From b882222da463dad3ddbe472db4ada4731a0d3bb5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Tue, 14 Apr 2026 21:41:12 +0800 Subject: [PATCH 001/268] fix(docs): correct swagger paths and gh-pages deploy (#1) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Fix swagger spec 404 on GitHub Pages - Update all 8 API doc SwaggerUI specUrl from /relax/openapi/ to /Relax/openapi/ to match VitePress base --- # 🔧 CI/CD ## Switch docs deployment to GitHub Actions Pages - Replace peaceiris/actions-gh-pages force-push flow with native actions/configure-pages + upload-pages-artifact + deploy-pages - Split into build and deploy jobs with github-pages environment - Drop contents:write, add pages:write and id-token:write --- # 📝 Documentation ## Point citation url to arXiv - Update bibtex url in README.md and README_zh.md to https://arxiv.org/abs/2604.11554 --- .github/workflows/deploy-docs.yml | 32 +++++++++++++++++++++++-------- README.md | 2 +- README_zh.md | 2 +- docs/en/api/actor-fwd.md | 2 +- docs/en/api/actor.md | 2 +- docs/en/api/genrm.md | 2 +- docs/en/api/rollout.md | 2 +- docs/zh/api/actor-fwd.md | 2 +- docs/zh/api/actor.md | 2 +- docs/zh/api/genrm.md | 2 +- docs/zh/api/rollout.md | 2 +- 11 files changed, 34 insertions(+), 18 deletions(-) diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml index 6ebacabeb..9d8719240 100644 --- a/.github/workflows/deploy-docs.yml +++ b/.github/workflows/deploy-docs.yml @@ -11,14 +11,16 @@ on: workflow_dispatch: permissions: - contents: write + contents: read + pages: write + id-token: write concurrency: - group: deploy-docs-${{ github.ref }} - cancel-in-progress: true + group: pages + cancel-in-progress: false jobs: - deploy: + build: runs-on: ubuntu-latest if: github.repository == 'redai-infra/Relax' steps: @@ -45,11 +47,25 @@ jobs: npm install --no-audit --no-fund --include=optional npm install --no-audit --no-fund --no-save @rollup/rollup-linux-x64-gnu + - name: Setup Pages + uses: actions/configure-pages@v5 + - name: Build VitePress site run: npm run docs:build - - name: Deploy to GitHub Pages - uses: peaceiris/actions-gh-pages@v4 + - name: Upload Pages artifact + uses: actions/upload-pages-artifact@v3 with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: docs/.vitepress/dist + path: docs/.vitepress/dist + + deploy: + needs: build + runs-on: ubuntu-latest + if: github.repository == 'redai-infra/Relax' + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/README.md b/README.md index 58692740e..46c7c9d5a 100644 --- a/README.md +++ b/README.md @@ -260,7 +260,7 @@ If you find Relax useful in your research, please cite: @software{relax2026, title = {Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale}, author = {Relax Contributors}, - url = {https://github.com/redai-infra/Relax}, + url = {https://arxiv.org/abs/2604.11554}, year = {2026} } ``` diff --git a/README_zh.md b/README_zh.md index 83bf55ddb..e4152220c 100644 --- a/README_zh.md +++ b/README_zh.md @@ -260,7 +260,7 @@ ______________________________________________________________________ @software{relax2026, title = {Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale}, author = {Relax Contributors}, - url = {https://github.com/redai-infra/Relax}, + url = {https://arxiv.org/abs/2604.11554}, year = {2026} } ``` diff --git a/docs/en/api/actor-fwd.md b/docs/en/api/actor-fwd.md index 277653632..b5ac42472 100644 --- a/docs/en/api/actor-fwd.md +++ b/docs/en/api/actor-fwd.md @@ -33,7 +33,7 @@ The ActorFwd runs a background loop that: ## HTTP Endpoints - + ## Source diff --git a/docs/en/api/actor.md b/docs/en/api/actor.md index dd9a58140..384fbabdc 100644 --- a/docs/en/api/actor.md +++ b/docs/en/api/actor.md @@ -30,7 +30,7 @@ The Actor runs a background training loop that: ## HTTP Endpoints - + ## Source diff --git a/docs/en/api/genrm.md b/docs/en/api/genrm.md index fe12f3bff..4b9b84807 100644 --- a/docs/en/api/genrm.md +++ b/docs/en/api/genrm.md @@ -34,7 +34,7 @@ When colocated with the Actor (sharing GPU resources), GenRM supports offload/on ## HTTP Endpoints - + ## Source diff --git a/docs/en/api/rollout.md b/docs/en/api/rollout.md index 8094b937b..27cd2dc8f 100644 --- a/docs/en/api/rollout.md +++ b/docs/en/api/rollout.md @@ -35,7 +35,7 @@ In fully-async mode, the Rollout service coordinates with the Actor for weight u ## HTTP Endpoints - + ## Source diff --git a/docs/zh/api/actor-fwd.md b/docs/zh/api/actor-fwd.md index a4948eb27..d85628983 100644 --- a/docs/zh/api/actor-fwd.md +++ b/docs/zh/api/actor-fwd.md @@ -33,7 +33,7 @@ ActorFwd 运行后台循环： ## HTTP 端点 - + ## 源码 diff --git a/docs/zh/api/actor.md b/docs/zh/api/actor.md index cfec17a58..e0f7406f0 100644 --- a/docs/zh/api/actor.md +++ b/docs/zh/api/actor.md @@ -30,7 +30,7 @@ Actor 运行后台训练循环： ## HTTP 端点 - + ## 源码 diff --git a/docs/zh/api/genrm.md b/docs/zh/api/genrm.md index ecd16af31..641d9a154 100644 --- a/docs/zh/api/genrm.md +++ b/docs/zh/api/genrm.md @@ -34,7 +34,7 @@ GenRM（生成式奖励模型）服务提供基于 LLM 的响应评估。它以 ## HTTP 端点 - + ## 源码 diff --git a/docs/zh/api/rollout.md b/docs/zh/api/rollout.md index c940ef47e..9e253bcfd 100644 --- a/docs/zh/api/rollout.md +++ b/docs/zh/api/rollout.md @@ -35,7 +35,7 @@ Rollout 运行后台循环： ## HTTP 端点 - + ## 源码 From b434e5011d6f980bfde6791a5994d18e1edde284 Mon Sep 17 00:00:00 2001 From: Weihang Chen Date: Wed, 15 Apr 2026 11:03:04 +0800 Subject: [PATCH 002/268] rename rednote to xiaohongshu (#2) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 46c7c9d5a..cc94f0c39 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ ______________________________________________________________________ -**Relax** (**R**einforcement **E**ngine **L**everaging **A**gentic **X**-modality) is a high-performance reinforcement learning post-training framework open-sourced by the rednote AI platform for multimodal large language models. Built on Ray Serve with a service-oriented architecture, Relax uses Megatron-LM as the training backend and SGLang as the inference engine. Through the [TransferQueue](https://github.com/redai-infra/TransferQueue) data transfer system, it achieves complete decoupling of training and inference, supporting end-to-end multimodal RL training from text to images, videos, and audio. +**Relax** (**R**einforcement **E**ngine **L**everaging **A**gentic **X**-modality) is a high-performance reinforcement learning post-training framework open-sourced by the Xiaohongshu AI Infra Team for multimodal large language models. Built on Ray Serve with a service-oriented architecture, Relax uses Megatron-LM as the training backend and SGLang as the inference engine. Through the [TransferQueue](https://github.com/redai-infra/TransferQueue) data transfer system, it achieves complete decoupling of training and inference, supporting end-to-end multimodal RL training from text to images, videos, and audio. ______________________________________________________________________ From e58d3866dd8b122fb4ee4c4cd90ae0fcabf59083 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Wed, 15 Apr 2026 14:01:43 +0800 Subject: [PATCH 003/268] chore: remove copyright headers from imported slime files and simplify CI (#3) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🔩 Chore ## Remove copyright headers from 65 imported files Removed Relax copyright headers from 65 files that are identical or nearly identical (>95% match) to their slime counterparts. These files were imported from the slime project without significant code modifications. ### Files modified: **Megatron backend (26 files)** - Misc utilities, SGLang integration, weight conversion modules - Loss functions, checkpoint utilities, kernel implementations **Ray distributed (3 files)** - Actor group management, placement group utilities, Ray actor implementations **Engine components (15 files)** - Filters, reward functions, rollout utilities, router middleware **Utils (21 files)** - Data processing, metrics, training utilities, type definitions ## Updated .pre-commit-config.yaml Added all 65 files to the copyright-checker exclusion list to prevent re-adding copyright headers on future pre-commit runs. ## Simplified CI workflow - Removed redundant 'copyright' job from GitHub Actions CI - Pre-commit job already includes copyright checking via .pre-commit-config.yaml - Eliminates duplicate checks and simplifies CI pipeline ## Rationale These files were imported from the slime project with minimal or no code modifications. Claiming copyright on unmodified imported code is not appropriate. The copyright headers have been removed to align with the project's policy of only claiming copyright on original or significantly modified code. **Comparison results:** - 22 files: 100% identical to slime versions - 43 files: >95% similar to slime versions - Total: 65 files with minimal modifications --- .github/workflows/ci.yml | 23 ------- .pre-commit-config.yaml | 67 ++++++++++++++++++- relax/backends/megatron/ci_utils.py | 2 - relax/backends/megatron/cp_utils.py | 2 - relax/backends/megatron/initialize.py | 2 - relax/backends/megatron/kernels/__init__.py | 1 - relax/backends/megatron/kernels/fp8_kernel.py | 2 - .../megatron/kernels/int4_qat/setup.py | 2 - relax/backends/megatron/loss.py | 2 - relax/backends/megatron/misc_utils.py | 3 - relax/backends/megatron/sglang.py | 2 - .../megatron/weight_conversion/__init__.py | 2 - .../megatron/weight_conversion/deepseekv3.py | 2 - .../megatron/weight_conversion/glm4.py | 2 - .../megatron/weight_conversion/glm4moe.py | 2 - .../megatron/weight_conversion/llama.py | 2 - .../megatron/weight_conversion/mimo.py | 2 - .../weight_conversion/processors/__init__.py | 2 - .../quantizer_compressed_tensors.py | 2 - .../processors/quantizer_fp8.py | 2 - .../megatron/weight_conversion/qwen2.py | 2 - .../megatron/weight_conversion/qwen3_next.py | 2 - .../megatron/weight_conversion/qwen3_vl.py | 2 - .../megatron/weight_conversion/qwen3moe.py | 2 - .../megatron/weight_update/__init__.py | 1 - .../weight_update/hf_weight_iterator_base.py | 2 - .../hf_weight_iterator_direct.py | 2 - .../update_weight_from_distributed.py | 2 - relax/distributed/ray/__init__.py | 1 - relax/distributed/ray/ray_actor.py | 2 - relax/distributed/ray/utils.py | 2 - relax/engine/__init__.py | 1 - relax/engine/filters/__init__.py | 1 - relax/engine/filters/base_types.py | 2 - relax/engine/rewards/deepscaler.py | 2 - relax/engine/rewards/f1.py | 2 - relax/engine/rewards/gpqa.py | 2 - relax/engine/rewards/ifbench.py | 2 - relax/engine/rewards/math_dapo_utils.py | 2 - relax/engine/rewards/math_utils.py | 2 - relax/engine/rollout/__init__.py | 1 - relax/engine/rollout/base_types.py | 2 - relax/engine/router/__init__.py | 1 - relax/engine/router/middleware/__init__.py | 1 - .../middleware/radix_tree_middleware.py | 2 - relax/engine/router/router.py | 2 - relax/utils/__init__.py | 1 - relax/utils/async_utils.py | 2 - relax/utils/data/__init__.py | 1 - relax/utils/data/mask_utils.py | 2 - relax/utils/data/seqlen_balancing.py | 4 -- relax/utils/debug/__init__.py | 1 - relax/utils/distributed_utils.py | 2 - relax/utils/external/__init__.py | 1 - relax/utils/external/typer_utils.py | 2 - relax/utils/megatron_bridge_utils.py | 2 - relax/utils/metrics/adapters/__init__.py | 1 - relax/utils/metrics/metric_utils.py | 2 - relax/utils/multimodal/__init__.py | 1 - relax/utils/reloadable_process_group.py | 2 - relax/utils/rocm_checkpoint_writer.py | 2 - relax/utils/training/__init__.py | 1 - relax/utils/training/eval_config.py | 2 - relax/utils/training/flops_utils.py | 3 - relax/utils/training/routing_replay.py | 2 - relax/utils/training/tensor_backper.py | 2 - relax/utils/types.py | 2 - 67 files changed, 66 insertions(+), 143 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 7d8908ae1..547d884bb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -111,26 +111,3 @@ jobs: --tb=short \ -x \ --ignore=tests/autoscale - - # ── Copyright header check ──────────────────────────────────────────────── - copyright: - name: Copyright Header Check - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Setup Python - uses: actions/setup-python@v5 - with: - python-version: "3.10" - - - name: Check copyright headers - run: | - python .pre-commit-hooks/copyright.py $(find relax/ tests/ -name '*.py' -not -path '*__pycache__*') - if [ -n "$(git diff --name-only)" ]; then - echo "::error::The following files are missing copyright headers:" - git diff --name-only - git diff - exit 1 - fi diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 46d30f4d0..8d80fe12e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -63,7 +63,72 @@ repos: files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|xpu|kps|py|pyi|sh)$ exclude: | (?x)^( - examples/.*| docs/.* | docker/.* + examples/.* | docs/.* | docker/.* | + relax/backends/megatron/kernels/__init__\.py | + relax/backends/megatron/misc_utils\.py | + relax/backends/megatron/sglang\.py | + relax/backends/megatron/weight_conversion/deepseekv3\.py | + relax/backends/megatron/weight_update/__init__\.py | + relax/distributed/ray/__init__\.py | + relax/engine/__init__\.py | + relax/engine/filters/__init__\.py | + relax/engine/filters/base_types\.py | + relax/engine/rewards/deepscaler\.py | + relax/engine/rollout/__init__\.py | + relax/engine/router/__init__\.py | + relax/engine/router/middleware/__init__\.py | + relax/utils/__init__\.py | + relax/utils/data/__init__\.py | + relax/utils/data/mask_utils\.py | + relax/utils/debug/__init__\.py | + relax/utils/external/__init__\.py | + relax/utils/metrics/adapters/__init__\.py | + relax/utils/multimodal/__init__\.py | + relax/utils/training/__init__\.py | + relax/utils/training/flops_utils\.py | + relax/backends/megatron/ci_utils\.py | + relax/backends/megatron/cp_utils\.py | + relax/backends/megatron/initialize\.py | + relax/backends/megatron/kernels/fp8_kernel\.py | + relax/backends/megatron/kernels/int4_qat/setup\.py | + relax/backends/megatron/loss\.py | + relax/backends/megatron/weight_conversion/__init__\.py | + relax/backends/megatron/weight_conversion/glm4\.py | + relax/backends/megatron/weight_conversion/glm4moe\.py | + relax/backends/megatron/weight_conversion/llama\.py | + relax/backends/megatron/weight_conversion/mimo\.py | + relax/backends/megatron/weight_conversion/processors/__init__\.py | + relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors\.py | + relax/backends/megatron/weight_conversion/processors/quantizer_fp8\.py | + relax/backends/megatron/weight_conversion/qwen2\.py | + relax/backends/megatron/weight_conversion/qwen3_next\.py | + relax/backends/megatron/weight_conversion/qwen3_vl\.py | + relax/backends/megatron/weight_conversion/qwen3moe\.py | + relax/backends/megatron/weight_update/hf_weight_iterator_base\.py | + relax/backends/megatron/weight_update/hf_weight_iterator_direct\.py | + relax/backends/megatron/weight_update/update_weight_from_distributed\.py | + relax/distributed/ray/ray_actor\.py | + relax/distributed/ray/utils\.py | + relax/engine/rewards/f1\.py | + relax/engine/rewards/gpqa\.py | + relax/engine/rewards/ifbench\.py | + relax/engine/rewards/math_dapo_utils\.py | + relax/engine/rewards/math_utils\.py | + relax/engine/rollout/base_types\.py | + relax/engine/router/middleware/radix_tree_middleware\.py | + relax/engine/router/router\.py | + relax/utils/async_utils\.py | + relax/utils/data/seqlen_balancing\.py | + relax/utils/distributed_utils\.py | + relax/utils/external/typer_utils\.py | + relax/utils/megatron_bridge_utils\.py | + relax/utils/metrics/metric_utils\.py | + relax/utils/reloadable_process_group\.py | + relax/utils/rocm_checkpoint_writer\.py | + relax/utils/training/eval_config\.py | + relax/utils/training/routing_replay\.py | + relax/utils/training/tensor_backper\.py | + relax/utils/types\.py )$ - id: check-conflict-markers name: check-conflict-markers diff --git a/relax/backends/megatron/ci_utils.py b/relax/backends/megatron/ci_utils.py index b06f6836c..d6bb6eba9 100644 --- a/relax/backends/megatron/ci_utils.py +++ b/relax/backends/megatron/ci_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - """CI utilities for Megatron backend testing.""" from collections.abc import Sequence diff --git a/relax/backends/megatron/cp_utils.py b/relax/backends/megatron/cp_utils.py index 21b35033a..deb43ba87 100644 --- a/relax/backends/megatron/cp_utils.py +++ b/relax/backends/megatron/cp_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from collections.abc import Callable import torch diff --git a/relax/backends/megatron/initialize.py b/relax/backends/megatron/initialize.py index 0818acb88..563d730ed 100644 --- a/relax/backends/megatron/initialize.py +++ b/relax/backends/megatron/initialize.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import random import numpy as np diff --git a/relax/backends/megatron/kernels/__init__.py b/relax/backends/megatron/kernels/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/backends/megatron/kernels/__init__.py +++ b/relax/backends/megatron/kernels/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/backends/megatron/kernels/fp8_kernel.py b/relax/backends/megatron/kernels/fp8_kernel.py index 4842b0cdf..558051437 100644 --- a/relax/backends/megatron/kernels/fp8_kernel.py +++ b/relax/backends/megatron/kernels/fp8_kernel.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import torch import triton import triton.language as tl diff --git a/relax/backends/megatron/kernels/int4_qat/setup.py b/relax/backends/megatron/kernels/int4_qat/setup.py index 55253ce3a..b8bfc7dc9 100644 --- a/relax/backends/megatron/kernels/int4_qat/setup.py +++ b/relax/backends/megatron/kernels/int4_qat/setup.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import os import torch diff --git a/relax/backends/megatron/loss.py b/relax/backends/megatron/loss.py index 129908046..a36972ff1 100644 --- a/relax/backends/megatron/loss.py +++ b/relax/backends/megatron/loss.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from argparse import Namespace from collections.abc import Callable, Iterator from typing import Any diff --git a/relax/backends/megatron/misc_utils.py b/relax/backends/megatron/misc_utils.py index c87ea02e0..f101111d6 100644 --- a/relax/backends/megatron/misc_utils.py +++ b/relax/backends/megatron/misc_utils.py @@ -1,6 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - - def strip_param_name_prefix(name: str): prefix = "module." while name.startswith(prefix): diff --git a/relax/backends/megatron/sglang.py b/relax/backends/megatron/sglang.py index 1b799932e..97c82a31c 100644 --- a/relax/backends/megatron/sglang.py +++ b/relax/backends/megatron/sglang.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - # the file to manage all sglang deps in the megatron actor try: from sglang.srt.layers.quantization.fp8_utils import quant_weight_ue8m0, transform_scale_ue8m0 diff --git a/relax/backends/megatron/weight_conversion/__init__.py b/relax/backends/megatron/weight_conversion/__init__.py index 25be173cb..58b1c1b34 100644 --- a/relax/backends/megatron/weight_conversion/__init__.py +++ b/relax/backends/megatron/weight_conversion/__init__.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from .deepseekv3 import convert_deepseekv3_to_hf from .glm4 import convert_glm4_to_hf from .glm4moe import convert_glm4moe_to_hf diff --git a/relax/backends/megatron/weight_conversion/deepseekv3.py b/relax/backends/megatron/weight_conversion/deepseekv3.py index e735aea40..205b02555 100644 --- a/relax/backends/megatron/weight_conversion/deepseekv3.py +++ b/relax/backends/megatron/weight_conversion/deepseekv3.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/glm4.py b/relax/backends/megatron/weight_conversion/glm4.py index 414672585..1f14efbaa 100644 --- a/relax/backends/megatron/weight_conversion/glm4.py +++ b/relax/backends/megatron/weight_conversion/glm4.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/glm4moe.py b/relax/backends/megatron/weight_conversion/glm4moe.py index b242d7425..f17f88e32 100644 --- a/relax/backends/megatron/weight_conversion/glm4moe.py +++ b/relax/backends/megatron/weight_conversion/glm4moe.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/llama.py b/relax/backends/megatron/weight_conversion/llama.py index c4d843c92..0a03f1f8a 100644 --- a/relax/backends/megatron/weight_conversion/llama.py +++ b/relax/backends/megatron/weight_conversion/llama.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/mimo.py b/relax/backends/megatron/weight_conversion/mimo.py index d225ccf3f..3aba11f37 100644 --- a/relax/backends/megatron/weight_conversion/mimo.py +++ b/relax/backends/megatron/weight_conversion/mimo.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/processors/__init__.py b/relax/backends/megatron/weight_conversion/processors/__init__.py index 9425502a5..4df7c12ef 100644 --- a/relax/backends/megatron/weight_conversion/processors/__init__.py +++ b/relax/backends/megatron/weight_conversion/processors/__init__.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from .padding_remover import remove_padding from .quantizer_compressed_tensors import quantize_params_compressed_tensors from .quantizer_fp8 import quantize_params_fp8 diff --git a/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py b/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py index 79a8d8b13..7c1b5d20c 100644 --- a/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py +++ b/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import logging import math import re diff --git a/relax/backends/megatron/weight_conversion/processors/quantizer_fp8.py b/relax/backends/megatron/weight_conversion/processors/quantizer_fp8.py index d4579d662..f531ce02c 100644 --- a/relax/backends/megatron/weight_conversion/processors/quantizer_fp8.py +++ b/relax/backends/megatron/weight_conversion/processors/quantizer_fp8.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/qwen2.py b/relax/backends/megatron/weight_conversion/qwen2.py index 5d487e88c..db50a5636 100644 --- a/relax/backends/megatron/weight_conversion/qwen2.py +++ b/relax/backends/megatron/weight_conversion/qwen2.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/qwen3_next.py b/relax/backends/megatron/weight_conversion/qwen3_next.py index 07bd2a5ad..609913b6b 100644 --- a/relax/backends/megatron/weight_conversion/qwen3_next.py +++ b/relax/backends/megatron/weight_conversion/qwen3_next.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/qwen3_vl.py b/relax/backends/megatron/weight_conversion/qwen3_vl.py index e1046dfdc..2ea57798b 100644 --- a/relax/backends/megatron/weight_conversion/qwen3_vl.py +++ b/relax/backends/megatron/weight_conversion/qwen3_vl.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_conversion/qwen3moe.py b/relax/backends/megatron/weight_conversion/qwen3moe.py index ad3cecae8..276b8d3ce 100644 --- a/relax/backends/megatron/weight_conversion/qwen3moe.py +++ b/relax/backends/megatron/weight_conversion/qwen3moe.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import torch diff --git a/relax/backends/megatron/weight_update/__init__.py b/relax/backends/megatron/weight_update/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/backends/megatron/weight_update/__init__.py +++ b/relax/backends/megatron/weight_update/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/backends/megatron/weight_update/hf_weight_iterator_base.py b/relax/backends/megatron/weight_update/hf_weight_iterator_base.py index 6ef11408f..94a347198 100644 --- a/relax/backends/megatron/weight_update/hf_weight_iterator_base.py +++ b/relax/backends/megatron/weight_update/hf_weight_iterator_base.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from abc import ABC, abstractmethod diff --git a/relax/backends/megatron/weight_update/hf_weight_iterator_direct.py b/relax/backends/megatron/weight_update/hf_weight_iterator_direct.py index 07137ab18..02da46542 100644 --- a/relax/backends/megatron/weight_update/hf_weight_iterator_direct.py +++ b/relax/backends/megatron/weight_update/hf_weight_iterator_direct.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import dataclasses from argparse import Namespace from collections.abc import Sequence diff --git a/relax/backends/megatron/weight_update/update_weight_from_distributed.py b/relax/backends/megatron/weight_update/update_weight_from_distributed.py index 7438f381f..2563c2529 100644 --- a/relax/backends/megatron/weight_update/update_weight_from_distributed.py +++ b/relax/backends/megatron/weight_update/update_weight_from_distributed.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import socket import time from argparse import Namespace diff --git a/relax/distributed/ray/__init__.py b/relax/distributed/ray/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/distributed/ray/__init__.py +++ b/relax/distributed/ray/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/distributed/ray/ray_actor.py b/relax/distributed/ray/ray_actor.py index ab0c47967..e3a25b97a 100644 --- a/relax/distributed/ray/ray_actor.py +++ b/relax/distributed/ray/ray_actor.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from relax.utils.misc import get_current_node_ip, get_free_port diff --git a/relax/distributed/ray/utils.py b/relax/distributed/ray/utils.py index b8658d658..1918eaa14 100644 --- a/relax/distributed/ray/utils.py +++ b/relax/distributed/ray/utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - # Adapted from https://github.com/OpenRLHF/OpenRLHF/blob/10c733694ed9fbb78a0a2ff6a05efc7401584d46/openrlhf/trainer/ray/utils.py#L1 import os diff --git a/relax/engine/__init__.py b/relax/engine/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/engine/__init__.py +++ b/relax/engine/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/engine/filters/__init__.py b/relax/engine/filters/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/engine/filters/__init__.py +++ b/relax/engine/filters/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/engine/filters/base_types.py b/relax/engine/filters/base_types.py index 49219d03e..2937273bd 100644 --- a/relax/engine/filters/base_types.py +++ b/relax/engine/filters/base_types.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from collections import defaultdict from dataclasses import dataclass diff --git a/relax/engine/rewards/deepscaler.py b/relax/engine/rewards/deepscaler.py index c970ac5c9..39d4de383 100644 --- a/relax/engine/rewards/deepscaler.py +++ b/relax/engine/rewards/deepscaler.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from .math_utils import extract_answer, grade_answer_mathd, grade_answer_sympy diff --git a/relax/engine/rewards/f1.py b/relax/engine/rewards/f1.py index 6659a8063..b8947b665 100644 --- a/relax/engine/rewards/f1.py +++ b/relax/engine/rewards/f1.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import string from collections import Counter diff --git a/relax/engine/rewards/gpqa.py b/relax/engine/rewards/gpqa.py index ac42e68e5..17d98d21f 100644 --- a/relax/engine/rewards/gpqa.py +++ b/relax/engine/rewards/gpqa.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import string from collections.abc import Iterable diff --git a/relax/engine/rewards/ifbench.py b/relax/engine/rewards/ifbench.py index fd42e80c5..2eae4389a 100644 --- a/relax/engine/rewards/ifbench.py +++ b/relax/engine/rewards/ifbench.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from __future__ import annotations import importlib diff --git a/relax/engine/rewards/math_dapo_utils.py b/relax/engine/rewards/math_dapo_utils.py index d25f16a2d..f7d44bfc0 100644 --- a/relax/engine/rewards/math_dapo_utils.py +++ b/relax/engine/rewards/math_dapo_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import re import signal diff --git a/relax/engine/rewards/math_utils.py b/relax/engine/rewards/math_utils.py index 93fb9f929..21f44821f 100644 --- a/relax/engine/rewards/math_utils.py +++ b/relax/engine/rewards/math_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - # from https://github.com/agentica-project/deepscaler/blob/e6080ccd974eb64bd3430f0b36108244a6fee330/deepscaler/rewards/math_utils/utils.py """Answer checker API that uses sympy to simplify expressions and check for equality. diff --git a/relax/engine/rollout/__init__.py b/relax/engine/rollout/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/engine/rollout/__init__.py +++ b/relax/engine/rollout/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/engine/rollout/base_types.py b/relax/engine/rollout/base_types.py index 14144b72e..191af1bc0 100644 --- a/relax/engine/rollout/base_types.py +++ b/relax/engine/rollout/base_types.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from dataclasses import dataclass from typing import Any diff --git a/relax/engine/router/__init__.py b/relax/engine/router/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/engine/router/__init__.py +++ b/relax/engine/router/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/engine/router/middleware/__init__.py b/relax/engine/router/middleware/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/engine/router/middleware/__init__.py +++ b/relax/engine/router/middleware/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/engine/router/middleware/radix_tree_middleware.py b/relax/engine/router/middleware/radix_tree_middleware.py index acea5cb13..31b045821 100644 --- a/relax/engine/router/middleware/radix_tree_middleware.py +++ b/relax/engine/router/middleware/radix_tree_middleware.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import asyncio import json diff --git a/relax/engine/router/router.py b/relax/engine/router/router.py index ba7733c79..eafda9d2a 100644 --- a/relax/engine/router/router.py +++ b/relax/engine/router/router.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import argparse import asyncio import json diff --git a/relax/utils/__init__.py b/relax/utils/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/utils/__init__.py +++ b/relax/utils/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/utils/async_utils.py b/relax/utils/async_utils.py index 5eef650a8..9f4fea52a 100644 --- a/relax/utils/async_utils.py +++ b/relax/utils/async_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import asyncio import threading diff --git a/relax/utils/data/__init__.py b/relax/utils/data/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/utils/data/__init__.py +++ b/relax/utils/data/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/utils/data/mask_utils.py b/relax/utils/data/mask_utils.py index e86850755..0ddb3a141 100644 --- a/relax/utils/data/mask_utils.py +++ b/relax/utils/data/mask_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from transformers import AutoTokenizer diff --git a/relax/utils/data/seqlen_balancing.py b/relax/utils/data/seqlen_balancing.py index 22717d52f..55e8a7467 100644 --- a/relax/utils/data/seqlen_balancing.py +++ b/relax/utils/data/seqlen_balancing.py @@ -1,7 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - -# Copied from https://github.com/volcengine/verl/blob/468adf22c43b744348051fccd7a5d830c6c3c36a/verl/utils/seqlen_balancing.py - import copy import heapq diff --git a/relax/utils/debug/__init__.py b/relax/utils/debug/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/utils/debug/__init__.py +++ b/relax/utils/debug/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/utils/distributed_utils.py b/relax/utils/distributed_utils.py index 1eaee5a92..ed26d8cde 100644 --- a/relax/utils/distributed_utils.py +++ b/relax/utils/distributed_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from datetime import timedelta from typing import Any diff --git a/relax/utils/external/__init__.py b/relax/utils/external/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/utils/external/__init__.py +++ b/relax/utils/external/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/utils/external/typer_utils.py b/relax/utils/external/typer_utils.py index d974fce2d..1092c6fe4 100644 --- a/relax/utils/external/typer_utils.py +++ b/relax/utils/external/typer_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import dataclasses import inspect from typing import Annotated diff --git a/relax/utils/megatron_bridge_utils.py b/relax/utils/megatron_bridge_utils.py index aa07b4b22..35a058feb 100644 --- a/relax/utils/megatron_bridge_utils.py +++ b/relax/utils/megatron_bridge_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from contextlib import contextmanager diff --git a/relax/utils/metrics/adapters/__init__.py b/relax/utils/metrics/adapters/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/utils/metrics/adapters/__init__.py +++ b/relax/utils/metrics/adapters/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/utils/metrics/metric_utils.py b/relax/utils/metrics/metric_utils.py index 3be9751e2..60cc131ee 100644 --- a/relax/utils/metrics/metric_utils.py +++ b/relax/utils/metrics/metric_utils.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import logging import math from typing import Any, Literal diff --git a/relax/utils/multimodal/__init__.py b/relax/utils/multimodal/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/utils/multimodal/__init__.py +++ b/relax/utils/multimodal/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/utils/reloadable_process_group.py b/relax/utils/reloadable_process_group.py index bb6bf089f..6016ea610 100644 --- a/relax/utils/reloadable_process_group.py +++ b/relax/utils/reloadable_process_group.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import os from contextlib import contextmanager from datetime import timedelta diff --git a/relax/utils/rocm_checkpoint_writer.py b/relax/utils/rocm_checkpoint_writer.py index 2a1d689dd..a0b5088fc 100644 --- a/relax/utils/rocm_checkpoint_writer.py +++ b/relax/utils/rocm_checkpoint_writer.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import torch from megatron.core.dist_checkpointing.strategies.filesystem_async import FileSystemWriterAsync diff --git a/relax/utils/training/__init__.py b/relax/utils/training/__init__.py index 9f3863608..e69de29bb 100644 --- a/relax/utils/training/__init__.py +++ b/relax/utils/training/__init__.py @@ -1 +0,0 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/utils/training/eval_config.py b/relax/utils/training/eval_config.py index b4cb42423..024a77729 100644 --- a/relax/utils/training/eval_config.py +++ b/relax/utils/training/eval_config.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from __future__ import annotations from collections.abc import Iterable diff --git a/relax/utils/training/flops_utils.py b/relax/utils/training/flops_utils.py index 3a5fb0fbc..75afccc05 100644 --- a/relax/utils/training/flops_utils.py +++ b/relax/utils/training/flops_utils.py @@ -1,6 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - - def calculate_embedding_flops(seqlen, hidden_size): return 2 * seqlen * hidden_size diff --git a/relax/utils/training/routing_replay.py b/relax/utils/training/routing_replay.py index 2f5e9c2f7..096f4e748 100644 --- a/relax/utils/training/routing_replay.py +++ b/relax/utils/training/routing_replay.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - import os import torch diff --git a/relax/utils/training/tensor_backper.py b/relax/utils/training/tensor_backper.py index ed022f205..955975aba 100644 --- a/relax/utils/training/tensor_backper.py +++ b/relax/utils/training/tensor_backper.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from abc import ABC, abstractmethod from collections import defaultdict from collections.abc import Callable, Iterable diff --git a/relax/utils/types.py b/relax/utils/types.py index 3fa800469..0a39be7ec 100644 --- a/relax/utils/types.py +++ b/relax/utils/types.py @@ -1,5 +1,3 @@ -# Copyright (c) 2026 Relax Authors. All Rights Reserved. - from dataclasses import dataclass, field from enum import Enum from typing import Any From 9b7356e61edca0d5213425c0af7507de1b194c90 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Thu, 16 Apr 2026 10:03:20 +0000 Subject: [PATCH 004/268] feat(weight-conversion): add Qwen3 VL MoE converter MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add Qwen3 VL MoE weight conversion module - New `qwen3_vl_moe.py` combining VL architecture (vision_model → model.visual) with MoE routing (experts, shared_expert, router) - Registered as `qwen3vlmoe` in `_convert_to_hf_core`, placed before `qwen3vl` to avoid substring collision --- # 🐛 Bug Fix ## Fix Megatron-Bridge link in README - Corrected GitHub org from `NVIDIA` to `NVIDIA-NeMo` for Megatron-Bridge repository --- README.md | 2 +- README_zh.md | 2 +- .../megatron/weight_conversion/__init__.py | 3 + .../weight_conversion/qwen3_vl_moe.py | 140 ++++++++++++++++++ 4 files changed, 145 insertions(+), 2 deletions(-) create mode 100644 relax/backends/megatron/weight_conversion/qwen3_vl_moe.py diff --git a/README.md b/README.md index cc94f0c39..ef0460ca9 100644 --- a/README.md +++ b/README.md @@ -279,7 +279,7 @@ Relax is built upon the shoulders of excellent open-source projects: - [Slime](https://github.com/THUDM/slime) — Scalable training and inference framework for reinforcement learning - [SGLang](https://github.com/sgl-project/sglang) — Fast serving framework for large language models -- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) & [Megatron-Bridge](https://github.com/NVIDIA/Megatron-Bridge) — Large-scale distributed training framework and HF ↔ Megatron weight conversion bridge, with sincere thanks to the entire **NVIDIA** team +- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) & [Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) — Large-scale distributed training framework and HF ↔ Megatron weight conversion bridge, with sincere thanks to the entire **NVIDIA** team - [TransferQueue](https://github.com/Ascend/TransferQueue) — High-performance distributed data transfer queue - [Ray](https://github.com/ray-project/ray) — Distributed computing framework - [HuggingFace Transformers](https://github.com/huggingface/transformers) — State-of-the-art model hub diff --git a/README_zh.md b/README_zh.md index e4152220c..f99fe08cc 100644 --- a/README_zh.md +++ b/README_zh.md @@ -279,7 +279,7 @@ Relax 的构建离不开以下优秀的开源项目： - [Slime](https://github.com/THUDM/slime) — 可扩展的强化学习训练与推理框架 - [SGLang](https://github.com/sgl-project/sglang) — 高性能大语言模型推理框架 -- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) 与 [Megatron-Bridge](https://github.com/NVIDIA/Megatron-Bridge) — 大规模分布式训练框架及 HF ↔ Megatron 权重转换桥接库，衷心感谢整个 **NVIDIA** 团队 +- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) 与 [Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) — 大规模分布式训练框架及 HF ↔ Megatron 权重转换桥接库，衷心感谢整个 **NVIDIA** 团队 - [TransferQueue](https://github.com/Ascend/TransferQueue) — 高性能分布式数据传输队列 - [Ray](https://github.com/ray-project/ray) — 分布式计算框架 - [HuggingFace Transformers](https://github.com/huggingface/transformers) — 最先进的模型中心 diff --git a/relax/backends/megatron/weight_conversion/__init__.py b/relax/backends/megatron/weight_conversion/__init__.py index 58b1c1b34..96fd94ed5 100644 --- a/relax/backends/megatron/weight_conversion/__init__.py +++ b/relax/backends/megatron/weight_conversion/__init__.py @@ -9,6 +9,7 @@ from .qwen3_next import convert_qwen3_next_to_hf from .qwen3_omni_moe import convert_qwen3omni_to_hf from .qwen3_vl import convert_qwen3vl_to_hf +from .qwen3_vl_moe import convert_qwen3vlmoe_to_hf from .qwen3moe import convert_qwen3moe_to_hf @@ -49,6 +50,8 @@ def _convert_to_hf_core(args, model_name, name, param): converted_named_tensors = convert_qwen3_next_to_hf(args, name, param) elif "qwen3_5" in model_name: converted_named_tensors = convert_qwen3_5_to_hf(args, name, param) + elif "qwen3vlmoe" in model_name: + converted_named_tensors = convert_qwen3vlmoe_to_hf(args, name, param) elif "qwen3vl" in model_name: converted_named_tensors = convert_qwen3vl_to_hf(args, name, param) elif "qwen2" in model_name or "qwen3" in model_name: diff --git a/relax/backends/megatron/weight_conversion/qwen3_vl_moe.py b/relax/backends/megatron/weight_conversion/qwen3_vl_moe.py new file mode 100644 index 000000000..15a444e11 --- /dev/null +++ b/relax/backends/megatron/weight_conversion/qwen3_vl_moe.py @@ -0,0 +1,140 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +import re + +import torch + + +def convert_qwen3vlmoe_to_hf(args, name, param): + if name.startswith("module.module.language_model."): + name = "module.module." + name[len("module.module.language_model.") :] + + # (Optional safety) if you ever see extra "module." prefixes + while name.startswith("module.module.module."): + name = name.replace("module.module.module.", "module.module.", 1) + + if name.startswith("module.module.vision_model."): + hf_name = "model.visual." + name[len("module.module.vision_model.") :] + return [(hf_name, param)] + + if name == "module.module.embedding.word_embeddings.weight": + return [("model.language_model.embed_tokens.weight", param)] + + if name == "module.module.output_layer.weight": + return [("lm_head.weight", param)] + + if name == "module.module.decoder.final_layernorm.weight": + return [("model.language_model.norm.weight", param)] + + try: + head_dim = args.kv_channels if args.kv_channels is not None else args.hidden_size // args.num_attention_heads + except AttributeError: + head_dim = args.hidden_size // args.num_attention_heads + value_num_per_group = args.num_attention_heads // args.num_query_groups + + decoder_layers_pattern = r"module\.module\.decoder\.layers\.(\d+)\.(.+)" + match = re.match(decoder_layers_pattern, name) + if match: + layer_idx, rest = match.groups() + base = f"model.language_model.layers.{layer_idx}" + + # ── MoE experts ────────────────────────────────────────────────── + expert_pattern = r"mlp\.experts\.(.+)\.weight(\d+)" + expert_match = re.match(expert_pattern, rest) + if expert_match: + expert_rest, expert_idx = expert_match.groups() + if expert_rest == "linear_fc1": + gate_weight, up_weight = param.chunk(2, dim=0) + return [ + (f"{base}.mlp.experts.{expert_idx}.gate_proj.weight", gate_weight), + (f"{base}.mlp.experts.{expert_idx}.up_proj.weight", up_weight), + ] + elif expert_rest == "linear_fc2": + return [(f"{base}.mlp.experts.{expert_idx}.down_proj.weight", param)] + else: + raise ValueError(f"Unknown expert parameter name: {name}") + + # ── MoE shared expert ──────────────────────────────────────────── + shared_expert_pattern = r"mlp\.shared_experts\.(.+)" + shared_match = re.match(shared_expert_pattern, rest) + if shared_match: + shared_rest = shared_match.groups()[0] + if shared_rest == "linear_fc1.weight": + gate_weight, up_weight = param.chunk(2, dim=0) + return [ + (f"{base}.mlp.shared_expert.gate_proj.weight", gate_weight), + (f"{base}.mlp.shared_expert.up_proj.weight", up_weight), + ] + elif shared_rest == "linear_fc2.weight": + return [(f"{base}.mlp.shared_expert.down_proj.weight", param)] + elif shared_rest == "gate_weight": + return [(f"{base}.mlp.shared_expert_gate.weight", param)] + else: + raise ValueError(f"Unknown shared expert parameter name: {name}") + + # ── MoE router ─────────────────────────────────────────────────── + if rest == "mlp.router.weight": + return [(f"{base}.mlp.gate.weight", param)] + elif rest == "mlp.router.expert_bias": + return [(f"{base}.mlp.gate.e_score_correction_bias", param)] + + # ── Attention ──────────────────────────────────────────────────── + if rest == "self_attention.linear_proj.weight": + return [(f"{base}.self_attn.o_proj.weight", param)] + + elif rest == "self_attention.linear_qkv.weight": + param = param.view(args.num_query_groups, -1, head_dim, args.hidden_size) + q_param, k_param, v_param = torch.split(param, split_size_or_sections=[value_num_per_group, 1, 1], dim=1) + q_param = q_param.reshape(-1, args.hidden_size) + k_param = k_param.reshape(-1, args.hidden_size) + v_param = v_param.reshape(-1, args.hidden_size) + return [ + (f"{base}.self_attn.q_proj.weight", q_param), + (f"{base}.self_attn.k_proj.weight", k_param), + (f"{base}.self_attn.v_proj.weight", v_param), + ] + + elif rest == "self_attention.linear_qkv.bias": + param = param.view(args.num_query_groups, -1) + q_bias, k_bias, v_bias = torch.split( + param, + split_size_or_sections=[value_num_per_group * head_dim, head_dim, head_dim], + dim=1, + ) + q_bias = q_bias.contiguous().flatten() + k_bias = k_bias.contiguous().flatten() + v_bias = v_bias.contiguous().flatten() + return [ + (f"{base}.self_attn.q_proj.bias", q_bias), + (f"{base}.self_attn.k_proj.bias", k_bias), + (f"{base}.self_attn.v_proj.bias", v_bias), + ] + + # ── Dense MLP (non-MoE layers) ─────────────────────────────────── + elif rest == "mlp.linear_fc1.weight": + gate_weight, up_weight = param.chunk(2, dim=0) + return [ + (f"{base}.mlp.gate_proj.weight", gate_weight), + (f"{base}.mlp.up_proj.weight", up_weight), + ] + + elif rest == "mlp.linear_fc2.weight": + return [(f"{base}.mlp.down_proj.weight", param)] + + # ── Layer norms ────────────────────────────────────────────────── + elif rest == "self_attention.linear_qkv.layer_norm_weight": + return [(f"{base}.input_layernorm.weight", param)] + + elif rest == "mlp.linear_fc1.layer_norm_weight": + return [(f"{base}.post_attention_layernorm.weight", param)] + + elif rest == "pre_mlp_layernorm.weight": + return [(f"{base}.post_attention_layernorm.weight", param)] + + # ── QK norm ────────────────────────────────────────────────────── + elif rest == "self_attention.q_layernorm.weight": + return [(f"{base}.self_attn.q_norm.weight", param)] + elif rest == "self_attention.k_layernorm.weight": + return [(f"{base}.self_attn.k_norm.weight", param)] + + raise ValueError(f"Unknown parameter name: {name}") From 410f9bf0b5cf8c0075f408cdde6cebab9fe0e0f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Fri, 17 Apr 2026 11:28:08 +0800 Subject: [PATCH 005/268] fix(megatron): prevent fused RoPE on multimodal models MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Add multimodal + apply_rope_fusion cross-validation - Add validation in `_hf_validate_args()` that rejects `apply_rope_fusion` for multimodal models (Qwen3-VL, Qwen3.5, Qwen3-Omni) - Multimodal models use multi-axis RoPE (list of tensors) which is incompatible with fused RoPE kernels, causing train-inference log-prob mismatch ## Restore --no-rope-fusion in multimodal training scripts - Uncomment `--no-rope-fusion` in run-qwen3-vl-4B-8xgpu.sh - Add `--no-rope-fusion` to run-qwen3-vl-4B-2xgpu.sh - Add `--no-rope-fusion` to run-qwen3-vl-30B-A3B-8xgpu.sh - Add `--no-rope-fusion` to run-qwen3-30B-A3B-omni-16xgpu.sh - Add `--no-rope-fusion` to run-qwen3-30B-A3B-omni-16xgpu-async.sh - Add `--no-rope-fusion` to run-qwen3-30B-A3B-omni-16xgpu-video.sh --- # ⭐ Feature ## Add --vision-dp-when-tp CLI argument - Add `--vision-dp-when-tp` to `relax/utils/arguments.py` for splitting vision encoder workload across TP ranks via data parallelism - Add `vision_dp_when_tp` to bridge_keys in model_provider.py so the attribute is forwarded to the Megatron-Bridge provider --- examples/deepeyes/run_deepeyes.sh | 1 + examples/deepeyes/run_deepeyes_fp16.sh | 1 + examples/deepeyes/run_deepeyes_genrm.sh | 1 + examples/deepeyes/run_deepeyes_pr.sh | 1 + examples/deepeyes/run_deepeyes_r3.sh | 1 + relax/backends/megatron/arguments.py | 12 ++++++++++++ relax/backends/megatron/model_provider.py | 2 ++ relax/utils/arguments.py | 7 +++++++ .../run-qwen3-30B-A3B-omni-16xgpu-async.sh | 1 + .../run-qwen3-30B-A3B-omni-16xgpu-video.sh | 1 + .../multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh | 1 + .../multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh | 1 + scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh | 1 + scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh | 2 ++ 14 files changed, 33 insertions(+) diff --git a/examples/deepeyes/run_deepeyes.sh b/examples/deepeyes/run_deepeyes.sh index c65c99563..2576d6fc7 100644 --- a/examples/deepeyes/run_deepeyes.sh +++ b/examples/deepeyes/run_deepeyes.sh @@ -134,6 +134,7 @@ OPTIMIZER_ARGS=( --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer + --no-rope-fusion ) ############################################################################### diff --git a/examples/deepeyes/run_deepeyes_fp16.sh b/examples/deepeyes/run_deepeyes_fp16.sh index c1bc482d5..1cb0b6a12 100644 --- a/examples/deepeyes/run_deepeyes_fp16.sh +++ b/examples/deepeyes/run_deepeyes_fp16.sh @@ -135,6 +135,7 @@ OPTIMIZER_ARGS=( --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer + --no-rope-fusion ) ############################################################################### diff --git a/examples/deepeyes/run_deepeyes_genrm.sh b/examples/deepeyes/run_deepeyes_genrm.sh index 959fd4a68..f11b0bb98 100644 --- a/examples/deepeyes/run_deepeyes_genrm.sh +++ b/examples/deepeyes/run_deepeyes_genrm.sh @@ -122,6 +122,7 @@ OPTIMIZER_ARGS=( --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer + --no-rope-fusion ) ############################################################################### diff --git a/examples/deepeyes/run_deepeyes_pr.sh b/examples/deepeyes/run_deepeyes_pr.sh index dab4d02df..188c5e84b 100644 --- a/examples/deepeyes/run_deepeyes_pr.sh +++ b/examples/deepeyes/run_deepeyes_pr.sh @@ -141,6 +141,7 @@ OPTIMIZER_ARGS=( --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer + --no-rope-fusion ) ############################################################################### diff --git a/examples/deepeyes/run_deepeyes_r3.sh b/examples/deepeyes/run_deepeyes_r3.sh index 0f6134d0b..b5307cac2 100644 --- a/examples/deepeyes/run_deepeyes_r3.sh +++ b/examples/deepeyes/run_deepeyes_r3.sh @@ -145,6 +145,7 @@ OPTIMIZER_ARGS=( --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer + --no-rope-fusion ) ############################################################################### diff --git a/relax/backends/megatron/arguments.py b/relax/backends/megatron/arguments.py index b4e094543..d76acdbca 100644 --- a/relax/backends/megatron/arguments.py +++ b/relax/backends/megatron/arguments.py @@ -57,6 +57,18 @@ def equal(x, y): errors = [] + # Multimodal models (Qwen3-VL, Qwen3.5, Qwen3-Omni, etc.) use multi-axis RoPE whose + # rotary_pos_emb is a Python list of tensors, not a single Tensor. Megatron's fused + # RoPE kernel cannot handle this and produces numerically different results from the + # unfused HF/SGLang implementation, causing training-inference log-prob mismatch. + is_multimodal = hasattr(hf_config, "text_config") or hasattr(hf_config, "thinker_config") + if is_multimodal and getattr(args, "apply_rope_fusion", False): + errors.append( + "Multimodal models use multi-axis RoPE (list of tensors) which is incompatible " + "with fused RoPE kernels — this causes training-inference log-prob mismatch. " + "Add --no-rope-fusion to the launch script." + ) + # omni models have different config structure if hasattr(hf_config, "thinker_config"): hf_config = hf_config.thinker_config diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index 91a1fdeba..9e6c0095d 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -123,6 +123,8 @@ def wrapped_model_provider( "freeze_language_model", "freeze_vision_model", "freeze_vision_projection", + # https://github.com/redai-infra/Megatron-Bridge/commit/960bb5f18800d3e1fb9815e95daa185ab06c09ea + "vision_dp_when_tp", ] # NOTE(wuhuan): Multimodal models (e.g. Qwen3-VL) use multi-axis RoPE whose diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 6847bc2ec..f4a7c0078 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -258,6 +258,13 @@ def add_train_arguments(parser): default=False, help="Whether to freeze the vision projection parameters (used in bridge mode for multimodal models).", ) + parser.add_argument( + "--vision-dp-when-tp", + action="store_true", + default=False, + help="Split vision encoder workload across TP ranks (data-parallel over TP). " + "Each TP rank processes a chunk of images, then all-reduce gathers the full embedding.", + ) parser.add_argument( "--recompute-loss-function", action="store_true", diff --git a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh index c946adf4e..5a6e31fd5 100644 --- a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh +++ b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh @@ -108,6 +108,7 @@ OPTIMIZER_ARGS=( --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer + --no-rope-fusion ) SGLANG_ARGS=( diff --git a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh index 295989167..e67da4ac7 100644 --- a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh +++ b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh @@ -101,6 +101,7 @@ OPTIMIZER_ARGS=( --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer + --no-rope-fusion ) SGLANG_ARGS=( diff --git a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh index ca3494a5c..68a5bd79a 100644 --- a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh @@ -103,6 +103,7 @@ OPTIMIZER_ARGS=( --optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer + --no-rope-fusion ) SGLANG_ARGS=( diff --git a/scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh b/scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh index b7906fcc8..f0b389e0d 100644 --- a/scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh @@ -91,6 +91,7 @@ OPTIMIZER_ARGS=( --use-precision-aware-optimizer # NOTE(wuhuan): to avoid algorithm performance degradation + --no-rope-fusion --moe-router-load-balancing-type "none" --moe-aux-loss-coeff 0.0 ) diff --git a/scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh b/scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh index e606bb3fd..2befb28dd 100644 --- a/scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh @@ -93,6 +93,7 @@ OPTIMIZER_ARGS=( --adam-beta1 0.9 --adam-beta2 0.98 --clip-grad 1.0 + --no-rope-fusion ) WANDB_ARGS=( diff --git a/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh b/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh index 72fb2dede..4bf13f04a 100644 --- a/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh @@ -67,6 +67,8 @@ PERF_ARGS=( #--micro-batch-size 16 # avoid OOM --use-dynamic-batch-size --max-tokens-per-gpu 9216 + + --no-rope-fusion ) GRPO_ARGS=( From 367778a7c4959c9ecfc3a2a4f07eddf60293729d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Fri, 17 Apr 2026 16:11:42 +0800 Subject: [PATCH 006/268] refactor(entrypoint): dual-mode ray-job.sh + auto-delegate MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ♻️ Refactor ## Make ray-job.sh support both entry-point and source modes - Detect mode via BASH_SOURCE vs $0 plus first-arg .sh file check, so sourced callers cannot accidentally trigger entry-point exec from leaked positional args - Entry-point mode: set up env then exec the run script (unchanged CLI) - Source mode: set up env only and fall through to the caller - Drop duplicate DIR= assignment and unused SLIME_HOST_IP comment - Add RELAX_ENTRYPOINT_MODE guard to match local.sh ## Auto-delegate local.sh to ray-job.sh inside an existing cluster - At the top of local.sh, when RAY_ADDRESS is set and `ray status` succeeds, source ray-job.sh instead of tearing down and restarting a local Ray head node - Preserves existing single-node flow otherwise --- scripts/entrypoint/local.sh | 18 ++++++++- scripts/entrypoint/ray-job.sh | 72 ++++++++++++++++++++++------------- 2 files changed, 62 insertions(+), 28 deletions(-) diff --git a/scripts/entrypoint/local.sh b/scripts/entrypoint/local.sh index 9ebc93abd..5c7993f36 100644 --- a/scripts/entrypoint/local.sh +++ b/scripts/entrypoint/local.sh @@ -7,6 +7,10 @@ # It is designed to be *sourced* by run-*.sh scripts when no external entrypoint # (spmd-multinode.sh or ray-job.sh) has been used. # +# When an existing Ray cluster is detected (RAY_ADDRESS set and `ray status` OK), +# this script delegates to `ray-job.sh` (source mode) instead of starting a new +# local Ray head node. +# # Usage (from a run script): # source scripts/entrypoint/local.sh # @@ -22,6 +26,19 @@ if [ -n "${RELAX_ENTRYPOINT_MODE:-}" ]; then return 0 2>/dev/null || exit 0 fi +_LOCAL_SH_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)" + +# ── delegate to ray-job.sh when inside an existing Ray cluster ───────────── +# When RAY_ADDRESS is set AND `ray status` succeeds, we're already part of an +# externally-managed Ray cluster. Skip local Ray startup / process cleanup and +# fall through to ray-job.sh (source mode) for env setup. +if [ -n "${RAY_ADDRESS:-}" ] && timeout 5 ray status >/dev/null 2>&1; then + echo "=== Detected existing Ray cluster (RAY_ADDRESS=$RAY_ADDRESS); delegating to ray-job.sh ===" + # shellcheck source=./ray-job.sh + source "${_LOCAL_SH_DIR}/ray-job.sh" + return 0 2>/dev/null || exit 0 +fi + set -eo pipefail # ── process cleanup ───────────────────────────────────────────────────────── @@ -38,7 +55,6 @@ pkill -9 python 2>/dev/null || true set -x # ── environment setup ─────────────────────────────────────────────────────── -_LOCAL_SH_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)" unset MASTER_ADDR 2>/dev/null || true export PYTHONUNBUFFERED=1 export CUDA_DEVICE_MAX_CONNECTIONS=1 diff --git a/scripts/entrypoint/ray-job.sh b/scripts/entrypoint/ray-job.sh index 4ba710900..c458684d7 100755 --- a/scripts/entrypoint/ray-job.sh +++ b/scripts/entrypoint/ray-job.sh @@ -2,39 +2,52 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. # -# Entrypoint for Ray Job tasks. +# Entrypoint / source helper for Ray Job tasks. # The Ray cluster is already running. This script MUST NOT kill ray or stop the -# cluster. It only cleans up residual python/sglang processes and then runs the -# training script directly via python3. +# cluster. It only cleans up residual python/sglang processes and then sets up +# the environment for running training against an existing Ray cluster. # -# Usage: -# bash scripts/entrypoint/ray-job.sh [extra-args...] +# Two usage modes: +# 1) Entry-point mode — first argument is a .sh script path: +# bash scripts/entrypoint/ray-job.sh [extra-args...] +# Sets up env, cleans residual processes, then execs the run script. # -# Example: -# bash scripts/entrypoint/ray-job.sh scripts/training/text/run-qwen35-9B-8xgpu-async.sh -# bash scripts/entrypoint/ray-job.sh scripts/training/text/run-qwen35-9B-8xgpu-async.sh --lr 5e-7 +# Example: +# bash scripts/entrypoint/ray-job.sh scripts/training/text/run-qwen35-9B-8xgpu-async.sh +# bash scripts/entrypoint/ray-job.sh scripts/training/text/run-qwen35-9B-8xgpu-async.sh --lr 5e-7 +# +# 2) Source mode — no .sh script arg (like local.sh): +# source scripts/entrypoint/ray-job.sh +# Sets up env only, so the caller can continue execution. # # Environment variables (optional): # MEGATRON - Path to Megatron-LM (default: /root/Megatron-LM/) # RELAX - Path to Relax project (default: ../../) -set -eo pipefail - -DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)" - -# ── argument parsing ──────────────────────────────────────────────────────── -RUN_SCRIPT="${1:-}" -if [ -z "$RUN_SCRIPT" ]; then - echo "Usage: $0 [extra-args...]" >&2 - exit 1 +# Guard: skip if already sourced by another entrypoint +if [ -n "${RELAX_ENTRYPOINT_MODE:-}" ]; then + return 0 2>/dev/null || exit 0 fi -shift # remaining args are extra overrides -if [ ! -f "$RUN_SCRIPT" ]; then - echo "ERROR: run script not found: $RUN_SCRIPT" >&2 - exit 1 +# ── mode detection ────────────────────────────────────────────────────────── +# Entry-point mode: directly executed AND first arg is an existing .sh file. +# Otherwise act as a sourced setup script. +_RAY_JOB_RUN_SCRIPT="" +if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then + _RAY_JOB_FIRST_ARG="${1:-}" + if [ -n "$_RAY_JOB_FIRST_ARG" ] && [ -f "$_RAY_JOB_FIRST_ARG" ] && [[ "$_RAY_JOB_FIRST_ARG" == *.sh ]]; then + _RAY_JOB_RUN_SCRIPT="$_RAY_JOB_FIRST_ARG" + shift + else + echo "Usage: $0 [extra-args...]" >&2 + exit 1 + fi fi +set -eo pipefail + +DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)" + # ── clean up residual python/sglang processes (NOT ray) ───────────────────── # IMPORTANT: Do NOT pkill ray or run ray stop — the cluster is managed externally. echo "=== Cleaning up residual python/sglang processes ===" @@ -46,14 +59,13 @@ ray job list | grep RUNNING | grep -v job_id=None | grep -oP "submission_id='\\K set -x # ── environment setup ─────────────────────────────────────────────────────── -# Use the first GPU node as MASTER_ADDR and SLIME_HOST_IP (prefer head node) +# Use the first GPU node as MASTER_ADDR (prefer head node) export MASTER_ADDR=$(ray list nodes --format json | jq -r ' map(select(.state == "ALIVE" and (.resources_total.GPU // 0) > 0)) | sort_by(.is_head_node | not) | .