diff --git a/.github/workflows/security.yml b/.github/workflows/security.yml index d297b8c..b526963 100644 --- a/.github/workflows/security.yml +++ b/.github/workflows/security.yml @@ -19,6 +19,7 @@ jobs: dependency-audit: name: Dependency CVE Scan (pip-audit) runs-on: ubuntu-latest + if: github.repository == 'uicacer/STREAM' steps: - uses: actions/checkout@v4 @@ -47,6 +48,7 @@ jobs: static-analysis: name: Static Security Analysis (bandit) runs-on: ubuntu-latest + if: github.repository == 'uicacer/STREAM' steps: - uses: actions/checkout@v4 @@ -85,6 +87,7 @@ jobs: secret-scan: name: Secret Scan (gitleaks) runs-on: ubuntu-latest + if: github.repository == 'uicacer/STREAM' steps: - uses: actions/checkout@v4 with: diff --git a/.gitignore b/.gitignore index ee2c7a7..0c8e94a 100644 --- a/.gitignore +++ b/.gitignore @@ -132,8 +132,8 @@ BACKUP_INSTRUCTIONS.md tutorial/ Research_Directions/ -# streamrelay standalone library (has its own repo: github.com/uicacer/streamrelay) -streamrelay_staging/ +# streamrelay standalone library (has its own repo at ~/Documents/CODES/streamrelay) +streamrelay_backup/ # Excalidraw source files *.excalidraw diff --git a/.gitleaks.toml b/.gitleaks.toml index d402f0b..c6f2c7e 100644 --- a/.gitleaks.toml +++ b/.gitleaks.toml @@ -10,4 +10,9 @@ paths = [ "docs/CLOUD_ROUTING_VIA_AGGREGATORS.md", "docs/LAKESHORE_CONNECTIVITY.md", "docs/WEBSOCKET_RELAY_TECHNICAL_REPORT.md", + "docs/test-plan.md", + "docs/proxy-vm-deployment.md", + "docs/vm-hpc-as-api-migration.md", + "streamrelay_staging_backup/streamrelay/crypto.py", + "streamrelay_staging_backup/README.md", ] diff --git a/Dockerfile.middleware b/Dockerfile.middleware index 54fc9a2..043a5cd 100644 --- a/Dockerfile.middleware +++ b/Dockerfile.middleware @@ -9,7 +9,7 @@ # ============================================================================= # Base image: Python 3.12 on Debian (slim = minimal size ~150MB) -FROM python:3.12-slim +FROM python:3.14-slim # Set working directory inside container WORKDIR /app diff --git a/docs/STREAM_Architecture.png b/docs/STREAM_Architecture.png index 3f19e77..655a8de 100644 Binary files a/docs/STREAM_Architecture.png and b/docs/STREAM_Architecture.png differ diff --git a/frontends/react/package-lock.json b/frontends/react/package-lock.json index 5b87141..4d585d7 100644 --- a/frontends/react/package-lock.json +++ b/frontends/react/package-lock.json @@ -16,29 +16,29 @@ "@tailwindcss/typography": "^0.5.19", "class-variance-authority": "^0.7.0", "clsx": "^2.1.0", - "geist": "^1.7.0", + "geist": "^1.7.1", "idb": "^8.0.0", - "katex": "^0.16.28", - "lucide-react": "^0.312.0", + "katex": "^0.16.33", + "lucide-react": "^0.575.0", "react": "^18.2.0", "react-dom": "^18.2.0", "react-markdown": "^10.1.0", "rehype-katex": "^7.0.1", "remark-math": "^6.0.0", "tailwind-merge": "^2.2.0", - "zustand": "^4.5.0" + "zustand": "^5.0.0" }, "devDependencies": { "@types/react": "^18.2.0", "@types/react-dom": "^18.2.0", "@vitejs/plugin-react": "^4.2.0", "autoprefixer": "^10.4.0", - "concurrently": "^8.2.0", + "concurrently": "^9.2.1", "postcss": "^8.4.0", "tailwindcss": "^3.4.0", "tailwindcss-animate": "^1.0.7", "typescript": "^5.3.0", - "vite": "^5.0.0" + "vite": "^7.3.1" } }, "node_modules/@alloc/quick-lru": { @@ -288,16 +288,6 @@ "@babel/core": "^7.0.0-0" } }, - "node_modules/@babel/runtime": { - "version": "7.28.6", - "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.28.6.tgz", - "integrity": "sha512-05WQkdpL9COIMz4LjTxGpPNCdlpyimKppYNoJ5Di5EUObifl8t4tuLuUBBZEpoLYOmfvIWrsp9fCl0HoPRVTdA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=6.9.0" - } - }, "node_modules/@babel/template": { "version": "7.28.6", "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.28.6.tgz", @@ -357,9 +347,9 @@ } }, "node_modules/@esbuild/aix-ppc64": { - "version": "0.21.5", - "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.21.5.tgz", - "integrity": "sha512-1SDgH6ZSPTlggy1yI6+Dbkiz8xzpHJEVAlF/AM1tHPLsf5STom9rwtjE4hKAF20FfXXNTFqEYXyJNWh1GiZedQ==", + "version": "0.27.7", + "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.27.7.tgz", + "integrity": "sha512-EKX3Qwmhz1eMdEJokhALr0YiD0lhQNwDqkPYyPhiSwKrh7/4KRjQc04sZ8db+5DVVnZ1LmbNDI1uAMPEUBnQPg==", "cpu": [ "ppc64" ], @@ -370,13 +360,13 @@ "aix" ], "engines": { - "node": ">=12" + "node": ">=18" } }, "node_modules/@esbuild/android-arm": { - "version": "0.21.5", - "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.21.5.tgz", - "integrity": "sha512-vCPvzSjpPHEi1siZdlvAlsPxXl7WbOVUBBAowWug4rJHb68Ox8KualB+1ocNvT5fjv6wpkX6o/iEpbDrf68zcg==", + "version": "0.27.7", + "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.27.7.tgz", + "integrity": "sha512-jbPXvB4Yj2yBV7HUfE2KHe4GJX51QplCN1pGbYjvsyCZbQmies29EoJbkEc+vYuU5o45AfQn37vZlyXy4YJ8RQ==", "cpu": [ "arm" ], @@ -387,13 +377,13 @@ "android" ], "engines": { - 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"esbuild": "^0.21.3", - "postcss": "^8.4.43", - "rollup": "^4.20.0" + "esbuild": "^0.27.0", + "fdir": "^6.5.0", + "picomatch": "^4.0.3", + "postcss": "^8.5.6", + "rollup": "^4.43.0", + "tinyglobby": "^0.2.15" }, "bin": { "vite": "bin/vite.js" }, "engines": { - "node": "^18.0.0 || >=20.0.0" + "node": "^20.19.0 || >=22.12.0" }, "funding": { "url": "https://github.com/vitejs/vite?sponsor=1" @@ -6142,19 +6147,25 @@ "fsevents": "~2.3.3" }, "peerDependencies": { - "@types/node": "^18.0.0 || >=20.0.0", - "less": "*", + "@types/node": "^20.19.0 || >=22.12.0", + "jiti": ">=1.21.0", + "less": "^4.0.0", "lightningcss": "^1.21.0", - "sass": "*", - "sass-embedded": "*", - "stylus": "*", - "sugarss": "*", - "terser": "^5.4.0" + "sass": "^1.70.0", + "sass-embedded": "^1.70.0", + "stylus": ">=0.54.8", + "sugarss": "^5.0.0", + "terser": "^5.16.0", + "tsx": "^4.8.1", + "yaml": "^2.4.2" }, "peerDependenciesMeta": { "@types/node": { "optional": true }, + "jiti": { + "optional": true + }, "less": { "optional": true }, @@ -6175,9 +6186,47 @@ }, "terser": { "optional": true + }, + "tsx": { + "optional": true + }, + "yaml": { + "optional": true + } + } + }, + "node_modules/vite/node_modules/fdir": { + "version": "6.5.0", + "resolved": "https://registry.npmjs.org/fdir/-/fdir-6.5.0.tgz", + "integrity": "sha512-tIbYtZbucOs0BRGqPJkshJUYdL+SDH7dVM8gjy+ERp3WAUjLEFJE+02kanyHtwjWOnwrKYBiwAmM0p4kLJAnXg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12.0.0" + }, + "peerDependencies": { + "picomatch": "^3 || ^4" + }, + "peerDependenciesMeta": { + "picomatch": { + "optional": true } } }, + "node_modules/vite/node_modules/picomatch": { + "version": "4.0.4", + "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.4.tgz", + "integrity": "sha512-QP88BAKvMam/3NxH6vj2o21R6MjxZUAd6nlwAS/pnGvN9IVLocLHxGYIzFhg6fUQ+5th6P4dv4eW9jX3DSIj7A==", + "dev": true, + "license": "MIT", + "peer": true, + "engines": { + "node": ">=12" + }, + "funding": { + "url": "https://github.com/sponsors/jonschlinkert" + } + }, "node_modules/web-namespaces": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/web-namespaces/-/web-namespaces-2.0.1.tgz", @@ -6253,20 +6302,18 @@ } }, "node_modules/zustand": { - "version": "4.5.7", - "resolved": "https://registry.npmjs.org/zustand/-/zustand-4.5.7.tgz", - "integrity": "sha512-CHOUy7mu3lbD6o6LJLfllpjkzhHXSBlX8B9+qPddUsIfeF5S/UZ5q0kmCsnRqT1UHFQZchNFDDzMbQsuesHWlw==", + "version": "5.0.13", + "resolved": "https://registry.npmjs.org/zustand/-/zustand-5.0.13.tgz", + "integrity": "sha512-efI2tVaVQPqtOh114loML/Z80Y4NP3yc+Ff0fYiZJPauNeWZeIp/bRFD7I9bfmCOYBh/PHxlglQ9+wvlwnPikQ==", "license": "MIT", - "dependencies": { - "use-sync-external-store": "^1.2.2" - }, "engines": { - "node": ">=12.7.0" + "node": ">=12.20.0" }, "peerDependencies": { - "@types/react": ">=16.8", + "@types/react": ">=18.0.0", "immer": ">=9.0.6", - "react": ">=16.8" + "react": ">=18.0.0", + "use-sync-external-store": ">=1.2.0" }, "peerDependenciesMeta": { "@types/react": { @@ -6277,6 +6324,9 @@ }, "react": { "optional": true + }, + "use-sync-external-store": { + "optional": true } } }, diff --git a/frontends/react/package.json b/frontends/react/package.json index ba94297..6624a1d 100644 --- a/frontends/react/package.json +++ b/frontends/react/package.json @@ -19,28 +19,28 @@ "@tailwindcss/typography": "^0.5.19", "class-variance-authority": "^0.7.0", "clsx": "^2.1.0", - "geist": "^1.7.0", + "geist": "^1.7.1", "idb": "^8.0.0", - "katex": "^0.16.28", - "lucide-react": "^0.312.0", + "katex": "^0.16.33", + "lucide-react": "^0.575.0", "react": "^18.2.0", "react-dom": "^18.2.0", "react-markdown": "^10.1.0", "rehype-katex": "^7.0.1", "remark-math": "^6.0.0", "tailwind-merge": "^2.2.0", - "zustand": "^4.5.0" + "zustand": "^5.0.0" }, "devDependencies": { "@types/react": "^18.2.0", "@types/react-dom": "^18.2.0", "@vitejs/plugin-react": "^4.2.0", "autoprefixer": "^10.4.0", - "concurrently": "^8.2.0", + "concurrently": "^9.2.1", "postcss": "^8.4.0", "tailwindcss": "^3.4.0", "tailwindcss-animate": "^1.0.7", "typescript": "^5.3.0", - "vite": "^5.0.0" + "vite": "^7.3.1" } } diff --git a/pyproject.toml b/pyproject.toml index d70818f..fcba9e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,12 +20,12 @@ dependencies = [ # UI (Streamlit) "streamlit>=1.52.2", # Middleware - Web Framework - "fastapi>=0.115.6", + "fastapi>=0.115.6,!=0.136.3", "uvicorn[standard]>=0.34.0", "pydantic>=2.10.4", # Middleware - HTTP & Networking "httpx>=0.28.1", - "python-multipart>=0.0.20", + "python-multipart>=0.0.32", # Database "psycopg2-binary>=2.9.10", # Utilities @@ -44,8 +44,10 @@ dependencies = [ "python-docx>=1.0.0", "openpyxl>=3.1.0", "python-pptx>=1.0.0", - "cryptography>=46.0.4", + "cryptography>=48.0.1", "anthropic>=0.102.0", + "aiohttp>=3.14.1", + "starlette>=1.3.1", ] [project.optional-dependencies] diff --git a/stream/gateway/litellm_config.yaml b/stream/gateway/litellm_config.yaml index f7adff8..7f96d74 100644 --- a/stream/gateway/litellm_config.yaml +++ b/stream/gateway/litellm_config.yaml @@ -160,7 +160,17 @@ model_list: # correct vLLM instance via Globus Compute (preferred) or SSH (fallback). # $0 per token — runs on university-owned GPUs. - # 72B VL AWQ model (multimodal flagship, runs on H100 NVL, ~25 tok/s) + # Gemma 4 31B Instruct (text + tool-use, runs on 2× A100 ga-002, 128K context) + - model_name: lakeshore-gemma4-31b + litellm_params: + model: openai/gemma4-31b + api_base: http://lakeshore-proxy:8001/v1 + api_key: dummy # pragma: allowlist secret + model_info: + input_cost_per_token: 0.0 + output_cost_per_token: 0.0 + + # Qwen2.5-VL-72B AWQ (multimodal, runs on H100 NVL ghi2-002) — offline - model_name: lakeshore-qwen-vl-72b litellm_params: model: openai/Qwen/Qwen2.5-VL-72B-Instruct-AWQ @@ -178,6 +188,7 @@ router_settings: model_timeout: local-llama: 90 local-vision: 120 + lakeshore-gemma4-31b: 180 lakeshore-qwen-vl-72b: 180 cloud-claude: 120 cloud-gpt: 120 diff --git a/stream/middleware/config.py b/stream/middleware/config.py index 1a2920b..eeffe93 100644 --- a/stream/middleware/config.py +++ b/stream/middleware/config.py @@ -210,18 +210,26 @@ # ============================================================================= # LAKESHORE MODELS # ============================================================================= -# Each Lakeshore model runs as a vLLM instance on the H100 NVL GPU (ghi2-002). +# Each Lakeshore model runs as a vLLM instance on the cluster. # The Globus Compute client uses this mapping to route to the correct vLLM URL. # -# GPU: Full H100 NVL (94 GB / 95,830 MiB VRAM) on ghi2-002 in batch_gpu2 partition. -# Only one model can run at a time (same port on same node). -# -# hf_name: The HuggingFace model ID that vLLM loads. This MUST match the model -# name passed to `vllm serve` in the SLURM script, because vLLM's OpenAI- -# compatible API uses this as the model identifier in chat completion requests. +# hf_name: The value passed to --served-model-name in the SLURM script. +# This MUST match exactly — vLLM's OpenAI-compatible API uses it as the model +# identifier in chat completion requests. LAKESHORE_MODELS = { - # 72B VL AWQ: Vision-Language flagship. Handles both text and image queries. - # ~25 tok/s with Marlin kernels on H100 NVL. + # Gemma 4 31B Instruct: text + tool-use flagship (currently running). + # 2× A100 SXM4 80GB on ga-002, batch_gpuapi, account ts_acer. + # 128K context window. Text-only (image/audio disabled in SLURM script). + # See scripts/vllm-gemma4-ga002.sh for SLURM launch