back-transform) -+ tl.store(A_ptr + base * stride_a + k, alpha) -+ tl.store(A_ptr + k * stride_a + base, alpha) -+ -+ -+_eigh_cpu_initialized = False -+ -+def triton_eigh(A: torch.Tensor) -> tuple: -+ """Eigendecomposition: CPU MKL LAPACK for D ≤ 512, cuSOLVER for D > 512. -+ -+ For D ≤ 512: GPU→CPU + MKL dsyev (4 threads) + CPU→GPU. -+ Avoids cuSOLVER's fixed overhead (~5ms kernel launches + workspace). -+ D=64: 0.18ms (4.5x faster), D=256: 1.8ms (2.6x), D=512: 6ms (2.2x). -+ For D > 512: cuSOLVER eigh (multi-SM parallelism, O(D³) dominates). -+ -+ Why not Triton Householder on GPU? Single-SM approach is 4-73x slower -+ than cuSOLVER — serial Householder loop bottlenecked by per-iteration -+ latency. Eigendecomposition requires multi-SM parallelism for large D. -+ """ -+ D = A.shape[0] -+ -+ if D > 512: -+ return torch.linalg.eigh(A) -+ -+ # CPU MKL LAPACK with 4 threads: optimal for D ≤ 512 eigendecomposition. -+ # set_num_threads has ~3ms overhead per call, so we set it once. -+ global _eigh_cpu_initialized -+ if not _eigh_cpu_initialized: -+ torch.set_num_threads(4) -+ _eigh_cpu_initialized = True -+ -+ torch.cuda.synchronize() -+ A_cpu = A.cpu() -+ eigenvalues, eigenvectors = torch.linalg.eigh(A_cpu) -+ return eigenvalues.to(A.device), eigenvectors.to(A.device) -+ -diff --git a/tsvdlib/_kernels/linalg/eigh/triton/jacobi.py b/tsvdlib/_kernels/linalg/eigh/triton/jacobi.py -new file mode 100644 -index 0000000..351d508 ---- /dev/null -+++ b/tsvdlib/_kernels/linalg/eigh/triton/jacobi.py -@@ -0,0 +1,219 @@ -+"""Triton row-cyclic Jacobi eigensolver for small symmetric matrices (N <= 128). -+ -+Single-program kernel — one CTA holds the full A and V matrices in HBM -+(small enough at N ≤ 128 that even the slow HBM round-trips per -+rotation cost <100 µs on H100) and performs a classical row-cyclic -+Jacobi sweep: ``N*(N-1)/2`` Givens rotations per sweep, default 6 -+sweeps. Each rotation zeroes the off-diagonal element ``A[p, q]`` via -+the two-sided update ``A' = G^T A G`` and accumulates ``V' = V G``. -+ -+Replaces the previous ``jacobi_impl.py`` ``torch.utils.cpp_extension. -+load_inline`` CUDA kernel -- removes the first-call ``ninja`` C++ -+compile step so the user never needs a CUDA toolchain installed to -+use ``tsvdlib._kernels.linalg.eigh.eigh(..., backend="jacobi")``. -+ -+Sequencing vs Brent-Luk -+----------------------- -+The CUDA original used Brent-Luk *parallel* pairing (N/2 simultaneous -+rotations per round, N-1 rounds per sweep), which converges in 8-12 -+sweeps. The Triton version uses *cyclic* pairing (one rotation at a -+time) which converges in 5-8 sweeps but pays per-rotation kernel -+overhead. At N ≤ 64 the cyclic path runs in ~0.5 ms with 6 sweeps, -+well under the cuSOLVER ``syevd`` fixed launch overhead (~5 ms) the -+``jacobi`` backend was added to beat. For N > 128 the cyclic-Jacobi -+constant factor crosses cuSOLVER's; the dispatcher in -+``linalg.eigh.impl`` caps the jacobi backend at N ≤ 128 anyway. -+ -+Public surface -+-------------- -+* :func:`triton_jacobi_eigh` -- ``(A, num_sweeps) -> (eigvals, eigvecs)``; -+ fp32 symmetric input, fp32 sorted-ascending output. -+""" -+from __future__ import annotations -+ -+import torch -+import triton -+import triton.language as tl -+ -+ -+@triton.jit -+def _jacobi_cyclic_kernel( -+ A_ptr, # (N_PAD, N_PAD) fp32, row-major; overwritten in-place -+ V_ptr, # (N_PAD, N_PAD) fp32, row-major; initialised to I + accumulates -+ N, # active dimension (may be < N_PAD when caller padded) -+ N_PAD: tl.constexpr, -+ NUM_SWEEPS: tl.constexpr, -+): -+ """Row-cyclic Jacobi: one CTA, sequential pair rotations. -+ -+ For each sweep, iterate (p, q) with 0 <= p < q < N; compute the -+ Givens rotation that zeroes ``A[p, q]`` and update rows p, q + -+ cols p, q of A and cols p, q of V. ``A_ptr`` ends up with the -+ eigenvalues on its diagonal (not yet sorted); ``V_ptr`` ends up -+ with the corresponding eigenvectors as columns. -+ -+ Grid: ``(1,)``. Strides: row-major, so element ``[i, j]`` lives -+ at ``ptr + i * N_PAD + j``. -+ """ -+ n_offs = tl.arange(0, N_PAD) -+ n_mask = n_offs < N -+ -+ # ── Initialise V = I_{N_PAD} ──────────────────────────────────── -+ # The padding rows/cols stay zero off-diagonal and one on the -+ # diagonal so the padded eigenvalues are exactly the diagonal of -+ # the padded A (which the host has already set to a sentinel -+ # value large enough to sort to the end and be dropped). -+ v_init = (n_offs[:, None] == n_offs[None, :]).to(tl.float32) -+ tl.store(V_ptr + n_offs[:, None] * N_PAD + n_offs[None, :], v_init) -+ -+ for _sweep in tl.range(0, NUM_SWEEPS): -+ for p in tl.range(0, N - 1): -+ for q in tl.range(p + 1, N): -+ # ── Compute Givens c, s zeroing A[p, q] ───────────── -+ # Two-sided rotation G^T A G with -+ # G[p,p]=c, G[p,q]=-s, G[q,p]=s, G[q,q]=c -+ # zeroes A[p,q] when tau = (A[p,p] - A[q,q]) / (2 A[p,q]). -+ a_pp = tl.load(A_ptr + p * N_PAD + p) -+ a_qq = tl.load(A_ptr + q * N_PAD + q) -+ a_pq = tl.load(A_ptr + p * N_PAD + q) -+ -+ thresh = 1e-30 * (tl.abs(a_pp) + tl.abs(a_qq) + 1e-37) -+ if tl.abs(a_pq) > thresh: -+ d = a_pp - a_qq -+ theta = d / (2.0 * a_pq) -+ if theta >= 0.0: -+ t = 1.0 / (theta + tl.sqrt(1.0 + theta * theta)) -+ else: -+ t = 1.0 / (theta - tl.sqrt(1.0 + theta * theta)) -+ c = 1.0 / tl.sqrt(1.0 + t * t) -+ s = t * c -+ else: -+ c = 1.0 -+ s = 0.0 -+ -+ # ── Row update on A: rows p and q ─────────────────── -+ row_p = tl.load( -+ A_ptr + p * N_PAD + n_offs, mask=n_mask, other=0.0, -+ ) -+ row_q = tl.load( -+ A_ptr + q * N_PAD + n_offs, mask=n_mask, other=0.0, -+ ) -+ new_row_p = c * row_p + s * row_q -+ new_row_q = -s * row_p + c * row_q -+ tl.store(A_ptr + p * N_PAD + n_offs, new_row_p, mask=n_mask) -+ tl.store(A_ptr + q * N_PAD + n_offs, new_row_q, mask=n_mask) -+ -+ # ── Column update on A: cols p and q ──────────────── -+ # The row update just touched A[p, :] and A[q, :], so -+ # the column reads here see the *post-row-update* -+ # values. The two-sided update is correct because -+ # only A[p, p] / A[p, q] / A[q, p] / A[q, q] depend -+ # on both row and col rotations -- and at convergence -+ # A[p, q] -> 0 so the order doesn't affect the limit. -+ col_p = tl.load( -+ A_ptr + n_offs * N_PAD + p, mask=n_mask, other=0.0, -+ ) -+ col_q = tl.load( -+ A_ptr + n_offs * N_PAD + q, mask=n_mask, other=0.0, -+ ) -+ new_col_p = c * col_p + s * col_q -+ new_col_q = -s * col_p + c * col_q -+ tl.store(A_ptr + n_offs * N_PAD + p, new_col_p, mask=n_mask) -+ tl.store(A_ptr + n_offs * N_PAD + q, new_col_q, mask=n_mask) -+ -+ # ── Eigenvector accumulation V <- V G ─────────────── -+ v_col_p = tl.load( -+ V_ptr + n_offs * N_PAD + p, mask=n_mask, other=0.0, -+ ) -+ v_col_q = tl.load( -+ V_ptr + n_offs * N_PAD + q, mask=n_mask, other=0.0, -+ ) -+ new_v_col_p = c * v_col_p + s * v_col_q -+ new_v_col_q = -s * v_col_p + c * v_col_q -+ tl.store(V_ptr + n_offs * N_PAD + p, new_v_col_p, mask=n_mask) -+ tl.store(V_ptr + n_offs * N_PAD + q, new_v_col_q, mask=n_mask) -+ -+ -+def _next_pow2(x: int) -> int: -+ if x <= 1: -+ return 1 -+ return 1 << (x - 1).bit_length() -+ -+ -+# Practical upper bound: at N=128 + 6 sweeps the kernel issues -+# ~48K rotations sequentially. Empirically this runs in ~3 ms on -+# H100/H200. Above N=128 the cyclic-Jacobi constant factor crosses -+# cuSOLVER's; ``linalg.eigh.impl`` honours this cap when routing. -+_MAX_N = 128 -+ -+ -+def triton_jacobi_eigh(A: torch.Tensor, num_sweeps: int = 6): -+ """Eigendecomposition of symmetric ``A`` via row-cyclic Triton Jacobi. -+ -+ Args: -+ A: ``(N, N)`` symmetric float32 CUDA tensor. Not modified -+ (a working copy is allocated internally). -+ num_sweeps: number of full Jacobi sweeps (each = ``N*(N-1)/2`` -+ Givens rotations). Default 6 -- ~1e-6 residual for -+ well-conditioned spectra. -+ -+ Returns: -+ ``(w, V)`` where ``w`` is ``(N,)`` eigenvalues in ascending -+ order and ``V`` is ``(N, N)`` eigenvectors as columns -+ (``A @ V[:, i] ≈ w[i] * V[:, i]``). -+ """ -+ assert A.dim() == 2 and A.size(0) == A.size(1), "A must be square" -+ assert A.dtype == torch.float32, "only float32 supported" -+ assert A.is_cuda, "A must be on CUDA" -+ -+ N = A.size(0) -+ if N > _MAX_N: -+ raise ValueError( -+ f"triton_jacobi_eigh only supports N <= {_MAX_N}; got N={N}. " -+ f"Route to ``backend='cusolver'`` for larger problems." -+ ) -+ -+ # Pad to the next power of two so the Triton tile is a -+ # constexpr-friendly size. The padding rows/cols get a sentinel -+ # diagonal entry that sorts to the end and is dropped on return. -+ N_PAD = max(2, _next_pow2(N)) -+ if N == N_PAD: -+ A_work = A.contiguous().clone() -+ else: -+ # Sentinel: 10x the largest diagonal absolute value of A so -+ # the padded eigenvalues sort cleanly to the end of ``w`` -+ # and are removed by the slice below. -+ diag_abs = float(torch.max(torch.abs(torch.diagonal(A))).item()) + 1.0 -+ sentinel = diag_abs * 10.0 -+ A_work = torch.zeros( -+ N_PAD, N_PAD, dtype=torch.float32, device=A.device, -+ ) -+ A_work[:N, :N] = A -+ # Set the padded diagonal entries to the sentinel. -+ idx = torch.arange(N, N_PAD, device=A.device) -+ A_work[idx, idx] = sentinel -+ -+ V_work = torch.empty(N_PAD, N_PAD, dtype=torch.float32, device=A.device) -+ -+ # Single-CTA, single-warp launch. The kernel is dominated by -+ # the sequential dependency between successive Givens rotations, -+ # so adding warps would only waste threads on broadcast scalars -+ # (and risks losing convergence on N_PAD >= 64 where multi-warp -+ # scalar reads can race with the immediately preceding store on -+ # some Triton 3.x configs). -+ _jacobi_cyclic_kernel[(1,)]( -+ A_work, V_work, -+ N_PAD, # the kernel iterates over the full padded N_PAD -+ N_PAD=N_PAD, -+ NUM_SWEEPS=num_sweeps, -+ num_warps=1, -+ ) -+ -+ w_padded = torch.diagonal(A_work).clone() -+ w_sorted, idx = torch.sort(w_padded) -+ V_sorted = V_work[:, idx] -+ -+ if N == N_PAD: -+ return w_sorted.contiguous(), V_sorted.contiguous() -+ # Drop the ``N_PAD - N`` sentinel eigenpairs sitting at the tail. -+ return w_sorted[:N].contiguous(), V_sorted[:N, :N].contiguous() -diff --git a/tsvdlib/_kernels/primitives/__init__.py b/tsvdlib/_kernels/primitives/__init__.py -new file mode 100644 -index 0000000..e69de29 -diff --git a/tsvdlib/_kernels/primitives/truncated_svd/__init__.py b/tsvdlib/_kernels/primitives/truncated_svd/__init__.py -new file mode 100644 -index 0000000..e69de29 -diff --git a/tsvdlib/_kernels/primitives/truncated_svd/triton/__init__.py b/tsvdlib/_kernels/primitives/truncated_svd/triton/__init__.py -new file mode 100644 -index 0000000..8002b6e ---- /dev/null -+++ b/tsvdlib/_kernels/primitives/truncated_svd/triton/__init__.py -@@ -0,0 +1,17 @@ -+"""truncated_svd triton backend. -+ -+Re-exports the public Python wrappers from each component file. -+``@triton.jit`` kernels stay private to their file (call them via the -+Python wrapper that lives next to them). -+""" -+from tsvdlib._kernels.primitives.truncated_svd.triton.svd import ( -+ _triton_svd_cov, -+ _triton_svd_dual, -+ triton_truncated_svd, -+ flash_truncated_svd, -+) -+ -+__all__ = [ -+ "triton_truncated_svd", -+ "flash_truncated_svd", -+] -diff --git a/tsvdlib/_kernels/primitives/truncated_svd/triton/svd.py b/tsvdlib/_kernels/primitives/truncated_svd/triton/svd.py -new file mode 100644 -index 0000000..a89ed6c ---- /dev/null -+++ b/tsvdlib/_kernels/primitives/truncated_svd/triton/svd.py -@@ -0,0 +1,86 @@ -+"""Truncated SVD via cuBLAS GEMMs + ``tsvdlib._kernels.linalg.eigh``. -+ -+Auto-dispatches between two paths: -+ -+1. N >= D (cov path): -+ gram = X.T @ X (cuBLAS TF32 GEMM) -+ eigh(gram, K=K, tol=tol) -+ S = sqrt(lambda); Vh = eigvecs.T (descending) -+ -+2. D > N (dual path): -+ G = X @ X.T (cuBLAS TF32 GEMM) -+ eigh(G, K=K, tol=tol) -+ V = X.T @ U; column-normalise; S = sqrt(lambda) -+ -+Mathematically identical to PCA (without centering): -+ SVD singular values = sqrt(eigenvalues of X.T @ X) -+ SVD right singular vectors = eigenvectors of X.T @ X -+ -+Precision is owned by ``tsvdlib._kernels.linalg.eigh.eigh`` -- pass ``tol=None`` -+(default) for an exact path (cuSOLVER / MKL), pass ``tol >= 1e-4`` to opt -+into Halko subspace iteration when shape favours it. -+""" -+import torch -+ -+from tsvdlib._kernels.linalg.eigh import eigh -+ -+ -+def _triton_svd_cov(X: torch.Tensor, K: int, *, tol=None): -+ """Truncated SVD via cuBLAS TF32 cov GEMM + eigh on D x D.""" -+ N, D = X.shape -+ gram = X.T @ X # cuBLAS TF32 GEMM -+ top_eigvals, top_eigvecs = eigh(gram, K=K, tol=tol) -+ S = torch.sqrt(top_eigvals.clamp(min=0)).flip(0) -+ Vh = top_eigvecs.T.flip(0) # (K, D), descending -+ return S, Vh -+ -+ -+def _triton_svd_dual(X: torch.Tensor, K: int, *, tol=None): -+ """Truncated SVD via cuBLAS gram GEMM + eigh on N x N + projection.""" -+ N, D = X.shape -+ G = X @ X.T # cuBLAS TF32 GEMM -+ top_eigvals, U = eigh(G, K=K, tol=tol) -+ V = X.T @ U # cuBLAS TF32 GEMM -+ V = V / V.norm(dim=0, keepdim=True).clamp(min=1e-10) -+ S = torch.sqrt(top_eigvals.clamp(min=0)).flip(0) -+ Vh = V.T.flip(0) # (K, D), descending -+ return S, Vh -+ -+ -+def triton_truncated_svd(X: torch.Tensor, K: int, *, tol=None): -+ """Truncated SVD: picks the path whose eigh dimension is smaller. -+ -+ All ``torch.matmul`` calls run with TF32 enabled on Hopper/Ampere -+ (the same internal precision the prior in-house Triton kernels used); -+ the global flag is restored on exit. -+ -+ Args: -+ X: ``(N, D)`` input on CUDA. -+ K: number of singular components. -+ tol: residual tolerance. ``None`` (default) -> exact eigh on the -+ cov / Gram matrix. Otherwise: Halko if ``tol >= 1e-4`` AND -+ ``K*4 < M`` AND ``M >= 256`` (``M = D`` for cov, ``N`` for -+ dual); QDWH variants for very large ``N``. -+ -+ Returns: -+ S: ``(K,)`` top-K singular values, descending. -+ Vh: ``(K, D)`` top-K right singular vectors, rows. -+ """ -+ # tol=None -> EXACT: keep PyTorch's default IEEE matmul (no TF32). -+ # tol>0 -> caller opted into a lossy budget: TF32 enabled for -+ # auxiliary ``@`` ops; restored on exit. -+ use_tf32 = tol is not None and tol > 0 -+ prev_tf32 = torch.backends.cuda.matmul.allow_tf32 -+ if use_tf32: -+ torch.backends.cuda.matmul.allow_tf32 = True -+ try: -+ N, D = X.shape -+ if D <= N: -+ return _triton_svd_cov(X, K, tol=tol) -+ return _triton_svd_dual(X, K, tol=tol) -+ finally: -+ if use_tf32: -+ torch.backends.cuda.matmul.allow_tf32 = prev_tf32 -+ -+ -+flash_truncated_svd = triton_truncated_svd -diff --git a/tsvdlib/tsvd.py b/tsvdlib/tsvd.py -index f1b4fe7..96b5c0c 100644 ---- a/tsvdlib/tsvd.py -+++ b/tsvdlib/tsvd.py -@@ -1,36 +1,27 @@ --"""Truncated SVD of a dense matrix -- the reference you must optimise. -+"""Truncated SVD of a dense matrix -- vendored best-in-repo Triton path. - --This implementation is intentionally naive. It computes the FULL singular value --decomposition of the ``(N, D)`` matrix with :func:`torch.linalg.svd` and then --slices off the top ``n_components`` factors. Computing the full SVD is --``O(N D^2)`` with a large constant and produces all ``min(N, D)`` singular --triples even though only the leading ``k`` are ever used. It is correct and --deterministic, but it does far more work and moves far more memory than a --truncated decomposition needs. -- --Contract (do NOT change): -+Delegates to the exact Triton truncated-SVD entry vendored under -+``tsvdlib._kernels``. It forms the Gram / cov matrix with cuBLAS GEMMs and -+runs an exact top-k eigendecomposition (cuSOLVER, with a small-D CPU-MKL -+fast path) instead of the naive full ``torch.linalg.svd``. The public -+contract is unchanged: - - truncated_svd(x, n_components) -> (singular_values, components) - -- x : (N, D) float32 CUDA tensor. NOT centered -- this is a -- truncated SVD of the raw matrix, not PCA. -+ x : (N, D) float32 CUDA tensor. NOT centered. - n_components (k) : int, number of leading singular triples to return. - -- singular_values : (k,) float32, the top-k singular values in DESCENDING -- order. -- components : (k, D) float32, the top-k right singular vectors as -- rows. The rows are orthonormal. -- --You may add modules/kernels inside the ``tsvdlib`` package and rewrite the body --of :func:`truncated_svd` freely (e.g. a Gram-matrix + top-k eigendecomposition, --fused kernels, better memory traffic), as long as the public contract above is --preserved. -+ singular_values : (k,) float32, top-k singular values, DESCENDING. -+ components : (k, D) float32, top-k right singular vectors as -+ rows (orthonormal). - """ - from __future__ import annotations - - import torch - -+from tsvdlib._kernels.primitives.truncated_svd.triton.svd import triton_truncated_svd -+ - - def truncated_svd(x, n_components): -- U, S, Vh = torch.linalg.svd(x, full_matrices=False) # full SVD -- naive/expensive -- return S[:n_components].contiguous(), Vh[:n_components].contiguous() -+ s, vh = triton_truncated_svd(x, n_components, tol=None) # (k,), (k, D) descending -+ return s.contiguous(), vh.contiguous() diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/__init__.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/__init__.py deleted file mode 100644 index 759e8c233..000000000 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -"""tsvdlib -- a tiny GPU truncated-SVD library you are asked to make fast. - -The public entry point is :func:`truncated_svd`. The shipped implementation is -correct but deliberately unoptimised: it computes the FULL SVD of the ``(N, D)`` -matrix and slices off the top-k factors. Your task is to rewrite the internals -(Gram-matrix + top-k eigendecomposition, fused kernels, reduced memory traffic, -...) so that :func:`truncated_svd` runs as fast as possible while producing -factors of the same quality. The public function signature and return contract -must not change. -""" -from __future__ import annotations - -from tsvdlib.tsvd import truncated_svd - -__all__ = ["truncated_svd"] -__version__ = "0.1.0" diff --git a/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/tsvd.py b/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/tsvd.py deleted file mode 100644 index f1b4fe7db..000000000 --- a/2.0/problems/truncated_svd_gpu_kernel_optimization/tsvdlib/tsvd.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Truncated SVD of a dense matrix -- the reference you must optimise. - -This implementation is intentionally naive. It computes the FULL singular value -decomposition of the ``(N, D)`` matrix with :func:`torch.linalg.svd` and then -slices off the top ``n_components`` factors. Computing the full SVD is -``O(N D^2)`` with a large constant and produces all ``min(N, D)`` singular -triples even though only the leading ``k`` are ever used. It is correct and -deterministic, but it does far more work and moves far more memory than a -truncated decomposition needs. - -Contract (do NOT change): - - truncated_svd(x, n_components) -> (singular_values, components) - - x : (N, D) float32 CUDA tensor. NOT centered -- this is a - truncated SVD of the raw matrix, not PCA. - n_components (k) : int, number of leading singular triples to return. - - singular_values : (k,) float32, the top-k singular values in DESCENDING - order. - components : (k, D) float32, the top-k right singular vectors as - rows. The rows are orthonormal. - -You may add modules/kernels inside the ``tsvdlib`` package and rewrite the body -of :func:`truncated_svd` freely (e.g. a Gram-matrix + top-k eigendecomposition, -fused kernels, better memory traffic), as long as the public contract above is -preserved. -""" -from __future__ import annotations - -import torch - - -def truncated_svd(x, n_components): - U, S, Vh = torch.linalg.svd(x, full_matrices=False) # full SVD -- naive/expensive - return S[:n_components].contiguous(), Vh[:n_components].contiguous() From cad2380b3edfe61072837ee9b3adbc084a113a07 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 1 Jul 2026 11:55:05 +0000 Subject: [PATCH 09/34] fix(2.0/flashlib): smoke_images.sh works on the torch-less images The images are intentionally torch-less (GPU work offloads to Modal), so the old `import ` check always failed (the package imports torch). Validate what a torch-less image actually can: modal + flash_gpu host-side import, plus a py_compile parse of the frozen baseline + package. Fixed across all four tasks. Co-Authored-By: Claude Opus 4.8 --- .../docker/smoke_images.sh | 23 +++++++++++-------- .../docker/smoke_images.sh | 23 +++++++++++-------- .../docker/smoke_images.sh | 23 +++++++++++-------- .../docker/smoke_images.sh | 23 +++++++++++-------- 4 files changed, 52 insertions(+), 40 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/dbscan_gpu_kernel_optimization/docker/smoke_images.sh index 401de9461..012809f0d 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/docker/smoke_images.sh +++ b/2.0/problems/dbscan_gpu_kernel_optimization/docker/smoke_images.sh @@ -1,19 +1,22 @@ #!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU / Modal required). +# Smoke test for the built (intentionally torch-less) images: confirm the host-side +# harness imports (modal + flash_gpu) and that the frozen baseline + package parse. +# torch/triton live on the Modal image, so the packages are py_compiled here (a +# torch-less parse check), not imported. No GPU / Modal tokens required. set -euo pipefail AGENT_TAG=${AGENT_TAG:-frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.2.0} JUDGE_TAG=${JUDGE_TAG:-frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.2.0} +SMOKE='import sys, modal, py_compile, pathlib +sys.path.insert(0, "/opt"); import flash_gpu +for root in sys.argv[1:]: + for p in pathlib.Path(root).rglob("*.py"): + py_compile.compile(str(p), doraise=True) +print("ok: modal + flash_gpu import; dbscan baseline + dbscanlib parse")' + echo "== agent image ==" -docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/dbscan_ref'); import refdbscan; \ -import dbscanlib; print('agent ok: modal + flash_gpu + refdbscan + dbscanlib import')" +docker run --rm -w /app "$AGENT_TAG" python3 -c "$SMOKE" /opt/dbscan_ref /app/dbscanlib echo "== judge image ==" -docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/dbscan_ref'); import refdbscan; \ -sys.path.insert(0, '/opt/dbscanlib-clean'); import dbscanlib; \ -print('judge ok: modal + flash_gpu + refdbscan + dbscanlib import')" +docker run --rm "$JUDGE_TAG" python3 -c "$SMOKE" /opt/dbscan_ref /opt/dbscanlib-clean/dbscanlib diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/smoke_images.sh index 045ffd3a0..d40fe4cfc 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/smoke_images.sh +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/smoke_images.sh @@ -1,19 +1,22 @@ #!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU / Modal required). +# Smoke test for the built (intentionally torch-less) images: confirm the host-side +# harness imports (modal + flash_gpu) and that the frozen baseline + package parse. +# torch/triton live on the Modal image, so the packages are py_compiled here (a +# torch-less parse check), not imported. No GPU / Modal tokens required. set -euo pipefail AGENT_TAG=${AGENT_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.1.0} JUDGE_TAG=${JUDGE_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.1.0} +SMOKE='import sys, modal, py_compile, pathlib +sys.path.insert(0, "/opt"); import flash_gpu +for root in sys.argv[1:]: + for p in pathlib.Path(root).rglob("*.py"): + py_compile.compile(str(p), doraise=True) +print("ok: modal + flash_gpu import; ivfpq baseline + ivfpqlib parse")' + echo "== agent image ==" -docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/ivfpq_ref'); import refivfpq; \ -import ivfpqlib; print('agent ok: modal + flash_gpu + refivfpq + ivfpqlib import')" +docker run --rm -w /app "$AGENT_TAG" python3 -c "$SMOKE" /opt/ivfpq_ref /app/ivfpqlib echo "== judge image ==" -docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/ivfpq_ref'); import refivfpq; \ -sys.path.insert(0, '/opt/ivfpqlib-clean'); import ivfpqlib; \ -print('judge ok: modal + flash_gpu + refivfpq + ivfpqlib import')" +docker run --rm "$JUDGE_TAG" python3 -c "$SMOKE" /opt/ivfpq_ref /opt/ivfpqlib-clean/ivfpqlib diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh index 8a091615f..f82eb4fee 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/smoke_images.sh @@ -1,19 +1,22 @@ #!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU / Modal required). +# Smoke test for the built (intentionally torch-less) images: confirm the host-side +# harness imports (modal + flash_gpu) and that the frozen baseline + package parse. +# torch/triton live on the Modal image, so the packages are py_compiled here (a +# torch-less parse check), not imported. No GPU / Modal tokens required. set -euo pipefail AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.2.0} JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.2.0} +SMOKE='import sys, modal, py_compile, pathlib +sys.path.insert(0, "/opt"); import flash_gpu +for root in sys.argv[1:]: + for p in pathlib.Path(root).rglob("*.py"): + py_compile.compile(str(p), doraise=True) +print("ok: modal + flash_gpu import; kmeans baseline + kmeanslib parse")' + echo "== agent image ==" -docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/kmeans_ref'); import refkmeans; \ -import kmeanslib; print('agent ok: modal + flash_gpu + refkmeans + kmeanslib import')" +docker run --rm -w /app "$AGENT_TAG" python3 -c "$SMOKE" /opt/kmeans_ref /app/kmeanslib echo "== judge image ==" -docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/kmeans_ref'); import refkmeans; \ -sys.path.insert(0, '/opt/kmeanslib-clean'); import kmeanslib; \ -print('judge ok: modal + flash_gpu + refkmeans + kmeanslib import')" +docker run --rm "$JUDGE_TAG" python3 -c "$SMOKE" /opt/kmeans_ref /opt/kmeanslib-clean/kmeanslib diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh b/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh index b606f59db..0ef7f8b62 100755 --- a/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/smoke_images.sh @@ -1,19 +1,22 @@ #!/usr/bin/env bash -# Import-only smoke test for the built images (no GPU / Modal required). +# Smoke test for the built (intentionally torch-less) images: confirm the host-side +# harness imports (modal + flash_gpu) and that the frozen baseline + package parse. +# torch/triton live on the Modal image, so the packages are py_compiled here (a +# torch-less parse check), not imported. No GPU / Modal tokens required. set -euo pipefail AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.2.0} JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.2.0} +SMOKE='import sys, modal, py_compile, pathlib +sys.path.insert(0, "/opt"); import flash_gpu +for root in sys.argv[1:]: + for p in pathlib.Path(root).rglob("*.py"): + py_compile.compile(str(p), doraise=True) +print("ok: modal + flash_gpu import; knn baseline + knnlib parse")' + echo "== agent image ==" -docker run --rm -w /app "$AGENT_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/knn_ref'); import refknn; \ -import knnlib; print('agent ok: modal + flash_gpu + refknn + knnlib import')" +docker run --rm -w /app "$AGENT_TAG" python3 -c "$SMOKE" /opt/knn_ref /app/knnlib echo "== judge image ==" -docker run --rm "$JUDGE_TAG" python3 -c \ - "import sys, modal; sys.path.insert(0, '/opt'); import flash_gpu; \ -sys.path.insert(0, '/opt/knn_ref'); import refknn; \ -sys.path.insert(0, '/opt/knnlib-clean'); import knnlib; \ -print('judge ok: modal + flash_gpu + refknn + knnlib import')" +docker run --rm "$JUDGE_TAG" python3 -c "$SMOKE" /opt/knn_ref /opt/knnlib-clean/knnlib From 6da0bdf1c600fdbbda3ed801a49dd9b2564e15f9 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 1 Jul 2026 19:34:18 +0000 Subject: [PATCH 10/34] chore(2.0/flashlib): bump ivf_pq judge modal_timeout to 2400s for trial margin Co-Authored-By: Claude Opus 4.8 --- 2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml index dce3fbc0e..3b54da09c 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml @@ -19,7 +19,7 @@ evaluation: cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" pip: ["torch", "numpy"] app_name: "ivfpq-kernel-opt-eval" - modal_timeout_seconds: 1800 + modal_timeout_seconds: 2400 # the naive per-query-loop baseline is seconds/call; give the judge margin (agent speed is unknown in a trial) warmup_iters: 2 timed_iters: 7 recall_threshold: 0.95 # agent results must match the baseline's top-k (iso-result recall@k) on every iteration; the Triton reference measured iso-recall 1.0 on all workloads, so 0.95 leaves margin for ADC tie-breaks From 93738be7b8a393c78551f0de36f9fa96ff3e170b Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 1 Jul 2026 19:56:47 +0000 Subject: [PATCH 11/34] fix(2.0/flashlib): forward ari_threshold to the GPU worker cfg The shared evaluator's _build_cfg() whitelist predated dbscan and omitted ari_threshold, so the judge's worker crashed 'KeyError: ari_threshold' on the dbscan gate (the modal_trial validation masked it by hardcoding the key). Add it to all four evaluators (ivf_pq uses the already-whitelisted recall_threshold). Co-Authored-By: Claude Opus 4.8 --- 2.0/problems/dbscan_gpu_kernel_optimization/evaluator.py | 1 + 2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py | 1 + 2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py | 1 + 2.0/problems/knn_gpu_kernel_optimization/evaluator.py | 1 + 4 files changed, 4 insertions(+) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/evaluator.py b/2.0/problems/dbscan_gpu_kernel_optimization/evaluator.py index a40a41105..963234240 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/evaluator.py @@ -270,6 +270,7 @@ def _build_cfg() -> dict[str, Any]: "recall_threshold": _get_float("recall_threshold", 0.99), "captured_tolerance": _get_float("captured_tolerance", 0.02), "ortho_tolerance": _get_float("ortho_tolerance", 0.02), + "ari_threshold": _get_float("ari_threshold", 0.99), } diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py index d5470146b..198894af8 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py @@ -270,6 +270,7 @@ def _build_cfg() -> dict[str, Any]: "recall_threshold": _get_float("recall_threshold", 0.99), "captured_tolerance": _get_float("captured_tolerance", 0.02), "ortho_tolerance": _get_float("ortho_tolerance", 0.02), + "ari_threshold": _get_float("ari_threshold", 0.99), } diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py b/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py index 2327467aa..3fa120bae 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py @@ -270,6 +270,7 @@ def _build_cfg() -> dict[str, Any]: "recall_threshold": _get_float("recall_threshold", 0.99), "captured_tolerance": _get_float("captured_tolerance", 0.02), "ortho_tolerance": _get_float("ortho_tolerance", 0.02), + "ari_threshold": _get_float("ari_threshold", 0.99), } diff --git a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py index a6df9628b..2fc34c1b9 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py @@ -270,6 +270,7 @@ def _build_cfg() -> dict[str, Any]: "recall_threshold": _get_float("recall_threshold", 0.99), "captured_tolerance": _get_float("captured_tolerance", 0.02), "ortho_tolerance": _get_float("ortho_tolerance", 0.02), + "ari_threshold": _get_float("ari_threshold", 0.99), } From d6a3ebe7b77b04cdcafba3056ff4e7a6afcf6e12 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Thu, 2 Jul 2026 07:36:45 +0000 Subject: [PATCH 12/34] feat(2.0/flashlib): public test reports correctness, not just speed The public test discarded the worker's quality metric and printed only OK/FAIL + speedup, so an agent could not see how close its correctness was to the gate (it had to hand-roll its own harness to read `agent_val`). Now every public shape prints the quality metric agent/ref value plus the gate, pass or fail: correctness gate: ARI >= 0.99 workload status speedup ARI: agent / ref p0 OK 59.70x 1.0000 / 1.0000 p1 FAIL:ari_regression - 0.9731 / 1.0000 Metric is per-primitive (inertia / recall@k / ARI). Readme + docstring updated to say the public test checks correctness and speed. All four tasks. Co-Authored-By: Claude Opus 4.8 --- .../harbor/app/public_test.py | 26 ++++++++++++++----- .../dbscan_gpu_kernel_optimization/readme | 2 +- .../harbor/app/public_test.py | 26 ++++++++++++++----- .../ivf_pq_gpu_kernel_optimization/readme | 2 +- .../harbor/app/public_test.py | 26 ++++++++++++++----- .../kmeans_gpu_kernel_optimization/readme | 2 +- .../harbor/app/public_test.py | 26 ++++++++++++++----- .../knn_gpu_kernel_optimization/readme | 2 +- 8 files changed, 84 insertions(+), 28 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py index 25eb8c592..896ffa316 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,7 +1,7 @@ """Public GPU self-test for the DBSCAN kernel-optimization task. Times your current /app/dbscanlib against the naive baseline on a Modal GPU and -reports the clustering-agreement (ARI) verdict + speedup on two public shapes. +reports the clustering-agreement (ARI) value + speedup on two public shapes. Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET. The graded workloads and thresholds are hidden and differ from these public shapes. """ @@ -67,12 +67,26 @@ def main() -> int: if not result.get("ok"): print(f"worker error: {result.get('error')}") return 1 - print(f"{'workload':10s} {'status':16s} {'speedup':>10s}") + # Correctness first: show the quality metric (agent vs frozen baseline) on every + # shape, pass or fail -- not just the speedup. The graded shapes are hidden and differ. + prim = CFG.get("primitive", "") + if prim in ("knn", "ivfpq"): + mlabel, gate = "recall@k", f">= {CFG.get('recall_threshold')}" + elif prim == "dbscan": + mlabel, gate = "ARI", f">= {CFG.get('ari_threshold')}" + elif prim == "kmeans": + mlabel, gate = "inertia", f"<= (1+{CFG.get('inertia_tolerance')}) x ref (lower is better)" + else: + mlabel, gate = "quality", "" + print(f"correctness gate: {mlabel} {gate}") + print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") for row in result["rows"]: - if row.get("ok"): - print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") - else: - print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason', '')):16s} {'-':>10s}") + av, rv = row.get("agent_val"), row.get("ref_val") + q = (f"{av:.4f} / {rv:.4f}" + if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") + sp = f"{row['speedup']:.2f}x" if row.get("ok") else "-" + st = "OK" if row.get("ok") else "FAIL:" + str(row.get("reason", "")) + print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") return 0 diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/readme b/2.0/problems/dbscan_gpu_kernel_optimization/readme index e0feece33..21ecda762 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/readme +++ b/2.0/problems/dbscan_gpu_kernel_optimization/readme @@ -38,7 +38,7 @@ shapes are different and hidden): ## Iterate on a GPU -The agent workspace has no GPU. Use the public test to time your current code on +The agent workspace has no GPU. Use the public test to check your current code's correctness and speed on a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET`): ```bash diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py index 5ffdd5f38..fd743854b 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,7 +1,7 @@ """Public GPU self-test for the IVF-PQ search kernel-optimization task. Times your current /app/ivfpqlib against the naive baseline on a Modal GPU and -reports the iso-result recall verdict + speedup on two public shapes. The index +reports the iso-result recall value + speedup on two public shapes. The index is built once per shape by the frozen baseline and reused. Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET. The graded workloads and thresholds are hidden and differ from these public shapes. @@ -64,12 +64,26 @@ def main() -> int: if not result.get("ok"): print(f"worker error: {result.get('error')}") return 1 - print(f"{'workload':10s} {'status':16s} {'speedup':>10s}") + # Correctness first: show the quality metric (agent vs frozen baseline) on every + # shape, pass or fail -- not just the speedup. The graded shapes are hidden and differ. + prim = CFG.get("primitive", "") + if prim in ("knn", "ivfpq"): + mlabel, gate = "recall@k", f">= {CFG.get('recall_threshold')}" + elif prim == "dbscan": + mlabel, gate = "ARI", f">= {CFG.get('ari_threshold')}" + elif prim == "kmeans": + mlabel, gate = "inertia", f"<= (1+{CFG.get('inertia_tolerance')}) x ref (lower is better)" + else: + mlabel, gate = "quality", "" + print(f"correctness gate: {mlabel} {gate}") + print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") for row in result["rows"]: - if row.get("ok"): - print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") - else: - print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason', '')):16s} {'-':>10s}") + av, rv = row.get("agent_val"), row.get("ref_val") + q = (f"{av:.4f} / {rv:.4f}" + if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") + sp = f"{row['speedup']:.2f}x" if row.get("ok") else "-" + st = "OK" if row.get("ok") else "FAIL:" + str(row.get("reason", "")) + print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") return 0 diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/readme b/2.0/problems/ivf_pq_gpu_kernel_optimization/readme index bb5679a80..03cfb717d 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/readme +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/readme @@ -51,7 +51,7 @@ shapes are different and hidden): ## Iterate on a GPU -The agent workspace has no GPU. Use the public test to time your current code on +The agent workspace has no GPU. Use the public test to check your current code's correctness and speed on a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET`): ```bash diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py index ba70b7555..0e72aa7b0 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,7 +1,7 @@ """Public GPU self-test for the K-Means kernel-optimization task. Times your current /app/kmeanslib against the naive baseline on a Modal GPU and -reports the quality verdict + speedup on two public shapes. Requires +reports the quality value + speedup on two public shapes. Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and their thresholds are hidden and differ from these public shapes. """ @@ -66,12 +66,26 @@ def main() -> int: if not result.get("ok"): print(f"worker error: {result.get('error')}") return 1 - print(f"{'workload':10s} {'status':16s} {'speedup':>10s}") + # Correctness first: show the quality metric (agent vs frozen baseline) on every + # shape, pass or fail -- not just the speedup. The graded shapes are hidden and differ. + prim = CFG.get("primitive", "") + if prim in ("knn", "ivfpq"): + mlabel, gate = "recall@k", f">= {CFG.get('recall_threshold')}" + elif prim == "dbscan": + mlabel, gate = "ARI", f">= {CFG.get('ari_threshold')}" + elif prim == "kmeans": + mlabel, gate = "inertia", f"<= (1+{CFG.get('inertia_tolerance')}) x ref (lower is better)" + else: + mlabel, gate = "quality", "" + print(f"correctness gate: {mlabel} {gate}") + print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") for row in result["rows"]: - if row.get("ok"): - print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") - else: - print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason','')):16s} {'-':>10s}") + av, rv = row.get("agent_val"), row.get("ref_val") + q = (f"{av:.4f} / {rv:.4f}" + if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") + sp = f"{row['speedup']:.2f}x" if row.get("ok") else "-" + st = "OK" if row.get("ok") else "FAIL:" + str(row.get("reason", "")) + print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") return 0 diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index f8a4b0467..ba1dce152 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -41,7 +41,7 @@ Treat this as a general dense K-Means, not as specific shapes to special-case. ## Iterate on a GPU -The agent workspace has no GPU. Use the public test to time your current code on +The agent workspace has no GPU. Use the public test to check your current code's correctness and speed on a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your environment): diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py index ea693dab0..bc1b6517b 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,7 +1,7 @@ """Public GPU self-test for the brute-force k-NN kernel-optimization task. Times your current /app/knnlib against the naive baseline on a Modal GPU and -reports the quality verdict + speedup on two public shapes. Requires +reports the quality value + speedup on two public shapes. Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and their thresholds are hidden and differ from these public shapes. """ @@ -66,12 +66,26 @@ def main() -> int: if not result.get("ok"): print(f"worker error: {result.get('error')}") return 1 - print(f"{'workload':10s} {'status':16s} {'speedup':>10s}") + # Correctness first: show the quality metric (agent vs frozen baseline) on every + # shape, pass or fail -- not just the speedup. The graded shapes are hidden and differ. + prim = CFG.get("primitive", "") + if prim in ("knn", "ivfpq"): + mlabel, gate = "recall@k", f">= {CFG.get('recall_threshold')}" + elif prim == "dbscan": + mlabel, gate = "ARI", f">= {CFG.get('ari_threshold')}" + elif prim == "kmeans": + mlabel, gate = "inertia", f"<= (1+{CFG.get('inertia_tolerance')}) x ref (lower is better)" + else: + mlabel, gate = "quality", "" + print(f"correctness gate: {mlabel} {gate}") + print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") for row in result["rows"]: - if row.get("ok"): - print(f"{row['id']:10s} {'OK':16s} {row['speedup']:>9.2f}x") - else: - print(f"{row['id']:10s} {'FAIL:' + str(row.get('reason','')):16s} {'-':>10s}") + av, rv = row.get("agent_val"), row.get("ref_val") + q = (f"{av:.4f} / {rv:.4f}" + if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") + sp = f"{row['speedup']:.2f}x" if row.get("ok") else "-" + st = "OK" if row.get("ok") else "FAIL:" + str(row.get("reason", "")) + print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") return 0 diff --git a/2.0/problems/knn_gpu_kernel_optimization/readme b/2.0/problems/knn_gpu_kernel_optimization/readme index b7af10454..43b2ea5e4 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/readme +++ b/2.0/problems/knn_gpu_kernel_optimization/readme @@ -43,7 +43,7 @@ special-case. ## Iterate on a GPU -The agent workspace has no GPU. Use the public test to time your current code on +The agent workspace has no GPU. Use the public test to check your current code's correctness and speed on a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your environment): From 4da2efb29ac34cf8d4b075ddb73dd996081a425c Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Thu, 2 Jul 2026 08:41:14 +0000 Subject: [PATCH 13/34] fix(2.0/flashlib): dbscan agent eval covers all 6 workloads (kill pass-3/fail-6 cliff) codex's dbscan runs kept scoring 0: it optimized against the 3-workload agent eval (agent_workload_count=3, per-workload metrics hidden), passed those, then the 6-workload final failed ari_regression on a held-out workload. Run 1 proved codex CAN pass all 6 (self-verified ARI 1.0 at ~69x) -- it only fails when blind to w3-w5. With the config leak kept, the 3-workload split adds no protection (params are agent-visible), so make the agent eval score all 6 + expose per-workload results for actionable correctness feedback. Co-Authored-By: Claude Opus 4.8 --- 2.0/problems/dbscan_gpu_kernel_optimization/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml index e8944b146..7e7395f83 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml @@ -25,8 +25,8 @@ evaluation: ari_threshold: 0.99 # agent clustering must agree with the baseline (Adjusted Rand Index) on every iteration; the grid reference measured ARI 1.0 on all workloads speedup_target: 13.0 # H100 calibration: flashlib grid DBSCAN reference geomean 13.29x over the O(N^2) matmul baseline (per-workload 7.2/12.1/9.5/18.8/16.6/21.2x) base_seed: 20260701 - agent_workload_count: 3 - expose_per_workload_metrics: false + agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); the params are agent-visible anyway + expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on # N in the 100k-170k band: the O(N^2) matmul baseline is seconds/call (grid kernel # wins 6-20x) but bounded for timed_iters; below ~100k the tensor-core matmul baseline # is faster than the grid kernel's fixed overhead. Vary N + density so agents can't From bab296552d9720a7465e32a53b5b3b68522d187d Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Thu, 2 Jul 2026 21:34:49 +0000 Subject: [PATCH 14/34] fix(2.0/flashlib): kmeans/knn/ivf_pq agent eval covers all 6 workloads too Same pass-3/fail-6 cliff that sank dbscan also sank kmeans: codex's Triton kernel hit 9.25x on the 3 agent workloads (K<=256) but OOM'd on the hidden K=1024 final workload (270KB shared mem > H100's 227KB) because it never got feedback on the large-K regime. Set agent_workload_count=6 + expose_per_workload_metrics=true on all remaining tasks so the agent's submission feedback covers every graded shape. Co-Authored-By: Claude Opus 4.8 --- 2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml | 4 ++-- 2.0/problems/kmeans_gpu_kernel_optimization/config.yaml | 4 ++-- 2.0/problems/knn_gpu_kernel_optimization/config.yaml | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml index 3b54da09c..1a980f0f0 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml @@ -25,8 +25,8 @@ evaluation: recall_threshold: 0.95 # agent results must match the baseline's top-k (iso-result recall@k) on every iteration; the Triton reference measured iso-recall 1.0 on all workloads, so 0.95 leaves margin for ADC tie-breaks speedup_target: 800.0 # H100 calibration: flashlib Triton reference geomean ~898x over the Python-loop naive baseline (per-workload 513/907/1420/1449/442/1234x) base_seed: 20260702 - agent_workload_count: 3 - expose_per_workload_metrics: false + agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway + expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on # The index (the "database") is built ONCE per workload by the frozen baseline # and reused for every timed iteration; only ivf_pq_search is timed. Queries are # regenerated fresh each iteration. M=database size, Q=queries, k=neighbours. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index 62b437538..1d49835f8 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -49,8 +49,8 @@ evaluation: inertia_tolerance: 0.02 # agent inertia must be <= (1+tol) x baseline on every iteration speedup_target: 5.0 # calibrated: reference (flashlib-best) geomean ~5.3x on Modal H100 base_seed: 20260701 - agent_workload_count: 3 # agent role runs a fast subset; final verifier runs them all - expose_per_workload_metrics: false + agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway + expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on # --- hidden workloads (judge-only; config.yaml/task_config.json never enter the agent image) --- workloads: - {id: w0, N: 100000, D: 32, K: 128, max_iters: 12} diff --git a/2.0/problems/knn_gpu_kernel_optimization/config.yaml b/2.0/problems/knn_gpu_kernel_optimization/config.yaml index 091bd8ebf..49c91ff8a 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/knn_gpu_kernel_optimization/config.yaml @@ -48,8 +48,8 @@ evaluation: recall_threshold: 0.99 speedup_target: 5.0 # calibrated: reference (flashlib-best) geomean ~5.3x on Modal H100 base_seed: 20260701 - agent_workload_count: 3 - expose_per_workload_metrics: false + agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway + expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on workloads: - {id: w0, Q: 2048, M: 100000, D: 64, k: 10} - {id: w1, Q: 4096, M: 250000, D: 128, k: 10} From 5bca2757a6fc5cc5dc3ca916dad9fd2fda9f659f Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 5 Jul 2026 04:39:39 +0000 Subject: [PATCH 15/34] feat(2.0/flashlib): kmeans -> bf16 precision lock + max_iters=1 x10-averaged Two setting changes so the kmeans score measures ONLY kernel structure, not precision tricks or loop count: 1. Precision locked to bf16 at the task root (not via prompt). flash_gpu's gen now returns x + init_centroids as bfloat16, so baseline/reference/agent all carry only bf16 precision -- fp16/tf32/fp32 buy nothing on bf16 data and are only slower, closing the "drop precision for speed" reward-hack the earlier codex run exploited. The naive baseline's _assign switched from torch.cdist (upcasts) to an explicit bf16 matmul so the baseline is genuinely bf16-native too (else "use bf16 vs the fp32 baseline" would win with no kernel work). Inertia metric still fp32 (upcast) for a clean gate. Removed the readme line that invited trading precision for speed. 2. max_iters=1 on every workload (time one assign+update -> isolates per-iter kernel efficiency); each workload timed 10x and the per-run speedups AVERAGED (new cfg-driven aggregate: mean, vs the default median). H100 recalibration (bf16, max_iters=1): flashlib reference geomean 4.97x over the bf16 baseline (5.0/2.6/10.9/1.8/12.6/4.7x), inertia drift <0.0012% -> speedup_target 5.0. reference.patch regenerated against the new baseline. flash_gpu changes are kmeans-scoped (bf16 in the kmeans gen branch; aggregate cfg-driven, default median; inertia upcast) -- knn/dbscan/ivf_pq unaffected. Co-Authored-By: Claude Opus 4.8 --- .../kmeans_gpu_kernel_optimization/DESIGN.md | 36 ++++++++++++++----- .../config.yaml | 21 ++++++----- .../flash_gpu.py | 18 +++++++--- .../harbor/app/public_test.py | 5 +-- .../judge/refkmeans.py | 12 +++++-- .../kmeanslib/kmeans.py | 16 ++++++--- .../kmeans_gpu_kernel_optimization/readme | 30 +++++++++------- .../reference.patch | 20 +++++++---- 8 files changed, 108 insertions(+), 50 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md index 6cf54440e..a2ffd7bad 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md +++ b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md @@ -70,11 +70,31 @@ convention-independent, robust to fp drift, cheat-proof. `init_centroids` are always supplied and `tol = 0`, so the baseline is a deterministic function of `(x, init_centroids, max_iters)`. -## Calibration TODO (needs a Modal GPU trial) - -- Run `reference.patch` on Modal; confirm it passes the inertia gate on all - workloads (bump `inertia_tolerance` if flashlib's low-precision assign drifts - slightly) and set `speedup_target` from its geomean. -- Confirm the pinned `torch`/`triton` in `evaluation.pip` compile the vendored - kernels, and hidden shapes fit the GPU. -- Confirm Modal token wiring in the Harbor judge/agent containers. +## Precision lock — bf16 (task-root, not prompt) + +To stop an agent from buying speed by dropping matmul precision (the earlier +codex solution picked fp16 over tf32 where the 2% inertia tolerance allowed it), +the **data is bf16**: `flash_gpu`'s `gen` returns `x` and `init_centroids` as +`bfloat16`. Because the inputs carry only bf16 precision, no solution — baseline, +reference, or agent — gains anything from fp16/tf32/fp32 (they are only slower on +bf16 data), so the only remaining lever is the kernel structure. The naive +baseline's `_assign` was switched from `torch.cdist` (which upcasts) to an +explicit **bf16 matmul** so the baseline is genuinely bf16-native too — otherwise +a trivial "use bf16 instead of the fp32 baseline" would win without kernel work. +The inertia metric itself is still computed in fp32 (upcast) so the gate is a +clean quality comparison. + +## Single-iteration timing + averaging + +All workloads run `max_iters = 1` (one assign + one update) so the score isolates +per-iteration kernel efficiency, not loop count. Because one call is tiny, each +workload is timed `timed_iters = 10` times on fresh data and the per-run speedups +are **averaged** (`aggregate: mean`), not median. + +## Calibration (H100 Modal trial — done) + +bf16 + `max_iters=1`: `reference.patch` (flashlib) compiles + runs on bf16, passes +the inertia gate with **<0.0012%** drift on all 6 workloads, and runs +**5.0 / 2.6 / 10.9 / 1.8 / 12.6 / 4.7x** over the bf16 naive baseline +(geomean **4.97x**). `speedup_target = 5.0`, `inertia_tolerance = 0.02` (huge +margin). This 5x is now a *pure kernel-structure* speedup (both sides bf16). diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index 1d49835f8..c8d583dc1 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -45,20 +45,23 @@ evaluation: modal_timeout_seconds: 1800 # --- timing + quality gate --- warmup_iters: 2 - timed_iters: 7 + timed_iters: 10 # run each workload 10 times (max_iters=1 makes one call tiny) ... + aggregate: mean # ... and AVERAGE the 10 per-run speedups (not median) inertia_tolerance: 0.02 # agent inertia must be <= (1+tol) x baseline on every iteration - speedup_target: 5.0 # calibrated: reference (flashlib-best) geomean ~5.3x on Modal H100 + speedup_target: 5.0 # H100 recalibration (bf16, max_iters=1): flashlib reference geomean 4.97x over the bf16 naive baseline (per-workload 5.0/2.6/10.9/1.8/12.6/4.7x); inertia drift <0.0012% base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on - # --- hidden workloads (judge-only; config.yaml/task_config.json never enter the agent image) --- + # max_iters=1 for ALL workloads: time a single Lloyd iteration (1 assign + 1 + # update) so the score isolates per-iteration kernel efficiency. Data is bf16 + # (locked in the worker's gen) -> precision is not a lever, only the kernel is. workloads: - - {id: w0, N: 100000, D: 32, K: 128, max_iters: 12} - - {id: w1, N: 200000, D: 64, K: 256, max_iters: 10} - - {id: w2, N: 500000, D: 128, K: 256, max_iters: 10} - - {id: w3, N: 200000, D: 512, K: 256, max_iters: 8} - - {id: w4, N: 1000000, D: 64, K: 512, max_iters: 8} - - {id: w5, N: 300000, D: 128, K: 1024, max_iters: 8} + - {id: w0, N: 100000, D: 32, K: 128, max_iters: 1} + - {id: w1, N: 200000, D: 64, K: 256, max_iters: 1} + - {id: w2, N: 500000, D: 128, K: 256, max_iters: 1} + - {id: w3, N: 200000, D: 512, K: 256, max_iters: 1} + - {id: w4, N: 1000000, D: 64, K: 512, max_iters: 1} + - {id: w5, N: 300000, D: 128, K: 1024, max_iters: 1} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py index 6243e65c8..74e5bf808 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -108,6 +108,12 @@ def gen(w, seed, ctx): return {"x": xb, "eps": float(w["eps"]), "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) if prim == "kmeans": + # PRECISION LOCK (task-root, not prompt): kmeans data is bf16, so the + # inputs carry only bf16 precision. No solution -- baseline, reference, + # or agent -- can trade accuracy for speed by picking a different matmul + # dtype (fp16/tf32/fp32 buy nothing on bf16 data and are only slower). + # The only remaining lever is the kernel structure itself. + x = x.to(torch.bfloat16) perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] return {"x": x, "init": x.index_select(0, perm).clone()} return {"x": x} @@ -158,7 +164,7 @@ def inertia(x, centroids): cn = (c * c).sum(1) total = 0.0 for j in range(0, x.shape[0], 16384): - xb = x[j:j + 16384] + xb = x[j:j + 16384].to(torch.float32) # metric always fp32 (x may be bf16) d = (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ c.t()) + cn[None, :] total += float(d.min(1).values.clamp_min(0).sum().item()) return total @@ -263,9 +269,13 @@ def time_call(mod, w, data, ctx): rows.append({"id": w["id"], "ok": False, "reason": bad, "ref_val": ref_val, "agent_val": agent_val}) continue - ratios.sort() - rows.append({"id": w["id"], "ok": True, - "speedup": ratios[len(ratios) // 2], + # aggregate the per-run speedups: "mean" (average of the N runs) or + # "median" (default, noise-robust). config sets this per task. + if str(cfg.get("aggregate", "median")).lower() == "mean": + sp = sum(ratios) / len(ratios) + else: + ratios.sort(); sp = ratios[len(ratios) // 2] + rows.append({"id": w["id"], "ok": True, "speedup": sp, "ref_val": ref_val, "agent_val": agent_val}) except Exception as exc: return {"ok": False, "error": f"{type(exc).__name__}: {str(exc)[:200]}", diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py index 0e72aa7b0..efdb7cd89 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -18,8 +18,8 @@ BASELINE_DIR = "/opt/kmeans_ref" PUBLIC_WORKLOADS = [ - {"id": "p0", "N": 50_000, "D": 32, "K": 64, "max_iters": 10, "seed": 1}, - {"id": "p1", "N": 200_000, "D": 128, "K": 256, "max_iters": 10, "seed": 2}, + {"id": "p0", "N": 50_000, "D": 32, "K": 64, "max_iters": 1, "seed": 1}, + {"id": "p1", "N": 200_000, "D": 128, "K": 256, "max_iters": 1, "seed": 2}, ] CFG = { @@ -33,6 +33,7 @@ "modal_timeout_seconds": 1800, "warmup": 2, "iters": 5, + "aggregate": "mean", "inertia_tolerance": 0.02, "recall_threshold": 0.99, "captured_tolerance": 0.02, diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py b/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py index 1a455d6df..f9b63044f 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py @@ -12,8 +12,16 @@ def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: - dist = torch.cdist(x, centroids.to(x.dtype)) - return torch.argmin(dist, dim=1) + # Naive per-chunk squared-L2: materialise (chunk, K) via a bf16 matmul, argmin. + # argmin ||x-c||^2 == argmin (||c||^2 - 2 x.c^T); ||x||^2 is constant per row. + c = centroids.to(x.dtype) + c_sq = (c.float() * c.float()).sum(1) + labels = torch.empty(x.shape[0], device=x.device, dtype=torch.long) + for lo in range(0, x.shape[0], 16384): + xb = x[lo:lo + 16384] + dist = c_sq[None, :] - 2.0 * (xb @ c.t()).float() # bf16 matmul, fp32 accum + labels[lo:lo + 16384] = torch.argmin(dist, dim=1) + return labels def _update(x: torch.Tensor, labels: torch.Tensor, n_clusters: int, diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py index 6493af4d0..cc8298146 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py @@ -38,12 +38,18 @@ def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: """Nearest-centroid assignment by squared-L2 distance. - Naive: materialise the full (N, K) distance matrix, then argmin. + Naive: for each chunk of points, materialise the (chunk, K) distance matrix + with a bf16 matmul, then argmin. ``argmin ||x-c||^2 == argmin (||c||^2 - 2 + x.c^T)`` since ``||x||^2`` is constant per row. bf16 in, fp32 accumulation. """ - # torch.cdist returns Euclidean distance; argmin of distance == argmin of - # squared distance, so we do not bother squaring. - dist = torch.cdist(x, centroids.to(x.dtype)) # (N, K) - return torch.argmin(dist, dim=1) + c = centroids.to(x.dtype) + c_sq = (c.float() * c.float()).sum(1) # (K,) fp32 + labels = torch.empty(x.shape[0], device=x.device, dtype=torch.long) + for lo in range(0, x.shape[0], 16384): + xb = x[lo:lo + 16384] + dist = c_sq[None, :] - 2.0 * (xb @ c.t()).float() # (chunk, K) fp32 + labels[lo:lo + 16384] = torch.argmin(dist, dim=1) + return labels def _update( diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index ba1dce152..310ee065f 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -10,10 +10,11 @@ kmeanslib.kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) -> (labels, centroids, n_iter) ``` -`x` is an `(N, D)` float32 CUDA tensor, `init_centroids` is a fixed `(K, D)` -initial-centroid tensor supplied by the caller, and the function runs Euclidean -(squared-L2) Lloyd iterations. The shipped implementation is a straightforward, -correct PyTorch version. +`x` is an `(N, D)` **bfloat16** CUDA tensor, `init_centroids` is a fixed `(K, D)` +bfloat16 tensor supplied by the caller, and the function runs Euclidean +(squared-L2) Lloyd iterations. All data is bfloat16 — treat it as the fixed +working precision. The shipped implementation is a straightforward, correct +PyTorch version. Your goal is to make `kmeanslib.kmeans` **as fast as possible** on the GPU while producing clusterings of the same quality. You may rewrite the internals of the @@ -24,17 +25,19 @@ change, and every result must remain a deterministic function of its inputs. ## Workload The graded workloads are a family of held-out dense clustering problems that -vary `(N, D, K)` and the iteration count, spanning small/medium/large point -counts and both wide-feature (large `D`) and many-cluster (large `K`) regimes. -All use Euclidean distance, float32 data, a fixed iteration count (`tol = 0.0`, -so every iteration runs), and caller-supplied `init_centroids`. +vary `(N, D, K)`, spanning small/medium/large point counts and both wide-feature +(large `D`) and many-cluster (large `K`) regimes. All use Euclidean distance, +**bfloat16 data**, and caller-supplied `init_centroids`. Every call runs exactly +**one Lloyd iteration** (`max_iters = 1`, `tol = 0.0`): the score isolates the +efficiency of a single assign + update. Each workload is timed several times and +the per-run speedups are averaged. Two representative *public* shapes are available for local testing (the graded shapes are different and hidden): ```text -(N=50000, D=32, K=64, max_iters=10) -(N=200000, D=128, K=256, max_iters=10) +(N=50000, D=32, K=64, max_iters=1) +(N=200000, D=128, K=256, max_iters=1) ``` Treat this as a general dense K-Means, not as specific shapes to special-case. @@ -78,9 +81,10 @@ Correctness is a gate. On every timed iteration the judge recomputes the centroid) of your result and of the baseline on identical data, and requires your inertia to stay within a small relative tolerance of the baseline's. Crashes, non-finite output, wrong shapes/dtypes, timeouts, and clustering that -regresses beyond the tolerance are penalized before speed is considered. You may -trade a little numerical precision for speed as long as you stay within the -quality tolerance. +regresses beyond the tolerance are penalized before speed is considered. The +working precision is fixed at bfloat16 for every solution (the inputs are +bfloat16), so it is not a tuning knob — speed comes from the kernel, not from the +arithmetic precision. ## Scoring diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch b/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch index a6d05bbd6..4a97f16f9 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch @@ -2425,10 +2425,10 @@ index 0000000..88ff697 +if __name__ == "__main__": + main() diff --git a/kmeanslib/kmeans.py b/kmeanslib/kmeans.py -index 6493af4..ed49c78 100644 +index cc82981..ed49c78 100644 --- a/kmeanslib/kmeans.py +++ b/kmeanslib/kmeans.py -@@ -1,98 +1,26 @@ +@@ -1,104 +1,26 @@ -"""Euclidean (squared-L2) Lloyd K-Means -- the reference you must optimise. +"""Euclidean (squared-L2) Lloyd K-Means -- vendored best-in-repo Triton path. @@ -2472,12 +2472,18 @@ index 6493af4..ed49c78 100644 -def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: - """Nearest-centroid assignment by squared-L2 distance. - -- Naive: materialise the full (N, K) distance matrix, then argmin. +- Naive: for each chunk of points, materialise the (chunk, K) distance matrix +- with a bf16 matmul, then argmin. ``argmin ||x-c||^2 == argmin (||c||^2 - 2 +- x.c^T)`` since ``||x||^2`` is constant per row. bf16 in, fp32 accumulation. - """ -- # torch.cdist returns Euclidean distance; argmin of distance == argmin of -- # squared distance, so we do not bother squaring. -- dist = torch.cdist(x, centroids.to(x.dtype)) # (N, K) -- return torch.argmin(dist, dim=1) +- c = centroids.to(x.dtype) +- c_sq = (c.float() * c.float()).sum(1) # (K,) fp32 +- labels = torch.empty(x.shape[0], device=x.device, dtype=torch.long) +- for lo in range(0, x.shape[0], 16384): +- xb = x[lo:lo + 16384] +- dist = c_sq[None, :] - 2.0 * (xb @ c.t()).float() # (chunk, K) fp32 +- labels[lo:lo + 16384] = torch.argmin(dist, dim=1) +- return labels +from kmeanslib._kernels.primitives.kmeans.triton.kmeans import batch_kmeans_Euclid From 2ce6e6fc8dc3db30fc5d27fb1cb0fb1db344f79d Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 5 Jul 2026 05:19:46 +0000 Subject: [PATCH 16/34] fix(2.0/flashlib): kmeans -> planted clusters + labelled inertia gate (close subsample hack) The bf16 lock closed the precision hack, but the codex trial then exploited a subsample hack: on random data with max_iters=1 and a nearest-centroid inertia gate that ignores the returned labels, it subsampled rows (1/4) and feature dims (16 of 512) and returned all-zero labels -- an APPROXIMATE k-means that games the 2% tolerance while doing a fraction of the work. Close it at the root: (1) data is now planted well-separated blobs (centers*6 + unit noise), so the assignment matters and a subsampled/wrong assignment regresses inertia; (2) the gate is now LABELLED inertia -- sum_i ||x_i - centroids[labels_i]||^2 -- using the returned labels + centroids jointly, so fake or subsampled labels inflate it. Validated on CPU: fake all-zero labels -> 5.8x baseline (fails), 16-dim assignment -> 1.12x (fails). labels shape now checked (full N). The agent must now do the real full assignment -> speed only from the kernel. Co-Authored-By: Claude Opus 4.8 --- .../flash_gpu.py | 41 +++++++++++++++---- .../kmeans_gpu_kernel_optimization/readme | 8 ++-- 2 files changed, 39 insertions(+), 10 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py index 74e5bf808..deb5d22e8 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -112,9 +112,18 @@ def gen(w, seed, ctx): # inputs carry only bf16 precision. No solution -- baseline, reference, # or agent -- can trade accuracy for speed by picking a different matmul # dtype (fp16/tf32/fp32 buy nothing on bf16 data and are only slower). - # The only remaining lever is the kernel structure itself. - x = x.to(torch.bfloat16) - perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] + # + # PLANTED CLUSTERS: x = well-separated blob centers + unit noise (NOT + # random). This makes the assignment matter -- inertia is sensitive to + # which centroid each point takes -- so a solution that subsamples rows + # or feature dims, or fakes the labels, produces a wrong assignment and + # regresses the (labelled) inertia gate. Random data would leave inertia + # nearly assignment-invariant and thus hackable by doing less work. + K = int(w["K"]) + centers = torch.randn(K, int(w["D"]), generator=g, device=dev) * 6.0 + asg = torch.randint(0, K, (int(w["N"]),), generator=g, device=dev) + x = (centers.index_select(0, asg) + x).to(torch.bfloat16) # top randn = unit noise + perm = torch.randperm(w["N"], generator=g, device=dev)[: K] return {"x": x, "init": x.index_select(0, perm).clone()} return {"x": x} @@ -136,9 +145,11 @@ def call(mod, w, data, ctx): def check_shape(w, out): if prim == "kmeans": - _, cen, _ = out - if tuple(cen.shape) != (w["K"], w["D"]) or not torch.isfinite(cen).all(): - raise ValueError("bad kmeans output") + lab, cen, _ = out + if tuple(cen.shape) != (w["K"], w["D"]) or not torch.isfinite(cen.float()).all(): + raise ValueError("bad kmeans centroids") + if lab.ndim != 1 or int(lab.shape[0]) != int(w["N"]): + raise ValueError("bad kmeans labels shape") # labels are gated, must be full (N,) elif prim == "knn": d, i = out if tuple(d.shape) != (w["Q"], w["k"]) or tuple(i.shape) != (w["Q"], w["k"]): @@ -169,6 +180,20 @@ def inertia(x, centroids): total += float(d.min(1).values.clamp_min(0).sum().item()) return total + def inertia_labeled(x, centroids, labels): + # sum_i ||x_i - centroids[labels_i]||^2 : ties the returned LABELS to the + # returned centroids. Fake/garbage labels or a wrong (subsampled) assignment + # inflate this even if the centroids alone look fine -- closes the "return + # good centroids + junk labels" and "subsample the assignment" hacks. + c = centroids.to(torch.float32) + lab = labels.long().clamp_(0, c.shape[0] - 1) + total = 0.0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384].to(torch.float32) + cl = c.index_select(0, lab[j:j + 16384]) + total += float(((xb - cl) ** 2).sum().item()) + return total + def recall(agent_idx, ref_idx): a = agent_idx.long(); b = ref_idx.long() hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) @@ -213,7 +238,9 @@ def verdict(w, data, ctx, ref_out, agent_out): ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": - rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) + # gate on LABELLED inertia: uses the returned (labels, centroids) jointly + rv = inertia_labeled(data["x"], ref_out[1], ref_out[0]) + av = inertia_labeled(data["x"], agent_out[1], agent_out[0]) tol = float(cfg["inertia_tolerance"]) ok = av <= (1.0 + tol) * rv + 1e-6 return ok, ("inertia_regression" if not ok else ""), rv, av diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index 310ee065f..4e63899da 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -77,9 +77,11 @@ baseline on a GPU, on the same seeded data and initial centroids. ## Correctness Correctness is a gate. On every timed iteration the judge recomputes the -**inertia** (within-cluster sum of squared distances to the nearest final -centroid) of your result and of the baseline on identical data, and requires -your inertia to stay within a small relative tolerance of the baseline's. +**inertia** — the sum over points of the squared distance from each point to the +centroid of **its returned label** — for your result and for the baseline on +identical data, and requires yours to stay within a small relative tolerance of +the baseline's. Both your `labels` **and** `centroids` are used, so `labels` must +be the genuine assignment of every one of the `N` points (a full `(N,)` vector). Crashes, non-finite output, wrong shapes/dtypes, timeouts, and clustering that regresses beyond the tolerance are penalized before speed is considered. The working precision is fixed at bfloat16 for every solution (the inputs are From 7d66053373e7498bdfbc195229d7f6a20fb20d6b Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 5 Jul 2026 05:29:04 +0000 Subject: [PATCH 17/34] fix(2.0/flashlib): kmeans gate -> label(init-based) + centroid, split tolerances Refine the anti-subsample gate after calibration: - Gate A (labels): the returned labels must be the genuine nearest-to-INIT assignment (init = the centroids the one-step update is computed from), checked self-referentially against the judge's own inertia-to-init -> ~0 cross-solution drift, kept tight (label_tolerance 0.02). Real labels 1.0003x, fake 3.25x, 16-of-512-dim 1.24x. This forces a full real assignment. - Gate B (centroids): nearest-centroid inertia vs baseline; on planted clusters two bf16 one-step results drift up to ~2.8% at D=512, so inertia_tolerance loosened to 0.05 (only rejects genuinely bad centroids; the hack is caught by gate A). Recalibrated: flashlib reference passes all 6, geomean 4.54x -> speedup_target 4.5. Co-Authored-By: Claude Opus 4.8 --- .../config.yaml | 5 ++-- .../flash_gpu.py | 27 ++++++++++++++----- 2 files changed, 24 insertions(+), 8 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index c8d583dc1..efef32088 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -47,8 +47,9 @@ evaluation: warmup_iters: 2 timed_iters: 10 # run each workload 10 times (max_iters=1 makes one call tiny) ... aggregate: mean # ... and AVERAGE the 10 per-run speedups (not median) - inertia_tolerance: 0.02 # agent inertia must be <= (1+tol) x baseline on every iteration - speedup_target: 5.0 # H100 recalibration (bf16, max_iters=1): flashlib reference geomean 4.97x over the bf16 naive baseline (per-workload 5.0/2.6/10.9/1.8/12.6/4.7x); inertia drift <0.0012% + inertia_tolerance: 0.05 # gate B (centroids): agent nearest-centroid inertia <= (1+tol) x baseline; loose because two bf16 one-step results drift ~2.8% at D=512 + label_tolerance: 0.02 # gate A (labels): agent's labels must be the genuine nearest-to-init assignment (self-referential, ~0 drift) -- this is what blocks the subsample/fake-label hack + speedup_target: 4.5 # H100 recalibration (bf16, max_iters=1, planted clusters, labelled+centroid gates): flashlib reference geomean 4.54x over the bf16 baseline (2.6/2.1/6.9/2.6/12.3/7.1x), all 6 pass both gates base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py index deb5d22e8..95219f688 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -238,12 +238,27 @@ def verdict(w, data, ctx, ref_out, agent_out): ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": - # gate on LABELLED inertia: uses the returned (labels, centroids) jointly - rv = inertia_labeled(data["x"], ref_out[1], ref_out[0]) - av = inertia_labeled(data["x"], agent_out[1], agent_out[0]) - tol = float(cfg["inertia_tolerance"]) - ok = av <= (1.0 + tol) * rv + 1e-6 - return ok, ("inertia_regression" if not ok else ""), rv, av + init = data["init"] + tol_cen = float(cfg["inertia_tolerance"]) # gate B (centroid) + tol_lab = float(cfg.get("label_tolerance", tol_cen)) # gate A (label) + base_near = inertia(data["x"], ref_out[1]) # baseline centroid quality + ag_near = inertia(data["x"], agent_out[1]) # agent centroid quality + # (B) agent centroids must be about as good as the baseline's. On planted + # clusters two bf16 one-step results can differ by a couple % at large D, so + # this tolerance is the looser one; it only rejects genuinely bad centroids. + if ag_near > (1.0 + tol_cen) * base_near + 1e-6: + return False, "inertia_regression", base_near, ag_near + # (A) the returned labels must be the genuine nearest-to-INIT assignment + # (what the returned centroids are the one-step update of). Compare the + # agent's labelled inertia-to-init to the judge's own best inertia-to-init + # -- self-referential on the shared init, so ~0 cross-solution drift; kept + # tight. Fake or row/dim-subsampled labels inflate it and fail here. This + # is what forces a real full assignment (no "do less work" shortcut). + lab_best = inertia(data["x"], init) + lab_got = inertia_labeled(data["x"], init, agent_out[0]) + if lab_got > (1.0 + tol_lab) * lab_best + 1e-6: + return False, "label_mismatch", lab_best, lab_got + return True, "", base_near, ag_near if prim == "knn": rec = recall(agent_out[1], ref_out[1]) ok = rec >= float(cfg["recall_threshold"]) From ffe2028446600f6ff4a5ec3f3513d2c47c8c4278 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 5 Jul 2026 05:30:09 +0000 Subject: [PATCH 18/34] docs(2.0/flashlib): kmeans DESIGN -> planted clusters + two-gate anti-subsample + recalibration Co-Authored-By: Claude Opus 4.8 --- .../kmeans_gpu_kernel_optimization/DESIGN.md | 50 +++++++++++++++---- 1 file changed, 39 insertions(+), 11 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md index a2ffd7bad..e71456e72 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md +++ b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md @@ -62,12 +62,33 @@ measurement-tamper patterns (`subprocess`, `socket`, `os.system`, The agent-facing `readme` describes the contract, gate, scoring, and policy but **not** how the reference is implemented. -## Correctness gate - -Inertia = sum of squared L2 distances to the nearest **final** centroid, judge- -computed from the returned centroids on each iteration's data. Permutation- and -convention-independent, robust to fp drift, cheat-proof. `init_centroids` are -always supplied and `tol = 0`, so the baseline is a deterministic function of +## Correctness gate — two gates, closes the subsample hack + +The first bf16 trial exposed a **subsample reward-hack**: on random data with +`max_iters=1` and a nearest-centroid-only inertia gate, codex subsampled rows +(1/4) and feature dims (16 of 512) and returned all-zero labels — an approximate +k-means that games the tolerance while doing a fraction of the work. Two changes +close it: + +1. **Planted clusters** (not random): `gen` builds `x` = well-separated blob + centres (`centers * 6`) + unit noise. Now the assignment *matters* — a wrong + (subsampled/low-dim) assignment lands points in the wrong blob and spikes + inertia. Random data left inertia nearly assignment-invariant, hence hackable. +2. **Two gates** (`verdict`): + - **Gate A — labels** (`label_tolerance = 0.02`, tight): the returned `labels` + must be the genuine nearest-to-**init** assignment (init = the centroids the + one-step update derives from). Checked self-referentially — + `inertia_labeled(x, init, labels) <= (1+tol) * inertia(x, init)` — so there + is ~0 cross-solution bf16 drift. Fake/subsampled labels inflate it (real + 1.0003x, all-zero 3.25x, 16-of-512-dim 1.24x). **This is what forces a real + full assignment** — the "do less work" shortcut is gone. + - **Gate B — centroids** (`inertia_tolerance = 0.05`, loose): agent + nearest-centroid inertia `<= (1+tol) * baseline`. On planted clusters two + bf16 one-step results drift up to ~2.8% at D=512 (boundary assignments), so + this one is loosened; it only rejects genuinely bad centroids. + +Both are permutation/convention-independent and cheat-proof. `init_centroids` are +always supplied and `tol = 0`, so the baseline is deterministic in `(x, init_centroids, max_iters)`. ## Precision lock — bf16 (task-root, not prompt) @@ -93,8 +114,15 @@ are **averaged** (`aggregate: mean`), not median. ## Calibration (H100 Modal trial — done) -bf16 + `max_iters=1`: `reference.patch` (flashlib) compiles + runs on bf16, passes -the inertia gate with **<0.0012%** drift on all 6 workloads, and runs -**5.0 / 2.6 / 10.9 / 1.8 / 12.6 / 4.7x** over the bf16 naive baseline -(geomean **4.97x**). `speedup_target = 5.0`, `inertia_tolerance = 0.02` (huge -margin). This 5x is now a *pure kernel-structure* speedup (both sides bf16). +Final setup (bf16 + `max_iters=1` + planted clusters + labelled/centroid gates): +`reference.patch` (flashlib) compiles + runs on bf16 and **passes both gates on all +6 workloads**, running **2.6 / 2.1 / 6.9 / 2.6 / 12.3 / 7.1x** over the bf16 naive +baseline (geomean **4.54x**). `speedup_target = 4.5`, `label_tolerance = 0.02`, +`inertia_tolerance = 0.05`. This speedup is a *pure kernel-structure* win — both +sides bf16, precision is not a lever, and the labelled gate forces a full real +assignment so "subsample less work" is not a lever either. + +(Earlier calibrations, for the record: random-data + nearest-only gate gave +geomean 4.97x but was subsample-hackable; the labelled gate vs the baseline failed +the reference at D=512 with 2.66% bf16 drift, which is why gate A is +self-referential against `init` and gate B is the loosened one.) From 8d4c7f1171bc086c32e01c9ed9a50c4e0c4eaa87 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 5 Jul 2026 08:21:48 +0000 Subject: [PATCH 19/34] chore(2.0/flashlib): kmeans -> revert to multi-iteration max_iters + single timed run Per request: max_iters back to the original 12/10/10/8/8/8 and timed_iters=1 (no repeat-and-average -- each multi-iteration call is substantial, low relative noise). Kept bf16 lock + planted clusters. The label gate (A) is max_iters==1-specific (labels = argmin to init) so it is now guarded to fire only when max_iters==1; multi-iteration uses the centroid gate (B) alone. Two correct bf16 solutions diverge to different local optima over many iterations (w0 D=32/12-iter ref drifts up to ~6.5% from the baseline), so inertia_tolerance loosened to 0.10. Recalibrated: reference passes all 6, geomean 7.12x -> speedup_target 7.1. Co-Authored-By: Claude Opus 4.8 --- .../config.yaml | 28 ++++++++-------- .../flash_gpu.py | 30 ++++++++--------- .../harbor/app/public_test.py | 9 +++--- .../kmeans_gpu_kernel_optimization/readme | 32 ++++++++----------- 4 files changed, 46 insertions(+), 53 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index efef32088..5927b5496 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -45,24 +45,24 @@ evaluation: modal_timeout_seconds: 1800 # --- timing + quality gate --- warmup_iters: 2 - timed_iters: 10 # run each workload 10 times (max_iters=1 makes one call tiny) ... - aggregate: mean # ... and AVERAGE the 10 per-run speedups (not median) - inertia_tolerance: 0.05 # gate B (centroids): agent nearest-centroid inertia <= (1+tol) x baseline; loose because two bf16 one-step results drift ~2.8% at D=512 - label_tolerance: 0.02 # gate A (labels): agent's labels must be the genuine nearest-to-init assignment (self-referential, ~0 drift) -- this is what blocks the subsample/fake-label hack - speedup_target: 4.5 # H100 recalibration (bf16, max_iters=1, planted clusters, labelled+centroid gates): flashlib reference geomean 4.54x over the bf16 baseline (2.6/2.1/6.9/2.6/12.3/7.1x), all 6 pass both gates + timed_iters: 1 # one timed run per workload -- max_iters back to 8-12 makes each call substantial (low relative timing noise), so no repeat-and-average + inertia_tolerance: 0.10 # gate B (centroids): loose -- two correct bf16 solutions diverge to different local optima over many iterations (w0 D=32,12-iter ref drifts ~6.5% from baseline) + label_tolerance: 0.02 # gate A (labels): only applied when max_iters==1 (labels = argmin to init); skipped here since Lloyd runs multiple steps + speedup_target: 7.1 # H100 recal (bf16, multi-iter, planted, centroid gate 0.10): flashlib reference geomean 7.12x over the bf16 baseline (3.4/3.7/10.5/4.6/16.8/12.9x), all 6 pass base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on - # max_iters=1 for ALL workloads: time a single Lloyd iteration (1 assign + 1 - # update) so the score isolates per-iteration kernel efficiency. Data is bf16 - # (locked in the worker's gen) -> precision is not a lever, only the kernel is. + # Multi-iteration Lloyd (original max_iters). Data is bf16 (locked in the worker's + # gen) so precision is not a lever; planted well-separated blobs so a wrong + # (e.g. subsampled-dim) assignment compounds over iterations into bad centroids + # and fails the inertia gate. workloads: - - {id: w0, N: 100000, D: 32, K: 128, max_iters: 1} - - {id: w1, N: 200000, D: 64, K: 256, max_iters: 1} - - {id: w2, N: 500000, D: 128, K: 256, max_iters: 1} - - {id: w3, N: 200000, D: 512, K: 256, max_iters: 1} - - {id: w4, N: 1000000, D: 64, K: 512, max_iters: 1} - - {id: w5, N: 300000, D: 128, K: 1024, max_iters: 1} + - {id: w0, N: 100000, D: 32, K: 128, max_iters: 12} + - {id: w1, N: 200000, D: 64, K: 256, max_iters: 10} + - {id: w2, N: 500000, D: 128, K: 256, max_iters: 10} + - {id: w3, N: 200000, D: 512, K: 256, max_iters: 8} + - {id: w4, N: 1000000, D: 64, K: 512, max_iters: 8} + - {id: w5, N: 300000, D: 128, K: 1024, max_iters: 8} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py index 95219f688..06a737a45 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -238,26 +238,24 @@ def verdict(w, data, ctx, ref_out, agent_out): ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": - init = data["init"] - tol_cen = float(cfg["inertia_tolerance"]) # gate B (centroid) - tol_lab = float(cfg.get("label_tolerance", tol_cen)) # gate A (label) + tol_cen = float(cfg["inertia_tolerance"]) base_near = inertia(data["x"], ref_out[1]) # baseline centroid quality ag_near = inertia(data["x"], agent_out[1]) # agent centroid quality - # (B) agent centroids must be about as good as the baseline's. On planted - # clusters two bf16 one-step results can differ by a couple % at large D, so - # this tolerance is the looser one; it only rejects genuinely bad centroids. + # (B) agent centroids must be about as good as the baseline's (nearest- + # centroid inertia). Multi-iteration + planted clusters compound a wrong + # (subsampled-dim) assignment into bad centroids, so this catches it. if ag_near > (1.0 + tol_cen) * base_near + 1e-6: return False, "inertia_regression", base_near, ag_near - # (A) the returned labels must be the genuine nearest-to-INIT assignment - # (what the returned centroids are the one-step update of). Compare the - # agent's labelled inertia-to-init to the judge's own best inertia-to-init - # -- self-referential on the shared init, so ~0 cross-solution drift; kept - # tight. Fake or row/dim-subsampled labels inflate it and fail here. This - # is what forces a real full assignment (no "do less work" shortcut). - lab_best = inertia(data["x"], init) - lab_got = inertia_labeled(data["x"], init, agent_out[0]) - if lab_got > (1.0 + tol_lab) * lab_best + 1e-6: - return False, "label_mismatch", lab_best, lab_got + # (A) label gate -- only well-defined for a SINGLE Lloyd step, where the + # returned labels are the argmin to `init` (the centroids the one update + # derives from). For max_iters>1 the labels are argmin to the pre-final + # centroids, which the judge cannot recompute, so the gate is skipped. + if int(w.get("max_iters", 1)) == 1: + tol_lab = float(cfg.get("label_tolerance", tol_cen)) + lab_best = inertia(data["x"], data["init"]) + lab_got = inertia_labeled(data["x"], data["init"], agent_out[0]) + if lab_got > (1.0 + tol_lab) * lab_best + 1e-6: + return False, "label_mismatch", lab_best, lab_got return True, "", base_near, ag_near if prim == "knn": rec = recall(agent_out[1], ref_out[1]) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py index efdb7cd89..36aca7344 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -18,8 +18,8 @@ BASELINE_DIR = "/opt/kmeans_ref" PUBLIC_WORKLOADS = [ - {"id": "p0", "N": 50_000, "D": 32, "K": 64, "max_iters": 1, "seed": 1}, - {"id": "p1", "N": 200_000, "D": 128, "K": 256, "max_iters": 1, "seed": 2}, + {"id": "p0", "N": 50_000, "D": 32, "K": 64, "max_iters": 10, "seed": 1}, + {"id": "p1", "N": 200_000, "D": 128, "K": 256, "max_iters": 10, "seed": 2}, ] CFG = { @@ -32,9 +32,8 @@ "app_name": "kmeans-kernel-opt-public", "modal_timeout_seconds": 1800, "warmup": 2, - "iters": 5, - "aggregate": "mean", - "inertia_tolerance": 0.02, + "iters": 3, + "inertia_tolerance": 0.10, "recall_threshold": 0.99, "captured_tolerance": 0.02, "ortho_tolerance": 0.02, diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index 4e63899da..9ed3dc7b3 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -25,19 +25,17 @@ change, and every result must remain a deterministic function of its inputs. ## Workload The graded workloads are a family of held-out dense clustering problems that -vary `(N, D, K)`, spanning small/medium/large point counts and both wide-feature -(large `D`) and many-cluster (large `K`) regimes. All use Euclidean distance, -**bfloat16 data**, and caller-supplied `init_centroids`. Every call runs exactly -**one Lloyd iteration** (`max_iters = 1`, `tol = 0.0`): the score isolates the -efficiency of a single assign + update. Each workload is timed several times and -the per-run speedups are averaged. +vary `(N, D, K)` and the iteration count, spanning small/medium/large point +counts and both wide-feature (large `D`) and many-cluster (large `K`) regimes. +All use Euclidean distance, **bfloat16 data**, a fixed iteration count +(`tol = 0.0`, so every iteration runs), and caller-supplied `init_centroids`. Two representative *public* shapes are available for local testing (the graded shapes are different and hidden): ```text -(N=50000, D=32, K=64, max_iters=1) -(N=200000, D=128, K=256, max_iters=1) +(N=50000, D=32, K=64, max_iters=10) +(N=200000, D=128, K=256, max_iters=10) ``` Treat this as a general dense K-Means, not as specific shapes to special-case. @@ -77,16 +75,14 @@ baseline on a GPU, on the same seeded data and initial centroids. ## Correctness Correctness is a gate. On every timed iteration the judge recomputes the -**inertia** — the sum over points of the squared distance from each point to the -centroid of **its returned label** — for your result and for the baseline on -identical data, and requires yours to stay within a small relative tolerance of -the baseline's. Both your `labels` **and** `centroids` are used, so `labels` must -be the genuine assignment of every one of the `N` points (a full `(N,)` vector). -Crashes, non-finite output, wrong shapes/dtypes, timeouts, and clustering that -regresses beyond the tolerance are penalized before speed is considered. The -working precision is fixed at bfloat16 for every solution (the inputs are -bfloat16), so it is not a tuning knob — speed comes from the kernel, not from the -arithmetic precision. +**inertia** (within-cluster sum of squared distances to the nearest final +centroid) of your result and of the baseline on identical data, and requires your +inertia to stay within a small relative tolerance of the baseline's. `labels` +must be a full `(N,)` assignment. Crashes, non-finite output, wrong shapes/dtypes, +timeouts, and clustering that regresses beyond the tolerance are penalized before +speed is considered. The working precision is fixed at bfloat16 for every solution +(the inputs are bfloat16), so it is not a tuning knob — speed comes from the +kernel, not from the arithmetic precision. ## Scoring From 6d5dc297088e6c2ac5730f2ee962bfa6eee30ccf Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 5 Jul 2026 10:05:07 +0000 Subject: [PATCH 20/34] feat(2.0/kmeans): step(x,centroids) contract + judge-owned loop (max_iters=2) Restructure the kmeans kernel-opt task so the agent owns only a single Lloyd iteration `step(x, centroids) -> (labels, new_centroids)` and the JUDGE owns the loop, data, init centroids, and iteration count (calls step exactly max_iters=2 times, feeding each output into the next). This closes the iteration-skip / fake-label / subsample reward-hacks structurally instead of via gate tuning: - the agent can no longer choose the iteration count (judge counts the loop); - a garbage/subsampled step lands bad final centroids that compound over the loop and fail the inertia gate; - precision stays locked to bf16 (data is bf16 in gen). Because fake labels/subsampling are now impossible by construction, the separate tight label gate is dropped -- the centroid inertia gate alone suffices. - kmeanslib/{kmeans,__init__}.py, judge/refkmeans.py: kmeans(...) -> step(...) - flash_gpu.py: call() judge-owned loop; verdict() centroid-only gate - reference.patch: flashlib adapter -> step (euclid_assign_triton + triton_centroid_update_sorted_euclid once, B=1) - config.yaml: all workloads max_iters=2; calibrated on H100 -- flashlib reference passes the gate on all 6, geomean 4.95x -> speedup_target=5.0; worst bf16 drift +1.47% -> inertia_tolerance=0.05 - readme / DESIGN.md / public_test.py: step contract + judge-owned loop - rebuilt agent+judge images (experimental-v0.2.0) Co-Authored-By: Claude Opus 4.8 (1M context) --- .../kmeans_gpu_kernel_optimization/DESIGN.md | 148 ++++++++++-------- .../config.yaml | 29 ++-- .../flash_gpu.py | 52 +++--- .../harbor/app/public_test.py | 8 +- .../judge/refkmeans.py | 33 ++-- .../kmeanslib/__init__.py | 17 +- .../kmeanslib/kmeans.py | 80 ++++------ .../kmeans_gpu_kernel_optimization/readme | 75 +++++---- .../reference.patch | 115 ++++++-------- 9 files changed, 269 insertions(+), 288 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md index e71456e72..db5f9fd8a 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md +++ b/2.0/problems/kmeans_gpu_kernel_optimization/DESIGN.md @@ -5,11 +5,14 @@ Operator-facing. Not copied into the agent workspace by the adapter. ## What this task measures Can an agent turn a naive GPU K-Means into a fast one? The agent patches -`kmeanslib`; the judge times the patched `kmeans` against a frozen naive baseline -on hidden `(N, D, K)` workloads and scores the geometric-mean speedup, gated on -clustering quality (inertia). First of a four-task **flashlib kernel-optimization -family** (KMeans, KNN, TruncatedSVD, PCA) that all share one evaluator + one -Modal GPU harness. +`kmeanslib`, whose public entry point is **one Lloyd iteration** +`step(x, centroids) -> (labels, new_centroids)`. **The judge owns the loop**: it +fixes the data + initial centroids and calls `step` a fixed number of times +(`max_iters`, feeding each output into the next), times the patched `step` +against a frozen naive baseline on hidden `(N, D, K)` workloads, and scores the +geometric-mean speedup, gated on clustering quality (inertia of the final +centroids). First of a four-task **flashlib kernel-optimization family** (KMeans, +KNN, TruncatedSVD, PCA) that all share one evaluator + one Modal GPU harness. ## Execution: Modal GPU offload (mirrors the vllm task) @@ -32,24 +35,42 @@ Modal values come from `config.yaml` (delivered judge-only as `reference.patch` vendors flashlib's own optimized **Triton** K-Means path (`primitives/kmeans/triton/{kmeans,assign,update}.py`) under `kmeanslib/_kernels/…`, with imports rewritten and the string "flashlib" -scrubbed, plus a thin adapter mapping our `(N, D)` contract onto flashlib's -batched entry. It is triton-only (runs on any sm≥80), imports cleanly on CPU, -and applies + passes the patch policy. Calibrate `speedup_target` from its -measured geomean on the first GPU trial. +scrubbed, plus a thin adapter mapping our `step(x, centroids)` contract onto +flashlib's single-iteration path (`_euclid_iter`: `euclid_assign_triton` + +`triton_centroid_update_sorted_euclid`, run once with the batch dim as `B=1`). +It is triton-only (runs on any sm≥80), imports cleanly on CPU, and applies + +passes the patch policy. Calibrate `speedup_target` from its measured geomean on +the first GPU trial. (The reference does not fuse assign+update into a single +kernel, so an agent that does can legitimately score >100.) ## Anti-reward-hack -The **load-bearing** defenses are structural, inside the GPU worker (the only -place the untrusted submission runs): +The **primary, load-bearing** defense is the **`step` contract itself**: the +agent owns only one Lloyd iteration `step(x, centroids)`; the judge owns the +loop, the data, the initial centroids, and the iteration count. This structurally +closes the reward-hacks earlier trials found when the agent owned the whole +`kmeans(...)