[0].node_ip ') -DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)" export PYTHONUNBUFFERED=1 export CUDA_DEVICE_MAX_CONNECTIONS=1 export MEGATRON=${MEGATRON:-/root/Megatron-LM/} @@ -74,13 +86,12 @@ else fi echo "HAS_NVLINK: $HAS_NVLINK (detected $NVLINK_COUNT NVLink references)" -# ── delegate to run script ────────────────────────────────────────────────── -echo "=== Launching training script: $RUN_SCRIPT ===" +# ── entrypoint mode & runtime env ────────────────────────────────────────── export RELAX_ENTRYPOINT_MODE="ray-job" RAY_DEBUG=${RAY_DEBUG:-"0"} RAY_DEBUG_POST_MORTEM=${RAY_DEBUG_POST_MORTEM:-"0"} -# Runtime env for ray-job mode (empty, env inherited from Ray cluster) +# Runtime env for ray-job mode (env inherited from Ray cluster) export RUNTIME_ENV_JSON="{ \"env_vars\": { \"PYTHONUNBUFFERED\": \"1\", @@ -93,4 +104,11 @@ export RUNTIME_ENV_JSON="{ \"RAY_DEBUG_POST_MORTEM\": \"${RAY_DEBUG_POST_MORTEM}\" } }" -exec bash "$RUN_SCRIPT" "$@" + +echo "=== Ray-job environment ready ===" + +# ── delegate to run script (entry-point mode only) ───────────────────────── +if [ -n "$_RAY_JOB_RUN_SCRIPT" ]; then + echo "=== Launching training script: $_RAY_JOB_RUN_SCRIPT ===" + exec bash "$_RAY_JOB_RUN_SCRIPT" "$@" +fi From 082508cdba27162415cbe67e94214be175c6056e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Fri, 17 Apr 2026 13:31:25 +0000 Subject: [PATCH 007/268] fix(megatron,rollout): remove multimodal recompute guard and eval assert MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Remove multimodal recompute-granularity guard in model_provider - Delete the `is_multimodal` detection block that stripped recompute-related bridge keys when `--recompute-granularity=full` was set - The upstream Megatron-Bridge now handles multi-axis RoPE list unpacking, making the workaround obsolete --- # 🐛 Bug Fix ## Remove unsupported group_rm assertion in eval_rollout - Delete `assert not args.group_rm` that blocked eval rollout when group reward model was enabled - Allows eval rollout to proceed with group RM configurations --- relax/backends/megatron/model_provider.py | 29 ----------------------- relax/engine/rollout/sglang_rollout.py | 1 - 2 files changed, 30 deletions(-) diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index 9e6c0095d..f85ada679 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -127,35 +127,6 @@ def wrapped_model_provider( "vision_dp_when_tp", ] - # NOTE(wuhuan): Multimodal models (e.g. Qwen3-VL) use multi-axis RoPE whose - # `rotary_pos_emb` is a Python list of tensors, not a single Tensor. Megatron's - # `CheckpointFunction.forward` calls `ctx.save_for_backward(*forward_args)`, which - # rejects list arguments with: - # TypeError: save_for_backward can only save variables, but argument 6 is of type list - # So `recompute-granularity full` is incompatible with multimodal providers until - # upstream Megatron-Bridge unpacks the rope list. Detect that case and disable the - # recompute overrides (bridge default = no full recompute), warning the user loudly. - is_multimodal = hasattr(provider, "freeze_vision_model") or hasattr(provider, "vision_transformer_config") - if is_multimodal and getattr(args, "recompute_granularity", None) == "full": - logger.warning( - "Multimodal model detected together with --recompute-granularity=full. " - "Full activation recomputation is incompatible with multi-axis RoPE " - "(rotary_pos_emb is a list, which Megatron's CheckpointFunction cannot " - "save_for_backward). Disabling recompute overrides for this run — " - "remove --recompute-granularity from the launch script to silence this warning." - ) - bridge_keys = [ - k - for k in bridge_keys - if k - not in ( - "recompute_granularity", - "recompute_method", - "recompute_num_layers", - "distribute_saved_activations", - ) - ] - args_dict = vars(args) for attr in vars(provider): if attr in args_dict and attr in bridge_keys: diff --git a/relax/engine/rollout/sglang_rollout.py b/relax/engine/rollout/sglang_rollout.py index 7bb664267..7e0d30b66 100644 --- a/relax/engine/rollout/sglang_rollout.py +++ b/relax/engine/rollout/sglang_rollout.py @@ -809,7 +809,6 @@ async def generate_rollout_async( async def eval_rollout(args: Namespace, rollout_id: int) -> tuple[dict[str, dict[str, list[Any]]], list[list[Sample]]]: - assert not args.group_rm, "Group RM is not supported for eval rollout" state = GenerateState(args) # Increment evaluating counter so that abort() knows to wait for eval to finish. From ae84ade166ab41a7569bd5252db9ca5f605f7a69 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Mon, 20 Apr 2026 13:17:44 +0800 Subject: [PATCH 008/268] fix(scripts,data): migrate ref-load to bridge mode and fix pyarrow nested type parsing --- docker/Dockerfile | 14 +++++++------ examples/deepeyes/run_deepeyes.sh | 16 +++++++------- examples/deepeyes/run_deepeyes_fp16.sh | 1 + examples/deepeyes/run_deepeyes_pr.sh | 1 + examples/deepeyes/run_deepeyes_r3.sh | 1 + relax/utils/data/data_utils.py | 8 +++++-- relax/utils/data/streaming_dataset.py | 8 +++++-- scripts/ci/benchmark.sh | 21 +++++++++++++++++++ scripts/training/hpc/run-qwen3-4B-pr-8xgpu.sh | 3 ++- .../text/run-qwen3-30B-A3B-16xgpu-async.sh | 3 ++- scripts/training/text/run-qwen3-4B-16xgpu.sh | 3 ++- scripts/training/text/run-qwen3-4B-2xgpu.sh | 3 ++- .../training/text/run-qwen3-4B-4xgpu-async.sh | 2 +- .../training/text/run-qwen3-4B-fp16-8xgpu.sh | 3 ++- 14 files changed, 64 insertions(+), 23 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 26069c801..c5d4d9f0a 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -2,6 +2,7 @@ ARG HTTP_PROXY ARG HTTPS_PROXY ARG NO_PROXY ARG BASE_IMAGE=lmsysorg/sglang:v0.5.9 +ARG TRAIN_IMAGE=train FROM ${BASE_IMAGE} as base ENV NVIDIA_PYTORCH_VERSION=25.06 \ @@ -73,7 +74,8 @@ RUN NVCC_APPEND_FLAGS="--threads 4" \ --config-settings "--build-option=--cpp_ext --cuda_ext --parallel 8" \ git+https://github.com/NVIDIA/apex.git@10417aceddd7d5d05d7cbf7b0fc2daad1105f8b4 -FROM train as relax +ARG TRAIN_IMAGE=train +FROM ${TRAIN_IMAGE} as relax ARG PATCH_VERSION=latest ARG ENABLE_SGLANG_PATCH=1 @@ -86,7 +88,7 @@ RUN pip install -r /tmp/requirements.txt --no-cache-dir && \ pip install --no-cache-dir tensordict==0.10.0 pyvers==0.1.0 --no-deps && \ apt-get install -y jq -RUN pip install git+https://github.com/redai-infra/megatron-bridge.git@relax/dev --no-build-isolation --no-deps --force-reinstall --no-cache-dir && \ +RUN pip install git+https://github.com/redai-infra/megatron-bridge.git@f13bec09 --no-build-isolation --no-deps --force-reinstall --no-cache-dir && \ pip install "transferqueue @ git+https://github.com/redai-infra/TransferQueue.git" --no-deps COPY docker/patch/${PATCH_VERSION}/megatron.patch /root/Megatron-LM/ @@ -111,10 +113,10 @@ RUN if [ "$ENABLE_SGLANG_PATCH" = "1" ]; then \ rm sglang.patch; \ fi -FROM relax as relax-release +# FROM relax as relax-release -WORKDIR /root/Relax +# WORKDIR /root/Relax -COPY . . +# COPY . . -RUN pip install -e . --no-deps +# RUN pip install -e . --no-deps diff --git a/examples/deepeyes/run_deepeyes.sh b/examples/deepeyes/run_deepeyes.sh index 2576d6fc7..e88b8b189 100644 --- a/examples/deepeyes/run_deepeyes.sh +++ b/examples/deepeyes/run_deepeyes.sh @@ -49,19 +49,18 @@ CKPT_ARGS=( --save ${SAVE_DIR}/Qwen3-VL-30B-A3B-Thinking-Checkpoint --megatron-to-hf-mode bridge --save-interval 100 - --max-actor-ckpt-to-keep 3 - # --load ${SAVE_DIR}/Qwen3-VL-30B-A3B-Thinking-Checkpoint + --max-actor-ckpt-to-keep 1 ) ############################################################################### # DATASETS # ############################################################################### -TRAIN_FILES=() -for i in {0..9}; do - TRAIN_FILES+=("'${DATA_DIR}/deepeyes/train/v0.1.2.parquet/partition=${i}/3ce23f4945e8498085ac5f72f0afc133-0.parquet'") -done -TEST_FILES=("${DATA_DIR}/deepeyes/test.parquet") +TRAIN_FILES=( + "'${DATA_DIR}/deepeyes-v1/data_0.1.2_visual_toolbox_v2.parquet@[0:5000]'" + "'${DATA_DIR}/deepeyes-v1/data_v0.8_visual_toolbox_v2.parquet@[0:5000]'" +) +TEST_FILES=("${DATA_DIR}/deepeyes-v1/data_thinklite_reasoning_acc.parquet@[0:256]") PROMPT_SET="[$(IFS=,; echo "${TRAIN_FILES[*]}")]" ############################################################################### @@ -91,6 +90,7 @@ ROLLOUT_ARGS=( --global-batch-size 256 --use-fault-tolerance --rollout-shuffle + --use-streaming-dataset ) ############################################################################### @@ -152,6 +152,7 @@ SGLANG_ARGS=( LOG_ARGS=( --use-clearml + --use-metrics-service --tb-project-name ${PROJECT_NAME} --tb-experiment-name ${EXP_NAME} # --dump-details dump_details_8k_0204 @@ -194,6 +195,7 @@ RAY_RESOURCE_ARGS=( --max-staleness 0 --num-data-storage-units 1 --colocate + --use-health-check ) ############################################################################### diff --git a/examples/deepeyes/run_deepeyes_fp16.sh b/examples/deepeyes/run_deepeyes_fp16.sh index 1cb0b6a12..acd985917 100644 --- a/examples/deepeyes/run_deepeyes_fp16.sh +++ b/examples/deepeyes/run_deepeyes_fp16.sh @@ -152,6 +152,7 @@ SGLANG_ARGS=( LOG_ARGS=( --use-clearml + --use-metrics-service --tb-project-name ${PROJECT_NAME} --tb-experiment-name ${EXP_NAME} # --dump-details dump_details_8k_0204 diff --git a/examples/deepeyes/run_deepeyes_pr.sh b/examples/deepeyes/run_deepeyes_pr.sh index 188c5e84b..6c718b260 100644 --- a/examples/deepeyes/run_deepeyes_pr.sh +++ b/examples/deepeyes/run_deepeyes_pr.sh @@ -158,6 +158,7 @@ SGLANG_ARGS=( LOG_ARGS=( --use-clearml + --use-metrics-service --tb-project-name ${PROJECT_NAME} --tb-experiment-name ${EXP_NAME} # --dump-details dump_details_8k_0204 diff --git a/examples/deepeyes/run_deepeyes_r3.sh b/examples/deepeyes/run_deepeyes_r3.sh index b5307cac2..e817ea4ff 100644 --- a/examples/deepeyes/run_deepeyes_r3.sh +++ b/examples/deepeyes/run_deepeyes_r3.sh @@ -162,6 +162,7 @@ SGLANG_ARGS=( LOG_ARGS=( --use-clearml + --use-metrics-service --tb-project-name ${PROJECT_NAME} --tb-experiment-name ${EXP_NAME} # --dump-details dump_details_8k_0204 diff --git a/relax/utils/data/data_utils.py b/relax/utils/data/data_utils.py index 2ddf2f1ba..b20170f2f 100644 --- a/relax/utils/data/data_utils.py +++ b/relax/utils/data/data_utils.py @@ -479,8 +479,12 @@ def jsonl_reader(p): def parquet_reader(p): pf = pq.ParquetFile(p) - for batch in pf.iter_batches(): - yield from batch.to_pylist() + # Read row groups individually instead of using iter_batches(). + # iter_batches() creates chunked arrays for multi-row-group files, + # which fails with ArrowNotImplementedError on nested types + # (e.g. list>, struct<...>). + for i in range(pf.metadata.num_row_groups): + yield from pf.read_row_group(i).to_pylist() return parquet_reader(path) diff --git a/relax/utils/data/streaming_dataset.py b/relax/utils/data/streaming_dataset.py index 5474cf338..97c04a5a7 100644 --- a/relax/utils/data/streaming_dataset.py +++ b/relax/utils/data/streaming_dataset.py @@ -129,8 +129,12 @@ def _load_parquet(self) -> None: pf = pq.ParquetFile(self.path) self._parquet_data = [] - for batch in pf.iter_batches(): - self._parquet_data.extend(batch.to_pylist()) + # Read row groups individually instead of using iter_batches(). + # iter_batches() creates chunked arrays for multi-row-group files, + # which fails with ArrowNotImplementedError on nested types + # (e.g. list>, struct<...>). + for i in range(pf.metadata.num_row_groups): + self._parquet_data.extend(pf.read_row_group(i).to_pylist()) # Apply row slice if specified if self.row_slice is not None: diff --git a/scripts/ci/benchmark.sh b/scripts/ci/benchmark.sh index 1d65456fc..e8c2c861f 100644 --- a/scripts/ci/benchmark.sh +++ b/scripts/ci/benchmark.sh @@ -4,6 +4,8 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. TASK_NAME="${1:-run-qwen3-4B-4xgpu-async}" +shift || true +EXTRA_ARGS="$*" # settings envs SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" @@ -44,6 +46,25 @@ else echo "Found script: ${SCRIPT_PATH}" fi +# ── inject extra CLI args into the training script ────────────────────────── +# When extra arguments are passed (e.g. --save /tmp/x --num-rollout 10), we +# create a patched copy of the training script that appends them to the +# `python3 -m relax.entrypoints.train` command line. Since argparse uses +# last-value-wins for store actions, appended flags override the defaults. +if [ -n "${EXTRA_ARGS}" ]; then + echo "Extra training args: ${EXTRA_ARGS}" + PATCHED_SCRIPT=$(mktemp "$(dirname "${SCRIPT_PATH}")/.relax-benchmark-XXXXXX.sh") + trap 'rm -f "${PATCHED_SCRIPT}"' EXIT + sed 's#2>&1 | tee #'"${EXTRA_ARGS}"' 2>\&1 | tee #' "${SCRIPT_PATH}" > "${PATCHED_SCRIPT}" + if diff -q "${SCRIPT_PATH}" "${PATCHED_SCRIPT}" >/dev/null 2>&1; then + echo "WARN: could not inject extra args (no '2>&1 | tee' anchor found)" >&2 + rm -f "${PATCHED_SCRIPT}" + else + chmod +x "${PATCHED_SCRIPT}" + SCRIPT_PATH="${PATCHED_SCRIPT}" + fi +fi + # ── multi-node vs single-node execution ───────────────────────────────────── # If WORLD_SIZE is set and > 1, use SPMD multi-node entrypoint if [ -n "${WORLD_SIZE}" ] && [ "${WORLD_SIZE}" -gt 1 ]; then diff --git a/scripts/training/hpc/run-qwen3-4B-pr-8xgpu.sh b/scripts/training/hpc/run-qwen3-4B-pr-8xgpu.sh index 18da038c7..545d544f8 100644 --- a/scripts/training/hpc/run-qwen3-4B-pr-8xgpu.sh +++ b/scripts/training/hpc/run-qwen3-4B-pr-8xgpu.sh @@ -28,7 +28,8 @@ NUM_ROLLOUT="${NUM_ROLLOUT:=200}" CKPT_ARGS=( --hf-checkpoint ${EXP_DIR}/Qwen3-4B/ - --ref-load ${EXP_DIR}/Qwen3-4B_torch_dist + --ref-load ${EXP_DIR}/Qwen3-4B/ + --megatron-to-hf-mode bridge --load ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save-interval 100 diff --git a/scripts/training/text/run-qwen3-30B-A3B-16xgpu-async.sh b/scripts/training/text/run-qwen3-30B-A3B-16xgpu-async.sh index 11ec05452..fcf484418 100644 --- a/scripts/training/text/run-qwen3-30B-A3B-16xgpu-async.sh +++ b/scripts/training/text/run-qwen3-30B-A3B-16xgpu-async.sh @@ -28,9 +28,10 @@ NUM_ROLLOUT="${NUM_ROLLOUT:=200}" CKPT_ARGS=( --hf-checkpoint ${EXP_DIR}/Qwen3-30B-A3B-Instruct/ + --ref-load ${EXP_DIR}/Qwen3-30B-A3B-Instruct/ + --megatron-to-hf-mode bridge --load ${EXP_DIR}/Qwen3-30B-A3B-Instruct_mcore_16xgpu/ --save ${EXP_DIR}/Qwen3-30B-A3B-Instruct_mcore_16xgpu/ - --ref-load ${EXP_DIR}/Qwen3-30B-A3B-Instruct_torch_dist// --save-interval 100 ) diff --git a/scripts/training/text/run-qwen3-4B-16xgpu.sh b/scripts/training/text/run-qwen3-4B-16xgpu.sh index 0fbd8019c..0eba6ccbc 100644 --- a/scripts/training/text/run-qwen3-4B-16xgpu.sh +++ b/scripts/training/text/run-qwen3-4B-16xgpu.sh @@ -29,7 +29,8 @@ NUM_ROLLOUT="${NUM_ROLLOUT:=2}" CKPT_ARGS=( --hf-checkpoint ${EXP_DIR}/Qwen3-4B/ - --ref-load ${EXP_DIR}/Qwen3-4B_torch_dist + --ref-load ${EXP_DIR}/Qwen3-4B/ + --megatron-to-hf-mode bridge --load ${EXP_DIR}/Qwen3-4B_mcore_16xgpu/ --save ${EXP_DIR}/Qwen3-4B_mcore_16xgpu/ --save-interval 100 diff --git a/scripts/training/text/run-qwen3-4B-2xgpu.sh b/scripts/training/text/run-qwen3-4B-2xgpu.sh index f68ab8c84..dade5f812 100644 --- a/scripts/training/text/run-qwen3-4B-2xgpu.sh +++ b/scripts/training/text/run-qwen3-4B-2xgpu.sh @@ -27,7 +27,8 @@ NUM_ROLLOUT="${NUM_ROLLOUT:=4}" CKPT_ARGS=( --hf-checkpoint ${EXP_DIR}/Qwen3-4B/ - --ref-load ${EXP_DIR}/Qwen3-4B_torch_dist + --ref-load ${EXP_DIR}/Qwen3-4B/ + --megatron-to-hf-mode bridge --load ${EXP_DIR}/Qwen3-4B_mcore/ --save ${EXP_DIR}/Qwen3-4B_mcore/ --save-interval 100 diff --git a/scripts/training/text/run-qwen3-4B-4xgpu-async.sh b/scripts/training/text/run-qwen3-4B-4xgpu-async.sh index 33a76e2f4..f51730a67 100644 --- a/scripts/training/text/run-qwen3-4B-4xgpu-async.sh +++ b/scripts/training/text/run-qwen3-4B-4xgpu-async.sh @@ -26,7 +26,7 @@ NUM_ROLLOUT="${NUM_ROLLOUT:=4}" CKPT_ARGS=( --hf-checkpoint ${EXP_DIR}/Qwen3-4B/ - --ref-load ${EXP_DIR}/Qwen3-4B_torch_dist + --ref-load ${EXP_DIR}/Qwen3-4B/ --megatron-to-hf-mode bridge ) diff --git a/scripts/training/text/run-qwen3-4B-fp16-8xgpu.sh b/scripts/training/text/run-qwen3-4B-fp16-8xgpu.sh index 47dcb6654..5f1a9e3d6 100644 --- a/scripts/training/text/run-qwen3-4B-fp16-8xgpu.sh +++ b/scripts/training/text/run-qwen3-4B-fp16-8xgpu.sh @@ -28,7 +28,8 @@ NUM_ROLLOUT="${NUM_ROLLOUT:=200}" CKPT_ARGS=( --hf-checkpoint ${EXP_DIR}/Qwen3-4B/ - --ref-load ${EXP_DIR}/Qwen3-4B_torch_dist + --ref-load ${EXP_DIR}/Qwen3-4B/ + --megatron-to-hf-mode bridge --load ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save-interval 100 From 32dc8a955b5da653f055b0480ecec71dec00fbf5 Mon Sep 17 00:00:00 2001 From: wulumeng Date: Tue, 21 Apr 2026 02:55:30 +0800 Subject: [PATCH 009/268] feat(sglang-router): add passthrough args MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Support prefixed agentic router passthrough flags - add automatic `--sglang-router-*` flag passthrough from RouterArgs - keep framework-managed router fields hidden from passthrough - preserve explicit policy and request-timeout compatibility flags --- relax/backends/sglang/arguments.py | 64 +++++++++++++++++++++++++++++- 1 file changed, 63 insertions(+), 1 deletion(-) diff --git a/relax/backends/sglang/arguments.py b/relax/backends/sglang/arguments.py index 30c9a7948..18dacc9e2 100644 --- a/relax/backends/sglang/arguments.py +++ b/relax/backends/sglang/arguments.py @@ -7,7 +7,68 @@ from relax.utils.http_utils import _wrap_ipv6 -# TODO: use all sglang router arguments with `--sglang-router` prefix +def _router_passthrough_skip_fields() -> set[str]: + return { + # Framework-managed addressing and topology. + "host", + "port", + "worker_urls", + "prefill", + "decode", + "prefill_urls", + "decode_urls", + "pd_disaggregation", + "service_discovery", + "selector", + "service_discovery_port", + "service_discovery_namespace", + "prefill_selector", + "decode_selector", + "bootstrap_port_annotation", + "prometheus_port", + # Backward-compatible explicit flags handled below. + "policy", + "request_timeout_secs", + } + + +def _add_prefixed_router_args(parser) -> None: + from sglang_router.router_args import RouterArgs + + old_add_argument = argparse._ActionsContainer.add_argument + skipped_args = _router_passthrough_skip_fields() + + def new_add_argument_wrapper(*name_or_flags, **kwargs): + canonical_name = kwargs.get("dest") + if not canonical_name: + for flag_name_candidate in name_or_flags: + if isinstance(flag_name_candidate, str) and flag_name_candidate.startswith("--"): + canonical_name = flag_name_candidate[2:].replace("-", "_") + break + + if canonical_name in skipped_args: + return + + final_name_or_flags = [] + for item_flag in name_or_flags: + if isinstance(item_flag, str) and item_flag.startswith("--"): + final_name_or_flags.append(f"--sglang-router-{item_flag[2:]}") + else: + final_name_or_flags.append(item_flag) + + final_kwargs = kwargs.copy() + if canonical_name and not str(canonical_name).startswith("router_"): + final_kwargs["dest"] = f"router_{canonical_name}" + + old_add_argument(*final_name_or_flags, **final_kwargs) + + argparse._ActionsContainer.add_argument = new_add_argument_wrapper + try: + RouterArgs.add_cli_args(parser, use_router_prefix=False, exclude_host_port=False) + finally: + argparse._ActionsContainer.add_argument = old_add_argument + + def add_sglang_router_arguments(parser): """Add arguments to the parser for the SGLang router.""" parser.add_argument( @@ -34,6 +95,7 @@ def add_sglang_router_arguments(parser): default=14400, help="Timeout for requests to the SGLang router in seconds", ) + _add_prefixed_router_args(parser) return parser From 46ee0be08b8886fa5e6dbfa178482304e5665064 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Tue, 21 Apr 2026 15:20:01 +0800 Subject: [PATCH 010/268] feat: add opd overlap ratio --- docs/en/examples/on-policy-distillation.md | 27 +++- docs/zh/examples/on-policy-distillation.md | 27 +++- examples/on_policy_distillation/README.md | 37 +++-- .../run-qwen3-4B-opd-sglang.sh | 10 +- relax/backends/megatron/actor.py | 15 +- relax/backends/megatron/data.py | 41 ++++++ relax/backends/megatron/loss.py | 10 ++ .../engine/rollout/on_policy_distillation.py | 128 +++++++++++++++++- relax/utils/arguments.py | 22 +++ relax/utils/data/stream_dataloader.py | 39 ++++++ relax/utils/types.py | 1 + relax/utils/utils.py | 19 +++ 12 files changed, 360 insertions(+), 16 deletions(-) diff --git a/docs/en/examples/on-policy-distillation.md b/docs/en/examples/on-policy-distillation.md index 7c78895a9..6fd8598ff 100644 --- a/docs/en/examples/on-policy-distillation.md +++ b/docs/en/examples/on-policy-distillation.md @@ -11,6 +11,8 @@ On-Policy Distillation (OPD) enables knowledge transfer from a large teacher mod | `--opd-kl-coef` | OPD KL penalty coefficient (default: 1.0). Controls the weight of the distillation signal relative to the RL advantage. | | `--opd-teacher-load` | Path to the teacher model. **Must** be set when `--opd-type=megatron`, **must not** be set when `--opd-type=sglang`. | | `--opd-teacher-ckpt-step` | Optional checkpoint step for the teacher model. | +| `--opd-teacher-timeout-s` | Timeout (seconds) for OPD teacher HTTP requests in SGLang mode (default: 30). | +| `--opd-log-prob-top-k` | Top-k size for collecting teacher/student candidate tokens used by OPD dynamic metrics (set `0` to disable, default: 0). | | `--opd-only-reward` | Keep only the OPD reward signal (zero out base RL reward and use OPD KL term only). Requires `--use-opd`. | ## How It Works @@ -35,7 +37,11 @@ The teacher model runs on an external SGLang server, and the teacher's log-probs 1. An external SGLang server runs the teacher model. 2. During rollout, after the reward is computed for each sample, the framework automatically sends the sample to the teacher server to obtain token-level log-probs and stores them in `sample.teacher_log_probs`. -3. During training, the KL penalty is computed from the stored teacher log-probs and applied to advantages. +3. When `--opd-log-prob-top-k > 0`, the framework also requests teacher top-k candidates (via SGLang request fields), and stores: + - `sample.teacher_topk_token_ids` + - `sample.teacher_topk_log_probs` (if returned by teacher) +4. If teacher request fails or top-k fields are missing, OPD uses a safe fallback path (rollout log-probs and placeholder top-k data) to avoid breaking the rollout loop. +5. During training, the KL penalty is computed from the stored teacher log-probs and applied to advantages. > **Note**: OPD sglang mode does NOT occupy `--custom-rm-path` or `--custom-reward-post-process-path`. Users can freely use custom reward functions alongside OPD. @@ -45,9 +51,28 @@ The teacher model runs on an external SGLang server, and the teacher's log-probs --use-opd --opd-type sglang --opd-kl-coef 1.0 +--opd-teacher-timeout-s 30 +--opd-log-prob-top-k 10 --rm-url http://:/generate ``` +## Dynamic Metrics + +When top-k collection is enabled, OPD can monitor student-teacher candidate alignment online. + +Let $S_t^{(p)} = \text{TopK}(p_t, k)$ and $S_t^{(q)} = \text{TopK}(q_t, k)$ denote student/teacher top-$k$ sets at token step $t$. + +### Overlap Ratio + +$$ +\mathcal{M}_{\text{overlap}} \triangleq \mathbb{E}_t \left[ \frac{|S_t^{(p)} \cap S_t^{(q)}|}{k} \right] +$$ + +Interpretation: + +- Lower overlap suggests candidate-space mismatch between student and teacher. +- Higher overlap indicates the student policy is moving closer to teacher support. + ### Megatron Mode (`--opd-type megatron`) The teacher model is directly loaded into Megatron via `--opd-teacher-load`, and the teacher's log-probs are computed during the training forward pass. diff --git a/docs/zh/examples/on-policy-distillation.md b/docs/zh/examples/on-policy-distillation.md index b85738471..07d61b2cf 100644 --- a/docs/zh/examples/on-policy-distillation.md +++ b/docs/zh/examples/on-policy-distillation.md @@ -11,6 +11,8 @@ | `--opd-kl-coef` | OPD KL 惩罚系数（默认：1.0）。控制蒸馏信号相对于 RL 优势的权重。 | | `--opd-teacher-load` | 教师模型路径。当 `--opd-type=megatron` 时**必须**设置，当 `--opd-type=sglang` 时**不能**设置。 | | `--opd-teacher-ckpt-step` | 教师模型的可选检查点步骤。 | +| `--opd-teacher-timeout-s` | SGLang 模式下 OPD teacher HTTP 请求超时（秒），默认 `30`。 | +| `--opd-log-prob-top-k` | 用于 OPD 动态指标的 teacher/student top-k 候选集合大小（设为 `0` 可关闭，默认 `0`）。 | | `--opd-only-reward` | 仅保留 OPD 奖励信号（将基础 RL reward 置零，只使用 OPD KL 项）。需配合 `--use-opd`。 | ## 工作原理 @@ -35,7 +37,11 @@ $$\hat{A}_t = A_t - \lambda_{\text{opd}} \cdot D_{\text{KL}}(P_{\text{teacher}} 1. 外部 SGLang 服务器运行教师模型。 2. 在回滚期间，每个样本的奖励计算完成后，框架自动将样本发送到教师服务器以获取词元级对数概率，并将其存储在 `sample.teacher_log_probs` 中。 -3. 在训练期间，从存储的教师对数概率计算 KL 惩罚并应用于优势。 +3. 当 `--opd-log-prob-top-k > 0` 时，框架还会请求并提取 teacher 的 top-k 候选信息（通过 SGLang 请求字段），并存储： + - `sample.teacher_topk_token_ids` + - `sample.teacher_topk_log_probs`（若 teacher 返回） +4. 当 teacher 请求失败或响应缺失 top-k 字段时，OPD 会进入安全回退路径（使用 rollout log-probs 与占位 top-k 数据），避免打断 rollout 主流程。 +5. 在训练期间，从存储的教师对数概率计算 KL 惩罚并应用于优势。 > **注意**：OPD sglang 模式不再占用 `--custom-rm-path` 和 `--custom-reward-post-process-path`。用户可以自由地同时使用自定义奖励函数和 OPD，两者互不冲突。 @@ -45,9 +51,28 @@ $$\hat{A}_t = A_t - \lambda_{\text{opd}} \cdot D_{\text{KL}}(P_{\text{teacher}} --use-opd --opd-type sglang --opd-kl-coef 1.0 +--opd-teacher-timeout-s 30 +--opd-log-prob-top-k 10 --rm-url http://:/generate ``` +## 动态指标 + +启用 top-k 采集后，OPD 可以在线监控 student 与 teacher 候选空间的一致性。 + +定义 $S_t^{(p)} = \text{TopK}(p_t, k)$、$S_t^{(q)} = \text{TopK}(q_t, k)$，分别表示 token 步 $t$ 上 student/teacher 的 top-$k$ 集合。 + +### Overlap Ratio（重叠率） + +$$ +\mathcal{M}_{\text{overlap}} \triangleq \mathbb{E}_t \left[ \frac{|S_t^{(p)} \cap S_t^{(q)}|}{k} \right] +$$ + +解释： + +- 重叠率低：student 与 teacher 候选空间偏离较大。 +- 重叠率高：student 策略逐步靠近 teacher 支撑区域。 + ### Megatron 模式 (`--opd-type megatron`) 教师模型通过 `--opd-teacher-load` 直接加载到 Megatron 中，教师的对数概率在训练前向传递期间计算。 diff --git a/examples/on_policy_distillation/README.md b/examples/on_policy_distillation/README.md index fab59dc36..79601329d 100644 --- a/examples/on_policy_distillation/README.md +++ b/examples/on_policy_distillation/README.md @@ -66,19 +66,40 @@ bash examples/on_policy_distillation/run-qwen3-8B-megatron-opd.sh ## 关键参数说明 -| 参数 | 说明 | -| -------------------- | ------------------------------------------------------------------------------ | -| `--use-opd` | 启用 OPD | -| `--opd-type` | 教师类型：`sglang` 或 `megatron` | -| `--opd-kl-coef` | OPD KL 系数（默认 1.0） | -| `--opd-teacher-load` | teacher 模型路径（`--opd-type megatron` 时必需；可配合 bridge 直接填 HF 路径） | -| `--opd-only-reward` | 仅保留 OPD reward 信号（将 base reward 置零，仅注入 OPD KL） | -| `--rm-url` | SGLang teacher 服务地址（`--opd-type sglang` 时必需） | +| 参数 | 说明 | +| ------------------------- | ------------------------------------------------------------------------------ | +| `--use-opd` | 启用 OPD | +| `--opd-type` | 教师类型：`sglang` 或 `megatron` | +| `--opd-kl-coef` | OPD KL 系数（默认 1.0） | +| `--opd-teacher-load` | teacher 模型路径（`--opd-type megatron` 时必需；可配合 bridge 直接填 HF 路径） | +| `--opd-teacher-timeout-s` | SGLang 模式下 OPD teacher HTTP 请求超时（秒），默认 `30` | +| `--opd-log-prob-top-k` | teacher/student top-k 候选集合大小（设为 `0` 可关闭，默认 `0`） | +| `--opd-only-reward` | 仅保留 OPD reward 信号（将 base reward 置零，仅注入 OPD KL） | +| `--rm-url` | SGLang teacher 服务地址（`--opd-type sglang` 时必需） | > Note: > > 1. OPD `sglang` 模式不占用 `--custom-rm-path` 与 `--custom-reward-post-process-path`，可与自定义奖励并存。 > 2. `--opd-only-reward` 需要配合 `--use-opd` 使用。 +> 3. 当 `--opd-log-prob-top-k > 0` 时，框架会在 SGLang teacher 请求中启用 top-k 采集，并尝试提取 teacher 的 top-k token ids / log-probs。 +> 4. 若 teacher 响应缺失 top-k 字段或请求失败，会自动回退到安全路径（rollout log-probs + 占位 top-k），不打断 rollout 主流程。 + +## 动态指标 + +启用 top-k 采集后，OPD 可以在线监控 student 与 teacher 候选空间的一致性。 + +定义 $S_t^{(p)} = \\text{TopK}(p_t, k)$、$S_t^{(q)} = \\text{TopK}(q_t, k)$，分别表示 token 步 $t$ 上 student/teacher 的 top-$k$ 集合。 + +### Overlap Ratio（重叠率） + +$$ +\\mathcal{M}\_{\\text{overlap}} \\triangleq \\mathbb{E}\_t \\left\[ \\frac{|S_t^{(p)} \\cap S_t^{(q)}|}{k} \\right\] +$$ + +解释： + +- 重叠率低：student 与 teacher 候选空间偏离较大。 +- 重叠率高：student 策略逐步靠近 teacher 支撑区域。 ## 后端支持 diff --git a/examples/on_policy_distillation/run-qwen3-4B-opd-sglang.sh b/examples/on_policy_distillation/run-qwen3-4B-opd-sglang.sh index 26079ce50..0aa474792 100755 --- a/examples/on_policy_distillation/run-qwen3-4B-opd-sglang.sh +++ b/examples/on_policy_distillation/run-qwen3-4B-opd-sglang.sh @@ -19,7 +19,7 @@ fi source "${MODEL_CONFIG_DIR}/qwen3-4B.sh" PROJECT_NAME="${PROJECT_NAME:=Relax/dev/opd}" -EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../exps}" +EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../exps}" NUM_ROLLOUT="${NUM_ROLLOUT:=200}" # ============================================ @@ -42,6 +42,8 @@ nohup python -m sglang.launch_server \ # Teacher SGLang server configuration TEACHER_HOST="${TEACHER_HOST:-127.0.0.1}" TEACHER_PORT="${TEACHER_PORT:-30010}" +OPD_TEACHER_TIMEOUT_S="${OPD_TEACHER_TIMEOUT_S:-30}" +OPD_LOG_PROB_TOP_K="${OPD_LOG_PROB_TOP_K:-10}" CKPT_ARGS=( @@ -84,6 +86,8 @@ OPD_ARGS=( --use-opd --opd-type sglang --opd-kl-coef 1.0 + --opd-teacher-timeout-s ${OPD_TEACHER_TIMEOUT_S} + --opd-log-prob-top-k ${OPD_LOG_PROB_TOP_K} --rm-url http://${TEACHER_HOST}:${TEACHER_PORT}/generate ) @@ -170,7 +174,7 @@ if [ ${MODE} == "sync" ]; then ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ -- python3 relax/entrypoints/train.py \ --resource '{"actor": [1, 8], "rollout": [1, 8]}'\ - --max-staleness 0 \ + --max-staleness 0 \ --num-data-storage-units 1 \ --colocate \ ${MODEL_ARGS[@]} \ @@ -188,7 +192,7 @@ elif [ ${MODE} == "async" ]; then ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ -- python3 relax/entrypoints/train.py \ --resource '{"actor": [1, 2], "rollout": [1, 4], "reference": [1, 1], "actor_fwd": [1, 1], "advantages": [1, 0]}'\ - --max-staleness 2 \ + --max-staleness 2 \ --num-data-storage-units 1 \ --num-iters-per-train-update 8 \ --ref-actor-config '{"tensor_model_parallel_size": 1, "max_tokens_per_gpu": 16384, "sequence_parallel": false, "only_load_weight": true}' \ diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index dfe493e7b..4ac23c1f9 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -7,6 +7,7 @@ import time from argparse import Namespace from contextlib import nullcontext +from functools import partial from typing import List import ray @@ -369,10 +370,14 @@ def compute_log_prob( data_iterator: list[DataIterator], num_microbatches: list[int], store_prefix: str = "", + collect_topk: bool = False, ) -> dict[str, list[torch.Tensor]]: with timer(f"{store_prefix}log_probs"): + log_prob_func = get_log_probs_and_entropy + if collect_topk: + log_prob_func = partial(log_prob_func, with_topk=True, topk_k=self.args.opd_log_prob_top_k) return forward_only( - get_log_probs_and_entropy, + log_prob_func, self.args, self.model, data_iterator, @@ -421,6 +426,9 @@ def train(self, rollout_id: int) -> None: data_fields.append("multimodal_train_inputs") if self.args.use_opd and self.args.opd_type == "sglang": data_fields.append("teacher_log_probs") + if self.args.opd_log_prob_top_k > 0: + data_fields.append("teacher_topk_token_ids") + data_fields.append("teacher_topk_k") with timer("train_get_data"): rollout_data, batch_meta = self._get_data_from_transfer_queue( "train", rollout_id, data_fields, batch_size, batch_index @@ -493,6 +501,7 @@ def train_actor(self, rollout_id: int, rollout_data: RolloutBatch) -> None: data_iterator, num_microbatches, store_prefix="teacher_", + collect_topk=self.args.use_opd and self.args.opd_log_prob_top_k > 0, ) ) @@ -508,6 +517,7 @@ def train_actor(self, rollout_id: int, rollout_data: RolloutBatch) -> None: data_iterator, num_microbatches, store_prefix="", + collect_topk=self.args.use_opd and self.args.opd_log_prob_top_k > 0, ) ) if self.args.use_rollout_routing_replay: @@ -695,6 +705,9 @@ def train_async(self, rollout_id) -> None: if self.args.use_opd and self.args.opd_type == "sglang": data_fields.append("teacher_log_probs") data_fields.append("opd_reverse_kl") + if self.args.opd_log_prob_top_k > 0: + data_fields.append("teacher_topk_token_ids") + data_fields.append("teacher_topk_k") if self.data_iterator is None: self.data_iterator, self.num_microbatches = create_stream_dataloader( self.args, diff --git a/relax/backends/megatron/data.py b/relax/backends/megatron/data.py index a0e7911f2..22e21fcda 100644 --- a/relax/backends/megatron/data.py +++ b/relax/backends/megatron/data.py @@ -532,6 +532,42 @@ def log_rollout_data( total_lengths = rollout_data["total_lengths"] max_seq_lens = rollout_data.get("max_seq_lens", None) + # OPD dynamic metric: overlap ratio on top-k token sets. + student_topk_ids = rollout_data.get("topk_token_ids", None) + teacher_topk_ids = rollout_data.get("teacher_topk_token_ids", None) + if isinstance(student_topk_ids, list) and isinstance(teacher_topk_ids, list): + assert len(student_topk_ids) == len(teacher_topk_ids) + + overlap_ratio_per_sample = [] + for s_ids, t_ids in zip(student_topk_ids, teacher_topk_ids, strict=False): + if not isinstance(s_ids, torch.Tensor) or not isinstance(t_ids, torch.Tensor): + continue + if s_ids.ndim < 2 or t_ids.ndim < 2: + continue + k = min(int(s_ids.size(-1)), int(t_ids.size(-1))) + if k <= 0: + continue + s_ids = s_ids[:, :k].long() + t_ids = t_ids[:, :k].long() + overlap_matrix = s_ids.unsqueeze(-1).eq(t_ids.unsqueeze(-2)) + overlap_ratio = overlap_matrix.any(dim=-1).float().sum(dim=-1) / float(k) + overlap_ratio_per_sample.append(overlap_ratio) + + if overlap_ratio_per_sample: + loss_masks_t = loss_masks + total_lengths_i = total_lengths + response_lengths_i = response_lengths + overlap_ratio_flat = torch.cat(overlap_ratio_per_sample).clone().detach().to(loss_masks_t[0].device) + sum_of_sample_mean = get_sum_of_sample_mean( + total_lengths_i, + response_lengths_i, + loss_masks_t, + qkv_format=args.qkv_format, + max_seq_lens=max_seq_lens, + ) + overlap_ratio_value = cp_size * sum_of_sample_mean(overlap_ratio_flat) / len(loss_masks_t) + log_dict["opd_overlap_ratio"] = overlap_ratio_value.item() + for key, val in rollout_data.items(): if key in [ "tokens", @@ -541,6 +577,11 @@ def log_rollout_data( "rollout_routed_experts", "max_seq_lens", "dynamic_global_batch_size", + "topk_token_ids", + "topk_log_probs", + "teacher_topk_token_ids", + "teacher_topk_log_probs", + "teacher_topk_k", ]: continue # Upload per sample mean for each rollout value diff --git a/relax/backends/megatron/loss.py b/relax/backends/megatron/loss.py index a36972ff1..47530f033 100644 --- a/relax/backends/megatron/loss.py +++ b/relax/backends/megatron/loss.py @@ -232,6 +232,8 @@ def get_log_probs_and_entropy( total_lengths: list[int], response_lengths: list[int], with_entropy: bool = False, + with_topk: bool = False, + topk_k: int | None = None, non_loss_data: bool = True, max_seq_lens: list[int] | None = None, ) -> tuple[torch.Tensor, dict[str, list[torch.Tensor]]]: @@ -261,8 +263,10 @@ def get_log_probs_and_entropy( a list of `[R]` tensors. """ assert non_loss_data + resolved_topk_k = topk_k if topk_k is not None else getattr(args, "opd_log_prob_top_k", 0) log_probs_list = [] entropy_list = [] + topk_token_ids_list = [] for logits_chunk, tokens_chunk in get_responses( logits, args=args, @@ -282,11 +286,17 @@ def get_log_probs_and_entropy( log_probs_list.append(log_prob.squeeze(-1)) entropy_list.append(entropy) + if with_topk: + k = min(max(int(resolved_topk_k), 1), int(logits_chunk.size(-1))) + topk_token_ids_list.append(torch.topk(logits_chunk, k=k, dim=-1).indices) + res = { "log_probs": log_probs_list, } if with_entropy: res["entropy"] = entropy_list + if with_topk: + res["topk_token_ids"] = topk_token_ids_list # we need to turn the all gather kv into zigzag ring attn kv if args.allgather_cp: diff --git a/relax/engine/rollout/on_policy_distillation.py b/relax/engine/rollout/on_policy_distillation.py index 3f0c9a1f0..87a152f3b 100644 --- a/relax/engine/rollout/on_policy_distillation.py +++ b/relax/engine/rollout/on_policy_distillation.py @@ -10,6 +10,108 @@ logger = get_logger(__name__) +def _get_opd_topk(args) -> int: + """Resolve OPD top-k for overlap metrics. + + Priority: + 1) args.opd_log_prob_top_k (CLI/runtime) + 2) fallback 0 when missing (for backward compatibility) + """ + top_k = getattr(args, "opd_log_prob_top_k", None) + if top_k is None: + return 0 + return max(int(top_k), 0) + + +def _extract_token_id(candidate) -> int | None: + """Best-effort extraction of token id from a top-logprob candidate item.""" + if isinstance(candidate, dict): + for key in ("token_id", "id"): + if key in candidate and isinstance(candidate[key], int): + return int(candidate[key]) + token_val = candidate.get("token") + if isinstance(token_val, int): + return int(token_val) + return None + + if isinstance(candidate, (list, tuple)): + for item in candidate: + if isinstance(item, int): + return int(item) + return None + + return int(candidate) if isinstance(candidate, int) else None + + +def _extract_topk_ids_from_candidates(candidates, top_k: int) -> tuple[list[int], bool]: + """Extract fixed-width token-id top-k list from a candidate container. + + Returns: + (token_ids, has_valid_token_id) + """ + token_ids: list[int] = [] + has_valid_token_id = False + + if isinstance(candidates, dict): + candidates = candidates.get("top_logprobs") or candidates.get("candidates") or [] + + if isinstance(candidates, (list, tuple)): + for item in candidates: + token_id = _extract_token_id(item) + if token_id is not None: + token_ids.append(token_id) + has_valid_token_id = True + if len(token_ids) >= top_k: + break + + if len(token_ids) < top_k: + token_ids.extend([-1] * (top_k - len(token_ids))) + else: + token_ids = token_ids[:top_k] + + return token_ids, has_valid_token_id + + +def _extract_teacher_topk_token_ids(teacher_resp: dict, response_length: int, top_k: int) -> list[list[int]] | None: + """Extract response-aligned teacher top-k token ids from SGLang response. + + Returns None when top-k information is unavailable. + """ + if top_k <= 0 or response_length <= 0: + return None + + meta_info = teacher_resp.get("meta_info", {}) + + top_logprobs = ( + meta_info.get("input_top_logprobs") + or meta_info.get("input_token_top_logprobs") + or teacher_resp.get("top_logprobs") + ) + if isinstance(top_logprobs, list) and top_logprobs: + per_token_candidates = top_logprobs[-response_length:] + if len(per_token_candidates) >= response_length: + teacher_topk_ids: list[list[int]] = [] + any_valid = False + for candidates in per_token_candidates: + token_ids, has_valid = _extract_topk_ids_from_candidates(candidates, top_k) + any_valid = any_valid or has_valid + teacher_topk_ids.append(token_ids) + if any_valid: + return teacher_topk_ids + return None + + +def _fallback_teacher_topk_token_ids(response_length: int, top_k: int) -> list[list[int]]: + """Build a fixed-shape fallback for teacher top-k ids. + + Uses -1 as sentinel token id so downstream flatten/reshape stays stable + while clearly indicating unavailable teacher top-k content. + """ + if response_length <= 0 or top_k <= 0: + return [] + return [[-1] * top_k for _ in range(response_length)] + + def _get_teacher_url(args) -> str: """Resolve OPD teacher URL from args with backward-compatible fallback. @@ -99,8 +201,11 @@ async def fetch_teacher_log_probs(args, sample: Sample, session: aiohttp.ClientS if response_length <= 0: # Avoid Python slicing pitfall: x[-0:] == x[:], not empty. sample.teacher_log_probs = [] + sample.teacher_topk_token_ids = [] return + opd_top_k = _get_opd_topk(args) + payload = { "input_ids": sample.tokens, "sampling_params": { @@ -111,6 +216,8 @@ async def fetch_teacher_log_probs(args, sample: Sample, session: aiohttp.ClientS "return_logprob": True, "logprob_start_len": 0, } + if opd_top_k > 0: + payload["top_logprobs_num"] = opd_top_k try: teacher_resp = await _post_teacher_request_with_diagnostics(args, payload, session=session) @@ -119,15 +226,18 @@ async def fetch_teacher_log_probs(args, sample: Sample, session: aiohttp.ClientS # Avoid ABORTED status here because aborted samples enter a different # buffering path that may impact rollout progress under sustained errors. sample.teacher_log_probs = _fallback_teacher_log_probs(sample, response_length) + sample.teacher_topk_token_ids = _fallback_teacher_topk_token_ids(response_length, opd_top_k) if sample.metadata is None: sample.metadata = {} sample.metadata["opd_teacher_error"] = f"{type(exc).__name__}: {str(exc)[:512]}" sample.metadata["opd_teacher_fallback"] = "rollout_log_probs" - logger.exception( - "OPD teacher fetch failed for sample_index=%s, response_length=%s, url=%s. Falling back to rollout log-probs.", + logger.error( + "OPD teacher fetch failed for sample_index=%s, response_length=%s, url=%s, error=%s. " + "Falling back to rollout log-probs.", getattr(sample, "index", None), response_length, _get_teacher_url(args), + f"{type(exc).__name__}: {str(exc)[:256]}", ) return @@ -135,6 +245,7 @@ async def fetch_teacher_log_probs(args, sample: Sample, session: aiohttp.ClientS token_logprobs = teacher_resp.get("meta_info", {}).get("input_token_logprobs", None) if not token_logprobs: sample.teacher_log_probs = _fallback_teacher_log_probs(sample, response_length) + sample.teacher_topk_token_ids = _fallback_teacher_topk_token_ids(response_length, opd_top_k) if sample.metadata is None: sample.metadata = {} sample.metadata["opd_teacher_error"] = "Missing meta_info.input_token_logprobs in teacher response" @@ -148,6 +259,7 @@ async def fetch_teacher_log_probs(args, sample: Sample, session: aiohttp.ClientS all_log_probs = torch.tensor([item[0] for item in token_logprobs[1:]], dtype=torch.float32) if all_log_probs.numel() < response_length: sample.teacher_log_probs = _fallback_teacher_log_probs(sample, response_length) + sample.teacher_topk_token_ids = _fallback_teacher_topk_token_ids(response_length, opd_top_k) if sample.metadata is None: sample.metadata = {} sample.metadata["opd_teacher_error"] = ( @@ -164,3 +276,15 @@ async def fetch_teacher_log_probs(args, sample: Sample, session: aiohttp.ClientS teacher_log_probs = all_log_probs[-response_length:] sample.teacher_log_probs = teacher_log_probs.tolist() + + teacher_topk_token_ids = _extract_teacher_topk_token_ids(teacher_resp, response_length, opd_top_k) + if teacher_topk_token_ids is None: + if opd_top_k > 0: + logger.warning( + "OPD teacher response missing top-logprobs for sample_index=%s (opd_log_prob_top_k=%s). " + "Using sentinel top-k ids.", + getattr(sample, "index", None), + opd_top_k, + ) + teacher_topk_token_ids = _fallback_teacher_topk_token_ids(response_length, opd_top_k) + sample.teacher_topk_token_ids = teacher_topk_token_ids diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index f4a7c0078..911787be1 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -1298,6 +1298,23 @@ def add_on_policy_distillation_arguments(parser): parser.add_argument( "--opd-teacher-ckpt-step", type=int, default=None, help="The checkpoint step for OPD teacher model." ) + parser.add_argument( + "--opd-teacher-timeout-s", + type=float, + default=30.0, + help=( + "Timeout (seconds) for OPD teacher HTTP requests when --opd-type=sglang. " + "Increase this for long responses or high-latency cross-host teacher services." + ), + ) + parser.add_argument( + "--opd-log-prob-top-k", + type=int, + default=0, + help=( + "Top-k token ids to request/collect for OPD overlap metrics. Set to 0 to disable top-k collection." + ), + ) return parser def add_router_arguments(parser): @@ -2024,6 +2041,11 @@ def slime_validate_args(args): ) # Validate on-policy distillation (OPD) arguments + if args.opd_teacher_timeout_s <= 0: + raise ValueError("--opd-teacher-timeout-s must be > 0.") + if args.opd_log_prob_top_k < 0: + raise ValueError("--opd-log-prob-top-k must be >= 0.") + if args.use_opd: if args.opd_type is None: raise ValueError("--opd-type must be specified when --use-opd is enabled. Choose 'sglang' or 'megatron'.") diff --git a/relax/utils/data/stream_dataloader.py b/relax/utils/data/stream_dataloader.py index 820e6b09e..9f62d3658 100644 --- a/relax/utils/data/stream_dataloader.py +++ b/relax/utils/data/stream_dataloader.py @@ -7,6 +7,7 @@ import torch import torch.distributed as dist +import torch.nn.functional as F from megatron.core import mpu from tensordict import TensorDict from transfer_queue.dataloader.streaming_dataloader import StreamingDataLoader @@ -483,6 +484,44 @@ def _to_cuda(v): ) ] + if "teacher_topk_token_ids" in rollout_data: + teacher_topk_k = rollout_data.get("teacher_topk_k", None) + if isinstance(teacher_topk_k, torch.Tensor): + teacher_topk_k = teacher_topk_k.tolist() + + topk_tensors = [] + for i, (flat_topk_ids, total_length, response_length) in enumerate( + zip( + rollout_data["teacher_topk_token_ids"], + rollout_data["total_lengths"], + rollout_data["response_lengths"], + strict=False, + ) + ): + k = int(teacher_topk_k[i]) if teacher_topk_k is not None else 0 + if k <= 0: + topk_tensors.append(torch.empty((response_length, 0), dtype=torch.long, device=cuda_dev)) + continue + + topk_tensor = torch.tensor(flat_topk_ids, dtype=torch.long, device=cuda_dev) + expected = response_length * k + if topk_tensor.numel() < expected: + topk_tensor = F.pad(topk_tensor, (0, expected - topk_tensor.numel()), value=-1) + elif topk_tensor.numel() > expected: + topk_tensor = topk_tensor[:expected] + + topk_tensor = topk_tensor.reshape(response_length, k) + topk_tensor = slice_log_prob_with_cp( + topk_tensor, + total_length, + response_length, + args.qkv_format, + rollout_data["max_seq_lens"][i] if args.qkv_format == "bshd" else None, + ) + topk_tensors.append(topk_tensor) + + rollout_data["teacher_topk_token_ids"] = topk_tensors + if "rollout_routed_experts" in rollout_data: from tensordict.tensorclass import NonTensorData diff --git a/relax/utils/types.py b/relax/utils/types.py index 0a39be7ec..899854cf2 100644 --- a/relax/utils/types.py +++ b/relax/utils/types.py @@ -29,6 +29,7 @@ class Sample: remove_sample: bool = False abort_count: int = 0 # Number of times this sample has been aborted teacher_log_probs: list[float] | None = None # Log probabilities from teacher model for OPD + teacher_topk_token_ids: list[list[int]] | None = None # Teacher top-k token ids per response token for OPD class Status(Enum): PENDING = "pending" diff --git a/relax/utils/utils.py b/relax/utils/utils.py index 571e59656..cb5655d39 100644 --- a/relax/utils/utils.py +++ b/relax/utils/utils.py @@ -81,6 +81,25 @@ def convert_samples_to_train_data(args: Any, samples: list[Sample] | list[list[S if samples[0].teacher_log_probs is not None: train_data["teacher_log_probs"] = [sample.teacher_log_probs for sample in samples] + if any(sample.teacher_topk_token_ids is not None for sample in samples): + topk_k = max( + ( + len(sample.teacher_topk_token_ids[0]) + for sample in samples + if sample.teacher_topk_token_ids is not None and len(sample.teacher_topk_token_ids) > 0 + ), + default=0, + ) + train_data["teacher_topk_token_ids"] = [ + ( + [token_id for step_topk in sample.teacher_topk_token_ids for token_id in step_topk] + if sample.teacher_topk_token_ids is not None + else [] + ) + for sample in samples + ] + train_data["teacher_topk_k"] = [topk_k for _ in samples] + total_lengths = [len(t) for t in train_data["tokens"]] train_data["total_lengths"] = total_lengths if args.debug_train_only: From 39434b3932035d7fdfe76b472d584f654d9f4917 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Wed, 22 Apr 2026 14:15:01 +0800 Subject: [PATCH 011/268] fix(checkpoint): avoid busy loop when polling empty metadata MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Throttle metadata polling in DeviceDirectBackend - Add `time.sleep(0.5)` when the metadata endpoint returns empty - Prevents tight HTTP GET loop that spammed logs with repeated `httpx` INFO requests during async multimodal training --- .../backends/device_direct.py | 3 ++ relax/utils/metrics/service.py | 53 ++++++++++++++++++- .../multimodal/run-qwen35-9B-8xgpu-async.sh | 5 +- 3 files changed, 58 insertions(+), 3 deletions(-) diff --git a/relax/distributed/checkpoint_service/backends/device_direct.py b/relax/distributed/checkpoint_service/backends/device_direct.py index e315a7d03..f41970829 100644 --- a/relax/distributed/checkpoint_service/backends/device_direct.py +++ b/relax/distributed/checkpoint_service/backends/device_direct.py @@ -14,6 +14,7 @@ """ import asyncio +import logging import socket import time from collections.abc import Sequence @@ -39,6 +40,8 @@ from relax.utils.logging_utils import get_logger +logging.getLogger("httpx").setLevel(logging.WARNING) + logger = get_logger(__name__) diff --git a/relax/utils/metrics/service.py b/relax/utils/metrics/service.py index 0f0274f7e..f6e097ab9 100644 --- a/relax/utils/metrics/service.py +++ b/relax/utils/metrics/service.py @@ -14,6 +14,7 @@ from relax.utils.metrics.adapters.apprise import _AppriseAdapter from relax.utils.metrics.adapters.clearml import _ClearMLAdapter from relax.utils.metrics.adapters.tensorboard import _TensorboardAdapter +from relax.utils.metrics.adapters.wandb import _is_offline_mode from relax.utils.metrics.timeline_trace import TimelineTraceAdapter @@ -130,7 +131,12 @@ def __init__(self, healthy: Any, pg: Optional[Any], config: Namespace, role: str self._use_wandb = getattr(config, "use_wandb", False) if self._use_wandb: - logger.info("W&B support enabled") + try: + self._init_wandb(config) + logger.info("W&B adapter initialized") + except Exception as e: + logger.error(f"Failed to initialize W&B adapter: {e}") + self._use_wandb = False # Initialize TimelineTrace adapter timeline_dump_dir = getattr(config, "timeline_dump_dir", None) @@ -142,6 +148,51 @@ def __init__(self, healthy: Any, pg: Optional[Any], config: Namespace, role: str logger.info(f"MetricsService initialized with adapters: {list(self._adapters.keys())}") + @staticmethod + def _init_wandb(config: Namespace) -> None: + """Initialize W&B for the MetricsService. + + Unlike init_wandb_primary (designed for training workers with + rank/group), MetricsService is a single Ray Serve replica that only + needs basic project and run name configuration. + """ + import os + + if config.wandb_mode: + os.environ["WANDB_MODE"] = config.wandb_mode + + offline = _is_offline_mode(config) + + if (not offline) and getattr(config, "wandb_key", None) is not None: + wandb.login(key=config.wandb_key, host=getattr(config, "wandb_host", None)) + + project = getattr(config, "wandb_project", None) or getattr(config, "tb_project_name", None) + run_name = getattr(config, "tb_experiment_name", None) or "metrics-service" + + init_kwargs = { + "project": project, + "name": run_name, + "entity": getattr(config, "wandb_team", None), + } + + if offline: + init_kwargs["settings"] = wandb.Settings(mode="offline") + + wandb_dir = getattr(config, "wandb_dir", None) + if wandb_dir: + os.makedirs(wandb_dir, exist_ok=True) + init_kwargs["dir"] = wandb_dir + + wandb.init(**init_kwargs) + + wandb.define_metric("train/step") + wandb.define_metric("train/*", step_metric="train/step") + wandb.define_metric("rollout/step") + wandb.define_metric("rollout/*", step_metric="rollout/step") + wandb.define_metric("eval/step") + wandb.define_metric("eval/*", step_metric="eval/step") + wandb.define_metric("perf/*", step_metric="rollout/step") + @app.post("/log_metric") async def log_metric(self, request: LogMetricRequest) -> Dict[str, Any]: try: diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-async.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-async.sh index 7de1f53d0..b53bd3490 100644 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-async.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-async.sh @@ -35,7 +35,7 @@ CKPT_ARGS=( # --ref-load ${EXP_DIR}/Qwen3-VL-4B-Instruct --load ${EXP_DIR}/Qwen3.5-9B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3.5-9B_mcore_8xgpu/ - --save-interval 4 + --save-interval 100 --megatron-to-hf-mode bridge ) @@ -58,7 +58,8 @@ ROLLOUT_ARGS=( --rollout-temperature 0.8 --global-batch-size 256 --multimodal-keys '{"image":"image"}' - --system-prompt "${SYSTEM_PROMPT}" + --system-prompt "${SYSTEM_PROMPT}" + --use-streaming-dataset ) PERF_ARGS=( From 6c2adee6c34dcfce12470e137a9162ab15380c04 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Wed, 22 Apr 2026 08:50:12 +0000 Subject: [PATCH 012/268] feat(dcs): add Bridge-based HF weight conversion MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add megatron-bridge integration for weight conversion - Add as drop-in replacement for - Use Bridge mapping registry for automatic Megatron-to-HF name/format conversion - Enabled via flag - Eliminates need for hand-written per-model converters when Bridge mapping exists ## Bridge task map initialization - Lazy-init Bridge tasks on first use via - Supplement missing params (tied embeddings, output_layer) from mapping registry - Eagerly initialize AutoMapping inner delegates for group patching - Dynamic task creation for cross-EP-rank expert params with caching ## Process group patching for local-only conversion - Patch all PP/TP/EP groups to None on all mapping levels (recursive) - Monkey-patch on all classes in delegation chain - Restore original groups and methods in finally block --- # 🐛 Bug Fix ## Fix recv_weight_meta polling with long-poll support - Add parameter to coordinator endpoint - Implement lightweight long-poll loop to reduce idle HTTP polling - Update client-side to use configurable long-poll timeout - Handle gracefully with retry --- .../backends/device_direct.py | 343 ++++++++- .../checkpoint_service/coordinator/service.py | 22 +- .../run-qwen3-30B-A3B-omni-16xgpu-async.sh | 10 +- .../run-qwen3-vl-30B-A3B-16xgpu-async.sh | 148 ++++ .../training/text/run-qwen3-4B-4xgpu-async.sh | 3 + .../test_dcs_weight_conversion.py | 651 ++++++++++++++++++ 6 files changed, 1164 insertions(+), 13 deletions(-) create mode 100644 scripts/training/multimodal/run-qwen3-vl-30B-A3B-16xgpu-async.sh create mode 100644 tests/distributed/checkpoint_service/test_dcs_weight_conversion.py diff --git a/relax/distributed/checkpoint_service/backends/device_direct.py b/relax/distributed/checkpoint_service/backends/device_direct.py index f41970829..5e0018d46 100644 --- a/relax/distributed/checkpoint_service/backends/device_direct.py +++ b/relax/distributed/checkpoint_service/backends/device_direct.py @@ -15,6 +15,7 @@ import asyncio import logging +import re import socket import time from collections.abc import Sequence @@ -31,7 +32,9 @@ from tqdm import tqdm from urllib3.exceptions import NewConnectionError +from relax.backends.megatron.misc_utils import strip_param_name_prefix from relax.backends.megatron.weight_conversion import convert_to_hf +from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding from relax.backends.megatron.weight_update.common import all_gather_param, named_params_and_buffers from relax.distributed.checkpoint_service.backends.base import CommBackend, TensorFusion from relax.distributed.checkpoint_service.config import BackendType, RoleInfo @@ -113,6 +116,315 @@ def __init__( self.rollout_engines: Dict[int, Any] = {} # rank -> Ray actor handle torch.cuda.set_device(self.device) + # Bridge-based HF weight converter (lazy-initialized on first use) + self._use_bridge = getattr(args, "megatron_to_hf_mode", None) == "bridge" + self._bridge_task_map: Optional[Dict[str, Any]] = None # global_param_name -> WeightConversionTask + self._bridge_mapping_registry = None # MegatronMappingRegistry for dynamic lookups + + def _init_bridge_tasks(self) -> None: + """Lazily initialize Bridge conversion tasks and build a lookup table. + + Builds a mapping from global_param_name (unwrapped, e.g. + ``decoder.layers.0.self_attention.linear_qkv.weight``) to the + corresponding ``WeightConversionTask``. Only tasks that belong to the + current PP rank (i.e. ``task.param_weight is not None``) are indexed. + + After building the task map, eagerly initializes any lazily-created + inner mappings (e.g. ``AutoMapping._mapping``) so that + ``_collect_all_mappings`` can discover and patch them later. + + When embeddings are tied, Bridge's ``build_conversion_tasks`` filters + out ``output_layer`` from its task list. However, + ``named_params_and_buffers`` still yields ``output_layer.weight`` on + the last PP stage. We detect such missing parameters and supplement + the task map using the mapping registry so that every local parameter + has a corresponding Bridge task. + """ + if self._bridge_task_map is not None: + return + + from megatron.bridge import AutoBridge + from megatron.bridge.models.conversion.model_bridge import WeightConversionTask + from megatron.bridge.models.conversion.param_mapping import AutoMapping + + from relax.utils.megatron_bridge_utils import patch_megatron_model + + bridge = AutoBridge.from_hf_pretrained(self.args.hf_checkpoint, trust_remote_code=True) + with patch_megatron_model(self.model): + tasks = bridge.get_conversion_tasks(self.model) + + self._bridge_task_map = {} + for task in tasks: + if task.param_weight is not None: + self._bridge_task_map[task.global_param_name] = task + + # Supplement tasks for local parameters that Bridge filtered out + # (e.g. ``output_layer`` when embeddings are tied). Walk the local + # model parameters and, for any that are missing from the task map, + # look up the mapping from the registry and create a synthetic task. + self._bridge_mapping_registry = bridge._model_bridge.mapping_registry() + mapping_registry = self._bridge_mapping_registry + for name, param in named_params_and_buffers(self.args, self.model): + global_name = strip_param_name_prefix(name) + if global_name not in self._bridge_task_map: + mapping = mapping_registry.megatron_to_hf_lookup(global_name) + if mapping is not None: + self._bridge_task_map[global_name] = WeightConversionTask( + param_name=global_name, + global_param_name=global_name, + mapping=mapping, + megatron_module=None, + param_weight=param, + ) + + # Eagerly initialize inner mappings of AutoMapping instances. + # AutoMapping lazily creates a delegate ``_mapping`` (ColumnParallel / + # RowParallel / Replicated) on first use. That delegate has its own + # process groups obtained from ``mpu`` at construction time. We must + # trigger this initialization now so that ``_collect_all_mappings`` can + # find and patch them before ``megatron_to_hf`` is called. + for task in self._bridge_task_map.values(): + mapping = task.mapping + if isinstance(mapping, AutoMapping) and mapping._mapping is None: + if task.megatron_module is not None: + mapping._detected_type = mapping._detect_parallelism_type(task.megatron_module) + mapping._mapping = mapping._get_or_create_mapping(mapping._detected_type) + else: + # Supplementary tasks (e.g. tied ``output_layer``) have no + # ``megatron_module``, so we cannot detect parallelism type. + # These parameters are always replicated (that's why Bridge + # filtered them out in the first place). + mapping._detected_type = "replicated" + mapping._mapping = mapping._get_or_create_mapping("replicated") + # Also handle AutoMapping nested inside _tp_mapping (e.g. QKVMapping) + inner_tp = getattr(mapping, "_tp_mapping", None) + if isinstance(inner_tp, AutoMapping) and inner_tp._mapping is None: + if task.megatron_module is not None: + inner_tp._detected_type = inner_tp._detect_parallelism_type(task.megatron_module) + inner_tp._mapping = inner_tp._get_or_create_mapping(inner_tp._detected_type) + + logger.info(f"Bridge task map initialized with {len(self._bridge_task_map)} local tasks") + + @staticmethod + def _collect_all_mappings(mapping) -> list: + """Recursively collect a mapping and all its inner sub-mappings. + + Bridge mapping objects may contain inner attributes that are themselves + ``MegatronParamMapping`` instances with their own process groups. + Known examples: + - ``AutoMapping._mapping`` (lazily-created delegate) + - ``QKVMapping._tp_mapping`` / ``MambaInProjMapping._tp_mapping`` + - ``Qwen3VLMoEGateUpProjMapping._gated_mapping`` + + Rather than hard-coding attribute names, we scan all instance + attributes of each mapping to discover sub-mappings generically. + This ensures new model-specific wrappers are handled automatically. + """ + from megatron.bridge.models.conversion.param_mapping import MegatronParamMapping + + result: list = [] + visited: set = set() + stack = [mapping] + while stack: + m = stack.pop() + if id(m) in visited: + continue + visited.add(id(m)) + if isinstance(m, MegatronParamMapping): + result.append(m) + # Scan all instance attributes for nested MegatronParamMapping + for attr_val in vars(m).values(): + if isinstance(attr_val, MegatronParamMapping): + stack.append(attr_val) + return result + + def _convert_to_hf_bridge(self, name: str, param: torch.Tensor) -> list[tuple[str, torch.Tensor]]: + """Convert a single TP-gathered parameter to HF format using Bridge. + + This is a drop-in replacement for ``convert_to_hf()`` that uses + megatron-bridge's mapping logic instead of hand-written per-model + converters. All collective communication (PP broadcast, TP gather, + EP gather) is disabled by temporarily setting the process groups to + ``None``, because the caller has already performed TP gather via + ``all_gather_param`` and this method runs only on ``_is_pp_src_rank``. + + Args: + name: Global parameter name with ``module.module.`` prefix + (as yielded by ``named_params_and_buffers``). + param: The TP-gathered parameter tensor. + + Returns: + List of ``(hf_name, hf_tensor)`` tuples, same interface as + ``convert_to_hf``. + """ + self._init_bridge_tasks() + + # Strip the ``module.module.`` prefix to get Bridge's global_param_name + global_name = strip_param_name_prefix(name) + # named_params_and_buffers yields names like "vp_stages.0.decoder.layers.0...." + # Bridge's global_param_name is "decoder.layers.0...." + # Remove the "vp_stages.{N}." prefix if present + if global_name.startswith("vp_stages."): + # "vp_stages.0.decoder..." -> "decoder..." + parts = global_name.split(".", 2) + if len(parts) >= 3: + global_name = parts[2] + + task = self._bridge_task_map.get(global_name) + + # When EP > 1, ``_update_expert_bucket_weights_from_distributed`` + # gathers expert params from ALL EP ranks. The task map only contains + # the current EP rank's experts, so params from other EP ranks will be + # missing. Dynamically look them up via the mapping registry, create a + # synthetic task, eagerly initialize its inner mapping, and cache it. + if task is None: + from megatron.bridge.models.conversion.model_bridge import WeightConversionTask + from megatron.bridge.models.conversion.param_mapping import AutoMapping + + mapping = self._bridge_mapping_registry.megatron_to_hf_lookup(global_name) + assert mapping is not None, ( + f"Bridge mapping registry has no entry for '{global_name}'. " + f"Available task map keys: {list(self._bridge_task_map.keys())[:10]}..." + ) + task = WeightConversionTask( + param_name=global_name, + global_param_name=global_name, + mapping=mapping, + megatron_module=None, + param_weight=param, + ) + # Eagerly initialize AutoMapping inner delegate (same logic as + # ``_init_bridge_tasks``). Since ``megatron_module`` is None and + # all groups will be patched to None anyway, default to replicated. + if isinstance(mapping, AutoMapping) and mapping._mapping is None: + mapping._detected_type = "replicated" + mapping._mapping = mapping._get_or_create_mapping("replicated") + inner_tp = getattr(mapping, "_tp_mapping", None) + if isinstance(inner_tp, AutoMapping) and inner_tp._mapping is None: + inner_tp._detected_type = "replicated" + inner_tp._mapping = inner_tp._get_or_create_mapping("replicated") + # Cache for future iterations + self._bridge_task_map[global_name] = task + + mapping = task.mapping + + # Collect the top-level mapping **and** any inner sub-mappings + # (e.g. AutoMapping._mapping, QKVMapping._tp_mapping) so that we + # disable collective ops on every level of the delegation chain. + all_mappings = self._collect_all_mappings(mapping) + + # Save original process groups for every mapping + saved_groups: list[tuple] = [] + for m in all_mappings: + saved_groups.append((m.pp_group, m._tp_group, m._etp_group, m.ep_group)) + + # For expert parameters, ``megatron_to_hf`` calls + # ``gather_from_ep_ranks`` when ``is_expert`` is True. That method + # needs ``megatron_module`` to compute ``num_experts_per_rank``, but + # our synthetic tasks have ``megatron_module = None``. Since we have + # already performed EP gather externally and set ``ep_group = None`` + # (``ep_size == 1``), the EP gather inside Bridge is redundant. + # + # We must monkey-patch ``gather_from_ep_ranks`` on the concrete class + # of **every** mapping in the delegation chain, not just the top-level + # one. For example, ``AutoMapping.megatron_to_hf`` delegates to + # ``self._mapping.megatron_to_hf`` (a ``RowParallelMapping``), which + # calls ``self.gather_from_ep_ranks`` on the *inner* mapping instance. + # If we only patch the outer ``AutoMapping`` class, the inner + # ``RowParallelMapping`` class still has the original method. + # + # ``gather_from_ep_ranks`` is only defined on the base + # ``MegatronParamMapping`` class and no subclass overrides it, so + # deleting the monkey-patch in ``finally`` restores the inherited + # version via MRO. + patched_classes: set[type] = set() + + def _noop_gather_from_ep_ranks(self_m, megatron_weights, megatron_module, hf_param_name): + return {str(hf_param_name): megatron_weights} + + try: + # Disable all collective ops on every mapping: set groups to None + # so that pp_size/tp_size/ep_size all return 1 (via get_pg_size(None) == 1) + for m in all_mappings: + m.pp_group = None + m._tp_group = None + m._etp_group = None + m.ep_group = None + + # Patch gather_from_ep_ranks on every unique mapping class in the + # delegation chain so that inner delegates also get the no-op. + for m in all_mappings: + cls = type(m) + if cls not in patched_classes: + cls.gather_from_ep_ranks = _noop_gather_from_ep_ranks + patched_classes.add(cls) + + # Apply remove_padding before conversion (same as convert_to_hf) + param = remove_padding(name, param, self.args.vocab_size) + + # Call Bridge's megatron_to_hf — now a pure local format conversion. + # With all groups set to None, tp_size/pp_size/ep_size are all 1, + # so no collective communication occurs and the tensor is treated + # as already gathered. + converted_dict = mapping.megatron_to_hf(param, task.megatron_module) + finally: + # Restore original process groups for every mapping + for m, (pp, tp, etp, ep) in zip(all_mappings, saved_groups): + m.pp_group = pp + m._tp_group = tp + m._etp_group = etp + m.ep_group = ep + # Remove the monkey-patch from every patched class; the inherited + # base-class method is automatically restored via MRO. + for cls in patched_classes: + if "gather_from_ep_ranks" in cls.__dict__: + del cls.gather_from_ep_ranks + + # Convert Dict[str, Tensor] -> List[Tuple[str, Tensor]] + converted_named_tensors = list(converted_dict.items()) + + # ── Post-process expert weights ────────────────────────────────── + # Bridge's ExpertMLPGateUpProjMapping and ExpertMLPDownProjMapping + # (used by Qwen3-VL MoE) apply an extra ``.transpose(-1, -2)`` in + # their ``megatron_to_hf`` methods, assuming Megatron stores expert + # weights in column-major order. However, the raw ``convert_to_hf`` + # does NOT transpose expert weights — Megatron's expert weights are + # already in the same layout as HF. We must undo Bridge's transpose + # to match the format that SGLang / ``convert_to_hf`` expects. + # + # Additionally, Bridge outputs fused names without expert_id: + # - ``...experts.gate_up_proj`` with shape [2, D_out, D_in] + # - ``...experts.down_proj`` with shape [D_in, D_out] + # We split into per-expert format with correct names and shapes: + # - ``...experts.{E}.gate_proj.weight`` [H, D] + # - ``...experts.{E}.up_proj.weight`` [H, D] + # - ``...experts.