script. + "lakeshore-gemma4-31b": { + "hf_name": "gemma4-31b", + "host": "ga-002", + "port": 8001, + "description": "Text + Tool-Use (Gemma 4 31B, 128K context)", + "multimodal": False, + }, + # Qwen2.5-VL-72B AWQ: Vision-Language model (offline — ghi2-002 not allocated). + # Full H100 NVL (94 GB) on ghi2-002, batch_gpu2 partition. # See scripts/vllm-qwen-vl-72b.sh for SLURM launch script. "lakeshore-qwen-vl-72b": { "hf_name": "Qwen/Qwen2.5-VL-72B-Instruct-AWQ", @@ -241,6 +249,7 @@ def get_lakeshore_vllm_url(model: str) -> str: from VLLM_SERVER_URL. Examples: + lakeshore-gemma4-31b (host=ga-002) → http://ga-002:8001 lakeshore-qwen-vl-72b (host=ghi2-002) → http://ghi2-002:8000 """ model_info = LAKESHORE_MODELS.get(model) @@ -561,7 +570,7 @@ def get_lakeshore_vllm_url(model: str) -> str: DEFAULT_MODELS = { "local": "local-llama", - "lakeshore": "lakeshore-qwen-vl-72b", + "lakeshore": "lakeshore-gemma4-31b", "cloud": DEFAULT_CLOUD_PROVIDER, # Now configurable! } @@ -609,6 +618,8 @@ def get_lakeshore_vllm_url(model: str) -> str: "local-vision", # Lakeshore: Qwen2.5-VL-72B (vision-language model on H100) "lakeshore-qwen-vl-72b", + # NOTE: lakeshore-gemma4-31b is text-only (--limit-mm-per-prompt image:0, audio:0) + # — it is intentionally NOT listed here. # Cloud (direct): Claude Sonnet 4, GPT-4o, and GPT-4o Mini all support vision "cloud-claude", "cloud-gpt", @@ -677,10 +688,11 @@ def get_lakeshore_vllm_url(model: str) -> str: # Gemma 3 4B supports 128K context, but images consume significant context. # Each image uses ~765 tokens, so we use 32K as a practical limit. "local-vision": {"total": 32768, "reserve_output": 2048}, - # Lakeshore: 64K context (vLLM --max-model-len=65536) on H100 NVL. - # reserve_output=4096 allows longer, more detailed responses (~2 pages), - # matching cloud models. The 72B models have enough VRAM headroom at 0.90 - # utilization with --enforce-eager for 64K context + 4K output reserve. + # Gemma 4 31B: 128K context (--max-model-len 131072) on 2× A100 SXM4 80GB. + # reserve_output=8192 allows long responses; 128K - 8K = 120K for input. + "lakeshore-gemma4-31b": {"total": 131072, "reserve_output": 8192}, + # Qwen2.5-VL-72B AWQ: 64K context (--max-model-len=65536) on H100 NVL. + # reserve_output=4096 allows ~2 pages of response. "lakeshore-qwen-vl-72b": {"total": 65536, "reserve_output": 4096}, # Cloud (direct): Full native context limits # max_input = 200000 - 4000 = 196000 tokens (~780KB of text) diff --git a/stream/middleware/core/globus_compute_client.py b/stream/middleware/core/globus_compute_client.py index 6e98349..fa9a42d 100644 --- a/stream/middleware/core/globus_compute_client.py +++ b/stream/middleware/core/globus_compute_client.py @@ -14,23 +14,24 @@ """ import asyncio +import contextlib import logging import os import time import warnings from typing import Any -from globus_compute_sdk import Executor +from globus_compute_sdk import Client, Executor from globus_compute_sdk.errors.error_types import DeserializationError, TaskExecutionFailed from globus_compute_sdk.serialize import AllCodeStrategies, ComputeSerializer from globus_sdk import GlobusAPIError +from globus_sdk.authorizers import AccessTokenAuthorizer from globus_sdk.login_flows.command_line_login_flow_manager import CommandLineLoginFlowEOFError from stream.middleware.config import ( GLOBUS_MAX_PAYLOAD_BYTES, LAKESHORE_MODELS, MODEL_CONTEXT_LIMITS, - RELAY_SECRET, get_lakeshore_vllm_url, ) from stream.middleware.utils.multimodal import strip_old_images @@ -87,7 +88,7 @@ # 4. exec() from source string → clean bytecode, works everywhere ✓ _REMOTE_FN_SOURCE = """\ -def remote_vllm_inference(vllm_url, model, messages, temperature, max_tokens, stream=False): +def remote_vllm_inference(vllm_url, model, messages, temperature, max_tokens, stream=False, chat_template_kwargs=None): try: import requests endpoint = f"{vllm_url}/v1/chat/completions" @@ -98,6 +99,8 @@ def remote_vllm_inference(vllm_url, model, messages, temperature, max_tokens, st "max_tokens": max_tokens, "stream": stream, } + if chat_template_kwargs: + payload["chat_template_kwargs"] = chat_template_kwargs