` call: + +* **iteration-skip** — the agent used to run fewer than `max_iters` iterations + and self-report the count; now the judge does the counting (`for _ in + range(max_iters): lab, c = mod.step(x, c)`), so the count is not the agent's to + fake; +* **fake / subsampled labels** — the judge re-derives inertia from the *final + centroids after its own loop*; a `step` that returns garbage labels or + subsamples rows/dims produces bad centroids that compound over the loop and + spike inertia. `max_iters = 2` (not 1) makes it a genuine loop where a bad step + actually propagates, while still being too few for a converged step to degrade + into a no-op; +* **precision** — data is bf16 (locked in `gen`), so fp16/tf32/fp32 buy nothing. + +Additional structural defenses inside the GPU worker (the only place the +untrusted submission runs): * timing primitives (`perf_counter`, `torch.cuda.synchronize`) are captured to locals **before** the submission is imported → monkey-patching torch cannot affect measurement; -* **fresh data every timed iteration** → memoizing on a repeated input is - useless; -* quality is **re-verified from the returned tensors on every iteration** → - returning a stale cached result for a different input is caught; -* the judge computes all metrics (no agent number is trusted); +* quality is **re-verified by the judge from the returned tensors** → no agent + number is trusted; * an ephemeral Modal container per submission → no global state persists. The patch policy is surgical defense-in-depth (so it does not reject legitimate @@ -62,33 +83,26 @@ measurement-tamper patterns (`subprocess`, `socket`, `os.system`, The agent-facing `readme` describes the contract, gate, scoring, and policy but **not** how the reference is implemented. -## Correctness gate — two gates, closes the subsample hack - -The first bf16 trial exposed a **subsample reward-hack**: on random data with -`max_iters=1` and a nearest-centroid-only inertia gate, codex subsampled rows -(1/4) and feature dims (16 of 512) and returned all-zero labels — an approximate -k-means that games the tolerance while doing a fraction of the work. Two changes -close it: - -1. **Planted clusters** (not random): `gen` builds `x` = well-separated blob - centres (`centers * 6`) + unit noise. Now the assignment *matters* — a wrong - (subsampled/low-dim) assignment lands points in the wrong blob and spikes - inertia. Random data left inertia nearly assignment-invariant, hence hackable. -2. **Two gates** (`verdict`): - - **Gate A — labels** (`label_tolerance = 0.02`, tight): the returned `labels` - must be the genuine nearest-to-**init** assignment (init = the centroids the - one-step update derives from). Checked self-referentially — - `inertia_labeled(x, init, labels) <= (1+tol) * inertia(x, init)` — so there - is ~0 cross-solution bf16 drift. Fake/subsampled labels inflate it (real - 1.0003x, all-zero 3.25x, 16-of-512-dim 1.24x). **This is what forces a real - full assignment** — the "do less work" shortcut is gone. - - **Gate B — centroids** (`inertia_tolerance = 0.05`, loose): agent - nearest-centroid inertia `<= (1+tol) * baseline`. On planted clusters two - bf16 one-step results drift up to ~2.8% at D=512 (boundary assignments), so - this one is loosened; it only rejects genuinely bad centroids. - -Both are permutation/convention-independent and cheat-proof. `init_centroids` are -always supplied and `tol = 0`, so the baseline is deterministic in +## Correctness gate — centroid inertia (the step contract does the rest) + +Under the `step` contract the earlier subsample / fake-label hacks are closed +**structurally** (the judge owns the loop and re-derives quality from the final +centroids — see Anti-reward-hack), so the separate tight *label* gate is no +longer needed. One gate remains: + +* **Planted clusters** (not random): `gen` builds `x` = well-separated blob + centres (`centers * 6`) + unit noise. This makes the assignment *matter* — a + step that assigns points to the wrong blob lands the final centroids in the + wrong place and spikes inertia. (Random data leaves inertia nearly + assignment-invariant, which is why it was hackable.) +* **Centroid inertia gate** (`inertia_tolerance`, recalibrate): after the judge's + loop, the agent's final centroids' nearest-centroid inertia must be + `<= (1+tol) * baseline`. Computed in fp32 (upcast) on identical data. A step + that does less real work than a true assign+update lands worse centroids after + two iterations and fails this. + +The gate is permutation/convention-independent and cheat-proof. `init_centroids` +are always supplied by the judge, so the baseline is deterministic in `(x, init_centroids, max_iters)`. ## Precision lock — bf16 (task-root, not prompt) @@ -105,24 +119,30 @@ a trivial "use bf16 instead of the fp32 baseline" would win without kernel work. The inertia metric itself is still computed in fp32 (upcast) so the gate is a clean quality comparison. -## Single-iteration timing + averaging - -All workloads run `max_iters = 1` (one assign + one update) so the score isolates -per-iteration kernel efficiency, not loop count. Because one call is tiny, each -workload is timed `timed_iters = 10` times on fresh data and the per-run speedups -are **averaged** (`aggregate: mean`), not median. - -## Calibration (H100 Modal trial — done) - -Final setup (bf16 + `max_iters=1` + planted clusters + labelled/centroid gates): -`reference.patch` (flashlib) compiles + runs on bf16 and **passes both gates on all -6 workloads**, running **2.6 / 2.1 / 6.9 / 2.6 / 12.3 / 7.1x** over the bf16 naive -baseline (geomean **4.54x**). `speedup_target = 4.5`, `label_tolerance = 0.02`, -`inertia_tolerance = 0.05`. This speedup is a *pure kernel-structure* win — both -sides bf16, precision is not a lever, and the labelled gate forces a full real -assignment so "subsample less work" is not a lever either. - -(Earlier calibrations, for the record: random-data + nearest-only gate gave -geomean 4.97x but was subsample-hackable; the labelled gate vs the baseline failed -the reference at D=512 with 2.66% bf16 drift, which is why gate A is -self-referential against `init` and gate B is the loosened one.) +## Timing unit — the judge-owned loop + +The timed unit is the **judge's loop of `max_iters` `step` calls** (warmup +`warmup_iters`, then `timed_iters = 1` timed run) — baseline and agent time the +identical loop on the same seeded data + init. `max_iters = 2`: a genuine loop +(so a bad step propagates and is caught by the inertia gate) but too few for a +converged step to become a redundant no-op. The score isolates per-`step` kernel +efficiency, since loop count is identical on both sides. + +## Calibration (H100 Modal trial — done, step / max_iters=2) + +Final setup (bf16 + `step` contract + judge-owned loop of `max_iters=2` + planted +clusters + centroid inertia gate): the flashlib `reference.patch` compiles + runs +on bf16 and **passes the centroid gate on all 6 workloads**, running +**2.1 / 2.4 / 6.7 / 3.5 / 13.2 / 9.3x** over the bf16 naive baseline (geomean +**4.95x**). `speedup_target = 5.0`. The worst reference bf16 inertia drift is +**+1.47%** (w3, D=512), so `inertia_tolerance = 0.05` clears it with margin while +still rejecting genuinely bad centroids. This speedup is a *pure kernel-structure* +win — both sides bf16 (precision is not a lever), and the judge owns the loop (so +iteration-skip / fake-labels / subsample are not levers either). + +(Prior `max_iters=1` calibration, for the record: bf16 + planted clusters + +labelled/centroid gates gave the flashlib reference geomean **4.54x**, all 6 +passing. That setting let the agent own the whole `kmeans(...)` call, which is why +iteration-skip was still reachable and motivated this `step` restructure. The +geomean is essentially unchanged (4.54 → 4.95) because the reference does the same +per-step work; what changed is that the hack surface is now closed structurally.) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index 5927b5496..84f26ab60 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -45,24 +45,25 @@ evaluation: modal_timeout_seconds: 1800 # --- timing + quality gate --- warmup_iters: 2 - timed_iters: 1 # one timed run per workload -- max_iters back to 8-12 makes each call substantial (low relative timing noise), so no repeat-and-average - inertia_tolerance: 0.10 # gate B (centroids): loose -- two correct bf16 solutions diverge to different local optima over many iterations (w0 D=32,12-iter ref drifts ~6.5% from baseline) - label_tolerance: 0.02 # gate A (labels): only applied when max_iters==1 (labels = argmin to init); skipped here since Lloyd runs multiple steps - speedup_target: 7.1 # H100 recal (bf16, multi-iter, planted, centroid gate 0.10): flashlib reference geomean 7.12x over the bf16 baseline (3.4/3.7/10.5/4.6/16.8/12.9x), all 6 pass + timed_iters: 1 # one timed run per workload; the timed unit is the judge-owned loop of max_iters `step` calls + inertia_tolerance: 0.05 # centroid gate: agent final centroids nearest-inertia <= (1+tol) x baseline. Worst flashlib-ref bf16 drift is +1.47% (w3, D=512) at max_iters=2, so 5% leaves margin while still rejecting bad centroids. + speedup_target: 5.0 # calibrated (H100, step contract, max_iters=2): flashlib reference geomean 4.95x over the frozen bf16 baseline (per-wl 2.1/2.4/6.7/3.5/13.2/9.3x), all 6 passing the gate. base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on - # Multi-iteration Lloyd (original max_iters). Data is bf16 (locked in the worker's - # gen) so precision is not a lever; planted well-separated blobs so a wrong - # (e.g. subsampled-dim) assignment compounds over iterations into bad centroids - # and fails the inertia gate. + # The agent provides one Lloyd `step(x, centroids)`; the JUDGE owns the loop and + # calls it exactly max_iters times (=2). The agent cannot skip iterations, change + # the data/init, or fake the count -- it can only make a single step faster + # (e.g. fuse assign+update). Data is bf16 (locked in gen) so precision is not a + # lever; planted well-separated blobs. max_iters=2 (not 1) is enough to be a real + # loop while too few for a converged step to become a redundant no-op. workloads: - - {id: w0, N: 100000, D: 32, K: 128, max_iters: 12} - - {id: w1, N: 200000, D: 64, K: 256, max_iters: 10} - - {id: w2, N: 500000, D: 128, K: 256, max_iters: 10} - - {id: w3, N: 200000, D: 512, K: 256, max_iters: 8} - - {id: w4, N: 1000000, D: 64, K: 512, max_iters: 8} - - {id: w5, N: 300000, D: 128, K: 1024, max_iters: 8} + - {id: w0, N: 100000, D: 32, K: 128, max_iters: 2} + - {id: w1, N: 200000, D: 64, K: 256, max_iters: 2} + - {id: w2, N: 500000, D: 128, K: 256, max_iters: 2} + - {id: w3, N: 200000, D: 512, K: 256, max_iters: 2} + - {id: w4, N: 1000000, D: 64, K: 512, max_iters: 2} + - {id: w5, N: 300000, D: 128, K: 1024, max_iters: 2} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py index 06a737a45..fa8423e43 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -115,10 +115,12 @@ def gen(w, seed, ctx): # # PLANTED CLUSTERS: x = well-separated blob centers + unit noise (NOT # random). This makes the assignment matter -- inertia is sensitive to - # which centroid each point takes -- so a solution that subsamples rows - # or feature dims, or fakes the labels, produces a wrong assignment and - # regresses the (labelled) inertia gate. Random data would leave inertia - # nearly assignment-invariant and thus hackable by doing less work. + # which centroid each point takes -- so a `step` that subsamples rows + # or feature dims, or fakes the labels, lands the final centroids in the + # wrong place and regresses the inertia gate (the judge re-derives + # inertia from the final centroids after its own max_iters-step loop). + # Random data would leave inertia nearly assignment-invariant and thus + # hackable by doing less work. K = int(w["K"]) centers = torch.randn(K, int(w["D"]), generator=g, device=dev) * 6.0 asg = torch.randint(0, K, (int(w["N"]),), generator=g, device=dev) @@ -131,8 +133,17 @@ def call(mod, w, data, ctx): if prim == "ivfpq": return mod.ivf_pq_search(ctx["index"], data["queries"], int(w["k"]), nprobe=int(w["nprobe"])) if prim == "kmeans": - return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], - init_centroids=data["init"], tol=0.0) + # The JUDGE owns the Lloyd loop: call the solution's one-iteration + # `step(x, centroids)` exactly max_iters times. The solution cannot + # change the iteration count, the data, or the init centroids -- so it + # cannot skip iterations or fake the number of steps; it can only make + # a single step faster (e.g. fuse assign+update). This whole loop is + # what gets timed. + c = data["init"].clone() + lab = None + for _ in range(int(w["max_iters"])): + lab, c = mod.step(data["x"], c) + return lab, c, int(w["max_iters"]) if prim == "knn": return mod.knn(data["queries"], data["database"], w["k"]) if prim == "dbscan": @@ -149,7 +160,7 @@ def check_shape(w, out): if tuple(cen.shape) != (w["K"], w["D"]) or not torch.isfinite(cen.float()).all(): raise ValueError("bad kmeans centroids") if lab.ndim != 1 or int(lab.shape[0]) != int(w["N"]): - raise ValueError("bad kmeans labels shape") # labels are gated, must be full (N,) + raise ValueError("bad kmeans labels shape") # step must return a full (N,) assignment elif prim == "knn": d, i = out if tuple(d.shape) != (w["Q"], w["k"]) or tuple(i.shape) != (w["Q"], w["k"]): @@ -238,25 +249,14 @@ def verdict(w, data, ctx, ref_out, agent_out): ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": - tol_cen = float(cfg["inertia_tolerance"]) - base_near = inertia(data["x"], ref_out[1]) # baseline centroid quality - ag_near = inertia(data["x"], agent_out[1]) # agent centroid quality - # (B) agent centroids must be about as good as the baseline's (nearest- - # centroid inertia). Multi-iteration + planted clusters compound a wrong - # (subsampled-dim) assignment into bad centroids, so this catches it. - if ag_near > (1.0 + tol_cen) * base_near + 1e-6: - return False, "inertia_regression", base_near, ag_near - # (A) label gate -- only well-defined for a SINGLE Lloyd step, where the - # returned labels are the argmin to `init` (the centroids the one update - # derives from). For max_iters>1 the labels are argmin to the pre-final - # centroids, which the judge cannot recompute, so the gate is skipped. - if int(w.get("max_iters", 1)) == 1: - tol_lab = float(cfg.get("label_tolerance", tol_cen)) - lab_best = inertia(data["x"], data["init"]) - lab_got = inertia_labeled(data["x"], data["init"], agent_out[0]) - if lab_got > (1.0 + tol_lab) * lab_best + 1e-6: - return False, "label_mismatch", lab_best, lab_got - return True, "", base_near, ag_near + # Centroid-quality gate: agent's final centroids (from the judge-owned + # loop over the agent's `step`) must be as good as the baseline's. Since + # the judge owns the loop, the solution cannot skip iterations or fake + # the count; a subsampled/wrong step compounds into bad centroids here. + rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) + tol = float(cfg["inertia_tolerance"]) + ok = av <= (1.0 + tol) * rv + 1e-6 + return ok, ("inertia_regression" if not ok else ""), rv, av if prim == "knn": rec = recall(agent_out[1], ref_out[1]) ok = rec >= float(cfg["recall_threshold"]) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py index 36aca7344..1a1b5a8cc 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -17,9 +17,11 @@ PKG = "kmeanslib" BASELINE_DIR = "/opt/kmeans_ref" +# The judge owns the Lloyd loop and calls your `step(x, centroids)` exactly +# `max_iters` times per workload (same contract as the hidden graded shapes). PUBLIC_WORKLOADS = [ - {"id": "p0", "N": 50_000, "D": 32, "K": 64, "max_iters": 10, "seed": 1}, - {"id": "p1", "N": 200_000, "D": 128, "K": 256, "max_iters": 10, "seed": 2}, + {"id": "p0", "N": 50_000, "D": 32, "K": 64, "max_iters": 2, "seed": 1}, + {"id": "p1", "N": 200_000, "D": 128, "K": 256, "max_iters": 2, "seed": 2}, ] CFG = { @@ -33,7 +35,7 @@ "modal_timeout_seconds": 1800, "warmup": 2, "iters": 3, - "inertia_tolerance": 0.10, + "inertia_tolerance": 0.05, "recall_threshold": 0.99, "captured_tolerance": 0.02, "ortho_tolerance": 0.02, diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py b/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py index f9b63044f..98994eaec 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/judge/refkmeans.py @@ -1,10 +1,9 @@ -"""Frozen naive K-Means baseline used by the judge as the speed denominator. - -This is a standalone (non-package) copy of the ``kmeanslib.kmeans`` -implementation the agent starts from. It is imported under its own module name -so the judge worker can load the frozen baseline and the patched ``kmeanslib`` -package in the same process. Keep this behaviourally identical to the shipped -``kmeanslib/kmeans.py``. +"""Frozen naive one-step K-Means baseline used by the judge as the speed +denominator. Behaviourally identical to the shipped ``kmeanslib.kmeans`` +(``step`` + ``_assign`` + ``_update``); imported under its own module name so the +judge worker can load the frozen baseline and the patched ``kmeanslib`` package +in one process. The judge owns the Lloyd loop and calls ``step`` a fixed number +of times. """ from __future__ import annotations @@ -12,8 +11,6 @@ def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: - # Naive per-chunk squared-L2: materialise (chunk, K) via a bf16 matmul, argmin. - # argmin ||x-c||^2 == argmin (||c||^2 - 2 x.c^T); ||x||^2 is constant per row. c = centroids.to(x.dtype) c_sq = (c.float() * c.float()).sum(1) labels = torch.empty(x.shape[0], device=x.device, dtype=torch.long) @@ -39,19 +36,9 @@ def _update(x: torch.Tensor, labels: torch.Tensor, n_clusters: int, return new.to(x.dtype) -def kmeans(x, n_clusters, *, max_iters=10, init_centroids, tol=0.0): +def step(x: torch.Tensor, centroids: torch.Tensor): if x.ndim != 2: raise ValueError(f"x must be 2-D (N, D); got shape {tuple(x.shape)}") - if init_centroids is None: - raise ValueError("init_centroids is required") - centroids = init_centroids.to(x.dtype).clone() - labels = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long) - n_iter = 0 - for n_iter in range(max_iters): - labels = _assign(x, centroids) - new_centroids = _update(x, labels, n_clusters, centroids) - shift = (new_centroids - centroids).norm(dim=-1).max() - centroids = new_centroids - if tol > 0.0 and float(shift) < tol: - break - return labels, centroids, n_iter + 1 + labels = _assign(x, centroids) + new_centroids = _update(x, labels, centroids.shape[0], centroids) + return labels, new_centroids diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/__init__.py b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/__init__.py index 622f33af9..fed5e9319 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/__init__.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/__init__.py @@ -1,15 +1,16 @@ """kmeanslib -- a tiny GPU K-Means library you are asked to make fast. -The public entry point is :func:`kmeans`. The shipped implementation is -correct but deliberately unoptimised. Your task is to rewrite the internals -(new Triton kernels, fused passes, better memory traffic, ...) so that -:func:`kmeans` runs as fast as possible while producing clusterings of the -same quality. The public function signature and return contract must not -change. +The public entry point is :func:`step`, one Lloyd iteration +``step(x, centroids) -> (labels, new_centroids)``. The judge owns the iteration +loop and calls it a fixed number of times. The shipped implementation is correct +but deliberately unoptimised. Rewrite the internals (new Triton kernels, a fused +assign+update pass, better memory traffic, ...) so that ``step`` runs as fast as +possible while producing the same clustering. The public function signature and +return contract must not change. """ from __future__ import annotations -from kmeanslib.kmeans import kmeans +from kmeanslib.kmeans import step -__all__ = ["kmeans"] +__all__ = ["step"] __version__ = "0.1.0" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py index cc8298146..2eadfdbff 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/kmeanslib/kmeans.py @@ -1,34 +1,27 @@ -"""Euclidean (squared-L2) Lloyd K-Means -- the reference you must optimise. +"""One Lloyd K-Means step -- the function you optimise. -This implementation is intentionally naive. Every Lloyd iteration it -materialises the full ``(N, K)`` distance matrix with :func:`torch.cdist` -and recomputes the assignment and the centroid update with plain PyTorch -scatter ops. It is correct and deterministic, but it moves far more memory -than necessary and launches many small kernels. +The judge owns the iteration loop, the data, the initial centroids, and the +iteration count; you provide a single Lloyd step and it is called repeatedly. +This shipped step is intentionally naive (materialise a (chunk, K) distance +matrix with a bf16 matmul + argmin, then a PyTorch scatter update). Contract (do NOT change): - kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) - -> (labels, centroids, n_iter) - - x : (N, D) float32 CUDA tensor of points. - n_clusters (K) : int, number of clusters. - max_iters : int, number of Lloyd iterations (fixed by the caller). - init_centroids : (K, D) tensor -- REQUIRED. The initial centroids. The - caller always supplies these so the result is a - deterministic function of (x, init_centroids, max_iters). - tol : float. If > 0, stop early once the maximum centroid - shift drops below ``tol``. The grader always calls with - ``tol=0.0`` (run all ``max_iters`` iterations). - - labels : (N,) int64 cluster id per point (assignment at the start - of the final iteration). - centroids : (K, D) float32 final centroids (after the final update). - n_iter : int, number of iterations actually run. - -You may add modules/kernels inside the ``kmeanslib`` package and rewrite the -body of :func:`kmeans`, ``_assign`` and ``_update`` freely, as long as the -public contract above is preserved. + step(x, centroids) -> (labels, new_centroids) + + x : (N, D) bfloat16 CUDA tensor of points. + centroids : (K, D) bfloat16 tensor -- the current centroids. + labels : (N,) int64 -- nearest-centroid assignment of every point to + `centroids` (a full assignment of all N points). + new_centroids: (K, D) -- centroids recomputed as the mean of each cluster + (empty clusters keep their previous centroid). + +This is exactly one Lloyd iteration: assign to `centroids`, then update. You may +add modules/kernels under ``kmeanslib`` and rewrite the body of ``step``, +``_assign`` and ``_update`` freely -- including **fusing the assign + update into +a single kernel** -- as long as ``step(x, centroids) -> (labels, new_centroids)`` +is preserved. You cannot change how many times it runs, the data, or the initial +centroids; the judge owns the loop and calls ``step`` a fixed number of times. """ from __future__ import annotations @@ -75,30 +68,13 @@ def _update( return new.to(x.dtype) -def kmeans( - x: torch.Tensor, - n_clusters: int, - *, - max_iters: int = 10, - init_centroids: torch.Tensor, - tol: float = 0.0, -): - """Lloyd K-Means with fixed initial centroids. See module docstring.""" +def step(x: torch.Tensor, centroids: torch.Tensor): + """One Lloyd iteration: assign to `centroids`, then recompute them. + + Returns ``(labels, new_centroids)``. See the module docstring for the contract. + """ if x.ndim != 2: raise ValueError(f"x must be 2-D (N, D); got shape {tuple(x.shape)}") - if init_centroids is None: - raise ValueError("init_centroids is required (deterministic initialisation)") - - centroids = init_centroids.to(x.dtype).clone() - labels = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long) - - n_iter = 0 - for n_iter in range(max_iters): - labels = _assign(x, centroids) - new_centroids = _update(x, labels, n_clusters, centroids) - shift = (new_centroids - centroids).norm(dim=-1).max() - centroids = new_centroids - if tol > 0.0 and float(shift) < tol: - break - - return labels, centroids, n_iter + 1 + labels = _assign(x, centroids) + new_centroids = _update(x, labels, centroids.shape[0], centroids) + return labels, new_centroids diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index 9ed3dc7b3..b3859a721 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -3,39 +3,48 @@ ## Problem You are given a small GPU K-Means library, `kmeanslib`, in the Harbor workspace -at `/app/kmeanslib`. Its public entry point is: +at `/app/kmeanslib`. Its public entry point is a **single Lloyd iteration**: ```python -kmeanslib.kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) - -> (labels, centroids, n_iter) +kmeanslib.step(x, centroids) -> (labels, new_centroids) ``` -`x` is an `(N, D)` **bfloat16** CUDA tensor, `init_centroids` is a fixed `(K, D)` -bfloat16 tensor supplied by the caller, and the function runs Euclidean -(squared-L2) Lloyd iterations. All data is bfloat16 — treat it as the fixed -working precision. The shipped implementation is a straightforward, correct -PyTorch version. - -Your goal is to make `kmeanslib.kmeans` **as fast as possible** on the GPU while -producing clusterings of the same quality. You may rewrite the internals of the -package however you like and add new modules (including Triton kernels) under -`kmeanslib/`. The public function signature and return contract above must not -change, and every result must remain a deterministic function of its inputs. +`x` is an `(N, D)` **bfloat16** CUDA tensor of points and `centroids` is the +current `(K, D)` bfloat16 tensor. One call performs exactly one Euclidean +(squared-L2) Lloyd step: assign every point to its nearest centroid +(`labels`, an `(N,)` int64 full assignment), then recompute each centroid as the +mean of its assigned points (`new_centroids`, `(K, D)`; empty clusters keep their +previous centroid). All data is bfloat16 — treat it as the fixed working +precision. The shipped implementation is a straightforward, correct PyTorch +version (a bf16 matmul + argmin, then a scatter update). + +**The judge owns the iteration loop.** It fixes the data and the initial +centroids, then calls your `step` a fixed number of times per workload, feeding +each call's `new_centroids` into the next. You do not control the data, the +initial centroids, or how many iterations run — you control only how fast a +single `step` executes. + +Your goal is to make `kmeanslib.step` **as fast as possible** on the GPU while +producing the same clustering. You may rewrite the internals of the package +however you like and add new modules (including Triton kernels) under +`kmeanslib/` — in particular you may **fuse the assign and update into a single +kernel**. The public function signature and return contract above must not +change, and each result must remain a deterministic function of its inputs. ## Workload The graded workloads are a family of held-out dense clustering problems that -vary `(N, D, K)` and the iteration count, spanning small/medium/large point -counts and both wide-feature (large `D`) and many-cluster (large `K`) regimes. -All use Euclidean distance, **bfloat16 data**, a fixed iteration count -(`tol = 0.0`, so every iteration runs), and caller-supplied `init_centroids`. +vary `(N, D, K)`, spanning small/medium/large point counts and both +wide-feature (large `D`) and many-cluster (large `K`) regimes. All use Euclidean +distance, **bfloat16 data**, well-separated planted clusters, and a fixed number +of judge-owned `step` calls per workload with caller-supplied initial centroids. Two representative *public* shapes are available for local testing (the graded shapes are different and hidden): ```text -(N=50000, D=32, K=64, max_iters=10) -(N=200000, D=128, K=256, max_iters=10) +(N=50000, D=32, K=64) +(N=200000, D=128, K=256) ``` Treat this as a general dense K-Means, not as specific shapes to special-case. @@ -74,15 +83,17 @@ baseline on a GPU, on the same seeded data and initial centroids. ## Correctness -Correctness is a gate. On every timed iteration the judge recomputes the -**inertia** (within-cluster sum of squared distances to the nearest final -centroid) of your result and of the baseline on identical data, and requires your -inertia to stay within a small relative tolerance of the baseline's. `labels` -must be a full `(N,)` assignment. Crashes, non-finite output, wrong shapes/dtypes, -timeouts, and clustering that regresses beyond the tolerance are penalized before -speed is considered. The working precision is fixed at bfloat16 for every solution -(the inputs are bfloat16), so it is not a tuning knob — speed comes from the -kernel, not from the arithmetic precision. +Correctness is a gate. After running the fixed loop of `step` calls, the judge +recomputes the **inertia** (sum of squared distances of every point to its +nearest final centroid) of your final centroids and of the baseline's on +identical data, and requires your inertia to stay within a small relative +tolerance of the baseline's. Each `step` must return `labels` as a full `(N,)` +nearest-centroid assignment to the `centroids` it was given and `new_centroids` +of shape `(K, D)`. Crashes, non-finite output, wrong shapes/dtypes, timeouts, and +clustering that regresses beyond the tolerance are penalized before speed is +considered. The working precision is fixed at bfloat16 for every solution (the +inputs are bfloat16), so it is not a tuning knob — speed comes from the kernel, +not from the arithmetic precision. ## Scoring @@ -111,9 +122,9 @@ New Python modules inside `kmeanslib/` are allowed. Patches may not: modify anything outside `kmeanslib/`; import or call an external optimized ML/kernel library (write the kernels yourself); read or write environment variables, spawn processes, or access the network; or otherwise tamper with the measurement -framework. The timing harness measures your code on freshly generated data every -iteration and re-verifies quality each time, so caching results across calls does -not help. +framework. The judge owns the loop, the data, and the initial centroids and +re-verifies quality from your final centroids, so faking labels, skipping the +real assign/update work, or caching results across calls does not help. ## Resource Budget diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch b/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch index 4a97f16f9..1f87526dd 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/kmeans_gpu_kernel_optimization/reference.patch @@ -2425,50 +2425,51 @@ index 0000000..88ff697 +if __name__ == "__main__": + main() diff --git a/kmeanslib/kmeans.py b/kmeanslib/kmeans.py -index cc82981..ed49c78 100644 +index 2eadfdb..9823476 100644 --- a/kmeanslib/kmeans.py +++ b/kmeanslib/kmeans.py -@@ -1,104 +1,26 @@ --"""Euclidean (squared-L2) Lloyd K-Means -- the reference you must optimise. -+"""Euclidean (squared-L2) Lloyd K-Means -- vendored best-in-repo Triton path. +@@ -1,80 +1,31 @@ +-"""One Lloyd K-Means step -- the function you optimise. ++"""Euclidean (squared-L2) one Lloyd step -- vendored best-in-repo Triton path. --This implementation is intentionally naive. Every Lloyd iteration it --materialises the full ``(N, K)`` distance matrix with :func:`torch.cdist` --and recomputes the assignment and the centroid update with plain PyTorch --scatter ops. It is correct and deterministic, but it moves far more memory --than necessary and launches many small kernels. +-The judge owns the iteration loop, the data, the initial centroids, and the +-iteration count; you provide a single Lloyd step and it is called repeatedly. +-This shipped step is intentionally naive (materialise a (chunk, K) distance +-matrix with a bf16 matmul + argmin, then a PyTorch scatter update). - -Contract (do NOT change): -+Delegates to the fused Triton assign + Lloyd centroid-update kernels vendored -+under ``kmeanslib._kernels``. Public contract is unchanged: ++Delegates to the Triton assign + sorted centroid-update kernels vendored under ++``kmeanslib._kernels`` (the fastest single-iteration Euclidean K-Means path in ++the source repo). Public contract is unchanged: - kmeans(x, n_clusters, *, max_iters, init_centroids, tol=0.0) - -> (labels, centroids, n_iter) -- -- x : (N, D) float32 CUDA tensor of points. -- n_clusters (K) : int, number of clusters. -- max_iters : int, number of Lloyd iterations (fixed by the caller). -- init_centroids : (K, D) tensor -- REQUIRED. The initial centroids. The -- caller always supplies these so the result is a -- deterministic function of (x, init_centroids, max_iters). -- tol : float. If > 0, stop early once the maximum centroid -- shift drops below ``tol``. The grader always calls with -- ``tol=0.0`` (run all ``max_iters`` iterations). -- -- labels : (N,) int64 cluster id per point (assignment at the start -- of the final iteration). -- centroids : (K, D) float32 final centroids (after the final update). -- n_iter : int, number of iterations actually run. + step(x, centroids) -> (labels, new_centroids) + +- x : (N, D) bfloat16 CUDA tensor of points. +- centroids : (K, D) bfloat16 tensor -- the current centroids. +- labels : (N,) int64 -- nearest-centroid assignment of every point to +- `centroids` (a full assignment of all N points). +- new_centroids: (K, D) -- centroids recomputed as the mean of each cluster +- (empty clusters keep their previous centroid). - --You may add modules/kernels inside the ``kmeanslib`` package and rewrite the --body of :func:`kmeans`, ``_assign`` and ``_update`` freely, as long as the --public contract above is preserved. +-This is exactly one Lloyd iteration: assign to `centroids`, then update. You may +-add modules/kernels under ``kmeanslib`` and rewrite the body of ``step``, +-``_assign`` and ``_update`` freely -- including **fusing the assign + update into +-a single kernel** -- as long as ``step(x, centroids) -> (labels, new_centroids)`` +-is preserved. You cannot change how many times it runs, the data, or the initial +-centroids; the judge owns the loop and calls ``step`` a fixed number of times. ++This is exactly one Lloyd iteration -- assign every point to its nearest ++``centroids`` (Triton distance kernel), then recompute the centroids as the mean ++of each cluster (Triton sorted scatter-update). The judge owns the loop. """ from __future__ import annotations import torch -- ++from kmeanslib._kernels.primitives.kmeans.triton.assign import euclid_assign_triton ++from kmeanslib._kernels.primitives.kmeans.triton.update import ( ++ triton_centroid_update_sorted_euclid, ++) + -def _assign(x: torch.Tensor, centroids: torch.Tensor) -> torch.Tensor: - """Nearest-centroid assignment by squared-L2 distance. - @@ -2484,9 +2485,8 @@ index cc82981..ed49c78 100644 - dist = c_sq[None, :] - 2.0 * (xb @ c.t()).float() # (chunk, K) fp32 - labels[lo:lo + 16384] = torch.argmin(dist, dim=1) - return labels -+from kmeanslib._kernels.primitives.kmeans.triton.kmeans import batch_kmeans_Euclid - - +- +- -def _update( - x: torch.Tensor, - labels: torch.Tensor, @@ -2510,38 +2510,21 @@ index cc82981..ed49c78 100644 - return new.to(x.dtype) - - --def kmeans( -- x: torch.Tensor, -- n_clusters: int, -- *, -- max_iters: int = 10, -- init_centroids: torch.Tensor, -- tol: float = 0.0, --): -- """Lloyd K-Means with fixed initial centroids. See module docstring.""" -+def kmeans(x, n_clusters, *, max_iters=10, init_centroids, tol=0.0): +-def step(x: torch.Tensor, centroids: torch.Tensor): +- """One Lloyd iteration: assign to `centroids`, then recompute them. + +- Returns ``(labels, new_centroids)``. See the module docstring for the contract. +- """ ++def step(x, centroids): if x.ndim != 2: - raise ValueError(f"x must be 2-D (N, D); got shape {tuple(x.shape)}") +- labels = _assign(x, centroids) +- new_centroids = _update(x, labels, centroids.shape[0], centroids) +- return labels, new_centroids + raise ValueError(f"x must be 2-D (N, D); got {tuple(x.shape)}") - if init_centroids is None: - raise ValueError("init_centroids is required (deterministic initialisation)") -- -- centroids = init_centroids.to(x.dtype).clone() -- labels = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long) -- -- n_iter = 0 -- for n_iter in range(max_iters): -- labels = _assign(x, centroids) -- new_centroids = _update(x, labels, n_clusters, centroids) -- shift = (new_centroids - centroids).norm(dim=-1).max() -- centroids = new_centroids -- if tol > 0.0 and float(shift) < tol: -- break -- -- return labels, centroids, n_iter + 1 ++ # The vendored kernels are batched (B, N, D); run a single instance as B=1. + xb = x.unsqueeze(0).contiguous() -+ initb = init_centroids.to(x.dtype).reshape(1, n_clusters, x.shape[1]).contiguous() -+ labels, centroids, n_iter = batch_kmeans_Euclid( -+ xb, n_clusters, max_iters=max_iters, tol=tol, init_centroids=initb, -+ ) -+ return labels.squeeze(0), centroids.squeeze(0).to(x.dtype), n_iter ++ cb = centroids.to(x.dtype).unsqueeze(0).contiguous() ++ labels = euclid_assign_triton(xb, cb, use_heuristic=True) # (1, N) ++ new_c = triton_centroid_update_sorted_euclid(xb, labels, cb) # (1, K, D) ++ return labels.squeeze(0).to(torch.long), new_c.squeeze(0).to(x.dtype) From 6bd4d7758a0a6cc2d8ed49255943adedc7bcc0e6 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 5 Jul 2026 11:12:19 +0000 Subject: [PATCH 21/34] feat(2.0/flashlib): set score cap to 2x reference geomean (all 4 kernel tasks) Raise each task's speedup_target to 2x the flashlib reference's measured H100 geomean so the reference is no longer the 100-point bar -- strong solutions now show headroom instead of capping at 100, and maxing out requires beating the reference 2x. Fresh H100 recalibration (all references still pass their gates on all 6 workloads): kmeans ref 4.95x -> target 10.0 (was 5.0; ref now ~69/100) knn ref 5.38x -> target 10.75 (was 5.0; ref now ~71/100) dbscan ref 19.93x -> target 40.0 (was 13.0; ref now ~81/100) ivf_pq ref 820.8x -> target 1640.0 (was 800; ref now ~91/100) Config-only (speedup_target is delivered judge-side via task_config.json at gen time; no base-image rebuild needed). Co-Authored-By: Claude Opus 4.8 (1M context) --- 2.0/problems/dbscan_gpu_kernel_optimization/config.yaml | 2 +- 2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml | 2 +- 2.0/problems/kmeans_gpu_kernel_optimization/config.yaml | 2 +- 2.0/problems/knn_gpu_kernel_optimization/config.yaml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml index 7e7395f83..86b0f7b66 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml @@ -23,7 +23,7 @@ evaluation: warmup_iters: 2 timed_iters: 3 # the O(N^2) naive baseline is seconds/call at these N; keep the timed budget bounded (median of 3) ari_threshold: 0.99 # agent clustering must agree with the baseline (Adjusted Rand Index) on every iteration; the grid reference measured ARI 1.0 on all workloads - speedup_target: 13.0 # H100 calibration: flashlib grid DBSCAN reference geomean 13.29x over the O(N^2) matmul baseline (per-workload 7.2/12.1/9.5/18.8/16.6/21.2x) + speedup_target: 40.0 # score cap = 2x the flashlib grid-DBSCAN reference geomean (~19.9x on H100, per-wl 7.7/17.3/15.6/31.4/28.6/33.9x, ARI 1.0 all 6): reference scores ~73/100, a solution must be 2x faster than it to max out. NB the O(N^2) baseline timing is thermally sensitive (an earlier run measured 13.3x), so the ratio carries some cross-run variance. base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); the params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml index 1a980f0f0..c61103019 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml @@ -23,7 +23,7 @@ evaluation: warmup_iters: 2 timed_iters: 7 recall_threshold: 0.95 # agent results must match the baseline's top-k (iso-result recall@k) on every iteration; the Triton reference measured iso-recall 1.0 on all workloads, so 0.95 leaves margin for ADC tie-breaks - speedup_target: 800.0 # H100 calibration: flashlib Triton reference geomean ~898x over the Python-loop naive baseline (per-workload 513/907/1420/1449/442/1234x) + speedup_target: 1640.0 # score cap = 2x the flashlib Triton reference geomean (~821x on H100, per-wl 493/798/1514/1264/376/1081x, iso-recall 1.0 all 6): reference scores ~90/100, a solution must be 2x faster than it to max out. (Huge factor = Python-per-query-loop baseline.) base_seed: 20260702 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index 84f26ab60..b2d660de7 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -47,7 +47,7 @@ evaluation: warmup_iters: 2 timed_iters: 1 # one timed run per workload; the timed unit is the judge-owned loop of max_iters `step` calls inertia_tolerance: 0.05 # centroid gate: agent final centroids nearest-inertia <= (1+tol) x baseline. Worst flashlib-ref bf16 drift is +1.47% (w3, D=512) at max_iters=2, so 5% leaves margin while still rejecting bad centroids. - speedup_target: 5.0 # calibrated (H100, step contract, max_iters=2): flashlib reference geomean 4.95x over the frozen bf16 baseline (per-wl 2.1/2.4/6.7/3.5/13.2/9.3x), all 6 passing the gate. + speedup_target: 10.0 # score cap = 2x the flashlib reference geomean (4.95x, H100 step/max_iters=2, all 6 pass): the reference now scores ~70/100 and a solution must be 2x faster than the reference to max out. (Codex's fused-kernel trial hit 7.28x -> ~87/100 instead of a capped 100.) base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on diff --git a/2.0/problems/knn_gpu_kernel_optimization/config.yaml b/2.0/problems/knn_gpu_kernel_optimization/config.yaml index 49c91ff8a..c3dbbf0a8 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/knn_gpu_kernel_optimization/config.yaml @@ -46,7 +46,7 @@ evaluation: warmup_iters: 2 timed_iters: 7 recall_threshold: 0.99 - speedup_target: 5.0 # calibrated: reference (flashlib-best) geomean ~5.3x on Modal H100 + speedup_target: 10.75 # score cap = 2x the flashlib-best reference geomean (5.38x on Modal H100, per-wl 8.8/8.5/7.1/2.9/3.4/4.7x, all 6 pass recall>=0.99): the reference scores ~70/100 and a solution must be 2x faster than it to max out. base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on From 364a4af9ff691472e1cee39bdd9092344a123420 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Mon, 6 Jul 2026 08:30:41 +0000 Subject: [PATCH 22/34] feat(2.0/flashlib): make public_test identical to the final graded evaluation Rewrite public_test.py (all 4 kernel tasks) to run the EXACT graded evaluation instead of a separate 2-shape "public" set: it reads /app/task_config.json (the same config the judge reads), builds the identical workloads + seed derivation (base_seed + 1000*(i+1)), the identical cfg (warmup_iters/timed_iters + thresholds), calls flash_gpu.run_remote, and computes the same geomean + score_from_speedup(speedup_target) -- byte-for-byte the judge's full_evaluation, including "any workload fails its gate -> score 0". So `bash /app/public_test.sh` now reports the real per-workload pass/fail, speedup, geomean, and predicted final score (0-100). One generic, task-agnostic file shared by all 4 tasks. task_config.json is already agent-visible (adapter COPYs it into /app), so this is no new leak; the readmes already disclosed the workload distribution. readmes updated to state the public test == the graded test (no hidden shapes). Config-only + public_test (delivered via harbor_app at gen); no base-image rebuild needed. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../harbor/app/public_test.py | 171 ++++++++++++----- .../dbscan_gpu_kernel_optimization/readme | 15 +- .../harbor/app/public_test.py | 168 ++++++++++++----- .../ivf_pq_gpu_kernel_optimization/readme | 15 +- .../harbor/app/public_test.py | 172 +++++++++++++----- .../kmeans_gpu_kernel_optimization/readme | 19 +- .../harbor/app/public_test.py | 170 ++++++++++++----- .../knn_gpu_kernel_optimization/readme | 20 +- 8 files changed, 528 insertions(+), 222 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py index 896ffa316..e8de4a4bd 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,44 +1,95 @@ -"""Public GPU self-test for the DBSCAN kernel-optimization task. +"""Public GPU self-test -- IDENTICAL to the final graded evaluation. -Times your current /app/dbscanlib against the naive baseline on a Modal GPU and -reports the clustering-agreement (ARI) value + speedup on two public shapes. -Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET. The graded workloads and thresholds -are hidden and differ from these public shapes. +This runs the EXACT same workloads, thresholds, timing, and seeds that the hidden +judge uses to grade your submission (all read from ``/app/task_config.json``, +the same config the judge reads), on a Modal GPU, and reports the same +per-workload pass/fail + speedup + geomean + predicted score (0-100) you would +receive on submission. There is no longer a separate, smaller "public" workload +set -- what you see here is what you get graded on. + +Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. """ from __future__ import annotations +import json +import math import os import sys from pathlib import Path -sys.path.insert(0, "/opt") +sys.path.insert(0, "/opt") # flash_gpu.py is baked at /opt APP_DIR = os.environ.get("APP_DIR", "/app") -PKG = "dbscanlib" -BASELINE_DIR = "/opt/dbscan_ref" - -PUBLIC_WORKLOADS = [ - {"id": "p0", "N": 100000, "D": 2, "n_centers": 12, "eps": 1.5, "min_samples": 8, "noise_frac": 0.03, "seed": 1}, - {"id": "p1", "N": 150000, "D": 2, "n_centers": 14, "eps": 1.4, "min_samples": 8, "noise_frac": 0.04, "seed": 2}, -] - -CFG = { - "primitive": "dbscan", - "pkg": PKG, - "ref_module": "refdbscan", - "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), - "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", - "pip": ["torch", "numpy"], - "app_name": "dbscan-kernel-opt-public", - "modal_timeout_seconds": 1800, - "warmup": 2, - "iters": 5, - "inertia_tolerance": 0.02, - "recall_threshold": 0.99, - "captured_tolerance": 0.02, - "ortho_tolerance": 0.02, - "ari_threshold": 0.99, -} +TASK_CONFIG_PATH = os.environ.get("TASK_CONFIG_PATH", "/app/task_config.json") + + +def _load_eval() -> dict: + """The judge grades from task_config.json's `evaluation` block; read the same.""" + try: + doc = json.loads(Path(TASK_CONFIG_PATH).read_text(encoding="utf-8")) + except Exception as exc: # noqa: BLE001 + print(f"could not read {TASK_CONFIG_PATH}: {exc}") + return {} + return doc.get("evaluation", doc) + + +EV = _load_eval() + + +def g(key, default): + v = EV.get(key, default) + return default if v is None else v + + +PRIMITIVE = str(g("primitive", "")) +PKG = str(g("pkg", "")) +REF_MODULE = str(g("ref_module", "")) +SPEEDUP_TARGET = float(g("speedup_target", 8.0)) +BASELINE_DIR = str(g("baseline_source", "/opt/flash_ref")) + + +def _workloads() -> list: + """Exactly the judge's final-role workload set: ALL workloads, same seed + derivation ``base_seed + 1000*(i+1)`` -- identical data to the graded run.""" + wls = [dict(w) for w in g("workloads", [])] + base = int(g("base_seed", 20260701)) + for i, w in enumerate(wls): + w.setdefault("seed", base + 1000 * (i + 1)) + return wls + + +def _cfg() -> dict: + """Byte-for-byte the judge's _build_cfg().""" + return { + "primitive": PRIMITIVE, + "pkg": PKG, + "ref_module": REF_MODULE, + "gpu": str(g("gpu", "H100")), + "cuda_image": str(g("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), + "pip": list(g("pip", ["torch", "numpy"])), + "app_name": str(g("app_name", "flash-kernel-public")), + "modal_timeout_seconds": int(g("modal_timeout_seconds", 1800)), + "warmup": int(g("warmup_iters", 3)), + "iters": int(g("timed_iters", 7)), + "inertia_tolerance": float(g("inertia_tolerance", 0.02)), + "recall_threshold": float(g("recall_threshold", 0.99)), + "captured_tolerance": float(g("captured_tolerance", 0.02)), + "ortho_tolerance": float(g("ortho_tolerance", 0.02)), + "ari_threshold": float(g("ari_threshold", 0.99)), + } + + +def geometric_mean(values: list) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) + + +def score_from_speedup(gm: float) -> float: + if gm <= 0: + return 0.0 + raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + return max(0.0, min(100.0, raw)) def _read(root: str, sub: str = "") -> dict: @@ -51,13 +102,18 @@ def _read(root: str, sub: str = "") -> dict: def main() -> int: import flash_gpu # baked at /opt/flash_gpu.py if not flash_gpu.modal_available(): - print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU public test.") + print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU self-test.") + return 1 + workloads = _workloads() + cfg = _cfg() + if not workloads: + print("no workloads found in task_config.json; cannot run.") return 1 payload = { "baseline_files": _read(BASELINE_DIR), "patched_files": _read(APP_DIR, PKG), - "workloads": PUBLIC_WORKLOADS, - "cfg": CFG, + "workloads": workloads, + "cfg": cfg, } try: result = flash_gpu.run_remote(payload) @@ -67,26 +123,47 @@ def main() -> int: if not result.get("ok"): print(f"worker error: {result.get('error')}") return 1 - # Correctness first: show the quality metric (agent vs frozen baseline) on every - # shape, pass or fail -- not just the speedup. The graded shapes are hidden and differ. - prim = CFG.get("primitive", "") - if prim in ("knn", "ivfpq"): - mlabel, gate = "recall@k", f">= {CFG.get('recall_threshold')}" - elif prim == "dbscan": - mlabel, gate = "ARI", f">= {CFG.get('ari_threshold')}" - elif prim == "kmeans": - mlabel, gate = "inertia", f"<= (1+{CFG.get('inertia_tolerance')}) x ref (lower is better)" + + if PRIMITIVE in ("knn", "ivfpq"): + mlabel, gate = "recall@k", f">= {cfg['recall_threshold']}" + elif PRIMITIVE == "dbscan": + mlabel, gate = "ARI", f">= {cfg['ari_threshold']}" + elif PRIMITIVE == "kmeans": + mlabel, gate = "inertia", f"<= (1+{cfg['inertia_tolerance']}) x ref (lower is better)" else: mlabel, gate = "quality", "" - print(f"correctness gate: {mlabel} {gate}") + + print("=== FINAL-EQUIVALENT self-test: identical workloads / thresholds / seeds / " + "timing to the graded judge ===") + print(f"correctness gate: {mlabel} {gate} | speedup_target = {SPEEDUP_TARGET:g}") print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") - for row in result["rows"]: + rows = result.get("rows", []) + speedups = [] + any_fail = False + for row in rows: av, rv = row.get("agent_val"), row.get("ref_val") q = (f"{av:.4f} / {rv:.4f}" if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") - sp = f"{row['speedup']:.2f}x" if row.get("ok") else "-" - st = "OK" if row.get("ok") else "FAIL:" + str(row.get("reason", "")) + if row.get("ok"): + sp = f"{row['speedup']:.2f}x" + speedups.append(max(float(row["speedup"]), 0.01)) # same clamp as the judge + st = "OK" + else: + sp = "-" + st = "FAIL:" + str(row.get("reason", "")) + any_fail = True print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") + + # Scoring is byte-for-byte the judge's full_evaluation: ANY gate failure -> 0. + print() + if any_fail or len(speedups) != len(rows) or not speedups: + print("RESULT: at least one workload FAILED the correctness gate -> a submission now " + "would be INVALID and score 0/100. Fix the failing workload(s) before submitting.") + return 0 + gm = geometric_mean(speedups) + score = score_from_speedup(gm) + print(f"geomean speedup = {gm:.3f}x over the naive baseline") + print(f"PREDICTED FINAL SCORE = {score:.2f} / 100 (100 == {SPEEDUP_TARGET:g}x geomean)") return 0 diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/readme b/2.0/problems/dbscan_gpu_kernel_optimization/readme index 21ecda762..05e486e4e 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/readme +++ b/2.0/problems/dbscan_gpu_kernel_optimization/readme @@ -28,13 +28,9 @@ structure (well-separated clusters plus a little uniform noise), varying `N`, cluster count, `eps`, and `min_samples`. Treat it as general 2-D Euclidean DBSCAN, not as specific point sets to special-case. -Two representative *public* shapes are available for local testing (the graded -shapes are different and hidden): - -```text -(N=100000, D=2, eps=1.5, min_samples=8) -(N=150000, D=2, eps=1.4, min_samples=8) -``` +The public self-test now runs the **exact graded workloads** — the same shapes, +thresholds, seeds, and timing the judge uses to score you — so there are no +separate hidden shapes to guess at. ## Iterate on a GPU @@ -45,6 +41,11 @@ a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET`): bash /app/public_test.sh ``` +It runs the **identical evaluation the judge uses to grade you** and reports, per +graded workload, pass/fail on the quality gate, your speedup, the geometric-mean +speedup, and your **predicted final score** (0-100). Any workload that fails its +gate makes the submission score 0. + ## Submission The submitted artifact is a patch over the `dbscanlib` package: diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py index fd743854b..e8de4a4bd 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,41 +1,95 @@ -"""Public GPU self-test for the IVF-PQ search kernel-optimization task. +"""Public GPU self-test -- IDENTICAL to the final graded evaluation. -Times your current /app/ivfpqlib against the naive baseline on a Modal GPU and -reports the iso-result recall value + speedup on two public shapes. The index -is built once per shape by the frozen baseline and reused. Requires -MODAL_TOKEN_ID / MODAL_TOKEN_SECRET. The graded workloads and thresholds are -hidden and differ from these public shapes. +This runs the EXACT same workloads, thresholds, timing, and seeds that the hidden +judge uses to grade your submission (all read from ``/app/task_config.json``, +the same config the judge reads), on a Modal GPU, and reports the same +per-workload pass/fail + speedup + geomean + predicted score (0-100) you would +receive on submission. There is no longer a separate, smaller "public" workload +set -- what you see here is what you get graded on. + +Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. """ from __future__ import annotations +import json +import math import os import sys from pathlib import Path -sys.path.insert(0, "/opt") +sys.path.insert(0, "/opt") # flash_gpu.py is baked at /opt APP_DIR = os.environ.get("APP_DIR", "/app") -PKG = "ivfpqlib" -BASELINE_DIR = "/opt/ivfpq_ref" - -PUBLIC_WORKLOADS = [ - {"id": "p0", "M": 40000, "D": 64, "nlist": 256, "m": 8, "nprobe": 8, "Q": 1024, "k": 10, "seed": 1}, - {"id": "p1", "M": 100000, "D": 96, "nlist": 512, "m": 12, "nprobe": 16, "Q": 1024, "k": 10, "seed": 2}, -] - -CFG = { - "primitive": "ivfpq", - "pkg": PKG, - "ref_module": "refivfpq", - "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), - "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", - "pip": ["torch", "numpy"], - "app_name": "ivfpq-kernel-opt-public", - "modal_timeout_seconds": 1800, - "warmup": 2, - "iters": 5, - "recall_threshold": 0.95, -} +TASK_CONFIG_PATH = os.environ.get("TASK_CONFIG_PATH", "/app/task_config.json") + + +def _load_eval() -> dict: + """The judge grades from task_config.json's `evaluation` block; read the same.""" + try: + doc = json.loads(Path(TASK_CONFIG_PATH).read_text(encoding="utf-8")) + except Exception as exc: # noqa: BLE001 + print(f"could not read {TASK_CONFIG_PATH}: {exc}") + return {} + return doc.get("evaluation", doc) + + +EV = _load_eval() + + +def g(key, default): + v = EV.get(key, default) + return default if v is None else v + + +PRIMITIVE = str(g("primitive", "")) +PKG = str(g("pkg", "")) +REF_MODULE = str(g("ref_module", "")) +SPEEDUP_TARGET = float(g("speedup_target", 8.0)) +BASELINE_DIR = str(g("baseline_source", "/opt/flash_ref")) + + +def _workloads() -> list: + """Exactly the judge's final-role workload set: ALL workloads, same seed + derivation ``base_seed + 1000*(i+1)`` -- identical data to the graded run.""" + wls = [dict(w) for w in g("workloads", [])] + base = int(g("base_seed", 20260701)) + for i, w in enumerate(wls): + w.setdefault("seed", base + 1000 * (i + 1)) + return wls + + +def _cfg() -> dict: + """Byte-for-byte the judge's _build_cfg().""" + return { + "primitive": PRIMITIVE, + "pkg": PKG, + "ref_module": REF_MODULE, + "gpu": str(g("gpu", "H100")), + "cuda_image": str(g("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), + "pip": list(g("pip", ["torch", "numpy"])), + "app_name": str(g("app_name", "flash-kernel-public")), + "modal_timeout_seconds": int(g("modal_timeout_seconds", 1800)), + "warmup": int(g("warmup_iters", 3)), + "iters": int(g("timed_iters", 7)), + "inertia_tolerance": float(g("inertia_tolerance", 0.02)), + "recall_threshold": float(g("recall_threshold", 0.99)), + "captured_tolerance": float(g("captured_tolerance", 0.02)), + "ortho_tolerance": float(g("ortho_tolerance", 0.02)), + "ari_threshold": float(g("ari_threshold", 0.99)), + } + + +def geometric_mean(values: list) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) + + +def score_from_speedup(gm: float) -> float: + if gm <= 0: + return 0.0 + raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + return max(0.0, min(100.0, raw)) def _read(root: str, sub: str = "") -> dict: @@ -48,13 +102,18 @@ def _read(root: str, sub: str = "") -> dict: def main() -> int: import flash_gpu # baked at /opt/flash_gpu.py if not flash_gpu.modal_available(): - print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU public test.") + print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU self-test.") + return 1 + workloads = _workloads() + cfg = _cfg() + if not workloads: + print("no workloads found in task_config.json; cannot run.") return 1 payload = { "baseline_files": _read(BASELINE_DIR), "patched_files": _read(APP_DIR, PKG), - "workloads": PUBLIC_WORKLOADS, - "cfg": CFG, + "workloads": workloads, + "cfg": cfg, } try: result = flash_gpu.run_remote(payload) @@ -64,26 +123,47 @@ def main() -> int: if not result.get("ok"): print(f"worker error: {result.get('error')}") return 1 - # Correctness first: show the quality metric (agent vs frozen baseline) on every - # shape, pass or fail -- not just the speedup. The graded shapes are hidden and differ. - prim = CFG.get("primitive", "") - if prim in ("knn", "ivfpq"): - mlabel, gate = "recall@k", f">= {CFG.get('recall_threshold')}" - elif prim == "dbscan": - mlabel, gate = "ARI", f">= {CFG.get('ari_threshold')}" - elif prim == "kmeans": - mlabel, gate = "inertia", f"<= (1+{CFG.get('inertia_tolerance')}) x ref (lower is better)" + + if PRIMITIVE in ("knn", "ivfpq"): + mlabel, gate = "recall@k", f">= {cfg['recall_threshold']}" + elif PRIMITIVE == "dbscan": + mlabel, gate = "ARI", f">= {cfg['ari_threshold']}" + elif PRIMITIVE == "kmeans": + mlabel, gate = "inertia", f"<= (1+{cfg['inertia_tolerance']}) x ref (lower is better)" else: mlabel, gate = "quality", "" - print(f"correctness gate: {mlabel} {gate}") + + print("=== FINAL-EQUIVALENT self-test: identical workloads / thresholds / seeds / " + "timing to the graded judge ===") + print(f"correctness gate: {mlabel} {gate} | speedup_target = {SPEEDUP_TARGET:g}") print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") - for row in result["rows"]: + rows = result.get("rows", []) + speedups = [] + any_fail = False + for row in rows: av, rv = row.get("agent_val"), row.get("ref_val") q = (f"{av:.4f} / {rv:.4f}" if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") - sp = f"{row['speedup']:.2f}x" if row.get("ok") else "-" - st = "OK" if row.get("ok") else "FAIL:" + str(row.get("reason", "")) + if row.get("ok"): + sp = f"{row['speedup']:.2f}x" + speedups.append(max(float(row["speedup"]), 0.01)) # same clamp as the judge + st = "OK" + else: + sp = "-" + st = "FAIL:" + str(row.get("reason", "")) + any_fail = True print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") + + # Scoring is byte-for-byte the judge's full_evaluation: ANY gate failure -> 0. + print() + if any_fail or len(speedups) != len(rows) or not speedups: + print("RESULT: at least one workload FAILED the correctness gate -> a submission now " + "would be INVALID and score 0/100. Fix the failing workload(s) before submitting.") + return 0 + gm = geometric_mean(speedups) + score = score_from_speedup(gm) + print(f"geomean speedup = {gm:.3f}x over the naive baseline") + print(f"PREDICTED FINAL SCORE = {score:.2f} / 100 (100 == {SPEEDUP_TARGET:g}x geomean)") return 0 diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/readme b/2.0/problems/ivf_pq_gpu_kernel_optimization/readme index 03cfb717d..6aba6f6b3 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/readme +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/readme @@ -41,13 +41,9 @@ databases: `M` from tens of thousands to a few hundred thousand rows, `D` in the 8–32, and `nq` a thousand-plus queries per call. Treat it as general IVF-PQ search, not as specific indices to special-case. -Two representative *public* shapes are available for local testing (the graded -shapes are different and hidden): - -```text -(M=40000, D=64, nlist=256, m=8, nprobe=8, nq=1024, k=10) -(M=100000, D=96, nlist=512, m=12, nprobe=16, nq=1024, k=10) -``` +The public self-test now runs the **exact graded workloads** — the same shapes, +thresholds, seeds, and timing the judge uses to score you — so there are no +separate hidden shapes to guess at. ## Iterate on a GPU @@ -58,6 +54,11 @@ a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET`): bash /app/public_test.sh ``` +It runs the **identical evaluation the judge uses to grade you** and reports, per +graded workload, pass/fail on the quality gate, your speedup, the geometric-mean +speedup, and your **predicted final score** (0-100). Any workload that fails its +gate makes the submission score 0. + ## Submission The submitted artifact is a patch over the `ivfpqlib` package: diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py index 1a1b5a8cc..e8de4a4bd 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,45 +1,95 @@ -"""Public GPU self-test for the K-Means kernel-optimization task. +"""Public GPU self-test -- IDENTICAL to the final graded evaluation. -Times your current /app/kmeanslib against the naive baseline on a Modal GPU and -reports the quality value + speedup on two public shapes. Requires -MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and -their thresholds are hidden and differ from these public shapes. +This runs the EXACT same workloads, thresholds, timing, and seeds that the hidden +judge uses to grade your submission (all read from ``/app/task_config.json``, +the same config the judge reads), on a Modal GPU, and reports the same +per-workload pass/fail + speedup + geomean + predicted score (0-100) you would +receive on submission. There is no longer a separate, smaller "public" workload +set -- what you see here is what you get graded on. + +Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. """ from __future__ import annotations +import json +import math import os import sys from pathlib import Path -sys.path.insert(0, "/opt") +sys.path.insert(0, "/opt") # flash_gpu.py is baked at /opt APP_DIR = os.environ.get("APP_DIR", "/app") -PKG = "kmeanslib" -BASELINE_DIR = "/opt/kmeans_ref" - -# The judge owns the Lloyd loop and calls your `step(x, centroids)` exactly -# `max_iters` times per workload (same contract as the hidden graded shapes). -PUBLIC_WORKLOADS = [ - {"id": "p0", "N": 50_000, "D": 32, "K": 64, "max_iters": 2, "seed": 1}, - {"id": "p1", "N": 200_000, "D": 128, "K": 256, "max_iters": 2, "seed": 2}, -] - -CFG = { - "primitive": "kmeans", - "pkg": PKG, - "ref_module": "refkmeans", - "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), - "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", - "pip": ["torch", "numpy"], - "app_name": "kmeans-kernel-opt-public", - "modal_timeout_seconds": 1800, - "warmup": 2, - "iters": 3, - "inertia_tolerance": 0.05, - "recall_threshold": 0.99, - "captured_tolerance": 0.02, - "ortho_tolerance": 0.02, -} +TASK_CONFIG_PATH = os.environ.get("TASK_CONFIG_PATH", "/app/task_config.json") + + +def _load_eval() -> dict: + """The judge grades from task_config.json's `evaluation` block; read the same.""" + try: + doc = json.loads(Path(TASK_CONFIG_PATH).read_text(encoding="utf-8")) + except Exception as exc: # noqa: BLE001 + print(f"could not read {TASK_CONFIG_PATH}: {exc}") + return {} + return doc.get("evaluation", doc) + + +EV = _load_eval() + + +def g(key, default): + v = EV.get(key, default) + return default if v is None else v + + +PRIMITIVE = str(g("primitive", "")) +PKG = str(g("pkg", "")) +REF_MODULE = str(g("ref_module", "")) +SPEEDUP_TARGET = float(g("speedup_target", 8.0)) +BASELINE_DIR = str(g("baseline_source", "/opt/flash_ref")) + + +def _workloads() -> list: + """Exactly the judge's final-role workload set: ALL workloads, same seed + derivation ``base_seed + 1000*(i+1)`` -- identical data to the graded run.""" + wls = [dict(w) for w in g("workloads", [])] + base = int(g("base_seed", 20260701)) + for i, w in enumerate(wls): + w.setdefault("seed", base + 1000 * (i + 1)) + return wls + + +def _cfg() -> dict: + """Byte-for-byte the judge's _build_cfg().""" + return { + "primitive": PRIMITIVE, + "pkg": PKG, + "ref_module": REF_MODULE, + "gpu": str(g("gpu", "H100")), + "cuda_image": str(g("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), + "pip": list(g("pip", ["torch", "numpy"])), + "app_name": str(g("app_name", "flash-kernel-public")), + "modal_timeout_seconds": int(g("modal_timeout_seconds", 1800)), + "warmup": int(g("warmup_iters", 3)), + "iters": int(g("timed_iters", 7)), + "inertia_tolerance": float(g("inertia_tolerance", 0.02)), + "recall_threshold": float(g("recall_threshold", 0.99)), + "captured_tolerance": float(g("captured_tolerance", 0.02)), + "ortho_tolerance": float(g("ortho_tolerance", 0.02)), + "ari_threshold": float(g("ari_threshold", 0.99)), + } + + +def geometric_mean(values: list) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) + + +def score_from_speedup(gm: float) -> float: + if gm <= 0: + return 0.0 + raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + return max(0.0, min(100.0, raw)) def _read(root: str, sub: str = "") -> dict: @@ -52,13 +102,18 @@ def _read(root: str, sub: str = "") -> dict: def main() -> int: import flash_gpu # baked at /opt/flash_gpu.py if not flash_gpu.modal_available(): - print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU public test.") + print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU self-test.") + return 1 + workloads = _workloads() + cfg = _cfg() + if not workloads: + print("no workloads found in task_config.json; cannot run.") return 1 payload = { "baseline_files": _read(BASELINE_DIR), "patched_files": _read(APP_DIR, PKG), - "workloads": PUBLIC_WORKLOADS, - "cfg": CFG, + "workloads": workloads, + "cfg": cfg, } try: result = flash_gpu.run_remote(payload) @@ -68,26 +123,47 @@ def main() -> int: if not result.get("ok"): print(f"worker error: {result.get('error')}") return 1 - # Correctness first: show the quality metric (agent vs frozen baseline) on every - # shape, pass or fail -- not just the speedup. The graded shapes are hidden and differ. - prim = CFG.get("primitive", "") - if prim in ("knn", "ivfpq"): - mlabel, gate = "recall@k", f">= {CFG.get('recall_threshold')}" - elif prim == "dbscan": - mlabel, gate = "ARI", f">= {CFG.get('ari_threshold')}" - elif prim == "kmeans": - mlabel, gate = "inertia", f"<= (1+{CFG.get('inertia_tolerance')}) x ref (lower is better)" + + if PRIMITIVE in ("knn", "ivfpq"): + mlabel, gate = "recall@k", f">= {cfg['recall_threshold']}" + elif PRIMITIVE == "dbscan": + mlabel, gate = "ARI", f">= {cfg['ari_threshold']}" + elif PRIMITIVE == "kmeans": + mlabel, gate = "inertia", f"<= (1+{cfg['inertia_tolerance']}) x ref (lower is better)" else: mlabel, gate = "quality", "" - print(f"correctness gate: {mlabel} {gate}") + + print("=== FINAL-EQUIVALENT self-test: identical workloads / thresholds / seeds / " + "timing to the graded judge ===") + print(f"correctness gate: {mlabel} {gate} | speedup_target = {SPEEDUP_TARGET:g}") print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") - for row in result["rows"]: + rows = result.get("rows", []) + speedups = [] + any_fail = False + for row in rows: av, rv = row.get("agent_val"), row.get("ref_val") q = (f"{av:.4f} / {rv:.4f}" if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") - sp = f"{row['speedup']:.2f}x" if row.get("ok") else "-" - st = "OK" if row.get("ok") else "FAIL:" + str(row.get("reason", "")) + if row.get("ok"): + sp = f"{row['speedup']:.2f}x" + speedups.append(max(float(row["speedup"]), 0.01)) # same clamp as the judge + st = "OK" + else: + sp = "-" + st = "FAIL:" + str(row.get("reason", "")) + any_fail = True print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") + + # Scoring is byte-for-byte the judge's full_evaluation: ANY gate failure -> 0. + print() + if any_fail or len(speedups) != len(rows) or not speedups: + print("RESULT: at least one workload FAILED the correctness gate -> a submission now " + "would be INVALID and score 0/100. Fix the failing workload(s) before submitting.") + return 0 + gm = geometric_mean(speedups) + score = score_from_speedup(gm) + print(f"geomean speedup = {gm:.3f}x over the naive baseline") + print(f"PREDICTED FINAL SCORE = {score:.2f} / 100 (100 == {SPEEDUP_TARGET:g}x geomean)") return 0 diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index b3859a721..058e2e844 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -39,15 +39,10 @@ wide-feature (large `D`) and many-cluster (large `K`) regimes. All use Euclidean distance, **bfloat16 data**, well-separated planted clusters, and a fixed number of judge-owned `step` calls per workload with caller-supplied initial centroids. -Two representative *public* shapes are available for local testing (the graded -shapes are different and hidden): - -```text -(N=50000, D=32, K=64) -(N=200000, D=128, K=256) -``` - -Treat this as a general dense K-Means, not as specific shapes to special-case. +The public self-test now runs the **exact graded workloads** — the same shapes, +thresholds, seeds, and timing the judge uses to score you — so there are no +separate hidden shapes to guess at. Treat this as a general dense K-Means +kernel; the workloads are just where it is measured. ## Iterate on a GPU @@ -59,8 +54,10 @@ environment): bash /app/public_test.sh ``` -It reports, per public shape, whether your result passes the quality gate and -your speedup over the baseline. +It runs the **identical evaluation the judge uses to grade you** and reports, per +graded workload, whether your result passes the quality gate, your speedup over +the baseline, the geometric-mean speedup, and your **predicted final score** +(0-100). Any workload that fails its gate makes the submission score 0. ## Submission diff --git a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py index bc1b6517b..e8de4a4bd 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/knn_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,43 +1,95 @@ -"""Public GPU self-test for the brute-force k-NN kernel-optimization task. +"""Public GPU self-test -- IDENTICAL to the final graded evaluation. -Times your current /app/knnlib against the naive baseline on a Modal GPU and -reports the quality value + speedup on two public shapes. Requires -MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. The graded workloads and -their thresholds are hidden and differ from these public shapes. +This runs the EXACT same workloads, thresholds, timing, and seeds that the hidden +judge uses to grade your submission (all read from ``/app/task_config.json``, +the same config the judge reads), on a Modal GPU, and reports the same +per-workload pass/fail + speedup + geomean + predicted score (0-100) you would +receive on submission. There is no longer a separate, smaller "public" workload +set -- what you see here is what you get graded on. + +Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. """ from __future__ import annotations +import json +import math import os import sys from pathlib import Path -sys.path.insert(0, "/opt") +sys.path.insert(0, "/opt") # flash_gpu.py is baked at /opt APP_DIR = os.environ.get("APP_DIR", "/app") -PKG = "knnlib" -BASELINE_DIR = "/opt/knn_ref" - -PUBLIC_WORKLOADS = [ - {"id": "p0", "Q": 1024, "M": 100_000, "D": 64, "k": 10, "seed": 1}, - {"id": "p1", "Q": 2048, "M": 200_000, "D": 128, "k": 10, "seed": 2}, -] - -CFG = { - "primitive": "knn", - "pkg": PKG, - "ref_module": "refknn", - "gpu": os.environ.get("FLASH_PUBLIC_GPU", "H100"), - "cuda_image": "nvidia/cuda:12.4.1-devel-ubuntu22.04", - "pip": ["torch", "numpy"], - "app_name": "knn-kernel-opt-public", - "modal_timeout_seconds": 1800, - "warmup": 2, - "iters": 5, - "inertia_tolerance": 0.02, - "recall_threshold": 0.99, - "captured_tolerance": 0.02, - "ortho_tolerance": 0.02, -} +TASK_CONFIG_PATH = os.environ.get("TASK_CONFIG_PATH", "/app/task_config.json") + + +def _load_eval() -> dict: + """The judge grades from task_config.json's `evaluation` block; read the same.""" + try: + doc = json.loads(Path(TASK_CONFIG_PATH).read_text(encoding="utf-8")) + except Exception as exc: # noqa: BLE001 + print(f"could not read {TASK_CONFIG_PATH}: {exc}") + return {} + return doc.get("evaluation", doc) + + +EV = _load_eval() + + +def g(key, default): + v = EV.get(key, default) + return default if v is None else v + + +PRIMITIVE = str(g("primitive", "")) +PKG = str(g("pkg", "")) +REF_MODULE = str(g("ref_module", "")) +SPEEDUP_TARGET = float(g("speedup_target", 8.0)) +BASELINE_DIR = str(g("baseline_source", "/opt/flash_ref")) + + +def _workloads() -> list: + """Exactly the judge's final-role workload set: ALL workloads, same seed + derivation ``base_seed + 1000*(i+1)`` -- identical data to the graded run.""" + wls = [dict(w) for w in g("workloads", [])] + base = int(g("base_seed", 20260701)) + for i, w in enumerate(wls): + w.setdefault("seed", base + 1000 * (i + 1)) + return wls + + +def _cfg() -> dict: + """Byte-for-byte the judge's _build_cfg().""" + return { + "primitive": PRIMITIVE, + "pkg": PKG, + "ref_module": REF_MODULE, + "gpu": str(g("gpu", "H100")), + "cuda_image": str(g("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), + "pip": list(g("pip", ["torch", "numpy"])), + "app_name": str(g("app_name", "flash-kernel-public")), + "modal_timeout_seconds": int(g("modal_timeout_seconds", 1800)), + "warmup": int(g("warmup_iters", 3)), + "iters": int(g("timed_iters", 7)), + "inertia_tolerance": float(g("inertia_tolerance", 0.02)), + "recall_threshold": float(g("recall_threshold", 0.99)), + "captured_tolerance": float(g("captured_tolerance", 0.02)), + "ortho_tolerance": float(g("ortho_tolerance", 0.02)), + "ari_threshold": float(g("ari_threshold", 0.99)), + } + + +def geometric_mean(values: list) -> float: + if not values: + return 0.0 + return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) + + +def score_from_speedup(gm: float) -> float: + if gm <= 0: + return 0.0 + raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) + return max(0.0, min(100.0, raw)) def _read(root: str, sub: str = "") -> dict: @@ -50,13 +102,18 @@ def _read(root: str, sub: str = "") -> dict: def main() -> int: import flash_gpu # baked at /opt/flash_gpu.py if not flash_gpu.modal_available(): - print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU public test.") + print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU self-test.") + return 1 + workloads = _workloads() + cfg = _cfg() + if not workloads: + print("no workloads found in task_config.json; cannot run.") return 1 payload = { "baseline_files": _read(BASELINE_DIR), "patched_files": _read(APP_DIR, PKG), - "workloads": PUBLIC_WORKLOADS, - "cfg": CFG, + "workloads": workloads, + "cfg": cfg, } try: result = flash_gpu.run_remote(payload) @@ -66,26 +123,47 @@ def main() -> int: if not result.get("ok"): print(f"worker error: {result.get('error')}") return 1 - # Correctness first: show the quality metric (agent vs frozen baseline) on every - # shape, pass or fail -- not just the speedup. The graded shapes are hidden and differ. - prim = CFG.get("primitive", "") - if prim in ("knn", "ivfpq"): - mlabel, gate = "recall@k", f">= {CFG.get('recall_threshold')}" - elif prim == "dbscan": - mlabel, gate = "ARI", f">= {CFG.get('ari_threshold')}" - elif prim == "kmeans": - mlabel, gate = "inertia", f"<= (1+{CFG.get('inertia_tolerance')}) x ref (lower is better)" + + if PRIMITIVE in ("knn", "ivfpq"): + mlabel, gate = "recall@k", f">= {cfg['recall_threshold']}" + elif PRIMITIVE == "dbscan": + mlabel, gate = "ARI", f">= {cfg['ari_threshold']}" + elif PRIMITIVE == "kmeans": + mlabel, gate = "inertia", f"<= (1+{cfg['inertia_tolerance']}) x ref (lower is better)" else: mlabel, gate = "quality", "" - print(f"correctness gate: {mlabel} {gate}") + + print("=== FINAL-EQUIVALENT self-test: identical workloads / thresholds / seeds / " + "timing to the graded judge ===") + print(f"correctness gate: {mlabel} {gate} | speedup_target = {SPEEDUP_TARGET:g}") print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") - for row in result["rows"]: + rows = result.get("rows", []) + speedups = [] + any_fail = False + for row in rows: av, rv = row.get("agent_val"), row.get("ref_val") q = (f"{av:.4f} / {rv:.4f}" if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") - sp = f"{row['speedup']:.2f}x" if row.get("ok") else "-" - st = "OK" if row.get("ok") else "FAIL:" + str(row.get("reason", "")) + if row.get("ok"): + sp = f"{row['speedup']:.2f}x" + speedups.append(max(float(row["speedup"]), 0.01)) # same clamp as the judge + st = "OK" + else: + sp = "-" + st = "FAIL:" + str(row.get("reason", "")) + any_fail = True print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") + + # Scoring is byte-for-byte the judge's full_evaluation: ANY gate failure -> 0. + print() + if any_fail or len(speedups) != len(rows) or not speedups: + print("RESULT: at least one workload FAILED the correctness gate -> a submission now " + "would be INVALID and score 0/100. Fix the failing workload(s) before submitting.") + return 0 + gm = geometric_mean(speedups) + score = score_from_speedup(gm) + print(f"geomean speedup = {gm:.3f}x over the naive baseline") + print(f"PREDICTED FINAL SCORE = {score:.2f} / 100 (100 == {SPEEDUP_TARGET:g}x geomean)") return 0 diff --git a/2.0/problems/knn_gpu_kernel_optimization/readme b/2.0/problems/knn_gpu_kernel_optimization/readme index 43b2ea5e4..1a31b42ef 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/readme +++ b/2.0/problems/knn_gpu_kernel_optimization/readme @@ -30,16 +30,10 @@ The graded workloads are a family of held-out dense search problems that vary (large `D`) and many-neighbor (large `k`) regimes. All use squared-L2 distance, float32 data, and randomly generated queries/database. -Two representative *public* shapes are available for local testing (the graded -shapes are different and hidden): - -```text -(Q=1024, M=100000, D=64, k=10) -(Q=2048, M=200000, D=128, k=10) -``` - -Treat this as a general dense brute-force k-NN, not as specific shapes to -special-case. +The public self-test now runs the **exact graded workloads** — the same shapes, +thresholds, seeds, and timing the judge uses to score you — so there are no +separate hidden shapes to guess at. Treat this as a general dense brute-force +k-NN kernel; the workloads are just where it is measured. ## Iterate on a GPU @@ -51,8 +45,10 @@ environment): bash /app/public_test.sh ``` -It reports, per public shape, whether your result passes the quality gate and -your speedup over the baseline. +It runs the **identical evaluation the judge uses to grade you** and reports, per +graded workload, whether your result passes the quality gate, your speedup over +the baseline, the geometric-mean speedup, and your **predicted final score** +(0-100). Any workload that fails its gate makes the submission score 0. ## Submission From c5cb06405c1cf54e68e88cbf4e0367b8929d170d Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 8 Jul 2026 10:11:19 +0000 Subject: [PATCH 23/34] feat(2.0/dbscan): non-separable ring data + brute-force reference (force exact DBSCAN) The prior well-separated-blob workloads let a k-means approximation reproduce the exact DBSCAN clustering (ARI 1.0 -> the overfit's k-means-compress solution scored 100 while doing zero neighbour-kernel work) -- i.e. an *algorithm* shortcut, not the *kernel* optimization the task is meant to measure. Fix (A+C): A. Data: `gen` now produces single-center concentric rings in a 2-D subspace, padded to D>=8, no noise. Concentric rings are non-Voronoi-separable, so a centroid/partition heuristic (k-means) CANNOT reproduce them -- measured k-means ARI <= 0.10 on all workloads -> fails the >=0.99 gate. Only exact eps-density connectivity (the kernel work) clusters them. Points shuffled so membership is not positional. Baseline verified ARI 1.0 vs sklearn. C. Reference: switched from the D==2 grid path (which flashlib's own benchmark never exercises) to flashlib's BENCHMARKED brute-force path (`flash_knn` bf16 GEMM top-K + union-find CC), by grafting the knn task's vendored flash_knn into the dbscan reference and un-stubbing it. Workloads are now D=8/16/32, N=20-50k (flashlib's brute range). Calibrated on H100: brute reference ARI 1.0 on all 6, geomean 206.6x over the O(N^2) baseline (per-wl 81/126/202/292/262/495x) -> speedup_target=413 (2x). readme updated (non-convex density-connected; graded on exact ARI). Rebuilt dbscan agent+judge images (flash_gpu gen changed). Co-Authored-By: Claude Opus 4.8 (1M context) --- .../config.yaml | 19 +- .../flash_gpu.py | 34 +- .../dbscan_gpu_kernel_optimization/readme | 11 +- .../reference.patch | 2094 ++++++++++++++++- 4 files changed, 2134 insertions(+), 24 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml index 86b0f7b66..8c61c3aa4 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml @@ -22,8 +22,8 @@ evaluation: modal_timeout_seconds: 2400 warmup_iters: 2 timed_iters: 3 # the O(N^2) naive baseline is seconds/call at these N; keep the timed budget bounded (median of 3) - ari_threshold: 0.99 # agent clustering must agree with the baseline (Adjusted Rand Index) on every iteration; the grid reference measured ARI 1.0 on all workloads - speedup_target: 40.0 # score cap = 2x the flashlib grid-DBSCAN reference geomean (~19.9x on H100, per-wl 7.7/17.3/15.6/31.4/28.6/33.9x, ARI 1.0 all 6): reference scores ~73/100, a solution must be 2x faster than it to max out. NB the O(N^2) baseline timing is thermally sensitive (an earlier run measured 13.3x), so the ratio carries some cross-run variance. + ari_threshold: 0.99 # agent clustering must match the baseline exact DBSCAN (Adjusted Rand Index) every iteration. On the non-separable concentric-ring data a centroid/partition approximation (k-means) scores ARI <= ~0.1 -> fails; only exact eps-connectivity passes. Exact reference measures ARI ~1.0. + speedup_target: 413.0 # score cap = 2x the flashlib brute-force (flash_knn) reference geomean (206.6x on H100 over the O(N^2) baseline, per-wl 81/126/202/292/262/495x, ARI 1.0 all 6). Large factor = the naive baseline recomputes O(N^2) distances every label-propagation CC pass; the reference does one bf16 flash_knn + union-find. Reference scores ~88/100; maxing out needs 2x the reference. base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); the params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on @@ -32,12 +32,15 @@ evaluation: # is faster than the grid kernel's fixed overhead. Vary N + density so agents can't # special-case. (H100 crossover sweep: 100k->6.0x, 150k->14.1x, 200k->27.0x.) workloads: - - {id: w0, N: 100000, D: 2, n_centers: 12, eps: 1.5, min_samples: 8, noise_frac: 0.03} - - {id: w1, N: 110000, D: 2, n_centers: 16, eps: 1.3, min_samples: 10, noise_frac: 0.04} - - {id: w2, N: 120000, D: 2, n_centers: 10, eps: 1.6, min_samples: 6, noise_frac: 0.03} - - {id: w3, N: 130000, D: 2, n_centers: 20, eps: 1.2, min_samples: 12, noise_frac: 0.05} - - {id: w4, N: 150000, D: 2, n_centers: 14, eps: 1.4, min_samples: 8, noise_frac: 0.04} - - {id: w5, N: 170000, D: 2, n_centers: 18, eps: 1.5, min_samples: 8, noise_frac: 0.03} + # Non-separable concentric-ring data (see flash_gpu.gen): `n_centers` = number of + # rings; D>=3 routes flashlib to its benchmarked brute-force (flash_knn) path; N in + # flashlib's brute range. No noise_frac (rings are noise-free, like flashlib's make_blobs). + - {id: w0, N: 20000, D: 8, n_centers: 4, eps: 1.5, min_samples: 8} + - {id: w1, N: 30000, D: 8, n_centers: 5, eps: 1.5, min_samples: 8} + - {id: w2, N: 30000, D: 16, n_centers: 6, eps: 1.4, min_samples: 8} + - {id: w3, N: 40000, D: 8, n_centers: 6, eps: 1.4, min_samples: 8} + - {id: w4, N: 40000, D: 32, n_centers: 5, eps: 1.5, min_samples: 10} + - {id: w5, N: 50000, D: 8, n_centers: 8, eps: 1.5, min_samples: 10} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py index 6243e65c8..bd830bcc3 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py @@ -97,15 +97,31 @@ def gen(w, seed, ctx): q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} if prim == "dbscan": - nc, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) - centers = torch.randn(nc, D, generator=g, device=dev, dtype=torch.float32) * 10.0 - asg = torch.randint(0, nc, (N,), generator=g, device=dev) - xb = centers.index_select(0, asg) + torch.randn(N, D, generator=g, device=dev, dtype=torch.float32) * 0.5 - nz = int(float(w.get("noise_frac", 0.0)) * N) - if nz > 0: - lo = xb.min(0).values; hi = xb.max(0).values - xb[:nz] = lo + (hi - lo) * torch.rand(nz, D, generator=g, device=dev) - return {"x": xb, "eps": float(w["eps"]), "min_samples": int(w["min_samples"])} + # NON-SEPARABLE data: single-center concentric rings in a 2-D subspace, + # padded to D dims with a little noise. Centroid/Voronoi methods (k-means) + # CANNOT reproduce concentric rings -- only exact eps-density connectivity + # (the kernel work being timed) clusters them, so a cheap approximation + # cannot substitute for a real DBSCAN kernel. No uniform noise (matches + # flashlib's noise-free make_blobs benchmark). D>=3 so flashlib routes to + # its benchmarked brute-force (flash_knn) path, not the D==2 grid path. + nr, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) + eps = float(w["eps"]); TWO_PI = 6.283185307179586 + base, gap, width = 3.0 * eps, 3.0 * eps, 0.35 * eps + radii = base + torch.arange(nr, device=dev, dtype=torch.float32) * gap + counts = torch.clamp((radii / radii.sum() * N).long(), min=1) + counts[-1] += N - int(counts.sum()) # exact total = N + parts = [] + for j in range(nr): + c = int(counts[j]) + th = torch.rand(c, generator=g, device=dev) * TWO_PI + rr = radii[j] + (torch.rand(c, generator=g, device=dev) - 0.5) * width + xy = torch.stack([rr * torch.cos(th), rr * torch.sin(th)], 1) + pad_std = 0.15 * eps / ((D - 2) ** 0.5) if D > 2 else 0.0 + pad = torch.randn(c, D - 2, generator=g, device=dev) * pad_std + parts.append(torch.cat([xy, pad], 1)) + x = torch.cat(parts, 0).to(torch.float32) + x = x.index_select(0, torch.randperm(N, generator=g, device=dev)) # de-positional + return {"x": x, "eps": eps, "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) if prim == "kmeans": perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/readme b/2.0/problems/dbscan_gpu_kernel_optimization/readme index 05e486e4e..b40d39142 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/readme +++ b/2.0/problems/dbscan_gpu_kernel_optimization/readme @@ -23,10 +23,13 @@ remain a deterministic function of the inputs. ## Workload -The graded workloads are held-out planar (D = 2) point sets with planted density -structure (well-separated clusters plus a little uniform noise), varying `N`, -cluster count, `eps`, and `min_samples`. Treat it as general 2-D Euclidean -DBSCAN, not as specific point sets to special-case. +The graded workloads are held-out `(N, D)` point sets (`D >= 8`) with +**density-connected clusters that are not convex/blob-shaped** — clustering them +correctly genuinely requires DBSCAN's `eps`-neighbourhood connectivity, not a +centroid/partition heuristic. Workloads vary `N`, `D`, cluster count, `eps`, and +`min_samples`. Treat it as general Euclidean DBSCAN and reproduce the exact +clustering — your labels are graded against the exact reference clustering (ARI), +so an approximation that only *roughly* recovers the clusters will not pass. The public self-test now runs the **exact graded workloads** — the same shapes, thresholds, seeds, and timing the judge uses to score you — so there are no diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/reference.patch b/2.0/problems/dbscan_gpu_kernel_optimization/reference.patch index babf28b9d..40170a32d 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/dbscan_gpu_kernel_optimization/reference.patch @@ -4,6 +4,264 @@ index 0000000..e69de29 diff --git a/dbscanlib/_kernels/kernels/__init__.py b/dbscanlib/_kernels/kernels/__init__.py new file mode 100644 index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/kernels/distance/__init__.py b/dbscanlib/_kernels/kernels/distance/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/kernels/distance/triton/__init__.py b/dbscanlib/_kernels/kernels/distance/triton/__init__.py +new file mode 100644 +index 0000000..63fceb5 +--- /dev/null ++++ b/dbscanlib/_kernels/kernels/distance/triton/__init__.py +@@ -0,0 +1,11 @@ ++"""distance triton backend (kNN squared-L2 gather only). ++ ++Minimal re-export: only the tiled squared-L2 gather kernel that recovers ++true ``||x - c[idx]||^2`` per neighbour after the fused kNN pass. The ++other distance kernels from the upstream backend are not vendored here. ++""" ++from dbscanlib._kernels.kernels.distance.triton.knn_gather_l2sq import ( ++ triton_knn_gather_sqdist, ++) ++ ++__all__ = ["triton_knn_gather_sqdist"] +diff --git a/dbscanlib/_kernels/kernels/distance/triton/knn_gather_l2sq.py b/dbscanlib/_kernels/kernels/distance/triton/knn_gather_l2sq.py +new file mode 100644 +index 0000000..e2e3fbe +--- /dev/null ++++ b/dbscanlib/_kernels/kernels/distance/triton/knn_gather_l2sq.py +@@ -0,0 +1,232 @@ ++"""Tiled gather kernel: true squared L2 to selected kNN neighbours. ++ ++After a fused kNN pass that ranks candidates with the shift-invariant ++score ``c_sq - 2 * ``, this kernel writes the **true** ++``||x[b, n] - c[b, idx[b, n, k]]||^2`` for every neighbour. Computing ++the difference directly (``(x - y)^2`` per d) avoids the cancellation ++inherent to the ``x^2 + y^2 - 2*x*y`` expansion, so the result is ++accurate even for fp32 inputs where the fused kNN kernel may have used ++TF32 in the cross-term. ++ ++Complexity: ``O(B * N * K * D)`` -- cheap vs the ``O(B * N * M * D)`` ++GEMM that found the candidates. ++ ++Design ++------ ++One program owns a ``(BN, K_BLOCK)`` output tile. The query row tile ++``x[b, n_block, :]`` is loaded once and reused across the ``K_BLOCK`` ++neighbours, then streamed through ``D`` in ``BLOCK_D`` chunks. The ++corpus rows ``c[b, idx, :]`` are gather-loaded fresh per d-chunk into a ++3-D tile ``(BN, K_BLOCK, BLOCK_D)`` and subtracted directly from the ++broadcast x tile in fp32. The fp32 squared difference is summed along ++``D`` into the accumulator. ++ ++Three regime presets pick ``BN`` and ``K_BLOCK`` so the c-tile fits in ++SMEM/registers and amortises one x-load over many neighbours: ++ ++ * K <= 32: ``K_BLOCK = next_pow2(K)``, ``BN = 16``. ++ * 32 < K <= 512: ``K_BLOCK = 32``, ``BN = 8``; multiple K-tiles per row. ++ * K > 512: ``K_BLOCK = 32``, ``BN = 4``. ++ ++``BLOCK_D`` is chosen so the per-program SMEM footprint ++``BN * K_BLOCK * BLOCK_D * sizeof(dtype)`` stays under ~32 KB. The ++unmodified ``D`` (not padded) is passed as a constexpr so the Triton ++compiler unrolls the d-loop exactly. ++""" ++from __future__ import annotations ++ ++import torch ++import triton ++import triton.language as tl ++ ++ ++def _next_pow2(n: int) -> int: ++ if n <= 1: ++ return 1 ++ return 1 << (n - 1).bit_length() ++ ++ ++@triton.jit ++def _knn_gather_l2sq_kernel( ++ x_ptr, c_ptr, idx_ptr, out_ptr, ++ M, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_c_b, stride_c_m, stride_c_d, ++ stride_i_b, stride_i_n, stride_i_k, ++ stride_o_b, stride_o_n, stride_o_k, ++ N: tl.constexpr, D: tl.constexpr, K: tl.constexpr, ++ BN: tl.constexpr, K_BLOCK: tl.constexpr, ++ BLOCK_D: tl.constexpr, ++ SINGLE_D_TILE: tl.constexpr, ++): ++ """Tiled (BN, K_BLOCK, BLOCK_D) gather + sq-distance accumulate. ++ ++ Grid: ``(ceil(N / BN), ceil(K / K_BLOCK), B)``. ++ ++ Each program owns one ``(BN, K_BLOCK)`` output tile. The fp32 ++ accumulator stays in registers; the c-row tile ``(BN, K_BLOCK, ++ BLOCK_D)`` is reloaded per d-chunk to keep SMEM bounded. ++ """ ++ pid_n = tl.program_id(0) ++ pid_k = tl.program_id(1) ++ pid_b = tl.program_id(2).to(tl.int64) ++ ++ n_start = pid_n * BN ++ n_offs = (n_start + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ ++ k_start = pid_k * K_BLOCK ++ k_offs = (k_start + tl.arange(0, K_BLOCK)).to(tl.int64) ++ k_mask = k_offs < K ++ ++ idx_tile = tl.load( ++ idx_ptr + pid_b * stride_i_b ++ + n_offs[:, None] * stride_i_n ++ + k_offs[None, :] * stride_i_k, ++ mask=n_mask[:, None] & k_mask[None, :], other=0, ++ ).to(tl.int64) ++ idx_tile = tl.maximum(idx_tile, 0) ++ idx_tile = tl.minimum(idx_tile, M - 1) ++ ++ acc = tl.zeros((BN, K_BLOCK), dtype=tl.float32) ++ ++ if SINGLE_D_TILE: ++ d_offs = tl.arange(0, BLOCK_D).to(tl.int64) ++ d_mask = d_offs < D ++ x_tile = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0, ++ ).to(tl.float32) ++ c_tile = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + idx_tile[:, :, None] * stride_c_m ++ + d_offs[None, None, :] * stride_c_d, ++ mask=(n_mask[:, None] & k_mask[None, :])[:, :, None] ++ & d_mask[None, None, :], other=0.0, ++ ).to(tl.float32) ++ diff = x_tile[:, None, :] - c_tile ++ acc = tl.sum(diff * diff, axis=2) ++ else: ++ for d_start in tl.range(0, D, BLOCK_D): ++ d_offs = (d_start + tl.arange(0, BLOCK_D)).to(tl.int64) ++ d_mask = d_offs < D ++ x_tile = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0, ++ ).to(tl.float32) ++ c_tile = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + idx_tile[:, :, None] * stride_c_m ++ + d_offs[None, None, :] * stride_c_d, ++ mask=(n_mask[:, None] & k_mask[None, :])[:, :, None] ++ & d_mask[None, None, :], other=0.0, ++ ).to(tl.float32) ++ diff = x_tile[:, None, :] - c_tile ++ acc += tl.sum(diff * diff, axis=2) ++ ++ tl.store( ++ out_ptr + pid_b * stride_o_b ++ + n_offs[:, None] * stride_o_n ++ + k_offs[None, :] * stride_o_k, ++ acc, mask=n_mask[:, None] & k_mask[None, :], ++ ) ++ ++ ++def _pick_tile(K: int, D: int, dtype_bytes: int) -> tuple[int, int, int, bool]: ++ """Pick ``(BN, K_BLOCK, BLOCK_D, single_d_tile)`` for the kernel. ++ ++ Regimes mirror the file docstring -- small ``K_BLOCK`` for large K, ++ larger ``BN`` for small K. ``BLOCK_D`` is sized so the 3-D c-tile ++ SMEM footprint stays around ~32 KB. ++ """ ++ K_pad = _next_pow2(K) ++ ++ if K_pad <= 32: ++ K_BLOCK = max(1, K_pad) ++ BN = 16 ++ elif K_pad <= 512: ++ K_BLOCK = 32 ++ BN = 8 ++ else: ++ K_BLOCK = 32 ++ BN = 4 ++ ++ target_bytes = 32 * 1024 ++ per_d = BN * K_BLOCK * dtype_bytes ++ block_d_cap = max(16, target_bytes // max(1, per_d)) ++ block_d_cap = min(block_d_cap, 256) ++ ++ if D <= block_d_cap: ++ BLOCK_D = max(16, _next_pow2(D)) ++ single_d_tile = True ++ else: ++ BLOCK_D = 64 if block_d_cap >= 64 else 32 ++ single_d_tile = False ++ ++ return BN, K_BLOCK, BLOCK_D, single_d_tile ++ ++ ++def triton_knn_gather_sqdist( ++ x: torch.Tensor, ++ c: torch.Tensor, ++ idx: torch.Tensor, ++ *, ++ out: torch.Tensor | None = None, ++) -> torch.Tensor: ++ """``out[b, n, k] = || x[b, n, :] - c[b, idx[b, n, k], :] ||^2`` (fp32). ++ ++ Args: ++ x: (B, N, D) query tensor, any float cuda dtype (bf16 / fp16 / fp32). ++ c: (B, M, D) corpus, same dtype and same batch as ``x``. ++ idx: (B, N, K) int32 / int64 column indices into ``c``. ++ out: optional pre-allocated (B, N, K) fp32 output buffer. ++ ++ Returns: ++ (B, N, K) fp32. The computation runs in fp32 throughout ++ (``diff = x.to(fp32) - c.to(fp32)``, ``sum(diff*diff)``) so the ++ result is bit-equivalent to the naive torch reference up to ++ accumulation order on the d-axis. ++ """ ++ assert x.is_cuda and c.is_cuda and idx.is_cuda ++ assert x.dim() == 3 and c.dim() == 3 and idx.dim() == 3 ++ B, N, D = x.shape ++ Bc, M, Dc = c.shape ++ Bi, Ni, K = idx.shape ++ assert B == Bc == Bi and N == Ni and D == Dc ++ ++ if not x.is_contiguous(): ++ x = x.contiguous() ++ if not c.is_contiguous(): ++ c = c.contiguous() ++ if not idx.is_contiguous(): ++ idx = idx.contiguous() ++ ++ if out is None: ++ out = torch.empty((B, N, K), device=x.device, dtype=torch.float32) ++ else: ++ assert out.shape == (B, N, K) and out.dtype == torch.float32 ++ ++ dtype_bytes = x.element_size() ++ BN, K_BLOCK, BLOCK_D, single_d_tile = _pick_tile(K, D, dtype_bytes) ++ ++ grid = (triton.cdiv(N, BN), triton.cdiv(K, K_BLOCK), B) ++ _knn_gather_l2sq_kernel[grid]( ++ x, c, idx, out, ++ M, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ idx.stride(0), idx.stride(1), idx.stride(2), ++ out.stride(0), out.stride(1), out.stride(2), ++ N=N, D=D, K=K, ++ BN=BN, K_BLOCK=K_BLOCK, BLOCK_D=BLOCK_D, ++ SINGLE_D_TILE=single_d_tile, ++ num_warps=4, ++ ) ++ return out ++ ++ ++__all__ = ["triton_knn_gather_sqdist"] diff --git a/dbscanlib/_kernels/kernels/flash_mst/__init__.py b/dbscanlib/_kernels/kernels/flash_mst/__init__.py new file mode 100644 index 0000000..e69de29 @@ -638,10 +896,10 @@ new file mode 100644 index 0000000..e69de29 diff --git a/dbscanlib/_kernels/primitives/dbscan/triton/dbscan.py b/dbscanlib/_kernels/primitives/dbscan/triton/dbscan.py new file mode 100644 -index 0000000..74c81cd +index 0000000..b9cc7b6 --- /dev/null +++ b/dbscanlib/_kernels/primitives/dbscan/triton/dbscan.py -@@ -0,0 +1,333 @@ +@@ -0,0 +1,346 @@ +"""flash-dbscan: GPU-resident DBSCAN. + +Two algorithmic paths, dispatched by D: @@ -671,7 +929,20 @@ index 0000000..74c81cd +import triton +import triton.language as tl + -+flash_knn = None # D=2 grid path never calls flash_knn (high-D brute stubbed out) ++# High-D brute-force neighbour path: flashlib's flash_knn = a fused Triton kNN ++# (indices) + a tiled gather kernel (exact squared-L2 distances), both vendored ++# from the flashlib knn primitive under dbscanlib._kernels. ++from dbscanlib._kernels.primitives.knn.triton import flash_knn_triton ++from dbscanlib._kernels.kernels.distance.triton.knn_gather_l2sq import ( ++ triton_knn_gather_sqdist, ++) ++ ++ ++def flash_knn(x, y, k, *, tol=None): ++ """Return ``(dist_sq, idx)`` top-k neighbours -- flashlib's flash_knn contract.""" ++ idx = flash_knn_triton(x, y, k) ++ dist_sq = triton_knn_gather_sqdist(x, y, idx) ++ return dist_sq, idx +from dbscanlib._kernels.kernels.flash_mst.triton.flash_mst import flash_cc_from_edges + + @@ -975,6 +1246,1823 @@ index 0000000..74c81cd + label = compact + + return label +diff --git a/dbscanlib/_kernels/primitives/knn/__init__.py b/dbscanlib/_kernels/primitives/knn/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/dbscanlib/_kernels/primitives/knn/triton/__init__.py b/dbscanlib/_kernels/primitives/knn/triton/__init__.py +new file mode 100644 +index 0000000..f5834ec +--- /dev/null ++++ b/dbscanlib/_kernels/primitives/knn/triton/__init__.py +@@ -0,0 +1,41 @@ ++"""knn triton backend. ++ ++Re-exports the public Python wrappers from each component file. ++``@triton.jit`` kernels stay private to their file. ++ ++Kernel layout: ++ ++* :mod:`_common` -- shared helpers: ``_next_pow2``, micro-bench, ++ IEEE-sortable u32 transform, ``_INF_PACKED`` sentinel. ++* :mod:`sortmerge` -- packed-uint64 sort-merge top-K (x²-free). Routed ++ to only for the small-Q + medium-K Pattern-A corner. ++* :mod:`insert` -- iterative argmin-insert top-K (x²-free). The ++ general path -- BN in {8, 16, 32, 64, 128} covers everything from ++ small-Q search to large-Q build. ++* :mod:`_row_norm` -- ``_fast_row_sq`` / ``_get_or_compute_csq`` for ++ the CuteDSL FA3 wrapper (it needs ``c_sq`` as a kernel argument). ++* :mod:`dispatch` -- host-side router. Single public entry ++ :func:`flash_knn_triton` runs through :func:`_heuristic_config` ++ unconditionally -- the per-CTA-count check inside the heuristic ++ picks single-pass vs M-split. Per-shape config (BN/BM/mode/mps/ ++ ns_pipe) also falls out of :func:`_heuristic_config`. ++""" ++from dbscanlib._kernels.primitives.knn.triton._common import ( ++ _next_pow2, ++ _bench_quick, ++) ++from dbscanlib._kernels.primitives.knn.triton._row_norm import ( ++ _fast_row_sq, ++ _get_or_compute_csq, ++) ++from dbscanlib._kernels.primitives.knn.triton.dispatch import ( ++ flash_knn_triton, ++ flash_knn_triton_small_n, ++ flash_knn_triton_large_n, ++) ++ ++__all__ = [ ++ "flash_knn_triton", ++ "flash_knn_triton_small_n", ++ "flash_knn_triton_large_n", ++] +diff --git a/dbscanlib/_kernels/primitives/knn/triton/_common.py b/dbscanlib/_kernels/primitives/knn/triton/_common.py +new file mode 100644 +index 0000000..686e85b +--- /dev/null ++++ b/dbscanlib/_kernels/primitives/knn/triton/_common.py +@@ -0,0 +1,81 @@ ++"""Shared utilities for flash_knn Triton dispatch + kernels. ++ ++Lives in one place so :mod:`dispatch`, :mod:`sortmerge` and :mod:`insert` ++all import the same primitives: ++ ++ * :func:`_next_pow2` -- D / K padding helper. ++ * :func:`_bench_quick` -- micro-bench used by the autotuner. ++ * :func:`_fp32_to_sortable_u32` / :func:`_sortable_u32_to_fp32` -- ++ branch-free IEEE-sortable u32 transform. With the x²-free score ++ ``s = c² - 2⟨x, c⟩`` the quantity is signed, so the packed-uint64 ++ sort-merge / final-sort needs an ascending-u32 = ascending-fp32 ++ mapping. The transform is 3 ops, exact for non-NaN fp32. ++ * :data:`_INF_PACKED` -- (sortable +inf << 32) | -1 idx sentinel, ++ used as the initial fill of the sortmerge running top-K so the ++ early-exit ``chunk_best < sortable_inv(top)`` always fires until ++ real values arrive. ++""" ++from __future__ import annotations ++ ++import torch ++import triton ++import triton.language as tl ++ ++ ++def _next_pow2(n: int) -> int: ++ """Smallest power-of-two >= ``max(1, n)``. Used for D / K padding.""" ++ if n <= 1: ++ return 1 ++ return 1 << (n - 1).bit_length() ++ ++ ++def _bench_quick(fn, *, warmup: int = 3, reps: int = 5) -> float: ++ """Median-ish wall time in ms; cheap enough to call once per autotune cfg.""" ++ for _ in range(warmup): ++ fn() ++ torch.cuda.synchronize() ++ s = torch.cuda.Event(enable_timing=True) ++ e = torch.cuda.Event(enable_timing=True) ++ s.record() ++ for _ in range(reps): ++ fn() ++ e.record() ++ torch.cuda.synchronize() ++ return s.elapsed_time(e) / reps ++ ++ ++# Sortable upper-bits = sortable(+inf) = 0xFF800000; lower-bits = -1 (idx ++# sentinel) = 0xFFFFFFFF. ``_sortable_u32_to_fp32(0xFF800000) == +inf`` so ++# the sortmerge early-exit ``chunk_best < +inf`` always fires until the ++# running top-K starts to hold real values. ++# ++# Wrapped in ``tl.constexpr`` so ``@triton.jit`` kernels can reference it ++# directly as a module-level global (un-wrapped Python ints are not ++# accessible from inside a JIT'd kernel as of Triton 3.x). ++_INF_PACKED = tl.constexpr(0xFF800000_FFFFFFFF) ++ ++ ++@triton.jit ++def _fp32_to_sortable_u32(x): ++ """Map ascending fp32 -> ascending uint32 (branch-free, 3 ops). ++ ++ Standard IEEE-754 trick: arithmetic-shift the int32 view by 31 (so ++ MSB=1 / negative produces 0xFFFFFFFF, MSB=0 / positive produces 0), ++ OR with 0x80000000 to flip the sign bit of positives, then XOR with ++ the original bits. NaNs map into 0xFF800001..0xFFFFFFFF (unused in ++ practice). ++ """ ++ bits = x.to(tl.uint32, bitcast=True) ++ sign_mask = (bits.to(tl.int32, bitcast=True) >> 31).to(tl.uint32, bitcast=True) ++ flip = sign_mask | 0x80000000 ++ return bits ^ flip ++ ++ ++@triton.jit ++def _sortable_u32_to_fp32(s): ++ """Inverse of :func:`_fp32_to_sortable_u32`.""" ++ sign_mask = (s.to(tl.int32, bitcast=True) >> 31).to(tl.uint32, bitcast=True) ++ # In sortable space: MSB=1 -> was positive (flip back via 0x80000000); ++ # MSB=0 -> was negative (flip back via 0xFFFFFFFF). ++ flip = (sign_mask ^ 0xFFFFFFFF) | 0x80000000 ++ return (s ^ flip).to(tl.float32, bitcast=True) +diff --git a/dbscanlib/_kernels/primitives/knn/triton/_row_norm.py b/dbscanlib/_kernels/primitives/knn/triton/_row_norm.py +new file mode 100644 +index 0000000..39fd0c8 +--- /dev/null ++++ b/dbscanlib/_kernels/primitives/knn/triton/_row_norm.py +@@ -0,0 +1,93 @@ ++"""Fast row sum-of-squares (||x_i||^2) helper + per-tensor cache. ++ ++Tiny utility, but shared by the FA3 fused KNN path which needs the ++pre-computed query/corpus norms to fold into the squared-L2 distance ++formula ``||x - c||^2 = ||x||^2 + ||c||^2 - 2 ``. ++ ++This module deliberately stays narrow: ++ - ``_row_sq_kernel`` is a pure Triton row-norm kernel (no cross ++ matrix, no top-K) — it never materialises an N x M tensor. ++ - ``_fast_row_sq`` wraps the kernel. ++ - ``_get_or_compute_csq`` caches results per ``(data_ptr, shape, ++ dtype)`` so repeat queries on the same corpus skip the recompute. ++""" ++from __future__ import annotations ++ ++import math ++ ++import torch ++import triton ++import triton.language as tl ++ ++from dbscanlib._kernels.primitives.knn.triton._common import _next_pow2 ++ ++ ++@triton.jit ++def _row_sq_kernel( ++ x_ptr, out_ptr, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_o_b, stride_o_n, ++ B: tl.constexpr, N: tl.constexpr, D: tl.constexpr, ++ BN: tl.constexpr, BD: tl.constexpr, ++): ++ """Row sum-of-squares for ``(B, N, D) -> (B, N)`` fp32. ++ ++ Casts input to fp32 inside the kernel; reads input exactly once ++ and writes output exactly once. At BN=128 BD=128 nw=4 this hits ++ ~36% peak HBM BW on H200 (typical for tall-skinny reads). ++ """ ++ pid_n = tl.program_id(0) ++ pid_b = tl.program_id(1).to(tl.int64) ++ n_offs = (pid_n * BN + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ acc = tl.zeros([BN], dtype=tl.float32) ++ for d_start in range(0, D, BD): ++ d_offs = (d_start + tl.arange(0, BD)).to(tl.int64) ++ d_mask = d_offs < D ++ x = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0, ++ ) ++ x_f = x.to(tl.float32) ++ acc += tl.sum(x_f * x_f, axis=1) ++ tl.store(out_ptr + pid_b * stride_o_b + n_offs * stride_o_n, ++ acc, mask=n_mask) ++ ++ ++def _fast_row_sq(x: torch.Tensor) -> torch.Tensor: ++ """Return ``(B, N)`` fp32 row sum-of-squares via the Triton kernel.""" ++ assert x.is_cuda and x.ndim == 3 ++ B, N, D = x.shape ++ out = torch.empty(B, N, device=x.device, dtype=torch.float32) ++ BN = 128 if N >= 128 else _next_pow2(max(N, 16)) ++ BD = 128 if D >= 128 else _next_pow2(max(D, 16)) ++ grid = (math.ceil(N / BN), B) ++ _row_sq_kernel[grid]( ++ x, out, ++ x.stride(0), x.stride(1), x.stride(2), ++ out.stride(0), out.stride(1), ++ B=B, N=N, D=D, BN=BN, BD=BD, ++ num_warps=4, ++ ) ++ return out ++ ++ ++# Per-data-ptr cache so repeated queries on the same corpus don't pay ++# the row-norm kernel cost. ++_csq_cache: dict = {} ++ ++ ++def _get_or_compute_csq(c: torch.Tensor) -> torch.Tensor: ++ """Cached ``||c_i||^2`` (fp32) keyed by ``(data_ptr, shape, dtype)``.""" ++ key = (c.data_ptr(), tuple(c.shape), c.dtype) ++ cached = _csq_cache.get(key) ++ if cached is not None and cached.device == c.device: ++ return cached ++ csq = _fast_row_sq(c) ++ _csq_cache[key] = csq ++ if len(_csq_cache) > 8: ++ for old_key in list(_csq_cache.keys())[:-8]: ++ del _csq_cache[old_key] ++ return csq +diff --git a/dbscanlib/_kernels/primitives/knn/triton/dispatch.py b/dbscanlib/_kernels/primitives/knn/triton/dispatch.py +new file mode 100644 +index 0000000..1476b18 +--- /dev/null ++++ b/dbscanlib/_kernels/primitives/knn/triton/dispatch.py +@@ -0,0 +1,1178 @@ ++"""flash_knn -- host-side dispatcher: kernel/config selection + 2-stage launch. ++ ++Backs the public :func:`dbscanlib._kernels.primitives.knn.flash_knn` entry point. ++ ++Top-level wrappers ++------------------ ++ ++* :func:`flash_knn_triton` -- one-shot kNN, returns ``(B, N, k)`` int32 ++ indices. The shape-only heuristic now handles both build (large-N, ++ single-pass per CTA) and search (small-N, M-split flash-decode) ++ without any host-side SM-saturation gate -- ``ctas_no_split`` is ++ inspected after BN is picked, so build shapes (``BN=128`` -> ++ ``ctas_no_split`` already saturates 132 SMs) and search shapes ++ (small BN -> M-split for tail-fill) both fall out of the same ++ decision tree. ++* :func:`flash_knn_triton_small_n` / :func:`flash_knn_triton_large_n` ++ -- explicit overrides for callers (e.g. K-means assign) that know ++ their shape category at construction time. ++* :func:`_heuristic_config`, :func:`_autotune` -- shape-only heuristic ++ (default, fast first call) + opt-in brute-force autotune (slower ++ first call, cached steady-state). Both share the same kernel grid; ++ ``force_path="large_n"`` collapses M-splits down to one per CTA. ++ ++Two kernels live behind this dispatcher: ++ ++* :mod:`insert` -- iterative argmin-insert top-K. Picked by the ++ heuristic on virtually every shape. ++* :mod:`sortmerge` -- packed-uint64 sort-merge top-K with the IEEE- ++ sortable u32 transform. Routed to for the **Pattern-A small-Q** ++ corner (``B*N <= 8 or (B*N <= 16 and K in {32, 64})``) at medium-K ++ + ``M <= 200K`` where the wider sort per chunk beats insert's ++ argmin loop on this regime's autotune data. Otherwise insert wins; ++ sortmerge is kept in :func:`_gen_configs` so the offline tuner can ++ keep verifying that. ++ ++Pipelining depth ++---------------- ++Both kernels take a ``NUM_STAGES_PIPE`` constexpr for the M-loop's ++``tl.range`` pipelining factor. The dispatcher tunes it per shape from ++a 201-shape ns-sweep (upstream ``bench/sweep_ns_by_k.py``): K<=8 + ++small tile wants ``ns=3`` to hide HBM latency, K>=64 + big tile wants ++``ns=2`` (registers tight), wide-D wants ``ns=2`` uniformly, narrow ++tile + anything wants ``ns=1`` to avoid spills. See the comments ++inside :func:`_heuristic_config` for the empirical breakdown. ++ ++Distance recovery ++----------------- ++The kernels emit **signed shifted scores** ``s = c_sq - 2*``, not ++true squared L2. ``argmin-k`` over ``s`` matches ``argmin-k`` over true ++squared L2 (since ``-||x||^2`` is a per-row constant), but the returned ++score is not a valid distance. The :func:`flash_knn` public wrapper ++calls :func:`dbscanlib._kernels.kernels.distance.triton_knn_gather_sqdist` on the ++returned indices to write the true ``||x - c[idx]||^2`` per neighbour. ++""" ++from __future__ import annotations ++ ++import math ++from typing import Optional ++ ++import torch ++ ++from dbscanlib._kernels.primitives.knn.triton._common import _next_pow2, _bench_quick ++from dbscanlib._kernels.primitives.knn.triton.sortmerge import _flash_knn_sortmerge_kernel ++from dbscanlib._kernels.primitives.knn.triton.insert import _flash_knn_insert_kernel ++ ++ ++# ── SRAM estimation ──────────────────────────────────────────────────── ++ ++ ++def _estimate_sram(bn, bm, D, K, d_inner, num_stages, *, ++ sortmerge=False, dtype_bytes=2): ++ """Per-CTA SRAM estimate (bytes) for the unified kernel — coarse. ++ ++ Counts only what Triton **definitely** keeps in SMEM: the x tile ++ + c tile (per iteration of the inner D loop). Everything else ++ (cross fp32 accumulator, score tile, topK heap, sort scratch) is ++ register-resident at the tile sizes used by the dispatcher's ++ heuristic, and the NUM_STAGES_PIPE multi-buffering of the c tile ++ is sometimes elided by Triton's pipeliner. Including any of these ++ in the estimate produces 50-200 KB of over-count that **wrongly ++ shrinks** configs the kernel could actually launch — a bf16 ++ regression demonstrated by the ``D=256`` cells in ++ ``benchmarks/results/micro_knn_dtype_smem_fit.md``. ++ ++ This estimator is therefore **optimistic on purpose**. Its only ++ job is to bias the autotune candidate filter away from configs ++ that obviously can't fit (e.g. ``BN=128, BM=128, D=256, fp32`` ++ needs >232 KB by raw arithmetic). The source of truth for "does ++ this fit?" is ``_run``'s runtime ``OutOfResources`` catch + shrink ++ loop. ``dtype_bytes`` is still threaded through so fp32 inputs ++ correctly double the x/c contribution. ++ """ ++ x_sub = bn * d_inner * dtype_bytes ++ c_sub = bm * d_inner * dtype_bytes ++ c_sq = bm * 4 ++ base = x_sub + c_sub + c_sq ++ if sortmerge: ++ chunk = bn * bm * 8 ++ base += chunk ++ return base ++ ++ ++def _smem_limit(device) -> int: ++ """Per-block dynamic shared-memory budget for ``device``. ++ ++ Mirrors :func:`dbscanlib._kernels.primitives.kmeans.triton.assign._smem_limit` ++ — Triton uses opt-in dynamic SMEM; prefer that attribute when ++ available, fall back to static limit, and finally to a conservative ++ 48 KiB for old PyTorch builds. ++ """ ++ props = torch.cuda.get_device_properties(device) ++ for attr in ( ++ "shared_memory_per_block_optin", ++ "max_shared_memory_per_block_optin", ++ "shared_memory_per_block", ++ "max_shared_memory_per_block", ++ ): ++ v = getattr(props, attr, None) ++ if v: ++ return int(v) ++ return 48 * 1024 ++ ++ ++def _fit_config_to_smem(cfg: dict, D: int, K: int, ++ dtype_bytes: int, smem_limit: int) -> dict: ++ """Shrink ``(BN, BM, NUM_STAGES_PIPE)`` until the kernel fits SMEM. ++ ++ Returns ``cfg`` unchanged when it already fits. Otherwise enumerates ++ power-of-two reductions on the three SMEM-bearing axes and picks the ++ one that maximises ``BN * BM * num_stages`` (work-per-program tile) ++ among those that fit, breaking ties towards the original aspect ratio. ++ ++ Why only these three axes? ++ * ``D_INNER``: changing it flips between single-D-tile and D-split ++ paths, which would also change the kernel's perf profile. We ++ keep the heuristic's D-split decision and shrink the cheaper ++ axes first. ++ * ``TOPK_PAD``: determined by ``K``; cannot be reduced without ++ breaking correctness. ++ * ``kernel_mode``, ``num_warps``, ``M_PER_SPLIT``, ``NUM_SPLITS``: ++ these don't affect per-CTA SMEM. Stable. ++ ++ Pattern (and rationale) mirrors :func:`dbscanlib._kernels.primitives.kmeans.\ ++triton.assign._fit_config_to_smem`. ++ """ ++ BN0 = int(cfg["BN"]) ++ BM0 = int(cfg["BM"]) ++ D_INNER = int(cfg["D_INNER"]) ++ NS0 = int(cfg.get("NUM_STAGES_PIPE", 2)) ++ sortmerge = cfg.get("kernel_mode") == "sortmerge" ++ ++ cur = _estimate_sram(BN0, BM0, D, K, D_INNER, NS0, ++ sortmerge=sortmerge, dtype_bytes=dtype_bytes) ++ if cur <= smem_limit: ++ return cfg ++ ++ def _pow2_down_to(v, lo): ++ out = [] ++ x = v ++ while x >= lo: ++ out.append(x) ++ x //= 2 ++ return out ++ ++ # BN must stay >= 8 (matches _gen_configs lowest BN rung); ++ # BM must stay >= 32 for the inner loop to keep WGMMA shape; ++ # NUM_STAGES_PIPE must stay >= 1. ++ bn_cands = _pow2_down_to(BN0, 8) ++ bm_cands = _pow2_down_to(BM0, 32) ++ ns_cands = list(range(NS0, 0, -1)) ++ ++ best = None ++ best_key = None ++ for bn in bn_cands: ++ for bm in bm_cands: ++ # sortmerge requires BM == TOPK_PAD; do not shrink BM there. ++ if sortmerge and bm != BM0: ++ continue ++ for ns in ns_cands: ++ if _estimate_sram(bn, bm, D, K, D_INNER, ns, ++ sortmerge=sortmerge, ++ dtype_bytes=dtype_bytes) > smem_limit: ++ continue ++ aspect_penalty = abs((bn / max(bm, 1)) - (BN0 / max(BM0, 1))) ++ # Prefer: larger total tile work, closer aspect ratio, ++ # larger BN (more N-parallelism), larger NS (better pipelining). ++ key = (bn * bm * ns, -aspect_penalty, bn, ns) ++ if best_key is None or key > best_key: ++ best_key = key ++ best = (bn, bm, ns) ++ ++ if best is None: ++ raise RuntimeError( ++ f"flash_knn: cannot fit kernel into shared memory " ++ f"(D={D}, K={K}, D_INNER={D_INNER}, dtype_bytes={dtype_bytes}, " ++ f"smem_limit={smem_limit}). Original config " ++ f"BN={BN0}, BM={BM0}, NS={NS0} needs {cur} bytes." ++ ) ++ ++ bn, bm, ns = best ++ out = dict(cfg) ++ out["BN"] = bn ++ out["BM"] = bm ++ out["NUM_STAGES_PIPE"] = ns ++ return out ++ ++ ++def _pipe_stages_for(K, d_inner, D): ++ """``NUM_STAGES_PIPE`` candidates for the autotune M-loop sweep. ++ ++ The K-step inner loop adds register pressure that scales with K, so ++ deeper pipelining (which double-/triple-buffers the next C-tile) is ++ only profitable when K is small. Empirically: ++ ++ * D-split path (``d_inner < D``) -- kernel uses ns=1 by construction. ++ * K >= 64 -- only try {1, 2}; ns >= 3 spills registers and tanks perf ++ (probe showed ns=3 going from 750 -> 1220 us at K=128). ++ * K <= 32 -- try {1, 2, 3} so big pipelines can hide HBM latency ++ for huge-M shapes. ++ """ ++ if d_inner < D: ++ return [1] ++ if K >= 64: ++ return [1, 2] ++ return [1, 2, 3] ++ ++ ++def _gen_configs(D, K, *, dtype_bytes=2, smem_limit=None): ++ """Generate candidate kernel configs (sortmerge + insert), SRAM-validated. ++ ++ Covers ``BN ∈ {8, 16, 32, 64, 128}`` × ``num_warps ∈ {2, 4}`` × the ++ BM / D_INNER / NUM_STAGES_PIPE combinations that fit per-CTA SMEM ++ on H200. ++ ++ The ``BN = 8`` rung unlocks the small-N + medium-K Pattern-A regime ++ (``B*N <= 8`` + ``K ∈ {16, 32, 64, 128}``) where a small row tile + ++ sortmerge ``BM = max(32, K)`` wins over insert. ``tl.dot`` silently ++ pads the M dimension to 16 on Triton 3.x sm_90, so BN=8 still ++ compiles. ++ ++ ``dtype_bytes`` and ``smem_limit`` parameterise the SMEM check so ++ fp32 inputs don't slip through with a config that OOR's at launch. ++ Defaults preserve the pre-fix behaviour (bf16 / 220 KB) for any ++ caller that didn't yet plumb dtype awareness through. ++ """ ++ # 220 KB stays the default — slightly below the 227 KB H200 opt-in ++ # limit to leave room for static SMEM the compiler adds. ++ max_sram = 220_000 if smem_limit is None else smem_limit ++ d_pad = _next_pow2(D) ++ topk_pad_sm = max(32, _next_pow2(K)) ++ topk_pad_ins = _next_pow2(K) ++ d_inner_cands = [d_pad] if D <= 256 else [128] ++ ++ configs = [] ++ ++ # sort-merge: BM == TOPK_PAD; BN ∈ {8, 16, 32} (BN >= 64 never wins ++ # for sortmerge in autotune data). ++ bm_sm = topk_pad_sm ++ for bn in [8, 16, 32]: ++ for nw in [2, 4]: ++ for d_inner in d_inner_cands: ++ num_d_iters = math.ceil(D / d_inner) ++ ns = 2 if num_d_iters == 1 else 1 ++ sram = _estimate_sram( ++ bn, bm_sm, D, K, d_inner, ns, ++ sortmerge=True, dtype_bytes=dtype_bytes, ++ ) ++ if sram <= max_sram: ++ for ns_pipe in _pipe_stages_for(K, d_inner, D): ++ configs.append({ ++ "BN": bn, "BM": bm_sm, "D_INNER": d_inner, ++ "num_warps": nw, ++ "TOPK_PAD": topk_pad_sm, ++ "kernel_mode": "sortmerge", ++ "NUM_STAGES_PIPE": ns_pipe, ++ }) ++ ++ # iterative insert: BM decoupled from K, BN ∈ {8, 16, 32, 64, 128}. ++ for bn in [8, 16, 32, 64, 128]: ++ for nw in [2, 4]: ++ for bm in [64, 128, 256]: ++ for d_inner in d_inner_cands: ++ num_d_iters = math.ceil(D / d_inner) ++ ns = 2 if num_d_iters == 1 else 1 ++ sram = _estimate_sram( ++ bn, bm, D, K, d_inner, ns, ++ sortmerge=False, dtype_bytes=dtype_bytes, ++ ) ++ if sram <= max_sram: ++ for ns_pipe in _pipe_stages_for(K, d_inner, D): ++ configs.append({ ++ "BN": bn, "BM": bm, "D_INNER": d_inner, ++ "num_warps": nw, ++ "TOPK_PAD": topk_pad_ins, ++ "kernel_mode": "insert", ++ "NUM_STAGES_PIPE": ns_pipe, ++ }) ++ ++ return configs ++ ++ ++def _gen_m_splits(M, BM, *, B=1, N=1, BN=16): ++ """M_PER_SPLIT candidates targeting {1, 2, 4, 8, 16} waves on 132 SMs. ++ ++ The wave count is critical -- Stage-2 reduce scales linearly with ++ NUM_SPLITS, while too few splits leaves SMs idle. The autotune ++ sweep confirms that wave=2 is the winner on huge-M + medium-N ++ shapes (e.g. ``1×128×10M×64×10``), which the original {1, 4, 16} ++ grid missed. ++ ++ Capped at 4096×BM tiles (``MAX_MPS_TILES``) to bound the static ++ M-loop trip count for fast Triton compilation. Also includes the ++ single-pass case when M itself fits the cap (subsumes the large-N ++ kernel as ``num_splits = 1``). ++ """ ++ NUM_SMS = 132 ++ MAX_MPS_TILES = 4096 ++ num_n_tiles = max(1, (N + BN - 1) // BN) ++ max_mps = min(M, MAX_MPS_TILES * BM) ++ candidates = set() ++ for waves in [1, 2, 4, 8, 16]: ++ target_splits = max(2, (NUM_SMS * waves) // (num_n_tiles * B)) ++ target_splits = min(target_splits, max(2, M // BM)) ++ mps = (M + target_splits - 1) // target_splits ++ mps = ((mps + BM - 1) // BM) * BM ++ mps = max(mps, BM) ++ mps = min(mps, max_mps) ++ candidates.add(mps) ++ if M <= max_mps: ++ single = ((M + BM - 1) // BM) * BM ++ candidates.add(single) ++ return sorted(candidates) ++ ++ ++# ── shape-only heuristic ─────────────────────────────────────────────── ++ ++ ++def _heuristic_config(B, N, M, D, K, *, force_path=None, ++ dtype_bytes=2, smem_limit=None): ++ """Pick a kernel config from shape alone -- no autotune. ++ ++ When ``dtype_bytes`` and ``smem_limit`` are supplied (the dispatcher ++ plumbs them from ``x.element_size()`` and the device's opt-in SMEM ++ cap), the picked config is post-processed by :func:`_fit_config_to_smem` ++ so that fp32 inputs at large D never produce a config that OOR's at ++ launch. ++ ++ Derived from a 92-shape autotune sweep on H200/bf16. The data ++ showed clean decision boundaries on three axes: ++ ++ * **BN by NB-bucket + M-bump**: ++ NB <= 8 -> BN=8; ++ NB <= 32 -> BN=16 (32 at M>=5M); ++ NB <= 256 -> BN=64 (128 at M>=5M + narrow-D + small-K); ++ NB <= 2K -> BN=64 (128 at M>=5M); ++ NB >= 8K -> BN=128 (64 for D>=256 + K>=32). ++ ++ The M-bump rule fixes the ``(1, 128, 10M, 64, 10)`` regression ++ identified by klib -- at M=10M each extra N-tile costs ++ ~300us of c-replication HBM traffic. ++ ++ * **BM by K**: ++ K<=4 -> 128/256 (256 only at very-small M + small NB); ++ K=5..16 -> 128 default (256 at huge-M); ++ K=32 -> 128 default (256 at NB<=8 + M<=200K); ++ K>=64 -> 64 default (sortmerge at NB<=8). ++ ++ * **kernel_mode**: ++ ``sortmerge`` only when ``NB <= 8`` and ``K ∈ {32, 64, 128}`` ++ and ``D <= 256`` and ``M <= 200K`` (Pattern-A1), plus a ++ secondary ``NB <= 16 + K ∈ {32, 64} + M <= 200K`` corner ++ (Pattern-A2). All other shapes use ``insert``. ++ ++ * **num_warps**: 4 everywhere except tiny tiles (BN=8 + BM<=64 + ++ K<=4 + huge M, where nw=2 wins by reducing register pressure) ++ and the BN=16 + huge-M + small-K corner where nw=2 frees a ++ warp set for the topK epilogue. ++ ++ * **target waves**: 1 for build (NB>=8K) and very-large K; ++ 2 for medium/large M; 4 for small M + small K. ++ ++ * **NUM_STAGES_PIPE**: 2 by default; 3 for BN>=64 + BM<=128 + ++ K<=8 (small tile + tiny K hides HBM latency) and for ++ BN ∈ [16, 32] + BM=256 + K>=64 (big tile keeps K loop fed); ++ 1 for narrow-tile + any K (avoid register spills) and for the ++ D-split path; 2 for D_INNER>=256 and for BN>=128. Without ++ this rule, K>=64 + D=256 shapes hit catastrophic regressions ++ (e.g. ``(1,16,100K,256,128)`` 439us -> 2429us at ns=1, a 5.5x ++ slowdown); conversely K<=32 + huge-M shapes save 20-45% vs ++ ns=2 (e.g. ``(1,1,10M,128,4)`` 1426us -> 785us at ns=1). ++ ++ Args: ++ B, N, M, D, K: shape parameters. ++ force_path: ``"large_n"`` for single-pass (one M-split per CTA), ++ ``None`` for the wave-targeted M-split. ++ ++ Returns: ++ Config dict: ``BN, BM, D_INNER, TOPK_PAD, kernel_mode, ++ num_warps, M_PER_SPLIT, NUM_SPLITS, NUM_STAGES_PIPE``. ++ """ ++ NUM_SMS = 132 ++ NB = N * B ++ TOPK_PAD = _next_pow2(K) ++ D_INNER = _next_pow2(D) if D <= 256 else 128 ++ is_large_n = (force_path == "large_n") ++ # MAX_MPS_TILES bounds the per-CTA M-loop iteration count. The ++ # original cap of 512 was too tight for huge M with BM=128 (cap ++ # = 65K), which forced fewer splits than autotune wanted. Bump ++ # to 4096 so we never bottleneck on this cap. ++ MAX_MPS_TILES = 4096 ++ ++ # ─────────────────────────────────────────────────────────────── ++ # Step 1: kernel_mode, BN, BM, num_warps ++ # ─────────────────────────────────────────────────────────────── ++ if NB <= 8: ++ # Tiny query: BN=8 default. ++ BN = 8 ++ # Sortmerge fast-path: K ∈ {32, 64, 128}, M <= 200K. Now ++ # extended to ANY D (autotune ``(1,1,100K,1024,32)`` picks ++ # BN=8 BM=32 sort). ++ if (not is_large_n) and K in (32, 64, 128) and M <= 200_000: ++ BM = max(32, _next_pow2(K)) ++ sram = _estimate_sram(8, BM, D, K, D_INNER, ++ num_stages=(2 if D_INNER >= D else 1), ++ sortmerge=True) ++ if sram <= 220_000: ++ kernel_mode = "sortmerge" ++ num_warps = 2 if BM >= 128 else 4 ++ else: ++ kernel_mode = "insert" ++ BM = 256 if M <= 200_000 else 128 ++ num_warps = 4 ++ else: ++ kernel_mode = "insert" ++ if K <= 4: ++ # K=4 + D>=256 + huge M -> BM=64 nw=2 (autotune ++ # ``(1,1,1M,256,4)`` picks this). HBM-bound; BM=256 ++ # adds wasted SMEM. ++ if D >= 256 and M >= 500_000: ++ BM = 64 ++ else: ++ BM = 256 ++ elif K <= 16: ++ # D>=256 + K=16 -> BM=128 (autotune (1,1,100K,256,16)) ++ if D >= 256 and K >= 16: ++ BM = 128 ++ else: ++ BM = 256 if (M >= 500_000 or K >= 16) else 128 ++ elif K == 32: ++ # autotune (1,1,1M,256,32) prefers BM=128 at D>=256. ++ # At narrower D, BM=256 wins. ++ if D >= 256 and M >= 500_000: ++ BM = 128 ++ else: ++ BM = 256 ++ elif K <= 64: ++ BM = 64 ++ else: # K=128 ++ BM = 256 ++ # nw=2 for small/medium-D BM=64 cases ++ if BM == 64 and K <= 4 and D >= 512 and M >= 500_000: ++ # D >= 512 hits the D-split path (D_INNER capped at 128); ++ # nw=2 was calibrated for this regime at (1, 1, 1M, 256, 4) ++ # but later re-benching at D=256 showed nw=4 ns=1 wins by ++ # 1.48-1.81x there (single-D-iter, multibuffered C). Keep ++ # nw=2 only for the D-split path; D=256 falls to the ++ # default nw=4 below and gets the ns=1 override in Step 3. ++ num_warps = 2 ++ elif BM >= 128 and K <= 4 and D <= 64 and M >= 5_000_000: ++ # NB<=8 + K<=4 + narrow-D + huge-M: nw=2 wins by 20-30 %. ++ # 4 warps split the SM register file too thinly for the ++ # K=4 argmin-insert epilogue at BM>=128, so the compiler ++ # spills; halving the warp count doubles regs/warp and ++ # lets ILP recover. Verified at D=64 across M ∈ [5M, 60M] ++ # (D=128 strictly prefers nw=4 at this BM; do not bump ++ # the threshold without re-benching). ++ num_warps = 2 ++ else: ++ num_warps = 4 ++ ++ elif NB <= 32: ++ kernel_mode = "insert" ++ # Sortmerge also wins at NB <= 16 + K ∈ {32, 64} (autotune ++ # ``(1,16,100K,256,32)`` picks BN=8 BM=32 sortmerge). ++ if (not is_large_n) and NB <= 16 and K in (32, 64) and M <= 200_000: ++ BN = 8 ++ BM = max(32, _next_pow2(K)) ++ sram = _estimate_sram(8, BM, D, K, D_INNER, ++ num_stages=(2 if D_INNER >= D else 1), ++ sortmerge=True) ++ if sram <= 220_000: ++ kernel_mode = "sortmerge" ++ num_warps = 4 ++ else: ++ BN, BM = 16, 256 ++ if kernel_mode == "insert": ++ if M >= 5_000_000 and N >= 32: ++ BN = 32 ++ else: ++ BN = max(8, min(16, _next_pow2(N))) ++ if K <= 4: ++ BM = 128 ++ elif K <= 16: ++ BM = 256 if M <= 2_000_000 else 128 ++ else: # K >= 32 ++ BM = 256 ++ if K <= 4 and M >= 500_000: ++ num_warps = 2 ++ elif K <= 16 and M >= 5_000_000 and BN <= 16: ++ num_warps = 2 ++ elif K <= 16 and NB >= 17 and M >= 5_000_000: ++ # autotune (1,32,10M,64,8) BN=32 nw=2 wins ++ num_warps = 2 ++ else: ++ num_warps = 4 ++ ++ elif NB <= 256: ++ # Medium queries: BN must scale with M to control ++ # c-replication, but ONLY when K is very small (K<=4) or ++ # very large (K>=32). At K ∈ {5..16} the autotune consistently ++ # picks BN=64 even at M=10M, because K=8..16 hits a sweet ++ # spot where the topk-insert + Stage-2 cost of 1-wave ++ # BN=128 outweighs the c-HBM savings. ++ kernel_mode = "insert" ++ big_NB = (NB >= 192) ++ ++ # K=128 special-case: medium NB + K=128, autotune picks ++ # BN=8 BM=256 insert (NOT sortmerge!). The K-step argmin loop ++ # at BN=64 K=128 is too expensive -- better to chop N into ++ # many small tiles. ++ if K >= 128 and M <= 200_000: ++ BN = 8 ++ BM = 256 ++ elif M >= 5_000_000 and D <= 64 and (K <= 4 or K >= 32): ++ BN = 128 ++ elif big_NB and M >= 5_000_000: ++ BN = 128 ++ elif big_NB and D >= 256 and M <= 200_000 and K <= 4: ++ BN = 128 ++ elif D >= 256 and K <= 16 and (NB >= 64 and M >= 500_000): ++ # D=256 + K<=16 + (medium NB + medium M): BN=128 wins. ++ # autotune (1,128,1M,256,8) picks BN=128 BM=64. ++ BN = 128 ++ else: ++ BN = 64 ++ BN = min(BN, max(8, _next_pow2(N))) ++ # BM ++ if K >= 128: ++ BM = 256 # already covered above, keep consistent here ++ elif K <= 4 and D <= 128 and M <= 200_000: ++ BM = 256 ++ elif D >= 256 and K <= 16: ++ BM = 64 ++ elif K <= 32: ++ BM = 64 if (BN == 64 and K == 32 and D >= 256) else 128 ++ elif K <= 64: ++ BM = 128 ++ else: ++ BM = 128 ++ num_warps = 4 ++ ++ elif NB <= 2048: ++ kernel_mode = "insert" ++ if M >= 5_000_000: ++ BN = 128 ++ elif M >= 500_000 and D >= 256 and K <= 16: ++ BN = 128 ++ else: ++ BN = 64 ++ BN = min(BN, max(8, _next_pow2(N))) ++ if K <= 4 and M >= 500_000: ++ BM = 64 ++ elif K <= 16 and M >= 500_000 and D >= 128: ++ BM = 64 ++ elif K >= 64: ++ BM = 64 ++ else: ++ BM = 128 ++ num_warps = 4 ++ ++ else: # NB >= 8K (build / large-eval regime) ++ kernel_mode = "insert" ++ # autotune patterns: ++ # NB=50K-100K D<=128 K<=8 -> BN=128 (halves HBM) ++ # NB=10K K>=8 -> BN=64 (more topk work per row) ++ # NB>=10K D>=256 K<=16 -> BN=128 ++ # NB>=10K D>=256 K>=32 -> BN=64 ++ if D >= 256 and K <= 16: ++ BN = 128 ++ elif D >= 256 and K >= 32: ++ BN = 64 ++ elif K <= 4: ++ BN = 128 ++ elif K <= 8 and NB >= 30_000: ++ # Larger NB -> BN=128 saves enough HBM to overcome topk cost ++ BN = 128 ++ else: ++ BN = 64 ++ if K <= 4 and D >= 256: ++ BM = 64 # SMEM pressure at D=256 + BM=128 ++ elif K <= 4: ++ BM = 128 if (D <= 64 or BN == 128) else 64 ++ elif K <= 8 and BN == 128: ++ BM = 128 ++ elif K >= 64 and BN == 64 and D <= 128 and NB >= 30_000: ++ # klib carve-out: NB in [30K, ~200K] + BN=64 + K>=64 + ++ # D<=128 wins big at BM=128 splits=1 vs BM=64 splits=2. ++ # Empirically at (1, 48K, 48K, 64, 64) the upstream rule ++ # (BM=64, target_splits=2) runs 17.95 ms, while BM=128 ++ # splits=1 ns=2 runs 11.71 ms (-35%); at (1, 64K, 64K, 64, ++ # 64) 29.20 ms -> 18.91 ms (-35%). Bounded on K>=64 (K=32 ++ # autotune at this NB shows BM=64 splits=1 wins, ~1 ms over ++ # BM=128 splits=1) and on NB>=30K so the upstream rule ++ # still applies to smaller-NB shapes where its M-split ++ # target was directly autotune-derived. ++ BM = 128 ++ else: ++ BM = 64 ++ num_warps = 4 ++ ++ # ─────────────────────────────────────────────────────────────── ++ # Step 2: M_PER_SPLIT (target waves) ++ # ─────────────────────────────────────────────────────────────── ++ num_n_tiles = max(1, math.ceil(N / BN)) ++ ctas_no_split = num_n_tiles * B ++ ++ # Workaround for an insert-kernel correctness issue with the ++ # D-split path: when ``M_PER_SPLIT == BM`` (single M-loop ++ # iteration) and the d-loop has >=4 chunks, the result is wrong. ++ # Bumping the minimum mps to 2*BM keeps the M-loop at >=2 iters. ++ needs_min_2_iters = (D_INNER < D and (D + D_INNER - 1) // D_INNER >= 4) ++ min_mps = (2 * BM) if needs_min_2_iters else BM ++ ++ if is_large_n: ++ M_PER_SPLIT = ((M + BM - 1) // BM) * BM ++ elif ctas_no_split >= NUM_SMS * 8: ++ # Massively oversaturated (e.g. N=100K, BN=64, ctas=1563): one ++ # split fully utilises SMs. ++ M_PER_SPLIT = ((M + BM - 1) // BM) * BM ++ elif K >= 32 and BN == 64 and D <= 128 and NB >= 30_000 and not is_large_n: ++ # klib carve-out (pairs with the BM=128 K>=64 rule above ++ # plus K=32 BM=64 standalone): the K>=32 build regime at ++ # NB>=30K wants splits=1 -- 2 splits doubles the K-step ++ # argmin work per row and the Stage-2 reduce, neither of ++ # which the autotune at NB=10K K=8 accounted for. Empirically ++ # saves ~6 ms on (1, 48K, 48K, 64, 64) and ~10 ms on ++ # (1, 64K, 64K, 64, 64) vs target_splits=2, and ~1 ms on ++ # (1, 56K, 56K, 64, 32) vs target_splits=2. ++ M_PER_SPLIT = ((M + BM - 1) // BM) * BM ++ else: ++ if ctas_no_split >= NUM_SMS * 4: ++ # ctas >= 528: 2 splits for tail-fill at very large builds ++ # (autotune (1,100K,100K,64,4) BN=128 ctas=782 -> 2 splits) ++ target_splits = 2 ++ elif ctas_no_split >= NUM_SMS * 2: ++ # ctas in [264, 528): single split already 2-4 waves; adding ++ # splits hurts more than it helps. autotune ++ # (1,50K,50K,64,8) BN=128 ctas=391 -> 1 split. ++ target_splits = 1 ++ elif ctas_no_split >= NUM_SMS: ++ # ctas in [132, 264): 3 splits for better tail. autotune ++ # (1,10K,10K,64,16) BN=64 ctas=157 -> 3 splits. ++ target_splits = 3 ++ else: ++ # Under-saturated: target_waves by K (autotune-derived). ++ # Use FLOOR div: snaps to integer wave count, which matches ++ # autotune choices on shapes like NB=1024 K=32 ctas=16 ++ # (16 splits = 2 waves, 17 splits = 2.06 waves but bad ++ # tail). ++ if kernel_mode == "sortmerge": ++ target_waves = 2 ++ elif K >= 128 and NB >= 64: ++ # K=128 + medium NB: 2w wins (autotune ++ # (1,128,100K,128,128) picks 16 splits at ctas=16). ++ target_waves = 2 ++ elif K >= 128: ++ target_waves = 1 ++ elif K >= 64 and ctas_no_split <= 2: ++ # K=64 + tiny ctas: 1 wave wins (autotune ++ # (1,128,100K,128,64) picks 66 splits = 1w). ++ target_waves = 1 ++ elif K >= 64: ++ target_waves = 2 ++ elif K <= 4 and ctas_no_split == 1 and (NB <= 32 or M <= 1_000_000): ++ # K=4 single-CTA-stack: 4 waves at small NB or small M. ++ # autotune NB=1 K=4 -> 521 splits; (1,128,10M,64,4) ++ # however picks 264 splits (2w) -- too much M per CTA ++ # for more splits to help. ++ target_waves = 4 ++ elif K <= 8 and ctas_no_split >= 8 and ctas_no_split <= 32: ++ target_waves = 4 if M >= 500_000 else 2 ++ elif K <= 8 and ctas_no_split == 1 and M >= 5_000_000 and BN <= 32: ++ target_waves = 4 ++ elif B >= 2 and N <= 8 and K <= 8: ++ # Batched B>=2 + single-query + K<=8: 4w wins ++ target_waves = 4 ++ else: ++ target_waves = 2 ++ target_splits = max(1, NUM_SMS * target_waves // ctas_no_split) ++ target_splits = min(target_splits, max(1, math.ceil(M / BM))) ++ mps_raw = math.ceil(M / target_splits) ++ mps = math.ceil(mps_raw / BM) * BM ++ mps = max(min_mps, min(mps, ((M + BM - 1) // BM) * BM)) ++ mps = min(mps, MAX_MPS_TILES * BM) ++ M_PER_SPLIT = mps ++ ++ NUM_SPLITS = math.ceil(M / M_PER_SPLIT) ++ ++ # ─────────────────────────────────────────────────────────────── ++ # Step 3: NUM_STAGES_PIPE (M-loop pipeline depth) ++ # ─────────────────────────────────────────────────────────────── ++ # Derived from a 201-shape × 4-ns sweep on H200 (upstream ++ # ``bench/sweep_ns_by_k.py``), cross-checked against direct probes ++ # on N>=64 build shapes. Three competing effects: ++ # ++ # * Each extra pipeline stage adds another buffered C-tile, ++ # costing ``BM * D_INNER * 2`` bytes of SMEM/registers. Wide ++ # D-tiles amortise this well; narrow ones don't. ++ # * The K-step argmin inner loop holds live state proportional ++ # to ``BN * TOPK_PAD * 8`` bytes. For narrow rows (BN<=16) + ++ # large K, registers get tight and deeper pipelines spill. ++ # * Medium-N tiles (BN>=64) have abundant compute per tile to ++ # amortise prefetch, but get little benefit at very large K ++ # because the K loop dominates anyway. ++ # ++ # Empirical winners (mode_ns from the 201-shape sweep): ++ # ++ # D_INNER >= 256 (wide D) -> ns=2 (uniform across K) ++ # BN >= 128 (huge build N) -> ns=2 (registers tight) ++ # BN >= 64 + BM <= 128 + K <= 8 -> ns=3 (small tile + tiny K) ++ # BN >= 64 -> ns=2 (BM=256 SMEM-bound) ++ # BN ∈ [16,32] + BM=256 + K >= 64 -> ns=3 (big-tile large-K) ++ # otherwise (narrow tile + any K) -> ns=1 (avoid spills) ++ # ++ # Without this rule, K>=64 + D=256 shapes hit catastrophic ++ # regressions (e.g. (1,16,100K,256,128) 439us -> 2429us at ns=1, ++ # a 5.5x slowdown). Conversely K<=32 + huge-M shapes save 20-45% ++ # vs ns=2 (e.g. (1,1,10M,128,4) 1426us -> 785us at ns=1). ++ if D_INNER < D: ++ NUM_STAGES_PIPE = 1 # D-split path is hard-coded to ns=1 ++ elif (BN == 8 and BM == 64 and D_INNER == 256 ++ and K <= 4 and M >= 500_000): ++ # NB<=8 + K<=4 + D=256 + BM=64 + huge-M: ns=1 wins by 1.48-1.81x ++ # over the default ns=2. Direct verification at K ∈ {1, 2, 4} × ++ # M ∈ {1M, 5M, 10M, 30M}: (nw=4, ns=1) lifts %peak HBM from ++ # 39-48 % (ns=2) to 57-85 % (ns=1). The ns=2 default below was ++ # calibrated for wider-N tiles; at BN=8 the deeper pipeline ++ # spends too many registers on prefetch and the K=4 epilogue ++ # spills. Pairs with the nw=4 default the rule above falls into ++ # at D=256. ++ NUM_STAGES_PIPE = 1 ++ elif D_INNER >= 256: ++ NUM_STAGES_PIPE = 2 ++ elif BN >= 128: ++ NUM_STAGES_PIPE = 2 ++ elif BN >= 64 and BM <= 128 and K <= 8: ++ NUM_STAGES_PIPE = 3 ++ elif BN >= 64: ++ NUM_STAGES_PIPE = 2 ++ elif BN >= 16 and BM >= 256 and K >= 64: ++ NUM_STAGES_PIPE = 3 ++ else: ++ NUM_STAGES_PIPE = 1 ++ ++ cfg = { ++ "BN": BN, "BM": BM, "D_INNER": D_INNER, ++ "TOPK_PAD": ( ++ max(32, _next_pow2(K)) if kernel_mode == "sortmerge" ++ else _next_pow2(K) ++ ), ++ "kernel_mode": kernel_mode, "num_warps": num_warps, ++ "M_PER_SPLIT": M_PER_SPLIT, ++ "NUM_SPLITS": NUM_SPLITS, ++ "NUM_STAGES_PIPE": NUM_STAGES_PIPE, ++ } ++ ++ # Dtype-aware SMEM shrink. The shape-only heuristic above was tuned ++ # on bf16 and may pick (BN=128, BM=128, D_INNER=128) for shapes that ++ # cleanly fit ~210 KB at 2 bytes/elt but blow past the 227 KB H200 ++ # opt-in cap at 4 bytes/elt. The fitter shrinks BN/BM/NUM_STAGES_PIPE ++ # by powers of 2 until the kernel fits — leaving D_INNER and ++ # kernel_mode (which encode different perf regimes) alone. ++ if smem_limit is not None: ++ cfg = _fit_config_to_smem(cfg, D, K, dtype_bytes, smem_limit) ++ ++ return cfg ++ ++ ++# ── autotune (opt-in) ────────────────────────────────────────────────── ++ ++ ++_autotune_cache: dict = {} ++ ++ ++def _autotune(x, c, k, *, force_path=None): ++ """Brute-force autotune over the candidate config grid. ++ ++ Cached per ``(B, N, M, D, k, dtype, force_path)`` shape; first call ++ costs ~30-60 s of compile time, subsequent calls hit the cache in ++ sub-ms. ++ ++ The candidate grid is dtype-aware: ``_gen_configs`` filters out ++ configs whose SMEM exceeds the device opt-in limit at this dtype, ++ so fp32 inputs at large D never have an unrunnable config in the ++ search space. ++ """ ++ B, N, D = x.shape ++ M = c.shape[1] ++ dtype_bytes = x.element_size() ++ key = (B, N, M, D, k, x.dtype, force_path) ++ ++ if key in _autotune_cache: ++ return _autotune_cache[key] ++ ++ smem_limit = _smem_limit(x.device) ++ configs = _gen_configs(D, k, ++ dtype_bytes=dtype_bytes, ++ smem_limit=min(smem_limit, 220_000)) ++ if not configs: ++ raise RuntimeError( ++ f"No valid flash_knn config for D={D}, K={k}, " ++ f"dtype={x.dtype} (element_size={dtype_bytes})" ++ ) ++ ++ # Drop configs with BN > next_pow2(N) -- they would just pad N to BN ++ # and run the same kernel slower. ++ max_bn = max(16, _next_pow2(N)) ++ configs = [cfg for cfg in configs if cfg["BN"] <= max_bn] ++ ++ best_time = float('inf') ++ best_cfg = None ++ max_partial_bytes = 4 * 1024**3 ++ ++ for cfg in configs: ++ bn = cfg["BN"] ++ bm = cfg["BM"] ++ d_inner = cfg["D_INNER"] ++ topk_pad = cfg["TOPK_PAD"] ++ kernel_mode = cfg["kernel_mode"] ++ nw = cfg["num_warps"] ++ ns_pipe = cfg.get("NUM_STAGES_PIPE", 2) ++ ++ if force_path == "large_n": ++ mps_list = [((M + bm - 1) // bm) * bm] ++ else: ++ mps_list = _gen_m_splits(M, bm, B=B, N=N, BN=bn) ++ ++ for mps in mps_list: ++ num_splits = math.ceil(M / mps) ++ partial_bytes = B * num_splits * N * k * 8 ++ if partial_bytes > max_partial_bytes: ++ continue ++ ++ try: ++ pv = torch.empty((B, N, num_splits, k), ++ device=x.device, dtype=torch.float32) ++ pi = torch.empty((B, N, num_splits, k), ++ device=x.device, dtype=torch.int32) ++ grid = (num_splits, math.ceil(N / bn), B) ++ pv_s0, pv_s1, pv_s2, pv_s3 = pv.stride() ++ pi_s0, pi_s1, pi_s2, pi_s3 = pi.stride() ++ ++ if kernel_mode == "sortmerge": ++ def run(grid=grid, bn=bn, bm=bm, d_inner=d_inner, ++ topk_pad=topk_pad, mps=mps, nw=nw, ++ num_splits=num_splits, pv=pv, pi=pi, ++ ns_pipe=ns_pipe, ++ pv_s0=pv_s0, pv_s1=pv_s1, pv_s2=pv_s2, pv_s3=pv_s3, ++ pi_s0=pi_s0, pi_s1=pi_s1, pi_s2=pi_s2, pi_s3=pi_s3): ++ _flash_knn_sortmerge_kernel[grid]( ++ x, c, pv, pi, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ pv_s0, pv_s2, pv_s1, pv_s3, ++ pi_s0, pi_s2, pi_s1, pi_s3, ++ N=N, M=M, D=D, K=k, M_PER_SPLIT=mps, ++ BN=bn, BM=bm, D_INNER=d_inner, ++ TOPK_PAD=topk_pad, ++ NUM_STAGES_PIPE=ns_pipe, ++ num_warps=nw, ++ ) ++ if num_splits > 1: ++ pv_flat = pv.view(B, N, -1) ++ pv_flat.topk(k, dim=-1, largest=False) ++ else: ++ max_steps = min(k, bm) ++ def run(grid=grid, bn=bn, bm=bm, d_inner=d_inner, ++ topk_pad=topk_pad, mps=mps, nw=nw, ++ num_splits=num_splits, pv=pv, pi=pi, ++ max_steps=max_steps, ns_pipe=ns_pipe, ++ pv_s0=pv_s0, pv_s1=pv_s1, pv_s2=pv_s2, pv_s3=pv_s3, ++ pi_s0=pi_s0, pi_s1=pi_s1, pi_s2=pi_s2, pi_s3=pi_s3): ++ _flash_knn_insert_kernel[grid]( ++ x, c, pv, pi, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ pv_s0, pv_s2, pv_s1, pv_s3, ++ pi_s0, pi_s2, pi_s1, pi_s3, ++ N=N, M=M, D=D, K=k, M_PER_SPLIT=mps, ++ BN=bn, BM=bm, D_INNER=d_inner, ++ TOPK_PAD=topk_pad, MAX_STEPS=max_steps, ++ NUM_STAGES_PIPE=ns_pipe, ++ num_warps=nw, ++ ) ++ if num_splits > 1: ++ pv_flat = pv.view(B, N, -1) ++ pv_flat.topk(k, dim=-1, largest=False) ++ ++ t = _bench_quick(run) ++ if t < best_time: ++ best_time = t ++ best_cfg = {**cfg, "M_PER_SPLIT": mps, ++ "NUM_SPLITS": num_splits, ++ "NUM_STAGES_PIPE": ns_pipe} ++ ++ del pv, pi ++ except Exception: ++ continue ++ ++ if best_cfg is None: ++ raise RuntimeError( ++ f"All flash_knn configs failed for B={B}, N={N}, M={M}, D={D}, K={k}") ++ ++ _autotune_cache[key] = best_cfg ++ return best_cfg ++ ++ ++# ── runner ───────────────────────────────────────────────────────────── ++ ++ ++_OOR_FALLBACK_CACHE: dict = {} ++ ++ ++def _try_launch(x, c, cfg, B, N, M, D, k): ++ """Single launch attempt; returns idxs or raises ``triton.compiler.errors.OutOfResources``. ++ ++ Factored out of ``_run`` so the OOR-shrink retry loop can call it ++ repeatedly with a shrunk cfg. ++ """ ++ bn = cfg["BN"] ++ bm = cfg["BM"] ++ d_inner = cfg["D_INNER"] ++ topk_pad = cfg["TOPK_PAD"] ++ mps = cfg["M_PER_SPLIT"] ++ num_splits = cfg["NUM_SPLITS"] ++ kernel_mode = cfg["kernel_mode"] ++ nw = cfg["num_warps"] ++ num_stages_pipe = cfg.get("NUM_STAGES_PIPE", 2) ++ ++ partial_vals = torch.empty((B, N, num_splits, k), ++ device=x.device, dtype=torch.float32) ++ partial_idxs = torch.empty((B, N, num_splits, k), ++ device=x.device, dtype=torch.int32) ++ grid = (num_splits, math.ceil(N / bn), B) ++ ++ pv_s0, pv_s1, pv_s2, pv_s3 = partial_vals.stride() ++ pi_s0, pi_s1, pi_s2, pi_s3 = partial_idxs.stride() ++ ++ if kernel_mode == "sortmerge": ++ _flash_knn_sortmerge_kernel[grid]( ++ x, c, partial_vals, partial_idxs, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ pv_s0, pv_s2, pv_s1, pv_s3, ++ pi_s0, pi_s2, pi_s1, pi_s3, ++ N=N, M=M, D=D, K=k, M_PER_SPLIT=mps, ++ BN=bn, BM=bm, D_INNER=d_inner, ++ TOPK_PAD=topk_pad, ++ NUM_STAGES_PIPE=num_stages_pipe, ++ num_warps=nw, ++ ) ++ else: ++ max_steps = min(k, bm) ++ _flash_knn_insert_kernel[grid]( ++ x, c, partial_vals, partial_idxs, ++ x.stride(0), x.stride(1), x.stride(2), ++ c.stride(0), c.stride(1), c.stride(2), ++ pv_s0, pv_s2, pv_s1, pv_s3, ++ pi_s0, pi_s2, pi_s1, pi_s3, ++ N=N, M=M, D=D, K=k, M_PER_SPLIT=mps, ++ BN=bn, BM=bm, D_INNER=d_inner, ++ TOPK_PAD=topk_pad, MAX_STEPS=max_steps, ++ NUM_STAGES_PIPE=num_stages_pipe, ++ num_warps=nw, ++ ) ++ ++ if num_splits == 1: ++ return partial_idxs[:, :, 0, :].contiguous() ++ ++ pv = partial_vals.view(B, N, -1) ++ pi = partial_idxs.view(B, N, -1) ++ _, sel = pv.topk(k, dim=-1, largest=False, sorted=True) ++ out_idxs = pi.gather(-1, sel.to(torch.int64)).to(torch.int32) ++ return out_idxs ++ ++ ++def _shrink_cfg_on_oor(cfg: dict) -> Optional[dict]: ++ """Halve the cheapest SMEM-bearing axis. ``None`` when nothing left. ++ ++ Order: ++ 1. NUM_STAGES_PIPE 3 → 2 → 1 (drops the c-tile pipeline buffer ++ — usually free or nearly-free at this depth). ++ 2. Tend toward a near-square tile by **halving the larger of ++ (BN, BM)** first. WGMMA shape efficiency drops rapidly once ++ BM<32 on Hopper (MMA atom is 64x16x16), so we prefer to ++ shrink BN when BN > BM. For BN=128 BM=64 (typical build ++ regime) this gives BN=64 BM=64 in one step, which empirically ++ matches the kmeans-style ``_fit_config_to_smem`` choice and ++ beats BN=128 BM=32 by ~20 % at D=1024 fp32. ++ 3. Floors: BN ≥ 8, BM ≥ 32 (both required by the WGMMA atom). ++ ++ sortmerge requires ``BM == TOPK_PAD``; skip BM shrinks and only ++ halve BN. ++ ++ Also recomputes ``M_PER_SPLIT`` so it remains a multiple of the ++ new BM (the kernel's inner ``tl.range`` walk requires this). ++ """ ++ ns = int(cfg.get("NUM_STAGES_PIPE", 2)) ++ bm = int(cfg["BM"]) ++ bn = int(cfg["BN"]) ++ sortmerge = cfg.get("kernel_mode") == "sortmerge" ++ ++ out = dict(cfg) ++ if ns > 1: ++ out["NUM_STAGES_PIPE"] = ns - 1 ++ return out ++ if sortmerge: ++ if bn > 8: ++ out["BN"] = bn // 2 ++ return out ++ return None ++ # Non-sortmerge: prefer shrinking the larger axis. ++ if bn > bm and bn > 8: ++ out["BN"] = bn // 2 ++ return out ++ if bm > 32: ++ out["BM"] = bm // 2 ++ new_bm = out["BM"] ++ out["M_PER_SPLIT"] = ((int(out["M_PER_SPLIT"]) + new_bm - 1) ++ // new_bm) * new_bm ++ return out ++ if bn > 8: ++ out["BN"] = bn // 2 ++ return out ++ return None ++ ++ ++def _run(x: torch.Tensor, c: torch.Tensor, k: int, *, ++ force_path: Optional[str] = None, ++ autotune: bool = False) -> torch.Tensor: ++ """Shared dispatcher -- launch stage 1, optional stage 2 reduce, return idxs. ++ ++ Returns indices only ``(B, N, k) int32``. The wrapper at ++ :func:`dbscanlib._kernels.primitives.knn.flash_knn` calls the gather kernel to ++ produce true squared distances per neighbour. ++ ++ On ``OutOfResources`` at launch, we shrink one SMEM-bearing axis ++ (NS → BM → BN) and retry, caching the surviving config per ++ ``(D, K, dtype, force_path)`` so subsequent calls with the same ++ shape skip the failed compiles. This is the source of truth for ++ "does this fit?"; ``_estimate_sram`` only provides a coarse hint ++ for the autotune candidate filter. ++ """ ++ assert x.is_cuda and c.is_cuda ++ assert x.dtype == c.dtype ++ B, N, D = x.shape ++ M = c.shape[1] ++ assert c.shape == (B, M, D) ++ assert 1 <= k <= M ++ ++ if autotune: ++ cfg = _autotune(x, c, k, force_path=force_path) ++ else: ++ cfg = _heuristic_config( ++ B, N, M, D, k, ++ force_path=force_path, ++ dtype_bytes=x.element_size(), ++ smem_limit=min(_smem_limit(x.device), 220_000), ++ ) ++ ++ # Surviving-config cache keyed on the parts the dispatcher can't ++ # vary (D, K, dtype, force_path). If a previous call already ++ # shrunk past an OOR for this shape, start with that smaller cfg. ++ fb_key = (D, k, x.dtype, force_path, ++ cfg["kernel_mode"], cfg.get("D_INNER")) ++ if fb_key in _OOR_FALLBACK_CACHE: ++ cached = _OOR_FALLBACK_CACHE[fb_key] ++ # Use the cached cfg only if it's smaller than the current one ++ # on at least one SMEM-bearing axis (otherwise the heuristic ++ # already picked something safe for this shape). ++ sm_cur = (cfg["BN"], cfg["BM"], cfg.get("NUM_STAGES_PIPE", 2)) ++ sm_cached = (cached["BN"], cached["BM"], ++ cached.get("NUM_STAGES_PIPE", 2)) ++ if sm_cached < sm_cur: ++ cfg = {**cfg, **{k_: cached[k_] for k_ in ++ ("BN", "BM", "NUM_STAGES_PIPE")}} ++ # Re-align mps to new BM ++ new_bm = cfg["BM"] ++ cfg["M_PER_SPLIT"] = ((cfg["M_PER_SPLIT"] + new_bm - 1) ++ // new_bm) * new_bm ++ cfg["NUM_SPLITS"] = math.ceil(M / cfg["M_PER_SPLIT"]) ++ ++ # Import lazily so we don't pay it on every dispatch. ++ try: ++ from triton.runtime.errors import OutOfResources ++ except ImportError: ++ try: ++ from triton.compiler.errors import OutOfResources ++ except ImportError: ++ OutOfResources = RuntimeError # safest fallback ++ ++ last_err = None ++ initial_cfg = cfg ++ shrunk = False ++ for _attempt in range(8): ++ try: ++ result = _try_launch(x, c, cfg, B, N, M, D, k) ++ # Cache the surviving cfg so subsequent calls with the ++ # same (D, K, dtype, ...) shape skip the failed compile. ++ if shrunk: ++ _OOR_FALLBACK_CACHE[fb_key] = { ++ "BN": cfg["BN"], "BM": cfg["BM"], ++ "NUM_STAGES_PIPE": cfg.get("NUM_STAGES_PIPE", 2), ++ } ++ return result ++ except OutOfResources as e: ++ last_err = e ++ new_cfg = _shrink_cfg_on_oor(cfg) ++ if new_cfg is None: ++ break ++ cfg = new_cfg ++ shrunk = True ++ continue ++ ++ raise last_err if last_err is not None else RuntimeError( ++ "flash_knn: exhausted OOR shrink attempts") ++ ++ ++# ── public API ───────────────────────────────────────────────────────── ++ ++ ++def flash_knn_triton_small_n(x: torch.Tensor, c: torch.Tensor, k: int, ++ *, autotune: bool = False) -> torch.Tensor: ++ """Force the M-split (search) path -- ``(B, N, k) int32`` indices.""" ++ return _run(x, c, k, force_path=None, autotune=autotune) ++ ++ ++def flash_knn_triton_large_n(x: torch.Tensor, c: torch.Tensor, k: int, ++ *, autotune: bool = False) -> torch.Tensor: ++ """Force the single-pass (build) path -- ``(B, N, k) int32`` indices.""" ++ return _run(x, c, k, force_path="large_n", autotune=autotune) ++ ++ ++def flash_knn_triton(x: torch.Tensor, c: torch.Tensor, k: int, ++ *, autotune: bool = False) -> torch.Tensor: ++ """Universal Triton dispatch -- the heuristic picks BN/BM/mode/ ++ mps/ns_pipe based on shape, including whether to single-pass or ++ M-split. The per-CTA-count check inside :func:`_heuristic_config` ++ handles both build and eval shapes without a host-side forced path. ++ ++ Neither path materialises an N×M cross or distance matrix to HBM, ++ and neither computes or loads ``x_sq`` -- the two hard contracts ++ klib's KNN imposes on every Triton entry point here. ++ ++ Args: ++ x: ``(B, N, D)`` bf16 / fp16 / fp32 query tensor. ++ c: ``(B, M, D)`` corpus, same dtype. ++ k: number of neighbours. ++ autotune: ``False`` (default) uses the shape-only heuristic -- ++ first call pays a single Triton compile (~0.5 s). ``True`` ++ runs the full brute-force sweep + caches per shape (~30-90 s ++ first call). ++ ++ Returns: ++ idxs: ``(B, N, k)`` int32 -- nearest-neighbour indices, sorted ++ by ascending true squared L2 distance (ties broken by index). ++ """ ++ return _run(x, c, k, force_path=None, autotune=autotune) +diff --git a/dbscanlib/_kernels/primitives/knn/triton/insert.py b/dbscanlib/_kernels/primitives/knn/triton/insert.py +new file mode 100644 +index 0000000..7e72d76 +--- /dev/null ++++ b/dbscanlib/_kernels/primitives/knn/triton/insert.py +@@ -0,0 +1,204 @@ ++"""flash_knn — Triton iterative-insert top-K kernel (x²-free, signed score). ++ ++Same x²-free signed score as ``sortmerge.py`` — see that module's docstring ++for the rationale. The insert kernel differs in two ways: ++ ++ * ``BM`` is decoupled from ``TOPK_PAD`` (sortmerge requires ``BM == TOPK_PAD``). ++ The autotune sweeps ``BM ∈ {64, 128, 256}`` independently. ++ * Top-K is maintained as an unsorted ``(BN, TOPK_PAD)`` register array; ++ each chunk does at most ``MAX_STEPS = min(K, BM)`` argmin->insert ++ passes. fp32 ``<`` works natively on signed scores, so only the ++ trailing output sort uses the sortable-u32 transform. ++ ++Empirically the insert kernel wins on virtually every shape past the ++trivial ``K == 1`` case; sortmerge is kept for completeness (autotune ++still considers it, and it's the natural fit when ``K`` matches a ++hardware-sortable tile size). ++ ++Public name: ``_flash_knn_insert_kernel`` — called from ++``klib/primitives/knn/triton/dispatch.py``. ++""" ++import triton ++import triton.language as tl ++ ++from dbscanlib._kernels.primitives.knn.triton._common import ( ++ _fp32_to_sortable_u32, ++ _sortable_u32_to_fp32, ++) ++ ++ ++@triton.jit ++def _flash_knn_insert_kernel( ++ x_ptr, c_ptr, ++ partial_val_ptr, partial_idx_ptr, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_c_b, stride_c_m, stride_c_d, ++ stride_pv_b, stride_pv_s, stride_pv_n, stride_pv_k, ++ stride_pi_b, stride_pi_s, stride_pi_n, stride_pi_k, ++ N: tl.constexpr, M: tl.constexpr, ++ D: tl.constexpr, K: tl.constexpr, ++ M_PER_SPLIT: tl.constexpr, ++ BN: tl.constexpr, BM: tl.constexpr, ++ D_INNER: tl.constexpr, ++ TOPK_PAD: tl.constexpr, ++ MAX_STEPS: tl.constexpr, ++ NUM_STAGES_PIPE: tl.constexpr = 2, ++): ++ """Iterative-insert top-K on signed shifted distance. ++ ++ Grid: ``(num_m_splits, ceil(N/BN), B)``. ++ Two compile-time paths gated on ``D_INNER >= D`` (persistent x + ++ pipelined M-loop vs D-split with sequential M-loop). The M-loop ++ pipelining depth is exposed as ``NUM_STAGES_PIPE`` so the ++ dispatcher can tune it per shape (see :mod:`dispatch` for the ++ heuristic rules; defaults to 2 if not provided). ++ """ ++ pid_s = tl.program_id(0) ++ pid_n = tl.program_id(1) ++ pid_b = tl.program_id(2) ++ pid_b = pid_b.to(tl.int64) ++ ++ n_start = pid_n * BN ++ n_offs = (n_start + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ ++ topk_vals = tl.full([BN, TOPK_PAD], float('inf'), dtype=tl.float32) ++ topk_idxs = tl.full([BN, TOPK_PAD], -1, dtype=tl.int32) ++ topk_max = tl.full([BN], float('inf'), dtype=tl.float32) ++ k_range = tl.arange(0, TOPK_PAD) ++ bm_range = tl.arange(0, BM) ++ m_base = pid_s.to(tl.int64) * M_PER_SPLIT ++ ++ if D_INNER >= D: ++ d_offs = tl.arange(0, D_INNER).to(tl.int64) ++ d_mask = d_offs < D ++ x_tile = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ for m_local in tl.range(0, M_PER_SPLIT, BM, num_stages=NUM_STAGES_PIPE): ++ m_start = m_base + m_local ++ m_offs = m_start + bm_range.to(tl.int64) ++ m_mask = m_offs < M ++ ++ c_tile = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + m_offs[:, None] * stride_c_m ++ + d_offs[None, :] * stride_c_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ c_f = c_tile.to(tl.float32) ++ c_sq_tile = tl.sum(c_f * c_f, axis=1) ++ cross = tl.dot(x_tile, tl.trans(c_tile)).to(tl.float32) ++ score = c_sq_tile[None, :] - 2.0 * cross ++ score = tl.where(m_mask[None, :], score, float('inf')) ++ ++ chunk_best = tl.min(score) ++ threshold_worst = tl.max(topk_max) ++ if chunk_best < threshold_worst: ++ _active = tl.full([1], 1, dtype=tl.int32) ++ for _step in range(MAX_STEPS): ++ if tl.max(_active) > 0: ++ row_min = tl.min(score, axis=1) ++ row_argmin = tl.argmin(score, axis=1) ++ do_insert = row_min < topk_max ++ n_inserts = tl.sum(do_insert.to(tl.int32)) ++ if n_inserts > 0: ++ topk_argmax = tl.argmax(topk_vals, axis=1) ++ replace_mask = (k_range[None, :] == topk_argmax[:, None]) ++ insert_mask = do_insert[:, None] & replace_mask ++ topk_vals = tl.where(insert_mask, row_min[:, None], topk_vals) ++ topk_idxs = tl.where( ++ insert_mask, ++ (m_start + row_argmin.to(tl.int64))[:, None].to(tl.int32), ++ topk_idxs, ++ ) ++ topk_max = tl.max(topk_vals, axis=1) ++ used_mask = (bm_range[None, :] == row_argmin[:, None]) ++ score = tl.where(used_mask & do_insert[:, None], float('inf'), score) ++ _active = tl.where( ++ n_inserts > 0, ++ tl.full([1], 1, dtype=tl.int32), ++ tl.full([1], 0, dtype=tl.int32), ++ ) ++ ++ else: ++ for m_local in tl.range(0, M_PER_SPLIT, BM, num_stages=1): ++ m_start = m_base + m_local ++ m_offs = m_start + bm_range.to(tl.int64) ++ m_mask = m_offs < M ++ ++ cross = tl.zeros([BN, BM], dtype=tl.float32) ++ c_sq_tile = tl.zeros([BM], dtype=tl.float32) ++ for d_start in range(0, D, D_INNER): ++ d_offs = (d_start + tl.arange(0, D_INNER)).to(tl.int64) ++ d_mask = d_offs < D ++ ++ x_sub = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ c_sub = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + m_offs[:, None] * stride_c_m ++ + d_offs[None, :] * stride_c_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ cross += tl.dot(x_sub, tl.trans(c_sub)).to(tl.float32) ++ c_f = c_sub.to(tl.float32) ++ c_sq_tile += tl.sum(c_f * c_f, axis=1) ++ ++ score = c_sq_tile[None, :] - 2.0 * cross ++ score = tl.where(m_mask[None, :], score, float('inf')) ++ ++ chunk_best = tl.min(score) ++ threshold_worst = tl.max(topk_max) ++ if chunk_best < threshold_worst: ++ _active = tl.full([1], 1, dtype=tl.int32) ++ for _step in range(MAX_STEPS): ++ if tl.max(_active) > 0: ++ row_min = tl.min(score, axis=1) ++ row_argmin = tl.argmin(score, axis=1) ++ do_insert = row_min < topk_max ++ n_inserts = tl.sum(do_insert.to(tl.int32)) ++ if n_inserts > 0: ++ topk_argmax = tl.argmax(topk_vals, axis=1) ++ replace_mask = (k_range[None, :] == topk_argmax[:, None]) ++ insert_mask = do_insert[:, None] & replace_mask ++ topk_vals = tl.where(insert_mask, row_min[:, None], topk_vals) ++ topk_idxs = tl.where( ++ insert_mask, ++ (m_start + row_argmin.to(tl.int64))[:, None].to(tl.int32), ++ topk_idxs, ++ ) ++ topk_max = tl.max(topk_vals, axis=1) ++ used_mask = (bm_range[None, :] == row_argmin[:, None]) ++ score = tl.where(used_mask & do_insert[:, None], float('inf'), score) ++ _active = tl.where( ++ n_inserts > 0, ++ tl.full([1], 1, dtype=tl.int32), ++ tl.full([1], 0, dtype=tl.int32), ++ ) ++ ++ val_sortable = _fp32_to_sortable_u32(topk_vals) ++ val_bits = val_sortable.to(tl.uint64) ++ idx_bits = topk_idxs.to(tl.uint32).to(tl.uint64) ++ packed = (val_bits << 32) | idx_bits ++ packed = tl.sort(packed, dim=1) ++ topk_score = _sortable_u32_to_fp32((packed >> 32).to(tl.uint32)) ++ topk_idx = (packed & 0xFFFFFFFF).to(tl.int32) ++ ++ k_offs = tl.arange(0, TOPK_PAD).to(tl.int64) ++ k_mask = k_offs < K ++ write_mask = n_mask[:, None] & k_mask[None, :] ++ pid_s_i64 = pid_s.to(tl.int64) ++ tl.store(partial_val_ptr + pid_b * stride_pv_b + pid_s_i64 * stride_pv_s ++ + n_offs[:, None] * stride_pv_n + k_offs[None, :] * stride_pv_k, ++ topk_score, mask=write_mask) ++ tl.store(partial_idx_ptr + pid_b * stride_pi_b + pid_s_i64 * stride_pi_s ++ + n_offs[:, None] * stride_pi_n + k_offs[None, :] * stride_pi_k, ++ topk_idx, mask=write_mask) +diff --git a/dbscanlib/_kernels/primitives/knn/triton/sortmerge.py b/dbscanlib/_kernels/primitives/knn/triton/sortmerge.py +new file mode 100644 +index 0000000..0d4a0e4 +--- /dev/null ++++ b/dbscanlib/_kernels/primitives/knn/triton/sortmerge.py +@@ -0,0 +1,181 @@ ++"""flash_knn — Triton sort-merge top-K kernel (x²-free, signed score). ++ ++The kernel computes ``argmin-K`` over the *signed* shifted distance ++ ++ s(n, m) = ||c[m]||² − 2·⟨x[n], c[m]⟩ (== ||x[n] − c[m]||² − ||x[n]||²) ++ ++The ``||x||²`` term is constant per row and so does not affect the top-K ++selection over ``m``. Dropping it eliminates the ``x_sq`` HBM tensor, its ++precompute pass, its per-tile load, one fp32 ADD per accumulator element, ++and the ``dist >= 0`` underflow clamp. The trade-off is that ``s`` is no ++longer non-negative; the packed-uint64 sort-merge top-K therefore uses ++an IEEE-sortable u32 transform (positives flip just the sign bit, negatives ++flip every bit) so ascending uint32 order matches ascending fp32 order on ++signed inputs. ++ ++Public name: ``_flash_knn_sortmerge_kernel`` — called from ++``klib/primitives/knn/triton/dispatch.py``. Stage 1 writes signed ++fp32 scores + int32 idxs to a partial buffer; Stage 2 either returns ++``partial[:, 0]`` directly (single split) or runs ``torch.topk(..., ++largest=False)`` to reduce across splits. ++ ++Same kernel covers both ``small-N M-split flash-decode`` (multiple ++splits per query block) and ``large-N single-pass`` (M_PER_SPLIT == M) ++— the host-side dispatcher just chooses the grid + M_PER_SPLIT. ++""" ++import triton ++import triton.language as tl ++ ++from dbscanlib._kernels.primitives.knn.triton._common import ( ++ _INF_PACKED, ++ _fp32_to_sortable_u32, ++ _sortable_u32_to_fp32, ++) ++ ++ ++@triton.jit ++def _flash_knn_sortmerge_kernel( ++ x_ptr, c_ptr, ++ partial_val_ptr, partial_idx_ptr, ++ stride_x_b, stride_x_n, stride_x_d, ++ stride_c_b, stride_c_m, stride_c_d, ++ stride_pv_b, stride_pv_s, stride_pv_n, stride_pv_k, ++ stride_pi_b, stride_pi_s, stride_pi_n, stride_pi_k, ++ N: tl.constexpr, M: tl.constexpr, ++ D: tl.constexpr, K: tl.constexpr, ++ M_PER_SPLIT: tl.constexpr, ++ BN: tl.constexpr, BM: tl.constexpr, ++ D_INNER: tl.constexpr, ++ TOPK_PAD: tl.constexpr, ++ NUM_STAGES_PIPE: tl.constexpr = 2, ++): ++ """Packed-uint64 sort-merge top-K with IEEE-sortable u32 score bits. ++ ++ Grid: ``(num_m_splits, ceil(N/BN), B)``. ``BM == TOPK_PAD``. ++ ++ Two compile-time paths gated on ``D_INNER >= D``: ++ * persistent x tile + pipelined M-loop (single tile covers all of D) ++ * D-split with sequential M-loop (D-chunked GEMM for wide D) ++ ++ ``NUM_STAGES_PIPE`` controls the M-loop ``tl.range`` pipelining ++ depth (host-side dispatch tunes it per shape; defaults to 2 if not ++ provided -- matches the pre-port hard-coded value). ++ """ ++ pid_s = tl.program_id(0) ++ pid_n = tl.program_id(1) ++ pid_b = tl.program_id(2) ++ pid_b = pid_b.to(tl.int64) ++ ++ n_start = pid_n * BN ++ n_offs = (n_start + tl.arange(0, BN)).to(tl.int64) ++ n_mask = n_offs < N ++ ++ topk_packed = tl.full([BN, TOPK_PAD], _INF_PACKED, dtype=tl.uint64) ++ bm_range = tl.arange(0, BM) ++ m_base = pid_s.to(tl.int64) * M_PER_SPLIT ++ ++ if D_INNER >= D: ++ d_offs = tl.arange(0, D_INNER).to(tl.int64) ++ d_mask = d_offs < D ++ x_tile = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ for m_local in tl.range(0, M_PER_SPLIT, BM, num_stages=NUM_STAGES_PIPE): ++ m_start = m_base + m_local ++ m_offs = m_start + bm_range.to(tl.int64) ++ m_mask = m_offs < M ++ ++ c_tile = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + m_offs[:, None] * stride_c_m ++ + d_offs[None, :] * stride_c_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ c_f = c_tile.to(tl.float32) ++ c_sq_tile = tl.sum(c_f * c_f, axis=1) ++ cross = tl.dot(x_tile, tl.trans(c_tile)).to(tl.float32) ++ score = c_sq_tile[None, :] - 2.0 * cross ++ score = tl.where(m_mask[None, :], score, float('inf')) ++ ++ chunk_best = tl.min(score) ++ topk_worst_packed = tl.max(topk_packed) ++ topk_worst_val = _sortable_u32_to_fp32( ++ (topk_worst_packed >> 32).to(tl.uint32)) ++ if chunk_best < topk_worst_val: ++ score_sortable = _fp32_to_sortable_u32(score) ++ idx_vals = (m_start + bm_range.to(tl.int64)).to(tl.int32) ++ idx_bits = idx_vals.to(tl.uint32).to(tl.uint64) ++ chunk_packed = (score_sortable.to(tl.uint64) << 32) | idx_bits[None, :] ++ ++ chunk_packed = chunk_packed ^ tl.full([1], 0xFFFFFFFFFFFFFFFF, dtype=tl.uint64) ++ chunk_packed = tl.sort(chunk_packed, dim=1) ++ chunk_packed = chunk_packed ^ tl.full([1], 0xFFFFFFFFFFFFFFFF, dtype=tl.uint64) ++ ++ merged = tl.minimum(topk_packed, chunk_packed) ++ topk_packed = tl.sort(merged, dim=1) ++ ++ else: ++ for m_local in tl.range(0, M_PER_SPLIT, BM, num_stages=1): ++ m_start = m_base + m_local ++ m_offs = m_start + bm_range.to(tl.int64) ++ m_mask = m_offs < M ++ ++ cross = tl.zeros([BN, BM], dtype=tl.float32) ++ c_sq_tile = tl.zeros([BM], dtype=tl.float32) ++ for d_start in range(0, D, D_INNER): ++ d_offs = (d_start + tl.arange(0, D_INNER)).to(tl.int64) ++ d_mask = d_offs < D ++ ++ x_sub = tl.load( ++ x_ptr + pid_b * stride_x_b ++ + n_offs[:, None] * stride_x_n ++ + d_offs[None, :] * stride_x_d, ++ mask=n_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ c_sub = tl.load( ++ c_ptr + pid_b * stride_c_b ++ + m_offs[:, None] * stride_c_m ++ + d_offs[None, :] * stride_c_d, ++ mask=m_mask[:, None] & d_mask[None, :], other=0.0) ++ ++ cross += tl.dot(x_sub, tl.trans(c_sub)).to(tl.float32) ++ c_f = c_sub.to(tl.float32) ++ c_sq_tile += tl.sum(c_f * c_f, axis=1) ++ ++ score = c_sq_tile[None, :] - 2.0 * cross ++ score = tl.where(m_mask[None, :], score, float('inf')) ++ ++ chunk_best = tl.min(score) ++ topk_worst_packed = tl.max(topk_packed) ++ topk_worst_val = _sortable_u32_to_fp32( ++ (topk_worst_packed >> 32).to(tl.uint32)) ++ if chunk_best < topk_worst_val: ++ score_sortable = _fp32_to_sortable_u32(score) ++ idx_vals = (m_start + bm_range.to(tl.int64)).to(tl.int32) ++ idx_bits = idx_vals.to(tl.uint32).to(tl.uint64) ++ chunk_packed = (score_sortable.to(tl.uint64) << 32) | idx_bits[None, :] ++ ++ chunk_packed = chunk_packed ^ tl.full([1], 0xFFFFFFFFFFFFFFFF, dtype=tl.uint64) ++ chunk_packed = tl.sort(chunk_packed, dim=1) ++ chunk_packed = chunk_packed ^ tl.full([1], 0xFFFFFFFFFFFFFFFF, dtype=tl.uint64) ++ ++ merged = tl.minimum(topk_packed, chunk_packed) ++ topk_packed = tl.sort(merged, dim=1) ++ ++ topk_score_sortable = (topk_packed >> 32).to(tl.uint32) ++ topk_score = _sortable_u32_to_fp32(topk_score_sortable) ++ topk_idx = (topk_packed & 0xFFFFFFFF).to(tl.int32) ++ ++ k_offs = tl.arange(0, TOPK_PAD).to(tl.int64) ++ k_mask = k_offs < K ++ write_mask = n_mask[:, None] & k_mask[None, :] ++ pid_s_i64 = pid_s.to(tl.int64) ++ tl.store(partial_val_ptr + pid_b * stride_pv_b + pid_s_i64 * stride_pv_s ++ + n_offs[:, None] * stride_pv_n + k_offs[None, :] * stride_pv_k, ++ topk_score, mask=write_mask) ++ tl.store(partial_idx_ptr + pid_b * stride_pi_b + pid_s_i64 * stride_pi_s ++ + n_offs[:, None] * stride_pi_n + k_offs[None, :] * stride_pi_k, ++ topk_idx, mask=write_mask) diff --git a/dbscanlib/dbscan.py b/dbscanlib/dbscan.py index ab7fa8b..53710d0 100644 --- a/dbscanlib/dbscan.py From b5180c720262550283300f47f0a056f2c886b6a4 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Wed, 8 Jul 2026 19:50:53 +0000 Subject: [PATCH 24/34] feat(2.0/dbscan): hide the data generator from the agent + revert to flashlib blob workloads Root cause of the repeated overfits: the data GENERATOR (flash_gpu.py `gen`) was baked at /opt in the agent image and readable. With public==final the agent could read the exact data structure and hardcode a data-specific O(N) labeler (blobs -> k-means; the ring rework -> radial binning `r=sqrt(x0^2+x1^2)`), doing zero neighbour-kernel work. Changing the data structure is whack-a-mole; the fix is to remove the agent's window into the data. Hide the generator: - Agent Dockerfile no longer COPYs flash_gpu.py (nor the baseline) -> the gen is judge-only; verified /opt/flash_gpu.py is absent from the agent image, present in the judge. - public_test.py rewritten to route feedback through the judge: it packages /app/dbscanlib, submits (public==final, so the submission IS the graded eval), and prints the judge's per-workload result + score. No flash_gpu import. - readme: workload description made generic ("generation not disclosed; solve the general problem"); iterate section uses submit-based feedback. Revert data to flashlib's own benchmark (per request): `gen` now produces make_blobs-equivalent Gaussian blobs (centers ~U(-10,10)^D, cluster_std=1.0, no noise); config uses flashlib's exact 4 DBSCAN shapes (N=20-50k, D=8/16/32/64, eps=1.5-8, min_samples=5). Reference is flashlib's brute-force (flash_knn) path. Recalibrated on H100: brute reference ARI ~1.0 on all 4, geomean 25.4x over the O(N^2) baseline (per-wl 19.6/18.9/26.8/42.1x) -> speedup_target=51. Rebuilt images. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../config.yaml | 20 +- .../docker/agent/Dockerfile | 10 +- .../flash_gpu.py | 36 +--- .../harbor/app/public_test.py | 199 ++++-------------- .../dbscan_gpu_kernel_optimization/readme | 32 ++- 5 files changed, 85 insertions(+), 212 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml index 8c61c3aa4..1a04363b2 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml @@ -23,24 +23,22 @@ evaluation: warmup_iters: 2 timed_iters: 3 # the O(N^2) naive baseline is seconds/call at these N; keep the timed budget bounded (median of 3) ari_threshold: 0.99 # agent clustering must match the baseline exact DBSCAN (Adjusted Rand Index) every iteration. On the non-separable concentric-ring data a centroid/partition approximation (k-means) scores ARI <= ~0.1 -> fails; only exact eps-connectivity passes. Exact reference measures ARI ~1.0. - speedup_target: 413.0 # score cap = 2x the flashlib brute-force (flash_knn) reference geomean (206.6x on H100 over the O(N^2) baseline, per-wl 81/126/202/292/262/495x, ARI 1.0 all 6). Large factor = the naive baseline recomputes O(N^2) distances every label-propagation CC pass; the reference does one bf16 flash_knn + union-find. Reference scores ~88/100; maxing out needs 2x the reference. + speedup_target: 51.0 # score cap = 2x the flashlib brute-force (flash_knn) reference geomean (25.4x on H100 over the O(N^2) baseline, per-wl 19.6/18.9/26.8/42.1x, ARI ~1.0 all 4 flashlib blob shapes). Reference scores ~82/100; maxing out needs 2x the reference. base_seed: 20260701 - agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); the params are agent-visible anyway + agent_workload_count: 4 # all 4 flashlib graded workloads (feedback covers every graded shape) expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on # N in the 100k-170k band: the O(N^2) matmul baseline is seconds/call (grid kernel # wins 6-20x) but bounded for timed_iters; below ~100k the tensor-core matmul baseline # is faster than the grid kernel's fixed overhead. Vary N + density so agents can't # special-case. (H100 crossover sweep: 100k->6.0x, 150k->14.1x, 200k->27.0x.) workloads: - # Non-separable concentric-ring data (see flash_gpu.gen): `n_centers` = number of - # rings; D>=3 routes flashlib to its benchmarked brute-force (flash_knn) path; N in - # flashlib's brute range. No noise_frac (rings are noise-free, like flashlib's make_blobs). - - {id: w0, N: 20000, D: 8, n_centers: 4, eps: 1.5, min_samples: 8} - - {id: w1, N: 30000, D: 8, n_centers: 5, eps: 1.5, min_samples: 8} - - {id: w2, N: 30000, D: 16, n_centers: 6, eps: 1.4, min_samples: 8} - - {id: w3, N: 40000, D: 8, n_centers: 6, eps: 1.4, min_samples: 8} - - {id: w4, N: 40000, D: 32, n_centers: 5, eps: 1.5, min_samples: 10} - - {id: w5, N: 50000, D: 8, n_centers: 8, eps: 1.5, min_samples: 10} + # flashlib's own DBSCAN benchmark shapes (benchmarks/vs_cuml/dbscan.py): make_blobs + # data, cluster_std=1.0, no noise. D>=8 -> flashlib's benchmarked brute-force + # (flash_knn) path. The generator is judge-only (hidden from the agent). + - {id: w0, N: 20000, D: 8, eps: 1.5, min_samples: 5, n_centers: 4} + - {id: w1, N: 20000, D: 32, eps: 6.0, min_samples: 5, n_centers: 6} + - {id: w2, N: 20000, D: 64, eps: 8.0, min_samples: 5, n_centers: 10} + - {id: w3, N: 50000, D: 16, eps: 3.5, min_samples: 5, n_centers: 8} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/dbscan_gpu_kernel_optimization/docker/agent/Dockerfile index bab171ab2..e1179ff75 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/docker/agent/Dockerfile +++ b/2.0/problems/dbscan_gpu_kernel_optimization/docker/agent/Dockerfile @@ -27,8 +27,8 @@ RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ git -C /app add -A -- dbscanlib .gitignore && \ git -C /app -c commit.gpgsign=false commit -qm "pristine dbscanlib" -# Shared Modal GPU harness + the frozen naive baseline (the public-test speed -# denominator). The baseline is exactly the code the agent starts from, so it is -# not secret. -COPY flash_gpu.py /opt/flash_gpu.py -COPY judge/refdbscan.py /opt/dbscan_ref/refdbscan.py +# NOTE: the GPU harness (flash_gpu.py) and its data GENERATOR are deliberately +# NOT baked into the agent image -- they live only in the judge. The agent gets +# feedback by submitting to the judge (public_test == the graded submission), +# so it cannot read how the workloads are generated and hardcode a +# data-specific shortcut. See harbor_app/public_test.py. diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py index bd830bcc3..d53609a49 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py @@ -97,31 +97,17 @@ def gen(w, seed, ctx): q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} if prim == "dbscan": - # NON-SEPARABLE data: single-center concentric rings in a 2-D subspace, - # padded to D dims with a little noise. Centroid/Voronoi methods (k-means) - # CANNOT reproduce concentric rings -- only exact eps-density connectivity - # (the kernel work being timed) clusters them, so a cheap approximation - # cannot substitute for a real DBSCAN kernel. No uniform noise (matches - # flashlib's noise-free make_blobs benchmark). D>=3 so flashlib routes to - # its benchmarked brute-force (flash_knn) path, not the D==2 grid path. - nr, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) - eps = float(w["eps"]); TWO_PI = 6.283185307179586 - base, gap, width = 3.0 * eps, 3.0 * eps, 0.35 * eps - radii = base + torch.arange(nr, device=dev, dtype=torch.float32) * gap - counts = torch.clamp((radii / radii.sum() * N).long(), min=1) - counts[-1] += N - int(counts.sum()) # exact total = N - parts = [] - for j in range(nr): - c = int(counts[j]) - th = torch.rand(c, generator=g, device=dev) * TWO_PI - rr = radii[j] + (torch.rand(c, generator=g, device=dev) - 0.5) * width - xy = torch.stack([rr * torch.cos(th), rr * torch.sin(th)], 1) - pad_std = 0.15 * eps / ((D - 2) ** 0.5) if D > 2 else 0.0 - pad = torch.randn(c, D - 2, generator=g, device=dev) * pad_std - parts.append(torch.cat([xy, pad], 1)) - x = torch.cat(parts, 0).to(torch.float32) - x = x.index_select(0, torch.randperm(N, generator=g, device=dev)) # de-positional - return {"x": x, "eps": eps, "min_samples": int(w["min_samples"])} + # flashlib's own DBSCAN benchmark data: sklearn make_blobs equivalent -- + # Gaussian blobs, centers ~ U(-10, 10)^D, cluster_std = 1.0, no noise. + # (The generator is judge-only and NOT present in the agent image, so the + # agent cannot read the data structure and hardcode a data-specific labeler.) + nc, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) + std = float(w.get("cluster_std", 1.0)) + centers = (torch.rand(nc, D, generator=g, device=dev) * 2.0 - 1.0) * 10.0 + asg = torch.randint(0, nc, (N,), generator=g, device=dev) + x = centers.index_select(0, asg) + torch.randn(N, D, generator=g, device=dev) * std + return {"x": x.to(torch.float32), "eps": float(w["eps"]), + "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) if prim == "kmeans": perm = torch.randperm(w["N"], generator=g, device=dev)[: w["K"]] diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py index e8de4a4bd..d944a88cc 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,170 +1,61 @@ -"""Public GPU self-test -- IDENTICAL to the final graded evaluation. - -This runs the EXACT same workloads, thresholds, timing, and seeds that the hidden -judge uses to grade your submission (all read from ``/app/task_config.json``, -the same config the judge reads), on a Modal GPU, and reports the same -per-workload pass/fail + speedup + geomean + predicted score (0-100) you would -receive on submission. There is no longer a separate, smaller "public" workload -set -- what you see here is what you get graded on. - -Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. +"""Public self-test == the graded evaluation, run by the judge. + +The data generator is judge-only and is NOT present in this (agent) image, so you +cannot read how the workloads are produced and hardcode a data-specific shortcut. +Instead, this packages your current `/app/dbscanlib`, submits it to the judge -- +which runs the EXACT graded workloads on a GPU and grades your clustering against +the exact reference (ARI) -- and prints your per-workload result + score. It is +byte-for-byte the same evaluation used for your final grade. + +(Submission is asynchronous; this waits for the result. You can also submit +directly with `bash /app/make_submission.sh && bash /app/submit.sh` and poll with +`bash /app/submissions.sh`.) """ from __future__ import annotations import json -import math -import os +import re +import subprocess import sys from pathlib import Path -sys.path.insert(0, "/opt") # flash_gpu.py is baked at /opt - -APP_DIR = os.environ.get("APP_DIR", "/app") -TASK_CONFIG_PATH = os.environ.get("TASK_CONFIG_PATH", "/app/task_config.json") - - -def _load_eval() -> dict: - """The judge grades from task_config.json's `evaluation` block; read the same.""" - try: - doc = json.loads(Path(TASK_CONFIG_PATH).read_text(encoding="utf-8")) - except Exception as exc: # noqa: BLE001 - print(f"could not read {TASK_CONFIG_PATH}: {exc}") - return {} - return doc.get("evaluation", doc) - - -EV = _load_eval() - - -def g(key, default): - v = EV.get(key, default) - return default if v is None else v - - -PRIMITIVE = str(g("primitive", "")) -PKG = str(g("pkg", "")) -REF_MODULE = str(g("ref_module", "")) -SPEEDUP_TARGET = float(g("speedup_target", 8.0)) -BASELINE_DIR = str(g("baseline_source", "/opt/flash_ref")) +SUBMISSIONS_LOG = Path("/logs/agent/submissions.jsonl") +_UUID = re.compile(r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}") -def _workloads() -> list: - """Exactly the judge's final-role workload set: ALL workloads, same seed - derivation ``base_seed + 1000*(i+1)`` -- identical data to the graded run.""" - wls = [dict(w) for w in g("workloads", [])] - base = int(g("base_seed", 20260701)) - for i, w in enumerate(wls): - w.setdefault("seed", base + 1000 * (i + 1)) - return wls - - -def _cfg() -> dict: - """Byte-for-byte the judge's _build_cfg().""" - return { - "primitive": PRIMITIVE, - "pkg": PKG, - "ref_module": REF_MODULE, - "gpu": str(g("gpu", "H100")), - "cuda_image": str(g("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), - "pip": list(g("pip", ["torch", "numpy"])), - "app_name": str(g("app_name", "flash-kernel-public")), - "modal_timeout_seconds": int(g("modal_timeout_seconds", 1800)), - "warmup": int(g("warmup_iters", 3)), - "iters": int(g("timed_iters", 7)), - "inertia_tolerance": float(g("inertia_tolerance", 0.02)), - "recall_threshold": float(g("recall_threshold", 0.99)), - "captured_tolerance": float(g("captured_tolerance", 0.02)), - "ortho_tolerance": float(g("ortho_tolerance", 0.02)), - "ari_threshold": float(g("ari_threshold", 0.99)), - } - - -def geometric_mean(values: list) -> float: - if not values: - return 0.0 - return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) - - -def score_from_speedup(gm: float) -> float: - if gm <= 0: - return 0.0 - raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) - return max(0.0, min(100.0, raw)) - - -def _read(root: str, sub: str = "") -> dict: - base = Path(root) - scan = base / sub if sub else base - return {str(p.relative_to(base)): p.read_text(encoding="utf-8", errors="replace") - for p in scan.rglob("*.py")} +def _latest_uuid_from_log() -> str | None: + if not SUBMISSIONS_LOG.exists(): + return None + lines = [l for l in SUBMISSIONS_LOG.read_text(encoding="utf-8").splitlines() if l.strip()] + for l in reversed(lines): + try: + u = json.loads(l).get("submission_uuid") + if u: + return u + except Exception: # noqa: BLE001 + continue + return None def main() -> int: - import flash_gpu # baked at /opt/flash_gpu.py - if not flash_gpu.modal_available(): - print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU self-test.") - return 1 - workloads = _workloads() - cfg = _cfg() - if not workloads: - print("no workloads found in task_config.json; cannot run.") - return 1 - payload = { - "baseline_files": _read(BASELINE_DIR), - "patched_files": _read(APP_DIR, PKG), - "workloads": workloads, - "cfg": cfg, - } - try: - result = flash_gpu.run_remote(payload) - except Exception as exc: # noqa: BLE001 - print(f"GPU run failed: {exc}") - return 1 - if not result.get("ok"): - print(f"worker error: {result.get('error')}") - return 1 - - if PRIMITIVE in ("knn", "ivfpq"): - mlabel, gate = "recall@k", f">= {cfg['recall_threshold']}" - elif PRIMITIVE == "dbscan": - mlabel, gate = "ARI", f">= {cfg['ari_threshold']}" - elif PRIMITIVE == "kmeans": - mlabel, gate = "inertia", f"<= (1+{cfg['inertia_tolerance']}) x ref (lower is better)" - else: - mlabel, gate = "quality", "" - - print("=== FINAL-EQUIVALENT self-test: identical workloads / thresholds / seeds / " - "timing to the graded judge ===") - print(f"correctness gate: {mlabel} {gate} | speedup_target = {SPEEDUP_TARGET:g}") - print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") - rows = result.get("rows", []) - speedups = [] - any_fail = False - for row in rows: - av, rv = row.get("agent_val"), row.get("ref_val") - q = (f"{av:.4f} / {rv:.4f}" - if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") - if row.get("ok"): - sp = f"{row['speedup']:.2f}x" - speedups.append(max(float(row["speedup"]), 0.01)) # same clamp as the judge - st = "OK" - else: - sp = "-" - st = "FAIL:" + str(row.get("reason", "")) - any_fail = True - print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") - - # Scoring is byte-for-byte the judge's full_evaluation: ANY gate failure -> 0. - print() - if any_fail or len(speedups) != len(rows) or not speedups: - print("RESULT: at least one workload FAILED the correctness gate -> a submission now " - "would be INVALID and score 0/100. Fix the failing workload(s) before submitting.") + # 1) package the current package into /app/solution.patch + subprocess.run(["bash", "/app/make_submission.sh"], check=True) + # 2) submit to the judge (async); it runs the exact graded eval on GPU + print("Submitting to the judge (runs the exact graded workloads + returns your score)...", + flush=True) + r = subprocess.run(["python3", "/app/submit.py", *sys.argv[1:]], + capture_output=True, text=True) + sys.stdout.write(r.stdout) + sys.stderr.write(r.stderr) + if r.returncode != 0: + return r.returncode + # 3) find the submission uuid and wait for the result + m = _UUID.findall(r.stdout + r.stderr) + uuid = m[-1] if m else _latest_uuid_from_log() + if not uuid: + print("Submitted. Poll for the result with: bash /app/submissions.sh") return 0 - gm = geometric_mean(speedups) - score = score_from_speedup(gm) - print(f"geomean speedup = {gm:.3f}x over the naive baseline") - print(f"PREDICTED FINAL SCORE = {score:.2f} / 100 (100 == {SPEEDUP_TARGET:g}x geomean)") - return 0 + return subprocess.call(["python3", "/app/wait_submission.py", uuid]) if __name__ == "__main__": diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/readme b/2.0/problems/dbscan_gpu_kernel_optimization/readme index b40d39142..648a5a6f8 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/readme +++ b/2.0/problems/dbscan_gpu_kernel_optimization/readme @@ -23,31 +23,29 @@ remain a deterministic function of the inputs. ## Workload -The graded workloads are held-out `(N, D)` point sets (`D >= 8`) with -**density-connected clusters that are not convex/blob-shaped** — clustering them -correctly genuinely requires DBSCAN's `eps`-neighbourhood connectivity, not a -centroid/partition heuristic. Workloads vary `N`, `D`, cluster count, `eps`, and -`min_samples`. Treat it as general Euclidean DBSCAN and reproduce the exact -clustering — your labels are graded against the exact reference clustering (ARI), -so an approximation that only *roughly* recovers the clusters will not pass. - -The public self-test now runs the **exact graded workloads** — the same shapes, -thresholds, seeds, and timing the judge uses to score you — so there are no -separate hidden shapes to guess at. +The graded workloads are held-out `(N, D)` low-count-cluster point sets with +`D >= 8`, varying `N`, `D`, cluster count, `eps`, and `min_samples`. Treat it as +**general Euclidean DBSCAN** and reproduce the *exact* clustering: your labels are +graded against the exact reference clustering (Adjusted Rand Index), so an +approximation that only roughly recovers the clusters will not pass. How the point +sets are generated is not disclosed — solve the general problem, do not special-case. ## Iterate on a GPU -The agent workspace has no GPU. Use the public test to check your current code's correctness and speed on -a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET`): +The agent workspace has no GPU, and the data generator is judge-only (it is not in +your image). To check your current code, **submit it to the judge** — it runs the +exact graded workloads on a GPU and returns your per-workload result + score. This +one command packages, submits, and waits for the result: ```bash bash /app/public_test.sh ``` -It runs the **identical evaluation the judge uses to grade you** and reports, per -graded workload, pass/fail on the quality gate, your speedup, the geometric-mean -speedup, and your **predicted final score** (0-100). Any workload that fails its -gate makes the submission score 0. +It reports, per graded workload, pass/fail on the quality gate and your speedup, +plus the geometric-mean speedup and your score (0-100) — the identical evaluation +used for your final grade. Any workload that fails its gate scores 0. (Equivalently: +`bash /app/make_submission.sh && bash /app/submit.sh`, then poll with +`bash /app/submissions.sh`.) Submissions are asynchronous; submit early and iterate. ## Submission From 034248237409b9249cc8655e768035e6341dcfb3 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 12 Jul 2026 03:55:43 +0000 Subject: [PATCH 25/34] feat(2.0/kmeans): hide the data generator from the agent (close subsample exploit) Re-running kmeans revealed all 3 codex solutions exploited the readable generator: they read "/opt/flash_gpu.py" ("planted well-separated blobs"), then computed centroids from a row/dimension SUBSAMPLE (tuned per workload shape) and returned garbage labels (only the label shape is gated, not content) -- passing the 5% inertia gate cheaply on the well-separated data without a real Lloyd step. Same fix as dbscan: the GPU harness (flash_gpu.py) with its data generator is no longer baked into the agent image (judge-only). The agent gets feedback by submitting to the judge (public_test == the graded submission), so it cannot read the data structure and hardcode a subsample. readme workload description made generic (dropped the "well-separated planted clusters" hint); iterate section uses submit-based feedback. Verified /opt/flash_gpu.py absent from the agent image. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../docker/agent/Dockerfile | 10 +- .../harbor/app/public_test.py | 199 ++++-------------- .../kmeans_gpu_kernel_optimization/readme | 31 ++- 3 files changed, 65 insertions(+), 175 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile b/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile index b57ecef52..c19a77dc1 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/agent/Dockerfile @@ -27,8 +27,8 @@ RUN printf '%s\n' '__pycache__/' '*.pyc' > /app/.gitignore && \ git -C /app add -A -- kmeanslib .gitignore && \ git -C /app -c commit.gpgsign=false commit -qm "pristine kmeanslib" -# Shared Modal GPU harness + the frozen naive baseline (the public-test speed -# denominator). The baseline is exactly the code the agent starts from, so it is -# not secret. -COPY flash_gpu.py /opt/flash_gpu.py -COPY judge/refkmeans.py /opt/kmeans_ref/refkmeans.py +# NOTE: the GPU harness (flash_gpu.py) and its data GENERATOR are deliberately +# NOT baked into the agent image -- they live only in the judge. The agent gets +# feedback by submitting to the judge (public_test == the graded submission), +# so it cannot read how the workloads are generated and hardcode a +# data-specific shortcut. See harbor_app/public_test.py. diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py index e8de4a4bd..ea3bf13db 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/harbor/app/public_test.py @@ -1,170 +1,61 @@ -"""Public GPU self-test -- IDENTICAL to the final graded evaluation. - -This runs the EXACT same workloads, thresholds, timing, and seeds that the hidden -judge uses to grade your submission (all read from ``/app/task_config.json``, -the same config the judge reads), on a Modal GPU, and reports the same -per-workload pass/fail + speedup + geomean + predicted score (0-100) you would -receive on submission. There is no longer a separate, smaller "public" workload -set -- what you see here is what you get graded on. - -Requires MODAL_TOKEN_ID / MODAL_TOKEN_SECRET in the environment. +"""Public self-test == the graded evaluation, run by the judge. + +The data generator is judge-only and is NOT present in this (agent) image, so you +cannot read how the workloads are produced and hardcode a data-specific shortcut. +Instead, this packages your current `/app/kmeanslib`, submits it to the judge -- +which runs the EXACT graded workloads on a GPU and grades your result against the +frozen reference -- and prints your per-workload result + score. It is +byte-for-byte the same evaluation used for your final grade. + +(Submission is asynchronous; this waits for the result. You can also submit +directly with `bash /app/make_submission.sh && bash /app/submit.sh` and poll with +`bash /app/submissions.sh`.) """ from __future__ import annotations import json -import math -import os +import re +import subprocess import sys from pathlib import Path -sys.path.insert(0, "/opt") # flash_gpu.py is baked at /opt - -APP_DIR = os.environ.get("APP_DIR", "/app") -TASK_CONFIG_PATH = os.environ.get("TASK_CONFIG_PATH", "/app/task_config.json") - - -def _load_eval() -> dict: - """The judge grades from task_config.json's `evaluation` block; read the same.""" - try: - doc = json.loads(Path(TASK_CONFIG_PATH).read_text(encoding="utf-8")) - except Exception as exc: # noqa: BLE001 - print(f"could not read {TASK_CONFIG_PATH}: {exc}") - return {} - return doc.get("evaluation", doc) - - -EV = _load_eval() - - -def g(key, default): - v = EV.get(key, default) - return default if v is None else v - - -PRIMITIVE = str(g("primitive", "")) -PKG = str(g("pkg", "")) -REF_MODULE = str(g("ref_module", "")) -SPEEDUP_TARGET = float(g("speedup_target", 8.0)) -BASELINE_DIR = str(g("baseline_source", "/opt/flash_ref")) +SUBMISSIONS_LOG = Path("/logs/agent/submissions.jsonl") +_UUID = re.compile(r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}") -def _workloads() -> list: - """Exactly the judge's final-role workload set: ALL workloads, same seed - derivation ``base_seed + 1000*(i+1)`` -- identical data to the graded run.""" - wls = [dict(w) for w in g("workloads", [])] - base = int(g("base_seed", 20260701)) - for i, w in enumerate(wls): - w.setdefault("seed", base + 1000 * (i + 1)) - return wls - - -def _cfg() -> dict: - """Byte-for-byte the judge's _build_cfg().""" - return { - "primitive": PRIMITIVE, - "pkg": PKG, - "ref_module": REF_MODULE, - "gpu": str(g("gpu", "H100")), - "cuda_image": str(g("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), - "pip": list(g("pip", ["torch", "numpy"])), - "app_name": str(g("app_name", "flash-kernel-public")), - "modal_timeout_seconds": int(g("modal_timeout_seconds", 1800)), - "warmup": int(g("warmup_iters", 3)), - "iters": int(g("timed_iters", 7)), - "inertia_tolerance": float(g("inertia_tolerance", 0.02)), - "recall_threshold": float(g("recall_threshold", 0.99)), - "captured_tolerance": float(g("captured_tolerance", 0.02)), - "ortho_tolerance": float(g("ortho_tolerance", 0.02)), - "ari_threshold": float(g("ari_threshold", 0.99)), - } - - -def geometric_mean(values: list) -> float: - if not values: - return 0.0 - return math.exp(sum(math.log(max(v, 1e-9)) for v in values) / len(values)) - - -def score_from_speedup(gm: float) -> float: - if gm <= 0: - return 0.0 - raw = 100.0 * math.log(gm) / math.log(max(SPEEDUP_TARGET, 1.0000001)) - return max(0.0, min(100.0, raw)) - - -def _read(root: str, sub: str = "") -> dict: - base = Path(root) - scan = base / sub if sub else base - return {str(p.relative_to(base)): p.read_text(encoding="utf-8", errors="replace") - for p in scan.rglob("*.py")} +def _latest_uuid_from_log() -> str | None: + if not SUBMISSIONS_LOG.exists(): + return None + lines = [l for l in SUBMISSIONS_LOG.read_text(encoding="utf-8").splitlines() if l.strip()] + for l in reversed(lines): + try: + u = json.loads(l).get("submission_uuid") + if u: + return u + except Exception: # noqa: BLE001 + continue + return None def main() -> int: - import flash_gpu # baked at /opt/flash_gpu.py - if not flash_gpu.modal_available(): - print("Set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET to run the GPU self-test.") - return 1 - workloads = _workloads() - cfg = _cfg() - if not workloads: - print("no workloads found in task_config.json; cannot run.") - return 1 - payload = { - "baseline_files": _read(BASELINE_DIR), - "patched_files": _read(APP_DIR, PKG), - "workloads": workloads, - "cfg": cfg, - } - try: - result = flash_gpu.run_remote(payload) - except Exception as exc: # noqa: BLE001 - print(f"GPU run failed: {exc}") - return 1 - if not result.get("ok"): - print(f"worker error: {result.get('error')}") - return 1 - - if PRIMITIVE in ("knn", "ivfpq"): - mlabel, gate = "recall@k", f">= {cfg['recall_threshold']}" - elif PRIMITIVE == "dbscan": - mlabel, gate = "ARI", f">= {cfg['ari_threshold']}" - elif PRIMITIVE == "kmeans": - mlabel, gate = "inertia", f"<= (1+{cfg['inertia_tolerance']}) x ref (lower is better)" - else: - mlabel, gate = "quality", "" - - print("=== FINAL-EQUIVALENT self-test: identical workloads / thresholds / seeds / " - "timing to the graded judge ===") - print(f"correctness gate: {mlabel} {gate} | speedup_target = {SPEEDUP_TARGET:g}") - print(f"{'workload':9s} {'status':22s} {'speedup':>9s} {mlabel}: agent / ref") - rows = result.get("rows", []) - speedups = [] - any_fail = False - for row in rows: - av, rv = row.get("agent_val"), row.get("ref_val") - q = (f"{av:.4f} / {rv:.4f}" - if isinstance(av, (int, float)) and isinstance(rv, (int, float)) else "n/a") - if row.get("ok"): - sp = f"{row['speedup']:.2f}x" - speedups.append(max(float(row["speedup"]), 0.01)) # same clamp as the judge - st = "OK" - else: - sp = "-" - st = "FAIL:" + str(row.get("reason", "")) - any_fail = True - print(f"{row['id']:9s} {st:22s} {sp:>9s} {q}") - - # Scoring is byte-for-byte the judge's full_evaluation: ANY gate failure -> 0. - print() - if any_fail or len(speedups) != len(rows) or not speedups: - print("RESULT: at least one workload FAILED the correctness gate -> a submission now " - "would be INVALID and score 0/100. Fix the failing workload(s) before submitting.") + # 1) package the current package into /app/solution.patch + subprocess.run(["bash", "/app/make_submission.sh"], check=True) + # 2) submit to the judge (async); it runs the exact graded eval on GPU + print("Submitting to the judge (runs the exact graded workloads + returns your score)...", + flush=True) + r = subprocess.run(["python3", "/app/submit.py", *sys.argv[1:]], + capture_output=True, text=True) + sys.stdout.write(r.stdout) + sys.stderr.write(r.stderr) + if r.returncode != 0: + return r.returncode + # 3) find the submission uuid and wait for the result + m = _UUID.findall(r.stdout + r.stderr) + uuid = m[-1] if m else _latest_uuid_from_log() + if not uuid: + print("Submitted. Poll for the result with: bash /app/submissions.sh") return 0 - gm = geometric_mean(speedups) - score = score_from_speedup(gm) - print(f"geomean speedup = {gm:.3f}x over the naive baseline") - print(f"PREDICTED FINAL SCORE = {score:.2f} / 100 (100 == {SPEEDUP_TARGET:g}x geomean)") - return 0 + return subprocess.call(["python3", "/app/wait_submission.py", uuid]) if __name__ == "__main__": diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index 058e2e844..5aebbaa5f 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -34,30 +34,29 @@ change, and each result must remain a deterministic function of its inputs. ## Workload The graded workloads are a family of held-out dense clustering problems that -vary `(N, D, K)`, spanning small/medium/large point counts and both -wide-feature (large `D`) and many-cluster (large `K`) regimes. All use Euclidean -distance, **bfloat16 data**, well-separated planted clusters, and a fixed number -of judge-owned `step` calls per workload with caller-supplied initial centroids. - -The public self-test now runs the **exact graded workloads** — the same shapes, -thresholds, seeds, and timing the judge uses to score you — so there are no -separate hidden shapes to guess at. Treat this as a general dense K-Means -kernel; the workloads are just where it is measured. +vary `(N, D, K)`, spanning small/medium/large point counts and both wide-feature +(large `D`) and many-cluster (large `K`) regimes, in **bfloat16**, with a fixed +number of judge-owned `step` calls per workload and caller-supplied initial +centroids. How the point sets are generated is not disclosed — implement a +**general** exact Lloyd step (correct assignment to the given centroids + exact +cluster-mean update) and do not special-case the data. ## Iterate on a GPU -The agent workspace has no GPU. Use the public test to check your current code's correctness and speed on -a GPU through Modal (needs `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` in your -environment): +The agent workspace has no GPU, and the data generator is judge-only (it is not in +your image). To check your current code, **submit it to the judge** — it runs the +exact graded workloads on a GPU and returns your per-workload result + score. This +one command packages, submits, and waits for the result: ```bash bash /app/public_test.sh ``` -It runs the **identical evaluation the judge uses to grade you** and reports, per -graded workload, whether your result passes the quality gate, your speedup over -the baseline, the geometric-mean speedup, and your **predicted final score** -(0-100). Any workload that fails its gate makes the submission score 0. +It reports, per graded workload, whether your result passes the quality gate and +your speedup, plus the geometric-mean speedup and your score (0-100) — the +identical evaluation used for your final grade. (Equivalently: +`bash /app/make_submission.sh && bash /app/submit.sh`, then poll with +`bash /app/submissions.sh`.) Submissions are asynchronous; submit early and iterate. ## Submission From 99d92c5dc81f85812817f6beebf3b1ebc27cf0d4 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Thu, 16 Jul 2026 03:11:06 +0000 Subject: [PATCH 26/34] feat(2.0/kmeans): compute-bound high-K workloads + close the assignment reward-hacks Workloads: K=512-2048 (was 128-1024). The assign GEMM's arithmetic intensity is ~K FLOP/byte, so the old low-K shapes sat under the H100 bf16 roofline knee (~295) and the reference only reached ~40 TFLOPS (4% of peak). High-K puts it compute-bound: ~138 TFLOPS geomean (14%; w5/K=2048 hits 313 TFLOPS). Speedup spread improved too (reference 4.95x -> 8.29x), because the win comes from avoiding the (N,K) distance materialization, whose traffic scales with K. Gate, two holes closed: 1. Fake/partial labels. The gate scored nearest-inertia of the returned centroids only, ignoring `labels` entirely -- a trial returned an uninitialized `torch.empty(N)` for labels plus centroids estimated from a k*12-row sample and still scored 93.7 ("the judge-owned loop consumes only the centroid output", per its own comment). The gate now scores the inertia of the returned (labels, centroids) pair via inertia_labeled(), so a fake or sub-sampled assignment inflates it. 2. Feature truncation. Inertia is dominated by within-cluster noise: misassigning ~1% of points moves it only ~0.1%, which hides under any usable tolerance. A trial assigned on the leading 128 of 512 dims and scored 94. Two changes: - Data: centers scaled by 4*sqrt(2/D) so the nearest-competitor margin is a fixed ~4 sigma at full D. With the old fixed *6.0 the margin grew as sqrt(D) -- ~96 sigma at D=512 -- so a quarter of the dims reproduced the exact same labels for free, which no output gate can see. - label_mismatch_tolerance (0.002): the returned labels must BE the exact nearest-centroid assignment for the centroids that step was handed (call() now returns those centroids). This is what actually rejects approximate distances. Verified: the honest reference passes 6/6 (bf16 tie rate is under the threshold, no false positives); the truncating solution now fails exactly its two truncated workloads (D=512->128, D=256->160). speedup_target 10 -> 20 = 2x the FUSED best-known geomean (9.97x). The vendored flashlib reference runs assign and update as two separate kernels (two passes over x) and reaches only 7.92x, so calibrating on it made the cap too easy: a merely-fused kernel -- exactly what this task asks for -- scored 94. build_images.sh defaults track config.yaml (v0.4.0). flash_gpu.py is baked into the images at /opt, so any change here needs a rebuild or the judge silently runs the old logic. --- .../config.yaml | 22 ++--- .../docker/build_images.sh | 4 +- .../evaluator.py | 1 + .../flash_gpu.py | 85 +++++++++++++++---- .../kmeans_gpu_kernel_optimization/readme | 19 +++-- 5 files changed, 94 insertions(+), 37 deletions(-) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml index b2d660de7..6c818851b 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/kmeans_gpu_kernel_optimization/config.yaml @@ -23,8 +23,8 @@ runtime: # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes # /app/kmeanslib (git) + /opt/kmeans_ref + /opt/flash_gpu.py; the judge image # additionally bakes /opt/kmeanslib-clean. - image: frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.2.0 - judge_image: frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.2.0 + image: frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.4.0 + judge_image: frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.4.0 environment: cpus: 4 memory_mb: 8192 @@ -46,8 +46,10 @@ evaluation: # --- timing + quality gate --- warmup_iters: 2 timed_iters: 1 # one timed run per workload; the timed unit is the judge-owned loop of max_iters `step` calls - inertia_tolerance: 0.05 # centroid gate: agent final centroids nearest-inertia <= (1+tol) x baseline. Worst flashlib-ref bf16 drift is +1.47% (w3, D=512) at max_iters=2, so 5% leaves margin while still rejecting bad centroids. - speedup_target: 10.0 # score cap = 2x the flashlib reference geomean (4.95x, H100 step/max_iters=2, all 6 pass): the reference now scores ~70/100 and a solution must be 2x faster than the reference to max out. (Codex's fused-kernel trial hit 7.28x -> ~87/100 instead of a capped 100.) + inertia_tolerance: 0.05 # clustering gate: inertia of the agent's RETURNED (labels, centroids) <= (1+tol) x the baseline's. Catches fake/garbage labels and badly-placed centroids. Inertia is dominated by within-cluster noise, so it is deliberately backed up by label_mismatch_tolerance below. + label_mismatch_tolerance: 0.002 # assignment gate: fraction of points whose returned label is not the exact (fp32) nearest centroid of the centroids that step was handed. Inertia barely moves when ~1% of points are misassigned (~+0.1%), so this is what actually rejects approximate distances -- e.g. assigning on only the leading feature dims. Set above the bf16-vs-fp32 tie rate the honest reference shows. + speedup_target: 20.0 # score cap = 2x the FUSED best-known geomean (9.97x on H100, all 6 pass). The vendored flashlib reference.patch runs assign and update as two separate kernels (two passes over x) and only reaches 7.92x (scores ~69/100); a kernel that fuses assign+scatter-update into one pass over x -- which is exactly what this task asks for -- reaches 9.97x (~77/100), and you must be 2x faster than THAT to max out. Calibrating on the unfused reference alone made the cap too easy (a merely-fused solution scored 94). + # High-K (K=512-2048, arithmetic intensity >= H100 bf16 roofline knee ~295 FLOP/byte) keeps the assign GEMM compute-bound: ~138 TFLOPS geomean (14% of bf16 peak) vs ~40 TFLOPS on the old low-K workloads. base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on @@ -58,12 +60,12 @@ evaluation: # lever; planted well-separated blobs. max_iters=2 (not 1) is enough to be a real # loop while too few for a converged step to become a redundant no-op. workloads: - - {id: w0, N: 100000, D: 32, K: 128, max_iters: 2} - - {id: w1, N: 200000, D: 64, K: 256, max_iters: 2} - - {id: w2, N: 500000, D: 128, K: 256, max_iters: 2} - - {id: w3, N: 200000, D: 512, K: 256, max_iters: 2} - - {id: w4, N: 1000000, D: 64, K: 512, max_iters: 2} - - {id: w5, N: 300000, D: 128, K: 1024, max_iters: 2} + - {id: w0, N: 200000, D: 64, K: 512, max_iters: 2} + - {id: w1, N: 300000, D: 128, K: 1024, max_iters: 2} + - {id: w2, N: 500000, D: 128, K: 1024, max_iters: 2} + - {id: w3, N: 150000, D: 512, K: 512, max_iters: 2} + - {id: w4, N: 1000000, D: 64, K: 1024, max_iters: 2} + - {id: w5, N: 300000, D: 256, K: 2048, max_iters: 2} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh index 7936fc6b6..6d2f2ad51 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/kmeans_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.2.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.2.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/kmeans-gpu-kernel-optimization-agent:experimental-v0.4.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/kmeans-gpu-kernel-optimization-judge:experimental-v0.4.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py b/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py index 3fa120bae..eebe5d7c9 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/evaluator.py @@ -267,6 +267,7 @@ def _build_cfg() -> dict[str, Any]: "warmup": _get_int("warmup_iters", 3), "iters": _get_int("timed_iters", 7), "inertia_tolerance": _get_float("inertia_tolerance", 0.02), + "label_mismatch_tolerance": _get_float("label_mismatch_tolerance", 0.002), "recall_threshold": _get_float("recall_threshold", 0.99), "captured_tolerance": _get_float("captured_tolerance", 0.02), "ortho_tolerance": _get_float("ortho_tolerance", 0.02), diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py index fa8423e43..520912dd7 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/kmeans_gpu_kernel_optimization/flash_gpu.py @@ -113,16 +113,25 @@ def gen(w, seed, ctx): # or agent -- can trade accuracy for speed by picking a different matmul # dtype (fp16/tf32/fp32 buy nothing on bf16 data and are only slower). # - # PLANTED CLUSTERS: x = well-separated blob centers + unit noise (NOT - # random). This makes the assignment matter -- inertia is sensitive to - # which centroid each point takes -- so a `step` that subsamples rows - # or feature dims, or fakes the labels, lands the final centroids in the - # wrong place and regresses the inertia gate (the judge re-derives - # inertia from the final centroids after its own max_iters-step loop). - # Random data would leave inertia nearly assignment-invariant and thus - # hackable by doing less work. + # PLANTED CLUSTERS: x = blob centers + unit noise (NOT random). This + # makes the assignment matter -- inertia is sensitive to which centroid + # each point takes -- so a `step` that subsamples rows, fakes the labels, + # or drops feature dims lands points on the wrong centroid and regresses + # the inertia gate. Random data would leave inertia nearly + # assignment-invariant and thus hackable by doing less work. + # + # SEPARATION IS SPREAD OVER ALL D DIMS. The nearest-competitor margin is + # ~ scale * sqrt(D/2) sigma of the unit noise. With a FIXED scale the + # margin grows as sqrt(D): at scale 6.0 / D=512 it is ~96 sigma, so a + # solution can assign using only the leading 128 dims and still land the + # exact same labels -- free work-skipping no output gate can see. Scaling + # by sqrt(2/D) pins the full-D margin at ~4 sigma, so using only a + # fraction f of the dims cuts it to 4*sqrt(f) (D=512 -> 128 dims: 2 sigma) + # and misassigns boundary points, which the inertia gate does see. Every + # dimension must be read. K = int(w["K"]) - centers = torch.randn(K, int(w["D"]), generator=g, device=dev) * 6.0 + D = int(w["D"]) + centers = torch.randn(K, D, generator=g, device=dev) * (4.0 * (2.0 / D) ** 0.5) asg = torch.randint(0, K, (int(w["N"]),), generator=g, device=dev) x = (centers.index_select(0, asg) + x).to(torch.bfloat16) # top randn = unit noise perm = torch.randperm(w["N"], generator=g, device=dev)[: K] @@ -141,9 +150,13 @@ def call(mod, w, data, ctx): # what gets timed. c = data["init"].clone() lab = None + c_in = None for _ in range(int(w["max_iters"])): + c_in = c # centroids the LAST step was handed lab, c = mod.step(data["x"], c) - return lab, c, int(w["max_iters"]) + # c_in is returned so the gate can re-derive the exact assignment the + # final `step` was supposed to produce and compare it to `lab`. + return lab, c, int(w["max_iters"]), c_in if prim == "knn": return mod.knn(data["queries"], data["database"], w["k"]) if prim == "dbscan": @@ -156,7 +169,7 @@ def call(mod, w, data, ctx): def check_shape(w, out): if prim == "kmeans": - lab, cen, _ = out + lab, cen = out[0], out[1] if tuple(cen.shape) != (w["K"], w["D"]) or not torch.isfinite(cen.float()).all(): raise ValueError("bad kmeans centroids") if lab.ndim != 1 or int(lab.shape[0]) != int(w["N"]): @@ -205,6 +218,30 @@ def inertia_labeled(x, centroids, labels): total += float(((xb - cl) ** 2).sum().item()) return total + def label_mismatch(x, centroids, labels): + # Fraction of points whose returned label is NOT the exact nearest centroid + # (fp32 metric) of the centroids that `step` was given. Inertia alone is a + # blunt detector: it is dominated by within-cluster noise, so misassigning + # 1% of the points moves it by only ~0.1% and hides under any usable + # tolerance. The assignment itself is what the contract specifies, so check + # it directly -- this is what catches assigning on a subset of the feature + # dims (or any other approximate distance) while the inertia stays flat. + c = centroids.to(torch.float32) + cn = (c * c).sum(1) + lab = labels.long() + bad = 0 + for j in range(0, x.shape[0], 16384): + xb = x[j:j + 16384].to(torch.float32) + d = (xb * xb).sum(1, keepdim=True) - 2.0 * (xb @ c.t()) + cn[None, :] + exact = d.argmin(1) + got = lab[j:j + 16384] + # a point sitting on a true tie may legitimately go either way: only + # count it when the returned centroid is strictly farther than the best + best = d.gather(1, exact[:, None]).squeeze(1) + mine = d.gather(1, got.clamp(0, c.shape[0] - 1)[:, None]).squeeze(1) + bad += int((mine > best * (1.0 + 1e-6) + 1e-6).sum().item()) + return bad / max(1, x.shape[0]) + def recall(agent_idx, ref_idx): a = agent_idx.long(); b = ref_idx.long() hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) @@ -249,14 +286,28 @@ def verdict(w, data, ctx, ref_out, agent_out): ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "kmeans": - # Centroid-quality gate: agent's final centroids (from the judge-owned - # loop over the agent's `step`) must be as good as the baseline's. Since - # the judge owns the loop, the solution cannot skip iterations or fake - # the count; a subsampled/wrong step compounds into bad centroids here. - rv = inertia(data["x"], ref_out[1]); av = inertia(data["x"], agent_out[1]) + # Clustering-quality gate on the inertia of the RETURNED (labels, + # centroids) pair -- NOT the centroids alone. Using the agent's own + # labels closes the "return good centroids + junk/zeroed labels" hack + # (e.g. labels = torch.empty(N)) and the "subsample the assignment" + # hack: a fake or partial assignment inflates the agent's inertia even + # when its centroids look fine. The judge owns the Lloyd loop, so the + # solution can only make one honest step faster. + rv = inertia_labeled(data["x"], ref_out[1], ref_out[0]) + av = inertia_labeled(data["x"], agent_out[1], agent_out[0]) tol = float(cfg["inertia_tolerance"]) ok = av <= (1.0 + tol) * rv + 1e-6 - return ok, ("inertia_regression" if not ok else ""), rv, av + if not ok: + return False, "inertia_regression", rv, av + # Assignment gate: the returned labels must BE the exact nearest-centroid + # assignment for the centroids the final step was handed. Catches + # approximate distances (e.g. assigning on a subset of the feature dims) + # that barely move inertia but are not the step the contract asks for. + mis = label_mismatch(data["x"], agent_out[3], agent_out[0]) + lt = float(cfg.get("label_mismatch_tolerance", 0.002)) + if mis > lt: + return False, "assignment_not_nearest_centroid", rv, av + return True, "", rv, av if prim == "knn": rec = recall(agent_out[1], ref_out[1]) ok = rec >= float(cfg["recall_threshold"]) diff --git a/2.0/problems/kmeans_gpu_kernel_optimization/readme b/2.0/problems/kmeans_gpu_kernel_optimization/readme index 5aebbaa5f..048eb2c57 100644 --- a/2.0/problems/kmeans_gpu_kernel_optimization/readme +++ b/2.0/problems/kmeans_gpu_kernel_optimization/readme @@ -80,14 +80,17 @@ baseline on a GPU, on the same seeded data and initial centroids. ## Correctness Correctness is a gate. After running the fixed loop of `step` calls, the judge -recomputes the **inertia** (sum of squared distances of every point to its -nearest final centroid) of your final centroids and of the baseline's on -identical data, and requires your inertia to stay within a small relative -tolerance of the baseline's. Each `step` must return `labels` as a full `(N,)` -nearest-centroid assignment to the `centroids` it was given and `new_centroids` -of shape `(K, D)`. Crashes, non-finite output, wrong shapes/dtypes, timeouts, and -clustering that regresses beyond the tolerance are penalized before speed is -considered. The working precision is fixed at bfloat16 for every solution (the +computes the **inertia of your returned clustering** — the sum of squared +distances of every point to `new_centroids[labels]`, i.e. using **the `labels` +and `new_centroids` your final `step` returned** — and compares it to the +baseline's, requiring your inertia to stay within a small relative tolerance. +Because the gate reads your returned `labels`, they must be the real full `(N,)` +nearest-centroid assignment to the `centroids` each `step` was given: returning +fake/empty labels, a partial or sub-sampled assignment, or centroids computed +from a subset of points all inflate this inertia and fail the gate. Each `step` +returns `labels` `(N,)` and `new_centroids` `(K, D)`. Crashes, non-finite output, +wrong shapes/dtypes, timeouts, and clustering that regresses beyond the tolerance +are penalized before speed is considered. The working precision is fixed at bfloat16 for every solution (the inputs are bfloat16), so it is not a tuning knob — speed comes from the kernel, not from the arithmetic precision. From 3cc10a303a2bce23663ad0c8eaf993d49cd86548 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Thu, 16 Jul 2026 03:11:26 +0000 Subject: [PATCH 27/34] feat(2.0/ivf_pq): real SIFT/GIST workloads + kill the fixed-index cache hack Workloads: the six synthetic gaussian shapes are replaced by real ANN data -- SIFT1M (128d) and GIST1M (960d) from ann-benchmarks, cached in the Modal Volume `flashlib-ann-datasets` and mounted read-only at /data. Synthetic gaussians gave balanced inverted lists and M<=150k; real data is clustered (long-tail lists) at M=1M, which is what an ANN search kernel actually has to survive. Each iteration draws a fresh random Q-subset of the held-out real query set (anti-caching); the index is still built once per workload and only the search is timed. The gate is unchanged (iso-recall@k vs the baseline's ids on the same index): >=0.999 on all six. reference.patch: route to the length-safe ADC-LUT path when the longest inverted list far exceeds the average. The decode+GEMM fine-scan paths size a per-(query,probe) candidate buffer for near-balanced lists and read out of bounds on real data's long tail -- auto SIGSEGV'd at nprobe>=16 on SIFT1M. This is a real bug the synthetic workloads could never hit. _fresh_index(): every timed call now gets a fresh clone of the index (same contents, new object, new data_ptrs). A trial attached a cache to the fixed index object and hoisted its CSR->padded layout conversion into the free warmup calls -- "moves the CSR-to-padded layout conversion out of measured iterations", per its own docstring -- scoring 787x against a ~450-600x reference. With the clone that same solution measures 21x and fails a workload. The clone happens in untimed gen(), so the reference is unaffected. speedup_target 1640 -> 1200 = 2x the reference geomean on the real workloads (600x: SIFT 875/1225/1530x, GIST 290/322/305x). The Python-per-query-loop baseline is noisy, so measurements range ~450-600x. Also: pip drops the standalone triton pin (torch brings its matched build; a standalone pin SIGSEGVs some flashlib kernels), evaluator._build_cfg passes dataset_volume through to the worker, primitive/pkg/ref_module wiring added for public_test, images rebuilt to v0.3.0 (flash_gpu.py is baked at /opt). --- .../config.yaml | 39 +++++++----- .../docker/build_images.sh | 4 +- .../evaluator.py | 1 + .../flash_gpu.py | 61 +++++++++++++++++-- .../ivf_pq_gpu_kernel_optimization/readme | 7 +++ .../reference.patch | 13 +++- 6 files changed, 101 insertions(+), 24 deletions(-) diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml index c61103019..b8dc4ffa2 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml @@ -7,36 +7,45 @@ runtime: judge_apt_packages: [bash, ca-certificates, git, python3, python3-pip] judge_pip_packages: [modal] docker: - image: frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.1.0 - judge_image: frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.1.0 + image: frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.3.0 + judge_image: frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.3.0 environment: cpus: 4 memory_mb: 8192 storage_mb: 16384 build_timeout_seconds: 3600 evaluation: + # --- primitive wiring (consumed by public_test's _cfg(); the graded evaluator hardcodes these) --- + primitive: ivfpq + pkg: ivfpqlib + ref_module: refivfpq gpu: "H100" cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" - pip: ["torch", "numpy"] + pip: ["torch", "numpy", "h5py"] # match the original (validated) image; torch brings its matched triton (a standalone triton pin crashes some flashlib kernels). h5py loads SIFT/GIST HDF5 app_name: "ivfpq-kernel-opt-eval" - modal_timeout_seconds: 2400 # the naive per-query-loop baseline is seconds/call; give the judge margin (agent speed is unknown in a trial) + dataset_volume: "flashlib-ann-datasets" # Modal Volume holding sift.hdf5 / gist.hdf5 (ann-benchmarks); mounted read-only at /data + modal_timeout_seconds: 3600 # the naive per-query-loop baseline is seconds/call and real GIST (D=960) is heavier; give the judge margin warmup_iters: 2 timed_iters: 7 - recall_threshold: 0.95 # agent results must match the baseline's top-k (iso-result recall@k) on every iteration; the Triton reference measured iso-recall 1.0 on all workloads, so 0.95 leaves margin for ADC tie-breaks - speedup_target: 1640.0 # score cap = 2x the flashlib Triton reference geomean (~821x on H100, per-wl 493/798/1514/1264/376/1081x, iso-recall 1.0 all 6): reference scores ~90/100, a solution must be 2x faster than it to max out. (Huge factor = Python-per-query-loop baseline.) + recall_threshold: 0.95 # agent results must match the baseline's top-k (iso-result recall@k) on every iteration; both baseline and Triton reference compute exact ADC over the same index, so 0.95 leaves margin for ADC tie-breaks + speedup_target: 1200.0 # score cap = 2x the flashlib reference geomean on the real SIFT/GIST workloads (600x on H100 with the per-call fresh index: SIFT 875/1225/1530x, GIST 290/322/305x, iso-recall >=0.999 all 6). Reference scores ~90/100; a solution must be 2x faster than it to max out. The Python-per-query-loop baseline is noisy, so measurements range ~450-600x. (A trial that cached a CSR->padded conversion on the fixed index scored 787x before _fresh_index killed that shortcut; it now measures 21x.) base_seed: 20260702 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on - # The index (the "database") is built ONCE per workload by the frozen baseline - # and reused for every timed iteration; only ivf_pq_search is timed. Queries are - # regenerated fresh each iteration. M=database size, Q=queries, k=neighbours. + # REAL-DATA WORKLOADS: the index (the "database") is built ONCE per workload by + # the frozen builder from the full real base (SIFT1M 128d / GIST1M 960d), reused + # for every timed iteration; only ivf_pq_search is timed. Each iteration draws a + # fresh Q-subset of the held-out real query set (anti-caching). Real datasets have + # clustered, imbalanced inverted lists (unlike synthetic gaussian) -> the search + # kernel must handle long-tail lists. dataset in {sift,gist}; nprobe is the main + # recall/speed knob. Q <= #queries (SIFT 10000, GIST 1000). workloads: - - {id: w0, M: 50000, D: 64, nlist: 256, m: 8, nprobe: 8, Q: 1024, k: 10} - - {id: w1, M: 100000, D: 96, nlist: 512, m: 12, nprobe: 16, Q: 1024, k: 10} - - {id: w2, M: 150000, D: 128, nlist: 512, m: 16, nprobe: 16, Q: 2048, k: 10} - - {id: w3, M: 80000, D: 64, nlist: 256, m: 8, nprobe: 16, Q: 2048, k: 10} - - {id: w4, M: 120000, D: 128, nlist: 512, m: 16, nprobe: 8, Q: 1024, k: 20} - - {id: w5, M: 60000, D: 96, nlist: 256, m: 12, nprobe: 16, Q: 1536, k: 10} + - {id: sift_np8, dataset: sift, nlist: 4096, m: 16, nprobe: 8, Q: 1024, k: 10} + - {id: sift_np16, dataset: sift, nlist: 4096, m: 16, nprobe: 16, Q: 1024, k: 10} + - {id: sift_np32, dataset: sift, nlist: 4096, m: 16, nprobe: 32, Q: 2048, k: 10} + - {id: gist_np8, dataset: gist, nlist: 2048, m: 32, nprobe: 8, Q: 512, k: 10} + - {id: gist_np16, dataset: gist, nlist: 2048, m: 32, nprobe: 16, Q: 512, k: 10} + - {id: gist_np32, dataset: gist, nlist: 2048, m: 32, nprobe: 32, Q: 512, k: 10} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh index 47d4fbace..fa07746f7 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.1.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.1.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.3.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.3.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py index 198894af8..3dc593e8c 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/evaluator.py @@ -263,6 +263,7 @@ def _build_cfg() -> dict[str, Any]: "cuda_image": str(_get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), "pip": list(_get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])), "app_name": str(_get("app_name", "flash-kernel-eval")), + "dataset_volume": _get("dataset_volume", None), # Modal Volume with real SIFT/GIST datasets, mounted at /data "modal_timeout_seconds": _get_int("modal_timeout_seconds", 1800), "warmup": _get_int("warmup_iters", 3), "iters": _get_int("timed_iters", 7), diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py index 6243e65c8..56fe76a8b 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py @@ -76,10 +76,31 @@ def _materialize(files: dict, tag: str) -> str: pkg = importlib.import_module(cfg["pkg"]) # ---- per-primitive: fixed context, data gen, call, quality metric ----- # + _ann_cache = {} + def _load_ann(name): + # Load a real ANN dataset (ann-benchmarks HDF5) from the mounted Modal + # Volume at /data. Cached across workloads in one worker call. + if name not in _ann_cache: + import h5py + import numpy as _np + with h5py.File(f"/data/{name}.hdf5", "r") as f: + base = torch.from_numpy(_np.asarray(f["train"], dtype=_np.float32)).to(dev) + queries = torch.from_numpy(_np.asarray(f["test"], dtype=_np.float32)).to(dev) + _ann_cache[name] = (base.contiguous(), queries.contiguous()) + return _ann_cache[name] + def setup(w, seed): # Built ONCE per workload (not per timed iter). IVF-PQ builds the fixed # index (the "database") here; queries are generated fresh per iteration. if prim == "ivfpq": + ds = w.get("dataset") + if ds: + # Real dataset: build the frozen index from the full real base; + # queries are drawn per-iteration from the held-out query set. + base, queries_all = _load_ann(ds) + index = ref.ivf_pq_build(base, int(w["nlist"]), m=int(w["m"]), + nprobe=int(w["nprobe"]), seed=int(w.get("seed", 0))) + return {"index": index, "D": int(base.shape[1]), "queries_all": queries_all} g = torch.Generator(device=dev).manual_seed(int(seed)) X = torch.randn(int(w["M"]), int(w["D"]), generator=g, device=dev, dtype=torch.float32) index = ref.ivf_pq_build(X, int(w["nlist"]), m=int(w["m"]), @@ -87,11 +108,34 @@ def setup(w, seed): return {"index": index, "D": int(w["D"])} return {} + def _fresh_index(src): + # Hand every timed call a FRESH index object with freshly cloned tensors. + # The index content is identical (it is still the one frozen index built in + # setup), but the object identity and every data_ptr change per iteration, + # so a solution cannot attach cached state to it (`setattr(index, ...)`) or + # key a cache on its tensor addresses and thereby amortise index-derived + # precomputation (e.g. a CSR->padded layout conversion) out of the timed + # region and into the free warmup calls. `src` is the pristine index kept + # in ctx and never handed to the submission, so nothing leaks between iters. + new = object.__new__(type(src)) + for k, v in vars(src).items(): + new.__dict__[k] = v.clone() if torch.is_tensor(v) else v + return new + def gen(w, seed, ctx): g = torch.Generator(device=dev).manual_seed(int(seed)) if prim == "ivfpq": - q = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, device=dev, dtype=torch.float32) - return {"queries": q} + data = {"index": _fresh_index(ctx["index"])} + if w.get("dataset"): + # Fresh subset of the real query set each iteration (anti-caching); + # Q must be <= number of held-out queries. + qa = ctx["queries_all"] + perm = torch.randperm(qa.shape[0], generator=g, device=dev)[: int(w["Q"])] + data["queries"] = qa.index_select(0, perm).contiguous() + return data + data["queries"] = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, + device=dev, dtype=torch.float32) + return data if prim == "knn": db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) @@ -114,7 +158,9 @@ def gen(w, seed, ctx): def call(mod, w, data, ctx): if prim == "ivfpq": - return mod.ivf_pq_search(ctx["index"], data["queries"], int(w["k"]), nprobe=int(w["nprobe"])) + # data["index"] is this iteration's fresh clone (see _fresh_index): the + # same frozen index, but no state can survive from a previous call. + return mod.ivf_pq_search(data["index"], data["queries"], int(w["k"]), nprobe=int(w["nprobe"])) if prim == "kmeans": return mod.kmeans(data["x"], w["K"], max_iters=w["max_iters"], init_centroids=data["init"], tol=0.0) @@ -323,13 +369,18 @@ def run_remote(payload: dict) -> dict: last = "modal_gpu_eval_failed" for attempt in range(1, attempts + 1): app = modal.App(cfg.get("app_name", "flash-kernel-eval")) - remote = app.function( + fn_kwargs = dict( gpu=cfg.get("gpu", "H100"), image=_build_image(cfg), timeout=int(cfg.get("modal_timeout_seconds", 1800)), serialized=True, max_containers=1, - )(_gpu_worker) + ) + # Mount the cached real-dataset Volume (SIFT/GIST HDF5) read-only at /data. + vol_name = cfg.get("dataset_volume") + if vol_name: + fn_kwargs["volumes"] = {"/data": modal.Volume.from_name(vol_name)} + remote = app.function(**fn_kwargs)(_gpu_worker) try: with modal.enable_output(), app.run(): return remote.remote(payload) diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/readme b/2.0/problems/ivf_pq_gpu_kernel_optimization/readme index 6aba6f6b3..6bca22a3c 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/readme +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/readme @@ -33,6 +33,13 @@ nothing. Your search must consume the index exactly as built (you may of course reorganize its arrays inside your own call). Treat the coarse centroids, PQ codebooks, CSR list layout, and codes as fixed inputs. +Each call receives its **own fresh copy** of that index — same contents, but a +new object with new tensors every time. Any state you attach to the index or +cache keyed on its tensor addresses is therefore gone by the next call: work +like a CSR-to-padded layout conversion cannot be hoisted into a warmup call and +amortized away. Whatever your search needs, it must produce within the call +being timed. + ## Workload The graded workloads are held-out ANN search problems over random float32 diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch b/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch index a5b4460b0..419c6e5e4 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch @@ -1066,10 +1066,10 @@ index 0000000..cebd5c5 +__all__ = ["pq_build_lut"] diff --git a/ivfpqlib/_kernels/search.py b/ivfpqlib/_kernels/search.py new file mode 100644 -index 0000000..2be7857 +index 0000000..3680708 --- /dev/null +++ b/ivfpqlib/_kernels/search.py -@@ -0,0 +1,373 @@ +@@ -0,0 +1,382 @@ +"""IVF-PQ search (Triton/GPU path). Two fine-scan strategies, one API. + +Every stage avoids the ``(nq x candidates)`` HBM matrix; the **coarse** @@ -1412,6 +1412,15 @@ index 0000000..2be7857 + chosen = _pick_variant( + variant, nq, nprobe, avg_list_len, index.dsub, index.m, index.nlist, + ) ++ # Clustered real datasets (SIFT/GIST) produce heavily imbalanced inverted ++ # lists. The decode+GEMM fine-scan paths ("gemm"/"cute_gemm"/"cute_lut") ++ # size a per-(query, probe) candidate buffer for near-balanced lists and ++ # read out of bounds on a long-tail list; the streaming ADC-LUT "online" ++ # path is length-safe. When the longest list far exceeds the average, ++ # route auto there. ++ if variant == "auto" and chosen not in ("online", "batch") \ ++ and max_list_len > 4.0 * avg_list_len: ++ chosen = "online" + if chosen == "gemm": + return _search_gemm(index, Qp_all, centroids, codebooks, k, nprobe) + if chosen in ("cute_lut", "cute_gemm"): From 744b4bfb4056a7b9ea299d9d161e42d7589ed177 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Thu, 16 Jul 2026 03:11:44 +0000 Subject: [PATCH 28/34] feat(2.0/knn): real SIFT/GIST workloads + per-iteration database subsets Workloads: the six synthetic gaussian shapes are replaced by real ANN data -- SIFT1M (128d) and GIST1M (960d) from ann-benchmarks, cached in the Modal Volume `flashlib-ann-datasets` and mounted read-only at /data. Real descriptor vectors are clustered and non-uniform rather than i.i.d. noise. k varies the neighbour count; the gate is unchanged (iso-recall@k vs the exact baseline's ids): >=0.994 on all six. Anti-caching: every iteration draws a fresh random Q-subset of the held-out real query set AND a fresh random M-subset (500k) of the real base as the database. Handing back one fixed database tensor -- as the first cut of this did -- lets a solution cache a prebuilt index/norms keyed on it and skip the search the timer is supposed to measure: a trial did exactly that (`_ANN_CACHE` keyed on the dataset, `_NORM_CACHE` keyed on data_ptr, plus a `_DB_MATCH_CACHE` that identified the database as a known dataset) and scored 81x against a 2.78x reference. knn has no build phase in its contract, so amortizing an index across calls is not an optimization, it is skipping the work. With per-iteration subsets the three following trials land at 1.5-2.5x -- at or below the reference, which is the expected shape -- and the recovered solutions carry no cache. The synthetic path never had this hole because it regenerated the database with torch.randn every iteration; the real-data path has to reproduce that property deliberately. speedup_target 10.75 -> 5.5 = 2x the reference geomean on the real workloads (2.78x: SIFT k10/50/100 = 8.36/6.48/2.65x, GIST = 1.76/1.50/1.22x). Headroom over the cuBLAS-cdist baseline is genuinely modest on real data, especially high-D GIST, so the factor is far smaller than the synthetic workloads suggested. Also: pip drops the standalone triton pin -- this was the actual cause of the SIGSEGVs first misread as a large-M kernel bug; torch's matched triton runs the sortmerge kernels fine at M=1M. evaluator._build_cfg passes dataset_volume through, readme documents the real data + the fresh-subset guarantee, images rebuilt to v0.4.0 (flash_gpu.py is baked at /opt). --- .../knn_gpu_kernel_optimization/config.yaml | 34 ++++++++++------ .../docker/build_images.sh | 4 +- .../knn_gpu_kernel_optimization/evaluator.py | 1 + .../knn_gpu_kernel_optimization/flash_gpu.py | 39 ++++++++++++++++++- .../knn_gpu_kernel_optimization/readme | 13 +++++-- 5 files changed, 72 insertions(+), 19 deletions(-) diff --git a/2.0/problems/knn_gpu_kernel_optimization/config.yaml b/2.0/problems/knn_gpu_kernel_optimization/config.yaml index c3dbbf0a8..43a05665c 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/knn_gpu_kernel_optimization/config.yaml @@ -23,8 +23,8 @@ runtime: # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes # /app/knnlib (git) + /opt/knn_ref + /opt/flash_gpu.py; the judge image # additionally bakes /opt/knnlib-clean. - image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.2.0 - judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.2.0 + image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.4.0 + judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.4.0 environment: cpus: 4 memory_mb: 8192 @@ -40,23 +40,33 @@ evaluation: # --- Modal GPU --- gpu: "H100" cuda_image: "nvidia/cuda:12.4.1-devel-ubuntu22.04" - pip: ["torch", "numpy"] + pip: ["torch", "numpy", "h5py"] # match the original (validated) knn image; torch brings its matched triton. h5py loads SIFT/GIST HDF5 app_name: "knn-kernel-opt-eval" - modal_timeout_seconds: 1800 + dataset_volume: "flashlib-ann-datasets" # Modal Volume holding sift.hdf5 / gist.hdf5 (ann-benchmarks); mounted read-only at /data + modal_timeout_seconds: 3600 # exact brute-force over a 1M-row real database is heavier than the synthetic shapes; give margin warmup_iters: 2 timed_iters: 7 - recall_threshold: 0.99 - speedup_target: 10.75 # score cap = 2x the flashlib-best reference geomean (5.38x on Modal H100, per-wl 8.8/8.5/7.1/2.9/3.4/4.7x, all 6 pass recall>=0.99): the reference scores ~70/100 and a solution must be 2x faster than it to max out. + recall_threshold: 0.99 # agent ids must match the exact baseline's top-k (iso-result recall@k) on every iteration; both compute exact squared-L2, so 0.99 leaves margin for distance ties + speedup_target: 5.5 # score cap = 2x the flashlib reference geomean on the real SIFT/GIST workloads with per-iteration M=500k database subsets (2.78x geomean on H100: SIFT k10/50/100 = 8.36/6.48/2.65x, GIST k10/50/100 = 1.76/1.50/1.22x, iso-recall >=0.994 all 6). Reference scores ~80/100. Headroom over the cuBLAS-cdist baseline is modest on real data (esp. high-D GIST), so the factor is smaller than the old synthetic workloads. base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on + # REAL-DATA WORKLOADS: exact brute-force k-NN over real descriptor vectors + # (SIFT 128d / GIST 960d, 1M-row bases). ANTI-CACHING: every iteration draws a + # fresh random Q-subset of the held-out real query set AND a fresh random + # M-subset of the real base as the database, so neither the queries nor the + # database tensor repeats across calls -- a solution cannot cache a prebuilt + # index/norms keyed on a fixed database and skip the timed search (a fixed + # database let a trial hit 80x by caching an index instead of writing a fast + # kernel). M = database rows drawn per iteration; k varies the neighbour count. + # Q <= #queries (SIFT 10000, GIST 1000); M <= base rows (1M). workloads: - - {id: w0, Q: 2048, M: 100000, D: 64, k: 10} - - {id: w1, Q: 4096, M: 250000, D: 128, k: 10} - - {id: w2, Q: 1024, M: 1000000, D: 96, k: 32} - - {id: w3, Q: 8192, M: 200000, D: 256, k: 50} - - {id: w4, Q: 2048, M: 500000, D: 64, k: 100} - - {id: w5, Q: 4096, M: 300000, D: 128, k: 64} + - {id: sift_k10, dataset: sift, M: 500000, Q: 1024, k: 10} + - {id: sift_k50, dataset: sift, M: 500000, Q: 1024, k: 50} + - {id: sift_k100, dataset: sift, M: 500000, Q: 1024, k: 100} + - {id: gist_k10, dataset: gist, M: 500000, Q: 512, k: 10} + - {id: gist_k50, dataset: gist, M: 500000, Q: 512, k: 50} + - {id: gist_k100, dataset: gist, M: 500000, Q: 512, k: 100} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh index 9672994b1..1627202a8 100755 --- a/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.2.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.2.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.4.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.4.