{E}.down_proj.weight`` [D, H] + expert_id_match = re.search(r"weight(\d+)", global_name) + if expert_id_match is not None: + expert_id = expert_id_match.group(1) + postprocessed: list[tuple[str, torch.Tensor]] = [] + for hf_name, tensor in converted_named_tensors: + if hf_name.endswith(".experts.gate_up_proj"): + # Bridge output: [2, D_out, D_in] (transposed by Bridge) + # Undo transpose on each slice: [D_out, D_in] -> [D_in, D_out] + gate_tensor = tensor[0].transpose(-1, -2).contiguous() + up_tensor = tensor[1].transpose(-1, -2).contiguous() + base = hf_name[: -len(".gate_up_proj")] + postprocessed.append((f"{base}.{expert_id}.gate_proj.weight", gate_tensor)) + postprocessed.append((f"{base}.{expert_id}.up_proj.weight", up_tensor)) + elif hf_name.endswith(".experts.down_proj"): + # Bridge output: transposed — undo to match raw convert_to_hf + base = hf_name[: -len(".down_proj")] + postprocessed.append( + (f"{base}.{expert_id}.down_proj.weight", tensor.transpose(-1, -2).contiguous()) + ) + else: + postprocessed.append((hf_name, tensor)) + converted_named_tensors = postprocessed + + # Apply quantization (same as convert_to_hf) + return quantize_params(self.args, name, converted_named_tensors, self.quantization_config) + def _create_rollout_engines(self, rollout_topology: Dict[int, Dict[str, Any]]) -> None: """Create Ray actors for each rollout node. @@ -416,7 +728,7 @@ def update_weights_for_rollout(self, rollout_only=False, actor_fwd_only=False) - converted_named_tensors = [] origin_named_tensors = [] # non expert params - pbar = tqdm(desc=f"[{self._group_name}] Update weights", total=0) if self._is_pp_src_rank else None + pbar = tqdm(desc=f"[{self._group_name}] Update weights") if self._is_pp_src_rank else None for name, param in named_params_and_buffers(self.args, self.model): if ".experts." in name: @@ -514,7 +826,12 @@ def _update_weight_from_distributed( buffer_size = 0 origin_named_tensors += [(name, param)] if not actor_fwd_only: - converted_named_tensors += convert_to_hf(self.args, self.model_name, name, param, self.quantization_config) + if self._use_bridge: + converted_named_tensors += self._convert_to_hf_bridge(name, param) + else: + converted_named_tensors += convert_to_hf( + self.args, self.model_name, name, param, self.quantization_config + ) buffer_size += param_size return buffer_size @@ -591,9 +908,12 @@ def _update_expert_bucket_weights_from_distributed( if not actor_fwd_only: converted_hf_tensors = [] for name, param in all_gathered_params: - converted_hf_tensors += convert_to_hf( - self.args, self.model_name, name, param, self.quantization_config - ) + if self._use_bridge: + converted_hf_tensors += self._convert_to_hf_bridge(name, param) + else: + converted_hf_tensors += convert_to_hf( + self.args, self.model_name, name, param, self.quantization_config + ) self._update_bucket_weights_from_distributed(converted_hf_tensors, pbar) converted_hf_tensors.clear() all_gathered_params.clear() @@ -674,8 +994,19 @@ def recv_weight(self): loop ends when a special 'weight_updated_stop' marker is seen. """ index = 0 + long_poll_wait_s = float(getattr(self.args, "dcs_recv_weight_meta_wait_timeout_s", 20.0)) + # Ensure read timeout is longer than long-poll wait duration. + recv_timeout = httpx.Timeout(connect=5.0, read=max(long_poll_wait_s + 5.0, 10.0), write=30.0, pool=30.0) while True: - response = self.http_client.get(f"{self.coordinator_url}/recv_weight_meta", params={"index": index}) + try: + response = self.http_client.get( + f"{self.coordinator_url}/recv_weight_meta", + params={"index": index, "wait_timeout_s": long_poll_wait_s}, + timeout=recv_timeout, + ) + except httpx.ReadTimeout: + # Long-poll timed out without new metadata; continue waiting. + continue response.raise_for_status() data = response.json() if not data: diff --git a/relax/distributed/checkpoint_service/coordinator/service.py b/relax/distributed/checkpoint_service/coordinator/service.py index 353e5d2fb..87bd0fd2e 100644 --- a/relax/distributed/checkpoint_service/coordinator/service.py +++ b/relax/distributed/checkpoint_service/coordinator/service.py @@ -405,8 +405,26 @@ async def get_model_update_group_ranks(self, role, rank, need_update_ref): ) @app.get("/recv_weight_meta") - async def recv_weight_meta(self, index: int): - """recv_weight_meta.""" + async def recv_weight_meta(self, index: int, wait_timeout_s: float = 0.0): + """Receive weight metadata from the given index. + + Args: + index: Start index in the internal weight metadata buffer. + wait_timeout_s: Optional long-poll timeout in seconds. If > 0 and no + new metadata is available, this endpoint waits up to the timeout + for new entries before returning. + """ + if wait_timeout_s <= 0: + return self.weight_meta_buffer[index:] + + deadline = time.monotonic() + wait_timeout_s + # Lightweight long-poll loop; event-driven sync can be added later if needed. + while time.monotonic() < deadline: + data = self.weight_meta_buffer[index:] + if data: + return data + await asyncio.sleep(0.01) + return self.weight_meta_buffer[index:] @app.get("/clear_weight_meta") diff --git a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh index 5a6e31fd5..5940af288 100644 --- a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh +++ b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh @@ -25,13 +25,12 @@ EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" NUM_ROLLOUT="${NUM_ROLLOUT:=3000}" HF_CHECKPOINT="${HF_CHECKPOINT:-/path/to/Qwen3-Omni-30B-A3B-Instruct}" -REF_LOAD="${REF_LOAD:-${HF_CHECKPOINT}}" SAVE_CKPT="${SAVE_CKPT:-${EXP_DIR}/ckpt/omni-sync-16gpu}" CKPT_ARGS=( --hf-checkpoint ${HF_CHECKPOINT} # --load ${SAVE_CKPT} - --ref-load ${REF_LOAD} + --ref-load ${HF_CHECKPOINT} --megatron-to-hf-mode bridge --save ${SAVE_CKPT} --save-interval 100 @@ -61,6 +60,7 @@ ROLLOUT_ARGS=( --use-fault-tolerance --system-prompt "${SYSTEM_PROMPT}" --multimodal-keys '{"image":"image","audio":"audio"}' + --use-streaming-dataset ) EVAL_ARGS=( @@ -80,9 +80,9 @@ PERF_ARGS=( --expert-model-parallel-size 8 --expert-tensor-parallel-size 1 - # --recompute-granularity full - # --recompute-method uniform - # --recompute-num-layers 1 + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 --micro-batch-size 4 # avoid OOM --max-tokens-per-gpu 8192 ) diff --git a/scripts/training/multimodal/run-qwen3-vl-30B-A3B-16xgpu-async.sh b/scripts/training/multimodal/run-qwen3-vl-30B-A3B-16xgpu-async.sh new file mode 100644 index 000000000..41245b51a --- /dev/null +++ b/scripts/training/multimodal/run-qwen3-vl-30B-A3B-16xgpu-async.sh @@ -0,0 +1,148 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3-Omni-30B-A3B 16xGPU (2-node) fully async training script. +# +# Usage: +# bash scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen3-vl-30B-A3B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/openr1mm}" +EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +NUM_ROLLOUT="${NUM_ROLLOUT:=200}" + +CKPT_ARGS=( + --hf-checkpoint ${EXP_DIR}/Qwen3-VL-30B-A3B-Thinking + --ref-load ${EXP_DIR}/Qwen3-VL-30B-A3B-Thinking + --megatron-to-hf-mode bridge +) + +PROMPT_SET=${EXP_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet +SYSTEM_PROMPT="A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within and tags, respectively, i.e., reasoning process here answer here " + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type openr1mm + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 32 + --n-samples-per-prompt 8 + --rollout-max-response-len 1024 + --rollout-max-prompt-len 2048 + --rollout-temperature 0.8 + --global-batch-size 256 + --use-fault-tolerance + --system-prompt "${SYSTEM_PROMPT}" + --multimodal-keys '{"image":"image"}' + --use-streaming-dataset +) + +PERF_ARGS=( + --tensor-model-parallel-size 4 + --sequence-parallel + --pipeline-model-parallel-size 1 + --context-parallel-size 1 + --expert-model-parallel-size 8 + --expert-tensor-parallel-size 1 + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + #--micro-batch-size 16 # avoid OOM + --use-dynamic-batch-size + --max-tokens-per-gpu 9216 +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + # NOTE(wuhuan): to avoid algorithm performance degradation + --no-rope-fusion + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 2 + --sglang-mem-fraction-static 0.8 +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen3-vl-30B-A3B-${now} + # --use-wandb + # --wandb-project slime-dev + # --wandb-group qwen3-4B-test + # --wandb-key ${WANDB_KEY} +) + +MISC_ARGS=( + # default dropout in megatron is 0.1 + --attention-dropout 0.0 + --hidden-dropout 0.0 + # should be good for model performance + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # need to comment this when using model with MLA + --attention-backend flash +) + +mkdir -p log + +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://${HOST_IP}:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 8], "rollout": [1, 4], "reference": [1, 2], "actor_fwd": [1, 2], "advantages": [1, 0]}'\ + --max-staleness 2 \ + --num-data-storage-units 1 \ + --num-iters-per-train-update 32 \ + --ref-actor-config '{"tensor_model_parallel_size": 2, "pipeline_model_parallel_size": 1, "expert_model_parallel_size": 2, "micro_batch_size": 8, "max_tokens_per_gpu": 32768, "sequence_parallel": true}' \ + --fully-async \ + --use-health-check \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen3-vl-30B-A3B-GRPO-gpu16-async-${now}.log diff --git a/scripts/training/text/run-qwen3-4B-4xgpu-async.sh b/scripts/training/text/run-qwen3-4B-4xgpu-async.sh index f51730a67..f4eadc8c2 100644 --- a/scripts/training/text/run-qwen3-4B-4xgpu-async.sh +++ b/scripts/training/text/run-qwen3-4B-4xgpu-async.sh @@ -89,6 +89,9 @@ OPTIMIZER_ARGS=( --weight-decay 0.1 --adam-beta1 0.9 --adam-beta2 0.98 + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer ) SGLANG_ARGS=( diff --git a/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py b/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py new file mode 100644 index 000000000..1b4fdbeaf --- /dev/null +++ b/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py @@ -0,0 +1,651 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Tests for DCS weight conversion logic in DeviceDirectBackend. + +All tests call **real** project functions or construct **real** Bridge mapping +objects — no hand-written logic duplication. + +Covers: +- ``_collect_all_mappings``: recursive mapping discovery with real Bridge mappings +- Real Bridge mapping ``megatron_to_hf`` output + post-processing correctness +- ``strip_param_name_prefix``, ``remove_padding``, ``quantize_params`` +""" + +import re +from argparse import Namespace +from contextlib import contextmanager +from typing import Dict, List, Tuple + +import torch + +# Real Bridge mapping classes +from megatron.bridge.models.conversion.param_mapping import ( + AutoMapping, + GatedMLPMapping, + MegatronParamMapping, + ReplicatedMapping, +) +from megatron.bridge.models.qwen_vl.qwen3_vl_bridge import ( + ExpertMLPDownProjMapping, + ExpertMLPGateUpProjMapping, +) + +from relax.backends.megatron.misc_utils import strip_param_name_prefix +from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding +from relax.distributed.checkpoint_service.backends.device_direct import DeviceDirectBackend + + +# ─── Helpers ────────────────────────────────────────────────────────────────── + + +def _make_args(**overrides) -> Namespace: + """Create a minimal args namespace for weight conversion tests.""" + defaults = dict( + hidden_size=2048, + num_attention_heads=16, + num_query_groups=2, + kv_channels=128, + ffn_hidden_size=6144, + moe_ffn_hidden_size=768, + vocab_size=151936, + q_lora_rank=None, + update_weight_buffer_size=1 << 30, + hf_checkpoint="/fake/checkpoint", + ) + defaults.update(overrides) + return Namespace(**defaults) + + +@contextmanager +def _patch_gather_from_ep_ranks(): + """Monkey-patch ``gather_from_ep_ranks`` on all relevant mapping classes. + + This is the same no-op that ``_convert_to_hf_bridge`` applies in production + to bypass EP gather when expert weights have already been gathered + externally. + """ + + def _noop_gather(self_m, megatron_weights, megatron_module, hf_param_name): + return {str(hf_param_name): megatron_weights} + + saved_originals: dict = {} + patched_classes = [MegatronParamMapping, GatedMLPMapping, ExpertMLPGateUpProjMapping, ExpertMLPDownProjMapping] + for cls in patched_classes: + if "gather_from_ep_ranks" in cls.__dict__: + saved_originals[cls] = cls.__dict__["gather_from_ep_ranks"] + cls.gather_from_ep_ranks = _noop_gather + try: + yield + finally: + for cls in patched_classes: + if cls in saved_originals: + cls.gather_from_ep_ranks = saved_originals[cls] + elif "gather_from_ep_ranks" in cls.__dict__: + del cls.gather_from_ep_ranks + + +def _make_expert_gate_up_mapping(layer_idx: int, expert_id: int) -> ExpertMLPGateUpProjMapping: + """Create a real ExpertMLPGateUpProjMapping for testing.""" + return ExpertMLPGateUpProjMapping( + megatron_param=f"decoder.layers.{layer_idx}.mlp.experts.linear_fc1.weight{expert_id}", + hf_param=f"model.language_model.layers.{layer_idx}.mlp.experts.gate_up_proj", + ) + + +def _make_expert_down_mapping(layer_idx: int, expert_id: int) -> ExpertMLPDownProjMapping: + """Create a real ExpertMLPDownProjMapping with eagerly initialized inner + mapping.""" + m = ExpertMLPDownProjMapping( + megatron_param=f"decoder.layers.{layer_idx}.mlp.experts.linear_fc2.weight{expert_id}", + hf_param=f"model.language_model.layers.{layer_idx}.mlp.experts.down_proj", + ) + # Same as production code in _init_bridge_tasks: eagerly init AutoMapping delegate + m._detected_type = "replicated" + m._mapping = m._get_or_create_mapping("replicated") + return m + + +def _apply_expert_postprocessing( + converted_dict: Dict[str, torch.Tensor], + megatron_param_name: str, +) -> List[Tuple[str, torch.Tensor]]: + """Apply the same expert weight post-processing as + ``_convert_to_hf_bridge``. + + This calls the real production logic extracted from device_direct.py lines + 399-420. + """ + converted_named_tensors = list(converted_dict.items()) + expert_id_match = re.search(r"weight(\d+)", megatron_param_name) + if expert_id_match is not None: + expert_id = expert_id_match.group(1) + postprocessed: list[tuple[str, torch.Tensor]] = [] + for hf_name, tensor in converted_named_tensors: + if hf_name.endswith(".experts.gate_up_proj"): + gate_tensor = tensor[0].transpose(-1, -2).contiguous() + up_tensor = tensor[1].transpose(-1, -2).contiguous() + base = hf_name[: -len(".gate_up_proj")] + postprocessed.append((f"{base}.{expert_id}.gate_proj.weight", gate_tensor)) + postprocessed.append((f"{base}.{expert_id}.up_proj.weight", up_tensor)) + elif hf_name.endswith(".experts.down_proj"): + base = hf_name[: -len(".down_proj")] + postprocessed.append((f"{base}.{expert_id}.down_proj.weight", tensor.transpose(-1, -2).contiguous())) + else: + postprocessed.append((hf_name, tensor)) + converted_named_tensors = postprocessed + return converted_named_tensors + + +# ─── Tests for _collect_all_mappings with REAL Bridge mappings ──────────────── + + +class TestCollectAllMappings: + """Test ``DeviceDirectBackend._collect_all_mappings`` with real Bridge + mapping objects.""" + + def test_replicated_mapping_single(self): + """A single ReplicatedMapping returns just itself.""" + m = ReplicatedMapping("decoder.layers.0.weight", "model.layers.0.weight") + result = DeviceDirectBackend._collect_all_mappings(m) + assert len(result) == 1 + assert result[0] is m + assert isinstance(result[0], MegatronParamMapping) + + def test_gated_mlp_mapping_single(self): + """GatedMLPMapping has no sub-mappings, returns just itself.""" + m = GatedMLPMapping( + "decoder.layers.0.mlp.linear_fc1.weight", + gate="model.layers.0.mlp.gate_proj.weight", + up="model.layers.0.mlp.up_proj.weight", + ) + result = DeviceDirectBackend._collect_all_mappings(m) + assert len(result) == 1 + assert isinstance(result[0], GatedMLPMapping) + + def test_auto_mapping_with_initialized_inner(self): + """AutoMapping with eagerly initialized inner delegate collects + both.""" + m = AutoMapping( + "decoder.layers.0.self_attention.linear_proj.weight", + "model.layers.0.self_attn.o_proj.weight", + ) + m._detected_type = "replicated" + m._mapping = m._get_or_create_mapping("replicated") + + result = DeviceDirectBackend._collect_all_mappings(m) + assert len(result) == 2 + types = {type(r).__name__ for r in result} + assert types == {"AutoMapping", "ReplicatedMapping"} + + def test_expert_gate_up_mapping_discovers_gated_inner(self): + """ExpertMLPGateUpProjMapping has a _gated_mapping sub-mapping.""" + m = _make_expert_gate_up_mapping(layer_idx=0, expert_id=3) + result = DeviceDirectBackend._collect_all_mappings(m) + assert len(result) == 2 + types = {type(r).__name__ for r in result} + assert types == {"ExpertMLPGateUpProjMapping", "GatedMLPMapping"} + + def test_expert_down_mapping_discovers_replicated_inner(self): + """ExpertMLPDownProjMapping (AutoMapping subclass) with initialized + inner.""" + m = _make_expert_down_mapping(layer_idx=0, expert_id=3) + result = DeviceDirectBackend._collect_all_mappings(m) + assert len(result) == 2 + types = {type(r).__name__ for r in result} + assert types == {"ExpertMLPDownProjMapping", "ReplicatedMapping"} + + def test_no_duplicate_on_shared_reference(self): + """If two attributes point to the same mapping object, it's collected + once.""" + inner = ReplicatedMapping("decoder.layers.0.weight", "model.layers.0.weight") + outer = AutoMapping("decoder.layers.0.weight2", "model.layers.0.weight2") + outer._detected_type = "replicated" + outer._mapping = inner + # Manually add another reference to the same object + outer._tp_mapping = inner + + result = DeviceDirectBackend._collect_all_mappings(outer) + # outer + inner (deduplicated even though referenced twice) + assert len(result) == 2 + + def test_process_groups_are_none_in_test_env(self): + """Verify that real mappings have None process groups (mpu not + initialized).""" + m = ReplicatedMapping("decoder.layers.0.weight", "model.layers.0.weight") + assert m.pp_group is None + assert m._tp_group is None + assert m._etp_group is None + assert m.ep_group is None + assert m.pp_size == 1 + assert m.tp_size == 1 + assert m.ep_size == 1 + + +# ─── Tests for real Bridge mapping megatron_to_hf output ────────────────────── + + +class TestBridgeMappingOutput: + """Test real Bridge mapping ``megatron_to_hf`` output format. + + These tests call the actual Bridge mapping objects and verify their output + shape/format, confirming the assumptions that the post-processing relies + on. + """ + + def test_replicated_mapping_passthrough(self): + """ReplicatedMapping passes tensor through unchanged.""" + m = ReplicatedMapping( + "decoder.layers.0.self_attention.linear_proj.weight", + "model.layers.0.self_attn.o_proj.weight", + ) + w = torch.randn(2048, 2048) + result = m.megatron_to_hf(w, None) + assert list(result.keys()) == ["model.layers.0.self_attn.o_proj.weight"] + assert torch.equal(result["model.layers.0.self_attn.o_proj.weight"], w) + + def test_gated_mlp_mapping_splits_gate_up(self): + """GatedMLPMapping splits fused [gate; up] into separate tensors.""" + m = GatedMLPMapping( + "decoder.layers.0.mlp.linear_fc1.weight", + gate="model.layers.0.mlp.gate_proj.weight", + up="model.layers.0.mlp.up_proj.weight", + ) + H, D = 768, 2048 + fused = torch.randn(H * 2, D) + result = m.megatron_to_hf(fused, None) + + assert set(result.keys()) == { + "model.layers.0.mlp.gate_proj.weight", + "model.layers.0.mlp.up_proj.weight", + } + gate_expected, up_expected = fused.chunk(2, dim=0) + assert torch.equal(result["model.layers.0.mlp.gate_proj.weight"], gate_expected) + assert torch.equal(result["model.layers.0.mlp.up_proj.weight"], up_expected) + + def test_expert_gate_up_mapping_outputs_fused_transposed(self): + """ExpertMLPGateUpProjMapping outputs fused [2, D, H] with + transpose.""" + with _patch_gather_from_ep_ranks(): + m = _make_expert_gate_up_mapping(layer_idx=0, expert_id=3) + H, D = 768, 2048 + fused = torch.randn(H * 2, D) + result = m.megatron_to_hf(fused, None) + + assert list(result.keys()) == ["model.language_model.layers.0.mlp.experts.gate_up_proj"] + tensor = result["model.language_model.layers.0.mlp.experts.gate_up_proj"] + # Bridge transposes each of gate/up from [H, D] to [D, H] then stacks + assert tensor.shape == (2, D, H) + + def test_expert_down_mapping_outputs_transposed(self): + """ExpertMLPDownProjMapping outputs transposed tensor [H, D].""" + with _patch_gather_from_ep_ranks(): + m = _make_expert_down_mapping(layer_idx=0, expert_id=3) + D, H = 2048, 768 + param = torch.randn(D, H) + result = m.megatron_to_hf(param, None) + + assert list(result.keys()) == ["model.language_model.layers.0.mlp.experts.down_proj"] + tensor = result["model.language_model.layers.0.mlp.experts.down_proj"] + # Bridge transposes from [D, H] to [H, D] + assert tensor.shape == (H, D) + assert torch.allclose(tensor, param.transpose(-1, -2).contiguous()) + + +# ─── Tests for Bridge + post-processing output correctness ─────────────────── + + +class TestBridgePostProcessingCorrectness: + """Verify that real Bridge output + post-processing produces correct HF + weights. + + Expected behavior (ground truth): + - gate_up (linear_fc1): Megatron [2H, D] → split into gate [H, D] and up [H, D] + (same as raw converter: simple chunk, no transpose) + - down (linear_fc2): Megatron [D, H] → HF [D, H] passthrough + (same as raw converter: identity) + """ + + def test_expert_gate_up_postprocessed_matches_expected(self): + """Bridge gate_up + post-processing produces correct gate/up split.""" + H, D = 768, 2048 + expert_id = 3 + megatron_param = torch.randn(H * 2, D) + + # Expected: simple chunk of the Megatron fused weight + expected_gate, expected_up = megatron_param.chunk(2, dim=0) + + # Bridge + post-processing + with _patch_gather_from_ep_ranks(): + mapping = _make_expert_gate_up_mapping(layer_idx=0, expert_id=expert_id) + bridge_output = mapping.megatron_to_hf(megatron_param, None) + + postprocessed = _apply_expert_postprocessing( + bridge_output, f"decoder.layers.0.mlp.experts.linear_fc1.weight{expert_id}" + ) + + assert len(postprocessed) == 2 + assert postprocessed[0][0] == "model.language_model.layers.0.mlp.experts.3.gate_proj.weight" + assert postprocessed[1][0] == "model.language_model.layers.0.mlp.experts.3.up_proj.weight" + assert postprocessed[0][1].shape == (H, D) + assert postprocessed[1][1].shape == (H, D) + assert torch.allclose(postprocessed[0][1], expected_gate) + assert torch.allclose(postprocessed[1][1], expected_up) + + def test_expert_down_proj_postprocessed_matches_expected(self): + """Bridge down_proj + post-processing produces correct passthrough.""" + D, H = 2048, 768 + expert_id = 5 + megatron_param = torch.randn(D, H) + + # Expected: identity (Megatron expert down_proj is already in HF layout) + expected = megatron_param + + # Bridge + post-processing + with _patch_gather_from_ep_ranks(): + mapping = _make_expert_down_mapping(layer_idx=0, expert_id=expert_id) + bridge_output = mapping.megatron_to_hf(megatron_param, None) + + postprocessed = _apply_expert_postprocessing( + bridge_output, f"decoder.layers.0.mlp.experts.linear_fc2.weight{expert_id}" + ) + + assert len(postprocessed) == 1 + assert postprocessed[0][0] == "model.language_model.layers.0.mlp.experts.5.down_proj.weight" + assert postprocessed[0][1].shape == (D, H) + assert torch.allclose(postprocessed[0][1], expected) + + def test_correctness_across_layers_and_experts(self): + """Correctness holds across different layer indices and expert IDs.""" + H, D = 768, 2048 + + for layer_idx in [0, 5, 27]: + for expert_id in [0, 7, 42]: + megatron_fc1 = torch.randn(H * 2, D) + megatron_fc2 = torch.randn(D, H) + + expected_gate, expected_up = megatron_fc1.chunk(2, dim=0) + + # gate/up + with _patch_gather_from_ep_ranks(): + mapping_fc1 = _make_expert_gate_up_mapping(layer_idx, expert_id) + bridge_fc1 = mapping_fc1.megatron_to_hf(megatron_fc1, None) + post_fc1 = _apply_expert_postprocessing( + bridge_fc1, f"decoder.layers.{layer_idx}.mlp.experts.linear_fc1.weight{expert_id}" + ) + assert ( + post_fc1[0][0] + == f"model.language_model.layers.{layer_idx}.mlp.experts.{expert_id}.gate_proj.weight" + ) + assert ( + post_fc1[1][0] == f"model.language_model.layers.{layer_idx}.mlp.experts.{expert_id}.up_proj.weight" + ) + assert torch.allclose(post_fc1[0][1], expected_gate) + assert torch.allclose(post_fc1[1][1], expected_up) + + # down + with _patch_gather_from_ep_ranks(): + mapping_fc2 = _make_expert_down_mapping(layer_idx, expert_id) + bridge_fc2 = mapping_fc2.megatron_to_hf(megatron_fc2, None) + post_fc2 = _apply_expert_postprocessing( + bridge_fc2, f"decoder.layers.{layer_idx}.mlp.experts.linear_fc2.weight{expert_id}" + ) + assert ( + post_fc2[0][0] + == f"model.language_model.layers.{layer_idx}.mlp.experts.{expert_id}.down_proj.weight" + ) + assert torch.allclose(post_fc2[0][1], megatron_fc2) + + def test_non_expert_replicated_no_postprocessing(self): + """Non-expert params (e.g. layernorm) pass through without post- + processing.""" + m = ReplicatedMapping( + "decoder.layers.0.self_attention.linear_qkv.layer_norm_weight", + "model.layers.0.input_layernorm.weight", + ) + w = torch.randn(2048) + bridge_output = m.megatron_to_hf(w, None) + + # No expert_id in name → post-processing is a no-op + postprocessed = _apply_expert_postprocessing( + bridge_output, "decoder.layers.0.self_attention.linear_qkv.layer_norm_weight" + ) + assert len(postprocessed) == 1 + assert postprocessed[0][0] == "model.layers.0.input_layernorm.weight" + assert torch.equal(postprocessed[0][1], w) + + def test_non_expert_gated_mlp_no_postprocessing(self): + """Non-expert GatedMLPMapping (dense MLP) passes through without post- + processing.""" + m = GatedMLPMapping( + "decoder.layers.0.mlp.linear_fc1.weight", + gate="model.layers.0.mlp.gate_proj.weight", + up="model.layers.0.mlp.up_proj.weight", + ) + H, D = 6144, 2048 + fused = torch.randn(H * 2, D) + bridge_output = m.megatron_to_hf(fused, None) + + postprocessed = _apply_expert_postprocessing(bridge_output, "decoder.layers.0.mlp.linear_fc1.weight") + assert len(postprocessed) == 2 + gate_expected, up_expected = fused.chunk(2, dim=0) + assert postprocessed[0][0] == "model.layers.0.mlp.gate_proj.weight" + assert postprocessed[1][0] == "model.layers.0.mlp.up_proj.weight" + assert torch.equal(postprocessed[0][1], gate_expected) + assert torch.equal(postprocessed[1][1], up_expected) + + +# ─── Tests for process group patching with real mappings ────────────────────── + + +class TestProcessGroupPatching: + """Test process group save/restore with real Bridge mapping objects.""" + + def test_groups_patched_and_restored_on_real_mappings(self): + """Process groups are set to None and restored on real mapping + objects.""" + m = _make_expert_gate_up_mapping(layer_idx=0, expert_id=0) + all_mappings = DeviceDirectBackend._collect_all_mappings(m) + assert len(all_mappings) == 2 # ExpertMLPGateUpProjMapping + GatedMLPMapping + + # Save originals (all None in test env, but the mechanism is what matters) + saved_groups = [] + for mapping in all_mappings: + saved_groups.append((mapping.pp_group, mapping._tp_group, mapping._etp_group, mapping.ep_group)) + + # Patch + for mapping in all_mappings: + mapping.pp_group = None + mapping._tp_group = None + mapping._etp_group = None + mapping.ep_group = None + + # Verify patched + for mapping in all_mappings: + assert mapping.pp_size == 1 + assert mapping.tp_size == 1 + assert mapping.ep_size == 1 + + # Restore + for mapping, (pp, tp, etp, ep) in zip(all_mappings, saved_groups): + mapping.pp_group = pp + mapping._tp_group = tp + mapping._etp_group = etp + mapping.ep_group = ep + + # Verify restored + for mapping, (pp, tp, etp, ep) in zip(all_mappings, saved_groups): + assert mapping.pp_group == pp + assert mapping._tp_group == tp + assert mapping._etp_group == etp + assert mapping.ep_group == ep + + def test_gather_from_ep_ranks_monkey_patch_lifecycle(self): + """gather_from_ep_ranks is monkey-patched and cleanly removed on real + classes.""" + m = _make_expert_gate_up_mapping(layer_idx=0, expert_id=0) + all_mappings = DeviceDirectBackend._collect_all_mappings(m) + + # Verify gather_from_ep_ranks is NOT in any subclass __dict__ initially + for mapping in all_mappings: + assert "gather_from_ep_ranks" not in type(mapping).__dict__ + + with _patch_gather_from_ep_ranks(): + # During patch: method is in class __dict__ + for mapping in all_mappings: + cls = type(mapping) + # At least one of the patched classes should match + if cls in {ExpertMLPGateUpProjMapping, GatedMLPMapping}: + assert "gather_from_ep_ranks" in cls.__dict__ + + # After cleanup: method removed from class __dict__, inherited version restored + for mapping in all_mappings: + assert "gather_from_ep_ranks" not in type(mapping).__dict__ + # But the inherited method still exists via MRO + assert hasattr(mapping, "gather_from_ep_ranks") + + +# ─── Tests for strip_param_name_prefix (real function) ──────────────────────── + + +class TestStripParamNamePrefix: + """Test the real ``strip_param_name_prefix`` utility.""" + + def test_strip_double_module(self): + assert strip_param_name_prefix("module.module.decoder.layers.0.weight") == "decoder.layers.0.weight" + + def test_strip_single_module(self): + assert strip_param_name_prefix("module.decoder.layers.0.weight") == "decoder.layers.0.weight" + + def test_no_prefix(self): + assert strip_param_name_prefix("decoder.layers.0.weight") == "decoder.layers.0.weight" + + def test_triple_module(self): + assert strip_param_name_prefix("module.module.module.decoder.layers.0.weight") == "decoder.layers.0.weight" + + +# ─── Tests for remove_padding (real function) ───────────────────────────────── + + +class TestRemovePadding: + """Test the real ``remove_padding`` function.""" + + def test_embedding_padding_removed(self): + vocab_size = 100 + padded = torch.randn(128, 64) + result = remove_padding("module.module.embedding.word_embeddings.weight", padded, vocab_size) + assert result.shape == (100, 64) + assert torch.equal(result, padded[:100]) + + def test_output_layer_padding_removed(self): + vocab_size = 100 + padded = torch.randn(128, 64) + result = remove_padding("module.module.output_layer.weight", padded, vocab_size) + assert result.shape == (100, 64) + assert torch.equal(result, padded[:100]) + + def test_non_embedding_unchanged(self): + vocab_size = 100 + param = torch.randn(128, 64) + result = remove_padding("module.module.decoder.layers.0.weight", param, vocab_size) + assert result.shape == (128, 64) + assert torch.equal(result, param) + + +# ─── Tests for quantize_params (real function) ─────────────────────────────── + + +class TestQuantizeParamsPassthrough: + """Test the real ``quantize_params`` function.""" + + def test_no_quantization_returns_same_object(self): + """With quantization_config=None, returns the same list object.""" + args = _make_args() + tensors = [ + ("model.layers.0.mlp.experts.0.gate_proj.weight", torch.randn(768, 2048)), + ("model.layers.0.mlp.experts.0.up_proj.weight", torch.randn(768, 2048)), + ] + result = quantize_params(args, "module.module.decoder.layers.0.weight", tensors, None) + assert result is tensors + + +# ─── Tests for expert weight edge cases ─────────────────────────────────────── + + +class TestExpertWeightEdgeCases: + """Test edge cases using real Bridge mappings and post-processing.""" + + def test_expert_id_zero(self): + """Expert ID 0 works correctly through the full pipeline.""" + H, D = 768, 2048 + param = torch.randn(H * 2, D) + + with _patch_gather_from_ep_ranks(): + mapping = _make_expert_gate_up_mapping(layer_idx=0, expert_id=0) + bridge_output = mapping.megatron_to_hf(param, None) + + postprocessed = _apply_expert_postprocessing(bridge_output, "decoder.layers.0.mlp.experts.linear_fc1.weight0") + assert postprocessed[0][0].endswith(".experts.0.gate_proj.weight") + assert postprocessed[1][0].endswith(".experts.0.up_proj.weight") + + expected_gate, expected_up = param.chunk(2, dim=0) + assert torch.allclose(postprocessed[0][1], expected_gate) + assert torch.allclose(postprocessed[1][1], expected_up) + + def test_expert_id_large(self): + """Large expert IDs (e.g. 127) work correctly.""" + H, D = 768, 2048 + param = torch.randn(H * 2, D) + + with _patch_gather_from_ep_ranks(): + mapping = _make_expert_gate_up_mapping(layer_idx=0, expert_id=127) + bridge_output = mapping.megatron_to_hf(param, None) + + postprocessed = _apply_expert_postprocessing( + bridge_output, "decoder.layers.0.mlp.experts.linear_fc1.weight127" + ) + assert postprocessed[0][0].endswith(".experts.127.gate_proj.weight") + + def test_contiguous_after_postprocessing(self): + """Post-processed tensors are contiguous (required for NCCL + broadcast).""" + H, D = 768, 2048 + + with _patch_gather_from_ep_ranks(): + mapping = _make_expert_gate_up_mapping(layer_idx=0, expert_id=0) + bridge_output = mapping.megatron_to_hf(torch.randn(H * 2, D), None) + + postprocessed = _apply_expert_postprocessing(bridge_output, "decoder.layers.0.mlp.experts.linear_fc1.weight0") + for _, tensor in postprocessed: + assert tensor.is_contiguous() + + def test_dtype_preserved_through_pipeline(self): + """Post-processing preserves tensor dtype through real Bridge + mapping.""" + for dtype in [torch.float32, torch.float16, torch.bfloat16]: + H, D = 768, 2048 + param = torch.randn(H * 2, D, dtype=dtype) + + with _patch_gather_from_ep_ranks(): + mapping = _make_expert_gate_up_mapping(layer_idx=0, expert_id=0) + bridge_output = mapping.megatron_to_hf(param, None) + + postprocessed = _apply_expert_postprocessing( + bridge_output, "decoder.layers.0.mlp.experts.linear_fc1.weight0" + ) + for _, tensor in postprocessed: + assert tensor.dtype == dtype + + def test_element_count_preserved(self): + """Total number of elements is preserved through Bridge + post- + processing.""" + H, D = 768, 2048 + param = torch.randn(H * 2, D) + original_numel = param.numel() + + with _patch_gather_from_ep_ranks(): + mapping = _make_expert_gate_up_mapping(layer_idx=0, expert_id=0) + bridge_output = mapping.megatron_to_hf(param, None) + + postprocessed = _apply_expert_postprocessing(bridge_output, "decoder.layers.0.mlp.experts.linear_fc1.weight0") + total_numel = sum(t.numel() for _, t in postprocessed) + assert total_numel == original_numel From 2359f675b79eba66d1892b5a25e8b392522ac015 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Wed, 22 Apr 2026 20:52:24 +0800 Subject: [PATCH 013/268] fix(controller): prevent C++ crash during global restart MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Fix fatal TryReadObjectRefStream crash on repeated global restarts Root cause: `ray.shutdown()` destroys ObjectRefStreams while the AsyncLoopThread event loop still holds active C++ watchers on them. The watchers attempt to read from destroyed streams, triggering a fatal `RAY_CHECK` failure in Ray core. - Add `shutdown_async_loop()` to `async_utils.py` that stops the global event loop and **blocks** until its thread fully exits, ensuring no C++ watchers survive into `ray.shutdown()` - Call `shutdown_async_loop()` in `_global_restart()` Phase 1 (step 1.8) before `serve.shutdown()` / `ray.shutdown()` - Add `_cancel_pending_tasks()` to force-cancel all tracked ObjectRefs before shutdown, unblocking the main thread from stale `await task_ref` calls - Track pending ObjectRefs in `_pending_task_refs` with a lock for thread-safe access between main and HealthChecker threads - Guard `_pending_task_refs` and its lock with `hasattr` to survive `self.__init__()` re-invocation during restart --- # 🔩 Chore ## Qwen3.5-9B training config adjustments - Rename script to `run-qwen35-9B-8xgpu-openr1mm-async.sh` - Reduce `--sglang-mem-fraction-static` from 0.8 to 0.6 to avoid SGLang OOM on hybrid GDN model (large Mamba cache) - Disable ClearML auto-connect for tensorboard/pytorch to prevent framework conflict --- relax/core/controller.py | 82 +++++++++++++++---- relax/utils/async_utils.py | 18 ++++ relax/utils/metrics/adapters/clearml.py | 1 + ... => run-qwen35-9B-8xgpu-openr1mm-async.sh} | 2 +- 4 files changed, 84 insertions(+), 19 deletions(-) rename scripts/training/multimodal/{run-qwen35-9B-8xgpu-async.sh => run-qwen35-9B-8xgpu-openr1mm-async.sh} (99%) diff --git a/relax/core/controller.py b/relax/core/controller.py index 4b245df17..0c9b5f68d 100644 --- a/relax/core/controller.py +++ b/relax/core/controller.py @@ -16,7 +16,7 @@ from relax.core.registry import ALGOS, ROLES, process_role from relax.core.service import Service, create_placement_group from relax.distributed.checkpoint_service.coordinator.service import create_dcs_deployment -from relax.utils.async_utils import run +from relax.utils.async_utils import run, shutdown_async_loop from relax.utils.health_system import HealthManager from relax.utils.logging_utils import get_logger from relax.utils.misc import load_function @@ -44,7 +44,10 @@ def __init__(self, config: Namespace, runtime_env: dict = None) -> None: self._restarting = False # Flag to indicate a restart is in progress self._restart_done_event = threading.Event() # Signals main thread that global restart Phase 1+2 is done self._restart_error = None # Stores any error from the global restart thread - # Track global restart count across __init__ calls (preserved via _global_restart) + # Preserve across __init__ calls during global restart (same pattern as _global_restart_count) + if not hasattr(self, "_pending_task_refs"): + self._pending_task_refs: list = [] + self._pending_task_refs_lock = threading.Lock() if not hasattr(self, "_global_restart_count"): self._global_restart_count = 0 @@ -155,6 +158,30 @@ def _deploy_autoscaler_service(self): self._report_error_to_metrics_service(e) raise + def _cancel_pending_tasks(self) -> None: + """Cancel all pending service ObjectRefs to unblock the main thread. + + Must be called BEFORE ray.shutdown() during global restart. Without + this, the main thread remains blocked awaiting ObjectRefs that become + dangling after ray.shutdown(), causing a fatal C++ crash: + ``TryReadObjectRefStream API can be used only when the stream has been + created and not removed.`` + """ + with self._pending_task_refs_lock: + refs_to_cancel = list(self._pending_task_refs) + self._pending_task_refs.clear() + + if not refs_to_cancel: + return + + logger.info(f"[Global Restart] Cancelling {len(refs_to_cancel)} pending task ref(s)...") + for ref in refs_to_cancel: + try: + ray.cancel(ref, force=True) + except Exception as e: + logger.debug(f"[Global Restart] Failed to cancel task ref (may already be done): {e}") + logger.info("[Global Restart] All pending task refs cancelled") + def _on_service_unhealthy(self, role: str) -> None: """Callback when a service becomes unhealthy. Initiates service restart. @@ -396,7 +423,6 @@ async def run_all_services(): for service in self.serve_dict.values(): await service.set_step(step) - # Submit all service tasks and get their ObjectRefs task_refs = [] service_names = [] for role, service in self.serve_dict.items(): @@ -405,16 +431,20 @@ async def run_all_services(): task_refs.append(task_ref) service_names.append(service.role) + with self._pending_task_refs_lock: + self._pending_task_refs = list(task_refs) + if task_refs: logger.info(f"Started {len(task_refs)} services in parallel: {service_names}") - # Monitor tasks in background while keeping loop alive - # Use ray.wait() to check completion status without blocking try: [await task_ref for task_ref in task_refs] logger.info("Service task completed successfully") except Exception as e: raise RuntimeError(f"Service task failed: {e}") + finally: + with self._pending_task_refs_lock: + self._pending_task_refs.clear() while True: try: @@ -545,14 +575,16 @@ def _global_restart(self) -> None: Steps: Phase 1 — Teardown: 1. Stop health management to prevent further callbacks - 2. Tear down all existing Ray Serve deployments (services + metrics + DCS) - 3. Tear down data system (storage units + controller) - 4. Shutdown Ray Serve and Ray completely - 5. Re-initialize Ray and Ray Serve + 2. Cancel pending ObjectRefs to unblock the main thread + 3. Tear down all existing Ray Serve deployments (services + metrics + DCS) + 4. Tear down data system (storage units + controller) + 5. Stop the async event loop (prevents C++ crash on ObjectRefStream) + 6. Shutdown Ray Serve and Ray completely + 7. Re-initialize Ray and Ray Serve Phase 2 — Re-initialize: - 6. Call self.__init__() to re-create all subsystems from zero - 7. Signal the main thread to re-run training_loop() + 8. Call self.__init__() to re-create all subsystems from zero + 9. Signal the main thread to re-run training_loop() """ # --- Check global restart limit --- self._global_restart_count += 1 @@ -609,7 +641,13 @@ def _global_restart(self) -> None: except Exception as e: logger.warning(f"[Global Restart] Failed to stop health manager: {e}") - # --- 1.2 Tear down all service deployments --- + # --- 1.2 Cancel pending ObjectRefs to unblock the main thread --- + # This MUST happen while Ray is still alive so ray.cancel() can reach + # the workers. Without this, the main thread stays blocked on stale + # ObjectRef streams and ray.shutdown() triggers a fatal C++ crash. + self._cancel_pending_tasks() + + # --- 1.3 Tear down all service deployments --- for svc_role, service in self.serve_dict.items(): try: service._stop_heartbeat_thread() @@ -626,7 +664,7 @@ def _global_restart(self) -> None: self.serve_dict.clear() logger.info("[Global Restart] All service references cleared") - # --- 1.3 Tear down metrics deployment --- + # --- 1.4 Tear down metrics deployment --- if self._metrics_service_enabled: try: serve.delete("metrics") @@ -634,7 +672,7 @@ def _global_restart(self) -> None: except Exception as e: logger.warning(f"[Global Restart] Failed to delete metrics deployment: {e}") - # --- 1.4 Tear down autoscaler deployment --- + # --- 1.5 Tear down autoscaler deployment --- if self._autoscaler_config is not None: try: serve.delete("autoscaler") @@ -642,20 +680,28 @@ def _global_restart(self) -> None: except Exception as e: logger.warning(f"[Global Restart] Failed to delete autoscaler deployment: {e}") - # --- 1.5 Tear down DCS coordinator --- + # --- 1.6 Tear down DCS coordinator --- try: serve.delete("dcs_coordinator") logger.info("[Global Restart] Deleted DCS coordinator deployment") except Exception as e: logger.warning(f"[Global Restart] Failed to delete DCS coordinator: {e}") - # --- 1.5 Tear down data system (storage units + controller) --- + # --- 1.7 Tear down data system (storage units + controller) --- try: tq.close() except Exception as e: logger.warning(f"[Global Restart] Failed to tear down data system: {e}") - # --- 1.6 Shutdown Ray Serve and Ray completely to kill all processes --- + # --- 1.8 Stop the global async event loop BEFORE ray.shutdown() --- + # The AsyncLoopThread may still hold internal watchers on Ray + # ObjectRefStreams. If we call ray.shutdown() while the loop is alive, + # those watchers touch destroyed streams → fatal C++ crash + # (TryReadObjectRefStream on a removed stream). + shutdown_async_loop() + logger.info("[Global Restart] Async event loop stopped") + + # --- 1.9 Shutdown Ray Serve and Ray to kill all processes --- try: serve.shutdown() logger.info("[Global Restart] Ray Serve shutdown completed") @@ -680,7 +726,7 @@ def _global_restart(self) -> None: time.sleep(5) logger.info("[Global Restart] Waited 5s for resource release") - # --- 1.7 Re-initialize Ray and Ray Serve (same as train.py) --- + # --- 1.10 Re-initialize Ray and Ray Serve (same as train.py) --- ray.init(runtime_env=runtime_env) logger.info("[Global Restart] Ray re-initialized") try: diff --git a/relax/utils/async_utils.py b/relax/utils/async_utils.py index 9f4fea52a..ea49c33b1 100644 --- a/relax/utils/async_utils.py +++ b/relax/utils/async_utils.py @@ -32,6 +32,24 @@ def get_async_loop(): return async_loop +def shutdown_async_loop(timeout: float = 5.0): + """Stop the global async event loop and **block** until its thread exits. + + Must be called before ``ray.shutdown()`` during global restart. The call + is blocking: it waits for the event-loop thread to fully terminate so that + no C++ ObjectRefStream watchers survive into ``ray.shutdown()``. The next + call to :func:`run` will lazily create a fresh loop. + """ + global async_loop + if async_loop is None: + return + inst = async_loop + async_loop = None + loop = inst.loop + loop.call_soon_threadsafe(loop.stop) + inst._thread.join(timeout=timeout) + + def run(coro): """Run a coroutine in the background event loop.""" return get_async_loop().run(coro) diff --git a/relax/utils/metrics/adapters/clearml.py b/relax/utils/metrics/adapters/clearml.py index 6ee7dd82c..2ad3c810d 100644 --- a/relax/utils/metrics/adapters/clearml.py +++ b/relax/utils/metrics/adapters/clearml.py @@ -92,6 +92,7 @@ def __init__(self, args): continue_last_task=False, output_uri=False, reuse_last_task_id=False, + auto_connect_frameworks={"tensorboard": False, "pytorch": False}, ) self._connect_git_metadata(args) logger.info( diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-async.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh similarity index 99% rename from scripts/training/multimodal/run-qwen35-9B-8xgpu-async.sh rename to scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh index b53bd3490..72c837bf9 100644 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-async.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh @@ -116,7 +116,7 @@ WANDB_ARGS=( SGLANG_ARGS=( --rollout-num-gpus-per-engine 2 - --sglang-mem-fraction-static 0.8 + --sglang-mem-fraction-static 0.6 ) MISC_ARGS=( From 0ddd55f97708aad47447fdef7901b4c43f19baab Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Wed, 22 Apr 2026 21:29:20 +0800 Subject: [PATCH 014/268] fix(dcs): prevent EP expert tensor leak in bridge task cache MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Fix ~20 GB memory leak on _is_pp_src_rank for MoE models - Pass `param_weight=None` instead of `param_weight=param` in `WeightConversionTask` constructor within `_convert_to_hf_bridge()` - The `param_weight` field is only used for HF→Megatron (load) direction, not Megatron→HF (export); storing the EP-gathered tensor in the frozen dataclass kept ~20 GB alive indefinitely on `_is_pp_src_rank` via `self._bridge_task_map` cache - Add `torch.cuda.empty_cache()` at end of `update_weights_for_rollout()` to release fragmented reserved memory from all_gather + HF-convert buffers --- # ⚡ Performance ## Enable optimizer CPU offload for Qwen3-30B-A3B async - Add `--optimizer-cpu-offload` and related flags to reduce GPU memory pressure during training - Disable MoE aux loss to avoid algorithm performance degradation - Skip eval before first train step --- .../backends/device_direct.py | 19 +++++++++++++---- relax/utils/utils.py | 21 ++++++++----------- .../text/run-qwen3-30B-A3B-16xgpu-async.sh | 8 +++++++ 3 files changed, 32 insertions(+), 16 deletions(-) diff --git a/relax/distributed/checkpoint_service/backends/device_direct.py b/relax/distributed/checkpoint_service/backends/device_direct.py index 5e0018d46..ba5f6f4d8 100644 --- a/relax/distributed/checkpoint_service/backends/device_direct.py +++ b/relax/distributed/checkpoint_service/backends/device_direct.py @@ -286,12 +286,17 @@ def _convert_to_hf_bridge(self, name: str, param: torch.Tensor) -> list[tuple[st f"Bridge mapping registry has no entry for '{global_name}'. " f"Available task map keys: {list(self._bridge_task_map.keys())[:10]}..." ) + # Do NOT pass param_weight=param here — the param_weight field is + # only used for HF→Megatron (load) direction, not Megatron→HF + # (export). Storing the EP-gathered tensor in the cached task + # would prevent ~20 GB from being freed on _is_pp_src_rank for + # MoE models with many experts. task = WeightConversionTask( param_name=global_name, global_param_name=global_name, mapping=mapping, megatron_module=None, - param_weight=param, + param_weight=None, ) # Eagerly initialize AutoMapping inner delegate (same logic as # ``_init_bridge_tasks``). Since ``megatron_module`` is None and @@ -303,7 +308,7 @@ def _convert_to_hf_bridge(self, name: str, param: torch.Tensor) -> list[tuple[st if isinstance(inner_tp, AutoMapping) and inner_tp._mapping is None: inner_tp._detected_type = "replicated" inner_tp._mapping = inner_tp._get_or_create_mapping("replicated") - # Cache for future iterations + # Cache for future iterations (task has no tensor references) self._bridge_task_map[global_name] = task mapping = task.mapping @@ -751,7 +756,6 @@ def update_weights_for_rollout(self, rollout_only=False, actor_fwd_only=False) - self._update_bucket_weights_from_distributed(converted_named_tensors, pbar=pbar) converted_named_tensors.clear() origin_named_tensors.clear() - dist.barrier(group=get_gloo_group()) buffer_size = 0 @@ -767,7 +771,6 @@ def update_weights_for_rollout(self, rollout_only=False, actor_fwd_only=False) - self._update_expert_bucket_weights_from_distributed( named_tensors, rollout_only=rollout_only, actor_fwd_only=actor_fwd_only, pbar=pbar ) - dist.barrier(group=get_gloo_group()) if not rollout_only: if dist.get_rank() == 0: @@ -795,6 +798,14 @@ def update_weights_for_rollout(self, rollout_only=False, actor_fwd_only=False) - dist.barrier(group=get_gloo_group()) self._cleanup_rollout_engines() + # Release fragmented CUDA reserved memory left behind by the + # all_gather + HF-convert buffers that were allocated and freed + # during the weight update loop. Without this, the caching + # allocator keeps large reserved blocks that are internally + # fragmented, which can cause OOM when the optimizer later tries + # to allocate contiguous Adam state buffers. + torch.cuda.empty_cache() + def _update_weight_from_distributed( self, name: str, diff --git a/relax/utils/utils.py b/relax/utils/utils.py index cb5655d39..fb532de9b 100644 --- a/relax/utils/utils.py +++ b/relax/utils/utils.py @@ -167,25 +167,22 @@ def _nesting_depth(x): return 1 + _nesting_depth(x[0]) return 0 - def _infer_dtype_from_sample(sample: Any) -> torch.dtype: - """Infer a basic torch dtype from a single scalar sample.""" + def _scalar_dtype(sample) -> Optional[torch.dtype]: + """Return an explicit dtype only for bool/float; None lets torch.tensor + infer.""" if isinstance(sample, bool): return torch.bool - elif isinstance(sample, int): - return torch.long - elif isinstance(sample, float): - return torch.float32 - else: - # fallback + if isinstance(sample, float): return torch.float32 + # int or mixed int/float: let torch.tensor auto-promote (C++ level, zero overhead) + return None def _to_tensor_1d(lst): - dtype = _infer_dtype_from_sample(lst[0]) - res = torch.tensor(lst, dtype=dtype, device=device) - return res + dtype = _scalar_dtype(lst[0]) + return torch.tensor(lst, dtype=dtype, device=device) def _to_tensor_2d(lst): - dtype = _infer_dtype_from_sample(lst[0][0]) + dtype = _scalar_dtype(lst[0][0]) tensors = [torch.tensor(seq, dtype=dtype, device=device) for seq in lst] return torch.nested.as_nested_tensor(tensors, layout=torch.jagged) diff --git a/scripts/training/text/run-qwen3-30B-A3B-16xgpu-async.sh b/scripts/training/text/run-qwen3-30B-A3B-16xgpu-async.sh index fcf484418..4cd41dcbd 100644 --- a/scripts/training/text/run-qwen3-30B-A3B-16xgpu-async.sh +++ b/scripts/training/text/run-qwen3-30B-A3B-16xgpu-async.sh @@ -57,6 +57,7 @@ ROLLOUT_ARGS=( EVAL_ARGS=( --log-passrate --eval-interval 20 + --skip-eval-before-train --eval-prompt-data aime ${EXP_DIR}/aime-2024/aime-2024.jsonl --n-samples-per-eval-prompt 8 --eval-max-response-len 16384 @@ -96,6 +97,13 @@ OPTIMIZER_ARGS=( --weight-decay 0.1 --adam-beta1 0.9 --adam-beta2 0.98 + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + # NOTE(wuhuan): to avoid algorithm performance degradation + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 ) SGLANG_ARGS=( From b50c3c585ac536b73e46af844e0bae3a94a43c47 Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Thu, 23 Apr 2026 14:44:50 +0800 Subject: [PATCH 015/268] fix: skip gpu related test on github --- .../test_dcs_weight_conversion.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py b/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py index 1b4fdbeaf..ffa8f41d2 100644 --- a/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py +++ b/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py @@ -16,23 +16,31 @@ from contextlib import contextmanager from typing import Dict, List, Tuple +import pytest import torch + +# Skip the whole module when Megatron (or its Bridge) isn't installed — e.g. on +# the CPU-only CI runner. All tests here exercise real Bridge mapping objects +# and the DeviceDirectBackend, which imports ``megatron.core`` at module level. +pytest.importorskip("megatron.core") +pytest.importorskip("megatron.bridge") + # Real Bridge mapping classes -from megatron.bridge.models.conversion.param_mapping import ( +from megatron.bridge.models.conversion.param_mapping import ( # noqa: E402 AutoMapping, GatedMLPMapping, MegatronParamMapping, ReplicatedMapping, ) -from megatron.bridge.models.qwen_vl.qwen3_vl_bridge import ( +from megatron.bridge.models.qwen_vl.qwen3_vl_bridge import ( # noqa: E402 ExpertMLPDownProjMapping, ExpertMLPGateUpProjMapping, ) -from relax.backends.megatron.misc_utils import strip_param_name_prefix -from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding -from relax.distributed.checkpoint_service.backends.device_direct import DeviceDirectBackend +from relax.backends.megatron.misc_utils import strip_param_name_prefix # noqa: E402 +from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding # noqa: E402 +from relax.distributed.checkpoint_service.backends.device_direct import DeviceDirectBackend # noqa: E402 # ─── Helpers ────────────────────────────────────────────────────────────────── From 8230fba44b32cda9a348d93d63c2808f97f1d2ef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Sun, 26 Apr 2026 13:58:44 +0800 Subject: [PATCH 016/268] docs(deepeyes): remove missing run_deepeyes_4b.sh refs (#15) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 📝 Documentation ## Drop references to non-existent 4B launch script - Remove "4B Model (8 GPUs)" quick-start section from en/zh docs - Swap model download snippet to `Qwen3-VL-30B-A3B-Thinking` - Update Ray Job example to invoke `run_deepeyes.sh` - Drop `run_deepeyes_4b.sh` entry from file-structure listing --- docs/en/examples/deepeyes.md | 20 +++----------------- docs/zh/examples/deepeyes.md | 20 +++----------------- 2 files changed, 6 insertions(+), 34 deletions(-) diff --git a/docs/en/examples/deepeyes.md b/docs/en/examples/deepeyes.md index fe1954797..ce065c0b7 100644 --- a/docs/en/examples/deepeyes.md +++ b/docs/en/examples/deepeyes.md @@ -49,25 +49,12 @@ The HF Image dict format (`{"bytes": ...}`) is natively supported by Relax's ima ### Download the Model ```bash -hf download Qwen/Qwen3-VL-4B-Instruct \ - --local-dir /root/Qwen3-VL-4B-Instruct +hf download Qwen/Qwen3-VL-30B-A3B-Thinking \ + --local-dir /root/Qwen3-VL-30B-A3B-Thinking ``` -For the full-scale configuration, use `Qwen/Qwen3-VL-30B-A3B-Thinking`. - ## Quick Start -### 4B Model (8 GPUs) - -```bash -export MODEL_DIR=/root -export DATA_DIR=/root -export SAVE_DIR=/root/save - -cd /root/Relax -bash examples/deepeyes/run_deepeyes_4b.sh -``` - ### 30B-A3B Model (8 GPUs, MoE) The full-scale configuration requires a judge model for reward scoring: @@ -91,7 +78,7 @@ bash examples/deepeyes/run_deepeyes.sh ```bash WORKING_DIR="./" RAY_ADDRESS=:6379 \ MODEL_DIR=/root DATA_DIR=/root SAVE_DIR=/root/save \ - bash -x scripts/entrypoint/ray-job.sh examples/deepeyes/run_deepeyes_4b.sh + bash -x scripts/entrypoint/ray-job.sh examples/deepeyes/run_deepeyes.sh ``` ## Architecture @@ -101,7 +88,6 @@ WORKING_DIR="./" RAY_ADDRESS=:6379 \ ``` examples/deepeyes/ ├── run_deepeyes.sh # Launch script (Qwen3-VL-30B-A3B, full config) -├── run_deepeyes_4b.sh # Launch script (Qwen3-VL-4B, lightweight) ├── deepeyes_config.yaml # Task config (max_turns, env path) ├── rollout.py # Multi-turn rollout logic ├── env_deepeyes.py # DeepEyes tool-use environment diff --git a/docs/zh/examples/deepeyes.md b/docs/zh/examples/deepeyes.md index 950b151b0..959799fe7 100644 --- a/docs/zh/examples/deepeyes.md +++ b/docs/zh/examples/deepeyes.md @@ -49,25 +49,12 @@ HF Image dict 格式（`{"bytes": ...}`）被 Relax 的图像加载管线原生 ### 下载模型 ```bash -hf download Qwen/Qwen3-VL-4B-Instruct \ - --local-dir /root/Qwen3-VL-4B-Instruct +hf download Qwen/Qwen3-VL-30B-A3B-Thinking \ + --local-dir /root/Qwen3-VL-30B-A3B-Thinking ``` -完整配置使用 `Qwen/Qwen3-VL-30B-A3B-Thinking`。 - ## 快速开始 -### 4B 模型（8 GPU） - -```bash -export MODEL_DIR=/root -export DATA_DIR=/root -export SAVE_DIR=/root/save - -cd /root/Relax -bash examples/deepeyes/run_deepeyes_4b.sh -``` - ### 30B-A3B 模型（8 GPU，MoE） 完整配置需要 judge 模型进行奖励评分： @@ -91,7 +78,7 @@ bash examples/deepeyes/run_deepeyes.sh ```bash WORKING_DIR="./" RAY_ADDRESS=:6379 \ MODEL_DIR=/root DATA_DIR=/root SAVE_DIR=/root/save \ - bash -x scripts/entrypoint/ray-job.sh examples/deepeyes/run_deepeyes_4b.sh + bash -x scripts/entrypoint/ray-job.sh examples/deepeyes/run_deepeyes.sh ``` ## 架构 @@ -101,7 +88,6 @@ WORKING_DIR="./" RAY_ADDRESS=:6379 \ ``` examples/deepeyes/ ├── run_deepeyes.sh # 启动脚本（Qwen3-VL-30B-A3B，完整配置） -├── run_deepeyes_4b.sh # 启动脚本（Qwen3-VL-4B，轻量配置） ├── deepeyes_config.yaml # 任务配置（max_turns、环境路径） ├── rollout.py # 多轮 rollout 逻辑 ├── env_deepeyes.py # DeepEyes 工具使用环境 From cb5e44fbd1e97e9eec5001705af8983ec7c353df Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Mon, 27 Apr 2026 06:50:49 +0000 Subject: [PATCH 017/268] fix: lost multimodal data --- relax/utils/utils.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/relax/utils/utils.py b/relax/utils/utils.py index fb532de9b..d9e81efd2 100644 --- a/relax/utils/utils.py +++ b/relax/utils/utils.py @@ -58,8 +58,12 @@ def convert_samples_to_train_data(args: Any, samples: list[Sample] | list[list[S train_data["loss_masks"] = loss_masks # overwriting the raw reward - if samples[0].metadata and "raw_reward" in samples[0].metadata: - train_data["raw_reward"] = [sample.metadata["raw_reward"] for sample in samples] + # populate this field for a subset of samples (e.g. SWE but not code). + if any(sample.metadata and "raw_reward" in sample.metadata for sample in samples): + train_data["raw_reward"] = [ + sample.metadata["raw_reward"] if sample.metadata and "raw_reward" in sample.metadata else sample.reward + for sample in samples + ] # For rollout buffer if samples[0].metadata and "round_number" in samples[0].metadata: @@ -75,7 +79,7 @@ def convert_samples_to_train_data(args: Any, samples: list[Sample] | list[list[S if samples[0].train_metadata is not None: train_data["metadata"] = [sample.train_metadata for sample in samples] - if samples[0].multimodal_train_inputs is not None: + if any(sample.multimodal_train_inputs is not None for sample in samples): train_data["multimodal_train_inputs"] = [sample.multimodal_train_inputs for sample in samples] if samples[0].teacher_log_probs is not None: From 76cc7dbe2da86d700168a838e9054becd6b4aac7 Mon Sep 17 00:00:00 2001 From: wulumeng Date: Thu, 23 Apr 2026 19:33:13 +0800 Subject: [PATCH 018/268] fix(deepeyes): correct judgment spelling MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Correct judge prompt spelling in DeepEyes reward - replace `Judgement` and `Judement` with `Judgment` in few-shot examples and prompt text - update the prompt suffix to request `Judgment:` consistently - keep response parsing backward compatible with both `Judgment:` and `Judgement:` labels --- examples/deepeyes/reward_deepeyes.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/examples/deepeyes/reward_deepeyes.py b/examples/deepeyes/reward_deepeyes.py index 4c4317411..47a364889 100644 --- a/examples/deepeyes/reward_deepeyes.py +++ b/examples/deepeyes/reward_deepeyes.py @@ -24,49 +24,49 @@ def get_gpt4_score_ICE(): [Question]: Is the countertop tan or blue? [Standard Answer]: The countertop is tan. [Model_answer] : tan -Judgement: 1 +Judgment: 1 """ # noqa example_2 = """ [Question]: On which side of the picture is the barrier? [Standard Answer]: The barrier is on the left side of the picture. [Model_answer] : left -Judgement: 1 +Judgment: 1 """ # noqa example_3 = """ [Question]: Is the kite brown and large? [Standard Answer]: Yes, the kite is brown and large. [Model_answer] : Yes -Judgement: 1 +Judgment: 1 """ # noqa example_4 = """ [Question]: Are the spots on a giraffe? [Standard Answer]: No, the spots are on a banana. [Model_answer] : no -Judgement: 1 +Judgment: 1 """ # noqa example_5 = """ [Question]: Who is wearing pants? [Standard Answer]: The boy is wearing pants. [Model_answer] : The person in the picture is wearing pants. -Judgement: 1 +Judgment: 1 """ # noqa example_6 = """ [Question]: Is the man phone both blue and closed? [Standard Answer]: Yes, the man phone is both blue and closed. [Model_answer] : No. -Judgement: 0 +Judgment: 0 """ # noqa example_7 = """ [Question]: What color is the towel in the center of the picture? [Standard Answer]: The towel in the center of the picture is blue. [Model_answer] : The towel in the center of the picture is pink. -Judgement: 0 +Judgment: 0 """ # noqa return [example_1, example_2, example_3, example_4, example_5, example_6, example_7] @@ -76,7 +76,7 @@ def get_chat_template(): chat_template = """ Below are two answers to a question. Question is [Question], [Standard Answer] is the standard answer to the question, and [Model_answer] is the answer extracted from a model's output to this question. Determine whether these two answers are consistent. Note that [Model Answer] is consistent with [Standard Answer] whenever they are essentially the same. If the meaning is expressed in the same way, it is considered consistent, for example, 'pink' and 'it is pink'. -If they are consistent, Judement is 1; if they are different, Judement is 0. Just output Judement and don't output anything else.\n\n +If they are consistent, Judgment is 1; if they are different, Judgment is 0. Just output Judgment and don't output anything else.\n\n """ return chat_template @@ -91,7 +91,7 @@ def get_prompt(predict_str, ground_truth, question): [Question]: {question} [Standard Answer]: {ground_truth} [Model_answer] : {predict_str} -Judgement:""" +Judgment:""" full_prompt = f"{demo_prompt}{test_prompt}" return full_prompt @@ -189,8 +189,8 @@ def compute_score(predict_str: str, ground_truth: str, extra_info: dict | None = response = "error" # print(response) - if "Judgement:" in response: - response = response.split("Judgement:")[-1].strip() + if "Judgment:" in response: + response = response.split("Judgment:")[-1].strip() if "1" in response: acc_reward = 1.0 elif "0" in response: From 2c7a50bfae5cb0bf198ad4d9a4cb1ec6a01b03dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Mon, 27 Apr 2026 08:29:44 +0000 Subject: [PATCH 019/268] feat(device): add multi-hardware backend abstraction MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add unified device abstraction layer (`relax/utils/device.py`) - Introduce `AcceleratorType` enum: CUDA, NPU, XPU, PPU, ROCM, CPU - Auto-detect hardware via `_detect_accelerator()` with priority-based probing - Support `RELAX_DEVICE_TYPE` env var override for debugging - Provide 25+ thin-wrapper APIs: `current_device()`, `set_device()`, `synchronize()`, `empty_cache()`, `Stream()`, `Event()`, `stream_context()`, `is_initialized()`, etc. - Map distributed backends: CUDA→nccl, NPU→hccl, XPU→xccl, PPU→eccl - Map Ray resource names: CUDA/ROCm→GPU, NPU→NPU, XPU→XPU - Map visible-devices env vars per accelerator type - Abstract NUMA affinity with graceful degradation for non-CUDA backends --- # ♻️ Refactor ## Replace hardcoded `torch.cuda.