try: response = requests.post(endpoint, json=payload, timeout=180) if response.status_code >= 400: @@ -166,7 +169,7 @@ def remote_vllm_inference(vllm_url, model, messages, temperature, max_tokens, st # Same exec() pattern as above — see comments on _REMOTE_FN_SOURCE for why. _REMOTE_STREAMING_FN_SOURCE = """\ -def remote_vllm_streaming(vllm_url, model, messages, temperature, max_tokens, relay_url, channel_id, relay_secret=""): +def remote_vllm_streaming(vllm_url, model, messages, temperature, max_tokens, relay_url, channel_id, chat_template_kwargs=None): # NOTE: All imports MUST be inside this function body. # This entire function is serialised as a source-code string and executed # remotely on Lakeshore by Globus Compute. There is no shared import @@ -176,9 +179,12 @@ def remote_vllm_streaming(vllm_url, model, messages, temperature, max_tokens, re import requests from websockets.sync.client import connect as ws_connect - # Read the encryption key from the endpoint environment, not from task arguments. - # This prevents the key from travelling over Globus Compute's AMQP channel. - # Set RELAY_ENCRYPTION_KEY in worker_init in ~/.globus_compute//config.yaml. + # Both relay credentials are read from the endpoint environment, not from + # task arguments. This prevents them from travelling over Globus Compute's + # AMQP channel. Set both in worker_init in ~/.globus_compute//config.yaml: + # export RELAY_SECRET= + # export RELAY_ENCRYPTION_KEY=<32-byte hex AES-256 key> + relay_secret = os.environ.get("RELAY_SECRET", "") encryption_key = os.environ.get("RELAY_ENCRYPTION_KEY", "") # ---- Encryption helper (used only when encryption_key is set) ---- @@ -227,7 +233,19 @@ def _send(ws, payload_dict): # never touches any log. The relay expects {"type":"auth","secret":"..."} # as the very first message and rejects the connection if it doesn't match. ws_url = f"{relay_url}/produce/{channel_id}" - ws = ws_connect(ws_url) + + # Build an explicit SSL context when connecting over wss://. + # Globus Compute worker processes on Lakeshore have a custom PYTHONPATH + # that can shadow the system SSL cert store, causing the default TLS + # handshake to fail silently. Passing an explicit context with the system + # CA bundle fixes this without changing the security model. + import ssl as _ssl + _ssl_ctx = _ssl.create_default_context() + _ssl_ctx.load_verify_locations( + _ssl.get_default_verify_paths().cafile or "/etc/pki/tls/cert.pem" + ) + _ws_kwargs = {"ssl": _ssl_ctx} if relay_url.startswith("wss://") else {} + ws = ws_connect(ws_url, **_ws_kwargs) # Send auth as first message (secret never appears in any HTTP log) if relay_secret: @@ -241,15 +259,18 @@ def _send(ws, payload_dict): # requests stream=True tells the requests library to not download the # full response body immediately, but to give us an iterator we can # read line by line. + _payload = { + "model": model, + "messages": messages, + "temperature": temperature, + "max_tokens": max_tokens, + "stream": True, + } + if chat_template_kwargs: + _payload["chat_template_kwargs"] = chat_template_kwargs response = requests.post( f"{vllm_url}/v1/chat/completions", - json={ - "model": model, - "messages": messages, - "temperature": temperature, - "max_tokens": max_tokens, - "stream": True, - }, + json=_payload, stream=True, timeout=180, ) @@ -297,12 +318,18 @@ def _send(ws, payload_dict): delta = choices[0].get("delta", {}) content = delta.get("content") + reasoning = delta.get("reasoning") or delta.get("reasoning_content") - # Forward the token to the relay (and thus to the consumer). + # Forward token and/or reasoning to the relay. # If encryption_key is set, _send() encrypts before transmitting — # the relay forwards an opaque blob instead of readable JSON. - if content: - _send(ws, {"type": "token", "content": content}) + if content or reasoning: + msg: dict = {"type": "token"} + if content: + msg["content"] = content + if reasoning: + msg["reasoning_content"] = reasoning + _send(ws, msg) tokens_sent += 1 # Capture usage stats from the final chunk (vLLM includes them @@ -495,6 +522,22 @@ def _reset_executor(self): self._executor = None logger.info("Executor reset — will reconnect on next request") + def _make_executor_for_token(self, globus_token: str) -> Executor: + """ + Create a short-lived Executor authenticated with the caller's own Globus token. + + Used for Mode A-1 (user brings their own Globus credentials). The job is + submitted under the caller's identity, giving full per-user SLURM attribution + on the HPC cluster. This Executor is NOT persistent — it is created per request + and shut down immediately after submission, so the AMQP setup cost (~1.5s) is + paid once per request for this auth mode. + """ + authorizer = AccessTokenAuthorizer(globus_token) + client = Client(authorizer=authorizer) + exe = Executor(endpoint_id=self.endpoint_id, client=client) + exe.serializer = ComputeSerializer(strategy_code=AllCodeStrategies()) + return exe + def shutdown(self): """ Clean up the persistent Executor when the app is shutting down. @@ -679,6 +722,7 @@ async def submit_inference( max_tokens: int | None = None, model: str = "Qwen/Qwen2.5-1.5B-Instruct", _retry: bool = False, # Internal flag to prevent infinite retry loops + chat_template_kwargs: dict | None = None, ) -> dict[str, Any]: """ Submit an inference task to Lakeshore via Globus Compute. @@ -805,6 +849,7 @@ async def submit_inference( temperature, max_tokens, False, # stream=False (streaming not yet implemented) + chat_template_kwargs, ) t_submit = time.perf_counter() @@ -1043,6 +1088,8 @@ async def submit_streaming_inference( max_tokens: int | None = None, model: str = "", relay_url: str = "", + globus_token: str | None = None, + chat_template_kwargs: dict | None = None, ) -> dict[str, Any]: """ Submit a streaming inference task to Lakeshore via Globus Compute. @@ -1063,6 +1110,11 @@ async def submit_streaming_inference( model: STREAM model key (e.g., "lakeshore-qwen-vl-72b", "lakeshore-qwen-vl-72b"). Resolved to the HuggingFace model name internally. relay_url: WebSocket URL of the relay server (e.g., wss://abc.trycloudflare.com) + globus_token: Optional caller Globus access token. When provided, the job is + submitted under the caller's own Globus identity (Mode A-1: per-user SLURM + attribution). When None, the proxy's stored credentials are used (Mode A-2). + Note: a per-request Executor is created for token-authenticated calls, so + the first request pays the ~1.5s AMQP setup cost. Returns: {"channel_id": "uuid-string"} on success @@ -1112,7 +1164,17 @@ async def submit_streaming_inference( channel_id = str(uuid.uuid4()) try: - gce = self._get_executor() + # Use caller's own Globus token (Mode A-1) when provided, + # otherwise fall back to the proxy's persistent executor (Mode A-2). + own_executor = None + if globus_token: + own_executor = self._make_executor_for_token(globus_token) + gce = own_executor + logger.info( + f"Using caller-token executor (per-user SLURM attribution, channel={channel_id[:8]})" + ) + else: + gce = self._get_executor() # Resolve the vLLM URL for this model (each model runs on its own port) vllm_url = get_lakeshore_vllm_url(model) @@ -1143,22 +1205,36 @@ async def submit_streaming_inference( max_tokens, relay_url, channel_id, - RELAY_SECRET, # auth: controls who may connect to the channel - # RELAY_ENCRYPTION_KEY is NOT passed here — the remote function - # reads it from os.environ on the endpoint to avoid sending the - # key over Globus Compute's AMQP channel. + chat_template_kwargs, + # No relay credentials are passed as task arguments. Both + # RELAY_SECRET (channel-access token) and RELAY_ENCRYPTION_KEY + # (AES-256-GCM payload key) are pre-provisioned in the Globus + # Compute endpoint's worker_init environment on the HPC cluster, + # so neither traverses Globus Compute's AMQP channel. ) logger.info(f"Streaming job submitted (channel={channel_id[:8]})") + # Clean up the per-request executor after submission (Mode A-1 only). + # The job is already enqueued in Globus — the executor is no longer needed. + if own_executor is not None: + with contextlib.suppress(Exception): + own_executor.shutdown(wait=False, cancel_futures=False) + return {"channel_id": channel_id} except Exception as e: logger.error(f"Failed to submit streaming inference: {e}", exc_info=True) + # Clean up per-request executor on failure too + if own_executor is not None: + with contextlib.suppress(Exception): + own_executor.shutdown(wait=False, cancel_futures=True) + error_str = str(e).lower() if "unable to open database file" in error_str or "login_required" in error_str: - self._reset_executor() + if own_executor is None: + self._reset_executor() return { "error": "Globus Compute authentication required.", "error_type": "AuthenticationError", diff --git a/stream/middleware/core/litellm_direct.py b/stream/middleware/core/litellm_direct.py index 326476d..5c65577 100644 --- a/stream/middleware/core/litellm_direct.py +++ b/stream/middleware/core/litellm_direct.py @@ -395,7 +395,14 @@ async def _check_relay_reachable(relay_url: str, timeout: float = 3.0) -> bool: from websockets.asyncio.client import connect as ws_connect try: - async with ws_connect(f"{relay_url}/health", open_timeout=timeout) as ws: + import ssl as _ssl + + _ssl_ctx = _ssl.create_default_context() + _ssl_ctx.load_verify_locations( + _ssl.get_default_verify_paths().cafile or "/etc/pki/tls/cert.pem" + ) + _ws_kwargs = {"ssl": _ssl_ctx} if relay_url.startswith("wss://") else {} + async with ws_connect(f"{relay_url}/health", open_timeout=timeout, **_ws_kwargs) as ws: await asyncio.wait_for(ws.recv(), timeout=timeout) return True except Exception as e: @@ -479,7 +486,16 @@ async def _forward_lakeshore_streaming( # Connect WITHOUT secret in the URL — secret travels as first message # after the handshake so it never appears in HTTP access logs. relay_consume_url = f"{RELAY_URL}/consume/{channel_id}" - async with ws_connect(relay_consume_url) as ws: + # Use an explicit SSL context for wss:// so the connection works even + # in environments where the default cert store may be shadowed. + import ssl as _ssl + + _ssl_ctx = _ssl.create_default_context() + _ssl_ctx.load_verify_locations( + _ssl.get_default_verify_paths().cafile or "/etc/pki/tls/cert.pem" + ) + _ws_kwargs = {"ssl": _ssl_ctx} if RELAY_URL.startswith("wss://") else {} + async with ws_connect(relay_consume_url, **_ws_kwargs) as ws: # Send auth as first message (post-handshake, fully TLS-encrypted) if RELAY_SECRET: import json as _json diff --git a/stream/middleware/utils/context_window.py b/stream/middleware/utils/context_window.py index f3cdc21..5e307c4 100644 --- a/stream/middleware/utils/context_window.py +++ b/stream/middleware/utils/context_window.py @@ -144,9 +144,9 @@ def get_tier_context_limits() -> dict: "note": "Ollama default (models capable of 128k if reconfigured)", }, "lakeshore": { - "max_input": get_max_input_tokens("lakeshore-qwen-vl-72b"), - "total": MODEL_CONTEXT_LIMITS["lakeshore-qwen-vl-72b"]["total"], - "note": "vLLM on H100 NVL (64K context)", + "max_input": get_max_input_tokens("lakeshore-gemma4-31b"), + "total": MODEL_CONTEXT_LIMITS["lakeshore-gemma4-31b"]["total"], + "note": "vLLM on 2× A100 (128K context)", }, "cloud": { "max_input": get_max_input_tokens("cloud-claude"), diff --git a/stream/proxy/app.py b/stream/proxy/app.py index c8a44a1..a8becc8 100644 --- a/stream/proxy/app.py +++ b/stream/proxy/app.py @@ -172,6 +172,7 @@ async def proxy_chat_completions( temperature = body.get("temperature", 0.7) stream = body.get("stream", False) + chat_template_kwargs = body.get("chat_template_kwargs", None) logger.info( f"Chat request: caller={caller.log_safe_id()}, model={model}, " @@ -225,7 +226,7 @@ async def proxy_chat_completions( # ========================================================================= # Look up context limits for the specific model, fall back to lakeshore-qwen-vl-72b lakeshore_limits = MODEL_CONTEXT_LIMITS.get( - model, MODEL_CONTEXT_LIMITS.get("lakeshore-qwen-vl-72b", {}) + model, MODEL_CONTEXT_LIMITS.get("lakeshore-gemma4-31b", {}) ) default_max_tokens = lakeshore_limits.get("reserve_output", 2048) max_tokens = body.get("max_tokens", default_max_tokens) @@ -235,12 +236,29 @@ async def proxy_chat_completions( ) if USE_GLOBUS_COMPUTE: - return await _route_via_globus_compute(model, messages, temperature, max_tokens, stream) + return await _route_via_globus_compute( + model, + messages, + temperature, + max_tokens, + stream, + globus_token=caller.globus_token, + chat_template_kwargs=chat_template_kwargs, + ) else: - return await _route_via_ssh(model, messages, temperature, max_tokens, stream) + return await _route_via_ssh( + model, + messages, + temperature, + max_tokens, + stream, + chat_template_kwargs=chat_template_kwargs, + ) -async def _route_via_globus_compute(model, messages, temperature, max_tokens, stream): +async def _route_via_globus_compute( + model, messages, temperature, max_tokens, stream, globus_token=None, chat_template_kwargs=None +): if not globus_client or not globus_client.is_available(): raise HTTPException(status_code=503, detail="Globus Compute not configured") @@ -252,7 +270,12 @@ async def _route_via_globus_compute(model, messages, temperature, max_tokens, st if stream and RELAY_URL: try: return await _route_via_globus_compute_streaming( - model, messages, temperature, max_tokens + model, + messages, + temperature, + max_tokens, + globus_token=globus_token, + chat_template_kwargs=chat_template_kwargs, ) except Exception as e: logger.warning(f"Relay streaming failed, falling back to batch mode: {e}") @@ -265,7 +288,11 @@ async def _route_via_globus_compute(model, messages, temperature, max_tokens, st try: logger.info(f"Submitting to Globus endpoint: {GLOBUS_COMPUTE_ENDPOINT_ID}") result = await globus_client.submit_inference( - messages=messages, temperature=temperature, max_tokens=max_tokens, model=model + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + model=model, + chat_template_kwargs=chat_template_kwargs, ) if "error" in result: @@ -301,7 +328,9 @@ async def _route_via_globus_compute(model, messages, temperature, max_tokens, st raise HTTPException(status_code=500, detail=f"Internal proxy error: {str(e)}") from e -async def _route_via_globus_compute_streaming(model, messages, temperature, max_tokens): +async def _route_via_globus_compute_streaming( + model, messages, temperature, max_tokens, globus_token=None, chat_template_kwargs=None +): """ TRUE streaming from Lakeshore via the WebSocket relay (server/Docker mode). @@ -315,13 +344,16 @@ async def _route_via_globus_compute_streaming(model, messages, temperature, max_ """ from websockets.asyncio.client import connect as ws_connect - # Submit the streaming job to Globus Compute + # Submit the streaming job to Globus Compute. + # Pass the caller's Globus token when available (Mode A-1: per-user SLURM attribution). result = await globus_client.submit_streaming_inference( messages=messages, temperature=temperature, max_tokens=max_tokens, model=model, relay_url=RELAY_URL, + globus_token=globus_token, + chat_template_kwargs=chat_template_kwargs, ) if "error" in result: @@ -358,9 +390,12 @@ async def sse_generator(): msg = json.loads(msg_str) if msg["type"] == "token": - chunk = { - "choices": [{"index": 0, "delta": {"content": msg["content"]}}], - } + delta: dict = {} + if "content" in msg: + delta["content"] = msg["content"] + if "reasoning_content" in msg: + delta["reasoning_content"] = msg["reasoning_content"] + chunk = {"choices": [{"index": 0, "delta": delta}]} yield f"data: {json.dumps(chunk)}\n\n" elif msg["type"] == "done": diff --git a/uv.lock b/uv.lock index 231f0c5..e73b76d 100644 --- a/uv.lock +++ b/uv.lock @@ -35,7 +35,7 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.13.5" +version = "3.14.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, @@ -44,78 +44,93 @@ dependencies = [ { name = "frozenlist" }, { name = "multidict" }, { name = "propcache" }, + { name = "typing-extensions", marker = "python_full_version < '3.13'" }, { name = "yarl" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/77/9a/152096d4808df8e4268befa55fba462f440f14beab85e8ad9bf990516918/aiohttp-3.13.5.tar.gz", hash = "sha256:9d98cc980ecc96be6eb4c1994ce35d28d8b1f5e5208a23b421187d1209dbb7d1", size = 7858271, upload-time = "2026-03-31T22:01:03.343Z" } -wheels = [ - { url = 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