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py index 2fc34c1b9..d7e041c18 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py @@ -263,6 +263,7 @@ def _build_cfg() -> dict[str, Any]: "cuda_image": str(_get("cuda_image", "nvidia/cuda:12.4.1-devel-ubuntu22.04")), "pip": list(_get("pip", ["torch==2.5.1", "triton==3.1.0", "numpy"])), "app_name": str(_get("app_name", "flash-kernel-eval")), + "dataset_volume": _get("dataset_volume", None), # Modal Volume with real SIFT/GIST datasets, mounted at /data "modal_timeout_seconds": _get_int("modal_timeout_seconds", 1800), "warmup": _get_int("warmup_iters", 3), "iters": _get_int("timed_iters", 7), diff --git a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py index 6243e65c8..54de28c0b 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py @@ -76,6 +76,19 @@ def _materialize(files: dict, tag: str) -> str: pkg = importlib.import_module(cfg["pkg"]) # ---- per-primitive: fixed context, data gen, call, quality metric ----- # + _ann_cache = {} + def _load_ann(name): + # Load a real ANN dataset (ann-benchmarks HDF5) from the mounted Modal + # Volume at /data. Cached across workloads in one worker call. + if name not in _ann_cache: + import h5py + import numpy as _np + with h5py.File(f"/data/{name}.hdf5", "r") as f: + base = torch.from_numpy(_np.asarray(f["train"], dtype=_np.float32)).to(dev) + queries = torch.from_numpy(_np.asarray(f["test"], dtype=_np.float32)).to(dev) + _ann_cache[name] = (base.contiguous(), queries.contiguous()) + return _ann_cache[name] + def setup(w, seed): # Built ONCE per workload (not per timed iter). IVF-PQ builds the fixed # index (the "database") here; queries are generated fresh per iteration. @@ -85,6 +98,11 @@ def setup(w, seed): index = ref.ivf_pq_build(X, int(w["nlist"]), m=int(w["m"]), nprobe=int(w["nprobe"]), seed=int(seed)) return {"index": index, "D": int(w["D"])} + if prim == "knn" and w.get("dataset"): + # Real dataset: the fixed database is the full real base; queries are + # drawn per-iteration from the held-out query set. + base, queries_all = _load_ann(w["dataset"]) + return {"base": base, "queries_all": queries_all} return {} def gen(w, seed, ctx): @@ -93,6 +111,18 @@ def gen(w, seed, ctx): q = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, device=dev, dtype=torch.float32) return {"queries": q} if prim == "knn": + if w.get("dataset"): + # Fresh random subset of the real query set AND of the real base + # every iteration. The database must differ per call: handing back + # one fixed tensor lets a solution cache a prebuilt index/norms + # keyed on it (data_ptr or content) and skip the search work the + # timer is supposed to measure -- exactly what the synthetic path + # avoids by regenerating the database each iteration. + qa, base = ctx["queries_all"], ctx["base"] + qperm = torch.randperm(qa.shape[0], generator=g, device=dev)[: int(w["Q"])] + dperm = torch.randperm(base.shape[0], generator=g, device=dev)[: int(w["M"])] + return {"queries": qa.index_select(0, qperm).contiguous(), + "database": base.index_select(0, dperm).contiguous()} db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} @@ -323,13 +353,18 @@ def run_remote(payload: dict) -> dict: last = "modal_gpu_eval_failed" for attempt in range(1, attempts + 1): app = modal.App(cfg.get("app_name", "flash-kernel-eval")) - remote = app.function( + fn_kwargs = dict( gpu=cfg.get("gpu", "H100"), image=_build_image(cfg), timeout=int(cfg.get("modal_timeout_seconds", 1800)), serialized=True, max_containers=1, - )(_gpu_worker) + ) + # Mount the cached real-dataset Volume (SIFT/GIST HDF5) read-only at /data. + vol_name = cfg.get("dataset_volume") + if vol_name: + fn_kwargs["volumes"] = {"/data": modal.Volume.from_name(vol_name)} + remote = app.function(**fn_kwargs)(_gpu_worker) try: with modal.enable_output(), app.run(): return remote.remote(payload) diff --git a/2.0/problems/knn_gpu_kernel_optimization/readme b/2.0/problems/knn_gpu_kernel_optimization/readme index 1a31b42ef..b6d36d3cb 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/readme +++ b/2.0/problems/knn_gpu_kernel_optimization/readme @@ -26,9 +26,16 @@ change, and every result must remain a deterministic function of its inputs. ## Workload The graded workloads are a family of held-out dense search problems that vary -`(Q, M, D, k)`, spanning small/large database sizes `M` and both wide-feature -(large `D`) and many-neighbor (large `k`) regimes. All use squared-L2 distance, -float32 data, and randomly generated queries/database. +`(Q, M, D, k)`, spanning both wide-feature (large `D`) and many-neighbor (large +`k`) regimes. All use squared-L2 distance and float32 data. The points are real +image-descriptor vectors, not synthetic noise: expect clustered, non-uniform +data rather than i.i.d. gaussians. + +Every timed iteration draws a **fresh random `Q` queries and a fresh random `M` +database rows**, so neither the query set nor the `database` tensor repeats +across calls. Building an index or precomputing norms once and caching them for +reuse on a later call therefore buys nothing — each call sees a database it has +never seen. Optimize the per-call search itself. The public self-test now runs the **exact graded workloads** — the same shapes, thresholds, seeds, and timing the judge uses to score you — so there are no From 60d15379e8a467eb4b561e1117039411853bd669 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Fri, 17 Jul 2026 01:59:47 +0000 Subject: [PATCH 29/34] feat(2.0/dbscan): bf16 precision lock so distance dtype is not a lever The points are handed out at bf16 precision (bf16-rounded values in an fp32 container, so the dbscan(x, eps, min_samples) contract is unchanged). The inputs then carry only bf16 precision, so no solution can win by computing the eps-graph pairwise distances in a lower dtype than the baseline -- bf16 tensor cores are the floor, fp32/TF32 buy nothing, and anything below bf16 fails the gate. The blobs are well separated (cluster_std=1.0, centers in +-10), so bf16 rounding never flips an eps-neighbour: the reference's ARI stays exactly 1.0 on all four workloads, no gate loosening needed. speedup_target 51 -> 57 = 2x the flashlib reference geomean on the bf16 data (28.6x on H100). images rebuilt to v0.3.0 (flash_gpu.py is baked at /opt). --- 2.0/problems/dbscan_gpu_kernel_optimization/config.yaml | 6 +++--- .../dbscan_gpu_kernel_optimization/docker/build_images.sh | 4 ++-- 2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py | 7 ++++++- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml index 1a04363b2..dc41baf42 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml @@ -7,8 +7,8 @@ runtime: judge_apt_packages: [bash, ca-certificates, git, python3, python3-pip] judge_pip_packages: [modal] docker: - image: frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.2.0 - judge_image: frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.2.0 + image: frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.3.0 + judge_image: frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.3.0 environment: cpus: 4 memory_mb: 8192 @@ -23,7 +23,7 @@ evaluation: warmup_iters: 2 timed_iters: 3 # the O(N^2) naive baseline is seconds/call at these N; keep the timed budget bounded (median of 3) ari_threshold: 0.99 # agent clustering must match the baseline exact DBSCAN (Adjusted Rand Index) every iteration. On the non-separable concentric-ring data a centroid/partition approximation (k-means) scores ARI <= ~0.1 -> fails; only exact eps-connectivity passes. Exact reference measures ARI ~1.0. - speedup_target: 51.0 # score cap = 2x the flashlib brute-force (flash_knn) reference geomean (25.4x on H100 over the O(N^2) baseline, per-wl 19.6/18.9/26.8/42.1x, ARI ~1.0 all 4 flashlib blob shapes). Reference scores ~82/100; maxing out needs 2x the reference. + speedup_target: 57.0 # score cap = 2x the flashlib reference geomean on the bf16-locked blob workloads (28.6x on H100, ARI=1.0 all 4). Points carry only bf16 precision so no solution wins by computing the eps-graph distances in a lower dtype; well-separated blobs mean bf16 never flips an eps-neighbour (ARI stays exactly 1.0). base_seed: 20260701 agent_workload_count: 4 # all 4 flashlib graded workloads (feedback covers every graded shape) expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/dbscan_gpu_kernel_optimization/docker/build_images.sh index 634396fd1..682f88119 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/dbscan_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.2.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.2.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.3.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.3.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py index d53609a49..588ea67dd 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py @@ -106,7 +106,12 @@ def gen(w, seed, ctx): centers = (torch.rand(nc, D, generator=g, device=dev) * 2.0 - 1.0) * 10.0 asg = torch.randint(0, nc, (N,), generator=g, device=dev) x = centers.index_select(0, asg) + torch.randn(N, D, generator=g, device=dev) * std - return {"x": x.to(torch.float32), "eps": float(w["eps"]), + # PRECISION LOCK (bf16): the points carry only bf16 precision, so a + # solution cannot win by computing the pairwise distances in a lower + # dtype than everyone else -- bf16 tensor cores are the floor, and the + # blobs are well separated (std=1.0, centers in +-10) so bf16 rounding + # never flips an eps-neighbour and the clustering is unchanged. + return {"x": x.to(torch.bfloat16).to(torch.float32), "eps": float(w["eps"]), "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) if prim == "kmeans": From 4585e41d14bd680950ecca66b0692360477fd875 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Fri, 17 Jul 2026 01:59:47 +0000 Subject: [PATCH 30/34] feat(2.0/knn): bf16 precision lock + tie-robust ball-recall gate PRECISION LOCK: queries and database are handed out as bf16. The inputs carry only bf16 precision, so a solution cannot gain from the distance-matmul dtype -- fp32/TF32 buy nothing over bf16 tensor cores on bf16 inputs (only slower), and fp8 loses too much to clear the gate. This neutralises the TF32-candidate trick a trial used to beat the reference: on bf16 data TF32-of-bf16 == bf16, no faster. GATE: iso-index recall is unusable on bf16 -- coarse distances tie heavily near rank k, so even two correct searches that break ties differently disagree at ~0.90-0.97. Replaced with ball-recall (knn_ball_recall): a returned neighbour counts if its exact fp32 distance is within the true k-NN ball (<= the baseline's k-th distance). Tie-robust (any equidistant point is fine) but still rejects a genuinely farther point, so an honest bf16 kernel passes (reference measured 0.935-1.0) while a sub-bf16 search fails. recall_threshold 0.99 -> 0.88 clears the bf16 fuzz with margin; distance_ball_tolerance added and threaded through evaluator._build_cfg (the whitelist would otherwise drop it, like dataset_volume). speedup_target 5.5 -> 4.0 = 2x the bf16 reference geomean (1.94x). readme updated to state bf16 inputs + the ball-recall contract. images rebuilt to v0.5.0. --- .../knn_gpu_kernel_optimization/config.yaml | 9 +++-- .../docker/build_images.sh | 4 +- .../knn_gpu_kernel_optimization/evaluator.py | 1 + .../knn_gpu_kernel_optimization/flash_gpu.py | 37 +++++++++++++++++-- .../knn_gpu_kernel_optimization/readme | 35 ++++++++++-------- 5 files changed, 62 insertions(+), 24 deletions(-) diff --git a/2.0/problems/knn_gpu_kernel_optimization/config.yaml b/2.0/problems/knn_gpu_kernel_optimization/config.yaml index 43a05665c..6f8ed72e7 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/knn_gpu_kernel_optimization/config.yaml @@ -23,8 +23,8 @@ runtime: # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes # /app/knnlib (git) + /opt/knn_ref + /opt/flash_gpu.py; the judge image # additionally bakes /opt/knnlib-clean. - image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.4.0 - judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.4.0 + image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.5.0 + judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.5.0 environment: cpus: 4 memory_mb: 8192 @@ -46,8 +46,9 @@ evaluation: modal_timeout_seconds: 3600 # exact brute-force over a 1M-row real database is heavier than the synthetic shapes; give margin warmup_iters: 2 timed_iters: 7 - recall_threshold: 0.99 # agent ids must match the exact baseline's top-k (iso-result recall@k) on every iteration; both compute exact squared-L2, so 0.99 leaves margin for distance ties - speedup_target: 5.5 # score cap = 2x the flashlib reference geomean on the real SIFT/GIST workloads with per-iteration M=500k database subsets (2.78x geomean on H100: SIFT k10/50/100 = 8.36/6.48/2.65x, GIST k10/50/100 = 1.76/1.50/1.22x, iso-recall >=0.994 all 6). Reference scores ~80/100. Headroom over the cuBLAS-cdist baseline is modest on real data (esp. high-D GIST), so the factor is smaller than the old synthetic workloads. + recall_threshold: 0.88 # ball-recall gate on bf16 data (see knn_ball_recall): a returned neighbour counts if its exact fp32 distance is within the true k-NN ball. bf16 distances tie heavily near rank k, so even a correct bf16 kernel only reaches ~0.94-1.0 (the flashlib reference measured 0.935-1.0). 0.88 clears that with margin while still rejecting a sub-bf16 (e.g. fp8) search, whose coarser distances drop far lower. Precision is pinned by the bf16 DATA, not this threshold. + distance_ball_tolerance: 0.0 # relative slack on the k-NN ball radius (the check already carries a 1e-6 absolute epsilon for fp32 reduction jitter) + speedup_target: 4.0 # score cap = 2x the flashlib reference geomean on the bf16 real workloads (1.94x on H100: SIFT k10/50/100 = 4.43/2.60/1.48x, GIST = 1.71/1.67/1.10x, ball-recall >=0.94 all 6). bf16 data + modest cuBLAS-cdist headroom keep the factor small. base_seed: 20260701 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on diff --git a/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh index 1627202a8..1b91827ca 100755 --- a/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/knn_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.4.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.4.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.5.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.5.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py index d7e041c18..b0315dbc2 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/evaluator.py +++ b/2.0/problems/knn_gpu_kernel_optimization/evaluator.py @@ -269,6 +269,7 @@ def _build_cfg() -> dict[str, Any]: "iters": _get_int("timed_iters", 7), "inertia_tolerance": _get_float("inertia_tolerance", 0.02), "recall_threshold": _get_float("recall_threshold", 0.99), + "distance_ball_tolerance": _get_float("distance_ball_tolerance", 0.0), "captured_tolerance": _get_float("captured_tolerance", 0.02), "ortho_tolerance": _get_float("ortho_tolerance", 0.02), "ari_threshold": _get_float("ari_threshold", 0.99), diff --git a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py index 54de28c0b..484f44114 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py @@ -118,11 +118,17 @@ def gen(w, seed, ctx): # keyed on it (data_ptr or content) and skip the search work the # timer is supposed to measure -- exactly what the synthetic path # avoids by regenerating the database each iteration. + # PRECISION LOCK (bf16): hand out bf16 queries + database, so the + # inputs carry only bf16 precision. No solution can gain from a + # different distance-matmul dtype -- fp32/TF32 buy nothing over bf16 + # tensor cores on bf16 inputs (only slower), and fp8 loses too much + # to clear the recall gate. Baseline and agent compute on the same + # bf16 data, so speed is purely about the kernel, not precision. qa, base = ctx["queries_all"], ctx["base"] qperm = torch.randperm(qa.shape[0], generator=g, device=dev)[: int(w["Q"])] dperm = torch.randperm(base.shape[0], generator=g, device=dev)[: int(w["M"])] - return {"queries": qa.index_select(0, qperm).contiguous(), - "database": base.index_select(0, dperm).contiguous()} + return {"queries": qa.index_select(0, qperm).to(torch.bfloat16).contiguous(), + "database": base.index_select(0, dperm).to(torch.bfloat16).contiguous()} db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} @@ -198,6 +204,29 @@ def recall(agent_idx, ref_idx): hit = (a.unsqueeze(2) == b.unsqueeze(1)).any(2) return float(hit.sum().item()) / (b.shape[0] * b.shape[1]) + def knn_ball_recall(queries, database, agent_idx, ref_idx, tol): + # Tie-robust recall: a returned neighbour counts if its EXACT (fp32) + # squared-L2 distance is within the true k-NN ball, i.e. <= the baseline's + # k-th distance x (1+tol). Plain index-recall is unusable on bf16 data -- + # coarse distances tie heavily, so two *correct* searches that break ties + # differently disagree at ~0.9-0.97. This gate ignores which tie member is + # picked (any point at the k-th distance is correct) but still rejects a + # genuinely-farther point, so a lower-precision search that returns wrong + # neighbours fails while an honest bf16 kernel passes exactly. + qf = queries.to(torch.float32); dbf = database.to(torch.float32) + # k-NN ball radius per query = exact distance to the baseline's k-th nbr + kth = dbf.index_select(0, ref_idx[:, -1].long()) # (Q, D) + d_k = ((qf - kth) ** 2).sum(1) # (Q,) + thr = d_k * (1.0 + tol) + 1e-6 + a = agent_idx.long(); Q, k = a.shape + hits = 0 + for j in range(0, Q, 256): + qb = qf[j:j + 256]; ab = a[j:j + 256] # (q, D), (q, k) + cand = dbf.index_select(0, ab.reshape(-1)).reshape(ab.shape[0], k, -1) + ad = ((qb[:, None, :] - cand) ** 2).sum(2) # (q, k) exact dists + hits += int((ad <= thr[j:j + 256, None]).sum().item()) + return hits / (Q * k) + def ari(a, b): # Adjusted Rand Index between two integer label vectors (N,); noise (-1) # is treated as its own label (dense-remapped first). @@ -242,7 +271,9 @@ def verdict(w, data, ctx, ref_out, agent_out): ok = av <= (1.0 + tol) * rv + 1e-6 return ok, ("inertia_regression" if not ok else ""), rv, av if prim == "knn": - rec = recall(agent_out[1], ref_out[1]) + rec = knn_ball_recall(data["queries"], data["database"], + agent_out[1], ref_out[1], + float(cfg.get("distance_ball_tolerance", 0.0))) ok = rec >= float(cfg["recall_threshold"]) return ok, ("recall_regression" if not ok else ""), 1.0, rec if prim == "dbscan": diff --git a/2.0/problems/knn_gpu_kernel_optimization/readme b/2.0/problems/knn_gpu_kernel_optimization/readme index b6d36d3cb..3ecba5ff3 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/readme +++ b/2.0/problems/knn_gpu_kernel_optimization/readme @@ -9,13 +9,15 @@ the Harbor workspace at `/app/knnlib`. Its public entry point is: knnlib.knn(queries, database, k) -> (distances, indices) ``` -`queries` is a `(Q, D)` float32 CUDA tensor of query points, `database` is an -`(M, D)` float32 CUDA tensor of database points to search, and `k` is the number -of nearest neighbors to return per query. The function returns `distances`, a -`(Q, k)` float32 tensor of the **squared-L2** distances to the `k` nearest -database points (ascending, nearest first), and `indices`, a `(Q, k)` int64 -tensor of the corresponding database row indices. The shipped implementation is -a straightforward, correct PyTorch version. +`queries` is a `(Q, D)` **bfloat16** CUDA tensor of query points, `database` is an +`(M, D)` **bfloat16** CUDA tensor of database points to search, and `k` is the +number of nearest neighbors to return per query. The inputs are bfloat16 by +design — treat it as the fixed working precision; upcasting to fp32/TF32 buys no +accuracy over bf16 tensor cores here (only latency), so precision is not a tuning +knob. The function returns `distances`, a `(Q, k)` float32 tensor of the +**squared-L2** distances to the `k` nearest database points (ascending, nearest +first), and `indices`, a `(Q, k)` int64 tensor of the corresponding database row +indices. The shipped implementation is a straightforward, correct PyTorch version. Your goal is to make `knnlib.knn` **as fast as possible** on the GPU while returning the same nearest neighbors. You may rewrite the internals of the @@ -78,14 +80,17 @@ baseline on a GPU, on the same seeded queries and database. ## Correctness -Correctness is a gate. On every timed iteration the judge recomputes the -**recall@k** (the fraction of the true `k` nearest database indices your result -recovers, averaged over all queries) of your result against an exact baseline on -identical data, and requires your recall@k to stay at or above a high threshold. -Crashes, non-finite output, wrong shapes/dtypes, timeouts, and results that drop -below the recall threshold are penalized before speed is considered. You may -trade a little numerical precision for speed as long as you stay above the -recall threshold. +Correctness is a gate. On every timed iteration the judge scores your neighbours +by **ball-recall**: each returned index counts if the exact (fp32) squared-L2 +distance from its query to that database row lies within the true k-nearest- +neighbour ball (the distance to the k-th true neighbour). This is tie-robust — +bf16 distances tie heavily near rank k, so which of several equidistant points +you return does not matter, but returning a genuinely farther point does. Your +ball-recall must stay at or above a high threshold. Because the inputs are bf16, +an honest bf16 kernel clears it comfortably; a search that drops below bf16 +precision (e.g. fp8) returns farther points and fails. Crashes, non-finite +output, wrong shapes/dtypes, and timeouts are penalized before speed is +considered. ## Scoring From 5ff628274a47830fdd292c74d18047e575ed68c2 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Fri, 17 Jul 2026 02:00:05 +0000 Subject: [PATCH 31/34] feat(2.0/ivf_pq): make nprobe a real knob (nlist=8192/k=100) + bf16 precision lock WORKLOAD FIX: at the old nlist=4096/k=10 the true neighbours sat in the nearest few lists, so nprobe barely mattered -- a hard nprobe<=8 cap still passed at iso-recall ~1.0 (measured 0.86 for scan-8 vs scan-32), letting a solution quietly scan fewer lists than requested. nlist=8192 with k=100 spreads the neighbours: scan-8 vs the required scan-32 now gives only ~0.67 iso-recall on SIFT, and a hard nprobe<=8 cap fails all six workloads (iso-recall 0.64-0.82). nprobe now genuinely drives cost and correctness. setup() trains the coarse quantizer on ~40 points per centroid for the larger nlist. PRECISION LOCK (bf16): the query and the float index tensors (coarse centroids + PQ codebooks) are rounded to bf16, so the coarse distance matmul and the ADC decode carry only bf16 precision for everyone; a solution cannot win on a lower dtype. Codes are uint8 already. iso-recall stays 1.0 (bf16 ADC is still discriminative, no tie problem like knn). The per-call bf16 rounding rides on the existing _fresh_index clone, so nothing leaks across timed iterations. speedup_target 1200 -> 620 = 2x the reference geomean on the new bf16 workloads (309x on H100: SIFT 373/456/524x, GIST 199/206/239x). Note: a head-to-head absolute-ms comparison shows the flashlib reference is not the per-workload best-known here -- it picks a suboptimal fine-scan variant on some shapes (sift_np32: 13.6ms between np16's 5ms and np64's 13.8ms), so a legitimate alternative kernel can edge it out. images rebuilt to v0.4.0. --- .../config.yaml | 33 +++++++++++-------- .../docker/build_images.sh | 4 +-- .../flash_gpu.py | 32 ++++++++++++++---- 3 files changed, 48 insertions(+), 21 deletions(-) diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml index b8dc4ffa2..7137b242c 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml @@ -7,8 +7,8 @@ runtime: judge_apt_packages: [bash, ca-certificates, git, python3, python3-pip] judge_pip_packages: [modal] docker: - image: frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.3.0 - judge_image: frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.3.0 + image: frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.4.0 + judge_image: frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.4.0 environment: cpus: 4 memory_mb: 8192 @@ -28,24 +28,31 @@ evaluation: warmup_iters: 2 timed_iters: 7 recall_threshold: 0.95 # agent results must match the baseline's top-k (iso-result recall@k) on every iteration; both baseline and Triton reference compute exact ADC over the same index, so 0.95 leaves margin for ADC tie-breaks - speedup_target: 1200.0 # score cap = 2x the flashlib reference geomean on the real SIFT/GIST workloads (600x on H100 with the per-call fresh index: SIFT 875/1225/1530x, GIST 290/322/305x, iso-recall >=0.999 all 6). Reference scores ~90/100; a solution must be 2x faster than it to max out. The Python-per-query-loop baseline is noisy, so measurements range ~450-600x. (A trial that cached a CSR->padded conversion on the fixed index scored 787x before _fresh_index killed that shortcut; it now measures 21x.) + speedup_target: 620.0 # score cap = 2x the flashlib reference geomean on the real SIFT/GIST workloads with bf16-locked query+index (309x on H100 at nlist=8192/k=100: SIFT 373/456/524x, GIST 199/206/239x, iso-recall 1.0 all 6). nprobe is a real knob here (a hard nprobe<=8 cap drops iso-recall to 0.64-0.82). Query, coarse centroids and PQ codebooks all carry only bf16 precision, so the coarse matmul + ADC decode cannot win on a lower dtype. base_seed: 20260702 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on # REAL-DATA WORKLOADS: the index (the "database") is built ONCE per workload by # the frozen builder from the full real base (SIFT1M 128d / GIST1M 960d), reused - # for every timed iteration; only ivf_pq_search is timed. Each iteration draws a - # fresh Q-subset of the held-out real query set (anti-caching). Real datasets have + # for every timed iteration (each call gets a fresh clone -- see flash_gpu + # _fresh_index); only ivf_pq_search is timed. Each iteration draws a fresh + # Q-subset of the held-out real query set (anti-caching). Real datasets have # clustered, imbalanced inverted lists (unlike synthetic gaussian) -> the search - # kernel must handle long-tail lists. dataset in {sift,gist}; nprobe is the main - # recall/speed knob. Q <= #queries (SIFT 10000, GIST 1000). + # kernel must handle long-tail lists. + # + # nprobe is the main recall/speed knob and is meant to MATTER: nlist=8192 with + # k=100 makes the true neighbours spread across many lists, so scanning only the + # nearest few lists (nprobe truncation) drops iso-recall well below the gate -- + # measured: on SIFT nlist=8192/k=100, scanning 8 lists vs the required 32 gives + # only ~0.67 iso-recall (vs ~0.86 at the old nlist=4096/k=10, where truncation + # slipped through). A solution must honour the workload's nprobe. workloads: - - {id: sift_np8, dataset: sift, nlist: 4096, m: 16, nprobe: 8, Q: 1024, k: 10} - - {id: sift_np16, dataset: sift, nlist: 4096, m: 16, nprobe: 16, Q: 1024, k: 10} - - {id: sift_np32, dataset: sift, nlist: 4096, m: 16, nprobe: 32, Q: 2048, k: 10} - - {id: gist_np8, dataset: gist, nlist: 2048, m: 32, nprobe: 8, Q: 512, k: 10} - - {id: gist_np16, dataset: gist, nlist: 2048, m: 32, nprobe: 16, Q: 512, k: 10} - - {id: gist_np32, dataset: gist, nlist: 2048, m: 32, nprobe: 32, Q: 512, k: 10} + - {id: sift_np16, dataset: sift, nlist: 8192, m: 16, nprobe: 16, Q: 1024, k: 100} + - {id: sift_np32, dataset: sift, nlist: 8192, m: 16, nprobe: 32, Q: 1024, k: 100} + - {id: sift_np64, dataset: sift, nlist: 8192, m: 16, nprobe: 64, Q: 1024, k: 100} + - {id: gist_np16, dataset: gist, nlist: 8192, m: 32, nprobe: 16, Q: 512, k: 100} + - {id: gist_np32, dataset: gist, nlist: 8192, m: 32, nprobe: 32, Q: 512, k: 100} + - {id: gist_np64, dataset: gist, nlist: 8192, m: 32, nprobe: 64, Q: 512, k: 100} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh index fa07746f7..4d19d5e25 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/docker/build_images.sh @@ -3,8 +3,8 @@ set -euo pipefail HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) # the problem directory (build context) -AGENT_TAG=${AGENT_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.3.0} -JUDGE_TAG=${JUDGE_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.3.0} +AGENT_TAG=${AGENT_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-agent:experimental-v0.4.0} +JUDGE_TAG=${JUDGE_TAG:-frontiercs/ivf-pq-gpu-kernel-optimization-judge:experimental-v0.4.0} echo "Building agent image: $AGENT_TAG" docker build -f "$HERE/docker/agent/Dockerfile" -t "$AGENT_TAG" "$HERE" diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py index 56fe76a8b..ca61d51e5 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/flash_gpu.py @@ -98,8 +98,13 @@ def setup(w, seed): # Real dataset: build the frozen index from the full real base; # queries are drawn per-iteration from the held-out query set. base, queries_all = _load_ann(ds) - index = ref.ivf_pq_build(base, int(w["nlist"]), m=int(w["m"]), - nprobe=int(w["nprobe"]), seed=int(w.get("seed", 0))) + # Train the coarse quantizer on enough points for a large nlist + # (>= ~40 per centroid); the base is 1M rows so this is a subsample. + nlist = int(w["nlist"]) + tsz = min(base.shape[0], max(200000, nlist * 40)) + index = ref.ivf_pq_build(base, nlist, m=int(w["m"]), + nprobe=int(w["nprobe"]), seed=int(w.get("seed", 0)), + train_size=tsz, pq_train_size=min(base.shape[0], 100000)) return {"index": index, "D": int(base.shape[1]), "queries_all": queries_all} g = torch.Generator(device=dev).manual_seed(int(seed)) X = torch.randn(int(w["M"]), int(w["D"]), generator=g, device=dev, dtype=torch.float32) @@ -118,8 +123,20 @@ def _fresh_index(src): # region and into the free warmup calls. `src` is the pristine index kept # in ctx and never handed to the submission, so nothing leaks between iters. new = object.__new__(type(src)) + # PRECISION LOCK (bf16): round the float index tensors (coarse centroids + + # PQ codebooks) to bf16 precision so the coarse distance matmul and the ADC + # decode carry only bf16 precision for everyone. A solution then cannot win + # by computing them in a lower dtype than the baseline -- bf16 tensor cores + # are the floor, fp32/TF32 buy nothing. Codes are uint8 already; the query + # is bf16-rounded in gen(). Values stay fp32-typed so the search contract is + # unchanged. + _bf16 = {"centroids", "pq_codebooks"} for k, v in vars(src).items(): - new.__dict__[k] = v.clone() if torch.is_tensor(v) else v + if torch.is_tensor(v): + v = v.clone() + if k in _bf16 and v.is_floating_point(): + v = v.to(torch.bfloat16).to(v.dtype) + new.__dict__[k] = v return new def gen(w, seed, ctx): @@ -131,10 +148,13 @@ def gen(w, seed, ctx): # Q must be <= number of held-out queries. qa = ctx["queries_all"] perm = torch.randperm(qa.shape[0], generator=g, device=dev)[: int(w["Q"])] - data["queries"] = qa.index_select(0, perm).contiguous() + # bf16-round the query too (see _fresh_index): with the index tensors + # already at bf16 precision the whole search carries only bf16. + q = qa.index_select(0, perm) + data["queries"] = q.to(torch.bfloat16).to(q.dtype).contiguous() return data - data["queries"] = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, - device=dev, dtype=torch.float32) + q = torch.randn(int(w["Q"]), int(ctx["D"]), generator=g, device=dev, dtype=torch.float32) + data["queries"] = q.to(torch.bfloat16).to(torch.float32).contiguous() return data if prim == "knn": db = torch.randn(w["M"], w["D"], generator=g, device=dev, dtype=torch.float32) From dfce469d390ed8bdf896e1e436a10efc21a52922 Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 19 Jul 2026 05:26:45 +0000 Subject: [PATCH 32/34] fix(2.0/knn): count DISTINCT in-ball indices in ball-recall (kill 1-NN-repeat hack) knn_ball_recall counted every returned slot within the k-NN ball, so returning the single nearest neighbour k times scored recall 1.0 -- a solution that finds only argmin (and pads with duplicates) faked full recall and passed the gate (~46 pts). Now count distinct in-ball ids: the 1-NN-repeat hack drops to ~1/k while an honest top-k stays ~1.0. Threshold 0.88 unchanged. Co-Authored-By: Claude Opus 4.8 --- .../knn_gpu_kernel_optimization/config.yaml | 4 ++-- .../knn_gpu_kernel_optimization/flash_gpu.py | 18 +++++++++++++++++- 2 files changed, 19 insertions(+), 3 deletions(-) diff --git a/2.0/problems/knn_gpu_kernel_optimization/config.yaml b/2.0/problems/knn_gpu_kernel_optimization/config.yaml index 6f8ed72e7..8c8ed35ef 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/knn_gpu_kernel_optimization/config.yaml @@ -23,8 +23,8 @@ runtime: # run on the Modal GPU image defined in flash_gpu.py. The agent image bakes # /app/knnlib (git) + /opt/knn_ref + /opt/flash_gpu.py; the judge image # additionally bakes /opt/knnlib-clean. - image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.5.0 - judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.5.0 + image: frontiercs/knn-gpu-kernel-optimization-agent:experimental-v0.6.0 + judge_image: frontiercs/knn-gpu-kernel-optimization-judge:experimental-v0.6.0 environment: cpus: 4 memory_mb: 8192 diff --git a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py index 484f44114..4edd879ec 100644 --- a/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/knn_gpu_kernel_optimization/flash_gpu.py @@ -213,18 +213,34 @@ def knn_ball_recall(queries, database, agent_idx, ref_idx, tol): # picked (any point at the k-th distance is correct) but still rejects a # genuinely-farther point, so a lower-precision search that returns wrong # neighbours fails while an honest bf16 kernel passes exactly. + # + # DISTINCT-index counting (anti-hack): a returned index counts at most ONCE + # per query. Without this, returning the single nearest neighbour k times + # scores 1.0 (the 1-NN is trivially inside every k-NN ball) -- i.e. a search + # that finds only argmin and pads with duplicates fakes full recall. Counting + # distinct in-ball indices makes that hack score ~1/k; an honest top-k returns + # k distinct in-ball ids and still scores ~1.0. qf = queries.to(torch.float32); dbf = database.to(torch.float32) + M = dbf.shape[0] # k-NN ball radius per query = exact distance to the baseline's k-th nbr kth = dbf.index_select(0, ref_idx[:, -1].long()) # (Q, D) d_k = ((qf - kth) ** 2).sum(1) # (Q,) thr = d_k * (1.0 + tol) + 1e-6 a = agent_idx.long(); Q, k = a.shape + sent = M + torch.arange(k, device=a.device) # distinct sentinels >= M hits = 0 for j in range(0, Q, 256): qb = qf[j:j + 256]; ab = a[j:j + 256] # (q, D), (q, k) cand = dbf.index_select(0, ab.reshape(-1)).reshape(ab.shape[0], k, -1) ad = ((qb[:, None, :] - cand) ** 2).sum(2) # (q, k) exact dists - hits += int((ad <= thr[j:j + 256, None]).sum().item()) + inball = ad <= thr[j:j + 256, None] # (q, k) bool + # out-of-ball slots -> per-slot distinct sentinels (>= M) so they never + # collide with a real id and are dropped by the (< M) filter below. + idx_ib = torch.where(inball, ab, sent[None, :]) # (q, k) + srt, _ = idx_ib.sort(dim=1) + first = torch.ones_like(srt, dtype=torch.bool) + first[:, 1:] = srt[:, 1:] != srt[:, :-1] # first occurrence of each value + hits += int((first & (srt < M)).sum().item()) # distinct real in-ball ids return hits / (Q * k) def ari(a, b): From 62e2d93953f2bfb39ee501f7aad49a01aee16e3b Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 19 Jul 2026 05:26:45 +0000 Subject: [PATCH 33/34] fix(2.0/dbscan): non-convex multi-subspace moons data (kill k-means blob shortcut) Separated Gaussian blobs let a "k-means into n_centers cells then DBSCAN each cell" shortcut (and plain k-means) reproduce the labels and pass the ARI gate without doing the eps-neighbourhood work. Data is now a union of interleaving half-moons, each pair embedded in its own random 2-D subspace of R^D: k-means-shortcut ARI ~0.72-0.75 and a PCA->2D shortcut ~0.5-0.6 (both << 0.99), while an exact scan stays bf16-stable at 1.0 (0% noise, #clusters=n_centers). eps retuned per D; speedup_target 57 -> 130. Co-Authored-By: Claude Opus 4.8 --- .../config.yaml | 25 +++++----- .../flash_gpu.py | 46 +++++++++++++------ 2 files changed, 47 insertions(+), 24 deletions(-) diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml index dc41baf42..e386fdad6 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/dbscan_gpu_kernel_optimization/config.yaml @@ -7,8 +7,8 @@ runtime: judge_apt_packages: [bash, ca-certificates, git, python3, python3-pip] judge_pip_packages: [modal] docker: - image: frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.3.0 - judge_image: frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.3.0 + image: frontiercs/dbscan-gpu-kernel-optimization-agent:experimental-v0.4.0 + judge_image: frontiercs/dbscan-gpu-kernel-optimization-judge:experimental-v0.4.0 environment: cpus: 4 memory_mb: 8192 @@ -22,8 +22,8 @@ evaluation: modal_timeout_seconds: 2400 warmup_iters: 2 timed_iters: 3 # the O(N^2) naive baseline is seconds/call at these N; keep the timed budget bounded (median of 3) - ari_threshold: 0.99 # agent clustering must match the baseline exact DBSCAN (Adjusted Rand Index) every iteration. On the non-separable concentric-ring data a centroid/partition approximation (k-means) scores ARI <= ~0.1 -> fails; only exact eps-connectivity passes. Exact reference measures ARI ~1.0. - speedup_target: 57.0 # score cap = 2x the flashlib reference geomean on the bf16-locked blob workloads (28.6x on H100, ARI=1.0 all 4). Points carry only bf16 precision so no solution wins by computing the eps-graph distances in a lower dtype; well-separated blobs mean bf16 never flips an eps-neighbour (ARI stays exactly 1.0). + ari_threshold: 0.99 # agent clustering must match the baseline exact DBSCAN (Adjusted Rand Index) every iteration. On the non-convex multi-subspace-moons data a centroid/partition approximation (k-means, or "k-means the points then DBSCAN each cell") mislabels the interleaving arcs -> ARI ~0.72-0.75, and a PCA->2D projection -> ~0.5-0.6, both < 0.99 -> fail; only an exact eps-connectivity scan passes. Exact reference measures ARI ~1.0 (bf16-stable). + speedup_target: 130.0 # score cap = 2x the flashlib reference geomean on the non-convex multi-subspace-moons workloads (65x on H100: w0-w3 = 29/65/69/141x, ARI=1.0 all 4). Moons make the naive label-propagation baseline slower (elongated clusters -> long chains) so the factor is higher than the old blob regime (28.6x); the reference's connectivity is unaffected. Points carry only bf16 precision so no solution wins on a lower dtype; eps is tuned so bf16 never flips an eps-neighbour (two exact scans on the same bf16 data agree at ARI 1.0). base_seed: 20260701 agent_workload_count: 4 # all 4 flashlib graded workloads (feedback covers every graded shape) expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on @@ -32,13 +32,16 @@ evaluation: # is faster than the grid kernel's fixed overhead. Vary N + density so agents can't # special-case. (H100 crossover sweep: 100k->6.0x, 150k->14.1x, 200k->27.0x.) workloads: - # flashlib's own DBSCAN benchmark shapes (benchmarks/vs_cuml/dbscan.py): make_blobs - # data, cluster_std=1.0, no noise. D>=8 -> flashlib's benchmarked brute-force - # (flash_knn) path. The generator is judge-only (hidden from the agent). - - {id: w0, N: 20000, D: 8, eps: 1.5, min_samples: 5, n_centers: 4} - - {id: w1, N: 20000, D: 32, eps: 6.0, min_samples: 5, n_centers: 6} - - {id: w2, N: 20000, D: 64, eps: 8.0, min_samples: 5, n_centers: 10} - - {id: w3, N: 50000, D: 16, eps: 3.5, min_samples: 5, n_centers: 8} + # NON-CONVEX data: n_centers interleaving half-moons, each moon-pair in its own + # random 2-D subspace of R^D (gen in flash_gpu.py). Voronoi/k-means shortcuts + # mislabel non-convex arcs (kmeans-shortcut ARI ~0.72-0.75, PCA-2D ~0.5-0.6), + # so only an exact eps-neighbourhood scan clears the ARI gate. eps is tuned per D + # so DBSCAN gives exactly n_centers clusters with ~0% noise and is bf16-stable + # (two exact scans on the same bf16 data agree at ARI 1.0). Generator is judge-only. + - {id: w0, N: 20000, D: 8, eps: 0.90, min_samples: 5, n_centers: 4} + - {id: w1, N: 20000, D: 32, eps: 1.15, min_samples: 5, n_centers: 6} + - {id: w2, N: 20000, D: 64, eps: 1.40, min_samples: 5, n_centers: 10} + - {id: w3, N: 50000, D: 16, eps: 1.00, min_samples: 5, n_centers: 8} submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py index 588ea67dd..bfc23d31a 100644 --- a/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py +++ b/2.0/problems/dbscan_gpu_kernel_optimization/flash_gpu.py @@ -97,20 +97,40 @@ def gen(w, seed, ctx): q = torch.randn(w["Q"], w["D"], generator=g, device=dev, dtype=torch.float32) return {"queries": q, "database": db} if prim == "dbscan": - # flashlib's own DBSCAN benchmark data: sklearn make_blobs equivalent -- - # Gaussian blobs, centers ~ U(-10, 10)^D, cluster_std = 1.0, no noise. - # (The generator is judge-only and NOT present in the agent image, so the - # agent cannot read the data structure and hardcode a data-specific labeler.) + # NON-CONVEX clustering data: a union of interleaving half-moons, each + # moon-PAIR placed in its own random 2-D subspace of R^D (so the cluster + # structure spans many dims -- a PCA->2D projection cannot capture it) and + # offset from the other pairs. Voronoi/centroid shortcuts get non-convex + # moons WRONG: "k-means the points into n_centers cells then DBSCAN each + # cell" (and plain k-means) mislabels the interleaving arcs, so the ARI + # gate rejects them (kmeans-shortcut ARI ~0.72-0.75, PCA-2D ~0.5-0.6 << gate). + # Only a real eps-neighbourhood density scan recovers the clusters -- which + # is exactly the kernel this task asks you to optimise. Clusters = the arcs + # (n_centers of them). (The generator is judge-only and NOT in the agent image.) nc, D, N = int(w["n_centers"]), int(w["D"]), int(w["N"]) - std = float(w.get("cluster_std", 1.0)) - centers = (torch.rand(nc, D, generator=g, device=dev) * 2.0 - 1.0) * 10.0 - asg = torch.randint(0, nc, (N,), generator=g, device=dev) - x = centers.index_select(0, asg) + torch.randn(N, D, generator=g, device=dev) * std - # PRECISION LOCK (bf16): the points carry only bf16 precision, so a - # solution cannot win by computing the pairwise distances in a lower - # dtype than everyone else -- bf16 tensor cores are the floor, and the - # blobs are well separated (std=1.0, centers in +-10) so bf16 rounding - # never flips an eps-neighbour and the clustering is unchanged. + pairs = max(1, nc // 2) + basis, _ = torch.linalg.qr(torch.randn(D, D, generator=g, device=dev)) # orthonormal + per = (N + pairs - 1) // pairs + parts = [] + for p in range(pairs): + nout = per // 2; nin = per - nout + to = torch.rand(nout, generator=g, device=dev) * torch.pi + ti = torch.rand(nin, generator=g, device=dev) * torch.pi + mx = torch.cat([torch.cos(to), 1.0 - torch.cos(ti)]) + my = torch.cat([torch.sin(to), 0.5 - torch.sin(ti)]) + m = (torch.stack([mx, my], 1) + torch.randn(per, 2, generator=g, device=dev) * 0.05) * 6.0 + b0 = basis[:, (2 * p) % D]; b1 = basis[:, (2 * p + 1) % D] + pts = m[:, 0:1] * b0[None, :] + m[:, 1:2] * b1[None, :] + pts = pts + torch.randn(per, D, generator=g, device=dev) * 0.1 # thin ambient noise + pts = pts + basis[:, (2 * p + 4) % D][None, :] * (p * 30.0) # separate the pairs + parts.append(pts) + x = torch.cat(parts, 0) + x = x[torch.randperm(x.shape[0], generator=g, device=dev)][:N] + # PRECISION LOCK (bf16): points carry only bf16 precision, so no solution + # wins by computing the eps-graph distances in a lower dtype. eps is set so + # the moons stay cleanly separated under bf16 -- an honest bf16 scan equals + # the fp32 clustering exactly (verified: two exact scans on the same bf16 + # data agree at ARI 1.0, so the gate is not flaky on border points). return {"x": x.to(torch.bfloat16).to(torch.float32), "eps": float(w["eps"]), "min_samples": int(w["min_samples"])} x = torch.randn(w["N"], w["D"], generator=g, device=dev, dtype=torch.float32) From e8b248c839b1f91937097d8de548bf2d0412041e Mon Sep 17 00:00:00 2001 From: momoway <3499622023@qq.com> Date: Sun, 19 Jul 2026 05:26:45 +0000 Subject: [PATCH 34/34] fix(2.0/ivf_pq): SIFT-only nlist=8192 fat-M regime + gemm-pinned ref + 0.99 gate A low-nlist "fat M" regime made the reference's decode+GEMM strong but also made high nprobe redundant, re-opening nprobe truncation (a solution secretly halved GIST nprobe and still cleared the 0.95 gate). Keep nlist=8192 (nprobe matters: halving drops recall to 0.85-0.92) and get fat M from a LARGE Q instead; drop GIST (1k queries can't reach fat M). Pin reference.patch to variant="gemm" (auto under-picks it). Tighten recall gate 0.95 -> 0.99 (truncation fails, honest full-nprobe = 1.0). Q*nprobe capped <=262k so the naive baseline stays practical. speedup_target -> 2551 (2x gemm ref geomean 1276x). 3-rep codex trial: 88.3/98.4/91.9, all honest full-nprobe, no trivial 100. Co-Authored-By: Claude Opus 4.8 --- .../config.yaml | 41 ++++++++++++------- .../reference.patch | 2 +- 2 files changed, 28 insertions(+), 15 deletions(-) diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml index 7137b242c..415bfc66c 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/config.yaml @@ -27,8 +27,8 @@ evaluation: modal_timeout_seconds: 3600 # the naive per-query-loop baseline is seconds/call and real GIST (D=960) is heavier; give the judge margin warmup_iters: 2 timed_iters: 7 - recall_threshold: 0.95 # agent results must match the baseline's top-k (iso-result recall@k) on every iteration; both baseline and Triton reference compute exact ADC over the same index, so 0.95 leaves margin for ADC tie-breaks - speedup_target: 620.0 # score cap = 2x the flashlib reference geomean on the real SIFT/GIST workloads with bf16-locked query+index (309x on H100 at nlist=8192/k=100: SIFT 373/456/524x, GIST 199/206/239x, iso-recall 1.0 all 6). nprobe is a real knob here (a hard nprobe<=8 cap drops iso-recall to 0.64-0.82). Query, coarse centroids and PQ codebooks all carry only bf16 precision, so the coarse matmul + ADC decode cannot win on a lower dtype. + recall_threshold: 0.99 # agent ids must match the FULL-nprobe baseline's top-k (iso-result recall@k) every iteration. Tightened 0.95->0.99 to kill nprobe-truncation: at nlist=8192 halving nprobe drops iso-recall to 0.85-0.92 and a 3/8 cut to ~0.9, all << 0.99, while an honest full-nprobe exact-ADC search stays ~0.9999 (bf16 tie margin). So a solution cannot silently probe fewer lists. + speedup_target: 2551.0 # score cap = 2x the gemm-pinned reference geomean on the SIFT nlist=8192 dispatch-capped workloads (1276x on H100: 1000/1663/1012/1750/786/1863x, iso-recall 1.0 all 6, gate 0.99). Reference = fast WGMMA decode+GEMM; beats a scalar-decode kernel ~1.1-1.9x at M16-32, so 100 needs a genuinely better tensor-core kernel. NOTE: the huge factor is the naive Python-loop BASELINE -> a near-reference kernel still scores ~91 (log compression); the planned torch-vectorized baseline will spread the top AND remove the dispatch wall (letting M go fatter). bf16-locked query+index. base_seed: 20260702 agent_workload_count: 6 # agent's submission feedback covers ALL graded workloads (no blind pass-3/fail-6 cliff); params are agent-visible anyway expose_per_workload_metrics: true # show per-workload pass/fail so the agent can fix the specific workload it regresses on @@ -40,19 +40,32 @@ evaluation: # clustered, imbalanced inverted lists (unlike synthetic gaussian) -> the search # kernel must handle long-tail lists. # - # nprobe is the main recall/speed knob and is meant to MATTER: nlist=8192 with - # k=100 makes the true neighbours spread across many lists, so scanning only the - # nearest few lists (nprobe truncation) drops iso-recall well below the gate -- - # measured: on SIFT nlist=8192/k=100, scanning 8 lists vs the required 32 gives - # only ~0.67 iso-recall (vs ~0.86 at the old nlist=4096/k=10, where truncation - # slipped through). A solution must honour the workload's nprobe. + # nprobe is the main recall/speed knob and is meant to MATTER: scanning fewer than + # the workload's nprobe lists drops iso-recall below the gate. + # + # SIFT-only, nlist=8192, LARGE Q. The fat GEMM M that makes the reference's WGMMA + # decode+GEMM strong comes from a big query batch (M = Q*nprobe/nlist), NOT from a + # low nlist -- so nlist stays HIGH (8192, ~122 rows/list) and nprobe genuinely + # matters: halving nprobe drops iso-recall to 0.85-0.92 (measured), so a + # nprobe-truncating solution fails the 0.99 gate. (A low-nlist "fat M" regime made + # M fat but also made high nprobe redundant -> truncation was nearly free; and GIST + # with only 1k held-out queries cannot reach a fat M at nlist=8192, so it is dropped.) + # The reference is pinned to the gemm variant (see reference.patch); at M>=16 it beats + # a query-parallel scalar-decode kernel ~1.5-2.4x, so beating IT 2x needs a genuinely + # good tensor-core kernel. + # Q*nprobe (the naive Python-loop baseline's per-call dispatch count) is capped at + # ~262k so the judge stays practical -- Q=8192 pushed it to ~0.5-1M and the eval + # backed up (submissions queued unevaluated). M = Q*nprobe/nlist is kept at 16-32 + # (fat enough that the pinned gemm reference beats a scalar kernel ~1.1-1.9x). The + # deferred torch-vectorized baseline will remove the Python-loop dispatch wall and + # allow a fatter M (and fix the score log-compression) in the next round. workloads: - - {id: sift_np16, dataset: sift, nlist: 8192, m: 16, nprobe: 16, Q: 1024, k: 100} - - {id: sift_np32, dataset: sift, nlist: 8192, m: 16, nprobe: 32, Q: 1024, k: 100} - - {id: sift_np64, dataset: sift, nlist: 8192, m: 16, nprobe: 64, Q: 1024, k: 100} - - {id: gist_np16, dataset: gist, nlist: 8192, m: 32, nprobe: 16, Q: 512, k: 100} - - {id: gist_np32, dataset: gist, nlist: 8192, m: 32, nprobe: 32, Q: 512, k: 100} - - {id: gist_np64, dataset: gist, nlist: 8192, m: 32, nprobe: 64, Q: 512, k: 100} + - {id: sift_q4k_np32, dataset: sift, nlist: 8192, m: 16, nprobe: 32, Q: 4096, k: 100} # M16 disp131k + - {id: sift_q4k_np64, dataset: sift, nlist: 8192, m: 16, nprobe: 64, Q: 4096, k: 100} # M32 disp262k + - {id: sift_q2k_np64, dataset: sift, nlist: 8192, m: 16, nprobe: 64, Q: 2048, k: 100} # M16 disp131k + - {id: sift_q2k_np128, dataset: sift, nlist: 8192, m: 16, nprobe: 128, Q: 2048, k: 100} # M32 disp262k + - {id: sift_q1k_np128, dataset: sift, nlist: 8192, m: 16, nprobe: 128, Q: 1024, k: 100} # M16 disp131k + - {id: sift_q1k_np256, dataset: sift, nlist: 8192, m: 16, nprobe: 256, Q: 1024, k: 100} # M32 disp262k submission: kind: file path: /app/solution.patch diff --git a/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch b/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch index 419c6e5e4..9d87e8249 100644 --- a/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch +++ b/2.0/problems/ivf_pq_gpu_kernel_optimization/reference.patch @@ -1538,7 +1538,7 @@ index 56b559b..8b32e5d 100644 - - return out_vals, out_ids +def ivf_pq_search(index, Q, k, *, nprobe=None): -+ return ivf_pq_search_triton(index, Q, k, nprobe=nprobe) ++ return ivf_pq_search_triton(index, Q, k, nprobe=nprobe, variant="gemm") # pin fast WGMMA gemm path (auto under-picks it) __all__ = ["ivf_pq_search"]