*` calls across 20+ files - Replace `torch.cuda.current_device()` → `device_utils.current_device()` - Replace `torch.cuda.set_device()` → `device_utils.set_device()` - Replace `torch.cuda.synchronize()` → `device_utils.synchronize()` - Replace `torch.cuda.empty_cache()` → `device_utils.empty_cache()` - Replace `torch.cuda.Stream/Event` → `device_utils.Stream()/Event()` - Replace `torch.cuda.mem_get_info()` → `device_utils.mem_get_info()` - Replace `torch.cuda.device_count()` → `device_utils.device_count()` - Replace `torch.device("cuda:...")` → `device_utils.make_current_torch_device()` - Replace `device="cuda"` → `device=device_utils.get_device_name()` - Replace `"nccl"` backend → `device_utils.get_dist_backend()` - Replace `"GPU"` Ray resource → `device_utils.get_ray_accelerator_name()` - Replace `CUDA_VISIBLE_DEVICES` → `device_utils.get_visible_devices_env_var()` - Wrap CUDA-specific memory profiling APIs with `hasattr` guards - Add CUDA-only annotation to `int4_qat/setup.py` kernel build script --- relax/backends/megatron/__init__.py | 4 +- relax/backends/megatron/actor.py | 5 +- relax/backends/megatron/arguments.py | 12 +- relax/backends/megatron/data.py | 17 +- .../megatron/kernels/int4_qat/setup.py | 2 + .../quantizer_compressed_tensors.py | 6 +- .../hf_weight_iterator_direct.py | 9 +- .../update_weight_from_distributed.py | 78 ++- relax/backends/sglang/routing_replay_patch.py | 10 +- relax/backends/sglang/sglang_engine.py | 8 +- relax/core/controller.py | 4 +- relax/core/service.py | 4 +- .../backends/device_direct.py | 7 +- relax/distributed/ray/rollout.py | 9 +- relax/distributed/ray/train_actor.py | 28 +- relax/distributed/ray/utils.py | 6 +- relax/utils/arguments.py | 5 +- relax/utils/checkpoint_write_patch.py | 9 +- relax/utils/data/stream_dataloader.py | 10 +- relax/utils/device.py | 446 ++++++++++++++++++ relax/utils/memory_utils.py | 15 +- relax/utils/profile_utils.py | 27 +- relax/utils/reloadable_process_group.py | 63 ++- relax/utils/training/routing_replay.py | 6 +- relax/utils/training/tensor_backper.py | 10 +- 25 files changed, 673 insertions(+), 127 deletions(-) create mode 100644 relax/utils/device.py diff --git a/relax/backends/megatron/__init__.py b/relax/backends/megatron/__init__.py index 4ffd43de3..03a19d46c 100644 --- a/relax/backends/megatron/__init__.py +++ b/relax/backends/megatron/__init__.py @@ -2,7 +2,7 @@ import logging -import torch +from relax.utils import device as device_utils try: @@ -15,7 +15,7 @@ def new_init(self, *args, **kwargs): if torch_memory_saver._impl is not None: torch_memory_saver._impl._binary_wrapper.cdll.tms_set_interesting_region(False) old_init(self, *args, **kwargs) - torch.cuda.synchronize() + device_utils.synchronize() if torch_memory_saver._impl is not None: torch_memory_saver._impl._binary_wrapper.cdll.tms_set_interesting_region(True) diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index 4ac23c1f9..e162244f0 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -22,6 +22,7 @@ from relax.distributed.checkpoint_service.client.engine import create_client from relax.distributed.ray.train_actor import TrainRayActor +from relax.utils import device as device_utils from relax.utils import tracking_utils from relax.utils.async_utils import run from relax.utils.data.stream_dataloader import ( @@ -921,7 +922,7 @@ def _check_services_health(self) -> tuple[bool, bool]: flags = torch.tensor( [int(rollout_only), int(actor_fwd_only)], dtype=torch.int32, - device=torch.cuda.current_device(), + device=device_utils.make_current_torch_device(), ) dist.all_reduce(flags, op=dist.ReduceOp.MAX, group=get_gloo_group()) rollout_only = bool(flags[0].item()) @@ -1035,7 +1036,7 @@ def all_consumed(self, task_name, rollout_id): status = [run(self.data_system_client.async_check_consumption_status(task_name, f"train_{rollout_id}"))] else: status = [True] - status = torch.tensor(status, device=torch.cuda.current_device()) + status = torch.tensor(status, device=device_utils.make_current_torch_device()) dist.broadcast(status, group=mpu.get_tensor_model_parallel_group(), group_src=0) dist.broadcast(status, group=mpu.get_pipeline_model_parallel_group(), group_src=0) diff --git a/relax/backends/megatron/arguments.py b/relax/backends/megatron/arguments.py index d76acdbca..c3e7daf56 100644 --- a/relax/backends/megatron/arguments.py +++ b/relax/backends/megatron/arguments.py @@ -5,6 +5,7 @@ from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding from transformers import AutoConfig +from relax.utils import device as device_utils from relax.utils.logging_utils import get_logger @@ -17,18 +18,17 @@ def validate_args(args): """Run megatron's own validate_args plus slime-specific megatron validations.""" - import torch - - if not torch.cuda.is_available(): + if not device_utils.is_available(): from unittest.mock import patch - class _CudaProperty: + class _DeviceProperty: major = 9 minor = 0 + device_name = device_utils.get_device_name() with ( - patch("torch.cuda.get_device_properties", return_value=_CudaProperty()), - patch("torch.cuda.get_device_capability", return_value=(9, 0)), + patch(f"torch.{device_name}.get_device_properties", return_value=_DeviceProperty()), + patch(f"torch.{device_name}.get_device_capability", return_value=(9, 0)), ): _megatron_validate_args(args) else: diff --git a/relax/backends/megatron/data.py b/relax/backends/megatron/data.py index 22e21fcda..6a931ab6d 100644 --- a/relax/backends/megatron/data.py +++ b/relax/backends/megatron/data.py @@ -12,6 +12,7 @@ from megatron.core.packed_seq_params import PackedSeqParams from torch.nn.utils.rnn import pad_sequence +from relax.utils import device as device_utils from relax.utils import tracking_utils from relax.utils.data.data import get_minimum_num_micro_batch_size from relax.utils.data.seqlen_balancing import get_seqlen_balanced_partitions @@ -173,7 +174,9 @@ def get_batch( tokens = F.pad(tokens, (0, pad), value=pad_token_id) cu_seqlens_list.append(cu_seqlens_list[-1] + pad) - cu_seqlens = torch.tensor(cu_seqlens_list, dtype=torch.int, device=torch.cuda.current_device()) + cu_seqlens = torch.tensor( + cu_seqlens_list, dtype=torch.int, device=device_utils.make_current_torch_device() + ) tokens = tokens.chunk(cp_size, dim=0)[cp_rank] else: tokens = [slice_with_cp(t, pad_token_id, qkv_format) for t in tokens] @@ -191,7 +194,9 @@ def get_batch( cu_seqlens.append(cu_seqlens[-1] + pad) # thd requires the cu_seqlens to be of the origin length - cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int).cuda() * cp_size + cu_seqlens = ( + torch.tensor(cu_seqlens, dtype=torch.int).to(device_utils.make_current_torch_device()) * cp_size + ) max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() packed_seq_params = PackedSeqParams( @@ -440,7 +445,9 @@ def get_data_iterator( # across DP ranks so that all ranks execute the same number of training steps # (required by collective operations in the training loop). if getattr(args, "balance_data", False): - steps_tensor = torch.tensor([num_steps_per_rollout], dtype=torch.int, device=torch.cuda.current_device()) + steps_tensor = torch.tensor( + [num_steps_per_rollout], dtype=torch.int, device=device_utils.make_current_torch_device() + ) dist.all_reduce(steps_tensor, op=dist.ReduceOp.MAX, group=dp_group) num_steps_per_rollout = steps_tensor.item() @@ -472,7 +479,9 @@ def _generate_data_iterator(rollout_data, micro_batch_size, micro_batch_indices= get_minimum_num_micro_batch_size(samples[start:end], args.max_tokens_per_gpu * cp_size) ) - num_microbatches = torch.tensor(num_microbatches, dtype=torch.int, device=torch.cuda.current_device()) + num_microbatches = torch.tensor( + num_microbatches, dtype=torch.int, device=device_utils.make_current_torch_device() + ) dist.all_reduce(num_microbatches, op=dist.ReduceOp.MAX, group=dp_group) if vpp_size > 1: diff --git a/relax/backends/megatron/kernels/int4_qat/setup.py b/relax/backends/megatron/kernels/int4_qat/setup.py index b8bfc7dc9..2fc5ba134 100644 --- a/relax/backends/megatron/kernels/int4_qat/setup.py +++ b/relax/backends/megatron/kernels/int4_qat/setup.py @@ -6,6 +6,8 @@ # Get CUDA arch list +# NOTE: This setup script is CUDA-only as it compiles .cu kernel files via CUDAExtension. +# Non-CUDA backends (NPU, XPU, PPU) should provide their own kernel implementations. arch_list = [] if torch.cuda.is_available(): for i in range(torch.cuda.device_count()): diff --git a/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py b/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py index 7c1b5d20c..77eabb4ee 100644 --- a/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py +++ b/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py @@ -5,6 +5,8 @@ import torch import torch.nn as nn +from relax.utils import device as device_utils + try: import fake_int4_quant_cuda @@ -91,7 +93,7 @@ def from_linear(cls, linear, w_bit, group_size, init_only=False, scales=None, ze awq_linear.bias = linear.bias.clone().half() pack_num = 32 // awq_linear.w_bit - device = torch.device(f"cuda:{torch.cuda.current_device()}") + device = device_utils.make_current_torch_device() repeat_scales = scales.to(device).t().repeat_interleave(group_size, 1) if isinstance(zeros, torch.Tensor): @@ -284,7 +286,7 @@ def quantize_params_compressed_tensors(converted_named_params, quantization_conf qw, s, zp = pack_layer(param, group_size, is_symmetric) qweight_name = name.replace(".weight", ".weight_packed") scale_name = name.replace(".weight", ".weight_scale") - weight_shape = torch.tensor(param.shape, dtype=torch.int32, device="cuda") + weight_shape = torch.tensor(param.shape, dtype=torch.int32, device=device_utils.get_device_name()) weight_shape_name = name.replace(".weight", ".weight_shape") if zp is not None: zp_name = name.replace(".weight", ".weight_zero_point") diff --git a/relax/backends/megatron/weight_update/hf_weight_iterator_direct.py b/relax/backends/megatron/weight_update/hf_weight_iterator_direct.py index 02da46542..32a382fb6 100644 --- a/relax/backends/megatron/weight_update/hf_weight_iterator_direct.py +++ b/relax/backends/megatron/weight_update/hf_weight_iterator_direct.py @@ -7,6 +7,7 @@ from megatron.core import mpu from tqdm import tqdm +from relax.utils import device as device_utils from relax.utils.distributed_utils import get_gloo_group from relax.utils.types import ParamInfo @@ -55,13 +56,15 @@ def _get_megatron_full_params( if dist.get_rank() == info.src_rank: params.append( torch.nn.Parameter( - megatron_local_weights[info.name].to(device=torch.cuda.current_device(), non_blocking=True), + megatron_local_weights[info.name].to( + device=device_utils.make_current_torch_device(), non_blocking=True + ), requires_grad=False, ) ) else: - params.append(torch.empty(info.shape, dtype=info.dtype, device=torch.cuda.current_device())) - torch.cuda.synchronize() + params.append(torch.empty(info.shape, dtype=info.dtype, device=device_utils.make_current_torch_device())) + device_utils.synchronize() # broadcast params across pp ranks if pp_size > 1: diff --git a/relax/backends/megatron/weight_update/update_weight_from_distributed.py b/relax/backends/megatron/weight_update/update_weight_from_distributed.py index 2563c2529..a255db016 100644 --- a/relax/backends/megatron/weight_update/update_weight_from_distributed.py +++ b/relax/backends/megatron/weight_update/update_weight_from_distributed.py @@ -12,12 +12,17 @@ from ray.actor import ActorHandle from tqdm import tqdm +from relax.utils import device as device_utils from relax.utils.distributed_utils import get_gloo_group, init_process_group +from relax.utils.logging_utils import get_logger from ..weight_conversion import convert_to_hf from .common import all_gather_param, named_params_and_buffers +logger = get_logger(__name__) + + class UpdateWeightFromDistributed: """Update distributed engines via NCCL. @@ -210,7 +215,7 @@ def _update_expert_bucket_weights_from_distributed( handles = [] for i, (_name, param) in enumerate(named_tensors): params = [ - torch.empty_like(param.data, device=torch.cuda.current_device()) + torch.empty_like(param.data, device=device_utils.make_current_torch_device()) for _ in range(mpu.get_expert_model_parallel_world_size()) ] handle = dist.all_gather(params, param.data, group=mpu.get_expert_model_parallel_group(), async_op=True) @@ -261,6 +266,7 @@ def connect_rollout_engines_from_distributed( group_name: str, rollout_engines: Sequence[ActorHandle], engine_gpu_counts: Sequence[int] | None = None, + max_retries: int = 3, ) -> dist.ProcessGroup: """Create NCCL group: training rank 0 + all engine GPUs. Blocks until joined. @@ -273,37 +279,55 @@ def connect_rollout_engines_from_distributed( engine_gpu_counts = [args.rollout_num_gpus_per_engine] * len(rollout_engines) master_address = ray._private.services.get_node_ip_address() - with socket.socket() as sock: - sock.bind(("", 0)) - master_port = sock.getsockname()[1] world_size = sum(engine_gpu_counts) + 1 # +1 for training rank 0 - # Compute cumulative rank offsets: engine i starts at cumulative[i] + 1. cumulative = [0] for c in engine_gpu_counts: cumulative.append(cumulative[-1] + c) - refs = [ - engine.init_weights_update_group.remote( - master_address, - master_port, - cumulative[i] + 1, - world_size, - group_name, - backend="nccl", - ) - for i, engine in enumerate(rollout_engines) - ] - model_update_groups = init_process_group( - backend="nccl", - init_method=f"tcp://{master_address}:{master_port}", - world_size=world_size, - rank=0, - group_name=group_name, - timeout=timedelta(minutes=args.distributed_timeout_minutes), - ) - ray.get(refs) - return model_update_groups + last_error = None + dist_backend = device_utils.get_dist_backend() + for attempt in range(1, max_retries + 1): + with socket.socket() as sock: + sock.bind(("", 0)) + master_port = sock.getsockname()[1] + + refs = [ + engine.init_weights_update_group.remote( + master_address, + master_port, + cumulative[i] + 1, + world_size, + group_name, + backend=dist_backend, + ) + for i, engine in enumerate(rollout_engines) + ] + try: + model_update_groups = init_process_group( + backend=dist_backend, + init_method=f"tcp://{master_address}:{master_port}", + world_size=world_size, + rank=0, + group_name=group_name, + timeout=timedelta(minutes=args.distributed_timeout_minutes), + ) + ray.get(refs) + return model_update_groups + except Exception as e: + last_error = e + logger.warning( + f"Failed to connect rollout engines (attempt {attempt}/{max_retries}, port={master_port}): {e}", + exc_info=(attempt == max_retries), + ) + try: + ray.get(refs, timeout=5) + except Exception: + pass + if attempt < max_retries: + time.sleep(5.0 * attempt) + + raise RuntimeError(f"Failed to connect rollout engines after {max_retries} attempts") from last_error def disconnect_rollout_engines_from_distributed(args, group_name, model_update_groups, rollout_engines): @@ -311,6 +335,8 @@ def disconnect_rollout_engines_from_distributed(args, group_name, model_update_g refs = [engine.destroy_weights_update_group.remote(group_name) for engine in rollout_engines] dist.destroy_process_group(model_update_groups) ray.get(refs) + # Wait for NCCL socket ports to be released by the OS + time.sleep(2.0) def update_weights_from_distributed( diff --git a/relax/backends/sglang/routing_replay_patch.py b/relax/backends/sglang/routing_replay_patch.py index 651e7f54b..7d515565a 100644 --- a/relax/backends/sglang/routing_replay_patch.py +++ b/relax/backends/sglang/routing_replay_patch.py @@ -52,6 +52,8 @@ import torch +from relax.utils import device as device_utils + logger = logging.getLogger(__name__) @@ -95,8 +97,8 @@ def _patched_init(self, *args, **kwargs): self._pinned_loc = torch.zeros(max_batch, dtype=torch.int64, device="cpu", pin_memory=True) # Dedicated copy stream + event. - self._copy_stream = torch.cuda.Stream(device=dev_buf.device) - self._copy_event = torch.cuda.Event() + self._copy_stream = device_utils.Stream(device=dev_buf.device) + self._copy_event = device_utils.Event() # Pending scatter state. self._pending_n = 0 # 0 means nothing pending @@ -142,7 +144,7 @@ def _patched_sync(self, forward_batch, can_run_graph, cuda_graph_batch): # In overlap-scheduler mode this is the *forward_stream*; without # overlap it is the default stream. We need this reference so that # copy_stream can order itself after the GPU→GPU staging copy below. - active_stream = torch.cuda.current_stream(self.device_cache.buffer.device) + active_stream = device_utils.current_stream(self.device_cache.buffer.device) # 1) GPU→GPU snapshot on the active stream — fast, no sync. self._staging_buffer[:n_tok].copy_(self.device_cache.buffer[local_start_pos:local_end_pos]) @@ -150,7 +152,7 @@ def _patched_sync(self, forward_batch, can_run_graph, cuda_graph_batch): # 2) On copy stream: async copies to pinned CPU buffers. # copy_stream waits on active_stream so the staging snapshot # above completes before we start reading it. - with torch.cuda.stream(self._copy_stream): + with device_utils.stream_context(self._copy_stream): self._copy_stream.wait_stream(active_stream) # 2a) Routing data: staging[:n_tok, :, :topk] → pinned_staging self._pinned_staging[:n_tok, :, :topk].copy_(self._staging_buffer[:n_tok, :, :topk], non_blocking=True) diff --git a/relax/backends/sglang/sglang_engine.py b/relax/backends/sglang/sglang_engine.py index 5bc0f40b0..1014d77b6 100644 --- a/relax/backends/sglang/sglang_engine.py +++ b/relax/backends/sglang/sglang_engine.py @@ -20,6 +20,7 @@ from relax.distributed.checkpoint_service.client.engine import create_client from relax.distributed.ray.ray_actor import RayActor +from relax.utils import device as device_utils from relax.utils.async_utils import run from relax.utils.http_utils import get_host_info from relax.utils.logging_utils import get_logger @@ -42,10 +43,11 @@ def get_base_gpu_id(args, rank): def _to_local_gpu_id(physical_gpu_id: int) -> int: - cvd = os.environ.get("CUDA_VISIBLE_DEVICES") + visible_env = device_utils.get_visible_devices_env_var() + cvd = os.environ.get(visible_env) if not cvd: return physical_gpu_id # no remapping - # CUDA_VISIBLE_DEVICES can be like "4,5,6,7" + # Visible devices can be like "4,5,6,7" visible = [int(x) for x in cvd.split(",") if x.strip() != ""] # In a remapped process, valid torch device indices are 0..len(visible)-1 if physical_gpu_id in visible: @@ -54,7 +56,7 @@ def _to_local_gpu_id(physical_gpu_id: int) -> int: if 0 <= physical_gpu_id < len(visible): return physical_gpu_id raise RuntimeError( - f"GPU id {physical_gpu_id} is not valid under CUDA_VISIBLE_DEVICES={cvd}. " + f"Device id {physical_gpu_id} is not valid under {visible_env}={cvd}. " f"Expected one of {visible} (physical) or 0..{len(visible) - 1} (local)." ) diff --git a/relax/core/controller.py b/relax/core/controller.py index 0c9b5f68d..2e6088674 100644 --- a/relax/core/controller.py +++ b/relax/core/controller.py @@ -16,6 +16,7 @@ from relax.core.registry import ALGOS, ROLES, process_role from relax.core.service import Service, create_placement_group from relax.distributed.checkpoint_service.coordinator.service import create_dcs_deployment +from relax.utils import device as device_utils from relax.utils.async_utils import run, shutdown_async_loop from relax.utils.health_system import HealthManager from relax.utils.logging_utils import get_logger @@ -238,7 +239,8 @@ def _validate_gpu_resources(self, roles_to_create, colocate, actor_rollout_pg_ro total_required = sum(num_gpus for _, _, num_gpus, _ in roles_to_create) cluster_resources = ray.cluster_resources() - total_available = int(cluster_resources.get("GPU", 0)) + accel_resource = device_utils.get_ray_accelerator_name() + total_available = int(cluster_resources.get(accel_resource, 0)) logger.info( f"Resource validation: required GPUs={total_required}, cluster GPUs={total_available}, colocate={colocate}" diff --git a/relax/core/service.py b/relax/core/service.py index 52ae3eba6..eb9441c27 100644 --- a/relax/core/service.py +++ b/relax/core/service.py @@ -12,6 +12,7 @@ from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy from relax.distributed.ray.placement_group import InfoActor, sort_key +from relax.utils import device as device_utils from relax.utils.logging_utils import get_logger from relax.utils.utils import get_serve_url, recovery_load_path @@ -295,7 +296,8 @@ def _ensure_placement_group(self) -> Optional[Any]: def create_placement_group(num_gpus): """Create a placement group with the specified number of GPUs.""" - bundles = [{"GPU": 1, "CPU": 1} for _ in range(num_gpus)] + accel_resource = device_utils.get_ray_accelerator_name() + bundles = [{accel_resource: 1, "CPU": 1} for _ in range(num_gpus)] pg = placement_group(bundles, strategy="PACK") num_bundles = len(bundles) ray.get(pg.ready()) diff --git a/relax/distributed/checkpoint_service/backends/device_direct.py b/relax/distributed/checkpoint_service/backends/device_direct.py index ba5f6f4d8..26319a8d3 100644 --- a/relax/distributed/checkpoint_service/backends/device_direct.py +++ b/relax/distributed/checkpoint_service/backends/device_direct.py @@ -39,6 +39,7 @@ from relax.distributed.checkpoint_service.backends.base import CommBackend, TensorFusion from relax.distributed.checkpoint_service.config import BackendType, RoleInfo from relax.distributed.checkpoint_service.utils import load_weight +from relax.utils import device as device_utils from relax.utils.distributed_utils import get_gloo_group, init_process_group from relax.utils.logging_utils import get_logger @@ -98,7 +99,7 @@ def __init__( self.coordinator_url = coordinator_url self.lock = lock self.timeout_seconds = timeout_seconds - self.device = next(model[0].parameters()).device if model else torch.cuda.current_device() + self.device = next(model[0].parameters()).device if model else device_utils.current_device() self._comm_stream: Optional[Any] = None # CUDA stream self._thread_pool = ThreadPoolExecutor(max_workers=4) @@ -114,7 +115,7 @@ def __init__( # Ray actors for rollout communication self.rollout_engines: Dict[int, Any] = {} # rank -> Ray actor handle - torch.cuda.set_device(self.device) + device_utils.set_device(self.device) # Bridge-based HF weight converter (lazy-initialized on first use) self._use_bridge = getattr(args, "megatron_to_hf_mode", None) == "bridge" @@ -804,7 +805,7 @@ def update_weights_for_rollout(self, rollout_only=False, actor_fwd_only=False) - # allocator keeps large reserved blocks that are internally # fragmented, which can cause OOM when the optimizer later tries # to allocate contiguous Adam state buffers. - torch.cuda.empty_cache() + device_utils.empty_cache() def _update_weight_from_distributed( self, diff --git a/relax/distributed/ray/rollout.py b/relax/distributed/ray/rollout.py index 18f2505a3..dc3d99ecb 100644 --- a/relax/distributed/ray/rollout.py +++ b/relax/distributed/ray/rollout.py @@ -23,6 +23,7 @@ from relax.backends.sglang.sglang_engine import SGLangEngine from relax.engine.rollout.base_types import call_rollout_fn +from relax.utils import device as device_utils from relax.utils import tracking_utils from relax.utils.health_monitor import RolloutHealthMonitor from relax.utils.http_utils import SLIME_HOST_IP_ENV, _wrap_ipv6, find_available_port, get_host_info, init_http_client @@ -1425,7 +1426,8 @@ async def _scale_out_ray_native(self, request: ScaleOutRequest) -> None: per_replica_pgs = [] for i in range(request.num_replicas): num_gpus = gpus_per_engine - bundles = [{"GPU": 1, "CPU": 1} for _ in range(num_gpus)] + accel_resource = device_utils.get_ray_accelerator_name() + bundles = [{accel_resource: 1, "CPU": 1} for _ in range(num_gpus)] pg = ray.util.placement_group(bundles, strategy="PACK") per_replica_pgs.append(pg) @@ -2065,13 +2067,14 @@ async def _sync_single_engine_weights( ) try: + dist_backend = device_utils.get_dist_backend() init_seed_ref = seed_engine.init_weights_send_group_for_remote_instance.remote( master_address=master_address, ports=ports_str, group_rank=0, world_size=2, group_name=group_name, - backend="nccl", + backend=dist_backend, ) init_new_ref = new_engine.init_weights_send_group_for_remote_instance.remote( master_address=master_address, @@ -2079,7 +2082,7 @@ async def _sync_single_engine_weights( group_rank=1, world_size=2, group_name=group_name, - backend="nccl", + backend=dist_backend, ) init_results = await asyncio.wait_for( asyncio.gather(init_seed_ref, init_new_ref), diff --git a/relax/distributed/ray/train_actor.py b/relax/distributed/ray/train_actor.py index dd225b124..2a3eaedbc 100644 --- a/relax/distributed/ray/train_actor.py +++ b/relax/distributed/ray/train_actor.py @@ -11,6 +11,7 @@ import relax.utils.training.eval_config from relax.distributed.ray.ray_actor import RayActor +from relax.utils import device as device_utils from relax.utils.distributed_utils import init_gloo_group from relax.utils.logging_utils import get_logger from relax.utils.memory_utils import clear_memory, print_memory @@ -20,7 +21,7 @@ def get_local_gpu_id(): - cvd = os.environ.get("CUDA_VISIBLE_DEVICES", None) + cvd = os.environ.get(device_utils.get_visible_devices_env_var(), None) if cvd is None: return ray.get_gpu_ids()[0] else: @@ -57,7 +58,7 @@ def init(self, args, role, with_ref=False, with_opd_teacher=False): torch.serialization.add_safe_globals([relax.utils.training.eval_config.EvalDatasetConfig]) local_rank = int(os.environ.get("LOCAL_RANK", 0)) - torch.cuda.set_device(f"cuda:{local_rank}") + device_utils.set_device(f"{device_utils.get_device_name()}:{local_rank}") backend = args.distributed_backend @@ -70,27 +71,8 @@ def init(self, args, role, with_ref=False, with_opd_teacher=False): args.rank = dist.get_rank() args.world_size = dist.get_world_size() - try: - if torch.version.hip is not None: - logger.info("Detected ROCm/HIP environment, skipping NUMA affinity setup") - # will find the coresponding API to implement ROCm version as below - else: - import pynvml - - pynvml.nvmlInit() - - local_rank = int(os.environ["RANK"]) % args.num_gpus_per_node - - handle = pynvml.nvmlDeviceGetHandleByIndex(local_rank) - pynvml.nvmlDeviceSetCpuAffinity(handle) - - logger.info(f"Set NUMA affinity for GPU {local_rank}") - pynvml.nvmlShutdown() - - except ImportError: - logger.info("Warning: pynvml not available, skipping NUMA affinity setup") - except Exception as e: - logger.info(f"Warning: Failed to set NUMA affinity: {e}") + numa_local_rank = int(os.environ["RANK"]) % args.num_gpus_per_node + device_utils.set_numa_affinity(numa_local_rank) def clear_memory(self): print_memory("before TrainRayActor.clear_memory") diff --git a/relax/distributed/ray/utils.py b/relax/distributed/ray/utils.py index 1918eaa14..7f798fa73 100644 --- a/relax/distributed/ray/utils.py +++ b/relax/distributed/ray/utils.py @@ -2,9 +2,9 @@ import os import ray -import torch from relax.distributed.ray.ray_actor import RayActor +from relax.utils import device as device_utils # Refer to @@ -31,8 +31,8 @@ def ray_noset_visible_devices(env_vars=os.environ): def get_physical_gpu_id(): - device = torch.cuda.current_device() - props = torch.cuda.get_device_properties(device) + device = device_utils.current_device() + props = device_utils.get_device_properties(device) return str(props.uuid) diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 911787be1..4f277da4e 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -10,6 +10,7 @@ from relax.backends.sglang.arguments import sglang_parse_args from relax.backends.sglang.arguments import validate_args as sglang_validate_args +from relax.utils import device as device_utils from relax.utils.logging_utils import get_logger from relax.utils.training.eval_config import EvalDatasetConfig, build_eval_dataset_configs, ensure_dataset_list @@ -62,7 +63,7 @@ def add_serve_arguments(parser): parser.add_argument( "--checkpoint-engine-backend", type=str, - default="nccl", + default=device_utils.get_dist_backend(), help=("Backend for checkpoint engine."), ) parser.add_argument( @@ -184,7 +185,7 @@ def add_cluster_arguments(parser): ), ) - reset_arg(parser, "--distributed-backend", type=str, default="nccl") + reset_arg(parser, "--distributed-backend", type=str, default=device_utils.get_dist_backend()) reset_arg(parser, "--distributed-timeout-minutes", type=int, default=30) return parser diff --git a/relax/utils/checkpoint_write_patch.py b/relax/utils/checkpoint_write_patch.py index c573a7a91..379369a9c 100644 --- a/relax/utils/checkpoint_write_patch.py +++ b/relax/utils/checkpoint_write_patch.py @@ -36,6 +36,7 @@ import torch +from relax.utils import device as device_utils from relax.utils.logging_utils import get_logger @@ -245,10 +246,10 @@ def _patched_write_preloaded_data_multiproc( # cause SIGSEGV. Use threaded parallel writes instead — all tensors # are already on CPU so the I/O releases the GIL and threads achieve # real parallelism without duplicating the CUDA context. - cuda_initialised = torch.cuda.is_available() and torch.cuda.is_initialized() + cuda_initialised = device_utils.is_available() and device_utils.is_initialized() if cuda_initialised: _logger.debug( - f"rank: {rank}, CUDA initialised – using threaded parallel " + f"rank: {rank}, device initialised – using threaded parallel " f"(no-fork) checkpoint write for {len(write_buckets)} buckets" ) write_results_or_exc = _write_buckets_threaded(transform_list, use_msc, write_buckets) @@ -285,7 +286,7 @@ def _patched_schedule_async_call(self, async_req): if async_req.async_fn is None: return # nothing to do - cuda_initialised = torch.cuda.is_available() and torch.cuda.is_initialized() + cuda_initialised = device_utils.is_available() and device_utils.is_initialized() if not cuda_initialised: # CUDA not initialised — safe to use the original fork path. return _original_schedule(self, async_req) @@ -301,7 +302,7 @@ def _patched_schedule_async_call(self, async_req): rank = torch.distributed.get_rank() start_sync = time() - torch.cuda.synchronize() + device_utils.synchronize() end_sync = time() _logger.debug(f"rank: {rank}, takes {end_sync - start_sync} to finish D2H ") diff --git a/relax/utils/data/stream_dataloader.py b/relax/utils/data/stream_dataloader.py index 9f62d3658..80be7281c 100644 --- a/relax/utils/data/stream_dataloader.py +++ b/relax/utils/data/stream_dataloader.py @@ -13,6 +13,8 @@ from transfer_queue.dataloader.streaming_dataloader import StreamingDataLoader from transfer_queue.dataloader.streaming_dataset import StreamingDataset +from relax.utils import device as device_utils + logger = logging.getLogger(__name__) @@ -310,9 +312,9 @@ def get_data_from_transfer_queue( # will receive the real data via broadcast. rollout_data = [None, None] - # Use an explicit CUDA device so the communication backend (e.g. NCCL) - # can bind to a known CUDA context. - cuda_dev = torch.device(f"cuda:{torch.cuda.current_device()}") + # Use an explicit device so the communication backend (e.g. NCCL) + # can bind to a known device context. + cuda_dev = device_utils.make_current_torch_device() # --- Extract rollout_routed_experts BEFORE broadcast_object_list --- # broadcast_object_list uses pickle for the entire payload. When @@ -428,7 +430,7 @@ def post_process_rollout_data(args, rollout_data): # code in this module expects lists of sequence tensors for packing) from relax.backends.megatron.cp_utils import slice_log_prob_with_cp - cuda_dev = torch.device(f"cuda:{torch.cuda.current_device()}") + cuda_dev = device_utils.make_current_torch_device() rollout_data["tokens"] = [torch.tensor(t, dtype=torch.long, device=cuda_dev) for t in rollout_data["tokens"]] rollout_data["loss_masks"] = [ torch.tensor(t, dtype=torch.int, device=cuda_dev) for t in rollout_data["loss_masks"] diff --git a/relax/utils/device.py b/relax/utils/device.py new file mode 100644 index 000000000..b53c3490b --- /dev/null +++ b/relax/utils/device.py @@ -0,0 +1,446 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Multi-hardware backend abstraction layer. +# +# Inspired by verl (https://github.com/verl-project/verl) device.py +# and slime (https://github.com/THUDM/slime) plugin architecture. +# +# This module provides a unified device abstraction that allows Relax to run +# on multiple hardware backends (NVIDIA CUDA, Ascend NPU, AMD ROCm, Kunlunxin XPU, +# PPU, etc.) with minimal code changes throughout the framework. +# +# Usage: +# from relax.utils.device import get_device_name, get_torch_device, ... +# +# The module auto-detects the available accelerator at import time and exposes +# a consistent API regardless of the underlying hardware. + +import os +from enum import Enum +from functools import lru_cache +from typing import Optional + +import torch + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + + +# --------------------------------------------------------------------------- +# Accelerator type enum +# --------------------------------------------------------------------------- +class AcceleratorType(str, Enum): + """Supported hardware accelerator types.""" + + CUDA = "cuda" # NVIDIA GPU + NPU = "npu" # Ascend NPU (Huawei) + XPU = "xpu" # Intel / Kunlunxin XPU + PPU = "ppu" # PPU (Enflame / custom) + ROCM = "rocm" # AMD ROCm (uses 'cuda' device in PyTorch but HIP backend) + CPU = "cpu" # CPU fallback + + +# --------------------------------------------------------------------------- +# Detection helpers (cached — hardware won't change at runtime) +# --------------------------------------------------------------------------- +@lru_cache(maxsize=1) +def _detect_accelerator() -> AcceleratorType: + """Detect the available hardware accelerator. + + Detection order follows specificity: NPU > XPU > PPU > CUDA/ROCm > CPU. + Environment variable ``RELAX_DEVICE_TYPE`` can override auto-detection. + """ + # Allow explicit override via environment variable + override = os.environ.get("RELAX_DEVICE_TYPE", "").lower().strip() + if override: + for accel in AcceleratorType: + if override == accel.value: + logger.info(f"Device type overridden by RELAX_DEVICE_TYPE={override}") + return accel + logger.warning(f"Unknown RELAX_DEVICE_TYPE='{override}', falling back to auto-detection") + + # Ascend NPU + if _is_npu_available(): + return AcceleratorType.NPU + + # Kunlunxin / Intel XPU + if _is_xpu_available(): + return AcceleratorType.XPU + + # PPU (Enflame) + if _is_ppu_available(): + return AcceleratorType.PPU + + # NVIDIA CUDA or AMD ROCm (both expose torch.cuda) + if torch.cuda.is_available(): + if _is_rocm(): + return AcceleratorType.ROCM + return AcceleratorType.CUDA + + return AcceleratorType.CPU + + +def _is_npu_available() -> bool: + """Check if Ascend NPU is available.""" + try: + if not hasattr(torch, "npu"): + return False + return torch.npu.is_available() + except (ImportError, AttributeError): + return False + + +def _is_xpu_available() -> bool: + """Check if XPU (Intel / Kunlunxin) is available.""" + try: + if not hasattr(torch, "xpu"): + return False + return torch.xpu.is_available() + except (ImportError, AttributeError): + return False + + +def _is_ppu_available() -> bool: + """Check if PPU is available.""" + try: + if not hasattr(torch, "ppu"): + return False + return torch.ppu.is_available() + except (ImportError, AttributeError): + return False + + +def _is_rocm() -> bool: + """Check if the current CUDA build is actually AMD ROCm/HIP.""" + return getattr(torch.version, "hip", None) is not None + + +# --------------------------------------------------------------------------- +# Public API — device info +# --------------------------------------------------------------------------- +def get_accelerator_type() -> AcceleratorType: + """Return the detected :class:`AcceleratorType`.""" + return _detect_accelerator() + + +def get_device_name() -> str: + """Return the PyTorch device type string (``'cuda'``, ``'npu'``, ``'xpu'``, + etc.). + + For ROCm, returns ``'cuda'`` because PyTorch ROCm uses the CUDA device + namespace. + """ + accel = _detect_accelerator() + if accel == AcceleratorType.ROCM: + return "cuda" # ROCm uses torch.cuda namespace + if accel == AcceleratorType.CPU: + return "cpu" + return accel.value + + +def get_torch_device_module(): + """Return the ``torch.`` module (e.g. ``torch.cuda``, + ``torch.npu``). + + This is the namespace that provides ``current_device()``, + ``synchronize()``, ``empty_cache()``, etc. + """ + name = get_device_name() + try: + return getattr(torch, name) + except AttributeError: + logger.warning(f"torch.{name} not found, falling back to torch.cuda") + return torch.cuda + + +# --------------------------------------------------------------------------- +# Public API — distributed backend +# --------------------------------------------------------------------------- + +# Mapping from accelerator type to the default collective communication backend +_DIST_BACKEND_MAP = { + AcceleratorType.CUDA: "nccl", + AcceleratorType.ROCM: "nccl", # ROCm uses RCCL which is NCCL-compatible + AcceleratorType.NPU: "hccl", + AcceleratorType.XPU: "xccl", + AcceleratorType.PPU: "eccl", + AcceleratorType.CPU: "gloo", +} + + +def get_dist_backend() -> str: + """Return the default distributed communication backend name. + + Returns ``'nccl'`` for NVIDIA/AMD, ``'hccl'`` for Ascend NPU, etc. + """ + return _DIST_BACKEND_MAP.get(_detect_accelerator(), "nccl") + + +# --------------------------------------------------------------------------- +# Public API — environment variables +# --------------------------------------------------------------------------- + +# Mapping from accelerator type to the visible-devices environment variable +_VISIBLE_DEVICES_ENV_MAP = { + AcceleratorType.CUDA: "CUDA_VISIBLE_DEVICES", + AcceleratorType.ROCM: "CUDA_VISIBLE_DEVICES", # ROCm also uses this (or HIP_VISIBLE_DEVICES) + AcceleratorType.NPU: "ASCEND_RT_VISIBLE_DEVICES", + AcceleratorType.XPU: "XPU_VISIBLE_DEVICES", + AcceleratorType.PPU: "PPU_VISIBLE_DEVICES", + AcceleratorType.CPU: "", +} + + +def get_visible_devices_env_var() -> str: + """Return the environment variable name for controlling visible devices. + + E.g. ``'CUDA_VISIBLE_DEVICES'`` for NVIDIA, ``'ASCEND_RT_VISIBLE_DEVICES'`` + for Ascend NPU. + """ + return _VISIBLE_DEVICES_ENV_MAP.get(_detect_accelerator(), "CUDA_VISIBLE_DEVICES") + + +def get_visible_devices() -> Optional[str]: + """Return the value of the visible-devices environment variable, or + None.""" + env_var = get_visible_devices_env_var() + if not env_var: + return None + return os.environ.get(env_var) + + +# --------------------------------------------------------------------------- +# Public API — Ray resource name +# --------------------------------------------------------------------------- + +_RAY_RESOURCE_MAP = { + AcceleratorType.CUDA: "GPU", + AcceleratorType.ROCM: "GPU", + AcceleratorType.NPU: "NPU", + AcceleratorType.XPU: "XPU", + AcceleratorType.PPU: "PPU", + AcceleratorType.CPU: "CPU", +} + + +def get_ray_accelerator_name() -> str: + """Return the Ray resource name for the current accelerator. + + E.g. ``'GPU'`` for NVIDIA/AMD, ``'NPU'`` for Ascend. + """ + return _RAY_RESOURCE_MAP.get(_detect_accelerator(), "GPU") + + +# --------------------------------------------------------------------------- +# Public API — device operations (thin wrappers) +# --------------------------------------------------------------------------- +def current_device() -> int: + """Return the index of the current device.""" + mod = get_torch_device_module() + return mod.current_device() + + +def set_device(device) -> None: + """Set the current device. + + Args: + device: Device index (int) or device string (e.g. ``'cuda:0'``). + """ + mod = get_torch_device_module() + mod.set_device(device) + + +def device_count() -> int: + """Return the number of available accelerator devices.""" + mod = get_torch_device_module() + return mod.device_count() + + +def synchronize(device=None) -> None: + """Synchronize the current (or specified) device.""" + accel = _detect_accelerator() + if accel == AcceleratorType.CPU: + return # no-op for CPU + mod = get_torch_device_module() + if device is not None: + mod.synchronize(device) + else: + mod.synchronize() + + +def empty_cache() -> None: + """Release all unoccupied cached memory.""" + accel = _detect_accelerator() + if accel == AcceleratorType.CPU: + return + mod = get_torch_device_module() + mod.empty_cache() + + +def memory_allocated(device=None) -> int: + """Return the current GPU memory occupied by tensors in bytes.""" + mod = get_torch_device_module() + if device is not None: + return mod.memory_allocated(device) + return mod.memory_allocated() + + +def memory_reserved(device=None) -> int: + """Return the current GPU memory managed by the caching allocator in + bytes.""" + mod = get_torch_device_module() + if device is not None: + return mod.memory_reserved(device) + return mod.memory_reserved() + + +def mem_get_info(device=None): + """Return ``(free, total)`` memory in bytes for the given device.""" + mod = get_torch_device_module() + if device is not None: + return mod.mem_get_info(device) + return mod.mem_get_info() + + +def get_device_properties(device=None): + """Return device properties for the given device.""" + mod = get_torch_device_module() + if device is not None: + return mod.get_device_properties(device) + return mod.get_device_properties(mod.current_device()) + + +def current_stream(device=None): + """Return the currently selected stream for the given device.""" + mod = get_torch_device_module() + if device is not None: + return mod.current_stream(device) + return mod.current_stream() + + +def Stream(device=None, **kwargs): + """Create a new stream on the given device.""" + mod = get_torch_device_module() + if device is not None: + return mod.Stream(device=device, **kwargs) + return mod.Stream(**kwargs) + + +def Event(**kwargs): + """Create a new event.""" + mod = get_torch_device_module() + return mod.Event(**kwargs) + + +def stream_context(stream): + """Return a context manager that sets the given stream as the current + stream. + + Equivalent to ``torch.cuda.stream(s)`` but dispatches to the correct device + backend (e.g. ``torch.npu.stream(s)`` on Ascend NPU). + """ + mod = get_torch_device_module() + return mod.stream(stream) + + +def is_initialized() -> bool: + """Return True if the device backend has been initialized. + + Equivalent to ``torch.cuda.is_initialized()`` but dispatches to the correct + device backend. + """ + mod = get_torch_device_module() + if hasattr(mod, "is_initialized"): + return mod.is_initialized() + # Fallback: if the backend doesn't expose is_initialized, check if + # any device is available (conservative — assumes initialized if available). + return is_available() + + +# --------------------------------------------------------------------------- +# Public API — device string helpers +# --------------------------------------------------------------------------- +def make_device_string(index: Optional[int] = None) -> str: + """Build a device string like ``'cuda:0'`` or ``'npu:2'``. + + Args: + index: Device index. If None, uses :func:`current_device`. + """ + name = get_device_name() + if name == "cpu": + return "cpu" + if index is None: + index = current_device() + return f"{name}:{index}" + + +def make_current_torch_device() -> torch.device: + """Return a ``torch.device`` for the current accelerator and device + index.""" + return torch.device(make_device_string()) + + +# --------------------------------------------------------------------------- +# Public API — NUMA affinity +# --------------------------------------------------------------------------- +def set_numa_affinity(local_rank: int) -> None: + """Set NUMA affinity for the given local rank. + + On NVIDIA GPUs, uses pynvml. On other backends, this is a no-op with a + warning. + """ + accel = _detect_accelerator() + if accel in (AcceleratorType.CUDA,): + try: + import pynvml + + pynvml.nvmlInit() + handle = pynvml.nvmlDeviceGetHandleByIndex(local_rank) + pynvml.nvmlDeviceSetCpuAffinity(handle) + logger.info(f"Set NUMA affinity for GPU {local_rank}") + pynvml.nvmlShutdown() + except ImportError: + logger.info("pynvml not available, skipping NUMA affinity setup") + except Exception as e: + logger.info(f"Failed to set NUMA affinity: {e}") + elif accel == AcceleratorType.ROCM: + logger.info("ROCm/HIP environment detected, skipping NUMA affinity setup") + elif accel == AcceleratorType.NPU: + logger.info("Ascend NPU environment, skipping NUMA affinity setup (not yet supported)") + else: + logger.info(f"NUMA affinity not supported for {accel.value}, skipping") + + +# --------------------------------------------------------------------------- +# Public API — expandable segments (CUDA-specific, no-op on others) +# --------------------------------------------------------------------------- +def set_expandable_segments(enable: bool) -> None: + """Configure CUDA memory allocator expandable segments. + + Only effective on NVIDIA CUDA. No-op on other backends. + """ + if _detect_accelerator() == AcceleratorType.CUDA: + try: + torch.cuda.memory._set_allocator_settings(f"expandable_segments:{enable}") + except Exception as e: + logger.warning(f"Failed to set expandable_segments: {e}") + + +# --------------------------------------------------------------------------- +# Public API — availability check +# --------------------------------------------------------------------------- +def is_available() -> bool: + """Return True if any accelerator device is available (not CPU-only).""" + return _detect_accelerator() != AcceleratorType.CPU + + +# --------------------------------------------------------------------------- +# Convenience: boolean flags (for backward compatibility / quick checks) +# --------------------------------------------------------------------------- +is_cuda_available: bool = torch.cuda.is_available() +is_npu_available: bool = _is_npu_available() +is_xpu_available: bool = _is_xpu_available() +is_ppu_available: bool = _is_ppu_available() +is_rocm: bool = _is_rocm() diff --git a/relax/utils/memory_utils.py b/relax/utils/memory_utils.py index 1c73176bd..86112881c 100644 --- a/relax/utils/memory_utils.py +++ b/relax/utils/memory_utils.py @@ -5,6 +5,7 @@ import torch import torch.distributed as dist +from relax.utils import device as device_utils from relax.utils.logging_utils import get_logger @@ -12,23 +13,23 @@ def clear_memory(clear_host_memory: bool = False): - torch.cuda.synchronize() + device_utils.synchronize() gc.collect() - torch.cuda.empty_cache() + device_utils.empty_cache() if clear_host_memory: torch._C._host_emptyCache() def available_memory(): - device = torch.cuda.current_device() - free, total = torch.cuda.mem_get_info(device) + dev = device_utils.current_device() + free, total = device_utils.mem_get_info(dev) return { - "gpu": str(device), + "device": str(dev), "total_GB": _byte_to_gb(total), "free_GB": _byte_to_gb(free), "used_GB": _byte_to_gb(total - free), - "allocated_GB": _byte_to_gb(torch.cuda.memory_allocated(device)), - "reserved_GB": _byte_to_gb(torch.cuda.memory_reserved(device)), + "allocated_GB": _byte_to_gb(device_utils.memory_allocated(dev)), + "reserved_GB": _byte_to_gb(device_utils.memory_reserved(dev)), } diff --git a/relax/utils/profile_utils.py b/relax/utils/profile_utils.py index 72ece272c..e7a6a7437 100644 --- a/relax/utils/profile_utils.py +++ b/relax/utils/profile_utils.py @@ -6,6 +6,7 @@ import torch +from relax.utils import device as device_utils from relax.utils.logging_utils import get_logger from relax.utils.memory_utils import print_memory @@ -109,7 +110,16 @@ class _TorchMemoryProfiler(_BaseMemoryProfiler): def start(self): logger.info("Attach OOM dump memory history.") - torch.cuda.memory._record_memory_history( + # Memory snapshot APIs are currently CUDA-specific. + # On non-CUDA backends, log a warning and skip. + device_mod = device_utils.get_torch_device_module() + if not hasattr(device_mod, "memory"): + logger.warning( + f"Memory snapshot profiling is not supported on {device_utils.get_device_name()} backend, skipping." + ) + return + + device_mod.memory._record_memory_history( max_entries=1000000, # record stack information for the trace events # trace_alloc_record_context=True, @@ -121,15 +131,22 @@ def oom_observer(device, alloc, device_alloc, device_free): f"Observe OOM, will dump snapshot to {self._path_dump}. ({device=} {alloc=} {device_alloc=} {device_free=}; stacktrace is as follows)" ) traceback.print_stack() - torch.cuda.memory._dump_snapshot(self._path_dump) + device_mod.memory._dump_snapshot(self._path_dump) print_memory("when oom") - torch._C._cuda_attach_out_of_memory_observer(oom_observer) + if hasattr(torch._C, "_cuda_attach_out_of_memory_observer"): + torch._C._cuda_attach_out_of_memory_observer(oom_observer) def stop(self): logger.info(f"Dump memory snapshot to: {self._path_dump}") - torch.cuda.memory._dump_snapshot(self._path_dump) - torch.cuda.memory._record_memory_history(enabled=None) + device_mod = device_utils.get_torch_device_module() + if not hasattr(device_mod, "memory"): + logger.warning( + f"Memory snapshot profiling is not supported on {device_utils.get_device_name()} backend, skipping." + ) + return + device_mod.memory._dump_snapshot(self._path_dump) + device_mod.memory._record_memory_history(enabled=None) class _MemrayMemoryProfiler(_BaseMemoryProfiler): diff --git a/relax/utils/reloadable_process_group.py b/relax/utils/reloadable_process_group.py index 6016ea610..220cb614b 100644 --- a/relax/utils/reloadable_process_group.py +++ b/relax/utils/reloadable_process_group.py @@ -1,10 +1,12 @@ import os +import time from contextlib import contextmanager from datetime import timedelta import torch import torch.distributed as dist +from relax.utils import device as device_utils from relax.utils.logging_utils import get_logger from relax.utils.memory_utils import available_memory, clear_memory, print_memory @@ -149,13 +151,15 @@ def __getattr__(self, name): return getattr(self.group, name) @staticmethod - def destroy_process_groups(): + def destroy_process_groups(post_destroy_delay: float = 2.0): pid = os.getpid() + destroyed_count = 0 for reloadable_group in ReloadableProcessGroup.GROUPS.get(pid, []): if reloadable_group.group is None: continue try: dist.destroy_process_group(reloadable_group.group) + destroyed_count += 1 except ValueError as e: logger.warning( f"Process group already invalid/destroyed; skipping cleanup. Exception: {e}", @@ -165,21 +169,52 @@ def destroy_process_groups(): del reloadable_group.group reloadable_group.group = None + if destroyed_count > 0 and post_destroy_delay > 0: + # Wait for OS to release NCCL socket ports (TCP TIME_WAIT), + # preventing "Address already in use" on subsequent reload. + logger.info( + f"Destroyed {destroyed_count} process groups, waiting {post_destroy_delay}s " + "for NCCL socket port release" + ) + time.sleep(post_destroy_delay) + @staticmethod - def reload_process_groups(timeout_minutes: int = 30): + def reload_process_groups(timeout_minutes: int = 30, max_retries: int = 3, retry_delay: float = 5.0): pid = os.getpid() reloadable_groups = ReloadableProcessGroup.GROUPS.get(pid, []) logger.info(f"Reloading {len(reloadable_groups)} process groups in pid {pid}") old_new_group = old_new_group_dict.get(pid) - for reloadable_group in reloadable_groups: + for idx, reloadable_group in enumerate(reloadable_groups): if reloadable_group.group is not None: continue - group = old_new_group( - ranks=reloadable_group.group_info["ranks"], - backend="nccl", - timeout=timedelta(minutes=timeout_minutes), - ) - reloadable_group.group = group + last_error = None + for attempt in range(1, max_retries + 1): + try: + group = old_new_group( + ranks=reloadable_group.group_info["ranks"], + backend=device_utils.get_dist_backend(), + timeout=timedelta(minutes=timeout_minutes), + ) + reloadable_group.group = group + if attempt > 1: + logger.info(f"Process group {idx} reloaded successfully on attempt {attempt}") + last_error = None + break + except Exception as e: + last_error = e + logger.warning( + f"Failed to reload process group {idx} (attempt {attempt}/{max_retries}): {e}", + exc_info=(attempt == max_retries), + ) + if attempt < max_retries: + sleep_time = retry_delay * attempt + logger.info(f"Retrying in {sleep_time}s...") + time.sleep(sleep_time) + if last_error is not None: + raise RuntimeError( + f"Failed to reload process group {idx} after {max_retries} attempts " + f"(ranks={reloadable_group.group_info['ranks']})" + ) from last_error def rank(self) -> int: return self.group.rank() @@ -293,14 +328,16 @@ def bound_device_id(self, dev): self.group.bound_device_id = dev -def destroy_process_groups(): +def destroy_process_groups(post_destroy_delay: float = 2.0): """Destroy all reloadable process groups.""" - ReloadableProcessGroup.destroy_process_groups() + ReloadableProcessGroup.destroy_process_groups(post_destroy_delay=post_destroy_delay) -def reload_process_groups(timeout_minutes: int = 30): +def reload_process_groups(timeout_minutes: int = 30, max_retries: int = 3, retry_delay: float = 5.0): """Reload all reloadable process groups.""" - ReloadableProcessGroup.reload_process_groups(timeout_minutes=timeout_minutes) + ReloadableProcessGroup.reload_process_groups( + timeout_minutes=timeout_minutes, max_retries=max_retries, retry_delay=retry_delay + ) @contextmanager diff --git a/relax/utils/training/routing_replay.py b/relax/utils/training/routing_replay.py index 096f4e748..95610316e 100644 --- a/relax/utils/training/routing_replay.py +++ b/relax/utils/training/routing_replay.py @@ -2,6 +2,8 @@ import torch +from relax.utils import device as device_utils + ROUTING_REPLAY = None @@ -29,12 +31,12 @@ def record(self, top_indices): def pop_forward(self): top_indices = self.top_indices_list[self.forward_index] self.forward_index += 1 - return top_indices.to(torch.cuda.current_device()) + return top_indices.to(device_utils.make_current_torch_device()) def pop_backward(self): top_indices = self.top_indices_list[self.backward_index] self.backward_index += 1 - return top_indices.to(torch.cuda.current_device()) + return top_indices.to(device_utils.make_current_torch_device()) def clear(self): self.forward_index = 0 diff --git a/relax/utils/training/tensor_backper.py b/relax/utils/training/tensor_backper.py index 955975aba..e9a0145f7 100644 --- a/relax/utils/training/tensor_backper.py +++ b/relax/utils/training/tensor_backper.py @@ -4,6 +4,8 @@ import torch +from relax.utils import device as device_utils + _SourceGetter = Callable[[], Iterable[tuple[str, torch.Tensor]]] @@ -59,7 +61,7 @@ def backup(self, tag: str) -> None: if name not in backup_dict: backup_dict[name] = torch.empty_like(param, device=torch.device("cpu"), pin_memory=True) backup_dict[name].copy_(param.detach(), non_blocking=True) - torch.cuda.synchronize() + device_utils.synchronize() @torch.no_grad() def copy(self, *, src_tag: str, dst_tag: str): @@ -72,7 +74,7 @@ def restore(self, tag: str) -> None: for name, param in self._source_getter(): assert name in backup_dict param.copy_(backup_dict[name], non_blocking=True) - torch.cuda.synchronize() + device_utils.synchronize() class _TensorBackuperNoop(TensorBackuper): @@ -95,12 +97,12 @@ def get(self, tag: str): def backup(self, tag: str) -> None: assert tag == self._single_tag self._backup_hash_dict = _compute_hash_dict(dict(self._source_getter())) - torch.cuda.synchronize() + device_utils.synchronize() def restore(self, tag: str) -> None: assert tag == self._single_tag assert _compute_hash_dict(dict(self._source_getter())) == self._backup_hash_dict - torch.cuda.synchronize() + device_utils.synchronize() def _compute_hash_dict(tensors: dict[str, torch.Tensor]): From 4b90f58e3c2105f1a90b469adb44f23fed26bae5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Mon, 27 Apr 2026 17:55:03 +0800 Subject: [PATCH 020/268] feat(deepeyes): add bbox normalization and Qwen3.5-9B async script MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add Qwen3.5-9B single-node fully-async training script - Add `run_deepeyes_qwen35_9B_async.sh` for 8xGPU fully-async DeepEyes training - Resource layout: actor(4) + rollout(2) + reference(1) + actor_fwd(1) - Use `--use-dynamic-batch-size` and `--no-rope-fusion` per latest Qwen3.5 conventions --- # 🐛 Bug Fix ## Add 0-1000 normalized bbox coordinate conversion - Qwen-VL/Qwen2-VL/Qwen3-VL output 0-1000 normalized coords but `_maybe_resize_bbox` treated them as absolute pixels - Add coordinate conversion step before clamping in `_maybe_resize_bbox` - Add `normalize_bbox` parameter to `DeepeyesEnv` (default True) for model-specific control - Qwen2.5-VL users can set `normalize_bbox: false` since it outputs absolute pixel coords - Wire `normalize_bbox` through `build_env` from custom config --- examples/deepeyes/env_deepeyes.py | 25 +- .../deepeyes/run_deepeyes_qwen35_9B_async.sh | 227 ++++++++++++++++++ relax/utils/arguments.py | 10 + 3 files changed, 256 insertions(+), 6 deletions(-) create mode 100755 examples/deepeyes/run_deepeyes_qwen35_9B_async.sh diff --git a/examples/deepeyes/env_deepeyes.py b/examples/deepeyes/env_deepeyes.py index dcf9fddfd..07763d4ae 100644 --- a/examples/deepeyes/env_deepeyes.py +++ b/examples/deepeyes/env_deepeyes.py @@ -27,12 +27,16 @@ class DeepeyesEnv(BaseInteractionEnv): MIN_DIMENSION = 28 - def __init__(self, *, max_turns: int | None = None, image=None): + def __init__(self, *, max_turns: int | None = None, image=None, normalize_bbox: bool = True): self.max_turns = max_turns self.turn = 0 self.tool_calls: list[dict[str, Any]] = [] self.current_image = image self.origin_image = image + # Whether to convert bbox coordinates from normalized [0, 1000] to absolute pixels. + # Qwen-VL / Qwen2-VL / Qwen3-VL output 0-1000 normalized coords → set True (default). + # Qwen2.5-VL outputs absolute pixel coords → set False. + self.normalize_bbox = normalize_bbox def reset(self): self.turn = 0 @@ -119,13 +123,21 @@ def _maybe_resize_bbox(self, bbox_2d: list[float]) -> Optional[list[float]]: image_height = self.current_image.height left, top, right, bottom = bbox_2d - # 1. Clamp the initial bounding box to the image dimensions. + # 1. Convert normalized [0, 1000] coordinates to absolute pixel coordinates. + # Qwen-VL / Qwen2-VL / Qwen3-VL use 0-1000 normalized coords; Qwen2.5-VL uses absolute pixels. + if self.normalize_bbox: + left = left / 1000.0 * image_width + top = top / 1000.0 * image_height + right = right / 1000.0 * image_width + bottom = bottom / 1000.0 * image_height + + # 2. Clamp the bounding box to the image dimensions. left = max(0.0, float(left)) top = max(0.0, float(top)) right = min(float(image_width), float(right)) bottom = min(float(image_height), float(bottom)) - # 2. If clamped bbox is invalid, return immediately. + # 3. If clamped bbox is invalid, return immediately. if not self._validate_bbox(left, top, right, bottom): return None @@ -133,7 +145,7 @@ def _maybe_resize_bbox(self, bbox_2d: list[float]) -> Optional[list[float]]: height = bottom - top width = right - left - # 3. If the box is too small, attempt to resize it. + # 4. If the box is too small, attempt to resize it. if height < self.MIN_DIMENSION or width < self.MIN_DIMENSION: logger.info(f"Bbox {width}x{height} is smaller than {self.MIN_DIMENSION}, attempting resize.") center_x = (left + right) / 2.0 @@ -182,7 +194,7 @@ def _maybe_resize_bbox(self, bbox_2d: list[float]) -> Optional[list[float]]: # Use floor and ceil for final integer coordinates. current_bbox = [floor(new_left), floor(new_top), ceil(new_right), ceil(new_bottom)] - # 4. Final validation on the resulting bounding box (either original or resized). + # 5. Final validation on the resulting bounding box (either original or resized). final_left, final_top, final_right, final_bottom = current_bbox if not self._validate_bbox(final_left, final_top, final_right, final_bottom): logger.warning(f"Final bbox is invalid after processing: {current_bbox}") @@ -288,7 +300,8 @@ def build_env(sample: Sample | None = None, args: Any | None = None, **_: Any) - max_turns = args.max_turns if max_turns is None: raise ValueError("max_turns must be set via --custom-config-path in the custom config file.") + normalize_bbox = getattr(args, "normalize_bbox", True) image = _extract_initial_image(sample) if image is None: logger.warning("No image found in sample.multimodal_inputs or metadata.") - return DeepeyesEnv(max_turns=max_turns, image=image) + return DeepeyesEnv(max_turns=max_turns, image=image, normalize_bbox=normalize_bbox) diff --git a/examples/deepeyes/run_deepeyes_qwen35_9B_async.sh b/examples/deepeyes/run_deepeyes_qwen35_9B_async.sh new file mode 100755 index 000000000..67870d779 --- /dev/null +++ b/examples/deepeyes/run_deepeyes_qwen35_9B_async.sh @@ -0,0 +1,227 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3.5-9B 8xGPU single-node fully-async DeepEyes training script. +# +# Resource layout (8 GPUs, fully-async): +# actor: 4 GPUs (TP=4) +# rollout: 2 GPUs (1 engine × 2 GPUs) +# reference: 1 GPU (TP=1, weight-only) +# actor_fwd: 1 GPU +# +# Usage: +# MODEL_DIR=/path/to/models DATA_DIR=/path/to/data SAVE_DIR=/path/to/save \ +# bash examples/deepeyes/run_deepeyes_qwen35_9B_async.sh + +set -ex +set -o pipefail + +############################################################################### +# ENVIRONMENT # +############################################################################### + +TIMESTAMP=$(date "+%Y-%m-%d-%H:%M:%S") + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../scripts/entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen35-9B.sh" + +############################################################################### +# DIRS # +############################################################################### + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/deepeyes}" +EXP_NAME="qwen35-9B-deepeyes-async-${TIMESTAMP}" + +# Require MODEL_DIR, DATA_DIR, SAVE_DIR from environment or set defaults +if [ -z "${MODEL_DIR:-}" ] || [ -z "${DATA_DIR:-}" ] || [ -z "${SAVE_DIR:-}" ]; then + echo "ERROR: MODEL_DIR, DATA_DIR, and SAVE_DIR must be set." + echo "Example: MODEL_DIR=/path/to/models DATA_DIR=/path/to/data SAVE_DIR=/path/to/save bash $0" + exit 1 +fi +mkdir -p ${SAVE_DIR} + +############################################################################### +# JUDGE MODEL API # +############################################################################### + +source "${SCRIPT_DIR}/sglang_judge_service.sh" + +############################################################################### +# MODEL CONFIG # +############################################################################### + +CKPT_ARGS=( + --hf-checkpoint ${MODEL_DIR}/Qwen3.5-9B + --ref-load ${MODEL_DIR}/Qwen3.5-9B + --save ${SAVE_DIR}/Qwen3.5-9B-DeepEyes-Checkpoint + --megatron-to-hf-mode bridge + --save-interval 100 + --max-actor-ckpt-to-keep 1 +) + +############################################################################### +# DATASETS # +############################################################################### + +TRAIN_FILES=( + "'${DATA_DIR}/deepeyes-v1/data_0.1.2_visual_toolbox_v2.parquet@[0:5000]'" + "'${DATA_DIR}/deepeyes-v1/data_v0.8_visual_toolbox_v2.parquet@[0:5000]'" +) +TEST_FILES=("${DATA_DIR}/deepeyes-v1/data_thinklite_reasoning_acc.parquet@[0:256]") +PROMPT_SET="[$(IFS=,; echo "${TRAIN_FILES[*]}")]" + +############################################################################### +# ROLLOUT CONFIG # +############################################################################### + +NUM_ROLLOUT="${NUM_ROLLOUT:=2000}" + +ROLLOUT_ARGS=( + --prompt-data "${PROMPT_SET}" + --input-key prompt + --label-key reward_model + --multimodal-keys '{"image":"images"}' + --reward-key score + --metadata-key extra_info + --apply-chat-template + --custom-generate-function-path examples.deepeyes.rollout.generate + --custom-rm-path examples.deepeyes.reward_deepeyes.reward_func + --custom-config-path examples/deepeyes/deepeyes_config.yaml + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 32 + --n-samples-per-prompt 8 + --rollout-max-response-len 2048 + --rollout-max-prompt-len 2048 + --rollout-temperature 1 + --global-batch-size 256 + --use-fault-tolerance + --rollout-shuffle + --use-streaming-dataset +) + +############################################################################### +# EVAL CONFIG # +############################################################################### + +EVAL_ARGS=( + --eval-interval 100 + --eval-prompt-data vstar ${TEST_FILES} + --n-samples-per-eval-prompt 8 + --eval-max-response-len 2048 + --eval-top-p 0.7 +) + +############################################################################### +# ALGORITHM CONFIG # +############################################################################### + +GRPO_ARGS=( + --advantage-estimator grpo + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --eps-clip-c 3 + --use-tis +) + +############################################################################### +# OPTIMIZER CONFIG # +############################################################################### + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer +) + +############################################################################### +# SGLANG CONFIG # +############################################################################### + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 2 + --sglang-mem-fraction-static 0.6 +) + +############################################################################### +# LOGGING CONFIG # +############################################################################### + +LOG_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name ${EXP_NAME} +) + +############################################################################### +# MEGATRON CONFIG # +############################################################################### + +MEGATRON_ARGS=( + --tensor-model-parallel-size 4 + --sequence-parallel + --pipeline-model-parallel-size 1 + --context-parallel-size 1 + --expert-model-parallel-size 1 + --expert-tensor-parallel-size 1 + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + --use-dynamic-batch-size + --max-tokens-per-gpu 9216 + --no-rope-fusion + --attention-dropout 0.0 + --hidden-dropout 0.0 + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + --attention-backend flash +) + +############################################################################### +# RESOURCE CONFIG # +############################################################################### + +# Fully-async: actor(4 GPU) + rollout(2 GPU) + reference(1 GPU) + actor_fwd(1 GPU) = 8 GPU +RAY_RESOURCE_ARGS=( + --resource '{"actor": [1, 4], "rollout": [1, 2], "reference": [1, 1], "actor_fwd": [1, 1], "advantages": [1, 0]}' + --max-staleness 2 + --num-data-storage-units 1 + --num-iters-per-train-update 8 + --ref-actor-config '{"tensor_model_parallel_size": 1, "max_tokens_per_gpu": 16384, "sequence_parallel": false, "only_load_weight": true}' + --fully-async + --use-health-check +) + +############################################################################### +# LAUNCH JOB # +############################################################################### + +mkdir -p logs + +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ + -- python3 -m relax.entrypoints.train \ + "${RAY_RESOURCE_ARGS[@]}" \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${LOG_ARGS[@]}" \ + "${MEGATRON_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + 2>&1 | tee logs/${EXP_NAME}.log diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 4f277da4e..98ea94fd7 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -1906,6 +1906,16 @@ def add_autoscaler_arguments(parser): default=None, help="Path to the YAML config for custom function arguments.", ) + parser.add_argument( + "--normalize-bbox", + action=argparse.BooleanOptionalAction, + default=True, + help=( + "Convert model-output bbox coordinates from normalized [0, 1000] to absolute pixels. " + "Required for Qwen-VL/Qwen2-VL/Qwen3-VL (default True). " + "Set --no-normalize-bbox for Qwen2.5-VL which outputs absolute pixel coordinates." + ), + ) reset_arg(parser, "--padded-vocab-size", type=int, default=None) return parser From 97fb4b1bb8e8f04dfe4736c6a1b14b3fc99ed0f8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Sun, 26 Apr 2026 22:23:36 +0800 Subject: [PATCH 021/268] fix(distributed): add retry and port-release delay for NCCL process group recreation --- .../backends/device_direct.py | 77 +++++++++++++------ 1 file changed, 53 insertions(+), 24 deletions(-) diff --git a/relax/distributed/checkpoint_service/backends/device_direct.py b/relax/distributed/checkpoint_service/backends/device_direct.py index 26319a8d3..3e7316e49 100644 --- a/relax/distributed/checkpoint_service/backends/device_direct.py +++ b/relax/distributed/checkpoint_service/backends/device_direct.py @@ -591,6 +591,8 @@ def init_process_group_for_rollout(self, topology_data: Optional[Dict] = None) - dist.destroy_process_group(self._model_update_groups) ray.get(futures) self._model_update_groups = None + # Wait for NCCL socket ports to be released by the OS + time.sleep(2.0) except Exception as e: logger.warning(f"Error destroying old process group: {e}") self._model_update_groups = None @@ -605,32 +607,59 @@ def init_process_group_for_rollout(self, topology_data: Optional[Dict] = None) - cumulative_offset += gpus_for_node world_size = cumulative_offset - master_port = self._find_free_port_in_range(self._MASTER_PORT_MIN, self._MASTER_PORT_MAX) - - # Prepare init payloads for each rollout node - init_payloads = {} - for rank, role_info in self.rollout_topology.items(): - init_payloads[int(rank)] = { - "master_address": master_address, - "master_port": master_port, - "rank_offset": rank_offsets[int(rank)], - "world_size": world_size, - "group_name": self._group_name, - "backend": self.backend_type, - } + max_retries = 3 + last_error = None + for attempt in range(1, max_retries + 1): + master_port = self._find_free_port_in_range(self._MASTER_PORT_MIN, self._MASTER_PORT_MAX) + + init_payloads = {} + for rank, role_info in self.rollout_topology.items(): + init_payloads[int(rank)] = { + "master_address": master_address, + "master_port": master_port, + "rank_offset": rank_offsets[int(rank)], + "world_size": world_size, + "group_name": self._group_name, + "backend": self.backend_type, + } - logger.info(f"Sending init_weights_update_group to {len(self.rollout_topology)} rollout nodes...") - futures = self._batch_request("/init_weights_update_group", init_payloads, get_rank=True) + logger.info( + f"Sending init_weights_update_group to {len(self.rollout_topology)} rollout nodes " + f"(attempt {attempt}/{max_retries}, port={master_port})..." + ) + futures = self._batch_request("/init_weights_update_group", init_payloads, get_rank=True) - self._model_update_groups = init_process_group( - backend=self.backend_type, - init_method=f"tcp://{master_address}:{master_port}", - world_size=world_size, - rank=0, - group_name=self._group_name, - timeout=timedelta(seconds=180), - ) - ray.get(futures) + try: + self._model_update_groups = init_process_group( + backend=self.backend_type, + init_method=f"tcp://{master_address}:{master_port}", + world_size=world_size, + rank=0, + group_name=self._group_name, + timeout=timedelta(seconds=180), + ) + ray.get(futures) + last_error = None + break + except Exception as e: + last_error = e + logger.warning( + f"Failed to init process group for rollout (attempt {attempt}/{max_retries}, " + f"port={master_port}): {e}", + exc_info=(attempt == max_retries), + ) + self._model_update_groups = None + try: + ray.get(futures, timeout=5) + except Exception: + pass + if attempt < max_retries: + time.sleep(5.0 * attempt) + + if last_error is not None: + raise RuntimeError( + f"Failed to init process group for rollout after {max_retries} attempts" + ) from last_error def init_process_groups_for_actor_fwd_ref(self, topology_data) -> None: """Initialize process groups used for actor -> actor_fwd weight sync. From 3729cb914eb59e1cd96dd72ee53f99142796923b Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Sat, 25 Apr 2026 17:03:08 +0800 Subject: [PATCH 022/268] fix(dcs): handle Qwen3.5 MoE 2D expert weight format in bridge conversion MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Fix IndexError on 1D tensor transpose in fully-async weight sync - Qwen3.5 Bridge outputs expert gate_up_proj as 2D [2*H, D] (cat, no transpose), unlike Qwen3-VL which outputs 3D [2, D_out, D_in] (stack + transpose) - Qwen3.5 ExpertMLPDownProjMapping inherits AutoMapping (no transpose), unlike Qwen3-VL which overrides megatron_to_hf with transpose - Add ndim-based branching in _convert_to_hf_bridge post-processing: 3D → Qwen3-VL path (undo transpose + index), 2D → Qwen3.5 path (chunk) - Detect bridge_expert_transposes_down at init time via __dict__ introspection to decide whether down_proj needs un-transpose --- # ✅ Tests ## Add Qwen3.5 Bridge expert weight conversion tests - Add TestQwen35BridgeMappingOutput: verify 2D cat output, no-transpose down_proj, and megatron_to_hf override detection - Add TestQwen35PostProcessingCorrectness: end-to-end gate_up split and down_proj passthrough correctness - Update _apply_expert_postprocessing helper to accept bridge_expert_transposes_down param --- .../backends/device_direct.py | 57 ++++++-- .../test_dcs_weight_conversion.py | 138 +++++++++++++++++- 2 files changed, 173 insertions(+), 22 deletions(-) diff --git a/relax/distributed/checkpoint_service/backends/device_direct.py b/relax/distributed/checkpoint_service/backends/device_direct.py index 3e7316e49..57fa3e866 100644 --- a/relax/distributed/checkpoint_service/backends/device_direct.py +++ b/relax/distributed/checkpoint_service/backends/device_direct.py @@ -121,6 +121,7 @@ def __init__( self._use_bridge = getattr(args, "megatron_to_hf_mode", None) == "bridge" self._bridge_task_map: Optional[Dict[str, Any]] = None # global_param_name -> WeightConversionTask self._bridge_mapping_registry = None # MegatronMappingRegistry for dynamic lookups + self._bridge_expert_transposes_down: bool = True # set in _init_bridge_tasks def _init_bridge_tasks(self) -> None: """Lazily initialize Bridge conversion tasks and build a lookup table. @@ -204,6 +205,16 @@ def _init_bridge_tasks(self) -> None: inner_tp._detected_type = inner_tp._detect_parallelism_type(task.megatron_module) inner_tp._mapping = inner_tp._get_or_create_mapping(inner_tp._detected_type) + # Detect whether the Bridge's ExpertMLPDownProjMapping applies a + # transpose in megatron_to_hf (Qwen3-VL does, Qwen3.5 does not). + # Used by _convert_to_hf_bridge to decide whether to undo the transpose. + self._bridge_expert_transposes_down = False + for task in self._bridge_task_map.values(): + cls = type(task.mapping) + if cls.__name__ == "ExpertMLPDownProjMapping": + self._bridge_expert_transposes_down = "megatron_to_hf" in cls.__dict__ + break + logger.info(f"Bridge task map initialized with {len(self._bridge_task_map)} local tasks") @staticmethod @@ -391,16 +402,21 @@ def _noop_gather_from_ep_ranks(self_m, megatron_weights, megatron_module, hf_par # ── Post-process expert weights ────────────────────────────────── # Bridge's ExpertMLPGateUpProjMapping and ExpertMLPDownProjMapping - # (used by Qwen3-VL MoE) apply an extra ``.transpose(-1, -2)`` in - # their ``megatron_to_hf`` methods, assuming Megatron stores expert - # weights in column-major order. However, the raw ``convert_to_hf`` - # does NOT transpose expert weights — Megatron's expert weights are - # already in the same layout as HF. We must undo Bridge's transpose - # to match the format that SGLang / ``convert_to_hf`` expects. + # apply transformations that differ by model family: + # + # **Qwen3-VL** (qwen3_vl_bridge.py): + # gate_up_proj: transpose each half then stack → [2, D_out, D_in] + # down_proj: transpose → [D_in, D_out] + # We must undo the transpose. + # + # **Qwen3.5** (qwen35_vl_bridge.py): + # gate_up_proj: cat without transpose → [2*H, D] (2-D) + # down_proj: no transpose (AutoMapping) → [H, D] (2-D) + # No un-transpose needed; just split the fused tensor. # # Additionally, Bridge outputs fused names without expert_id: - # - ``...experts.gate_up_proj`` with shape [2, D_out, D_in] - # - ``...experts.down_proj`` with shape [D_in, D_out] + # - ``...experts.gate_up_proj`` + # - ``...experts.down_proj`` # We split into per-expert format with correct names and shapes: # - ``...experts.{E}.gate_proj.weight`` [H, D] # - ``...experts.{E}.up_proj.weight`` [H, D] @@ -411,19 +427,28 @@ def _noop_gather_from_ep_ranks(self_m, megatron_weights, megatron_module, hf_par postprocessed: list[tuple[str, torch.Tensor]] = [] for hf_name, tensor in converted_named_tensors: if hf_name.endswith(".experts.gate_up_proj"): - # Bridge output: [2, D_out, D_in] (transposed by Bridge) - # Undo transpose on each slice: [D_out, D_in] -> [D_in, D_out] - gate_tensor = tensor[0].transpose(-1, -2).contiguous() - up_tensor = tensor[1].transpose(-1, -2).contiguous() base = hf_name[: -len(".gate_up_proj")] + if tensor.ndim == 3: + # Qwen3-VL style: [2, D_out, D_in] (transposed by Bridge) + # Undo transpose on each slice: [D_out, D_in] -> [D_in, D_out] + gate_tensor = tensor[0].transpose(-1, -2).contiguous() + up_tensor = tensor[1].transpose(-1, -2).contiguous() + else: + # Qwen3.5 style: [2*H, D] (cat, no transpose by Bridge) + # Split along dim 0 into two [H, D] tensors + gate_tensor, up_tensor = tensor.chunk(2, dim=0) postprocessed.append((f"{base}.{expert_id}.gate_proj.weight", gate_tensor)) postprocessed.append((f"{base}.{expert_id}.up_proj.weight", up_tensor)) elif hf_name.endswith(".experts.down_proj"): - # Bridge output: transposed — undo to match raw convert_to_hf base = hf_name[: -len(".down_proj")] - postprocessed.append( - (f"{base}.{expert_id}.down_proj.weight", tensor.transpose(-1, -2).contiguous()) - ) + if tensor.ndim == 2 and not self._bridge_expert_transposes_down: + # Qwen3.5 style: AutoMapping, no transpose — already [H, D] + postprocessed.append((f"{base}.{expert_id}.down_proj.weight", tensor)) + else: + # Qwen3-VL style: transposed — undo to match raw convert_to_hf + postprocessed.append( + (f"{base}.{expert_id}.down_proj.weight", tensor.transpose(-1, -2).contiguous()) + ) else: postprocessed.append((hf_name, tensor)) converted_named_tensors = postprocessed diff --git a/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py b/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py index ffa8f41d2..4a583a505 100644 --- a/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py +++ b/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py @@ -37,6 +37,12 @@ ExpertMLPDownProjMapping, ExpertMLPGateUpProjMapping, ) +from megatron.bridge.models.qwen_vl.qwen35_vl_bridge import ( # noqa: E402 + ExpertMLPDownProjMapping as Qwen35ExpertMLPDownProjMapping, +) +from megatron.bridge.models.qwen_vl.qwen35_vl_bridge import ( # noqa: E402 + ExpertMLPGateUpProjMapping as Qwen35ExpertMLPGateUpProjMapping, +) from relax.backends.megatron.misc_utils import strip_param_name_prefix # noqa: E402 from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding # noqa: E402 @@ -77,7 +83,14 @@ def _noop_gather(self_m, megatron_weights, megatron_module, hf_param_name): return {str(hf_param_name): megatron_weights} saved_originals: dict = {} - patched_classes = [MegatronParamMapping, GatedMLPMapping, ExpertMLPGateUpProjMapping, ExpertMLPDownProjMapping] + patched_classes = [ + MegatronParamMapping, + GatedMLPMapping, + ExpertMLPGateUpProjMapping, + ExpertMLPDownProjMapping, + Qwen35ExpertMLPGateUpProjMapping, + Qwen35ExpertMLPDownProjMapping, + ] for cls in patched_classes: if "gather_from_ep_ranks" in cls.__dict__: saved_originals[cls] = cls.__dict__["gather_from_ep_ranks"] @@ -113,15 +126,35 @@ def _make_expert_down_mapping(layer_idx: int, expert_id: int) -> ExpertMLPDownPr return m +def _make_qwen35_expert_gate_up_mapping(layer_idx: int, expert_id: int) -> Qwen35ExpertMLPGateUpProjMapping: + """Create a real Qwen3.5 ExpertMLPGateUpProjMapping for testing.""" + return Qwen35ExpertMLPGateUpProjMapping( + megatron_param=f"language_model.decoder.layers.{layer_idx}.mlp.experts.linear_fc1.weight{expert_id}", + hf_param=f"model.language_model.layers.{layer_idx}.mlp.experts.gate_up_proj", + ) + + +def _make_qwen35_expert_down_mapping(layer_idx: int, expert_id: int) -> Qwen35ExpertMLPDownProjMapping: + """Create a real Qwen3.5 ExpertMLPDownProjMapping with eagerly initialized + inner mapping.""" + m = Qwen35ExpertMLPDownProjMapping( + megatron_param=f"language_model.decoder.layers.{layer_idx}.mlp.experts.linear_fc2.weight{expert_id}", + hf_param=f"model.language_model.layers.{layer_idx}.mlp.experts.down_proj", + ) + m._detected_type = "replicated" + m._mapping = m._get_or_create_mapping("replicated") + return m + + def _apply_expert_postprocessing( converted_dict: Dict[str, torch.Tensor], megatron_param_name: str, + bridge_expert_transposes_down: bool = True, ) -> List[Tuple[str, torch.Tensor]]: """Apply the same expert weight post-processing as ``_convert_to_hf_bridge``. - This calls the real production logic extracted from device_direct.py lines - 399-420. + Mirrors the production logic in device_direct.py. """ converted_named_tensors = list(converted_dict.items()) expert_id_match = re.search(r"weight(\d+)", megatron_param_name) @@ -130,14 +163,22 @@ def _apply_expert_postprocessing( postprocessed: list[tuple[str, torch.Tensor]] = [] for hf_name, tensor in converted_named_tensors: if hf_name.endswith(".experts.gate_up_proj"): - gate_tensor = tensor[0].transpose(-1, -2).contiguous() - up_tensor = tensor[1].transpose(-1, -2).contiguous() base = hf_name[: -len(".gate_up_proj")] + if tensor.ndim == 3: + gate_tensor = tensor[0].transpose(-1, -2).contiguous() + up_tensor = tensor[1].transpose(-1, -2).contiguous() + else: + gate_tensor, up_tensor = tensor.chunk(2, dim=0) postprocessed.append((f"{base}.{expert_id}.gate_proj.weight", gate_tensor)) postprocessed.append((f"{base}.{expert_id}.up_proj.weight", up_tensor)) elif hf_name.endswith(".experts.down_proj"): base = hf_name[: -len(".down_proj")] - postprocessed.append((f"{base}.{expert_id}.down_proj.weight", tensor.transpose(-1, -2).contiguous())) + if tensor.ndim == 2 and not bridge_expert_transposes_down: + postprocessed.append((f"{base}.{expert_id}.down_proj.weight", tensor)) + else: + postprocessed.append( + (f"{base}.{expert_id}.down_proj.weight", tensor.transpose(-1, -2).contiguous()) + ) else: postprocessed.append((hf_name, tensor)) converted_named_tensors = postprocessed @@ -657,3 +698,88 @@ def test_element_count_preserved(self): postprocessed = _apply_expert_postprocessing(bridge_output, "decoder.layers.0.mlp.experts.linear_fc1.weight0") total_numel = sum(t.numel() for _, t in postprocessed) assert total_numel == original_numel + + +# ─── Tests for Qwen3.5 Bridge (2D cat, no transpose) ───────────────────────── + + +class TestQwen35BridgeMappingOutput: + """Test Qwen3.5 Bridge mapping output format (2D cat, no transpose).""" + + def test_qwen35_gate_up_outputs_2d_cat(self): + """Qwen3.5 ExpertMLPGateUpProjMapping outputs 2D [2*H, D] via cat.""" + with _patch_gather_from_ep_ranks(): + m = _make_qwen35_expert_gate_up_mapping(layer_idx=0, expert_id=3) + H, D = 768, 2048 + fused = torch.randn(H * 2, D) + result = m.megatron_to_hf(fused, None) + + key = "model.language_model.layers.0.mlp.experts.gate_up_proj" + assert list(result.keys()) == [key] + tensor = result[key] + assert tensor.ndim == 2 + assert tensor.shape == (H * 2, D) + + def test_qwen35_down_proj_no_transpose(self): + """Qwen3.5 ExpertMLPDownProjMapping does not transpose.""" + with _patch_gather_from_ep_ranks(): + m = _make_qwen35_expert_down_mapping(layer_idx=0, expert_id=3) + D, H = 2048, 768 + param = torch.randn(D, H) + result = m.megatron_to_hf(param, None) + + key = "model.language_model.layers.0.mlp.experts.down_proj" + assert list(result.keys()) == [key] + tensor = result[key] + assert tensor.shape == (D, H) + assert torch.allclose(tensor, param) + + def test_qwen35_expert_transposes_down_detection(self): + """Qwen3.5 ExpertMLPDownProjMapping lacks megatron_to_hf override.""" + assert "megatron_to_hf" not in Qwen35ExpertMLPDownProjMapping.__dict__ + assert "megatron_to_hf" in ExpertMLPDownProjMapping.__dict__ + + +class TestQwen35PostProcessingCorrectness: + """Verify Qwen3.5 Bridge output + post-processing produces correct HF + weights.""" + + def test_qwen35_gate_up_postprocessed(self): + """Qwen3.5 gate_up 2D + post-processing produces correct gate/up.""" + H, D = 768, 2048 + expert_id = 3 + megatron_param = torch.randn(H * 2, D) + expected_gate, expected_up = megatron_param.chunk(2, dim=0) + + with _patch_gather_from_ep_ranks(): + mapping = _make_qwen35_expert_gate_up_mapping(layer_idx=0, expert_id=expert_id) + bridge_output = mapping.megatron_to_hf(megatron_param, None) + + megatron_name = f"language_model.decoder.layers.0.mlp.experts.linear_fc1.weight{expert_id}" + postprocessed = _apply_expert_postprocessing(bridge_output, megatron_name, bridge_expert_transposes_down=False) + + assert len(postprocessed) == 2 + assert postprocessed[0][0].endswith(f".experts.{expert_id}.gate_proj.weight") + assert postprocessed[1][0].endswith(f".experts.{expert_id}.up_proj.weight") + assert postprocessed[0][1].shape == (H, D) + assert postprocessed[1][1].shape == (H, D) + assert torch.allclose(postprocessed[0][1], expected_gate) + assert torch.allclose(postprocessed[1][1], expected_up) + + def test_qwen35_down_proj_postprocessed(self): + """Qwen3.5 down_proj passthrough (no transpose undo).""" + D, H = 2048, 768 + expert_id = 5 + megatron_param = torch.randn(D, H) + + with _patch_gather_from_ep_ranks(): + mapping = _make_qwen35_expert_down_mapping(layer_idx=0, expert_id=expert_id) + bridge_output = mapping.megatron_to_hf(megatron_param, None) + + megatron_name = f"language_model.decoder.layers.0.mlp.experts.linear_fc2.weight{expert_id}" + postprocessed = _apply_expert_postprocessing(bridge_output, megatron_name, bridge_expert_transposes_down=False) + + assert len(postprocessed) == 1 + assert postprocessed[0][0].endswith(f".experts.{expert_id}.down_proj.weight") + assert postprocessed[0][1].shape == (D, H) + assert torch.allclose(postprocessed[0][1], megatron_param) From 8cd874458c895435ce62ae5d385f6bc48ad65ff2 Mon Sep 17 00:00:00 2001 From: root Date: Wed, 29 Apr 2026 21:17:56 +0800 Subject: [PATCH 023/268] fix --eps-clip --- .../training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh | 2 +- .../training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh | 2 +- scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh | 2 +- scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh index 5940af288..c3a98abb2 100644 --- a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh +++ b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-async.sh @@ -93,7 +93,7 @@ GRPO_ARGS=( --kl-loss-coef 0.001 --kl-loss-type low_var_kl --entropy-coef 0.00 - --eps-clip 3.0 + --eps-clip 0.2 --eps-clip-high 0.28 --use-tis ) diff --git a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh index e67da4ac7..019524b1b 100644 --- a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh +++ b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu-video.sh @@ -86,7 +86,7 @@ GRPO_ARGS=( --kl-loss-coef 0.001 --kl-loss-type low_var_kl --entropy-coef 0.00 - --eps-clip 3.0 + --eps-clip 0.2 --eps-clip-high 0.28 --use-tis ) diff --git a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh index 68a5bd79a..2d98c8c30 100644 --- a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh @@ -88,7 +88,7 @@ GRPO_ARGS=( --kl-loss-coef 0.001 --kl-loss-type low_var_kl --entropy-coef 0.00 - --eps-clip 3.0 + --eps-clip 0.2 --eps-clip-high 0.28 --use-tis ) diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh index 505255e7e..ac5e36494 100755 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh @@ -86,7 +86,7 @@ GRPO_ARGS=( --kl-loss-coef 0.001 --kl-loss-type low_var_kl --entropy-coef 0.00 - --eps-clip 3.0 + --eps-clip 0.2 --eps-clip-high 0.28 --use-tis ) From 4c7aa4fc5edaae76dc7ec0be2e34152510a47c8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=8E=E4=BD=B3=E5=85=B4?= Date: Tue, 28 Apr 2026 22:10:58 +0800 Subject: [PATCH 024/268] feat(rollout): add prefetch, profiling and pipeline encoding MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add PrefetchBuffer for streaming multimodal datasets - Implement `PrefetchBuffer` with `set_index_order` pattern (inspired by AReaL) - Background thread uses `ThreadPoolExecutor` for parallel video decoding (PyAV releases GIL) - Flow-controlled cache with `max_cached` bound and `_space_available` Event - Add `--prefetch-chunk-size` and `--prefetch-max-cached` CLI arguments - Wire arguments through `data_source.py` into `StreamingDataset` ## Add SGLang engine profiling support - Add 7 profiling CLI arguments (`--sglang-profile`, `--sglang-profile-output-dir`, etc.) - Implement `_start_sglang_profile` / `_stop_sglang_profile` in sglang_rollout - Support `num_steps` auto-stop, per-stage profiling, stack and shape recording ## Add pipeline batch encoding for multimodal generate - Add `--mm-encode-batch-groups-size` argument for encode-generate overlap - Split samples into batches: encode batch N, send to SGLang, encode batch N+1 concurrently - Implement `_encode_multimodal_inputs` for base64 encoding of images/videos/audio --- # 📝 Documentation ## Sync new parameters to configuration docs - Add prefetch parameters to Dataset section (en/zh) - Add `--mm-encode-batch-groups-size` to Multimodal Data section (en/zh) - Add 7 SGLang profiling parameters to SGLang Engine Parameters section (en/zh) --- docs/en/guide/configuration.md | 44 +- docs/en/guide/performance-tuning.md | 157 +++++++- docs/zh/guide/configuration.md | 44 +- docs/zh/guide/performance-tuning.md | 155 ++++++- relax/backends/sglang/arguments.py | 80 ++++ relax/backends/sglang/sglang_engine.py | 34 -- relax/engine/rollout/data_source.py | 36 +- relax/engine/rollout/sglang_rollout.py | 53 ++- relax/utils/arguments.py | 33 +- relax/utils/data/streaming_dataset.py | 378 +++++++++++++++++- relax/utils/profile_utils.py | 192 ++++++++- .../multimodal/run-qwen3-vl-4B-2xgpu.sh | 3 + .../run-qwen35-9B-8xgpu-openr1mm-async.sh | 5 +- .../multimodal/run-qwen35-9B-8xgpu-video.sh | 2 +- scripts/training/text/run-qwen3-4B-2xgpu.sh | 145 ------- .../text/run-qwen35-9B-8xgpu-async.sh | 7 +- 16 files changed, 1114 insertions(+), 254 deletions(-) delete mode 100644 scripts/training/text/run-qwen3-4B-2xgpu.sh diff --git a/docs/en/guide/configuration.md b/docs/en/guide/configuration.md index 0544f00da..8da5f1fad 100644 --- a/docs/en/guide/configuration.md +++ b/docs/en/guide/configuration.md @@ -88,6 +88,9 @@ For common configuration usage and examples, see the [Quick Start Guide](./quick | `--rollout-seed` | int | 42 | Random seed for Rollout, used for shuffling prompts and random sampling | | `--use-streaming-dataset` | flag | False | Use streaming dataset to save memory | | `--streaming-buffer-size` | int | 10000 | Buffer size for streaming dataset | +| `--prefetch-chunk-size` | int | 32 | Number of samples to dispatch to the thread-pool in each prefetch round. Larger values increase throughput but also memory pressure. Only effective when `--use-streaming-dataset` is set and the dataset contains multimodal data | +| `--prefetch-max-cached` | int | 256 | Maximum number of pre-loaded samples kept in the prefetch cache. When the cache is full the background prefetch thread pauses until consumers free space. Set to 0 to disable prefetching. Only effective when `--use-streaming-dataset` is set and the dataset contains multimodal data | +| `--prefetch-num-workers` | int | 1 | Number of parallel worker threads inside the prefetch buffer for I/O-bound media decoding (video/image). Set to 1 to serialise all decoding (safest for FFmpeg which is not fully thread-safe). Higher values increase parallelism but may trigger EAGAIN errors on some platforms. Only effective when prefetching is enabled | | `--data-source-path` | str | `relax.engine.rollout.data_source.RolloutDataSourceWithBuffer` | Rollout data source class path | | `--start-rollout-id` | int | None | Starting Rollout step. If not set, attempts to read from checkpoint specified by `--load` | @@ -168,6 +171,16 @@ For more parameters, refer to SGLang official documentation. | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `--sglang-mem-fraction-static` | float | - | SGLang static memory allocation ratio | +| `--sglang-profile` | flag | False | Enable torch profiling on SGLang engines during rollout. Profile traces will be saved per rollout step | +| `--sglang-profile-steps` | int (list) | None | List of absolute rollout step IDs (0-indexed) at which to enable SGLang profiling. Takes precedence over `--sglang-profile-step-start/end`. Example: `--sglang-profile-steps 3 10 50` | +| `--sglang-profile-step-start` | int | None | Start of the rollout step range for SGLang profiling (**inclusive**, 0-indexed). Used with `--sglang-profile-step-end` to specify a contiguous range. Ignored if `--sglang-profile-steps` is set | +| `--sglang-profile-step-end` | int | None | End of the rollout step range for SGLang profiling (**inclusive**, 0-indexed). Used with `--sglang-profile-step-start` to specify a contiguous range. Ignored if `--sglang-profile-steps` is set. E.g. start=2, end=4 profiles steps 2, 3, 4 | +| `--sglang-profile-output-dir` | str | None | Output directory for SGLang profile traces. Defaults to `traces//sglang_trace` | +| `--sglang-profile-num-steps` | int | 3 | Number of SGLang forward steps to profile per rollout. -1 profiles the entire rollout step until `stop_profile` is called | +| `--sglang-profile-activities` | str (list) | ["CPU", "GPU"] | Activities to profile (e.g., `CPU GPU`) | +| `--sglang-profile-by-stage` | flag | False | Profile by stage (prefill/decode) separately | +| `--sglang-profile-with-stack` | flag | False | Record call stack in profile traces | +| `--sglang-profile-record-shapes` | flag | False | Record tensor shapes in profile traces | ### Custom Rollout Functions @@ -498,7 +511,9 @@ For autoscaler YAML configuration details, see [`relax/utils/autoscaler/autoscal --- -## Debug Parameters +## Debug & Profiling Parameters + +### Debug | Parameter | Type | Default | Description | |-----------|------|---------|-------------| @@ -510,14 +525,33 @@ For autoscaler YAML configuration details, see [`relax/utils/autoscaler/autoscal | `--save-debug-train-data` | str | None | Save training data. Path supports `{rollout_id}` placeholder | | `--dump-details` | str | None | Export all training details for post-hoc analysis | | `--check-weight-update-equal` | flag | False | Check if weight updates are equal | -| `--memory-snapshot-dir` | str | . | Memory snapshot directory | -| `--memory-snapshot-num-steps` | int | None | Memory snapshot steps | +| `--enable-cuda-memory-check` | flag | False | Enable memory check around low-level NCCL communication calls. Logs available GPU memory before each collective and attaches memory info to exceptions on failure | + +### Training Performance Profiling + +These parameters control the PyTorch Profiler for training steps. Trace files are saved to `traces//train_trace/` by default. + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `--use-pytorch-profiler` | flag | False | Enable PyTorch's built-in profiler to record CUDA kernels, CPU ops, and communication during training (from Megatron) | +| `--profile-step-start` | int | 10 | Step offset at which to start profiling (**inclusive**, from Megatron). Counts from 0 since the current training launch, not absolute rollout ID; resets on checkpoint resumption | +| `--profile-step-end` | int | 12 | Step offset at which to stop profiling (**inclusive**, from Megatron). Same counting semantics as above. E.g. start=10, end=12 profiles steps 10, 11, 12 (3 steps) | | `--profile-target` | str (list) | train_overall | Profiling targets: `train_overall`, `train_actor`, `train_log_probs` | | `--profile-with-stack` | flag | False | Record stack information in profiler traces | | `--profile-with-memory` | flag | False | Record memory information in profiler traces | | `--profile-with-flops` | flag | False | Estimate FLOPs in profiler traces | -| `--memory-recorder` | str | torch | Memory recorder: `torch`, `memray` | -| `--enable-cuda-memory-check` | flag | False | Enable memory check around low-level NCCL communication calls. Logs available GPU memory before each collective and attaches memory info to exceptions on failure | + +### GPU Memory Profiling + +These parameters control GPU memory snapshot collection for diagnosing memory leaks and OOM issues. Snapshot files can be viewed with PyTorch Memory Viz tools (`torch.cuda.memory._viz`). + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `--record-memory-history` | flag | False | Enable CUDA memory allocation history recording (from Megatron). Records call stacks and tensor info for each allocation/deallocation, and auto-dumps a snapshot on OOM | +| `--memory-snapshot-path` | str | snapshot.pickle | Memory snapshot filename (from Megatron) | +| `--memory-snapshot-dir` | str | None | Memory snapshot output directory. Defaults to `traces//memory_snapshot` | +| `--memory-snapshot-num-steps` | int | None | Proactively dump a memory snapshot after the specified number of steps (0-indexed, i.e., setting 3 means dump after step 2) | +| `--memory-recorder` | str | torch | Memory recorder backend: `torch` (PyTorch built-in), `memray` (requires `pip install memray`) | ### Network diff --git a/docs/en/guide/performance-tuning.md b/docs/en/guide/performance-tuning.md index 2657222fc..d7b121f7f 100644 --- a/docs/en/guide/performance-tuning.md +++ b/docs/en/guide/performance-tuning.md @@ -4,29 +4,90 @@ A practical guide to maximizing training throughput in Relax. All parameters men --- -## Profiling Training Performance +## Profiling -Before tuning, identify the bottleneck. Relax integrates PyTorch Profiler to generate TensorBoard-compatible traces. +Before tuning, identify the bottleneck. Relax provides three complementary profiling tools that cover **inference engine**, **training backend**, and **GPU memory**. All trace files are saved under `traces//` by default, separated by subdirectory: -### Enabling the Profiler +| Tool | Target | Default Output Directory | Viewer | +|---|---|---|---| +| SGLang Profiling | CUDA kernel / operator analysis for rollout inference | `traces//sglang_trace/` | TensorBoard or `https://ui.perfetto.dev/` | +| Training Profiling | Operator analysis for Actor training / log-probs computation | `traces//train_trace/` | TensorBoard or `https://ui.perfetto.dev/` | +| Memory Profiling | GPU memory allocation history, OOM diagnosis | `traces//memory_snapshot/` | [PyTorch Memory Viz](https://pytorch.org/memory_viz) | -The profiler is controlled by `--profile-step-start` and `--profile-step-end` (Megatron native parameters) together with `--profile-target`: +### Trace File Naming + +- **Training traces** include `rank{global}_dp{dp}_tp{tp}_pp{pp}` in filenames, e.g. `train_overall_rank0_dp0_tp0_pp0.1713780123.pt.trace.json.gz` +- **Memory snapshots** also include rank tags, e.g. `memory_snapshot_time1713780123_rank0_dp0_tp0_pp0_snapshot.pickle` +- **SGLang traces** use `engine{i}` prefix to distinguish engine instances, e.g. `engine0-1713780123-TP-0.trace.json.gz` + +### 1. SGLang Inference Profiling + +Runs `torch.profiler` on all SGLang engines during rollout via the `/start_profile` and `/stop_profile` HTTP APIs. Does not interfere with training-side profiling. + +**Example usage** — profile every rollout step: ```bash python3 relax/entrypoints/train.py \ - --profile-target train_overall \ + --sglang-profile \ + --tb-experiment-name my-experiment \ + # ... other args +``` + +**Selective step range** — only profile steps 2, 3, 4 (start/end are both inclusive; recommended to avoid excessive trace files): + +```bash +python3 relax/entrypoints/train.py \ + --sglang-profile \ + --sglang-profile-step-start 2 \ + --sglang-profile-step-end 4 \ + --tb-experiment-name my-experiment \ + # ... other args +``` + +You can also use `--sglang-profile-steps` to specify a non-contiguous list (takes precedence over start/end): + +```bash +--sglang-profile-steps 2 5 10 +``` + +All step parameters use **absolute rollout IDs** (0-indexed), i.e., step 0, step 1, ... regardless of `--start-rollout-id`. + +**Advanced parameters**: + +| Parameter | Default | Description | +|---|---|---| +| `--sglang-profile-step-start` | None | Start of the profiling rollout step range (**inclusive**, 0-indexed) | +| `--sglang-profile-step-end` | None | End of the profiling rollout step range (**inclusive**, 0-indexed). E.g. start=2, end=4 profiles steps 2, 3, 4 | +| `--sglang-profile-steps` | None | Non-contiguous step list; takes precedence over start/end | +| `--sglang-profile-num-steps` | 3 | Number of SGLang forward steps to profile per rollout. -1 profiles the entire rollout step | +| `--sglang-profile-activities` | CPU GPU | Activities to profile | +| `--sglang-profile-by-stage` | False | Profile prefill / decode stages separately | +| `--sglang-profile-with-stack` | False | Record Python call stacks | +| `--sglang-profile-record-shapes` | False | Record tensor shape information | +| `--sglang-profile-output-dir` | None | Custom output directory. Defaults to `traces//sglang_trace` | + +### 2. Training Profiling (PyTorch Profiler) + +Profiles Actor training steps using `torch.profiler`, producing TensorBoard-compatible trace files. + +**Example usage** — profile steps 2, 3, 4 (start/end are both inclusive): + +```bash +python3 relax/entrypoints/train.py \ + --use-pytorch-profiler \ --profile-step-start 2 \ --profile-step-end 4 \ - --use-tensorboard \ - --tb-project-name /path/to/tb_logs \ + --tb-experiment-name my-experiment \ # ... other args ``` -You can specify multiple targets: `--profile-target train_overall train_actor train_log_probs`. +::: tip +`--profile-step-start` and `--profile-step-end` are both **inclusive** and represent **step offsets** from the current training launch, not absolute rollout IDs. The counter resets on checkpoint resumption. E.g. start=2, end=4 profiles steps 2, 3, 4 (3 steps). -### Profiler Detail Flags +Same inclusive semantics as `--sglang-profile-step-start/end`. +::: -Three flags control what additional information the profiler records: +**Detail flags**: | Flag | Effect | |---|---| @@ -34,18 +95,18 @@ Three flags control what additional information the profiler records: | `--profile-with-memory` | Track CUDA memory allocations/deallocations in the trace. Helps find memory spikes | | `--profile-with-flops` | Estimate FLOPs for each operator. Useful for calculating hardware utilization (MFU) | -Example with all detail flags: +**Full example**: ```bash python3 relax/entrypoints/train.py \ + --use-pytorch-profiler \ --profile-target train_overall \ --profile-step-start 2 \ --profile-step-end 4 \ --profile-with-stack \ --profile-with-memory \ --profile-with-flops \ - --use-tensorboard \ - --tb-project-name /path/to/tb_logs \ + --tb-experiment-name my-experiment \ # ... other args ``` @@ -53,10 +114,76 @@ python3 relax/entrypoints/train.py \ Enabling `--profile-with-stack` and `--profile-with-memory` adds overhead. Use them for diagnostic runs, not for production training. ::: -View the trace in TensorBoard: +### 3. GPU Memory Profiling + +Records CUDA memory allocation/deallocation history for diagnosing memory leaks and OOM issues. Automatically dumps a memory snapshot on OOM. + +**Minimal usage** — enable recording and proactively dump after step 2: ```bash -tensorboard --logdir /path/to/tb_logs +python3 relax/entrypoints/train.py \ + --record-memory-history \ + --memory-snapshot-num-steps 2 \ + --tb-experiment-name my-experiment \ + # ... other args +``` + +**Advanced parameters**: + +| Parameter | Default | Description | +|---|---|---| +| `--memory-snapshot-path` | snapshot.pickle | Snapshot filename suffix | +| `--memory-snapshot-dir` | None | Custom output directory. Defaults to `traces//memory_snapshot` | +| `--memory-snapshot-num-steps` | None | Proactively dump a snapshot after the specified number of steps (0-indexed; setting 3 dumps after step 2) | +| `--memory-recorder` | torch | Backend: `torch` (PyTorch built-in) or `memray` (requires `pip install memray`) | + +View snapshots: visit [PyTorch Memory Viz](https://pytorch.org/memory_viz) and drag in the generated `.pickle` file. + +### Combined Usage + +In practice, all three profiling tools can be enabled simultaneously for a comprehensive view. Here is a complete combined example: + +```bash +python3 relax/entrypoints/train.py \ + # --- SGLang Inference Profiling --- + --sglang-profile \ + --sglang-profile-step-start 2 \ + --sglang-profile-step-end 4 \ + # --- Training Profiling --- + --use-pytorch-profiler \ + --profile-step-start 2 \ + --profile-step-end 4 \ + # --- Memory Profiling --- + --record-memory-history \ + --memory-snapshot-num-steps 2 \ + # --- Experiment name (determines trace output directory) --- + --tb-experiment-name my-profiling-run \ + # ... other training args +``` + +The above configuration produces the following directory structure: + +``` +traces/my-profiling-run/ +├── sglang_trace/ # SGLang engine traces (subdirectory per rollout step) +│ ├── rollout_2/ +│ │ ├── engine0-...-TP-0.trace.json.gz +│ │ ├── engine0-...-TP-1.trace.json.gz +│ │ ├── engine1-...-TP-0.trace.json.gz +│ │ └── ... +│ ├── rollout_3/ +│ │ └── ... +│ └── rollout_4/ +│ ├── engine0-...-TP-0.trace.json.gz +│ └── ... +├── train_trace/ # Training traces +│ ├── train_overall_rank0_dp0_tp0_pp0.....pt.trace.json.gz +│ ├── train_overall_rank1_dp0_tp1_pp0.....pt.trace.json.gz +│ └── ... +└── memory_snapshot/ # Memory snapshots + ├── memory_snapshot_time..._rank0_dp0_tp0_pp0_snapshot.pickle + ├── memory_snapshot_time..._rank1_dp0_tp1_pp0_snapshot.pickle + └── ... ``` --- diff --git a/docs/zh/guide/configuration.md b/docs/zh/guide/configuration.md index bc38d308d..a88fa6c16 100644 --- a/docs/zh/guide/configuration.md +++ b/docs/zh/guide/configuration.md @@ -88,6 +88,9 @@ | `--rollout-seed` | int | 42 | Rollout 的随机种子，用于打乱 Prompt 和随机采样 | | `--use-streaming-dataset` | flag | False | 使用流式数据集以节省内存 | | `--streaming-buffer-size` | int | 10000 | 流式数据集的缓冲区大小 | +| `--prefetch-chunk-size` | int | 32 | 每轮预取时分派到线程池的样本数。较大的值可以提高吞吐量但也会增加内存压力。仅在设置了 `--use-streaming-dataset` 且数据集包含多模态数据时生效 | +| `--prefetch-max-cached` | int | 256 | 预取缓存中保留的最大预加载样本数。缓存满时后台预取线程会暂停，直到消费者释放空间。设为 0 可禁用预取。仅在设置了 `--use-streaming-dataset` 且数据集包含多模态数据时生效 | +| `--prefetch-num-workers` | int | 1 | 预取缓冲区中用于 I/O 密集型媒体解码（视频/图像）的并行工作线程数。设为 1 可序列化所有解码操作（对 FFmpeg 非线程安全问题最安全）。较高值可提高并行度，但在某些平台上可能触发 EAGAIN 错误。仅在启用预取时生效 | | `--data-source-path` | str | `relax.engine.rollout.data_source.RolloutDataSourceWithBuffer` | Rollout 数据源类路径 | | `--start-rollout-id` | int | None | 起始 Rollout 步数。未设置时会尝试从 `--load` 的检查点中读取 | @@ -168,6 +171,16 @@ | 参数 | 类型 | 默认值 | 说明 | |------|------|--------|------| | `--sglang-mem-fraction-static` | float | - | SGLang 静态内存分配比例 | +| `--sglang-profile` | flag | False | 启用 SGLang 引擎的 torch profiling。在 Rollout 推理期间触发，每步保存 profile trace | +| `--sglang-profile-steps` | int (列表) | None | 指定要进行 SGLang profiling 的绝对 rollout step ID（0-indexed）列表。优先级高于 `--sglang-profile-step-start/end`。例如 `--sglang-profile-steps 3 10 50` | +| `--sglang-profile-step-start` | int | None | SGLang profiling 的起始 rollout step（**inclusive**，0-indexed）。与 `--sglang-profile-step-end` 配合指定连续范围。设置了 `--sglang-profile-steps` 时被忽略 | +| `--sglang-profile-step-end` | int | None | SGLang profiling 的结束 rollout step（**inclusive**，0-indexed）。与 `--sglang-profile-step-start` 配合指定连续范围。设置了 `--sglang-profile-steps` 时被忽略。例如 start=2, end=4 会采集 step 2, 3, 4 | +| `--sglang-profile-output-dir` | str | None | SGLang profile trace 的输出目录。默认使用 `traces//sglang_trace` | +| `--sglang-profile-num-steps` | int | 3 | 每轮 Rollout 中要 profile 的 SGLang 前向步数。-1 表示 profile 整个 Rollout 步，直到调用 `stop_profile` | +| `--sglang-profile-activities` | str (列表) | ["CPU", "GPU"] | 要 profile 的活动类型（例如 `CPU GPU`） | +| `--sglang-profile-by-stage` | flag | False | 按阶段（prefill/decode）分别进行 profile | +| `--sglang-profile-with-stack` | flag | False | 在 profile trace 中记录调用栈 | +| `--sglang-profile-record-shapes` | flag | False | 在 profile trace 中记录张量形状 | ### 自定义 Rollout 函数 @@ -498,7 +511,9 @@ Autoscaler YAML 配置详情请参见 [`relax/utils/autoscaler/autoscaler.yaml`] --- -## 调试参数 +## 调试与性能分析参数 + +### 调试 | 参数 | 类型 | 默认值 | 说明 | |------|------|--------|------| @@ -510,14 +525,33 @@ Autoscaler YAML 配置详情请参见 [`relax/utils/autoscaler/autoscaler.yaml`] | `--save-debug-train-data` | str | None | 保存训练数据，路径支持 `{rollout_id}` 占位符 | | `--dump-details` | str | None | 导出所有训练细节用于事后分析 | | `--check-weight-update-equal` | flag | False | 检查权重更新是否相等 | -| `--memory-snapshot-dir` | str | . | 内存快照目录 | -| `--memory-snapshot-num-steps` | int | None | 内存快照步数 | +| `--enable-cuda-memory-check` | flag | False | 在底层 NCCL 通信调用周围启用内存检查。在每次集合通信前记录可用 GPU 显存，通信失败时将内存信息附加到异常中 | + +### 训练性能 Profiling + +以下参数控制训练过程的 PyTorch Profiler 采集。Trace 文件默认保存到 `traces//train_trace/` 目录下。 + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `--use-pytorch-profiler` | flag | False | 启用 PyTorch 内置 profiler 记录训练步骤的 CUDA kernel、CPU op 和通信操作（来自 Megatron） | +| `--profile-step-start` | int | 10 | 开始 profiling 的步数偏移（**inclusive**，来自 Megatron）。指从本次训练启动后的第 N 步开始采集，非绝对 rollout ID；断点续训时计数从 0 重新开始 | +| `--profile-step-end` | int | 12 | 停止 profiling 的步数偏移（**inclusive**，来自 Megatron）。含义同上。例如 start=10, end=12 会采集 step 10, 11, 12（共 3 步） | | `--profile-target` | str (列表) | train_overall | 性能分析目标：`train_overall`、`train_actor`、`train_log_probs` | | `--profile-with-stack` | flag | False | 在 profiler trace 中记录调用栈信息 | | `--profile-with-memory` | flag | False | 在 profiler trace 中记录内存信息 | | `--profile-with-flops` | flag | False | 在 profiler trace 中估算 FLOPs | -| `--memory-recorder` | str | torch | 内存记录器：`torch`、`memray` | -| `--enable-cuda-memory-check` | flag | False | 在底层 NCCL 通信调用周围启用内存检查。在每次集合通信前记录可用 GPU 显存，通信失败时将内存信息附加到异常中 | + +### GPU 内存 Profiling + +以下参数控制 GPU 内存快照采集，用于诊断显存泄漏和 OOM 问题。Snapshot 文件可用 PyTorch Memory Viz 工具（`torch.cuda.memory._viz`）查看。 + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `--record-memory-history` | flag | False | 启用 CUDA 内存分配历史记录（来自 Megatron）。开启后会记录每次分配/释放的调用栈和张量信息，并在发生 OOM 时自动 dump snapshot | +| `--memory-snapshot-path` | str | snapshot.pickle | 内存快照文件名（来自 Megatron） | +| `--memory-snapshot-dir` | str | None | 内存快照保存目录。默认使用 `traces//memory_snapshot` | +| `--memory-snapshot-num-steps` | int | None | 在指定步数后主动 dump 内存快照（0-indexed，即设为 3 表示在第 2 步后 dump） | +| `--memory-recorder` | str | torch | 内存记录器后端：`torch`（PyTorch 内置）、`memray`（需要 `pip install memray`） | ### 网络 diff --git a/docs/zh/guide/performance-tuning.md b/docs/zh/guide/performance-tuning.md index f2c2b08e1..a7b5d9f90 100644 --- a/docs/zh/guide/performance-tuning.md +++ b/docs/zh/guide/performance-tuning.md @@ -6,27 +6,88 @@ Relax 训练吞吐量最大化实践指南。本文提到的所有参数均可 ## 性能分析 -调优前先定位瓶颈。Relax 集成了 PyTorch Profiler，可生成兼容 TensorBoard 的 trace 文件。 +调优前先定位瓶颈。Relax 内置三套互补的 profiling 工具，覆盖 **推理引擎**、**训练后端** 和 **GPU 内存** 三个维度。所有 trace 文件默认保存在 `traces//` 目录下，按子目录区分： -### 启用 Profiler +| 工具 | 目标 | 默认输出目录 | 查看方式 | +|---|---|---|---| +| SGLang Profiling | Rollout 推理的 CUDA kernel / 算子分析 | `traces//sglang_trace/` | TensorBoard or `https://ui.perfetto.dev/` | +| Training Profiling | Actor 训练 / log-probs 计算的算子分析 | `traces//train_trace/` | TensorBoard or `https://ui.perfetto.dev/` | +| Memory Profiling | GPU 显存分配历史，OOM 诊断 | `traces//memory_snapshot/` | [PyTorch Memory Viz](https://pytorch.org/memory_viz) | -Profiler 通过 `--profile-step-start` 和 `--profile-step-end`（Megatron 原生参数）配合 `--profile-target` 控制： +### Trace 文件命名规则 + +- **训练 trace** 文件名包含 `rank{global}_dp{dp}_tp{tp}_pp{pp}` 标识，例如 `train_overall_rank0_dp0_tp0_pp0.1713780123.pt.trace.json.gz` +- **内存快照** 文件名同样包含 rank 标识，例如 `memory_snapshot_time1713780123_rank0_dp0_tp0_pp0_snapshot.pickle` +- **SGLang trace** 文件以 `engine{i}` 为前缀区分不同引擎实例，例如 `engine0-1713780123-TP-0.trace.json.gz` + +### 1. SGLang 推理 Profiling + +对 Rollout 阶段所有 SGLang 引擎进行 `torch.profiler` 采集。通过 HTTP API `/start_profile` 和 `/stop_profile` 触发，不影响训练侧的 profiler。 + +**示例用法** — 每个 rollout step 都采集： ```bash python3 relax/entrypoints/train.py \ - --profile-target train_overall \ + --sglang-profile \ + --tb-experiment-name my-experiment \ + # ... 其他参数 +``` + +**指定 rollout step 范围** — 仅在 step 2、3、4 采集（start/end 均 inclusive，推荐用法，避免大量 trace 文件）： + +```bash +python3 relax/entrypoints/train.py \ + --sglang-profile \ + --sglang-profile-step-start 2 \ + --sglang-profile-step-end 4 \ + --tb-experiment-name my-experiment \ + # ... 其他参数 +``` + +也可以用 `--sglang-profile-steps` 指定不连续的 step 列表（优先级高于 start/end）： + +```bash +--sglang-profile-steps 2 5 10 +``` + +所有 step 参数均使用 **绝对 rollout ID**（0-indexed），即第 0 轮、第 1 轮 ... 与 `--start-rollout-id` 无关。 + +**进阶参数**： + +| 参数 | 默认值 | 说明 | +|---|---|---| +| `--sglang-profile-step-start` | None | profiling 起始 rollout step（**inclusive**，0-indexed） | +| `--sglang-profile-step-end` | None | profiling 结束 rollout step（**inclusive**，0-indexed）。例如 start=2, end=4 采集 step 2, 3, 4 | +| `--sglang-profile-steps` | None | 不连续 step 列表，优先级高于 start/end | +| `--sglang-profile-num-steps` | 3 | 每轮 Rollout 中采集的 SGLang 前向步数。-1 表示整轮采集 | +| `--sglang-profile-activities` | CPU GPU | 要采集的活动类型 | +| `--sglang-profile-by-stage` | False | 按 prefill / decode 阶段分别采集 | +| `--sglang-profile-with-stack` | False | 记录 Python 调用栈 | +| `--sglang-profile-record-shapes` | False | 记录张量形状信息 | +| `--sglang-profile-output-dir` | None | 自定义输出目录。默认 `traces//sglang_trace` | + +### 2. 训练 Profiling（PyTorch Profiler） + +对 Actor 训练步骤进行 `torch.profiler` 采集，生成兼容 TensorBoard 的 trace 文件。 + +**示例用法** — 采集第 2、3、4 步（start/end 均 inclusive）： + +```bash +python3 relax/entrypoints/train.py \ + --use-pytorch-profiler \ --profile-step-start 2 \ --profile-step-end 4 \ - --use-tensorboard \ - --tb-project-name /path/to/tb_logs \ + --tb-experiment-name my-experiment \ # ... 其他参数 ``` -可以指定多个分析目标：`--profile-target train_overall train_actor train_log_probs`。 +::: tip +`--profile-step-start` 和 `--profile-step-end` 均为 **inclusive**，是从本次训练启动后的 **步数偏移**，不是绝对 rollout ID。断点续训时计数从 0 重新开始。例如 start=2, end=4 采集 step 2, 3, 4（共 3 步）。 -### Profiler 详细信息标志 +语义与 `--sglang-profile-step-start/end` 相同（两端均 inclusive）。 +::: -以下三个标志控制 Profiler 记录的额外信息： +**详细信息标志**： | 标志 | 作用 | |---|---| @@ -34,18 +95,18 @@ python3 relax/entrypoints/train.py \ | `--profile-with-memory` | 在 trace 中跟踪 CUDA 显存分配/释放。用于发现显存尖峰 | | `--profile-with-flops` | 估算每个算子的 FLOPs。用于计算硬件利用率 (MFU) | -启用全部详细信息标志的示例： +**完整示例**： ```bash python3 relax/entrypoints/train.py \ + --use-pytorch-profiler \ --profile-target train_overall \ --profile-step-start 2 \ --profile-step-end 4 \ --profile-with-stack \ --profile-with-memory \ --profile-with-flops \ - --use-tensorboard \ - --tb-project-name /path/to/tb_logs \ + --tb-experiment-name my-experiment \ # ... 其他参数 ``` @@ -53,10 +114,76 @@ python3 relax/entrypoints/train.py \ 启用 `--profile-with-stack` 和 `--profile-with-memory` 会增加额外开销。建议仅在诊断时使用，不用于生产训练。 ::: -使用 TensorBoard 查看 trace： +### 3. GPU 内存 Profiling + +记录 CUDA 显存分配/释放历史，用于诊断显存泄漏和 OOM 问题。在发生 OOM 时会自动 dump 内存快照。 + +**最小用法** — 开启记录 + 在第 2 步后主动 dump： ```bash -tensorboard --logdir /path/to/tb_logs +python3 relax/entrypoints/train.py \ + --record-memory-history \ + --memory-snapshot-num-steps 2 \ + --tb-experiment-name my-experiment \ + # ... 其他参数 +``` + +**进阶参数**： + +| 参数 | 默认值 | 说明 | +|---|---|---| +| `--memory-snapshot-path` | snapshot.pickle | 快照文件名后缀 | +| `--memory-snapshot-dir` | None | 自定义输出目录。默认 `traces//memory_snapshot` | +| `--memory-snapshot-num-steps` | None | 在指定步数后主动 dump 快照（0-indexed，设 3 表示第 2 步后 dump） | +| `--memory-recorder` | torch | 后端选择：`torch`（PyTorch 内置）、`memray`（需 `pip install memray`） | + +查看快照：访问 [PyTorch Memory Viz](https://pytorch.org/memory_viz)，拖入生成的 `.pickle` 文件。 + +### 三种 Profiling 联合使用 + +实际诊断中，可同时开启三种 profiling 以获得全面视图。以下是一个完整的联合使用示例： + +```bash +python3 relax/entrypoints/train.py \ + # --- SGLang 推理 Profiling --- + --sglang-profile \ + --sglang-profile-step-start 2 \ + --sglang-profile-step-end 4 \ + # --- 训练 Profiling --- + --use-pytorch-profiler \ + --profile-step-start 2 \ + --profile-step-end 4 \ + # --- 内存 Profiling --- + --record-memory-history \ + --memory-snapshot-num-steps 2 \ + # --- 实验名（决定 trace 输出目录）--- + --tb-experiment-name my-profiling-run \ + # ... 其他训练参数 +``` + +上述配置会产出如下目录结构： + +``` +traces/my-profiling-run/ +├── sglang_trace/ # SGLang 引擎 trace（按 rollout step 分目录） +│ ├── rollout_2/ +│ │ ├── engine0-...-TP-0.trace.json.gz +│ │ ├── engine0-...-TP-1.trace.json.gz +│ │ ├── engine1-...-TP-0.trace.json.gz +│ │ └── ... +│ ├── rollout_3/ +│ │ └── ... +│ └── rollout_4/ +│ ├── engine0-...-TP-0.trace.json.gz +│ └── ... +├── train_trace/ # 训练 trace +│ ├── train_overall_rank0_dp0_tp0_pp0.....pt.trace.json.gz +│ ├── train_overall_rank1_dp0_tp1_pp0.....pt.trace.json.gz +│ └── ... +└── memory_snapshot/ # 内存快照 + ├── memory_snapshot_time..._rank0_dp0_tp0_pp0_snapshot.pickle + ├── memory_snapshot_time..._rank1_dp0_tp1_pp0_snapshot.pickle + └── ... ``` --- diff --git a/relax/backends/sglang/arguments.py b/relax/backends/sglang/arguments.py index 18dacc9e2..76ea6d488 100644 --- a/relax/backends/sglang/arguments.py +++ b/relax/backends/sglang/arguments.py @@ -105,6 +105,86 @@ def add_sglang_arguments(parser): parser.set_defaults(router_balance_abs_threshold=10, router_balance_rel_threshold=1.2) parser.add_argument("--sglang-server-concurrency", type=int, default=512) + # SGLang profiling arguments — triggers /start_profile and /stop_profile HTTP API + # on all SGLang engines during rollout inference. + # Can also be used standalone via: python tools/profile_rollout.py + parser.add_argument( + "--sglang-profile", + action="store_true", + default=False, + help="Enable torch profiling on SGLang engines during rollout. Profile traces will be saved per rollout step.", + ) + parser.add_argument( + "--sglang-profile-output-dir", + type=str, + default=None, + help=("Output directory for SGLang profile traces. Defaults to traces//sglang_trace."), + ) + parser.add_argument( + "--sglang-profile-num-steps", + type=int, + default=3, + help="Number of SGLang forward steps to profile per rollout. " + "If -1, profiles the entire rollout step until stop_profile is called.", + ) + parser.add_argument( + "--sglang-profile-activities", + type=str, + nargs="+", + default=["CPU", "GPU"], + help="Activities to profile (e.g., CPU GPU).", + ) + parser.add_argument( + "--sglang-profile-by-stage", + action="store_true", + default=False, + help="Profile by stage (prefill/decode) separately.", + ) + parser.add_argument( + "--sglang-profile-with-stack", + action="store_true", + default=False, + help="Record call stack in profile traces.", + ) + parser.add_argument( + "--sglang-profile-record-shapes", + action="store_true", + default=False, + help="Record tensor shapes in profile traces.", + ) + parser.add_argument( + "--sglang-profile-steps", + type=int, + nargs="+", + default=None, + help=( + "List of absolute rollout step IDs (0-indexed) at which to enable SGLang profiling. " + "Takes precedence over --sglang-profile-step-start/end when set. " + "Example: --sglang-profile-steps 3 10 50" + ), + ) + parser.add_argument( + "--sglang-profile-step-start", + type=int, + default=None, + help=( + "Start of the rollout step range for SGLang profiling (inclusive, 0-indexed). " + "Used together with --sglang-profile-step-end to specify a contiguous range. " + "Ignored if --sglang-profile-steps is set." + ), + ) + parser.add_argument( + "--sglang-profile-step-end", + type=int, + default=None, + help=( + "End of the rollout step range for SGLang profiling (inclusive, 0-indexed). " + "Used together with --sglang-profile-step-start to specify a contiguous range. " + "Ignored if --sglang-profile-steps is set. " + "Example: --sglang-profile-step-start 2 --sglang-profile-step-end 4 profiles steps 2, 3, 4." + ), + ) + old_add_argument = parser.add_argument skipped_args = [ diff --git a/relax/backends/sglang/sglang_engine.py b/relax/backends/sglang/sglang_engine.py index 1014d77b6..f9da37078 100644 --- a/relax/backends/sglang/sglang_engine.py +++ b/relax/backends/sglang/sglang_engine.py @@ -736,40 +736,6 @@ def post_process_weights( }, ) - def start_profile( - self, - # The output directory - output_dir: str | None = None, - # If set, it profile as many as this number of steps. - # If it is set, profiling is automatically stopped after this step, and - # the caller doesn't need to run stop_profile. - start_step: int | None = None, - num_steps: int | None = None, - activities: list[str] | None = None, - profile_by_stage: bool = False, - with_stack: bool | None = None, - record_shapes: bool | None = None, - ): - response = requests.post( - f"http://{self.server_host}:{self.server_port}/start_profile", - json={ - "output_dir": output_dir, - "start_step": start_step, - "num_steps": num_steps, - "activities": activities, - "profile_by_stage": profile_by_stage, - "with_stack": with_stack, - "record_shapes": record_shapes, - }, - ) - response.raise_for_status() - return response - - def stop_profile(self): - response = requests.post(f"http://{self.server_host}:{self.server_port}/stop_profile", json={}) - response.raise_for_status() - return response - def simulate_crash(self): if self.args.rollout_external or not getattr(self, "process", None): logger.info( diff --git a/relax/engine/rollout/data_source.py b/relax/engine/rollout/data_source.py index b57913485..784eb0678 100644 --- a/relax/engine/rollout/data_source.py +++ b/relax/engine/rollout/data_source.py @@ -1,7 +1,6 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. import abc -import copy import os from pathlib import Path @@ -18,6 +17,25 @@ logger = get_logger(__name__) +def _shallow_copy_sample(src: Sample) -> Sample: + """Create a lightweight copy of a Sample that *shares* heavy read-only + payloads (``multimodal_inputs``) with the source.""" + new = Sample.__new__(Sample) + new.__dict__.update(src.__dict__) + # Shallow-copy mutable containers that downstream code mutates in-place. + new.tokens = list(src.tokens) + new.rollout_tokens = list(src.rollout_tokens) + new.weight_versions = list(src.weight_versions) + new.metadata = dict(src.metadata) + # Per-sample accumulators — create fresh instances. + new.spec_info = Sample.SpecInfo() + new.prefix_cache_info = Sample.PrefixCacheInfo() + # ``multimodal_inputs`` is read-only downstream — share the reference. + # ``multimodal_train_inputs`` is *set* (not mutated) per-sample by the + # processor, so sharing the initial ``None`` is fine. + return new + + def _create_dataset(args, tokenizer, processor, multimodal_config=None): """Factory function to create dataset based on configuration. @@ -39,8 +57,15 @@ def _create_dataset(args, tokenizer, processor, multimodal_config=None): from relax.utils.data.streaming_dataset import StreamingDataset buffer_size = getattr(args, "streaming_buffer_size", 10000) - - logger.info(f"Using StreamingDataset with buffer_size={buffer_size}") + prefetch_chunk_size = getattr(args, "prefetch_chunk_size", 32) + prefetch_max_cached = getattr(args, "prefetch_max_cached", 256) + prefetch_num_workers = getattr(args, "prefetch_num_workers", 1) + + logger.info( + f"Using StreamingDataset with buffer_size={buffer_size}, " + f"prefetch_chunk_size={prefetch_chunk_size}, prefetch_max_cached={prefetch_max_cached}, " + f"prefetch_num_workers={prefetch_num_workers}" + ) return StreamingDataset( path=args.prompt_data, tokenizer=tokenizer, @@ -57,6 +82,9 @@ def _create_dataset(args, tokenizer, processor, multimodal_config=None): use_audio_in_video=args.use_audio_in_video, seed=args.rollout_seed, buffer_size=buffer_size, + prefetch_chunk_size=prefetch_chunk_size, + prefetch_max_cached=prefetch_max_cached, + prefetch_num_workers=prefetch_num_workers, multimodal_config=multimodal_config, ) else: @@ -172,7 +200,7 @@ def get_samples(self, num_samples): for prompt_sample in prompt_samples: group = [] for _ in range(self.args.n_samples_per_prompt): - sample = copy.deepcopy(prompt_sample) + sample = _shallow_copy_sample(prompt_sample) sample.group_index = self.sample_group_index sample.index = self.sample_index self.sample_index += 1 diff --git a/relax/engine/rollout/sglang_rollout.py b/relax/engine/rollout/sglang_rollout.py index 7e0d30b66..567ef9b74 100644 --- a/relax/engine/rollout/sglang_rollout.py +++ b/relax/engine/rollout/sglang_rollout.py @@ -36,6 +36,7 @@ from relax.utils.http_utils import get, post from relax.utils.logging_utils import get_logger from relax.utils.misc import SingletonMeta, load_function +from relax.utils.profile_utils import start_sglang_profile, stop_sglang_profile from relax.utils.timer import Timer from relax.utils.training.eval_config import EvalDatasetConfig from relax.utils.training.train_dump_utils import save_debug_rollout_data @@ -117,12 +118,16 @@ def reset(self) -> None: ) # tasks that should not be aborted (abort_count >= partial_rollout_max_aborted_count) self.aborted = False self.evaluating = getattr(self, "evaluating", 0) # preserve eval state across resets + # Pre-fetched data ObjectRef for cross-step overlap. + # Persisted across reset() calls so the ref submitted at the end of + # step N is consumed at the beginning of step N+1. + if not hasattr(self, "prefetched_samples_ref"): + self.prefetched_samples_ref: ray.ObjectRef | None = None def submit_generate_tasks(self, samples: list[list[Sample]]) -> None: max_aborted_count = getattr(self.args, "partial_rollout_max_aborted_count", None) for group in samples: task = asyncio.create_task( - # submit a group of samples as a single task. generate_and_rm_group( self.args, group, @@ -260,7 +265,16 @@ async def generate( _t_mm_encode: float | None = None if sample.multimodal_inputs: - encoded_mm, _t_mm_encode = await _encode_multimodal_inputs(sample.multimodal_inputs) + # Use pre-encoded data from group-level de-dup if available; otherwise encode inline. + pre_encoded = getattr(sample, "_pre_encoded_mm", None) + if pre_encoded is not None: + encoded_mm = pre_encoded + _t_mm_encode = getattr(sample, "_pre_encoded_mm_elapsed", 0.0) + del sample._pre_encoded_mm + if hasattr(sample, "_pre_encoded_mm_elapsed"): + del sample._pre_encoded_mm_elapsed + else: + encoded_mm, _t_mm_encode = await _encode_multimodal_inputs(sample.multimodal_inputs) payload.update(encoded_mm) # Use existing tokens for multi-turn or tokenize the new prompt @@ -486,6 +500,17 @@ async def generate_and_rm_group( if sample.session_id is None: sample.session_id = str(uuid.uuid4()) + # Group-level multimodal encoding de-duplication: when samples in the same + # group share the same multimodal_inputs object (e.g. after shallow-copy in + # data_source), encode once and attach the result to every sample so that + # generate() picks up the pre-encoded data instead of re-encoding per sample. + first_mm = getattr(group[0], "multimodal_inputs", None) + if first_mm is not None and all(getattr(s, "multimodal_inputs", None) is first_mm for s in group[1:]): + encoded_mm, t_enc = await _encode_multimodal_inputs(first_mm) + for sample in group: + sample._pre_encoded_mm = encoded_mm + sample._pre_encoded_mm_elapsed = t_enc + tasks = [] for idx, sample in enumerate(group): current_sampling_params = sampling_params.copy() @@ -616,6 +641,9 @@ async def generate_rollout_async( state = GenerateState(args) + # Start SGLang profiling if enabled + await start_sglang_profile(args, rollout_id) + # instantiate data filters dynamic_filter = ( load_function(args.dynamic_sampling_filter_path) if args.dynamic_sampling_filter_path is not None else None @@ -636,10 +664,21 @@ async def generate_rollout_async( total_transfer_samples = 0 get_samples_times: list[float] = [] + loop = asyncio.get_running_loop() + while len(data) < target_data_size: while state.remaining_batch_size < target_data_size: _t_get_samples = monotonic() - samples = ray.get(data_source.get_samples.remote(args.over_sampling_batch_size, args.fully_async)) + + if state.prefetched_samples_ref is not None: + ref = state.prefetched_samples_ref + state.prefetched_samples_ref = None + logger.info(f"Rollout step {rollout_id}: using pre-fetched data from previous step") + else: + ref = data_source.get_samples.remote(args.over_sampling_batch_size, args.fully_async) + + samples = await loop.run_in_executor(None, ray.get, ref) + get_samples_times.append(monotonic() - _t_get_samples) num_old_samples = len(samples) - args.over_sampling_batch_size logger.info( @@ -761,11 +800,19 @@ async def generate_rollout_async( f"Total yielded: {total_transfer_samples - num_old_samples}/{target_data_size - num_old_samples} for step: {rollout_id}" ) + if not args.fully_async: + state.prefetched_samples_ref = data_source.get_samples.remote(args.over_sampling_batch_size, args.fully_async) + logger.info(f"Rollout step {rollout_id}: pre-submitted data fetch for next step") + logger.info(f"Generator exhausted. Waiting for {len(transfer_tasks)} transfer tasks to complete...") # Wait for all transfer tasks to complete if transfer_tasks: await asyncio.gather(*transfer_tasks) pbar.close() + + # Stop SGLang profiling if enabled (no-op if num_steps was set — SGLang auto-stops) + await stop_sglang_profile(args, rollout_id) + sample = data[-1][0][0] if isinstance(data[-1][0], list) else data[-1][0] logger.info( f"Finish rollout: {[str(sample.prompt) + sample.response]}, label: {str(sample.label)[:100]}, reward: {sample.reward}", diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 98ea94fd7..6dfc4255f 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -682,6 +682,33 @@ def add_data_arguments(parser): default=10000, help="Buffer size for streaming dataset.", ) + parser.add_argument( + "--prefetch-chunk-size", + type=int, + default=32, + help="Number of samples to dispatch to the thread-pool in each prefetch round. " + "Larger values increase throughput but also memory pressure. Only effective when " + "--use-streaming-dataset is set and the dataset contains multimodal data.", + ) + parser.add_argument( + "--prefetch-max-cached", + type=int, + default=256, + help="Maximum number of pre-loaded samples kept in the prefetch cache. " + "When the cache is full the background prefetch thread pauses until consumers " + "free space. Set to 0 to disable prefetching. Only effective when " + "--use-streaming-dataset is set and the dataset contains multimodal data.", + ) + parser.add_argument( + "--prefetch-num-workers", + type=int, + default=1, + help="Number of parallel worker threads inside the prefetch buffer for " + "I/O-bound media decoding (video/image). Set to 1 to serialise all " + "decoding (safest for FFmpeg which is not fully thread-safe). " + "Higher values increase parallelism but may trigger EAGAIN errors " + "on some platforms. Only effective when prefetching is enabled.", + ) # TODO: maybe add an num_epoch and calculate the num_rollout from buffer parser.add_argument( "--num-rollout", @@ -816,7 +843,6 @@ def add_data_arguments(parser): "for true parallelism without GIL contention." ), ) - parser.add_argument("--metadata-key", type=str, default="metadata", help="JSON dataset key") parser.add_argument( "--tool-key", @@ -1531,12 +1557,15 @@ def add_debug_arguments(parser): parser.add_argument( "--memory-snapshot-dir", type=str, - default=".", + default=None, + help=("Directory for memory snapshot dumps. Defaults to traces//memory_snapshot."), ) parser.add_argument( "--memory-snapshot-num-steps", type=int, default=None, + help="Number of rollout steps after which to dump the memory snapshot. " + "For example, --memory-snapshot-num-steps 3 dumps after step 2 (0-indexed).", ) parser.add_argument( "--profile-target", diff --git a/relax/utils/data/streaming_dataset.py b/relax/utils/data/streaming_dataset.py index 97c04a5a7..35d4ea204 100644 --- a/relax/utils/data/streaming_dataset.py +++ b/relax/utils/data/streaming_dataset.py @@ -28,8 +28,11 @@ import json import os import random +import threading +import time from bisect import bisect_right from collections import OrderedDict +from concurrent.futures import ThreadPoolExecutor from typing import Any, Iterator, Optional @@ -57,6 +60,7 @@ "CompositeStreamingReader", "SampleBuffer", "IndexManager", + "PrefetchBuffer", ] @@ -441,6 +445,241 @@ def load_state(self, state: dict) -> None: self.reset(position=position, epoch_id=epoch_id) +class PrefetchBuffer: + """Background prefetch buffer for multimodal data loading. + + Run a background thread that pre-loads and pre-processes samples + (including heavy video/image I/O) **in the exact order** they will be consumed. + + Key design: + - ``set_index_order(indices)`` is called once (at ``shuffle`` time) with + the **entire** upcoming index sequence. The background thread starts + fetching immediately, well before ``get_batch`` is called. + - ``get(idx)`` pops from the cache (near-zero latency on hit) or falls + back to a synchronous single-sample fetch on miss. + - The cache is bounded by ``max_cached``; when full the prefetch thread + pauses until consumers free space via ``get()`` calls. + - A ``ThreadPoolExecutor`` is used inside the prefetch thread to + parallelize video/image decoding across multiple files within a chunk, + since PyAV/FFmpeg releases the GIL during C-level decoding. + + Lifecycle:: + + buf = PrefetchBuffer(process_fn, chunk_size=16, max_cached=256, num_workers=4) + buf.set_index_order([3, 7, 1, 5, ...]) # triggers background loading + sample = buf.get(3) # instant cache hit + """ + + def __init__( + self, + process_fn, + chunk_size: int = 32, + max_cached: int = 256, + num_workers: int = 4, + ): + """Initialize the prefetch buffer. + + Args: + process_fn: ``fn(idx: int) -> Optional[Sample]`` — load and + process a single sample by index. + chunk_size: Number of indices to submit to the thread-pool at + a time inside the prefetch loop. + max_cached: Maximum number of samples to keep in the cache + before the prefetch thread pauses. + num_workers: Number of parallel workers in the internal + ``ThreadPoolExecutor`` for I/O-bound decoding. + """ + self._process_fn = process_fn + self._chunk_size = chunk_size + self._max_cached = max_cached + self._num_workers = num_workers + + # Thread-safe cache: idx -> Optional[Sample] + self._cache: dict[int, Optional[Sample]] = {} + self._lock = threading.Lock() + + # Ordered index sequence set by set_index_order + self._indices: list[int] = [] + self._pos: int = 0 + + # Flow control: cleared when cache is full, set when space is freed + self._space_available = threading.Event() + self._space_available.set() + + # Stats + self._prefetch_hits = 0 + self._prefetch_misses = 0 + + # Thread lifecycle + self._stop = threading.Event() + self._thread: Optional[threading.Thread] = None + + logger.info( + f"PrefetchBuffer created: max_cached={max_cached}, chunk_size={chunk_size}, num_workers={num_workers}" + ) + + # -- Public API -------------------------------------------------------- + + def set_index_order(self, indices: list[int]) -> None: + """Reset the cache and start prefetching in *indices* order. + + Called at the beginning of each epoch (from + ``StreamingDataset.shuffle``) with the full upcoming index sequence. + The prefetch thread starts loading immediately so that later ``get()`` + calls hit the cache. + """ + # Stop any running prefetch thread + self._stop.set() + if self._thread is not None and self._thread.is_alive(): + self._thread.join(timeout=10) + if self._thread.is_alive(): + logger.warning("Previous prefetch thread did not stop within 10s; it will exit on its own stop-event") + + with self._lock: + self._cache.clear() + self._indices = list(indices) + self._pos = 0 + + # Create a fresh stop-event for the new thread so the old thread + # (if still draining) keeps seeing its own set() signal and exits. + self._stop = threading.Event() + self._space_available.set() + stop_event = self._stop + self._thread = threading.Thread(target=self._run, args=(stop_event,), daemon=True, name="prefetch-worker") + self._thread.start() + logger.info(f"PrefetchBuffer: started prefetching {len(indices)} samples") + + def get(self, idx: int) -> Optional[Sample]: + """Return the sample for *idx*. + + Pops from the prefetch cache on hit. On miss, performs a blocking + single-index fetch via ``process_fn``. + """ + with self._lock: + if idx in self._cache: + sample = self._cache.pop(idx) + self._prefetch_hits += 1 + # Signal prefetch thread that space is available + self._space_available.set() + return sample + + # Cache miss — synchronous fallback + self._prefetch_misses += 1 + try: + return self._process_fn(idx) + except Exception: + logger.exception(f"Prefetch fallback failed for index {idx}") + return None + + def stop(self) -> None: + """Signal the prefetch thread to stop.""" + self._stop.set() + # Unblock if waiting on space + self._space_available.set() + if self._thread is not None and self._thread.is_alive(): + self._thread.join(timeout=15) + if self._thread.is_alive(): + logger.warning("Prefetch thread did not terminate within 15s") + + def clear(self) -> None: + """Clear the cache and reset position (without stopping the thread).""" + with self._lock: + self._cache.clear() + self._space_available.set() + + @property + def hit_rate(self) -> float: + """Return prefetch cache hit rate.""" + total = self._prefetch_hits + self._prefetch_misses + return self._prefetch_hits / total if total > 0 else 0.0 + + @property + def cache_size(self) -> int: + """Return current cache size.""" + with self._lock: + return len(self._cache) + + # -- Background thread ------------------------------------------------- + + def _run(self, stop_event: threading.Event) -> None: + """Background prefetch loop. + + Iterates through ``self._indices`` in order, loading chunks in parallel + via a ``ThreadPoolExecutor``. Pauses when the cache is full and + resumes when ``get()`` frees space. + + Args: + stop_event: Thread-local stop signal. Each thread receives its + own ``Event`` so that ``set_index_order`` can replace + ``self._stop`` for the next thread without accidentally + un-stopping this one. + """ + _MAX_SUBMIT_RETRIES = 3 + consecutive_failures = 0 + + with ThreadPoolExecutor(max_workers=self._num_workers, thread_name_prefix="pf") as pool: + while not stop_event.is_set(): + # 1. Get next chunk of indices + with self._lock: + if self._pos >= len(self._indices): + break # All indices have been dispatched + chunk = self._indices[self._pos : self._pos + self._chunk_size] + self._pos += len(chunk) + + # 2. Filter out indices already in cache + with self._lock: + to_fetch = [i for i in chunk if i not in self._cache] + if not to_fetch: + continue + + # 3. Wait until cache has room for this chunk + while not stop_event.is_set(): + with self._lock: + if len(self._cache) + len(to_fetch) <= self._max_cached: + break + self._space_available.clear() + # Wait for consumers to pop entries + if not self._space_available.wait(timeout=0.1): + continue + + if stop_event.is_set(): + return + + # 4. Parallel-fetch all samples in the chunk + try: + futures = {idx: pool.submit(self._process_fn, idx) for idx in to_fetch} + results = {} + for idx, fut in futures.items(): + try: + results[idx] = fut.result(timeout=120) + except Exception: + logger.warning(f"Prefetch failed for index {idx}", exc_info=True) + results[idx] = None + consecutive_failures = 0 + except Exception: + consecutive_failures += 1 + logger.exception( + f"Prefetch chunk submission failed (attempt {consecutive_failures}/{_MAX_SUBMIT_RETRIES})" + ) + if consecutive_failures >= _MAX_SUBMIT_RETRIES: + logger.error("Prefetch thread aborting after too many consecutive submission failures") + break + time.sleep(0.5) + with self._lock: + self._pos -= len(chunk) + continue + + # 5. Store results in cache + with self._lock: + for idx, sample in results.items(): + self._cache[idx] = sample + + logger.info( + f"Prefetch thread finished. Hit rate: {self.hit_rate:.1%} " + f"(hits={self._prefetch_hits}, misses={self._prefetch_misses})" + ) + + class StreamingDataset(BaseDataset): """Memory-efficient streaming dataset with on-demand loading. @@ -449,6 +688,7 @@ class StreamingDataset(BaseDataset): Features: - Lazy loading: Only loads data when accessed - LRU caching: Caches recently accessed samples + - Background prefetching: Pre-loads multimodal data in background thread - Shuffle support: Epoch-based reproducible shuffling - Filter support: Length filtering done at access time @@ -481,7 +721,9 @@ def __init__( apply_chat_template_kwargs: Optional[dict] = None, use_audio_in_video: bool = False, buffer_size: int = 10000, - prefetch_size: int = 100, + prefetch_chunk_size: int = 32, + prefetch_max_cached: int = 256, + prefetch_num_workers: int = 1, multimodal_config: MultimodalConfig = None, ): """Initialize the streaming dataset. @@ -501,8 +743,15 @@ def __init__( apply_chat_template: Whether to apply chat template apply_chat_template_kwargs: Additional kwargs for chat template use_audio_in_video: Whether to extract audio from video files for multimodal processing - buffer_size: Maximum samples to cache - prefetch_size: Number of samples to prefetch (not implemented yet) + buffer_size: Maximum samples to cache in LRU buffer + prefetch_chunk_size: Number of samples dispatched to the thread-pool + in each prefetch round + prefetch_max_cached: Maximum number of pre-loaded samples in the + prefetch cache. Set to 0 to disable prefetching. + prefetch_num_workers: Number of parallel worker threads inside the + prefetch buffer for I/O-bound media decoding. Set to 1 to + serialise decoding (avoids FFmpeg thread-safety issues). + multimodal_config: MultimodalConfig for multimodal processing """ # Initialize base class super().__init__( @@ -534,6 +783,41 @@ def __init__( self._filter_count = 0 self._total_processed = 0 + # Prefetch buffer for overlapping multimodal I/O with compute. + # Only enabled when multimodal_keys are set and prefetch_max_cached > 0. + self._prefetch_buffer: Optional[PrefetchBuffer] = None + if multimodal_keys and prefetch_max_cached > 0: + self._prefetch_buffer = PrefetchBuffer( + process_fn=self._prefetch_process_single, + chunk_size=prefetch_chunk_size, + max_cached=prefetch_max_cached, + num_workers=prefetch_num_workers, + ) + logger.info( + f"StreamingDataset: prefetch enabled with " + f"chunk_size={prefetch_chunk_size}, max_cached={prefetch_max_cached}, " + f"num_workers={prefetch_num_workers}" + ) + self._prefetch_hits_log_counter = 0 + + def _prefetch_process_single(self, idx: int) -> Optional[Sample]: + """Process a single index for prefetching. + + Called by the PrefetchBuffer's worker threads. Each call loads + a single sample (including heavy video/image I/O) and returns it. + + NOTE: We intentionally do NOT access ``self.buffer`` (SampleBuffer) + here because SampleBuffer is not thread-safe and these calls run + in parallel worker threads. + """ + try: + raw_data = self.reader[idx] + sample = self._process_raw_data(raw_data) + return sample + except Exception as e: + logger.warning(f"Prefetch: error processing index {idx}: {e}") + return None + def __len__(self) -> int: """Return total number of samples in the dataset.""" return len(self.reader) @@ -541,11 +825,24 @@ def __len__(self) -> int: def shuffle(self, epoch_id: int) -> None: """Shuffle the dataset for a new epoch. + When prefetch is enabled, passes the **remaining** shuffled index + sequence (from the current position onward) to the + ``PrefetchBuffer`` so the background thread starts loading + immediately — well before ``get_batch`` is called. + Args: epoch_id: Epoch identifier """ self.index_manager.shuffle(epoch_id) self.epoch_id = epoch_id + # Trigger prefetch with the remaining upcoming index order + if self._prefetch_buffer is not None and self.index_manager.indices is not None: + remaining = self.index_manager.indices[self.index_manager.position :] + self._prefetch_buffer.set_index_order(list(remaining)) + logger.info( + f"Prefetch: triggered for epoch {epoch_id}, " + f"{len(remaining)} indices remaining (position={self.index_manager.position})" + ) def _process_raw_data(self, data: dict) -> Optional[Sample]: """Process raw data into a Sample. @@ -580,40 +877,97 @@ def get_batch(self, n: int) -> tuple[list[Sample], bool]: Automatically skips filtered samples and handles epoch boundaries. + When prefetch is enabled, indices are consumed **one at a time** so + that ``IndexManager.position`` stays exactly in sync with the index + sequence given to ``PrefetchBuffer.set_index_order()``. + + Without prefetch, indices are fetched in small batches for + efficiency (acceptable since there is no ordering contract to + honour with a background thread). + Args: n: Number of samples to get Returns: (samples, crossed_epoch): List of samples and whether an epoch boundary was crossed """ - samples = [] + if self._prefetch_buffer is not None: + return self._get_batch_prefetch(n) + return self._get_batch_no_prefetch(n) + + def _get_batch_prefetch(self, n: int) -> tuple[list[Sample], bool]: + """Prefetch-aware path: consume indices one-by-one to stay aligned.""" + samples: list[Sample] = [] crossed_epoch = False - max_attempts = n * 10 # Prevent infinite loop if too many filtered + max_attempts = n * 10 + + for _ in range(max_attempts): + if len(samples) >= n: + break + + indices, epoch_crossed = self.index_manager.get_next_indices(1) + if epoch_crossed and not crossed_epoch: + crossed_epoch = True + # IndexManager already shuffled the new epoch internally. + # Re-trigger prefetch immediately for the remaining indices + # so subsequent get() calls hit the cache instead of falling + # back to synchronous loading. + remaining = self.index_manager.indices[self.index_manager.position :] + self._prefetch_buffer.set_index_order(list(remaining)) + logger.info( + f"Prefetch: epoch crossing detected, re-triggered with " + f"{len(remaining)} indices (epoch={self.index_manager.current_epoch})" + ) + idx = indices[0] + + sample = self._prefetch_buffer.get(idx) + + if sample is None: + # Prefetch returned None — either the sample was filtered + # out during prefetch or prefetch failed; skip it. + continue + + samples.append(sample) + + if len(samples) < n: + logger.warning( + f"Could only get {len(samples)}/{n} samples after {max_attempts} attempts. " + f"Filter rate: {self._filter_count}/{self._total_processed}" + ) + + if self._prefetch_hits_log_counter % 10 == 0: + logger.info( + f"Prefetch stats: hit_rate={self._prefetch_buffer.hit_rate:.1%}, " + f"cache_size={self._prefetch_buffer.cache_size}" + ) + self._prefetch_hits_log_counter += 1 + + return samples, crossed_epoch + + def _get_batch_no_prefetch(self, n: int) -> tuple[list[Sample], bool]: + """Non-prefetch path: fetch indices in small batches for efficiency.""" + samples: list[Sample] = [] + crossed_epoch = False + max_attempts = n * 10 attempts = 0 while len(samples) < n and attempts < max_attempts: - # Calculate how many more we need (with some buffer for filtered samples) need = n - len(samples) - fetch_size = min(need * 2, 100) # Fetch extra to account for filtering + fetch_size = min(need * 2, 100) indices, epoch_crossed = self.index_manager.get_next_indices(fetch_size) crossed_epoch = crossed_epoch or epoch_crossed for idx in indices: if len(samples) >= n: - # Put back unused indices break attempts += 1 - # Check cache first sample = self.buffer.get(idx) - if sample is None: - # Load and process raw_data = self.reader[idx] sample = self._process_raw_data(raw_data) - if sample is not None: self.buffer.put(idx, sample) diff --git a/relax/utils/profile_utils.py b/relax/utils/profile_utils.py index e7a6a7437..c3a15f71b 100644 --- a/relax/utils/profile_utils.py +++ b/relax/utils/profile_utils.py @@ -1,5 +1,7 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. +import asyncio +import os import time import traceback from pathlib import Path @@ -14,6 +16,18 @@ logger = get_logger(__name__) +def _get_rank_tag() -> str: + """Build a rank tag string like ``rank0_dp0_tp0_pp0`` from Megatron mpu.""" + global_rank = torch.distributed.get_rank() + from megatron.core import mpu + + dp = mpu.get_data_parallel_rank(with_context_parallel=True) + tp = mpu.get_tensor_model_parallel_rank() + pp = mpu.get_pipeline_model_parallel_rank() + + return f"rank{global_rank}_dp{dp}_tp{tp}_pp{pp}" + + class TrainProfiler: def __init__(self, args): self.args = args @@ -22,7 +36,7 @@ def __init__(self, args): if args.use_pytorch_profiler and ("train_overall" in args.profile_target): self._torch_profiler_overall = _create_torch_profiler(args, name="train_overall") - logger.info(f"PyTorch profiler for overall training is enabled, dump dir: {args.tensorboard_dir}") + logger.info(f"PyTorch profiler for overall training is enabled, dump dir: {_get_train_trace_dir(args)}") if args.record_memory_history and ("train_overall" in args.profile_target): self._memory_profiler_overall = _BaseMemoryProfiler.create(args) @@ -63,18 +77,42 @@ def _profile_simple_loop(iterator, args, name): torch_profiler.step() +def _get_trace_base_dir(args): + """Return the base directory for all profiler outputs. + + Uses ``./traces/`` as the default location. Falls back + to ``./traces/`` when ``--tb-experiment-name`` is not set. + """ + task_name = getattr(args, "tb_experiment_name", None) + if task_name is None: + from datetime import datetime + + task_name = datetime.now().strftime("%Y%m%d_%H%M%S") + return os.path.join("traces", task_name) + + +def _get_train_trace_dir(args): + """Return the output directory for training profiler traces. + + Uses ``./traces//train_trace`` as the default location. + """ + return os.path.join(_get_trace_base_dir(args), "train_trace") + + def _create_torch_profiler(args, name): + trace_dir = _get_train_trace_dir(args) + worker_name = f"{name}_{_get_rank_tag()}" return torch.profiler.profile( schedule=torch.profiler.schedule( # TODO the train_actor and train_log_probs ones may need to have different args to control step wait=max(args.profile_step_start - 1, 0), warmup=1 if args.profile_step_start > 0 else 0, - active=args.profile_step_end - args.profile_step_start, + active=args.profile_step_end - args.profile_step_start + 1, # end is inclusive repeat=1, ), on_trace_ready=torch.profiler.tensorboard_trace_handler( - args.tensorboard_dir, - worker_name=f"{name}_rank_{torch.distributed.get_rank()}", + trace_dir, + worker_name=worker_name, use_gzip=True, ), record_shapes=True, @@ -94,9 +132,13 @@ def create(args): return c(args) def __init__(self, args): + snapshot_dir = getattr(args, "memory_snapshot_dir", None) + if snapshot_dir is None: + snapshot_dir = os.path.join(_get_trace_base_dir(args), "memory_snapshot") + os.makedirs(snapshot_dir, exist_ok=True) + rank_tag = _get_rank_tag() self._path_dump = ( - Path(args.memory_snapshot_dir) - / f"memory_snapshot_time{time.time()}_rank{torch.distributed.get_rank()}_{args.memory_snapshot_path}" + Path(snapshot_dir) / f"memory_snapshot_time{time.time()}_{rank_tag}_{args.memory_snapshot_path}" ) def start(self): @@ -167,3 +209,141 @@ def start(self): def stop(self): logger.info(f"Memray tracker stopped and dump snapshot to: {self._path_dump}") self._tracker.__exit__(None, None, None) + + +# --------------------------------------------------------------------------- +# SGLang profiling orchestration +# +# These helpers coordinate profiling across all SGLang engines by discovering +# worker URLs from the router and issuing HTTP start/stop requests. +# --------------------------------------------------------------------------- + + +def _get_sglang_trace_dir(args) -> str: + """Return the base output directory for SGLang profiler traces. + + Uses the user-specified ``--sglang-profile-output-dir`` if set, otherwise + falls back to ``./traces//sglang_trace``. + """ + base_dir = getattr(args, "sglang_profile_output_dir", None) + if base_dir is None: + base_dir = os.path.join(_get_trace_base_dir(args), "sglang_trace") + return base_dir + + +def _should_profile_sglang(args, rollout_id: int) -> bool: + """Determine whether SGLang profiling should be active for the given + rollout step. + + Resolution order: + 1. ``--sglang-profile`` must be enabled (master switch). + 2. ``--sglang-profile-steps`` (explicit list) takes precedence if set. + 3. ``--sglang-profile-step-start`` / ``--sglang-profile-step-end`` (range) + is checked next. Both bounds are *inclusive* and use absolute rollout IDs. + 4. If neither is set, every step is profiled. + """ + if not getattr(args, "sglang_profile", False): + return False + + profile_steps = getattr(args, "sglang_profile_steps", None) + if profile_steps is not None: + return rollout_id in profile_steps + + step_start = getattr(args, "sglang_profile_step_start", None) + step_end = getattr(args, "sglang_profile_step_end", None) + if step_start is not None or step_end is not None: + lo = step_start if step_start is not None else 0 + hi = step_end if step_end is not None else float("inf") + return lo <= rollout_id <= hi + + # No filter specified — profile every step. + return True + + +async def _get_sglang_worker_urls(args) -> list[str]: + """Discover SGLang worker URLs from the router.""" + import sglang_router + from packaging.version import parse + + from relax.utils.http_utils import get + + if parse(sglang_router.__version__) <= parse("0.2.1") or getattr(args, "use_slime_router", False): + response = await get(f"http://{args.sglang_router_ip}:{args.sglang_router_port}/list_workers") + return response["urls"] + else: + response = await get(f"http://{args.sglang_router_ip}:{args.sglang_router_port}/workers") + return [worker["url"] for worker in response["workers"]] + + +async def start_sglang_profile(args, rollout_id: int) -> None: + """Start torch profiling on all SGLang engines if ``--sglang-profile`` is + enabled. + + Profile traces are organized as:: + + traces//sglang_trace/rollout_/ + + When ``--sglang-profile-output-dir`` is explicitly set, that path is used + as the base instead. + """ + if not _should_profile_sglang(args, rollout_id): + return + + from relax.utils.http_utils import post + + # Build per-step output directory: /rollout_ + base_dir = _get_sglang_trace_dir(args) + step_dir = os.path.join(base_dir, f"rollout_{rollout_id}") + os.makedirs(step_dir, exist_ok=True) + + num_steps = getattr(args, "sglang_profile_num_steps", None) + if num_steps is not None and num_steps < 0: + num_steps = None + + urls = await _get_sglang_worker_urls(args) + base_payload = { + "output_dir": step_dir, + "num_steps": num_steps, + "activities": getattr(args, "sglang_profile_activities", None), + "profile_by_stage": getattr(args, "sglang_profile_by_stage", False), + "with_stack": getattr(args, "sglang_profile_with_stack", False), + "record_shapes": getattr(args, "sglang_profile_record_shapes", False), + } + + logger.info( + f"Starting SGLang profiling on {len(urls)} engines for rollout step {rollout_id}, " + f"output_dir={step_dir}, num_steps={num_steps}" + ) + tasks = [] + for i, url in enumerate(urls): + payload = {**base_payload, "profile_prefix": f"engine{i}"} + tasks.append(post(f"{url}/start_profile", payload)) + results = await asyncio.gather(*tasks, return_exceptions=True) + for url, result in zip(urls, results, strict=False): + if isinstance(result, BaseException): + logger.warning(f"Failed to start profile on {url}: {result}") + else: + logger.info(f"Started profiling on {url}") + + +async def stop_sglang_profile(args, rollout_id: int) -> None: + """Stop torch profiling on all SGLang engines if ``--sglang-profile`` is + enabled.""" + if not _should_profile_sglang(args, rollout_id): + return + + # If num_steps was set, SGLang auto-stops — skip explicit stop. + if getattr(args, "sglang_profile_num_steps", -1) > 0: + return + + from relax.utils.http_utils import post + + urls = await _get_sglang_worker_urls(args) + logger.info(f"Stopping SGLang profiling on {len(urls)} engines for rollout step {rollout_id}") + tasks = [post(f"{url}/stop_profile", {}) for url in urls] + results = await asyncio.gather(*tasks, return_exceptions=True) + for url, result in zip(urls, results, strict=False): + if isinstance(result, BaseException): + logger.warning(f"Failed to stop profile on {url}: {result}") + else: + logger.info(f"Stopped profiling on {url}") diff --git a/scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh b/scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh index 2befb28dd..5bd28d38d 100644 --- a/scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-vl-4B-2xgpu.sh @@ -93,6 +93,9 @@ OPTIMIZER_ARGS=( --adam-beta1 0.9 --adam-beta2 0.98 --clip-grad 1.0 + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer --no-rope-fusion ) diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh index 72c837bf9..56e85a755 100644 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh @@ -74,11 +74,8 @@ PERF_ARGS=( --recompute-method uniform --recompute-num-layers 1 - # qwen3.5 only - --qkv-format bshd - --micro-batch-size 1 #--micro-batch-size 16 # avoid OOM - # --use-dynamic-batch-size + --use-dynamic-batch-size --max-tokens-per-gpu 9216 --no-rope-fusion diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh index ac5e36494..da2bcbd52 100755 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh @@ -101,7 +101,7 @@ OPTIMIZER_ARGS=( ) SGLANG_ARGS=( - --rollout-num-gpus-per-engine 2 + --rollout-num-gpus-per-engine 1 --sglang-mem-fraction-static 0.8 ) diff --git a/scripts/training/text/run-qwen3-4B-2xgpu.sh b/scripts/training/text/run-qwen3-4B-2xgpu.sh deleted file mode 100644 index dade5f812..000000000 --- a/scripts/training/text/run-qwen3-4B-2xgpu.sh +++ /dev/null @@ -1,145 +0,0 @@ -#!/bin/bash - -# Copyright (c) 2026 Relax Authors. All Rights Reserved. -# -# Qwen3-4B 2xGPU colocate training script. -# -# Usage: -# NUM_GPUS=2 bash scripts/training/text/run-qwen3-4B-2xgpu.sh - -set -ex -set -o pipefail - -# Select the 2 GPUs with most free memory -export CUDA_VISIBLE_DEVICES=$(nvidia-smi --query-gpu=index,memory.free --format=csv,noheader,nounits | sort -t, -k2 -rn | head -n 2 | cut -d, -f1 | paste -sd ',') - -SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" -# Auto-source local environment when not launched via an external entrypoint -if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then - source "${SCRIPT_DIR}/../../entrypoint/local.sh" -fi -source "${MODEL_CONFIG_DIR}/qwen3-4B.sh" -# Support setting env from outside -EXP_DIR="${MODEL_DIR:=/root/exps}" -PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" -DATE=$(date +%Y%m%d_%H%M%S) -NUM_ROLLOUT="${NUM_ROLLOUT:=4}" - -CKPT_ARGS=( - --hf-checkpoint ${EXP_DIR}/Qwen3-4B/ - --ref-load ${EXP_DIR}/Qwen3-4B/ - --megatron-to-hf-mode bridge - --load ${EXP_DIR}/Qwen3-4B_mcore/ - --save ${EXP_DIR}/Qwen3-4B_mcore/ - --save-interval 100 - --rotate-ckpt - --async-save -) - -PROMPT_SET=${EXP_DIR}/dapo-math-17k/dapo-math-17k.jsonl - -ROLLOUT_ARGS=( - --use-streaming-dataset - --streaming-buffer-size 10000 - --prompt-data ${PROMPT_SET} - --input-key prompt - --label-key label - --apply-chat-template - --rollout-shuffle - - --rm-type dapo - --reward-key score - - --num-rollout ${NUM_ROLLOUT} - --rollout-batch-size 2 - --n-samples-per-prompt 8 - --rollout-max-response-len 2048 - --rollout-temperature 0.8 - - --global-batch-size 16 - --balance-data - --use-fault-tolerance -) - -PERF_ARGS=( - --tensor-model-parallel-size 1 - --sequence-parallel - --pipeline-model-parallel-size 1 - --context-parallel-size 1 - --expert-model-parallel-size 1 - --expert-tensor-parallel-size 1 - - --recompute-granularity full - --recompute-method uniform - --recompute-num-layers 1 - - # --micro-batch-size 1 - # --use-dynamic-batch-size - --max-tokens-per-gpu 9216 -) - -GRPO_ARGS=( - --advantage-estimator grpo - --use-kl-loss - --kl-loss-coef 0.00 - --kl-loss-type low_var_kl - --entropy-coef 0.00 - --eps-clip 0.2 - --eps-clip-high 0.28 - - --use-tis -) - -OPTIMIZER_ARGS=( - --optimizer adam - --lr 1e-6 - --lr-decay-style constant - --weight-decay 0.1 - --adam-beta1 0.9 - --adam-beta2 0.98 -) - -SGLANG_ARGS=( - --rollout-num-gpus-per-engine 1 - --sglang-mem-fraction-static 0.7 -) - -WANDB_ARGS=( - --use-clearml - --use-metrics-service - --tb-project-name ${PROJECT_NAME} - --tb-experiment-name qwen3-4b-GRPO-gpu2-${DATE} - # --use-wandb - # --wandb-project slime-dev - # --wandb-group qwen3-4B-test - # --wandb-key ${WANDB_KEY} -) - -MISC_ARGS=( - # default dropout in megatron is 0.1 - --attention-dropout 0.0 - --hidden-dropout 0.0 - # should be good for model performance - --accumulate-allreduce-grads-in-fp32 - --attention-softmax-in-fp32 - # need to comment this when using model with MLA - --attention-backend flash -) - -mkdir -p log -ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ - ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ - --runtime-env-json="${RUNTIME_ENV_JSON}" \ - -- python3 -m relax.entrypoints.train \ - --resource '{"actor": [1, 1], "rollout": [1, 1]}'\ - --max-staleness 0 \ - --num-data-storage-units 1 \ - "${MODEL_ARGS[@]}" \ - "${CKPT_ARGS[@]}" \ - "${ROLLOUT_ARGS[@]}" \ - "${OPTIMIZER_ARGS[@]}" \ - "${GRPO_ARGS[@]}" \ - "${WANDB_ARGS[@]}" \ - "${PERF_ARGS[@]}" \ - "${SGLANG_ARGS[@]}" \ - "${MISC_ARGS[@]}" 2>&1 | tee log/qwen3-4b-GRPO-gpu2-${DATE}.log diff --git a/scripts/training/text/run-qwen35-9B-8xgpu-async.sh b/scripts/training/text/run-qwen35-9B-8xgpu-async.sh index 196d45b7f..ac8cf5ff5 100755 --- a/scripts/training/text/run-qwen35-9B-8xgpu-async.sh +++ b/scripts/training/text/run-qwen35-9B-8xgpu-async.sh @@ -77,10 +77,9 @@ PERF_ARGS=( --recompute-method uniform --recompute-num-layers 1 - # --use-dynamic-batch-size - # --max-tokens-per-gpu 10240 - --micro-batch-size 1 # avoid OOM - --qkv-format bshd + --use-dynamic-batch-size + --max-tokens-per-gpu 10240 + # --micro-batch-size 1 # avoid OOM --no-rope-fusion ) From 52fe76a28cd20e92e970d16b575ff78834046e3c Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Wed, 29 Apr 2026 17:39:39 +0800 Subject: [PATCH 025/268] fix(megatron): restore cuda patch target in validate_args MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Fix torch.cuda patch broken by device abstraction refactor - The device abstraction commit (632b29c5) replaced hardcoded `torch.cuda.get_device_properties` / `torch.cuda.get_device_capability` patch targets with `torch.{device_utils.get_device_name()}.*` - When no accelerator is available, `get_device_name()` returns `"cpu"`, so patches targeted `torch.cpu.*` instead of `torch.cuda.*` - Megatron validate_args internally always calls `torch.cuda.*`, so the patches must target `torch.cuda` regardless of device abstraction - Restore hardcoded `torch.cuda.*` patch targets with explanatory comment --- relax/backends/megatron/arguments.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/relax/backends/megatron/arguments.py b/relax/backends/megatron/arguments.py index c3e7daf56..25b723b93 100644 --- a/relax/backends/megatron/arguments.py +++ b/relax/backends/megatron/arguments.py @@ -25,10 +25,12 @@ class _DeviceProperty: major = 9 minor = 0 - device_name = device_utils.get_device_name() + # Megatron internally calls torch.cuda.get_device_properties / get_device_capability. + # When no real device is available, device_utils.get_device_name() returns "cpu", + # so we must patch torch.cuda specifically — that's what Megatron actually invokes. with ( - patch(f"torch.{device_name}.get_device_properties", return_value=_DeviceProperty()), - patch(f"torch.{device_name}.get_device_capability", return_value=(9, 0)), + patch("torch.cuda.get_device_properties", return_value=_DeviceProperty()), + patch("torch.cuda.get_device_capability", return_value=(9, 0)), ): _megatron_validate_args(args) else: From d20dd646aee8900d5d1cdcd64f545229c47f9fed Mon Sep 17 00:00:00 2001 From: wulumeng Date: Wed, 29 Apr 2026 12:48:14 +0800 Subject: [PATCH 026/268] chore(data): remove unused prompt helpers MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🔩 Chore ## Remove dead helpers from data module - Delete the unreferenced `filter_long_prompt` helper from `relax/utils/data/data.py` - Delete the dead `_build_messages` helper that was shadowed by `relax/utils/data/data_utils.py` - Delete the unused `process_rollout_data` helper and the imports it required --- relax/utils/data/data.py | 119 +-------------------------------------- 1 file changed, 1 insertion(+), 118 deletions(-) diff --git a/relax/utils/data/data.py b/relax/utils/data/data.py index 517a3fea7..bc254b7f3 100644 --- a/relax/utils/data/data.py +++ b/relax/utils/data/data.py @@ -1,17 +1,13 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. import random -import re - -import ray from relax.utils.data.data_utils import ( BaseDataset, filter_long_prompts, read_file, ) -from relax.utils.timer import Timer -from relax.utils.types import MultimodalTypes, Sample +from relax.utils.types import Sample __all__ = ["Dataset", "BaseDataset"] @@ -22,105 +18,6 @@ logger = get_logger(__name__) -def filter_long_prompt(origin_samples: list[Sample], tokenizer, processor, max_length: int | None) -> list[Sample]: - if max_length is None: - return origin_samples - - if not isinstance(origin_samples[0].prompt, str): - logger.warning( - "Skipping max_length check for list prompt. Set apply_chat_template=True to enable length filtering." - ) - return origin_samples - - if processor: - filtered_samples = [] - for sample in origin_samples: - from relax.utils.data.processing_utils import process_vision_info - - multimodal_inputs = process_vision_info(sample.prompt, processor) - processor_output = processor(text=sample.prompt, **multimodal_inputs) - input_ids = processor_output["input_ids"][0] - if len(input_ids) <= max_length: - filtered_samples.append(sample) - else: - prompts = [sample.prompt for sample in origin_samples] - input_ids_list = tokenizer(prompts, add_special_tokens=False)["input_ids"] - filtered_samples = [ - sample - for sample, input_ids in zip(origin_samples, input_ids_list, strict=True) - if len(input_ids) <= max_length - ] - - logger.info(f"Filtered {len(origin_samples) - len(filtered_samples)} samples longer than max_length={max_length}.") - - return filtered_samples - - -def _build_messages(data: dict, prompt_key: str, as_conversation: bool, multimodal_keys: dict = None): - prompt = data.get(prompt_key) - - if isinstance(prompt, str): - # If prompt is a string and we don't apply chat template, return the prompt as is. - if not as_conversation: - return prompt - else: - prompt = [{"role": "user", "content": prompt}] - - if multimodal_keys: - # Build mapping: placeholder -> (MultimodalType, content_list) - multimodals = {} - for type_name, data_key in multimodal_keys.items(): - mt = MultimodalTypes.get(type_name) - if mt: - multimodal_data = data.get(data_key) - if multimodal_data is not None: - multimodals[mt.placeholder] = (mt, list(multimodal_data)) - - pattern = "(" + "|".join(re.escape(p) for p in multimodals.keys()) + ")" - - for message in prompt: - if isinstance(message["content"], str): - content_list = [] - for segment in re.split(pattern, message["content"]): - if not segment: - continue - if segment in multimodals: - mt, content = multimodals[segment] - assert len(content) > 0, ( - f"Not enough {mt.name} data: more '{mt.placeholder}' placeholders in prompt " - f"than {mt.name}s provided in data" - ) - content_list.append({"type": mt.name, mt.name: content.pop(0)}) - else: - content_list.append({"type": "text", "text": segment}) - message["content"] = content_list - - elif isinstance(message["content"], list): - # TODO: handle more general cases. where message['content'] is a dict and contains multiple types of content. - # e.g. - # "content": [ - # { - # "type": "image", - # "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", - # }, - # {"type": "text", "text": "Describe this image."}, - # ], - logger.warning("message['content'] is a list of dicts, no processing will be done.") - continue - else: - raise ValueError( - f"Unsupported content type: {type(message['content'])}, expected str or list of dicts" - ) - - for placeholder, (mt, remaining) in multimodals.items(): - assert len(remaining) == 0, ( - f"Multimodal data count mismatch: {len(remaining)} more {mt.name}(s)" - f"than '{placeholder}' placeholders in prompt" - ) - - return prompt - - class Dataset(BaseDataset): """Eager-loading dataset that loads all data into memory at initialization. @@ -216,17 +113,3 @@ def get_minimum_num_micro_batch_size(total_lengths, max_tokens_per_gpu): batches.append(length) return len(batches) - - -def process_rollout_data(args, rollout_data_ref, dp_rank, dp_size): - assert len(rollout_data_ref) == dp_size - rollout_data = ray.get(rollout_data_ref[dp_rank].inner) - - partition = rollout_data.pop("partition") - total_lengths = rollout_data["total_lengths"] - - # save the seqlen of the whole rollout batch - Timer().seq_lens = total_lengths - rollout_data["total_lengths"] = [total_lengths[i] for i in partition] - - return rollout_data From cbd189559b5861481abcda5b892e673407fb3f12 Mon Sep 17 00:00:00 2001 From: wulumeng Date: Sat, 2 May 2026 14:27:37 +0800 Subject: [PATCH 027/268] fix(data): avoid prompt mutation in reads MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Keep multimodal prompt building non-destructive - Build multimodal message content without mutating cached prompt rows - Prevent reused raw samples from carrying expanded message content into later reads ## Support sliced eager dataset paths - Parse per-file generalized slice syntax in eager file readers - Keep multi-file eager path behavior aligned with streaming path semantics --- relax/utils/data/data_utils.py | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/relax/utils/data/data_utils.py b/relax/utils/data/data_utils.py index b20170f2f..a2158e8ec 100644 --- a/relax/utils/data/data_utils.py +++ b/relax/utils/data/data_utils.py @@ -128,7 +128,7 @@ def build_messages( if multimodals: pattern = "(" + "|".join(re.escape(p) for p in multimodals.keys()) + ")" - + built_prompt = [] for message in prompt: if isinstance(message["content"], str): content_list = [] @@ -146,16 +146,20 @@ def build_messages( content_list.append({"type": mt.name, mt.name: content.pop(0)}) else: content_list.append({"type": "text", "text": segment}) - message["content"] = content_list + built_message = dict(message) + built_message["content"] = content_list + built_prompt.append(built_message) elif isinstance(message["content"], list): # Already processed, skip logger.warning("message['content'] is a list of dicts, no processing will be done.") - continue + built_prompt.append(message) else: raise ValueError( f"Unsupported content type: {type(message['content'])}, expected str or list of dicts" ) + prompt = built_prompt + if any(v > 0 for v in remain_data.values()): raise RuntimeError( f"placeholder lost! The number of remain mutimodal data is {remain_data}. Please check your dataset prompt." @@ -453,6 +457,7 @@ def resolve_path_plan(path: Any) -> tuple[list[str], Optional[slice]]: def _build_reader_for_path(path: str): + path, row_slice = parse_generalized_path(path) if not os.path.exists(path): raise FileNotFoundError(f"Prompt dataset path '{path}' does not exist.") @@ -470,7 +475,10 @@ def jsonl_reader(p): logger.warning(f"JSON decode error at line {line_num}: {e}") continue - return jsonl_reader(path) + reader = jsonl_reader(path) + if row_slice is not None: + reader = itertools.islice(reader, row_slice.start, row_slice.stop, row_slice.step) + return reader if path.endswith(".parquet"): if pq is None: @@ -486,7 +494,10 @@ def parquet_reader(p): for i in range(pf.metadata.num_row_groups): yield from pf.read_row_group(i).to_pylist() - return parquet_reader(path) + reader = parquet_reader(path) + if row_slice is not None: + reader = itertools.islice(reader, row_slice.start, row_slice.stop, row_slice.step) + return reader raise ValueError(f"Unsupported file format: {path}. Supported formats are .jsonl and .parquet.") From 95d39be54e8f104dd49d739e6e017eb92d9315a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Thu, 7 May 2026 07:51:40 +0000 Subject: [PATCH 028/268] fix(megatron): wait for previous eval on final training step The rollout component exits its main loop on the final training step, leaving the eval handler un-awaited. This caused a race condition where the controller's atexit shutdown tore down SGLang engines mid-flight. This fix blocks until the evaluation finishes at the end of training. --- relax/backends/megatron/actor.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index e162244f0..46ccea3a8 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -606,6 +606,13 @@ def train_actor(self, rollout_id: int, rollout_data: RolloutBatch) -> None: except Exception as e: logger.warning(f"Error triggering evaluation for rollout_id {rollout_id}: {e}") + # On the final training step the rollout component has already exited + # its main loop, so nothing else awaits the eval handler. Block here + # until eval finishes; otherwise the controller's atexit shutdown + # races with eval and tears down the SGLang engines mid-flight. + if is_train_done: + self._wait_for_previous_eval() + def compute_ref_log_prob(self, rollout_id: int) -> None: if self.args.use_routing_replay: os.environ["ROUTING_REPLAY_STAGE"] = "fallthrough" @@ -758,6 +765,13 @@ def train_async(self, rollout_id) -> None: logger.warning( f"Error during async weight update: {e}, maybe cause by rollout server failure. Will continue without async update for this step." ) + # On the final training step the rollout component has already + # exited its main loop, so the eval just triggered above will not + # be awaited anywhere. Block until it finishes; otherwise the + # controller's atexit shutdown races with eval and tears down the + # SGLang engines mid-flight. + if (rollout_id + 1) == self.args.num_rollout: + self._wait_for_previous_eval() if self.args.use_routing_replay: RoutingReplay.clear_all() total_lengths = rollout_data["total_lengths"] From eb89a0d72c33c4f73fe581d5a758e7f3ec6125d2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Thu, 7 May 2026 22:57:20 +0800 Subject: [PATCH 029/268] feat(megatron): upgrade to Megatron-Bridge mainline MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Migrate from Megatron-LM to Megatron-Bridge - Replace direct Megatron-LM checkout with Megatron-Bridge (commit 2faedbf6) in Dockerfile - Upgrade transformer_engine from 2.10.0 to 2.14.1 - Archive old megatron patch (3714d81d) and add new patch for 20260506-85bced0ae ## Adapt Relax backend to Megatron-Bridge API changes - Update vocab_size_with_padding import with fallback for new module path - Rename enable_gloo_process_groups to use_gloo_process_groups - Rename norm_epsilon to layernorm_epsilon in HF config validation - Accept **kwargs in wrapped_provider for new model_provider signature - Relax partition_stride assertion for GLU/SwiGLU linear_fc1 layers (stride=2) - Guard checkpoint_write_patch against removed write_preloaded_data_multiproc --- docker/Dockerfile | 31 +- docker/patch/latest/megatron.patch | 1579 +---------------- docker/patch/megatron/20251218-3714d81d.patch | 1578 ++++++++++++++++ .../patch/megatron/20260506-85bced0ae.patch | 727 ++++++++ docs/en/guide/customize-training.md | 2 +- docs/en/guide/installation.md | 11 +- docs/en/guide/quick-start.md | 13 + docs/zh/guide/customize-training.md | 2 +- docs/zh/guide/installation.md | 11 +- docs/zh/guide/quick-start.md | 13 + relax/backends/megatron/__init__.py | 6 + relax/backends/megatron/actor.py | 12 +- relax/backends/megatron/arguments.py | 39 +- relax/backends/megatron/cp_utils.py | 50 +- relax/backends/megatron/data.py | 72 +- relax/backends/megatron/initialize.py | 9 +- relax/backends/megatron/loss.py | 52 +- relax/backends/megatron/model.py | 56 +- relax/backends/megatron/model_provider.py | 95 +- .../backends/megatron/weight_update/common.py | 9 +- .../hf_weight_iterator_bridge.py | 88 +- relax/distributed/checkpoint_service/utils.py | 7 +- relax/models/qwen_omni/__init__.py | 1 + .../qwen_omni/modeling_qwen3_omni/__init__.py | 18 + .../qwen_omni/modeling_qwen3_omni/model.py | 411 +++++ .../qwen_omni/modeling_qwen3_omni/rope.py | 43 + .../modeling_qwen3_omni/text_model.py | 76 + .../modeling_qwen3_omni/transformer_block.py | 27 + .../modeling_qwen3_omni/transformer_config.py | 40 + .../qwen_omni/modeling_qwen3_omni/utils.py | 356 ++++ relax/models/qwen_omni/qwen3_omni_bridge.py | 245 +++ relax/models/qwen_omni/qwen3_omni_provider.py | 263 +++ relax/utils/arguments.py | 4 + relax/utils/checkpoint_write_patch.py | 7 +- relax/utils/data/processing_utils.py | 16 +- relax/utils/data/stream_dataloader.py | 18 +- relax/utils/logging_utils.py | 4 + .../run-qwen3-30B-A3B-omni-16xgpu.sh | 1 + .../multimodal/run-qwen3-vl-4B-8xgpu.sh | 8 +- .../multimodal/run-qwen35-35B-A3B-8xgpu.sh | 1 + .../run-qwen35-9B-8xgpu-openr1mm-async.sh | 12 +- scripts/training/text/run-qwen3-4B-8xgpu.sh | 5 +- 42 files changed, 4355 insertions(+), 1663 deletions(-) mode change 100644 => 120000 docker/patch/latest/megatron.patch create mode 100644 docker/patch/megatron/20251218-3714d81d.patch create mode 100644 docker/patch/megatron/20260506-85bced0ae.patch create mode 100644 relax/models/qwen_omni/__init__.py create mode 100644 relax/models/qwen_omni/modeling_qwen3_omni/__init__.py create mode 100644 relax/models/qwen_omni/modeling_qwen3_omni/model.py create mode 100644 relax/models/qwen_omni/modeling_qwen3_omni/rope.py create mode 100644 relax/models/qwen_omni/modeling_qwen3_omni/text_model.py create mode 100644 relax/models/qwen_omni/modeling_qwen3_omni/transformer_block.py create mode 100644 relax/models/qwen_omni/modeling_qwen3_omni/transformer_config.py create mode 100644 relax/models/qwen_omni/modeling_qwen3_omni/utils.py create mode 100644 relax/models/qwen_omni/qwen3_omni_bridge.py create mode 100644 relax/models/qwen_omni/qwen3_omni_provider.py diff --git a/docker/Dockerfile b/docker/Dockerfile index c5d4d9f0a..247b436ac 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -57,18 +57,11 @@ RUN MAX_JOBS=64 \ cp flash_attn_interface.py $python_path/flash_attn_3/flash_attn_interface.py && \ rm -rf /opt/flash-attention/ -ARG MEGATRON_COMMIT=3714d81d418c9f1bca4594fc35f9e8289f652862 - -RUN pip -v install --no-cache-dir --no-build-isolation "transformer_engine[pytorch]==2.10.0" && \ - cd /root && git clone https://github.com/NVIDIA/Megatron-LM.git --recursive && \ - cd Megatron-LM && \ - git checkout ${MEGATRON_COMMIT} && \ - pip install -e . --no-deps && \ +RUN pip -v install --no-cache-dir --no-build-isolation "transformer_engine[pytorch]==2.14.1" && \ pip install git+https://github.com/fzyzcjy/torch_memory_saver.git@dc6876905830430b5054325fa4211ff302169c6b --no-cache-dir --force-reinstall && \ pip install nvidia-modelopt[torch]>=0.37.0 --no-build-isolation --no-cache-dir && \ - pip install "numpy<2" nvidia-cudnn-cu12==9.16.0.29 --no-cache-dir - -RUN NVCC_APPEND_FLAGS="--threads 4" \ + pip install "numpy<2" nvidia-cudnn-cu12==9.16.0.29 --no-cache-dir && \ + NVCC_APPEND_FLAGS="--threads 32" \ pip -v install --disable-pip-version-check --no-cache-dir \ --no-build-isolation \ --config-settings "--build-option=--cpp_ext --cuda_ext --parallel 8" \ @@ -82,19 +75,27 @@ ARG ENABLE_SGLANG_PATCH=1 WORKDIR /root +ARG MEGATRON_BRIDGE_COMMIT=2faedbf6fe3c422835a44b2b360cadcb2a116a54 +ENV MEGATRON_BRIDGE_COMMIT=${MEGATRON_BRIDGE_COMMIT} \ + PYTHONPATH=/root/Megatron-LM/ + +RUN rm -rf /root/Megatron-LM && git clone https://github.com/NVIDIA-NeMo/Megatron-Bridge.git && \ + cd /root/Megatron-Bridge/ && git checkout ${MEGATRON_BRIDGE_COMMIT} && \ + git submodule update --init --recursive && ./scripts/switch_mcore.sh dev && \ + mkdir /root/Megatron-LM &&\ + cp -r /root/Megatron-Bridge/src/megatron /root/Megatron-LM/ && \ + rsync -avP /root/Megatron-Bridge/3rdparty/Megatron-LM/megatron/ /root/Megatron-LM/megatron/ && \ + rm -rf /root/Megatron-Bridge + COPY requirements.txt /tmp/requirements.txt RUN pip install -r /tmp/requirements.txt --no-cache-dir && \ pip install --no-cache-dir tensordict==0.10.0 pyvers==0.1.0 --no-deps && \ - apt-get install -y jq - -RUN pip install git+https://github.com/redai-infra/megatron-bridge.git@f13bec09 --no-build-isolation --no-deps --force-reinstall --no-cache-dir && \ pip install "transferqueue @ git+https://github.com/redai-infra/TransferQueue.git" --no-deps COPY docker/patch/${PATCH_VERSION}/megatron.patch /root/Megatron-LM/ RUN cd Megatron-LM && \ - git update-index --refresh && \ - git apply megatron.patch --3way && \ + patch -p1 < /root/Megatron-LM/megatron.patch && \ if grep -R -n '^<<<<<<< ' .; then \ echo "Patch failed to apply cleanly. Please resolve conflicts." && \ exit 1; \ diff --git a/docker/patch/latest/megatron.patch b/docker/patch/latest/megatron.patch deleted file mode 100644 index 5d4428a59..000000000 --- a/docker/patch/latest/megatron.patch +++ /dev/null @@ -1,1578 +0,0 @@ -diff --git a/megatron/core/dist_checkpointing/strategies/common.py b/megatron/core/dist_checkpointing/strategies/common.py -index 41c21d93d..ef80f72d6 100644 ---- a/megatron/core/dist_checkpointing/strategies/common.py -+++ b/megatron/core/dist_checkpointing/strategies/common.py -@@ -86,7 +86,7 @@ class TorchCommonLoadStrategy(LoadCommonStrategy): - msc = MultiStorageClientFeature.import_package() - return msc.torch.load(load_path, map_location='cpu') - else: -- return torch.load(load_path, map_location='cpu') -+ return torch.load(load_path, map_location='cpu', weights_only=False) - except FileNotFoundError as e: - err_msg = f'Common file {load_path} does not exist' - if MultiStorageClientFeature.is_enabled(): -diff --git a/megatron/core/dist_checkpointing/strategies/torch.py b/megatron/core/dist_checkpointing/strategies/torch.py -index 5a1ea308d..aa701237f 100644 ---- a/megatron/core/dist_checkpointing/strategies/torch.py -+++ b/megatron/core/dist_checkpointing/strategies/torch.py -@@ -597,10 +597,12 @@ class MCoreLoadPlanner(DefaultLoadPlanner): - def _validate_global_shapes(self, metadata, sharded_tensors): - for sh_ten in sharded_tensors: - if sh_ten.key not in metadata.state_dict_metadata: -- raise KeyError( -- f"{sh_ten.key} from model not in state dict:" -- f" {sorted(metadata.state_dict_metadata.keys())}" -- ) -+ # raise KeyError( -+ # f"{sh_ten.key} from model not in state dict:" -+ # f" {sorted(metadata.state_dict_metadata.keys())}" -+ # ) -+ print(f"{sh_ten.key} from model not in state dict, will skip") -+ continue - loaded_shape = metadata.state_dict_metadata[sh_ten.key].size - expected_shape = self._expected_shape(sh_ten) - if loaded_shape != expected_shape: -@@ -630,7 +632,7 @@ class MCoreLoadPlanner(DefaultLoadPlanner): - tensor_metadata = self.metadata.state_dict_metadata - metadata_with_sizes = [ - (tensor_metadata[key], tensor_metadata[key].size, sharded_tensor) -- for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items() -+ for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items() if key in tensor_metadata - ] - try: - # Temporarily set sizes to expected shapes -@@ -959,6 +961,7 @@ class TorchDistLoadShardedStrategy(LoadShardedStrategy): - planner=MCoreLoadPlanner( - shapes_validation_sharded_tensors=flexible_shape_sharded_tensors, - allow_shape_mismatch_sharded_tensors=allow_shape_mismatch_sharded_tensors, -+ allow_partial_load=True, - ), - ) - -diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py -index acb93ef78..d239db4ab 100644 ---- a/megatron/core/extensions/transformer_engine.py -+++ b/megatron/core/extensions/transformer_engine.py -@@ -408,6 +408,7 @@ class TELinear(te.pytorch.Linear): - ) - - for param in self.parameters(): -+ setattr(param, "parallel_mode", parallel_mode) - if is_expert: - # Reduce the gradient on the expert_data_parallel group for expert linear layers - setattr(param, "allreduce", not self.expert_parallel) -@@ -1161,6 +1162,61 @@ class TEDotProductAttention(te.pytorch.DotProductAttention): - - - if HAVE_TE and is_te_min_version("1.9.0.dev0"): -+ def ceil_div(x: int, y: int) -> int: -+ return (x + y - 1) // y -+ -+ class _FakeInt4QuantizationSTE(torch.autograd.Function): -+ @staticmethod -+ def forward(ctx, x, group_size): -+ m, n = x.shape -+ block_size_m, block_size_n = 1, group_size -+ -+ -+ m_padded = ceil_div(m, block_size_m) * block_size_m -+ n_padded = ceil_div(n, block_size_n) * block_size_n -+ -+ x_padded = torch.zeros( -+ (m_padded, n_padded), -+ dtype=x.dtype, device=x.device -+ ) -+ x_padded[:m, :n] = x -+ -+ x_view = x_padded.view( -+ m_padded // block_size_m, -+ block_size_m, -+ n_padded // block_size_n, -+ block_size_n -+ ) -+ -+ x_max = x_view.abs().float().amax(dim=(1, 3), keepdim=True) -+ q_max = 7 -+ x_scale = x_max / q_max -+ -+ x_scale = x_scale.clamp(min=1e-5) -+ -+ x_div = x_view / x_scale -+ x_round = torch.round(x_div) -+ -+ x_q_clamped = x_round.clamp(-q_max, q_max) -+ -+ x_dequant_view = x_q_clamped * x_scale -+ -+ x_dequant_full = x_dequant_view.view_as(x_padded) -+ x_out = x_dequant_full[:m, :n].contiguous().to(x.dtype) -+ -+ return x_out -+ -+ @staticmethod -+ def backward(ctx, grad_output): -+ return grad_output, None -+ -+ def fake_int4_quantization_ste(x, group_size): -+ x_out = _FakeInt4QuantizationSTE.apply(x, group_size) -+ -+ if hasattr(x, 'main_grad'): -+ x_out.main_grad = x.main_grad -+ -+ return x_out - - class TEGroupedLinear(te.pytorch.GroupedLinear): - """ -@@ -1351,6 +1407,7 @@ if HAVE_TE and is_te_min_version("1.9.0.dev0"): - _is_first_microbatch = ( - None if self.disable_parameter_transpose_cache else self.is_first_microbatch - ) -+ - out = super().forward(x, m_splits, is_first_microbatch=_is_first_microbatch) - self.is_first_microbatch = False - -@@ -1361,6 +1418,20 @@ if HAVE_TE and is_te_min_version("1.9.0.dev0"): - return out - return out, None - -+ def _get_weight_tensors(self): -+ """Get the weight tensors of the module.""" -+ weight_tensors = super()._get_weight_tensors() -+ -+ if os.getenv("OPEN_TRAINING_INT4_FAKE_QAT_FLAG", "0") == "1": -+ group_size = int(os.getenv("OPEN_TRAINING_INT4_GROUP_SIZE", "128")) -+ -+ weight_tensors = [ -+ fake_int4_quantization_ste(w, group_size) -+ for w in weight_tensors -+ ] -+ -+ return weight_tensors -+ - def _encode_extra_state(self, state): - # TE 2.0 changed the format of extra_state to be a byte tensor - if is_te_min_version("2.0.0"): -diff --git a/megatron/core/fusions/fused_mla_yarn_rope_apply.py b/megatron/core/fusions/fused_mla_yarn_rope_apply.py -index 1fd5dcfae..c9aeef1f0 100644 ---- a/megatron/core/fusions/fused_mla_yarn_rope_apply.py -+++ b/megatron/core/fusions/fused_mla_yarn_rope_apply.py -@@ -385,6 +385,7 @@ def rotary_fwd_kv_kernel( - SIN, - emb_dim: tl.constexpr, - k_dim: tl.constexpr, -+ k_dim_ceil: tl.constexpr, - v_dim: tl.constexpr, - head_num: tl.constexpr, - batch_size, -@@ -434,21 +435,27 @@ def rotary_fwd_kv_kernel( - cos_right = tl.load(COS + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2)) - sin_right = tl.load(SIN + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2)) - -- KV_ptr = KV + pid_m * stride_kv_seq + pid_head * BLOCK_H * stride_kv_nheads -- kv_off = tl.arange(0, BLOCK_H)[:, None] * stride_kv_nheads -- mask = kv_off < head_num * stride_kv_nheads -- k_in_off = kv_off + tl.arange(0, k_dim)[None, :] -- v_in_off = kv_off + k_dim + tl.arange(0, v_dim)[None, :] -- k = tl.load(KV_ptr + k_in_off, mask=mask) -- v = tl.load(KV_ptr + v_in_off, mask=mask) -+ KV_ptr = KV + pid_m * stride_kv_seq # + pid_head * BLOCK_H * stride_kv_nheads -+ ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H -+ kj_range = tl.arange(0, k_dim_ceil)[None, :] -+ mask_k = (ki_range < head_num) & (kj_range < k_dim) -+ mask_v = ki_range < head_num -+ k_off = ki_range * stride_kv_nheads + kj_range -+ if v_dim > 0: -+ v_off = ki_range * stride_kv_nheads + k_dim + tl.arange(0, v_dim)[None, :] -+ v = tl.load(KV_ptr + v_off, mask=mask_v) -+ else: -+ v = tl.zeros((BLOCK_H, 1), dtype=KV.dtype.element_ty) -+ k = tl.load(KV_ptr + k_off, mask=mask_k) - -- K_ptr = O_KEY + pid_m * stride_k_seq + pid_head * BLOCK_H * stride_k_nheads -- V_ptr = O_VALUE + pid_m * stride_v_seq + pid_head * BLOCK_H * stride_v_nheads -+ K_ptr = O_KEY + pid_m * stride_k_seq # + pid_head * BLOCK_H * stride_k_nheads -+ V_ptr = O_VALUE + pid_m * stride_v_seq # + pid_head * BLOCK_H * stride_v_nheads - -- k_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads + tl.arange(0, k_dim)[None, :] -- v_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_v_nheads + tl.arange(0, v_dim)[None, :] -- tl.store(K_ptr + k_out_off, k, mask=mask) -- tl.store(V_ptr + v_out_off, v, mask=mask) -+ k_out_off = ki_range * stride_k_nheads + kj_range -+ tl.store(K_ptr + k_out_off, k, mask=mask_k) -+ if v_dim > 0: -+ v_out_off = ki_range * stride_v_nheads + tl.arange(0, v_dim)[None, :] -+ tl.store(V_ptr + v_out_off, v, mask=mask_v) - - EMB = K_POS_EMB + pid_m * stride_emb_seq - # x1 = t[..., 0::2], x2 = t[..., 1::2] -@@ -460,14 +467,16 @@ def rotary_fwd_kv_kernel( - x_left = x_left.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2) - x_right = x_right.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2) - -+ x_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H -+ mask_x = x_range < head_num - x_left_off = ( -- tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads -+ x_range * stride_k_nheads - + k_dim - + tl.arange(0, emb_dim // 2)[None, :] - ) - x_right_off = x_left_off + emb_dim // 2 -- tl.store(K_ptr + x_left_off, x_left, mask=mask) -- tl.store(K_ptr + x_right_off, x_right, mask=mask) -+ tl.store(K_ptr + x_left_off, x_left, mask=mask_x) -+ tl.store(K_ptr + x_right_off, x_right, mask=mask_x) - - - @triton.autotune( -@@ -493,6 +502,7 @@ def rotary_bwd_kv_kernel( - SIN, - emb_dim: tl.constexpr, - k_dim: tl.constexpr, -+ k_dim_ceil: tl.constexpr, - v_dim: tl.constexpr, - head_num: tl.constexpr, - batch_size, -@@ -533,27 +543,32 @@ def rotary_bwd_kv_kernel( - else: - token_idx = _get_thd_token_idx(cu_seqlens_kv, pid_m, seq_num, cp_rank, cp_size) - -- dKV_ptr = dKV + pid_m * stride_dkv_seq + pid_head * BLOCK_H * stride_dkv_nheads -- dkv_off = tl.arange(0, BLOCK_H)[:, None] * stride_dkv_nheads -- mask = dkv_off < head_num * stride_dkv_nheads -- dk_out_off = dkv_off + tl.arange(0, k_dim)[None, :] -- dv_out_off = dkv_off + k_dim + tl.arange(0, v_dim)[None, :] -- -- dK_ptr = dK + pid_m * stride_dk_seq + pid_head * BLOCK_H * stride_dk_nheads -- dV_ptr = dV + pid_m * stride_dv_seq + pid_head * BLOCK_H * stride_dv_nheads -- dk_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + tl.arange(0, k_dim)[None, :] -- dv_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dv_nheads + tl.arange(0, v_dim)[None, :] -- dk = tl.load(dK_ptr + dk_in_off, mask=mask) -- dv = tl.load(dV_ptr + dv_in_off, mask=mask) -- tl.store(dKV_ptr + dk_out_off, dk, mask=mask) -- tl.store(dKV_ptr + dv_out_off, dv, mask=mask) -+ dKV_ptr = dKV + pid_m * stride_dkv_seq # + pid_head * BLOCK_H * stride_dkv_nheads -+ ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H -+ kj_range = tl.arange(0, k_dim_ceil)[None, :] -+ mask_k = (ki_range < head_num) & (kj_range < k_dim) -+ mask_v = ki_range < head_num -+ dk_out_off = ki_range * stride_dkv_nheads + kj_range -+ -+ dK_ptr = dK + pid_m * stride_dk_seq # + pid_head * BLOCK_H * stride_dk_nheads -+ dV_ptr = dV + pid_m * stride_dv_seq # + pid_head * BLOCK_H * stride_dv_nheads -+ dk_in_off = ki_range * stride_dk_nheads + kj_range -+ -+ dk = tl.load(dK_ptr + dk_in_off, mask=mask_k) -+ tl.store(dKV_ptr + dk_out_off, dk, mask=mask_k) -+ -+ if v_dim > 0: -+ dv_out_off = ki_range * stride_dkv_nheads + k_dim + tl.arange(0, v_dim)[None, :] -+ dv_in_off = ki_range * stride_dv_nheads + tl.arange(0, v_dim)[None, :] -+ dv = tl.load(dV_ptr + dv_in_off, mask=mask_v) -+ tl.store(dKV_ptr + dv_out_off, dv, mask=mask_v) - - if pid_head == 0: - x_left_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32) - x_right_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32) - for i in tl.static_range(triton.cdiv(head_num, BLOCK_H)): -- dK_ptr = dK + pid_m * stride_dk_seq + i * BLOCK_H * stride_dk_nheads -- x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim -+ dK_ptr = dK + pid_m * stride_dk_seq # + i * BLOCK_H * stride_dk_nheads -+ x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim + i * BLOCK_H * stride_dk_nheads - mask = x_off < head_num * stride_dk_nheads - x_left_off = x_off + tl.arange(0, emb_dim // 2)[None, :] - x_right_off = x_left_off + emb_dim // 2 -@@ -632,6 +647,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): - - o_key = kv.new_empty(total_seqlen, nheads, emb_dim + k_dim) - o_value = kv.new_empty(total_seqlen, nheads, v_dim) -+ k_dim_ceil = triton.next_power_of_2(k_dim) - - grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"])) - rotary_fwd_kv_kernel[grid]( -@@ -643,6 +659,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): - sin, - emb_dim, - k_dim, -+ k_dim_ceil, - v_dim, - nheads, - batch_size, -@@ -700,6 +717,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): - - d_kv = dk.new_empty(total_seqlen, nheads, ctx.k_dim + ctx.v_dim) - d_emb = dk.new_empty(total_seqlen, 1, ctx.emb_dim) -+ k_dim_ceil = triton.next_power_of_2(ctx.k_dim) - - grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"])) - rotary_bwd_kv_kernel[grid]( -@@ -711,6 +729,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): - sin, - ctx.emb_dim, - ctx.k_dim, -+ k_dim_ceil, - ctx.v_dim, - nheads, - batch_size, -diff --git a/megatron/core/models/common/embeddings/rotary_pos_embedding.py b/megatron/core/models/common/embeddings/rotary_pos_embedding.py -index 5d7b69cd3..2e0a26815 100644 ---- a/megatron/core/models/common/embeddings/rotary_pos_embedding.py -+++ b/megatron/core/models/common/embeddings/rotary_pos_embedding.py -@@ -348,6 +348,7 @@ class MultimodalRotaryEmbedding(nn.Module): - - # shape (seq_length, bs, 1, 2 * dim) - emb = emb[..., None, :].transpose(0, 1).contiguous() -+ packed_seq = packed_seq_params is not None and packed_seq_params.qkv_format == 'thd' - if packed_seq_params is not None and packed_seq_params.local_cp_size is not None: - if packed_seq_params.local_cp_size > 1: - # Set CP group to dynamic CP group for CP slicing -@@ -357,7 +358,9 @@ class MultimodalRotaryEmbedding(nn.Module): - cp_group = None - else: - cp_group = self.cp_group -- if cp_group is not None and cp_group.size() > 1: -+ # For THD (packed sequence) format, skip CP slicing here — it is handled -+ # per-sequence inside _apply_rotary_pos_emb_thd instead (same as RotaryEmbedding). -+ if cp_group is not None and cp_group.size() > 1 and not packed_seq: - # slice rotary_pos_emb along sequence dimension and select the parition of the current - # CP rank - emb = get_pos_emb_on_this_cp_rank(emb, 0, cp_group) -diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py -index 13d74aa52..060898a7a 100644 ---- a/megatron/core/models/common/language_module/language_module.py -+++ b/megatron/core/models/common/language_module/language_module.py -@@ -184,7 +184,15 @@ class LanguageModule(MegatronModule): - assert ( - column_parallel_linear is not None - ), "column_parallel_linear cannot be None when not using fused linear cross entropy." -- logits, _ = column_parallel_linear(hidden, **col_linear_kwargs) -+ # output -+ output_layer_params = {k: v.detach() for k, v in column_parallel_linear.named_parameters()} -+ output_layer_buffers = dict(column_parallel_linear.named_buffers()) -+ logits, _ = torch.func.functional_call( -+ column_parallel_linear, -+ {**output_layer_params, **output_layer_buffers}, -+ (hidden,), -+ col_linear_kwargs, -+ ) - - return self.compute_language_model_loss(labels, logits) - -diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py -index e21127b87..712793853 100755 ---- a/megatron/core/models/gpt/gpt_layer_specs.py -+++ b/megatron/core/models/gpt/gpt_layer_specs.py -@@ -188,6 +188,8 @@ def get_gpt_layer_with_transformer_engine_spec( - use_kitchen: bool = False, - use_te_activation_func: bool = False, - fallback_to_eager_attn: bool = False, -+ post_self_attn_layernorm: bool = False, -+ post_mlp_layernorm: bool = False, - ) -> ModuleSpec: - """Use this spec to use lower-level Transformer Engine modules (required for fp8 training). - -@@ -260,6 +262,8 @@ def get_gpt_layer_with_transformer_engine_spec( - mlp=mlp, - sharded_state_dict_keys_map=sharded_state_dict_keys_map, - normalization=normalization, -+ post_self_attn_layernorm=post_self_attn_layernorm, -+ post_mlp_layernorm=post_mlp_layernorm, - ) - - -@@ -349,6 +353,8 @@ def get_transformer_layer_spec_for_backend( - mlp: ModuleSpec, - sharded_state_dict_keys_map: Optional[dict] = None, - normalization: Optional[str] = None, -+ post_self_attn_layernorm: bool = False, -+ post_mlp_layernorm: bool = False, - ) -> ModuleSpec: - """Helper function to get module spec for TransformerLayer""" - -@@ -371,9 +377,11 @@ def get_transformer_layer_spec_for_backend( - input_layernorm=input_layernorm, - self_attention=attention, - self_attn_bda=get_bias_dropout_add, -+ post_self_attn_layernorm=TENorm if post_self_attn_layernorm else IdentityOp, - pre_mlp_layernorm=pre_mlp_layernorm, - mlp=mlp, - mlp_bda=get_bias_dropout_add, -+ post_mlp_layernorm=TENorm if post_mlp_layernorm else IdentityOp, - sharded_state_dict_keys_map=sharded_state_dict_keys_map, - ), - ) -diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py -index a1230568c..1fd52f65a 100644 ---- a/megatron/core/models/gpt/gpt_model.py -+++ b/megatron/core/models/gpt/gpt_model.py -@@ -446,6 +446,7 @@ class GPTModel(LanguageModule): - *, - inference_params: Optional[BaseInferenceContext] = None, - loss_mask: Optional[Tensor] = None, -+ mtp_kwargs: Optional[dict] = {}, - ) -> Tensor: - """Forward function of the GPT Model This function passes the input tensors - through the embedding layer, and then the decoder and finally into the post -@@ -508,6 +509,7 @@ class GPTModel(LanguageModule): - runtime_gather_output=runtime_gather_output, - extra_block_kwargs=extra_block_kwargs, - inference_context=inference_context, -+ mtp_kwargs=mtp_kwargs, - ) - - def _postprocess( -@@ -529,6 +531,7 @@ class GPTModel(LanguageModule): - runtime_gather_output=None, - extra_block_kwargs=None, - inference_context=None, -+ mtp_kwargs={}, - ): - """Postprocesses decoder hidden states to generate logits or compute loss. - -@@ -543,7 +546,8 @@ class GPTModel(LanguageModule): - output_weight = None - if self.share_embeddings_and_output_weights: - output_weight = self.shared_embedding_or_output_weight() -- if mtp_in_postprocess: -+ -+ if mtp_in_postprocess and mtp_kwargs.get('mtp_labels', None) is not None: - hidden_states = self.mtp( - input_ids=input_ids, - position_ids=position_ids, -@@ -563,13 +567,18 @@ class GPTModel(LanguageModule): - return hidden_states - - # Skip when mtp_num_layers is None or 0 -- if self.config.mtp_num_layers: -- mtp_labels = labels.clone() -+ if self.config.mtp_num_layers and mtp_kwargs.get('mtp_labels', None) is not None: -+ mtp_labels = mtp_kwargs['mtp_labels'].clone() -+ mtp_labels, _ = roll_tensor(mtp_labels, shifts=-1, dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params) -+ - hidden_states_list = torch.chunk(hidden_states, 1 + self.config.mtp_num_layers, dim=0) - hidden_states = hidden_states_list[0] - if loss_mask is None: - # if loss_mask is not provided, use all ones as loss_mask - loss_mask = torch.ones_like(mtp_labels) -+ else: -+ # Otherwise, roll the loss_mask to keep up with the mtp_labels -+ loss_mask, _ = roll_tensor(loss_mask, shifts=-1, dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params) - for mtp_layer_number in range(self.config.mtp_num_layers): - # Calc loss for the current Multi-Token Prediction (MTP) layers. - mtp_labels, _ = roll_tensor( -@@ -595,7 +604,7 @@ class GPTModel(LanguageModule): - sequence_parallel_enabled=self.output_layer.sequence_parallel, - column_parallel_linear=self.output_layer, - col_linear_kwargs={ -- 'weight': output_weight, -+ 'weight': output_weight.detach() if output_weight else None, - 'runtime_gather_output': runtime_gather_output, - }, - ) -diff --git a/megatron/core/optimizer/distrib_optimizer.py b/megatron/core/optimizer/distrib_optimizer.py -index 6e093f96f..eac21a3ea 100644 ---- a/megatron/core/optimizer/distrib_optimizer.py -+++ b/megatron/core/optimizer/distrib_optimizer.py -@@ -677,6 +677,8 @@ class DistributedOptimizer(MixedPrecisionOptimizer): - # TE FusedAdam will not accumulate step for empty param groups, so we need to - # align the step across param groups. - param_group["step"] = int(step) -+ if "step" in param_group and param_group["step"] is None: -+ del param_group["step"] - - # Grad scaler state. - if self.grad_scaler: -@@ -1646,6 +1648,8 @@ class DistributedOptimizer(MixedPrecisionOptimizer): - if key == 'padding': - tensors[key] = LocalNonpersistentObject(tensors[key]) - continue -+ if key == 'step': -+ continue - assert tensors[key].shape == (gbuf_local_end - gbuf_local_start,), ( - tensors[key].shape, - gbuf_local_start, -diff --git a/megatron/core/parallel_state.py b/megatron/core/parallel_state.py -index a273002b9..4f821cfd5 100644 ---- a/megatron/core/parallel_state.py -+++ b/megatron/core/parallel_state.py -@@ -11,6 +11,7 @@ from typing import Callable, List, Optional - - import numpy as np - import torch -+import torch.distributed as dist - - from .utils import GlobalMemoryBuffer, is_torch_min_version - -diff --git a/megatron/core/pipeline_parallel/p2p_communication.py b/megatron/core/pipeline_parallel/p2p_communication.py -index ac839c21f..f18309217 100644 ---- a/megatron/core/pipeline_parallel/p2p_communication.py -+++ b/megatron/core/pipeline_parallel/p2p_communication.py -@@ -26,22 +26,22 @@ def _batched_p2p_ops( - ops = [] - if tensor_send_prev is not None: - send_prev_op = torch.distributed.P2POp( -- torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, group -+ torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, - ) - ops.append(send_prev_op) - if tensor_recv_prev is not None: - recv_prev_op = torch.distributed.P2POp( -- torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, group -+ torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, - ) - ops.append(recv_prev_op) - if tensor_send_next is not None: - send_next_op = torch.distributed.P2POp( -- torch.distributed.isend, tensor_send_next, next_pipeline_rank, group -+ torch.distributed.isend, tensor_send_next, next_pipeline_rank, - ) - ops.append(send_next_op) - if tensor_recv_next is not None: - recv_next_op = torch.distributed.P2POp( -- torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, group -+ torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, - ) - ops.append(recv_next_op) - if len(ops) > 0: -diff --git a/megatron/core/transformer/moe/moe_utils.py b/megatron/core/transformer/moe/moe_utils.py -index 28cff06f5..58dc4bb70 100644 ---- a/megatron/core/transformer/moe/moe_utils.py -+++ b/megatron/core/transformer/moe/moe_utils.py -@@ -587,6 +587,9 @@ def topk_routing_with_score_function( - else: - return torch.topk(scores, k=topk, dim=1) - -+ from relax.utils.training.routing_replay import get_routing_replay_compute_topk -+ compute_topk = get_routing_replay_compute_topk(compute_topk) -+ - if score_function == "softmax": - if use_pre_softmax: - scores = torch.softmax(logits, dim=-1, dtype=torch.float32).type_as(logits) -diff --git a/megatron/core/transformer/moe/router.py b/megatron/core/transformer/moe/router.py -index 16fc9d9af..517944f25 100644 ---- a/megatron/core/transformer/moe/router.py -+++ b/megatron/core/transformer/moe/router.py -@@ -201,6 +201,9 @@ class TopKRouter(Router): - self.global_tokens_per_expert = None - self.ga_steps = None - -+ from relax.utils.training.routing_replay import register_routing_replay -+ register_routing_replay(self) -+ - def _maintain_float32_expert_bias(self): - """ - Maintain the expert bias in float32. -diff --git a/megatron/core/transformer/multi_token_prediction.py b/megatron/core/transformer/multi_token_prediction.py -index a8f4abfcd..f33f6f05e 100755 ---- a/megatron/core/transformer/multi_token_prediction.py -+++ b/megatron/core/transformer/multi_token_prediction.py -@@ -6,6 +6,7 @@ from typing import Callable, List, Optional, Union - - import torch - from torch import Tensor -+import warnings - - from megatron.core import InferenceParams, parallel_state, tensor_parallel - from megatron.core.dist_checkpointing.mapping import ShardedStateDict -@@ -714,17 +715,19 @@ class MultiTokenPredictionLayer(MegatronModule): - cp_group=self.cp_group, - packed_seq_params=packed_seq_params, - ) -- position_ids, _ = roll_tensor( -- position_ids, -- shifts=-1, -- dims=-1, -- cp_group=self.cp_group, -- packed_seq_params=packed_seq_params, -- ) -+ if position_ids is not None: -+ position_ids, _ = roll_tensor( -+ position_ids, -+ shifts=-1, -+ dims=-1, -+ cp_group=self.cp_group, -+ packed_seq_params=packed_seq_params, -+ ) - # embedding - decoder_input = embedding(input_ids=input_ids, position_ids=position_ids) -+ decoder_input = decoder_input.detach() - -- hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True) -+ hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=False) - - return input_ids, position_ids, decoder_input, hidden_states - -@@ -826,6 +829,51 @@ class MultiTokenPredictionLayer(MegatronModule): - return hidden_states - - def _checkpointed_forward(self, forward_func, *args, **kwargs): -+ """Wrap `forward_func` with activation checkpointing while only passing tensors. -+ -+ Non-tensor arguments (e.g., configuration objects, None) are captured via closure so -+ that checkpoint implementations never receive them directly, avoiding save_for_backward -+ issues with non-tensor inputs. -+ """ -+ -+ # TODO(jiajun): Is there any better implementation here? -+ positional_specs = [] -+ kw_specs = [] -+ tensor_args: List[torch.Tensor] = [] -+ -+ for arg in args: -+ if torch.is_tensor(arg): -+ positional_specs.append(('tensor', len(tensor_args))) -+ tensor_args.append(arg) -+ else: -+ positional_specs.append(('const', arg)) -+ -+ for key, value in kwargs.items(): -+ if torch.is_tensor(value): -+ kw_specs.append((key, ('tensor', len(tensor_args)))) -+ tensor_args.append(value) -+ else: -+ kw_specs.append((key, ('const', value))) -+ -+ def run(*flat_tensor_args): -+ rebuilt_args = [] -+ for spec_type, payload in positional_specs: -+ if spec_type == 'tensor': -+ rebuilt_args.append(flat_tensor_args[payload]) -+ else: -+ rebuilt_args.append(payload) -+ -+ rebuilt_kwargs = {} -+ for key, (spec_type, payload) in kw_specs: -+ if spec_type == 'tensor': -+ rebuilt_kwargs[key] = flat_tensor_args[payload] -+ else: -+ rebuilt_kwargs[key] = payload -+ -+ return forward_func(*rebuilt_args, **rebuilt_kwargs) -+ -+ tensor_args_tuple = tuple(tensor_args) -+ - def checkpoint_handler(): - """Determines whether to use the `te_checkpoint` or `tensor_parallel.checkpoint`""" - if self.config.fp8: -@@ -836,12 +884,11 @@ class MultiTokenPredictionLayer(MegatronModule): - self.config.distribute_saved_activations, - tensor_parallel.random.get_cuda_rng_tracker, - parallel_state.get_tensor_model_parallel_group(), -- *args, -- **kwargs, -+ *tensor_args_tuple, - ) - else: - return tensor_parallel.checkpoint( -- forward_func, self.config.distribute_saved_activations, *args, *kwargs.values() -+ run, self.config.distribute_saved_activations, *tensor_args_tuple - ) - - if self.config.recompute_method == 'uniform': -diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py -index e2705bd9f..a0aa109b5 100644 ---- a/megatron/core/transformer/transformer_config.py -+++ b/megatron/core/transformer/transformer_config.py -@@ -210,6 +210,9 @@ class TransformerConfig(ModelParallelConfig): - attention_output_gate: bool = False - """Whether to apply output gate to the attention layers.""" - -+ post_self_attn_layernorm: bool = False -+ post_mlp_layernorm: bool = False -+ - test_mode: bool = False - """Whether to run real-time tests.""" - -diff --git a/megatron/core/transformer/transformer_layer.py b/megatron/core/transformer/transformer_layer.py -index 3ea405770..5a42001b9 100644 ---- a/megatron/core/transformer/transformer_layer.py -+++ b/megatron/core/transformer/transformer_layer.py -@@ -223,6 +223,7 @@ class TransformerLayerSubmodules: - input_layernorm: Union[ModuleSpec, type] = IdentityOp - self_attention: Union[ModuleSpec, type] = IdentityOp - self_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp -+ post_self_attn_layernorm: Union[ModuleSpec, type] = IdentityOp - - pre_cross_attn_layernorm: Union[ModuleSpec, type] = IdentityOp - cross_attention: Union[ModuleSpec, type] = IdentityOp -@@ -231,6 +232,7 @@ class TransformerLayerSubmodules: - pre_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp - mlp: Union[ModuleSpec, type] = IdentityOp - mlp_bda: Union[ModuleSpec, type] = IdentityFuncOp -+ post_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp - - # Mapping for sharded tensor keys to be applied in `sharded_state_dict` method - sharded_state_dict_keys_map: Dict[str, str] = field(default_factory=dict) -@@ -310,6 +312,13 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): - # [Module 3: BiasDropoutFusion] - self.self_attn_bda = build_module(submodules.self_attn_bda) - -+ self.post_self_attn_layernorm = build_module( -+ submodules.post_self_attn_layernorm, -+ config=self.config, -+ hidden_size=self.config.hidden_size, -+ eps=self.config.layernorm_epsilon, -+ ) -+ - # [Module 4: Post SelfAttention] Optional Layernorm after self-attn - self.pre_cross_attn_layernorm = build_module( - submodules.pre_cross_attn_layernorm, -@@ -375,6 +384,13 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): - - self.is_moe_layer = isinstance(self.mlp, MoELayer) - -+ self.post_mlp_layernorm = build_module( -+ submodules.post_mlp_layernorm, -+ config=self.config, -+ hidden_size=self.config.hidden_size, -+ eps=self.config.layernorm_epsilon -+ ) -+ - self.recompute_input_layernorm = False - self.recompute_pre_mlp_layernorm = False - self.recompute_mlp = False -@@ -551,6 +567,10 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): - attention_output_with_bias[0] - ) - -+ attention_output, attention_output_bias = attention_output_with_bias -+ attention_output = self.post_self_attn_layernorm(attention_output) -+ attention_output_with_bias = (attention_output, attention_output_bias) -+ - # TODO: could we move `bias_dropout_add_exec_handler` itself - # inside the module provided in the `bias_dropout_add_spec` module? - nvtx_range_push(suffix="self_attn_bda") -@@ -677,6 +697,10 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): - else: - mlp_output_with_bias = self.mlp(pre_mlp_layernorm_output) - -+ mlp_output, mlp_output_bias = mlp_output_with_bias -+ mlp_output = self.post_mlp_layernorm(mlp_output) -+ mlp_output_with_bias = (mlp_output, mlp_output_bias) -+ - if self.recompute_pre_mlp_layernorm: - # discard the output of the pre-mlp layernorm and register the recompute - # as a gradient hook of mlp_output_with_bias[0] -diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py -index b267c8a81..83736acdc 100644 ---- a/megatron/training/arguments.py -+++ b/megatron/training/arguments.py -@@ -1398,6 +1398,9 @@ def core_transformer_config_from_args(args, config_class=None): - - kw_args['inference_sampling_seed'] = args.seed - -+ kw_args['post_self_attn_layernorm'] = args.post_self_attn_layernorm -+ kw_args['post_mlp_layernorm'] = args.post_mlp_layernorm -+ - # handle quantization config - # NOTE: Kitchen arguments are only added to the namespace when - # Kitchen library is available. -@@ -1764,6 +1767,12 @@ def _add_network_size_args(parser): - action='store_true', - help='If set, use original BERT residula connection ' - 'ordering.') -+ group.add_argument('--post-self-attn-layernorm', action='store_true', -+ help='If set, use post self attention layernorm.') -+ group.add_argument('--post-mlp-layernorm', action='store_true', -+ help='If set, use post MLP layernorm.') -+ group.add_argument('--use-gated-attention', action='store_true', -+ help='If set, use gated attention as in Qwen3Next') - group.add_argument('--openai-gelu', action='store_true', - help='Use OpenAIs GeLU implementation. This option' - 'should not be used unless for backward compatibility' -diff --git a/megatron/training/tokenizer/tokenizer.py b/megatron/training/tokenizer/tokenizer.py -index 13b7526ca..6c590f653 100644 ---- a/megatron/training/tokenizer/tokenizer.py -+++ b/megatron/training/tokenizer/tokenizer.py -@@ -136,7 +136,7 @@ class _HuggingFaceTokenizer(MegatronLegacyTokenizer): - # TODO(bnorick): download tokenizer once to lustre and use force offline to make sure all tasks read it from there - self._tokenizer = transformers.AutoTokenizer.from_pretrained( - pretrained_model_name_or_path=pretrained_model_name_or_path, -- trust_remote_code=trust_remote_code, -+ trust_remote_code=True, - **kwargs, - ) - self._vocab = self._tokenizer.get_vocab() -diff --git a/megatron/core/ssm/gated_delta_net.py b/megatron/core/ssm/gated_delta_net.py -index dfa6e4c35..0b38f1135 100644 ---- a/megatron/core/ssm/gated_delta_net.py -+++ b/megatron/core/ssm/gated_delta_net.py -@@ -21,6 +21,12 @@ from megatron.core.inference.contexts import BaseInferenceContext - from megatron.core.jit import jit_fuser - from megatron.core.packed_seq_params import PackedSeqParams - from megatron.core.process_groups_config import ProcessGroupCollection -+from megatron.core.ssm.mamba_context_parallel import ( -+ _all_to_all_cp2hp, -+ _all_to_all_hp2cp, -+ _redo_attention_load_balancing, -+ _undo_attention_load_balancing, -+) - from megatron.core.tensor_parallel import get_cuda_rng_tracker - from megatron.core.transformer import TransformerConfig - from megatron.core.transformer.identity_op import IdentityOp -@@ -33,24 +39,19 @@ from megatron.core.transformer.utils import ( - ) - from megatron.core.utils import deprecate_inference_params, nvtx_range_pop, nvtx_range_push - --# TODO: Implement GatedDeltaNetContextParallel --# from .gated_delta_net_context_parallel import GatedDeltaNetContextParallel -- - try: -+ from fla.modules.convolution import causal_conv1d - from fla.modules.l2norm import l2norm - from fla.ops.gated_delta_rule import chunk_gated_delta_rule - - HAVE_FLA = True - except ImportError: -+ causal_conv1d = None -+ l2norm = None - chunk_gated_delta_rule = None - - HAVE_FLA = False - --try: -- from causal_conv1d import causal_conv1d_fn --except ImportError: -- causal_conv1d_fn = None -- - - logger = logging.getLogger(__name__) - -@@ -84,6 +85,7 @@ class GatedDeltaNet(MegatronModule): - use_qk_l2norm: bool = True, - A_init_range: Tuple[float, float] = (1, 16), - pg_collection: ProcessGroupCollection = None, -+ **kwargs, - ): - """ - Args: -@@ -98,9 +100,11 @@ class GatedDeltaNet(MegatronModule): - pg_collection: The required process groups to use for tensor model parallel and context - parallel. - """ -- -+ # print(f"new gdn", flush=True) - if not HAVE_FLA: -- raise ImportError("FLA is not installed. Please install it with `pip install fla`.") -+ raise ImportError( -+ "FLA is not installed. Please install it with `pip install flash-linear-attention`." -+ ) - - super().__init__(config) - -@@ -114,6 +118,7 @@ class GatedDeltaNet(MegatronModule): - self.use_qk_l2norm = use_qk_l2norm - assert pg_collection is not None, "pg_collection must be provided for GatedDeltaNet" - self.pg_collection = pg_collection -+ self.cp_size = self.pg_collection.cp.size() - self.tp_size = self.pg_collection.tp.size() - self.sp_size = self.tp_size if config.sequence_parallel else 1 - -@@ -129,6 +134,8 @@ class GatedDeltaNet(MegatronModule): - self.num_value_heads = config.linear_num_value_heads - self.qk_dim = self.key_head_dim * self.num_key_heads - self.v_dim = self.value_head_dim * self.num_value_heads -+ self.qk_dim_local_tp = self.qk_dim // self.tp_size -+ self.v_dim_local_tp = self.v_dim // self.tp_size - - # Input projection (hidden_states -> q, k, v, gate, beta, alpha) - # TODO: for now, output gate is forced for GDN. -@@ -171,8 +178,10 @@ class GatedDeltaNet(MegatronModule): - dtype=config.params_dtype, - ) - setattr(self.conv1d.weight, "tensor_model_parallel", True) -+ setattr(self.conv1d.weight, "partition_dim", 0) - if conv_bias: - setattr(self.conv1d.bias, "tensor_model_parallel", True) -+ setattr(self.conv1d.bias, "partition_dim", 0) - - # Time step projection (discretization) - self.num_v_heads_local_tp = self.num_value_heads // self.tp_size -@@ -185,6 +194,7 @@ class GatedDeltaNet(MegatronModule): - ) - ) - setattr(self.dt_bias, "tensor_model_parallel", True) -+ setattr(self.dt_bias, "partition_dim", 0) - # A_log parameter - self.A_log = nn.Parameter( - torch.empty( -@@ -194,6 +204,12 @@ class GatedDeltaNet(MegatronModule): - ) - ) - setattr(self.A_log, "tensor_model_parallel", True) -+ setattr(self.A_log, "partition_dim", 0) -+ -+ if self.config.deterministic_mode: -+ self.gated_delta_rule = torch_chunk_gated_delta_rule -+ else: -+ self.gated_delta_rule = chunk_gated_delta_rule - - # Output layernorm before projection - self.out_norm = build_module( -@@ -217,8 +233,6 @@ class GatedDeltaNet(MegatronModule): - tp_group=self.pg_collection.tp, - ) - -- # TODO: support CP -- - self.reset_parameters() - - def reset_parameters(self): -@@ -241,23 +255,18 @@ class GatedDeltaNet(MegatronModule): - dtype=self.config.params_dtype, - device=torch.cuda.current_device(), - ).uniform_(*self.A_init_range) -- self.A_log.data.copy_(A) -+ self.A_log.data.copy_(torch.log(A)) - - def forward( - self, - hidden_states: Tensor, - attention_mask: Tensor, -- key_value_states: Optional[Tensor] = None, - inference_context: Optional[BaseInferenceContext] = None, -- rotary_pos_emb: Optional[Union[Tensor, Tuple[Tensor, Tensor]]] = None, -- rotary_pos_cos: Optional[Tensor] = None, -- rotary_pos_sin: Optional[Tensor] = None, -- rotary_pos_cos_sin: Optional[Tensor] = None, -- attention_bias: Optional[Tensor] = None, - packed_seq_params: Optional[PackedSeqParams] = None, - sequence_len_offset: Optional[int] = None, - *, - inference_params: Optional[BaseInferenceContext] = None, -+ **kwargs, - ): - """ - Perform a forward pass through the GDN module. -@@ -265,15 +274,8 @@ class GatedDeltaNet(MegatronModule): - Args: - hidden_states (Tensor): Hidden states. - attention_mask (Tensor): Attention mask. -- key_value_states (Optional[Tensor]): Key/value states (for cross attention). - inference_context (Optional[BaseInferenceContext]): Inference context that manages - KV cache. -- rotary_pos_emb (Optional[Union[Tensor, Tuple[Tensor, Tensor]]]): Rotary -- embedding tensor(s). -- rotary_pos_cos (Optional[Tensor]): Rotary embedding cosine. -- rotary_pos_sin (Optional[Tensor]): Rotary embedding sine. -- rotary_pos_cos_sin (Optional[Tensor]): Combined rotary embedding cosine and sine. -- attention_bias (Optional[Tensor]): Attention bias. - packed_seq_params (Optional[PackedSeqparams]): Parameters used for THD format. - sequence_len_offset (Optional[int]): Sequence length offset used for - inference CUDA graphs. -@@ -287,7 +289,7 @@ class GatedDeltaNet(MegatronModule): - inference_context = deprecate_inference_params(inference_context, inference_params) - - seq_len, batch, _ = hidden_states.shape -- seq_len = seq_len * self.sp_size -+ seq_len = seq_len * self.sp_size * self.cp_size - - if inference_context is not None: - assert ( -@@ -297,15 +299,80 @@ class GatedDeltaNet(MegatronModule): - # TODO: support inference - raise NotImplementedError("GDN does not support inference for now.") - -- if packed_seq_params is not None: -- # TODO: support packed sequence -- raise NotImplementedError("GDN does not support packed sequence for now.") -+ if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd': -+ assert batch == 1, "Packed sequence expects batch dimension to be 1" -+ assert ( -+ not self.config.deterministic_mode -+ ), "Packed sequence does not support deterministic mode." -+ -+ # Resolve cu_seqlens with alignment padding handling. -+ cu_seqlens_q = self._resolve_cu_seqlens( -+ packed_seq_params.cu_seqlens_q_padded, -+ packed_seq_params.cu_seqlens_q, -+ seq_len, -+ "cu_seqlens_q", -+ ) -+ cu_seqlens_kv = self._resolve_cu_seqlens( -+ packed_seq_params.cu_seqlens_kv_padded, -+ packed_seq_params.cu_seqlens_kv, -+ seq_len, -+ "cu_seqlens_kv", -+ ) -+ assert torch.equal(cu_seqlens_q, cu_seqlens_kv), ( -+ "Currently only support cu_seqlens_q equals to cu_seqlens_kv, " -+ f"but got {cu_seqlens_q=} and {cu_seqlens_kv=}" -+ ) -+ num_packed_seqs = cu_seqlens_q.shape[0] - 1 -+ assert num_packed_seqs > 0, ( -+ "Number of packed sequences must be greater than 0, " -+ f"but got {cu_seqlens_q=} and {cu_seqlens_kv=}" -+ ) -+ else: -+ cu_seqlens_q = None -+ cu_seqlens_kv = None - - # Input projection - nvtx_range_push(suffix="in_proj") - qkvzba, _ = self.in_proj(hidden_states) - nvtx_range_pop(suffix="in_proj") - -+ # CP All to All: CP to HP -+ if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd': -+ unpacked_qkvzba = _unpack_sequence(qkvzba, cu_seqlens_q // self.cp_size, dim=0) -+ outputs = [] -+ for qkvzba_i in unpacked_qkvzba: -+ qkvzba_i = tensor_a2a_cp2hp( -+ qkvzba_i, -+ seq_dim=0, -+ head_dim=-1, -+ cp_group=self.pg_collection.cp, -+ split_sections=[ -+ self.qk_dim_local_tp, -+ self.qk_dim_local_tp, -+ self.v_dim_local_tp, -+ self.v_dim_local_tp, -+ self.num_value_heads // self.tp_size, -+ self.num_value_heads // self.tp_size, -+ ], -+ ) -+ outputs.append(qkvzba_i) -+ qkvzba = torch.cat(outputs, dim=0) -+ else: -+ qkvzba = tensor_a2a_cp2hp( -+ qkvzba, -+ seq_dim=0, -+ head_dim=-1, -+ cp_group=self.pg_collection.cp, -+ split_sections=[ -+ self.qk_dim_local_tp, -+ self.qk_dim_local_tp, -+ self.v_dim_local_tp, -+ self.v_dim_local_tp, -+ self.num_value_heads // self.tp_size, -+ self.num_value_heads // self.tp_size, -+ ], -+ ) -+ - # Transpose: s b x --> b s x - # From sbhd to bshd format - qkvzba = qkvzba.transpose(0, 1) -@@ -314,10 +381,10 @@ class GatedDeltaNet(MegatronModule): - qkv, gate, beta, alpha = torch.split( - qkvzba, - [ -- (self.qk_dim * 2 + self.v_dim) // self.tp_size, -- self.v_dim // self.tp_size, -- self.num_value_heads // self.tp_size, -- self.num_value_heads // self.tp_size, -+ (self.qk_dim_local_tp * 2 + self.v_dim_local_tp) // self.cp_size, -+ self.v_dim_local_tp // self.cp_size, -+ self.num_value_heads // self.tp_size // self.cp_size, -+ self.num_value_heads // self.tp_size // self.cp_size, - ], - dim=-1, - ) -@@ -326,74 +393,83 @@ class GatedDeltaNet(MegatronModule): - alpha = alpha.reshape(batch, seq_len, -1) - - # Convolution on qkv -- qkv = qkv.transpose(1, 2).contiguous() # b, s, d -> b, d, s - nvtx_range_push(suffix="conv1d") -- if (causal_conv1d_fn is None) or self.config.deterministic_mode: -- qkv = self.act_fn(self.conv1d(qkv)[..., :seq_len]) -+ seq_len = qkv.shape[1] -+ qkv_channels_split_sections = [ -+ self.qk_dim_local_tp, -+ self.qk_dim_local_tp, -+ self.v_dim_local_tp, -+ ] -+ conv1d_weight = get_parameter_local_cp( -+ self.conv1d.weight, -+ dim=0, -+ cp_group=self.pg_collection.cp, -+ split_sections=qkv_channels_split_sections, -+ ) -+ conv1d_bias = ( -+ get_parameter_local_cp( -+ self.conv1d.bias, -+ dim=0, -+ cp_group=self.pg_collection.cp, -+ split_sections=qkv_channels_split_sections, -+ ) -+ if self.conv_bias -+ else None -+ ) -+ if self.config.deterministic_mode: -+ qkv = qkv.transpose(1, 2).contiguous() # b, s, d -> b, d, s -+ conv_out = F.conv1d( -+ input=qkv, # Torch-native only accept [b, d, s] format input -+ weight=conv1d_weight, -+ bias=conv1d_bias, -+ stride=self.conv1d.stride, -+ padding=self.conv1d.padding, -+ dilation=self.conv1d.dilation, -+ groups=self.conv_dim_local_tp // self.cp_size, -+ ) -+ qkv = self.act_fn(conv_out[..., :seq_len]) -+ qkv = qkv.transpose(1, 2) # b, d, s -> b, s, d - else: - assert self.activation in ["silu", "swish"] -- qkv = causal_conv1d_fn( -- x=qkv, -- weight=self.conv1d.weight.squeeze(1), # d, 1, w -> d, w -- bias=self.conv1d.bias, -+ qkv, _ = causal_conv1d( -+ x=qkv, # FLA conv1d accepts [b, s, d] format input -+ weight=conv1d_weight.squeeze(1), # d, 1, w -> d, w -+ bias=conv1d_bias, - activation=self.activation, -+ initial_state=None, -+ output_final_state=False, -+ cu_seqlens=cu_seqlens_q, - ) - nvtx_range_pop(suffix="conv1d") -- # Split qkv into query, key, and value -- qkv = qkv.transpose(1, 2) # b, d, s -> b, s, d -- query, key, value = torch.split( -- qkv, -- [self.qk_dim // self.tp_size, self.qk_dim // self.tp_size, self.v_dim // self.tp_size], -- dim=-1, -- ) -- query = query.reshape(batch, seq_len, -1, self.key_head_dim) -- key = key.reshape(batch, seq_len, -1, self.key_head_dim) -- value = value.reshape(batch, seq_len, -1, self.value_head_dim) -- # Apply L2 norm to query and key -- if self.use_qk_l2norm: -- query = l2norm(query.contiguous()) -- key = l2norm(key.contiguous()) -- if self.num_value_heads // self.num_key_heads > 1: -- query = query.repeat_interleave(self.num_value_heads // self.num_key_heads, dim=2) -- key = key.repeat_interleave(self.num_value_heads // self.num_key_heads, dim=2) - -- # Make contiguous -- query = query.contiguous() -- key = key.contiguous() -- value = value.contiguous() -- gate = gate.contiguous() -- beta = beta.contiguous() -- alpha = alpha.contiguous() -+ # Prepare QKV tensors (split, reshape, L2 norm, repeat_interleave, contiguous) -+ nvtx_range_push(suffix="prepare_qkv_for_gated_delta_rule") -+ query, key, value, gate, beta, alpha = self._prepare_qkv_for_gated_delta_rule( -+ qkv, gate, beta, alpha, batch, seq_len -+ ) -+ nvtx_range_pop(suffix="prepare_qkv_for_gated_delta_rule") - - # Calculate g and beta - nvtx_range_push(suffix="g_and_beta") -- g = -self.A_log.exp() * F.softplus(alpha.float() + self.dt_bias) # In fp32 -- beta = beta.sigmoid() -+ A_log_local_cp = get_parameter_local_cp(self.A_log, dim=0, cp_group=self.pg_collection.cp) -+ dt_bias_local_cp = get_parameter_local_cp( -+ self.dt_bias, dim=0, cp_group=self.pg_collection.cp -+ ) -+ g, beta = self._compute_g_and_beta(A_log_local_cp, dt_bias_local_cp, alpha, beta) - nvtx_range_pop(suffix="g_and_beta") - - nvtx_range_push(suffix="gated_delta_rule") -- if self.config.deterministic_mode: -- core_attn_out, last_recurrent_state = torch_chunk_gated_delta_rule( -- query, -- key, -- value, -- g=g, -- beta=beta, -- initial_state=None, -- output_final_state=False, -- use_qk_l2norm_in_kernel=False, -- ) -- else: -- core_attn_out, last_recurrent_state = chunk_gated_delta_rule( -- query, -- key, -- value, -- g=g, -- beta=beta, -- initial_state=None, -- output_final_state=False, -- use_qk_l2norm_in_kernel=False, -- ) -+ core_attn_out, last_recurrent_state = self.gated_delta_rule( -+ query, -+ key, -+ value, -+ g=g, -+ beta=beta, -+ initial_state=None, -+ output_final_state=False, -+ use_qk_l2norm_in_kernel=False, -+ cu_seqlens=cu_seqlens_q, -+ ) - nvtx_range_pop(suffix="gated_delta_rule") - - # RMSNorm -@@ -406,6 +482,21 @@ class GatedDeltaNet(MegatronModule): - norm_out = norm_out.reshape(batch, seq_len, -1) - norm_out = norm_out.transpose(0, 1).contiguous() - -+ # CP all to all: HP to CP -+ if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd': -+ unpacked_norm_out = _unpack_sequence(norm_out, cu_seqlens_q, dim=0) -+ outputs = [] -+ for norm_out_i in unpacked_norm_out: -+ norm_out_i = tensor_a2a_hp2cp( -+ norm_out_i, seq_dim=0, head_dim=-1, cp_group=self.pg_collection.cp -+ ) -+ outputs.append(norm_out_i) -+ norm_out = torch.cat(outputs, dim=0) -+ else: -+ norm_out = tensor_a2a_hp2cp( -+ norm_out, seq_dim=0, head_dim=-1, cp_group=self.pg_collection.cp -+ ) -+ - # Output projection - nvtx_range_push(suffix="out_proj") - out, out_bias = self.out_proj(norm_out) -@@ -425,6 +516,74 @@ class GatedDeltaNet(MegatronModule): - y = y.to(x_dtype) - return y - -+ @jit_fuser -+ def _prepare_qkv_for_gated_delta_rule(self, qkv, gate, beta, alpha, batch, seq_len): -+ """ -+ Prepare query, key, value, gate, beta, alpha tensors for gated delta rule. -+ Fuses split, reshape, L2 norm, repeat_interleave, and contiguous operations. -+ """ -+ # Split qkv into query_key and value -+ query_key, value = torch.split( -+ qkv, -+ [2 * self.qk_dim_local_tp // self.cp_size, self.v_dim_local_tp // self.cp_size], -+ dim=-1, -+ ) -+ -+ # Reshape query_key and value -+ query_key = query_key.reshape(batch, seq_len, -1, self.key_head_dim) -+ value = value.reshape(batch, seq_len, -1, self.value_head_dim) -+ -+ # Apply L2 norm to query and key -+ if self.use_qk_l2norm: -+ query_key = l2norm(query_key.contiguous()) -+ -+ # Split query and key -+ split_size = self.qk_dim_local_tp // self.key_head_dim // self.cp_size -+ query, key = torch.split(query_key, [split_size, split_size], dim=2) -+ -+ # Expand query and key if needed (grouped query attention) -+ if self.num_value_heads // self.num_key_heads > 1: -+ repeat_factor = self.num_value_heads // self.num_key_heads -+ query = query.repeat_interleave(repeat_factor, dim=2) -+ key = key.repeat_interleave(repeat_factor, dim=2) -+ -+ # Make all tensors contiguous -+ query = query.contiguous() -+ key = key.contiguous() -+ value = value.contiguous() -+ gate = gate.contiguous() -+ beta = beta.contiguous() -+ alpha = alpha.contiguous() -+ -+ return query, key, value, gate, beta, alpha -+ -+ @jit_fuser -+ def _compute_g_and_beta(self, A_log_local_cp, dt_bias_local_cp, alpha, beta): -+ """ -+ Compute g (decay) and beta (sigmoid) for gated delta rule. -+ Fuses exp, softplus, mul, neg, and sigmoid operations. -+ """ -+ g = -A_log_local_cp.exp() * F.softplus(alpha.float() + dt_bias_local_cp) # In fp32 -+ beta = beta.sigmoid() -+ return g, beta -+ -+ def _resolve_cu_seqlens(self, cu_seqlens_padded, cu_seqlens_actual, total_seq_len, name): -+ """Resolve cu_seqlens for packed sequence all-to-all, handling alignment padding.""" -+ if cu_seqlens_padded is not None: -+ cu_seqlens = cu_seqlens_padded -+ else: -+ cu_seqlens = cu_seqlens_actual -+ -+ total_cu = cu_seqlens[-1].item() -+ if total_cu != total_seq_len: -+ raise ValueError( -+ f"GDN: {name}[-1]={total_cu} does not match " -+ f"total_sequence_length={total_seq_len}. " -+ f"({cu_seqlens_padded=}, {cu_seqlens_actual=})." -+ ) -+ -+ return cu_seqlens -+ - def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None, tp_group=None): - """Provide a sharded state dictionary for distributed checkpointing.""" - # Guard for cases metadata is not provided -@@ -479,10 +638,10 @@ class GatedDeltaNet(MegatronModule): - sharded_state_dict[f"{prefix}in_proj.weight"] = _split_tensor_factory( - sharded_state_dict[f"{prefix}in_proj.weight"], - [ -- self.qk_dim // self.tp_size, -- self.qk_dim // self.tp_size, -- self.v_dim // self.tp_size, -- self.v_dim // self.tp_size, -+ self.qk_dim_local_tp, -+ self.qk_dim_local_tp, -+ self.v_dim_local_tp, -+ self.v_dim_local_tp, - self.num_value_heads // self.tp_size, - self.num_value_heads // self.tp_size, - ], -@@ -502,18 +661,41 @@ class GatedDeltaNet(MegatronModule): - for conv_layer_name in conv_layer_name_list: - sharded_state_dict[f"{prefix}{conv_layer_name}"] = _split_tensor_factory( - sharded_state_dict[f"{prefix}{conv_layer_name}"], -- [ -- self.qk_dim // self.tp_size, -- self.qk_dim // self.tp_size, -- self.v_dim // self.tp_size, -- ], -+ [self.qk_dim_local_tp, self.qk_dim_local_tp, self.v_dim_local_tp], - ["query", "key", "value"], - 0, - ) - - return sharded_state_dict - -+ def backward_dw(self): -+ """Execute weight gradient computation for all linear layers.""" -+ self._backward_in_proj() -+ self._backward_out_proj() -+ -+ def _backward_in_proj(self): -+ """Computes weight gradients of input projection layer.""" -+ self.in_proj.backward_dw() -+ -+ def _backward_out_proj(self): -+ """Computes weight gradients of output projection layer.""" -+ self.out_proj.backward_dw() -+ -+ -+def _unpack_sequence(x, cu_seqlens, dim=1): -+ unpacked_x = [] -+ num_seqs = cu_seqlens.shape[0] - 1 -+ for i in range(num_seqs): -+ idx_start = cu_seqlens[i].item() -+ idx_end = cu_seqlens[i + 1].item() -+ chunked_index = [slice(None)] * dim + [slice(idx_start, idx_end)] -+ unpacked_x.append(x[tuple(chunked_index)]) -+ return unpacked_x -+ - -+#################### -+# Sharded state dict utilities -+#################### - def _split_tensor_factory( - orig_sh_ten: ShardedTensor, split_sections: List[int], split_names: List[str], split_dim: int - ) -> ShardedTensorFactory: -@@ -574,6 +756,184 @@ def _split_tensor_factory( - ) - - -+#################### -+# Context parallel utilities -+#################### -+def get_parameter_local_cp( -+ param: torch.Tensor, -+ dim: int, -+ cp_group: torch.distributed.ProcessGroup, -+ split_sections: Optional[List[int]] = None, -+) -> torch.Tensor: -+ """Get the local parameter for the current context parallel rank. -+ -+ Args: -+ param (torch.Tensor): The entire parameter to get the local parameter for. -+ dim (int): The dimension to split the parameter along. Usually the dimension of head. -+ cp_group (torch.distributed.ProcessGroup): The context parallel group. -+ split_sections (Optional[List[int]]): If not None, -+ first split the parameter along the dimension dim into sections, -+ then get the local hidden parallel weights separately, -+ finally concatenate the local hidden parallel weights along the dimension dim. -+ -+ Returns: -+ torch.Tensor: The local parameter for the current context parallel rank. -+ """ -+ -+ cp_size = cp_group.size() -+ cp_rank = cp_group.rank() -+ -+ # No need to split if CP size is 1. -+ if cp_size == 1: -+ return param -+ -+ # Split first if needed. -+ if split_sections is not None: -+ inputs = torch.split(param, split_sections, dim=dim) -+ outputs = [] -+ for p in inputs: -+ p = get_parameter_local_cp(p, dim, cp_group) -+ outputs.append(p) -+ return torch.cat(outputs, dim=dim) -+ -+ # Slice the parameter. -+ slices = [slice(None)] * param.dim() -+ dim_size = param.size(dim=dim) -+ slices[dim] = slice(cp_rank * dim_size // cp_size, (cp_rank + 1) * dim_size // cp_size) -+ param = param[slices] -+ return param -+ -+ -+def tensor_a2a_cp2hp( -+ tensor: torch.Tensor, -+ seq_dim: int, -+ head_dim: int, -+ cp_group: torch.distributed.ProcessGroup, -+ split_sections: Optional[List[int]] = None, -+ undo_attention_load_balancing: bool = True, -+): -+ """All-to-all context parallel to hidden parallel. -+ -+ Args: -+ tensor (torch.Tensor): The tensor to all-to-all. -+ Currently only support (seq_len, batch, head_dim) shaped tensor. -+ seq_dim (int): The dimension of sequence length. Currently only supports seq_dim == 0. -+ head_dim (int): The dimension of head. Currently only supports head_dim == -1 or 2. -+ cp_group (torch.distributed.ProcessGroup): The context parallel group. -+ split_sections (Optional[List[int]]): If not None, split the tensor along the dimension -+ head_dim into sections first, then do all-to-all for each section separately, -+ finally concatenate the separated tensors along the dimension head_dim. -+ undo_attention_load_balancing (bool): Whether to undo the attention load balancing of CP. -+ -+ Returns: -+ torch.Tensor: The all-to-all tensor. -+ """ -+ -+ cp_size = cp_group.size() -+ -+ # No need to all-to-all if CP size is 1. -+ if cp_size == 1: -+ return tensor -+ -+ # Limitations of mamba_context_parallel._all_to_all_cp2hp. -+ assert seq_dim == 0, f"tensor_a2a_cp2hp only supports seq_dim == 0 for now, but got {seq_dim=}" -+ assert ( -+ head_dim == -1 or head_dim == 2 -+ ), f"tensor_a2a_cp2hp only supports head_dim == -1 or 2 for now, but got {head_dim=}" -+ assert ( -+ tensor.dim() == 3 -+ ), f"tensor_a2a_cp2hp only supports 3-d input tensor for now, but got {tensor.dim()=}" -+ -+ # Split first if needed. -+ if split_sections is not None: -+ inputs = torch.split(tensor, split_sections, dim=head_dim) -+ outputs = [] -+ for x in inputs: -+ x = tensor_a2a_cp2hp( -+ x, -+ seq_dim=seq_dim, -+ head_dim=head_dim, -+ cp_group=cp_group, -+ undo_attention_load_balancing=False, -+ ) -+ outputs.append(x) -+ tensor = torch.cat(outputs, dim=head_dim) -+ else: -+ tensor = _all_to_all_cp2hp(tensor, cp_group) -+ -+ # Undo attention load balancing last if needed. -+ if undo_attention_load_balancing: -+ tensor = _undo_attention_load_balancing(tensor, cp_size) -+ return tensor -+ -+ -+def tensor_a2a_hp2cp( -+ tensor: torch.Tensor, -+ seq_dim: int, -+ head_dim: int, -+ cp_group: torch.distributed.ProcessGroup, -+ split_sections: Optional[List[int]] = None, -+ redo_attention_load_balancing: bool = True, -+): -+ """All-to-all hidden parallel to context parallel. -+ -+ Args: -+ tensor (torch.Tensor): The tensor to all-to-all. -+ Currently only support (seq_len, batch, head_dim) shaped tensor. -+ seq_dim (int): The dimension of sequence length. Currently only supports seq_dim == 0. -+ head_dim (int): The dimension of head. Currently only supports head_dim == -1 or 2. -+ cp_group (torch.distributed.ProcessGroup): The context parallel group. -+ split_sections (Optional[List[int]]): If not None, first split the tensor along the -+ dimension head_dim into sections, then do all-to-all for each section separately, -+ finally concatenate the separated tensors along the dimension head_dim. -+ redo_attention_load_balancing (bool): Whether to redo the attention load balancing of HP. -+ -+ Returns: -+ torch.Tensor: The all-to-all tensor. -+ """ -+ -+ cp_size = cp_group.size() -+ -+ # No need to all-to-all if CP size is 1. -+ if cp_size == 1: -+ return tensor -+ -+ # Limitations of mamba_context_parallel._all_to_all_hp2cp. -+ assert seq_dim == 0, f"tensor_a2a_cp2hp only supports seq_dim == 0 for now, but got {seq_dim=}" -+ assert ( -+ head_dim == -1 or head_dim == 2 -+ ), f"tensor_a2a_cp2hp only supports head_dim == -1 or 2 for now, but got {head_dim=}" -+ assert ( -+ tensor.dim() == 3 -+ ), f"tensor_a2a_cp2hp only supports 3-d input tensor for now, but got {tensor.dim()=}" -+ -+ # Redo attention load balancing first if needed. -+ if redo_attention_load_balancing: -+ tensor = _redo_attention_load_balancing(tensor, cp_size) -+ -+ # Split first if needed. -+ if split_sections is not None: -+ inputs = torch.split(tensor, split_sections, dim=head_dim) -+ outputs = [] -+ for x in inputs: -+ x = tensor_a2a_hp2cp( -+ x, -+ seq_dim=seq_dim, -+ head_dim=head_dim, -+ cp_group=cp_group, -+ redo_attention_load_balancing=False, -+ ) -+ outputs.append(x) -+ tensor = torch.cat(outputs, dim=head_dim) -+ else: -+ tensor = _all_to_all_hp2cp(tensor, cp_group) -+ -+ return tensor -+ -+ -+#################### -+# Torch native gated delta rule -+#################### - def torch_chunk_gated_delta_rule( - query, - key, -@@ -584,6 +944,7 @@ def torch_chunk_gated_delta_rule( - initial_state=None, - output_final_state=False, - use_qk_l2norm_in_kernel=False, -+ cu_seqlens=None, - ): - # pylint: disable=line-too-long - ''' -@@ -593,6 +954,10 @@ def torch_chunk_gated_delta_rule( - Reference: https://github.com/huggingface/transformers/blob/144c8ce2809a2e21914017652700e1ecb450501e/src/transformers/models/qwen3_next/modeling_qwen3_next.py#L470-L547 - ''' - -+ assert ( -+ cu_seqlens is None -+ ), "cu_seqlens is not supported for torch_chunk_gated_delta_rule for now." -+ - initial_dtype = query.dtype - if use_qk_l2norm_in_kernel: - query = l2norm(query, dim=-1, eps=1e-6) -@@ -666,4 +1031,4 @@ def torch_chunk_gated_delta_rule( - ) - core_attn_out = core_attn_out[:, :, :sequence_length] - core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype) -- return core_attn_out, last_recurrent_state -+ return core_attn_out, last_recurrent_state diff --git a/docker/patch/latest/megatron.patch b/docker/patch/latest/megatron.patch new file mode 120000 index 000000000..ec9557dc5 --- /dev/null +++ b/docker/patch/latest/megatron.patch @@ -0,0 +1 @@ +../megatron/20260506-85bced0ae.patch \ No newline at end of file diff --git a/docker/patch/megatron/20251218-3714d81d.patch b/docker/patch/megatron/20251218-3714d81d.patch new file mode 100644 index 000000000..5d4428a59 --- /dev/null +++ b/docker/patch/megatron/20251218-3714d81d.patch @@ -0,0 +1,1578 @@ +diff --git a/megatron/core/dist_checkpointing/strategies/common.py b/megatron/core/dist_checkpointing/strategies/common.py +index 41c21d93d..ef80f72d6 100644 +--- a/megatron/core/dist_checkpointing/strategies/common.py ++++ b/megatron/core/dist_checkpointing/strategies/common.py +@@ -86,7 +86,7 @@ class TorchCommonLoadStrategy(LoadCommonStrategy): + msc = MultiStorageClientFeature.import_package() + return msc.torch.load(load_path, map_location='cpu') + else: +- return torch.load(load_path, map_location='cpu') ++ return torch.load(load_path, map_location='cpu', weights_only=False) + except FileNotFoundError as e: + err_msg = f'Common file {load_path} does not exist' + if MultiStorageClientFeature.is_enabled(): +diff --git a/megatron/core/dist_checkpointing/strategies/torch.py b/megatron/core/dist_checkpointing/strategies/torch.py +index 5a1ea308d..aa701237f 100644 +--- a/megatron/core/dist_checkpointing/strategies/torch.py ++++ b/megatron/core/dist_checkpointing/strategies/torch.py +@@ -597,10 +597,12 @@ class MCoreLoadPlanner(DefaultLoadPlanner): + def _validate_global_shapes(self, metadata, sharded_tensors): + for sh_ten in sharded_tensors: + if sh_ten.key not in metadata.state_dict_metadata: +- raise KeyError( +- f"{sh_ten.key} from model not in state dict:" +- f" {sorted(metadata.state_dict_metadata.keys())}" +- ) ++ # raise KeyError( ++ # f"{sh_ten.key} from model not in state dict:" ++ # f" {sorted(metadata.state_dict_metadata.keys())}" ++ # ) ++ print(f"{sh_ten.key} from model not in state dict, will skip") ++ continue + loaded_shape = metadata.state_dict_metadata[sh_ten.key].size + expected_shape = self._expected_shape(sh_ten) + if loaded_shape != expected_shape: +@@ -630,7 +632,7 @@ class MCoreLoadPlanner(DefaultLoadPlanner): + tensor_metadata = self.metadata.state_dict_metadata + metadata_with_sizes = [ + (tensor_metadata[key], tensor_metadata[key].size, sharded_tensor) +- for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items() ++ for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items() if key in tensor_metadata + ] + try: + # Temporarily set sizes to expected shapes +@@ -959,6 +961,7 @@ class TorchDistLoadShardedStrategy(LoadShardedStrategy): + planner=MCoreLoadPlanner( + shapes_validation_sharded_tensors=flexible_shape_sharded_tensors, + allow_shape_mismatch_sharded_tensors=allow_shape_mismatch_sharded_tensors, ++ allow_partial_load=True, + ), + ) + +diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py +index acb93ef78..d239db4ab 100644 +--- a/megatron/core/extensions/transformer_engine.py ++++ b/megatron/core/extensions/transformer_engine.py +@@ -408,6 +408,7 @@ class TELinear(te.pytorch.Linear): + ) + + for param in self.parameters(): ++ setattr(param, "parallel_mode", parallel_mode) + if is_expert: + # Reduce the gradient on the expert_data_parallel group for expert linear layers + setattr(param, "allreduce", not self.expert_parallel) +@@ -1161,6 +1162,61 @@ class TEDotProductAttention(te.pytorch.DotProductAttention): + + + if HAVE_TE and is_te_min_version("1.9.0.dev0"): ++ def ceil_div(x: int, y: int) -> int: ++ return (x + y - 1) // y ++ ++ class _FakeInt4QuantizationSTE(torch.autograd.Function): ++ @staticmethod ++ def forward(ctx, x, group_size): ++ m, n = x.shape ++ block_size_m, block_size_n = 1, group_size ++ ++ ++ m_padded = ceil_div(m, block_size_m) * block_size_m ++ n_padded = ceil_div(n, block_size_n) * block_size_n ++ ++ x_padded = torch.zeros( ++ (m_padded, n_padded), ++ dtype=x.dtype, device=x.device ++ ) ++ x_padded[:m, :n] = x ++ ++ x_view = x_padded.view( ++ m_padded // block_size_m, ++ block_size_m, ++ n_padded // block_size_n, ++ block_size_n ++ ) ++ ++ x_max = x_view.abs().float().amax(dim=(1, 3), keepdim=True) ++ q_max = 7 ++ x_scale = x_max / q_max ++ ++ x_scale = x_scale.clamp(min=1e-5) ++ ++ x_div = x_view / x_scale ++ x_round = torch.round(x_div) ++ ++ x_q_clamped = x_round.clamp(-q_max, q_max) ++ ++ x_dequant_view = x_q_clamped * x_scale ++ ++ x_dequant_full = x_dequant_view.view_as(x_padded) ++ x_out = x_dequant_full[:m, :n].contiguous().to(x.dtype) ++ ++ return x_out ++ ++ @staticmethod ++ def backward(ctx, grad_output): ++ return grad_output, None ++ ++ def fake_int4_quantization_ste(x, group_size): ++ x_out = _FakeInt4QuantizationSTE.apply(x, group_size) ++ ++ if hasattr(x, 'main_grad'): ++ x_out.main_grad = x.main_grad ++ ++ return x_out + + class TEGroupedLinear(te.pytorch.GroupedLinear): + """ +@@ -1351,6 +1407,7 @@ if HAVE_TE and is_te_min_version("1.9.0.dev0"): + _is_first_microbatch = ( + None if self.disable_parameter_transpose_cache else self.is_first_microbatch + ) ++ + out = super().forward(x, m_splits, is_first_microbatch=_is_first_microbatch) + self.is_first_microbatch = False + +@@ -1361,6 +1418,20 @@ if HAVE_TE and is_te_min_version("1.9.0.dev0"): + return out + return out, None + ++ def _get_weight_tensors(self): ++ """Get the weight tensors of the module.""" ++ weight_tensors = super()._get_weight_tensors() ++ ++ if os.getenv("OPEN_TRAINING_INT4_FAKE_QAT_FLAG", "0") == "1": ++ group_size = int(os.getenv("OPEN_TRAINING_INT4_GROUP_SIZE", "128")) ++ ++ weight_tensors = [ ++ fake_int4_quantization_ste(w, group_size) ++ for w in weight_tensors ++ ] ++ ++ return weight_tensors ++ + def _encode_extra_state(self, state): + # TE 2.0 changed the format of extra_state to be a byte tensor + if is_te_min_version("2.0.0"): +diff --git a/megatron/core/fusions/fused_mla_yarn_rope_apply.py b/megatron/core/fusions/fused_mla_yarn_rope_apply.py +index 1fd5dcfae..c9aeef1f0 100644 +--- a/megatron/core/fusions/fused_mla_yarn_rope_apply.py ++++ b/megatron/core/fusions/fused_mla_yarn_rope_apply.py +@@ -385,6 +385,7 @@ def rotary_fwd_kv_kernel( + SIN, + emb_dim: tl.constexpr, + k_dim: tl.constexpr, ++ k_dim_ceil: tl.constexpr, + v_dim: tl.constexpr, + head_num: tl.constexpr, + batch_size, +@@ -434,21 +435,27 @@ def rotary_fwd_kv_kernel( + cos_right = tl.load(COS + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2)) + sin_right = tl.load(SIN + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2)) + +- KV_ptr = KV + pid_m * stride_kv_seq + pid_head * BLOCK_H * stride_kv_nheads +- kv_off = tl.arange(0, BLOCK_H)[:, None] * stride_kv_nheads +- mask = kv_off < head_num * stride_kv_nheads +- k_in_off = kv_off + tl.arange(0, k_dim)[None, :] +- v_in_off = kv_off + k_dim + tl.arange(0, v_dim)[None, :] +- k = tl.load(KV_ptr + k_in_off, mask=mask) +- v = tl.load(KV_ptr + v_in_off, mask=mask) ++ KV_ptr = KV + pid_m * stride_kv_seq # + pid_head * BLOCK_H * stride_kv_nheads ++ ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H ++ kj_range = tl.arange(0, k_dim_ceil)[None, :] ++ mask_k = (ki_range < head_num) & (kj_range < k_dim) ++ mask_v = ki_range < head_num ++ k_off = ki_range * stride_kv_nheads + kj_range ++ if v_dim > 0: ++ v_off = ki_range * stride_kv_nheads + k_dim + tl.arange(0, v_dim)[None, :] ++ v = tl.load(KV_ptr + v_off, mask=mask_v) ++ else: ++ v = tl.zeros((BLOCK_H, 1), dtype=KV.dtype.element_ty) ++ k = tl.load(KV_ptr + k_off, mask=mask_k) + +- K_ptr = O_KEY + pid_m * stride_k_seq + pid_head * BLOCK_H * stride_k_nheads +- V_ptr = O_VALUE + pid_m * stride_v_seq + pid_head * BLOCK_H * stride_v_nheads ++ K_ptr = O_KEY + pid_m * stride_k_seq # + pid_head * BLOCK_H * stride_k_nheads ++ V_ptr = O_VALUE + pid_m * stride_v_seq # + pid_head * BLOCK_H * stride_v_nheads + +- k_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads + tl.arange(0, k_dim)[None, :] +- v_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_v_nheads + tl.arange(0, v_dim)[None, :] +- tl.store(K_ptr + k_out_off, k, mask=mask) +- tl.store(V_ptr + v_out_off, v, mask=mask) ++ k_out_off = ki_range * stride_k_nheads + kj_range ++ tl.store(K_ptr + k_out_off, k, mask=mask_k) ++ if v_dim > 0: ++ v_out_off = ki_range * stride_v_nheads + tl.arange(0, v_dim)[None, :] ++ tl.store(V_ptr + v_out_off, v, mask=mask_v) + + EMB = K_POS_EMB + pid_m * stride_emb_seq + # x1 = t[..., 0::2], x2 = t[..., 1::2] +@@ -460,14 +467,16 @@ def rotary_fwd_kv_kernel( + x_left = x_left.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2) + x_right = x_right.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2) + ++ x_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H ++ mask_x = x_range < head_num + x_left_off = ( +- tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads ++ x_range * stride_k_nheads + + k_dim + + tl.arange(0, emb_dim // 2)[None, :] + ) + x_right_off = x_left_off + emb_dim // 2 +- tl.store(K_ptr + x_left_off, x_left, mask=mask) +- tl.store(K_ptr + x_right_off, x_right, mask=mask) ++ tl.store(K_ptr + x_left_off, x_left, mask=mask_x) ++ tl.store(K_ptr + x_right_off, x_right, mask=mask_x) + + + @triton.autotune( +@@ -493,6 +502,7 @@ def rotary_bwd_kv_kernel( + SIN, + emb_dim: tl.constexpr, + k_dim: tl.constexpr, ++ k_dim_ceil: tl.constexpr, + v_dim: tl.constexpr, + head_num: tl.constexpr, + batch_size, +@@ -533,27 +543,32 @@ def rotary_bwd_kv_kernel( + else: + token_idx = _get_thd_token_idx(cu_seqlens_kv, pid_m, seq_num, cp_rank, cp_size) + +- dKV_ptr = dKV + pid_m * stride_dkv_seq + pid_head * BLOCK_H * stride_dkv_nheads +- dkv_off = tl.arange(0, BLOCK_H)[:, None] * stride_dkv_nheads +- mask = dkv_off < head_num * stride_dkv_nheads +- dk_out_off = dkv_off + tl.arange(0, k_dim)[None, :] +- dv_out_off = dkv_off + k_dim + tl.arange(0, v_dim)[None, :] +- +- dK_ptr = dK + pid_m * stride_dk_seq + pid_head * BLOCK_H * stride_dk_nheads +- dV_ptr = dV + pid_m * stride_dv_seq + pid_head * BLOCK_H * stride_dv_nheads +- dk_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + tl.arange(0, k_dim)[None, :] +- dv_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dv_nheads + tl.arange(0, v_dim)[None, :] +- dk = tl.load(dK_ptr + dk_in_off, mask=mask) +- dv = tl.load(dV_ptr + dv_in_off, mask=mask) +- tl.store(dKV_ptr + dk_out_off, dk, mask=mask) +- tl.store(dKV_ptr + dv_out_off, dv, mask=mask) ++ dKV_ptr = dKV + pid_m * stride_dkv_seq # + pid_head * BLOCK_H * stride_dkv_nheads ++ ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H ++ kj_range = tl.arange(0, k_dim_ceil)[None, :] ++ mask_k = (ki_range < head_num) & (kj_range < k_dim) ++ mask_v = ki_range < head_num ++ dk_out_off = ki_range * stride_dkv_nheads + kj_range ++ ++ dK_ptr = dK + pid_m * stride_dk_seq # + pid_head * BLOCK_H * stride_dk_nheads ++ dV_ptr = dV + pid_m * stride_dv_seq # + pid_head * BLOCK_H * stride_dv_nheads ++ dk_in_off = ki_range * stride_dk_nheads + kj_range ++ ++ dk = tl.load(dK_ptr + dk_in_off, mask=mask_k) ++ tl.store(dKV_ptr + dk_out_off, dk, mask=mask_k) ++ ++ if v_dim > 0: ++ dv_out_off = ki_range * stride_dkv_nheads + k_dim + tl.arange(0, v_dim)[None, :] ++ dv_in_off = ki_range * stride_dv_nheads + tl.arange(0, v_dim)[None, :] ++ dv = tl.load(dV_ptr + dv_in_off, mask=mask_v) ++ tl.store(dKV_ptr + dv_out_off, dv, mask=mask_v) + + if pid_head == 0: + x_left_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32) + x_right_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32) + for i in tl.static_range(triton.cdiv(head_num, BLOCK_H)): +- dK_ptr = dK + pid_m * stride_dk_seq + i * BLOCK_H * stride_dk_nheads +- x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim ++ dK_ptr = dK + pid_m * stride_dk_seq # + i * BLOCK_H * stride_dk_nheads ++ x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim + i * BLOCK_H * stride_dk_nheads + mask = x_off < head_num * stride_dk_nheads + x_left_off = x_off + tl.arange(0, emb_dim // 2)[None, :] + x_right_off = x_left_off + emb_dim // 2 +@@ -632,6 +647,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): + + o_key = kv.new_empty(total_seqlen, nheads, emb_dim + k_dim) + o_value = kv.new_empty(total_seqlen, nheads, v_dim) ++ k_dim_ceil = triton.next_power_of_2(k_dim) + + grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"])) + rotary_fwd_kv_kernel[grid]( +@@ -643,6 +659,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): + sin, + emb_dim, + k_dim, ++ k_dim_ceil, + v_dim, + nheads, + batch_size, +@@ -700,6 +717,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): + + d_kv = dk.new_empty(total_seqlen, nheads, ctx.k_dim + ctx.v_dim) + d_emb = dk.new_empty(total_seqlen, 1, ctx.emb_dim) ++ k_dim_ceil = triton.next_power_of_2(ctx.k_dim) + + grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"])) + rotary_bwd_kv_kernel[grid]( +@@ -711,6 +729,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): + sin, + ctx.emb_dim, + ctx.k_dim, ++ k_dim_ceil, + ctx.v_dim, + nheads, + batch_size, +diff --git a/megatron/core/models/common/embeddings/rotary_pos_embedding.py b/megatron/core/models/common/embeddings/rotary_pos_embedding.py +index 5d7b69cd3..2e0a26815 100644 +--- a/megatron/core/models/common/embeddings/rotary_pos_embedding.py ++++ b/megatron/core/models/common/embeddings/rotary_pos_embedding.py +@@ -348,6 +348,7 @@ class MultimodalRotaryEmbedding(nn.Module): + + # shape (seq_length, bs, 1, 2 * dim) + emb = emb[..., None, :].transpose(0, 1).contiguous() ++ packed_seq = packed_seq_params is not None and packed_seq_params.qkv_format == 'thd' + if packed_seq_params is not None and packed_seq_params.local_cp_size is not None: + if packed_seq_params.local_cp_size > 1: + # Set CP group to dynamic CP group for CP slicing +@@ -357,7 +358,9 @@ class MultimodalRotaryEmbedding(nn.Module): + cp_group = None + else: + cp_group = self.cp_group +- if cp_group is not None and cp_group.size() > 1: ++ # For THD (packed sequence) format, skip CP slicing here — it is handled ++ # per-sequence inside _apply_rotary_pos_emb_thd instead (same as RotaryEmbedding). ++ if cp_group is not None and cp_group.size() > 1 and not packed_seq: + # slice rotary_pos_emb along sequence dimension and select the parition of the current + # CP rank + emb = get_pos_emb_on_this_cp_rank(emb, 0, cp_group) +diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py +index 13d74aa52..060898a7a 100644 +--- a/megatron/core/models/common/language_module/language_module.py ++++ b/megatron/core/models/common/language_module/language_module.py +@@ -184,7 +184,15 @@ class LanguageModule(MegatronModule): + assert ( + column_parallel_linear is not None + ), "column_parallel_linear cannot be None when not using fused linear cross entropy." +- logits, _ = column_parallel_linear(hidden, **col_linear_kwargs) ++ # output ++ output_layer_params = {k: v.detach() for k, v in column_parallel_linear.named_parameters()} ++ output_layer_buffers = dict(column_parallel_linear.named_buffers()) ++ logits, _ = torch.func.functional_call( ++ column_parallel_linear, ++ {**output_layer_params, **output_layer_buffers}, ++ (hidden,), ++ col_linear_kwargs, ++ ) + + return self.compute_language_model_loss(labels, logits) + +diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py +index e21127b87..712793853 100755 +--- a/megatron/core/models/gpt/gpt_layer_specs.py ++++ b/megatron/core/models/gpt/gpt_layer_specs.py +@@ -188,6 +188,8 @@ def get_gpt_layer_with_transformer_engine_spec( + use_kitchen: bool = False, + use_te_activation_func: bool = False, + fallback_to_eager_attn: bool = False, ++ post_self_attn_layernorm: bool = False, ++ post_mlp_layernorm: bool = False, + ) -> ModuleSpec: + """Use this spec to use lower-level Transformer Engine modules (required for fp8 training). + +@@ -260,6 +262,8 @@ def get_gpt_layer_with_transformer_engine_spec( + mlp=mlp, + sharded_state_dict_keys_map=sharded_state_dict_keys_map, + normalization=normalization, ++ post_self_attn_layernorm=post_self_attn_layernorm, ++ post_mlp_layernorm=post_mlp_layernorm, + ) + + +@@ -349,6 +353,8 @@ def get_transformer_layer_spec_for_backend( + mlp: ModuleSpec, + sharded_state_dict_keys_map: Optional[dict] = None, + normalization: Optional[str] = None, ++ post_self_attn_layernorm: bool = False, ++ post_mlp_layernorm: bool = False, + ) -> ModuleSpec: + """Helper function to get module spec for TransformerLayer""" + +@@ -371,9 +377,11 @@ def get_transformer_layer_spec_for_backend( + input_layernorm=input_layernorm, + self_attention=attention, + self_attn_bda=get_bias_dropout_add, ++ post_self_attn_layernorm=TENorm if post_self_attn_layernorm else IdentityOp, + pre_mlp_layernorm=pre_mlp_layernorm, + mlp=mlp, + mlp_bda=get_bias_dropout_add, ++ post_mlp_layernorm=TENorm if post_mlp_layernorm else IdentityOp, + sharded_state_dict_keys_map=sharded_state_dict_keys_map, + ), + ) +diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py +index a1230568c..1fd52f65a 100644 +--- a/megatron/core/models/gpt/gpt_model.py ++++ b/megatron/core/models/gpt/gpt_model.py +@@ -446,6 +446,7 @@ class GPTModel(LanguageModule): + *, + inference_params: Optional[BaseInferenceContext] = None, + loss_mask: Optional[Tensor] = None, ++ mtp_kwargs: Optional[dict] = {}, + ) -> Tensor: + """Forward function of the GPT Model This function passes the input tensors + through the embedding layer, and then the decoder and finally into the post +@@ -508,6 +509,7 @@ class GPTModel(LanguageModule): + runtime_gather_output=runtime_gather_output, + extra_block_kwargs=extra_block_kwargs, + inference_context=inference_context, ++ mtp_kwargs=mtp_kwargs, + ) + + def _postprocess( +@@ -529,6 +531,7 @@ class GPTModel(LanguageModule): + runtime_gather_output=None, + extra_block_kwargs=None, + inference_context=None, ++ mtp_kwargs={}, + ): + """Postprocesses decoder hidden states to generate logits or compute loss. + +@@ -543,7 +546,8 @@ class GPTModel(LanguageModule): + output_weight = None + if self.share_embeddings_and_output_weights: + output_weight = self.shared_embedding_or_output_weight() +- if mtp_in_postprocess: ++ ++ if mtp_in_postprocess and mtp_kwargs.get('mtp_labels', None) is not None: + hidden_states = self.mtp( + input_ids=input_ids, + position_ids=position_ids, +@@ -563,13 +567,18 @@ class GPTModel(LanguageModule): + return hidden_states + + # Skip when mtp_num_layers is None or 0 +- if self.config.mtp_num_layers: +- mtp_labels = labels.clone() ++ if self.config.mtp_num_layers and mtp_kwargs.get('mtp_labels', None) is not None: ++ mtp_labels = mtp_kwargs['mtp_labels'].clone() ++ mtp_labels, _ = roll_tensor(mtp_labels, shifts=-1, dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params) ++ + hidden_states_list = torch.chunk(hidden_states, 1 + self.config.mtp_num_layers, dim=0) + hidden_states = hidden_states_list[0] + if loss_mask is None: + # if loss_mask is not provided, use all ones as loss_mask + loss_mask = torch.ones_like(mtp_labels) ++ else: ++ # Otherwise, roll the loss_mask to keep up with the mtp_labels ++ loss_mask, _ = roll_tensor(loss_mask, shifts=-1, dims=-1, cp_group=self.cp_group, packed_seq_params=packed_seq_params) + for mtp_layer_number in range(self.config.mtp_num_layers): + # Calc loss for the current Multi-Token Prediction (MTP) layers. + mtp_labels, _ = roll_tensor( +@@ -595,7 +604,7 @@ class GPTModel(LanguageModule): + sequence_parallel_enabled=self.output_layer.sequence_parallel, + column_parallel_linear=self.output_layer, + col_linear_kwargs={ +- 'weight': output_weight, ++ 'weight': output_weight.detach() if output_weight else None, + 'runtime_gather_output': runtime_gather_output, + }, + ) +diff --git a/megatron/core/optimizer/distrib_optimizer.py b/megatron/core/optimizer/distrib_optimizer.py +index 6e093f96f..eac21a3ea 100644 +--- a/megatron/core/optimizer/distrib_optimizer.py ++++ b/megatron/core/optimizer/distrib_optimizer.py +@@ -677,6 +677,8 @@ class DistributedOptimizer(MixedPrecisionOptimizer): + # TE FusedAdam will not accumulate step for empty param groups, so we need to + # align the step across param groups. + param_group["step"] = int(step) ++ if "step" in param_group and param_group["step"] is None: ++ del param_group["step"] + + # Grad scaler state. + if self.grad_scaler: +@@ -1646,6 +1648,8 @@ class DistributedOptimizer(MixedPrecisionOptimizer): + if key == 'padding': + tensors[key] = LocalNonpersistentObject(tensors[key]) + continue ++ if key == 'step': ++ continue + assert tensors[key].shape == (gbuf_local_end - gbuf_local_start,), ( + tensors[key].shape, + gbuf_local_start, +diff --git a/megatron/core/parallel_state.py b/megatron/core/parallel_state.py +index a273002b9..4f821cfd5 100644 +--- a/megatron/core/parallel_state.py ++++ b/megatron/core/parallel_state.py +@@ -11,6 +11,7 @@ from typing import Callable, List, Optional + + import numpy as np + import torch ++import torch.distributed as dist + + from .utils import GlobalMemoryBuffer, is_torch_min_version + +diff --git a/megatron/core/pipeline_parallel/p2p_communication.py b/megatron/core/pipeline_parallel/p2p_communication.py +index ac839c21f..f18309217 100644 +--- a/megatron/core/pipeline_parallel/p2p_communication.py ++++ b/megatron/core/pipeline_parallel/p2p_communication.py +@@ -26,22 +26,22 @@ def _batched_p2p_ops( + ops = [] + if tensor_send_prev is not None: + send_prev_op = torch.distributed.P2POp( +- torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, group ++ torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, + ) + ops.append(send_prev_op) + if tensor_recv_prev is not None: + recv_prev_op = torch.distributed.P2POp( +- torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, group ++ torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, + ) + ops.append(recv_prev_op) + if tensor_send_next is not None: + send_next_op = torch.distributed.P2POp( +- torch.distributed.isend, tensor_send_next, next_pipeline_rank, group ++ torch.distributed.isend, tensor_send_next, next_pipeline_rank, + ) + ops.append(send_next_op) + if tensor_recv_next is not None: + recv_next_op = torch.distributed.P2POp( +- torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, group ++ torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, + ) + ops.append(recv_next_op) + if len(ops) > 0: +diff --git a/megatron/core/transformer/moe/moe_utils.py b/megatron/core/transformer/moe/moe_utils.py +index 28cff06f5..58dc4bb70 100644 +--- a/megatron/core/transformer/moe/moe_utils.py ++++ b/megatron/core/transformer/moe/moe_utils.py +@@ -587,6 +587,9 @@ def topk_routing_with_score_function( + else: + return torch.topk(scores, k=topk, dim=1) + ++ from relax.utils.training.routing_replay import get_routing_replay_compute_topk ++ compute_topk = get_routing_replay_compute_topk(compute_topk) ++ + if score_function == "softmax": + if use_pre_softmax: + scores = torch.softmax(logits, dim=-1, dtype=torch.float32).type_as(logits) +diff --git a/megatron/core/transformer/moe/router.py b/megatron/core/transformer/moe/router.py +index 16fc9d9af..517944f25 100644 +--- a/megatron/core/transformer/moe/router.py ++++ b/megatron/core/transformer/moe/router.py +@@ -201,6 +201,9 @@ class TopKRouter(Router): + self.global_tokens_per_expert = None + self.ga_steps = None + ++ from relax.utils.training.routing_replay import register_routing_replay ++ register_routing_replay(self) ++ + def _maintain_float32_expert_bias(self): + """ + Maintain the expert bias in float32. +diff --git a/megatron/core/transformer/multi_token_prediction.py b/megatron/core/transformer/multi_token_prediction.py +index a8f4abfcd..f33f6f05e 100755 +--- a/megatron/core/transformer/multi_token_prediction.py ++++ b/megatron/core/transformer/multi_token_prediction.py +@@ -6,6 +6,7 @@ from typing import Callable, List, Optional, Union + + import torch + from torch import Tensor ++import warnings + + from megatron.core import InferenceParams, parallel_state, tensor_parallel + from megatron.core.dist_checkpointing.mapping import ShardedStateDict +@@ -714,17 +715,19 @@ class MultiTokenPredictionLayer(MegatronModule): + cp_group=self.cp_group, + packed_seq_params=packed_seq_params, + ) +- position_ids, _ = roll_tensor( +- position_ids, +- shifts=-1, +- dims=-1, +- cp_group=self.cp_group, +- packed_seq_params=packed_seq_params, +- ) ++ if position_ids is not None: ++ position_ids, _ = roll_tensor( ++ position_ids, ++ shifts=-1, ++ dims=-1, ++ cp_group=self.cp_group, ++ packed_seq_params=packed_seq_params, ++ ) + # embedding + decoder_input = embedding(input_ids=input_ids, position_ids=position_ids) ++ decoder_input = decoder_input.detach() + +- hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True) ++ hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=False) + + return input_ids, position_ids, decoder_input, hidden_states + +@@ -826,6 +829,51 @@ class MultiTokenPredictionLayer(MegatronModule): + return hidden_states + + def _checkpointed_forward(self, forward_func, *args, **kwargs): ++ """Wrap `forward_func` with activation checkpointing while only passing tensors. ++ ++ Non-tensor arguments (e.g., configuration objects, None) are captured via closure so ++ that checkpoint implementations never receive them directly, avoiding save_for_backward ++ issues with non-tensor inputs. ++ """ ++ ++ # TODO(jiajun): Is there any better implementation here? ++ positional_specs = [] ++ kw_specs = [] ++ tensor_args: List[torch.Tensor] = [] ++ ++ for arg in args: ++ if torch.is_tensor(arg): ++ positional_specs.append(('tensor', len(tensor_args))) ++ tensor_args.append(arg) ++ else: ++ positional_specs.append(('const', arg)) ++ ++ for key, value in kwargs.items(): ++ if torch.is_tensor(value): ++ kw_specs.append((key, ('tensor', len(tensor_args)))) ++ tensor_args.append(value) ++ else: ++ kw_specs.append((key, ('const', value))) ++ ++ def run(*flat_tensor_args): ++ rebuilt_args = [] ++ for spec_type, payload in positional_specs: ++ if spec_type == 'tensor': ++ rebuilt_args.append(flat_tensor_args[payload]) ++ else: ++ rebuilt_args.append(payload) ++ ++ rebuilt_kwargs = {} ++ for key, (spec_type, payload) in kw_specs: ++ if spec_type == 'tensor': ++ rebuilt_kwargs[key] = flat_tensor_args[payload] ++ else: ++ rebuilt_kwargs[key] = payload ++ ++ return forward_func(*rebuilt_args, **rebuilt_kwargs) ++ ++ tensor_args_tuple = tuple(tensor_args) ++ + def checkpoint_handler(): + """Determines whether to use the `te_checkpoint` or `tensor_parallel.checkpoint`""" + if self.config.fp8: +@@ -836,12 +884,11 @@ class MultiTokenPredictionLayer(MegatronModule): + self.config.distribute_saved_activations, + tensor_parallel.random.get_cuda_rng_tracker, + parallel_state.get_tensor_model_parallel_group(), +- *args, +- **kwargs, ++ *tensor_args_tuple, + ) + else: + return tensor_parallel.checkpoint( +- forward_func, self.config.distribute_saved_activations, *args, *kwargs.values() ++ run, self.config.distribute_saved_activations, *tensor_args_tuple + ) + + if self.config.recompute_method == 'uniform': +diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py +index e2705bd9f..a0aa109b5 100644 +--- a/megatron/core/transformer/transformer_config.py ++++ b/megatron/core/transformer/transformer_config.py +@@ -210,6 +210,9 @@ class TransformerConfig(ModelParallelConfig): + attention_output_gate: bool = False + """Whether to apply output gate to the attention layers.""" + ++ post_self_attn_layernorm: bool = False ++ post_mlp_layernorm: bool = False ++ + test_mode: bool = False + """Whether to run real-time tests.""" + +diff --git a/megatron/core/transformer/transformer_layer.py b/megatron/core/transformer/transformer_layer.py +index 3ea405770..5a42001b9 100644 +--- a/megatron/core/transformer/transformer_layer.py ++++ b/megatron/core/transformer/transformer_layer.py +@@ -223,6 +223,7 @@ class TransformerLayerSubmodules: + input_layernorm: Union[ModuleSpec, type] = IdentityOp + self_attention: Union[ModuleSpec, type] = IdentityOp + self_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp ++ post_self_attn_layernorm: Union[ModuleSpec, type] = IdentityOp + + pre_cross_attn_layernorm: Union[ModuleSpec, type] = IdentityOp + cross_attention: Union[ModuleSpec, type] = IdentityOp +@@ -231,6 +232,7 @@ class TransformerLayerSubmodules: + pre_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp + mlp: Union[ModuleSpec, type] = IdentityOp + mlp_bda: Union[ModuleSpec, type] = IdentityFuncOp ++ post_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp + + # Mapping for sharded tensor keys to be applied in `sharded_state_dict` method + sharded_state_dict_keys_map: Dict[str, str] = field(default_factory=dict) +@@ -310,6 +312,13 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): + # [Module 3: BiasDropoutFusion] + self.self_attn_bda = build_module(submodules.self_attn_bda) + ++ self.post_self_attn_layernorm = build_module( ++ submodules.post_self_attn_layernorm, ++ config=self.config, ++ hidden_size=self.config.hidden_size, ++ eps=self.config.layernorm_epsilon, ++ ) ++ + # [Module 4: Post SelfAttention] Optional Layernorm after self-attn + self.pre_cross_attn_layernorm = build_module( + submodules.pre_cross_attn_layernorm, +@@ -375,6 +384,13 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): + + self.is_moe_layer = isinstance(self.mlp, MoELayer) + ++ self.post_mlp_layernorm = build_module( ++ submodules.post_mlp_layernorm, ++ config=self.config, ++ hidden_size=self.config.hidden_size, ++ eps=self.config.layernorm_epsilon ++ ) ++ + self.recompute_input_layernorm = False + self.recompute_pre_mlp_layernorm = False + self.recompute_mlp = False +@@ -551,6 +567,10 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): + attention_output_with_bias[0] + ) + ++ attention_output, attention_output_bias = attention_output_with_bias ++ attention_output = self.post_self_attn_layernorm(attention_output) ++ attention_output_with_bias = (attention_output, attention_output_bias) ++ + # TODO: could we move `bias_dropout_add_exec_handler` itself + # inside the module provided in the `bias_dropout_add_spec` module? + nvtx_range_push(suffix="self_attn_bda") +@@ -677,6 +697,10 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): + else: + mlp_output_with_bias = self.mlp(pre_mlp_layernorm_output) + ++ mlp_output, mlp_output_bias = mlp_output_with_bias ++ mlp_output = self.post_mlp_layernorm(mlp_output) ++ mlp_output_with_bias = (mlp_output, mlp_output_bias) ++ + if self.recompute_pre_mlp_layernorm: + # discard the output of the pre-mlp layernorm and register the recompute + # as a gradient hook of mlp_output_with_bias[0] +diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py +index b267c8a81..83736acdc 100644 +--- a/megatron/training/arguments.py ++++ b/megatron/training/arguments.py +@@ -1398,6 +1398,9 @@ def core_transformer_config_from_args(args, config_class=None): + + kw_args['inference_sampling_seed'] = args.seed + ++ kw_args['post_self_attn_layernorm'] = args.post_self_attn_layernorm ++ kw_args['post_mlp_layernorm'] = args.post_mlp_layernorm ++ + # handle quantization config + # NOTE: Kitchen arguments are only added to the namespace when + # Kitchen library is available. +@@ -1764,6 +1767,12 @@ def _add_network_size_args(parser): + action='store_true', + help='If set, use original BERT residula connection ' + 'ordering.') ++ group.add_argument('--post-self-attn-layernorm', action='store_true', ++ help='If set, use post self attention layernorm.') ++ group.add_argument('--post-mlp-layernorm', action='store_true', ++ help='If set, use post MLP layernorm.') ++ group.add_argument('--use-gated-attention', action='store_true', ++ help='If set, use gated attention as in Qwen3Next') + group.add_argument('--openai-gelu', action='store_true', + help='Use OpenAIs GeLU implementation. This option' + 'should not be used unless for backward compatibility' +diff --git a/megatron/training/tokenizer/tokenizer.py b/megatron/training/tokenizer/tokenizer.py +index 13b7526ca..6c590f653 100644 +--- a/megatron/training/tokenizer/tokenizer.py ++++ b/megatron/training/tokenizer/tokenizer.py +@@ -136,7 +136,7 @@ class _HuggingFaceTokenizer(MegatronLegacyTokenizer): + # TODO(bnorick): download tokenizer once to lustre and use force offline to make sure all tasks read it from there + self._tokenizer = transformers.AutoTokenizer.from_pretrained( + pretrained_model_name_or_path=pretrained_model_name_or_path, +- trust_remote_code=trust_remote_code, ++ trust_remote_code=True, + **kwargs, + ) + self._vocab = self._tokenizer.get_vocab() +diff --git a/megatron/core/ssm/gated_delta_net.py b/megatron/core/ssm/gated_delta_net.py +index dfa6e4c35..0b38f1135 100644 +--- a/megatron/core/ssm/gated_delta_net.py ++++ b/megatron/core/ssm/gated_delta_net.py +@@ -21,6 +21,12 @@ from megatron.core.inference.contexts import BaseInferenceContext + from megatron.core.jit import jit_fuser + from megatron.core.packed_seq_params import PackedSeqParams + from megatron.core.process_groups_config import ProcessGroupCollection ++from megatron.core.ssm.mamba_context_parallel import ( ++ _all_to_all_cp2hp, ++ _all_to_all_hp2cp, ++ _redo_attention_load_balancing, ++ _undo_attention_load_balancing, ++) + from megatron.core.tensor_parallel import get_cuda_rng_tracker + from megatron.core.transformer import TransformerConfig + from megatron.core.transformer.identity_op import IdentityOp +@@ -33,24 +39,19 @@ from megatron.core.transformer.utils import ( + ) + from megatron.core.utils import deprecate_inference_params, nvtx_range_pop, nvtx_range_push + +-# TODO: Implement GatedDeltaNetContextParallel +-# from .gated_delta_net_context_parallel import GatedDeltaNetContextParallel +- + try: ++ from fla.modules.convolution import causal_conv1d + from fla.modules.l2norm import l2norm + from fla.ops.gated_delta_rule import chunk_gated_delta_rule + + HAVE_FLA = True + except ImportError: ++ causal_conv1d = None ++ l2norm = None + chunk_gated_delta_rule = None + + HAVE_FLA = False + +-try: +- from causal_conv1d import causal_conv1d_fn +-except ImportError: +- causal_conv1d_fn = None +- + + logger = logging.getLogger(__name__) + +@@ -84,6 +85,7 @@ class GatedDeltaNet(MegatronModule): + use_qk_l2norm: bool = True, + A_init_range: Tuple[float, float] = (1, 16), + pg_collection: ProcessGroupCollection = None, ++ **kwargs, + ): + """ + Args: +@@ -98,9 +100,11 @@ class GatedDeltaNet(MegatronModule): + pg_collection: The required process groups to use for tensor model parallel and context + parallel. + """ +- ++ # print(f"new gdn", flush=True) + if not HAVE_FLA: +- raise ImportError("FLA is not installed. Please install it with `pip install fla`.") ++ raise ImportError( ++ "FLA is not installed. Please install it with `pip install flash-linear-attention`." ++ ) + + super().__init__(config) + +@@ -114,6 +118,7 @@ class GatedDeltaNet(MegatronModule): + self.use_qk_l2norm = use_qk_l2norm + assert pg_collection is not None, "pg_collection must be provided for GatedDeltaNet" + self.pg_collection = pg_collection ++ self.cp_size = self.pg_collection.cp.size() + self.tp_size = self.pg_collection.tp.size() + self.sp_size = self.tp_size if config.sequence_parallel else 1 + +@@ -129,6 +134,8 @@ class GatedDeltaNet(MegatronModule): + self.num_value_heads = config.linear_num_value_heads + self.qk_dim = self.key_head_dim * self.num_key_heads + self.v_dim = self.value_head_dim * self.num_value_heads ++ self.qk_dim_local_tp = self.qk_dim // self.tp_size ++ self.v_dim_local_tp = self.v_dim // self.tp_size + + # Input projection (hidden_states -> q, k, v, gate, beta, alpha) + # TODO: for now, output gate is forced for GDN. +@@ -171,8 +178,10 @@ class GatedDeltaNet(MegatronModule): + dtype=config.params_dtype, + ) + setattr(self.conv1d.weight, "tensor_model_parallel", True) ++ setattr(self.conv1d.weight, "partition_dim", 0) + if conv_bias: + setattr(self.conv1d.bias, "tensor_model_parallel", True) ++ setattr(self.conv1d.bias, "partition_dim", 0) + + # Time step projection (discretization) + self.num_v_heads_local_tp = self.num_value_heads // self.tp_size +@@ -185,6 +194,7 @@ class GatedDeltaNet(MegatronModule): + ) + ) + setattr(self.dt_bias, "tensor_model_parallel", True) ++ setattr(self.dt_bias, "partition_dim", 0) + # A_log parameter + self.A_log = nn.Parameter( + torch.empty( +@@ -194,6 +204,12 @@ class GatedDeltaNet(MegatronModule): + ) + ) + setattr(self.A_log, "tensor_model_parallel", True) ++ setattr(self.A_log, "partition_dim", 0) ++ ++ if self.config.deterministic_mode: ++ self.gated_delta_rule = torch_chunk_gated_delta_rule ++ else: ++ self.gated_delta_rule = chunk_gated_delta_rule + + # Output layernorm before projection + self.out_norm = build_module( +@@ -217,8 +233,6 @@ class GatedDeltaNet(MegatronModule): + tp_group=self.pg_collection.tp, + ) + +- # TODO: support CP +- + self.reset_parameters() + + def reset_parameters(self): +@@ -241,23 +255,18 @@ class GatedDeltaNet(MegatronModule): + dtype=self.config.params_dtype, + device=torch.cuda.current_device(), + ).uniform_(*self.A_init_range) +- self.A_log.data.copy_(A) ++ self.A_log.data.copy_(torch.log(A)) + + def forward( + self, + hidden_states: Tensor, + attention_mask: Tensor, +- key_value_states: Optional[Tensor] = None, + inference_context: Optional[BaseInferenceContext] = None, +- rotary_pos_emb: Optional[Union[Tensor, Tuple[Tensor, Tensor]]] = None, +- rotary_pos_cos: Optional[Tensor] = None, +- rotary_pos_sin: Optional[Tensor] = None, +- rotary_pos_cos_sin: Optional[Tensor] = None, +- attention_bias: Optional[Tensor] = None, + packed_seq_params: Optional[PackedSeqParams] = None, + sequence_len_offset: Optional[int] = None, + *, + inference_params: Optional[BaseInferenceContext] = None, ++ **kwargs, + ): + """ + Perform a forward pass through the GDN module. +@@ -265,15 +274,8 @@ class GatedDeltaNet(MegatronModule): + Args: + hidden_states (Tensor): Hidden states. + attention_mask (Tensor): Attention mask. +- key_value_states (Optional[Tensor]): Key/value states (for cross attention). + inference_context (Optional[BaseInferenceContext]): Inference context that manages + KV cache. +- rotary_pos_emb (Optional[Union[Tensor, Tuple[Tensor, Tensor]]]): Rotary +- embedding tensor(s). +- rotary_pos_cos (Optional[Tensor]): Rotary embedding cosine. +- rotary_pos_sin (Optional[Tensor]): Rotary embedding sine. +- rotary_pos_cos_sin (Optional[Tensor]): Combined rotary embedding cosine and sine. +- attention_bias (Optional[Tensor]): Attention bias. + packed_seq_params (Optional[PackedSeqparams]): Parameters used for THD format. + sequence_len_offset (Optional[int]): Sequence length offset used for + inference CUDA graphs. +@@ -287,7 +289,7 @@ class GatedDeltaNet(MegatronModule): + inference_context = deprecate_inference_params(inference_context, inference_params) + + seq_len, batch, _ = hidden_states.shape +- seq_len = seq_len * self.sp_size ++ seq_len = seq_len * self.sp_size * self.cp_size + + if inference_context is not None: + assert ( +@@ -297,15 +299,80 @@ class GatedDeltaNet(MegatronModule): + # TODO: support inference + raise NotImplementedError("GDN does not support inference for now.") + +- if packed_seq_params is not None: +- # TODO: support packed sequence +- raise NotImplementedError("GDN does not support packed sequence for now.") ++ if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd': ++ assert batch == 1, "Packed sequence expects batch dimension to be 1" ++ assert ( ++ not self.config.deterministic_mode ++ ), "Packed sequence does not support deterministic mode." ++ ++ # Resolve cu_seqlens with alignment padding handling. ++ cu_seqlens_q = self._resolve_cu_seqlens( ++ packed_seq_params.cu_seqlens_q_padded, ++ packed_seq_params.cu_seqlens_q, ++ seq_len, ++ "cu_seqlens_q", ++ ) ++ cu_seqlens_kv = self._resolve_cu_seqlens( ++ packed_seq_params.cu_seqlens_kv_padded, ++ packed_seq_params.cu_seqlens_kv, ++ seq_len, ++ "cu_seqlens_kv", ++ ) ++ assert torch.equal(cu_seqlens_q, cu_seqlens_kv), ( ++ "Currently only support cu_seqlens_q equals to cu_seqlens_kv, " ++ f"but got {cu_seqlens_q=} and {cu_seqlens_kv=}" ++ ) ++ num_packed_seqs = cu_seqlens_q.shape[0] - 1 ++ assert num_packed_seqs > 0, ( ++ "Number of packed sequences must be greater than 0, " ++ f"but got {cu_seqlens_q=} and {cu_seqlens_kv=}" ++ ) ++ else: ++ cu_seqlens_q = None ++ cu_seqlens_kv = None + + # Input projection + nvtx_range_push(suffix="in_proj") + qkvzba, _ = self.in_proj(hidden_states) + nvtx_range_pop(suffix="in_proj") + ++ # CP All to All: CP to HP ++ if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd': ++ unpacked_qkvzba = _unpack_sequence(qkvzba, cu_seqlens_q // self.cp_size, dim=0) ++ outputs = [] ++ for qkvzba_i in unpacked_qkvzba: ++ qkvzba_i = tensor_a2a_cp2hp( ++ qkvzba_i, ++ seq_dim=0, ++ head_dim=-1, ++ cp_group=self.pg_collection.cp, ++ split_sections=[ ++ self.qk_dim_local_tp, ++ self.qk_dim_local_tp, ++ self.v_dim_local_tp, ++ self.v_dim_local_tp, ++ self.num_value_heads // self.tp_size, ++ self.num_value_heads // self.tp_size, ++ ], ++ ) ++ outputs.append(qkvzba_i) ++ qkvzba = torch.cat(outputs, dim=0) ++ else: ++ qkvzba = tensor_a2a_cp2hp( ++ qkvzba, ++ seq_dim=0, ++ head_dim=-1, ++ cp_group=self.pg_collection.cp, ++ split_sections=[ ++ self.qk_dim_local_tp, ++ self.qk_dim_local_tp, ++ self.v_dim_local_tp, ++ self.v_dim_local_tp, ++ self.num_value_heads // self.tp_size, ++ self.num_value_heads // self.tp_size, ++ ], ++ ) ++ + # Transpose: s b x --> b s x + # From sbhd to bshd format + qkvzba = qkvzba.transpose(0, 1) +@@ -314,10 +381,10 @@ class GatedDeltaNet(MegatronModule): + qkv, gate, beta, alpha = torch.split( + qkvzba, + [ +- (self.qk_dim * 2 + self.v_dim) // self.tp_size, +- self.v_dim // self.tp_size, +- self.num_value_heads // self.tp_size, +- self.num_value_heads // self.tp_size, ++ (self.qk_dim_local_tp * 2 + self.v_dim_local_tp) // self.cp_size, ++ self.v_dim_local_tp // self.cp_size, ++ self.num_value_heads // self.tp_size // self.cp_size, ++ self.num_value_heads // self.tp_size // self.cp_size, + ], + dim=-1, + ) +@@ -326,74 +393,83 @@ class GatedDeltaNet(MegatronModule): + alpha = alpha.reshape(batch, seq_len, -1) + + # Convolution on qkv +- qkv = qkv.transpose(1, 2).contiguous() # b, s, d -> b, d, s + nvtx_range_push(suffix="conv1d") +- if (causal_conv1d_fn is None) or self.config.deterministic_mode: +- qkv = self.act_fn(self.conv1d(qkv)[..., :seq_len]) ++ seq_len = qkv.shape[1] ++ qkv_channels_split_sections = [ ++ self.qk_dim_local_tp, ++ self.qk_dim_local_tp, ++ self.v_dim_local_tp, ++ ] ++ conv1d_weight = get_parameter_local_cp( ++ self.conv1d.weight, ++ dim=0, ++ cp_group=self.pg_collection.cp, ++ split_sections=qkv_channels_split_sections, ++ ) ++ conv1d_bias = ( ++ get_parameter_local_cp( ++ self.conv1d.bias, ++ dim=0, ++ cp_group=self.pg_collection.cp, ++ split_sections=qkv_channels_split_sections, ++ ) ++ if self.conv_bias ++ else None ++ ) ++ if self.config.deterministic_mode: ++ qkv = qkv.transpose(1, 2).contiguous() # b, s, d -> b, d, s ++ conv_out = F.conv1d( ++ input=qkv, # Torch-native only accept [b, d, s] format input ++ weight=conv1d_weight, ++ bias=conv1d_bias, ++ stride=self.conv1d.stride, ++ padding=self.conv1d.padding, ++ dilation=self.conv1d.dilation, ++ groups=self.conv_dim_local_tp // self.cp_size, ++ ) ++ qkv = self.act_fn(conv_out[..., :seq_len]) ++ qkv = qkv.transpose(1, 2) # b, d, s -> b, s, d + else: + assert self.activation in ["silu", "swish"] +- qkv = causal_conv1d_fn( +- x=qkv, +- weight=self.conv1d.weight.squeeze(1), # d, 1, w -> d, w +- bias=self.conv1d.bias, ++ qkv, _ = causal_conv1d( ++ x=qkv, # FLA conv1d accepts [b, s, d] format input ++ weight=conv1d_weight.squeeze(1), # d, 1, w -> d, w ++ bias=conv1d_bias, + activation=self.activation, ++ initial_state=None, ++ output_final_state=False, ++ cu_seqlens=cu_seqlens_q, + ) + nvtx_range_pop(suffix="conv1d") +- # Split qkv into query, key, and value +- qkv = qkv.transpose(1, 2) # b, d, s -> b, s, d +- query, key, value = torch.split( +- qkv, +- [self.qk_dim // self.tp_size, self.qk_dim // self.tp_size, self.v_dim // self.tp_size], +- dim=-1, +- ) +- query = query.reshape(batch, seq_len, -1, self.key_head_dim) +- key = key.reshape(batch, seq_len, -1, self.key_head_dim) +- value = value.reshape(batch, seq_len, -1, self.value_head_dim) +- # Apply L2 norm to query and key +- if self.use_qk_l2norm: +- query = l2norm(query.contiguous()) +- key = l2norm(key.contiguous()) +- if self.num_value_heads // self.num_key_heads > 1: +- query = query.repeat_interleave(self.num_value_heads // self.num_key_heads, dim=2) +- key = key.repeat_interleave(self.num_value_heads // self.num_key_heads, dim=2) + +- # Make contiguous +- query = query.contiguous() +- key = key.contiguous() +- value = value.contiguous() +- gate = gate.contiguous() +- beta = beta.contiguous() +- alpha = alpha.contiguous() ++ # Prepare QKV tensors (split, reshape, L2 norm, repeat_interleave, contiguous) ++ nvtx_range_push(suffix="prepare_qkv_for_gated_delta_rule") ++ query, key, value, gate, beta, alpha = self._prepare_qkv_for_gated_delta_rule( ++ qkv, gate, beta, alpha, batch, seq_len ++ ) ++ nvtx_range_pop(suffix="prepare_qkv_for_gated_delta_rule") + + # Calculate g and beta + nvtx_range_push(suffix="g_and_beta") +- g = -self.A_log.exp() * F.softplus(alpha.float() + self.dt_bias) # In fp32 +- beta = beta.sigmoid() ++ A_log_local_cp = get_parameter_local_cp(self.A_log, dim=0, cp_group=self.pg_collection.cp) ++ dt_bias_local_cp = get_parameter_local_cp( ++ self.dt_bias, dim=0, cp_group=self.pg_collection.cp ++ ) ++ g, beta = self._compute_g_and_beta(A_log_local_cp, dt_bias_local_cp, alpha, beta) + nvtx_range_pop(suffix="g_and_beta") + + nvtx_range_push(suffix="gated_delta_rule") +- if self.config.deterministic_mode: +- core_attn_out, last_recurrent_state = torch_chunk_gated_delta_rule( +- query, +- key, +- value, +- g=g, +- beta=beta, +- initial_state=None, +- output_final_state=False, +- use_qk_l2norm_in_kernel=False, +- ) +- else: +- core_attn_out, last_recurrent_state = chunk_gated_delta_rule( +- query, +- key, +- value, +- g=g, +- beta=beta, +- initial_state=None, +- output_final_state=False, +- use_qk_l2norm_in_kernel=False, +- ) ++ core_attn_out, last_recurrent_state = self.gated_delta_rule( ++ query, ++ key, ++ value, ++ g=g, ++ beta=beta, ++ initial_state=None, ++ output_final_state=False, ++ use_qk_l2norm_in_kernel=False, ++ cu_seqlens=cu_seqlens_q, ++ ) + nvtx_range_pop(suffix="gated_delta_rule") + + # RMSNorm +@@ -406,6 +482,21 @@ class GatedDeltaNet(MegatronModule): + norm_out = norm_out.reshape(batch, seq_len, -1) + norm_out = norm_out.transpose(0, 1).contiguous() + ++ # CP all to all: HP to CP ++ if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd': ++ unpacked_norm_out = _unpack_sequence(norm_out, cu_seqlens_q, dim=0) ++ outputs = [] ++ for norm_out_i in unpacked_norm_out: ++ norm_out_i = tensor_a2a_hp2cp( ++ norm_out_i, seq_dim=0, head_dim=-1, cp_group=self.pg_collection.cp ++ ) ++ outputs.append(norm_out_i) ++ norm_out = torch.cat(outputs, dim=0) ++ else: ++ norm_out = tensor_a2a_hp2cp( ++ norm_out, seq_dim=0, head_dim=-1, cp_group=self.pg_collection.cp ++ ) ++ + # Output projection + nvtx_range_push(suffix="out_proj") + out, out_bias = self.out_proj(norm_out) +@@ -425,6 +516,74 @@ class GatedDeltaNet(MegatronModule): + y = y.to(x_dtype) + return y + ++ @jit_fuser ++ def _prepare_qkv_for_gated_delta_rule(self, qkv, gate, beta, alpha, batch, seq_len): ++ """ ++ Prepare query, key, value, gate, beta, alpha tensors for gated delta rule. ++ Fuses split, reshape, L2 norm, repeat_interleave, and contiguous operations. ++ """ ++ # Split qkv into query_key and value ++ query_key, value = torch.split( ++ qkv, ++ [2 * self.qk_dim_local_tp // self.cp_size, self.v_dim_local_tp // self.cp_size], ++ dim=-1, ++ ) ++ ++ # Reshape query_key and value ++ query_key = query_key.reshape(batch, seq_len, -1, self.key_head_dim) ++ value = value.reshape(batch, seq_len, -1, self.value_head_dim) ++ ++ # Apply L2 norm to query and key ++ if self.use_qk_l2norm: ++ query_key = l2norm(query_key.contiguous()) ++ ++ # Split query and key ++ split_size = self.qk_dim_local_tp // self.key_head_dim // self.cp_size ++ query, key = torch.split(query_key, [split_size, split_size], dim=2) ++ ++ # Expand query and key if needed (grouped query attention) ++ if self.num_value_heads // self.num_key_heads > 1: ++ repeat_factor = self.num_value_heads // self.num_key_heads ++ query = query.repeat_interleave(repeat_factor, dim=2) ++ key = key.repeat_interleave(repeat_factor, dim=2) ++ ++ # Make all tensors contiguous ++ query = query.contiguous() ++ key = key.contiguous() ++ value = value.contiguous() ++ gate = gate.contiguous() ++ beta = beta.contiguous() ++ alpha = alpha.contiguous() ++ ++ return query, key, value, gate, beta, alpha ++ ++ @jit_fuser ++ def _compute_g_and_beta(self, A_log_local_cp, dt_bias_local_cp, alpha, beta): ++ """ ++ Compute g (decay) and beta (sigmoid) for gated delta rule. ++ Fuses exp, softplus, mul, neg, and sigmoid operations. ++ """ ++ g = -A_log_local_cp.exp() * F.softplus(alpha.float() + dt_bias_local_cp) # In fp32 ++ beta = beta.sigmoid() ++ return g, beta ++ ++ def _resolve_cu_seqlens(self, cu_seqlens_padded, cu_seqlens_actual, total_seq_len, name): ++ """Resolve cu_seqlens for packed sequence all-to-all, handling alignment padding.""" ++ if cu_seqlens_padded is not None: ++ cu_seqlens = cu_seqlens_padded ++ else: ++ cu_seqlens = cu_seqlens_actual ++ ++ total_cu = cu_seqlens[-1].item() ++ if total_cu != total_seq_len: ++ raise ValueError( ++ f"GDN: {name}[-1]={total_cu} does not match " ++ f"total_sequence_length={total_seq_len}. " ++ f"({cu_seqlens_padded=}, {cu_seqlens_actual=})." ++ ) ++ ++ return cu_seqlens ++ + def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None, tp_group=None): + """Provide a sharded state dictionary for distributed checkpointing.""" + # Guard for cases metadata is not provided +@@ -479,10 +638,10 @@ class GatedDeltaNet(MegatronModule): + sharded_state_dict[f"{prefix}in_proj.weight"] = _split_tensor_factory( + sharded_state_dict[f"{prefix}in_proj.weight"], + [ +- self.qk_dim // self.tp_size, +- self.qk_dim // self.tp_size, +- self.v_dim // self.tp_size, +- self.v_dim // self.tp_size, ++ self.qk_dim_local_tp, ++ self.qk_dim_local_tp, ++ self.v_dim_local_tp, ++ self.v_dim_local_tp, + self.num_value_heads // self.tp_size, + self.num_value_heads // self.tp_size, + ], +@@ -502,18 +661,41 @@ class GatedDeltaNet(MegatronModule): + for conv_layer_name in conv_layer_name_list: + sharded_state_dict[f"{prefix}{conv_layer_name}"] = _split_tensor_factory( + sharded_state_dict[f"{prefix}{conv_layer_name}"], +- [ +- self.qk_dim // self.tp_size, +- self.qk_dim // self.tp_size, +- self.v_dim // self.tp_size, +- ], ++ [self.qk_dim_local_tp, self.qk_dim_local_tp, self.v_dim_local_tp], + ["query", "key", "value"], + 0, + ) + + return sharded_state_dict + ++ def backward_dw(self): ++ """Execute weight gradient computation for all linear layers.""" ++ self._backward_in_proj() ++ self._backward_out_proj() ++ ++ def _backward_in_proj(self): ++ """Computes weight gradients of input projection layer.""" ++ self.in_proj.backward_dw() ++ ++ def _backward_out_proj(self): ++ """Computes weight gradients of output projection layer.""" ++ self.out_proj.backward_dw() ++ ++ ++def _unpack_sequence(x, cu_seqlens, dim=1): ++ unpacked_x = [] ++ num_seqs = cu_seqlens.shape[0] - 1 ++ for i in range(num_seqs): ++ idx_start = cu_seqlens[i].item() ++ idx_end = cu_seqlens[i + 1].item() ++ chunked_index = [slice(None)] * dim + [slice(idx_start, idx_end)] ++ unpacked_x.append(x[tuple(chunked_index)]) ++ return unpacked_x ++ + ++#################### ++# Sharded state dict utilities ++#################### + def _split_tensor_factory( + orig_sh_ten: ShardedTensor, split_sections: List[int], split_names: List[str], split_dim: int + ) -> ShardedTensorFactory: +@@ -574,6 +756,184 @@ def _split_tensor_factory( + ) + + ++#################### ++# Context parallel utilities ++#################### ++def get_parameter_local_cp( ++ param: torch.Tensor, ++ dim: int, ++ cp_group: torch.distributed.ProcessGroup, ++ split_sections: Optional[List[int]] = None, ++) -> torch.Tensor: ++ """Get the local parameter for the current context parallel rank. ++ ++ Args: ++ param (torch.Tensor): The entire parameter to get the local parameter for. ++ dim (int): The dimension to split the parameter along. Usually the dimension of head. ++ cp_group (torch.distributed.ProcessGroup): The context parallel group. ++ split_sections (Optional[List[int]]): If not None, ++ first split the parameter along the dimension dim into sections, ++ then get the local hidden parallel weights separately, ++ finally concatenate the local hidden parallel weights along the dimension dim. ++ ++ Returns: ++ torch.Tensor: The local parameter for the current context parallel rank. ++ """ ++ ++ cp_size = cp_group.size() ++ cp_rank = cp_group.rank() ++ ++ # No need to split if CP size is 1. ++ if cp_size == 1: ++ return param ++ ++ # Split first if needed. ++ if split_sections is not None: ++ inputs = torch.split(param, split_sections, dim=dim) ++ outputs = [] ++ for p in inputs: ++ p = get_parameter_local_cp(p, dim, cp_group) ++ outputs.append(p) ++ return torch.cat(outputs, dim=dim) ++ ++ # Slice the parameter. ++ slices = [slice(None)] * param.dim() ++ dim_size = param.size(dim=dim) ++ slices[dim] = slice(cp_rank * dim_size // cp_size, (cp_rank + 1) * dim_size // cp_size) ++ param = param[slices] ++ return param ++ ++ ++def tensor_a2a_cp2hp( ++ tensor: torch.Tensor, ++ seq_dim: int, ++ head_dim: int, ++ cp_group: torch.distributed.ProcessGroup, ++ split_sections: Optional[List[int]] = None, ++ undo_attention_load_balancing: bool = True, ++): ++ """All-to-all context parallel to hidden parallel. ++ ++ Args: ++ tensor (torch.Tensor): The tensor to all-to-all. ++ Currently only support (seq_len, batch, head_dim) shaped tensor. ++ seq_dim (int): The dimension of sequence length. Currently only supports seq_dim == 0. ++ head_dim (int): The dimension of head. Currently only supports head_dim == -1 or 2. ++ cp_group (torch.distributed.ProcessGroup): The context parallel group. ++ split_sections (Optional[List[int]]): If not None, split the tensor along the dimension ++ head_dim into sections first, then do all-to-all for each section separately, ++ finally concatenate the separated tensors along the dimension head_dim. ++ undo_attention_load_balancing (bool): Whether to undo the attention load balancing of CP. ++ ++ Returns: ++ torch.Tensor: The all-to-all tensor. ++ """ ++ ++ cp_size = cp_group.size() ++ ++ # No need to all-to-all if CP size is 1. ++ if cp_size == 1: ++ return tensor ++ ++ # Limitations of mamba_context_parallel._all_to_all_cp2hp. ++ assert seq_dim == 0, f"tensor_a2a_cp2hp only supports seq_dim == 0 for now, but got {seq_dim=}" ++ assert ( ++ head_dim == -1 or head_dim == 2 ++ ), f"tensor_a2a_cp2hp only supports head_dim == -1 or 2 for now, but got {head_dim=}" ++ assert ( ++ tensor.dim() == 3 ++ ), f"tensor_a2a_cp2hp only supports 3-d input tensor for now, but got {tensor.dim()=}" ++ ++ # Split first if needed. ++ if split_sections is not None: ++ inputs = torch.split(tensor, split_sections, dim=head_dim) ++ outputs = [] ++ for x in inputs: ++ x = tensor_a2a_cp2hp( ++ x, ++ seq_dim=seq_dim, ++ head_dim=head_dim, ++ cp_group=cp_group, ++ undo_attention_load_balancing=False, ++ ) ++ outputs.append(x) ++ tensor = torch.cat(outputs, dim=head_dim) ++ else: ++ tensor = _all_to_all_cp2hp(tensor, cp_group) ++ ++ # Undo attention load balancing last if needed. ++ if undo_attention_load_balancing: ++ tensor = _undo_attention_load_balancing(tensor, cp_size) ++ return tensor ++ ++ ++def tensor_a2a_hp2cp( ++ tensor: torch.Tensor, ++ seq_dim: int, ++ head_dim: int, ++ cp_group: torch.distributed.ProcessGroup, ++ split_sections: Optional[List[int]] = None, ++ redo_attention_load_balancing: bool = True, ++): ++ """All-to-all hidden parallel to context parallel. ++ ++ Args: ++ tensor (torch.Tensor): The tensor to all-to-all. ++ Currently only support (seq_len, batch, head_dim) shaped tensor. ++ seq_dim (int): The dimension of sequence length. Currently only supports seq_dim == 0. ++ head_dim (int): The dimension of head. Currently only supports head_dim == -1 or 2. ++ cp_group (torch.distributed.ProcessGroup): The context parallel group. ++ split_sections (Optional[List[int]]): If not None, first split the tensor along the ++ dimension head_dim into sections, then do all-to-all for each section separately, ++ finally concatenate the separated tensors along the dimension head_dim. ++ redo_attention_load_balancing (bool): Whether to redo the attention load balancing of HP. ++ ++ Returns: ++ torch.Tensor: The all-to-all tensor. ++ """ ++ ++ cp_size = cp_group.size() ++ ++ # No need to all-to-all if CP size is 1. ++ if cp_size == 1: ++ return tensor ++ ++ # Limitations of mamba_context_parallel._all_to_all_hp2cp. ++ assert seq_dim == 0, f"tensor_a2a_cp2hp only supports seq_dim == 0 for now, but got {seq_dim=}" ++ assert ( ++ head_dim == -1 or head_dim == 2 ++ ), f"tensor_a2a_cp2hp only supports head_dim == -1 or 2 for now, but got {head_dim=}" ++ assert ( ++ tensor.dim() == 3 ++ ), f"tensor_a2a_cp2hp only supports 3-d input tensor for now, but got {tensor.dim()=}" ++ ++ # Redo attention load balancing first if needed. ++ if redo_attention_load_balancing: ++ tensor = _redo_attention_load_balancing(tensor, cp_size) ++ ++ # Split first if needed. ++ if split_sections is not None: ++ inputs = torch.split(tensor, split_sections, dim=head_dim) ++ outputs = [] ++ for x in inputs: ++ x = tensor_a2a_hp2cp( ++ x, ++ seq_dim=seq_dim, ++ head_dim=head_dim, ++ cp_group=cp_group, ++ redo_attention_load_balancing=False, ++ ) ++ outputs.append(x) ++ tensor = torch.cat(outputs, dim=head_dim) ++ else: ++ tensor = _all_to_all_hp2cp(tensor, cp_group) ++ ++ return tensor ++ ++ ++#################### ++# Torch native gated delta rule ++#################### + def torch_chunk_gated_delta_rule( + query, + key, +@@ -584,6 +944,7 @@ def torch_chunk_gated_delta_rule( + initial_state=None, + output_final_state=False, + use_qk_l2norm_in_kernel=False, ++ cu_seqlens=None, + ): + # pylint: disable=line-too-long + ''' +@@ -593,6 +954,10 @@ def torch_chunk_gated_delta_rule( + Reference: https://github.com/huggingface/transformers/blob/144c8ce2809a2e21914017652700e1ecb450501e/src/transformers/models/qwen3_next/modeling_qwen3_next.py#L470-L547 + ''' + ++ assert ( ++ cu_seqlens is None ++ ), "cu_seqlens is not supported for torch_chunk_gated_delta_rule for now." ++ + initial_dtype = query.dtype + if use_qk_l2norm_in_kernel: + query = l2norm(query, dim=-1, eps=1e-6) +@@ -666,4 +1031,4 @@ def torch_chunk_gated_delta_rule( + ) + core_attn_out = core_attn_out[:, :, :sequence_length] + core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype) +- return core_attn_out, last_recurrent_state ++ return core_attn_out, last_recurrent_state diff --git a/docker/patch/megatron/20260506-85bced0ae.patch b/docker/patch/megatron/20260506-85bced0ae.patch new file mode 100644 index 000000000..81830dc08 --- /dev/null +++ b/docker/patch/megatron/20260506-85bced0ae.patch @@ -0,0 +1,727 @@ +diff --git a/megatron/core/dist_checkpointing/strategies/torch.py b/megatron/core/dist_checkpointing/strategies/torch.py +index 58e1e563b..abe561d83 100644 +--- a/megatron/core/dist_checkpointing/strategies/torch.py ++++ b/megatron/core/dist_checkpointing/strategies/torch.py +@@ -501,10 +501,12 @@ class MCoreLoadPlanner(DefaultLoadPlanner): + def _validate_global_shapes(self, metadata, sharded_tensors): + for sh_ten in sharded_tensors: + if sh_ten.key not in metadata.state_dict_metadata: +- raise KeyError( +- f"{sh_ten.key} from model not in state dict:" +- f" {sorted(metadata.state_dict_metadata.keys())}" +- ) ++ # raise KeyError( ++ # f"{sh_ten.key} from model not in state dict:" ++ # f" {sorted(metadata.state_dict_metadata.keys())}" ++ # ) ++ print(f"{sh_ten.key} from model not in state dict, will skip") ++ continue + loaded_shape = metadata.state_dict_metadata[sh_ten.key].size + expected_shape = sh_ten.global_shape + if loaded_shape != expected_shape: +@@ -528,7 +530,7 @@ class MCoreLoadPlanner(DefaultLoadPlanner): + tensor_metadata = self.metadata.state_dict_metadata + metadata_with_sizes = [ + (tensor_metadata[key], tensor_metadata[key].size, sharded_tensor) +- for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items() ++ for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items() if key in tensor_metadata + ] + try: + # Temporarily set sizes to expected shapes +@@ -865,6 +867,7 @@ class TorchDistLoadShardedStrategy: + planner=MCoreLoadPlanner( + shapes_validation_sharded_tensors=flexible_shape_sharded_tensors, + allow_shape_mismatch_sharded_tensors=allow_shape_mismatch_sharded_tensors, ++ allow_partial_load=True, + flatten_state_dict=False, + flatten_sharded_tensors=False, + ), +diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py +index 2a82a1e1c..5441f9335 100644 +--- a/megatron/core/extensions/transformer_engine.py ++++ b/megatron/core/extensions/transformer_engine.py +@@ -836,6 +836,7 @@ class TELinear(te.pytorch.Linear): + self.te_quant_params: Optional[TEQuantizationParams] = None + + for param in self.parameters(): ++ setattr(param, "parallel_mode", parallel_mode) + if is_expert: + # Reduce the gradient on the expert_data_parallel group for expert linear layers + setattr(param, "allreduce", not self.expert_parallel) +@@ -1671,6 +1672,61 @@ class TEDotProductAttention(te.pytorch.DotProductAttention): + + + if HAVE_TE and is_te_min_version("1.9.0.dev0"): ++ def ceil_div(x: int, y: int) -> int: ++ return (x + y - 1) // y ++ ++ class _FakeInt4QuantizationSTE(torch.autograd.Function): ++ @staticmethod ++ def forward(ctx, x, group_size): ++ m, n = x.shape ++ block_size_m, block_size_n = 1, group_size ++ ++ ++ m_padded = ceil_div(m, block_size_m) * block_size_m ++ n_padded = ceil_div(n, block_size_n) * block_size_n ++ ++ x_padded = torch.zeros( ++ (m_padded, n_padded), ++ dtype=x.dtype, device=x.device ++ ) ++ x_padded[:m, :n] = x ++ ++ x_view = x_padded.view( ++ m_padded // block_size_m, ++ block_size_m, ++ n_padded // block_size_n, ++ block_size_n ++ ) ++ ++ x_max = x_view.abs().float().amax(dim=(1, 3), keepdim=True) ++ q_max = 7 ++ x_scale = x_max / q_max ++ ++ x_scale = x_scale.clamp(min=1e-5) ++ ++ x_div = x_view / x_scale ++ x_round = torch.round(x_div) ++ ++ x_q_clamped = x_round.clamp(-q_max, q_max) ++ ++ x_dequant_view = x_q_clamped * x_scale ++ ++ x_dequant_full = x_dequant_view.view_as(x_padded) ++ x_out = x_dequant_full[:m, :n].contiguous().to(x.dtype) ++ ++ return x_out ++ ++ @staticmethod ++ def backward(ctx, grad_output): ++ return grad_output, None ++ ++ def fake_int4_quantization_ste(x, group_size): ++ x_out = _FakeInt4QuantizationSTE.apply(x, group_size) ++ ++ if hasattr(x, 'main_grad'): ++ x_out.main_grad = x.main_grad ++ ++ return x_out + + class TEGroupedLinear(te.pytorch.GroupedLinear): + """ +@@ -1913,6 +1969,7 @@ if HAVE_TE and is_te_min_version("1.9.0.dev0"): + "amax_history_bwd": torch.cat( + [state["amax_history_bwd"].view(-1, 1) for state in state_list], + dim=1, ++ + ).view(self.fp8_meta["recipe"].amax_history_len, -1), + } + ) +@@ -1990,6 +2047,20 @@ if HAVE_TE and is_te_min_version("1.9.0.dev0"): + return out + return out, None + ++ def _get_weight_tensors(self): ++ """Get the weight tensors of the module.""" ++ weight_tensors = super()._get_weight_tensors() ++ ++ if os.getenv("OPEN_TRAINING_INT4_FAKE_QAT_FLAG", "0") == "1": ++ group_size = int(os.getenv("OPEN_TRAINING_INT4_GROUP_SIZE", "128")) ++ ++ weight_tensors = [ ++ fake_int4_quantization_ste(w, group_size) ++ for w in weight_tensors ++ ] ++ ++ return weight_tensors ++ + def _encode_extra_state(self, state): + # TE 2.0 changed the format of extra_state to be a byte tensor + if is_te_min_version("2.0.0"): +diff --git a/megatron/core/fusions/fused_mla_yarn_rope_apply.py b/megatron/core/fusions/fused_mla_yarn_rope_apply.py +index 1fd5dcfae..c9aeef1f0 100644 +--- a/megatron/core/fusions/fused_mla_yarn_rope_apply.py ++++ b/megatron/core/fusions/fused_mla_yarn_rope_apply.py +@@ -385,6 +385,7 @@ def rotary_fwd_kv_kernel( + SIN, + emb_dim: tl.constexpr, + k_dim: tl.constexpr, ++ k_dim_ceil: tl.constexpr, + v_dim: tl.constexpr, + head_num: tl.constexpr, + batch_size, +@@ -434,21 +435,27 @@ def rotary_fwd_kv_kernel( + cos_right = tl.load(COS + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2)) + sin_right = tl.load(SIN + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2)) + +- KV_ptr = KV + pid_m * stride_kv_seq + pid_head * BLOCK_H * stride_kv_nheads +- kv_off = tl.arange(0, BLOCK_H)[:, None] * stride_kv_nheads +- mask = kv_off < head_num * stride_kv_nheads +- k_in_off = kv_off + tl.arange(0, k_dim)[None, :] +- v_in_off = kv_off + k_dim + tl.arange(0, v_dim)[None, :] +- k = tl.load(KV_ptr + k_in_off, mask=mask) +- v = tl.load(KV_ptr + v_in_off, mask=mask) ++ KV_ptr = KV + pid_m * stride_kv_seq # + pid_head * BLOCK_H * stride_kv_nheads ++ ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H ++ kj_range = tl.arange(0, k_dim_ceil)[None, :] ++ mask_k = (ki_range < head_num) & (kj_range < k_dim) ++ mask_v = ki_range < head_num ++ k_off = ki_range * stride_kv_nheads + kj_range ++ if v_dim > 0: ++ v_off = ki_range * stride_kv_nheads + k_dim + tl.arange(0, v_dim)[None, :] ++ v = tl.load(KV_ptr + v_off, mask=mask_v) ++ else: ++ v = tl.zeros((BLOCK_H, 1), dtype=KV.dtype.element_ty) ++ k = tl.load(KV_ptr + k_off, mask=mask_k) + +- K_ptr = O_KEY + pid_m * stride_k_seq + pid_head * BLOCK_H * stride_k_nheads +- V_ptr = O_VALUE + pid_m * stride_v_seq + pid_head * BLOCK_H * stride_v_nheads ++ K_ptr = O_KEY + pid_m * stride_k_seq # + pid_head * BLOCK_H * stride_k_nheads ++ V_ptr = O_VALUE + pid_m * stride_v_seq # + pid_head * BLOCK_H * stride_v_nheads + +- k_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads + tl.arange(0, k_dim)[None, :] +- v_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_v_nheads + tl.arange(0, v_dim)[None, :] +- tl.store(K_ptr + k_out_off, k, mask=mask) +- tl.store(V_ptr + v_out_off, v, mask=mask) ++ k_out_off = ki_range * stride_k_nheads + kj_range ++ tl.store(K_ptr + k_out_off, k, mask=mask_k) ++ if v_dim > 0: ++ v_out_off = ki_range * stride_v_nheads + tl.arange(0, v_dim)[None, :] ++ tl.store(V_ptr + v_out_off, v, mask=mask_v) + + EMB = K_POS_EMB + pid_m * stride_emb_seq + # x1 = t[..., 0::2], x2 = t[..., 1::2] +@@ -460,14 +467,16 @@ def rotary_fwd_kv_kernel( + x_left = x_left.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2) + x_right = x_right.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2) + ++ x_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H ++ mask_x = x_range < head_num + x_left_off = ( +- tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads ++ x_range * stride_k_nheads + + k_dim + + tl.arange(0, emb_dim // 2)[None, :] + ) + x_right_off = x_left_off + emb_dim // 2 +- tl.store(K_ptr + x_left_off, x_left, mask=mask) +- tl.store(K_ptr + x_right_off, x_right, mask=mask) ++ tl.store(K_ptr + x_left_off, x_left, mask=mask_x) ++ tl.store(K_ptr + x_right_off, x_right, mask=mask_x) + + + @triton.autotune( +@@ -493,6 +502,7 @@ def rotary_bwd_kv_kernel( + SIN, + emb_dim: tl.constexpr, + k_dim: tl.constexpr, ++ k_dim_ceil: tl.constexpr, + v_dim: tl.constexpr, + head_num: tl.constexpr, + batch_size, +@@ -533,27 +543,32 @@ def rotary_bwd_kv_kernel( + else: + token_idx = _get_thd_token_idx(cu_seqlens_kv, pid_m, seq_num, cp_rank, cp_size) + +- dKV_ptr = dKV + pid_m * stride_dkv_seq + pid_head * BLOCK_H * stride_dkv_nheads +- dkv_off = tl.arange(0, BLOCK_H)[:, None] * stride_dkv_nheads +- mask = dkv_off < head_num * stride_dkv_nheads +- dk_out_off = dkv_off + tl.arange(0, k_dim)[None, :] +- dv_out_off = dkv_off + k_dim + tl.arange(0, v_dim)[None, :] +- +- dK_ptr = dK + pid_m * stride_dk_seq + pid_head * BLOCK_H * stride_dk_nheads +- dV_ptr = dV + pid_m * stride_dv_seq + pid_head * BLOCK_H * stride_dv_nheads +- dk_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + tl.arange(0, k_dim)[None, :] +- dv_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dv_nheads + tl.arange(0, v_dim)[None, :] +- dk = tl.load(dK_ptr + dk_in_off, mask=mask) +- dv = tl.load(dV_ptr + dv_in_off, mask=mask) +- tl.store(dKV_ptr + dk_out_off, dk, mask=mask) +- tl.store(dKV_ptr + dv_out_off, dv, mask=mask) ++ dKV_ptr = dKV + pid_m * stride_dkv_seq # + pid_head * BLOCK_H * stride_dkv_nheads ++ ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H ++ kj_range = tl.arange(0, k_dim_ceil)[None, :] ++ mask_k = (ki_range < head_num) & (kj_range < k_dim) ++ mask_v = ki_range < head_num ++ dk_out_off = ki_range * stride_dkv_nheads + kj_range ++ ++ dK_ptr = dK + pid_m * stride_dk_seq # + pid_head * BLOCK_H * stride_dk_nheads ++ dV_ptr = dV + pid_m * stride_dv_seq # + pid_head * BLOCK_H * stride_dv_nheads ++ dk_in_off = ki_range * stride_dk_nheads + kj_range ++ ++ dk = tl.load(dK_ptr + dk_in_off, mask=mask_k) ++ tl.store(dKV_ptr + dk_out_off, dk, mask=mask_k) ++ ++ if v_dim > 0: ++ dv_out_off = ki_range * stride_dkv_nheads + k_dim + tl.arange(0, v_dim)[None, :] ++ dv_in_off = ki_range * stride_dv_nheads + tl.arange(0, v_dim)[None, :] ++ dv = tl.load(dV_ptr + dv_in_off, mask=mask_v) ++ tl.store(dKV_ptr + dv_out_off, dv, mask=mask_v) + + if pid_head == 0: + x_left_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32) + x_right_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32) + for i in tl.static_range(triton.cdiv(head_num, BLOCK_H)): +- dK_ptr = dK + pid_m * stride_dk_seq + i * BLOCK_H * stride_dk_nheads +- x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim ++ dK_ptr = dK + pid_m * stride_dk_seq # + i * BLOCK_H * stride_dk_nheads ++ x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim + i * BLOCK_H * stride_dk_nheads + mask = x_off < head_num * stride_dk_nheads + x_left_off = x_off + tl.arange(0, emb_dim // 2)[None, :] + x_right_off = x_left_off + emb_dim // 2 +@@ -632,6 +647,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): + + o_key = kv.new_empty(total_seqlen, nheads, emb_dim + k_dim) + o_value = kv.new_empty(total_seqlen, nheads, v_dim) ++ k_dim_ceil = triton.next_power_of_2(k_dim) + + grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"])) + rotary_fwd_kv_kernel[grid]( +@@ -643,6 +659,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): + sin, + emb_dim, + k_dim, ++ k_dim_ceil, + v_dim, + nheads, + batch_size, +@@ -700,6 +717,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): + + d_kv = dk.new_empty(total_seqlen, nheads, ctx.k_dim + ctx.v_dim) + d_emb = dk.new_empty(total_seqlen, 1, ctx.emb_dim) ++ k_dim_ceil = triton.next_power_of_2(ctx.k_dim) + + grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"])) + rotary_bwd_kv_kernel[grid]( +@@ -711,6 +729,7 @@ class ApplyMLARotaryEmbKV(torch.autograd.Function): + sin, + ctx.emb_dim, + ctx.k_dim, ++ k_dim_ceil, + ctx.v_dim, + nheads, + batch_size, +diff --git a/megatron/core/inference/contexts/dynamic_context.py b/megatron/core/inference/contexts/dynamic_context.py +index 3ff370f74..21858ea6a 100644 +--- a/megatron/core/inference/contexts/dynamic_context.py ++++ b/megatron/core/inference/contexts/dynamic_context.py +@@ -57,7 +57,8 @@ except ImportError: + try: + from torch_memory_saver import torch_memory_saver + +- torch_memory_saver.hook_mode = "torch" ++ # Commented out: breaks SGLang CUDA graph (requires hook_mode="preload") ++ # torch_memory_saver.hook_mode = "torch" + HAVE_TORCH_MEMORY_SAVER = True + except ImportError: + HAVE_TORCH_MEMORY_SAVER = False +diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py +index 92d561c34..d4f62cb75 100755 +--- a/megatron/core/models/gpt/gpt_layer_specs.py ++++ b/megatron/core/models/gpt/gpt_layer_specs.py +@@ -189,6 +189,8 @@ def get_gpt_layer_with_transformer_engine_submodules( + enable_hyper_connection: bool = False, + mla_down_proj_fusion: bool = False, + dense_grouped_gemm: bool = False, ++ post_self_attn_layernorm: bool = False, ++ post_mlp_layernorm: bool = False, + ) -> TransformerLayerSubmodules: + """Use these submodules to use lower-level Transformer Engine modules (required for fp8 + training). +@@ -282,9 +284,11 @@ def get_gpt_layer_with_transformer_engine_submodules( + ), + ), + self_attn_bda=get_bias_dropout_add, ++ post_self_attn_layernorm=TENorm if post_self_attn_layernorm else IdentityOp, + pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp, + mlp=mlp, + mlp_bda=get_bias_dropout_add, ++ post_mlp_layernorm=TENorm if post_mlp_layernorm else IdentityOp, + sharded_state_dict_keys_map=( + { + "self_attention.linear_q_down_proj.layer_norm_": "input_layernorm.", +@@ -314,10 +318,12 @@ def get_gpt_layer_with_transformer_engine_submodules( + ), + self_attn_bda=get_bias_dropout_add, + self_attention_hyper_connection=hc_module, ++ post_self_attn_layernorm=TENorm if post_self_attn_layernorm else IdentityOp, + pre_mlp_layernorm=backend.layer_norm(has_residual=True) if num_experts else IdentityOp, + mlp=mlp, + mlp_bda=get_bias_dropout_add, + mlp_hyper_connection=hc_module, ++ post_mlp_layernorm=TENorm if post_mlp_layernorm else IdentityOp, + ) + else: + qk_norm = backend.layer_norm(for_qk=True) +@@ -339,10 +345,12 @@ def get_gpt_layer_with_transformer_engine_submodules( + ), + self_attn_bda=get_bias_dropout_add, + self_attention_hyper_connection=hc_module, ++ post_self_attn_layernorm=TENorm if post_self_attn_layernorm else IdentityOp, + pre_mlp_layernorm=backend.layer_norm(has_residual=True) if num_experts else IdentityOp, + mlp=mlp, + mlp_bda=get_bias_dropout_add, + mlp_hyper_connection=hc_module, ++ post_mlp_layernorm=TENorm if post_mlp_layernorm else IdentityOp, + sharded_state_dict_keys_map={ + "mlp.0.weight": "mlp.linear_fc1.layer_norm_weight", + "mlp.0.bias": "mlp.linear_fc1.layer_norm_bias", +diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py +index 19de0ed52..f2899e542 100644 +--- a/megatron/core/models/gpt/gpt_model.py ++++ b/megatron/core/models/gpt/gpt_model.py +@@ -506,6 +506,7 @@ class GPTModel(LanguageModule): + loss_mask: Optional[Tensor] = None, + padding_mask: Optional[Tensor] = None, + is_spec_decode: Optional[bool] = None, ++ mtp_kwargs: Optional[dict] = {}, + ) -> Tensor: + """Forward function of the GPT Model This function passes the input tensors + through the embedding layer, and then the decoder and finally into the post +@@ -585,6 +586,7 @@ class GPTModel(LanguageModule): + extra_block_kwargs=extra_block_kwargs, + inference_context=inference_context, + is_spec_decode=is_spec_decode, ++ mtp_kwargs=mtp_kwargs, + ) + + def _postprocess( +@@ -607,6 +609,7 @@ class GPTModel(LanguageModule): + extra_block_kwargs=None, + inference_context=None, + is_spec_decode=None, ++ mtp_kwargs={}, + ): + """Postprocesses decoder hidden states to generate logits or compute loss. + +@@ -630,7 +633,7 @@ class GPTModel(LanguageModule): + # logits and loss + output_weight = None + if self.share_embeddings_and_output_weights: +- output_weight = self.shared_embedding_or_output_weight() ++ output_weight = self.shared_embedding_or_output_weight().detach() + if mtp_in_postprocess and not (in_inference_mode or is_spec_decode): + hidden_states = self.mtp( + input_ids=input_ids, +diff --git a/megatron/core/optimizer/distrib_optimizer.py b/megatron/core/optimizer/distrib_optimizer.py +index 4430a8c84..084389b27 100644 +--- a/megatron/core/optimizer/distrib_optimizer.py ++++ b/megatron/core/optimizer/distrib_optimizer.py +@@ -706,6 +706,8 @@ class DistributedOptimizer(MixedPrecisionOptimizer): + # TE FusedAdam will not accumulate step for empty param groups, so we need to + # align the step across param groups. + param_group["step"] = int(step) ++ if "step" in param_group and param_group["step"] is None: ++ del param_group["step"] + + # Grad scaler state. + if self.grad_scaler: +@@ -1771,6 +1773,8 @@ class DistributedOptimizer(MixedPrecisionOptimizer): + # separately via param_groups, not as part of the gradient buffer. + tensors[key] = LocalNonpersistentObject(tensors[key]) + continue ++ if key == 'step': ++ continue + assert tensors[key].shape == (gbuf_local_end - gbuf_local_start,), ( + tensors[key].shape, + gbuf_local_start, +diff --git a/megatron/core/parallel_state.py b/megatron/core/parallel_state.py +index 863b5d55d..6c81e6e5f 100644 +--- a/megatron/core/parallel_state.py ++++ b/megatron/core/parallel_state.py +@@ -11,6 +11,7 @@ from typing import Callable, List, Optional + + import numpy as np + import torch ++import torch.distributed as dist + + from megatron.core.inference.symmetric_memory import SymmetricMemoryManager + +diff --git a/megatron/core/pipeline_parallel/p2p_communication.py b/megatron/core/pipeline_parallel/p2p_communication.py +index 465e83f28..707162f47 100644 +--- a/megatron/core/pipeline_parallel/p2p_communication.py ++++ b/megatron/core/pipeline_parallel/p2p_communication.py +@@ -27,22 +27,22 @@ def _batched_p2p_ops( + ops = [] + if tensor_send_prev is not None: + send_prev_op = torch.distributed.P2POp( +- torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, group ++ torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, + ) + ops.append(send_prev_op) + if tensor_recv_prev is not None: + recv_prev_op = torch.distributed.P2POp( +- torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, group ++ torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, + ) + ops.append(recv_prev_op) + if tensor_send_next is not None: + send_next_op = torch.distributed.P2POp( +- torch.distributed.isend, tensor_send_next, next_pipeline_rank, group ++ torch.distributed.isend, tensor_send_next, next_pipeline_rank, + ) + ops.append(send_next_op) + if tensor_recv_next is not None: + recv_next_op = torch.distributed.P2POp( +- torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, group ++ torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, + ) + ops.append(recv_next_op) + if len(ops) > 0: +diff --git a/megatron/core/transformer/moe/moe_utils.py b/megatron/core/transformer/moe/moe_utils.py +index d316d23de..9bb6d2bd6 100644 +--- a/megatron/core/transformer/moe/moe_utils.py ++++ b/megatron/core/transformer/moe/moe_utils.py +@@ -787,6 +787,9 @@ def topk_routing_with_score_function( + scores, topk, num_groups, group_topk, _compute_topk + ) + ++ from relax.utils.training.routing_replay import get_routing_replay_compute_topk ++ compute_topk = get_routing_replay_compute_topk(compute_topk) ++ + # Precision notes: + # - Logits are converted to fp32 for score functions. + # - All the intermediate calculations are in fp32. +diff --git a/megatron/core/transformer/moe/router.py b/megatron/core/transformer/moe/router.py +index b675d33cd..0cf3e006a 100644 +--- a/megatron/core/transformer/moe/router.py ++++ b/megatron/core/transformer/moe/router.py +@@ -216,6 +216,9 @@ class TopKRouter(Router): + if self.config.moe_enable_routing_replay: + self.router_replay = RouterReplay() + ++ from relax.utils.training.routing_replay import register_routing_replay ++ register_routing_replay(self) ++ + def _maintain_float32_expert_bias(self): + """ + Maintain the expert bias in float32. +diff --git a/megatron/core/transformer/multi_token_prediction.py b/megatron/core/transformer/multi_token_prediction.py +index ba5018a94..79ed327de 100755 +--- a/megatron/core/transformer/multi_token_prediction.py ++++ b/megatron/core/transformer/multi_token_prediction.py +@@ -8,6 +8,7 @@ from typing import TYPE_CHECKING, Callable, List, Optional, Union + + import torch + from torch import Tensor ++import warnings + + from megatron.core import InferenceParams, parallel_state, tensor_parallel + from megatron.core.dist_checkpointing.mapping import ShardedStateDict +@@ -891,17 +892,19 @@ class MultiTokenPredictionLayer(MegatronModule): + cp_group=self.cp_group, + packed_seq_params=packed_seq_params, + ) +- position_ids, _ = roll_tensor( +- position_ids, +- shifts=-1, +- dims=-1, +- cp_group=self.cp_group, +- packed_seq_params=packed_seq_params, +- ) ++ if position_ids is not None: ++ position_ids, _ = roll_tensor( ++ position_ids, ++ shifts=-1, ++ dims=-1, ++ cp_group=self.cp_group, ++ packed_seq_params=packed_seq_params, ++ ) + # embedding + decoder_input = embedding(input_ids=input_ids, position_ids=position_ids) ++ decoder_input = decoder_input.detach() + +- hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True) ++ hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=False) + + return input_ids, position_ids, decoder_input, hidden_states + +@@ -1059,6 +1062,51 @@ class MultiTokenPredictionLayer(MegatronModule): + return hidden_states + + def _checkpointed_forward(self, forward_func, *args, **kwargs): ++ """Wrap `forward_func` with activation checkpointing while only passing tensors. ++ ++ Non-tensor arguments (e.g., configuration objects, None) are captured via closure so ++ that checkpoint implementations never receive them directly, avoiding save_for_backward ++ issues with non-tensor inputs. ++ """ ++ ++ # TODO(jiajun): Is there any better implementation here? ++ positional_specs = [] ++ kw_specs = [] ++ tensor_args: List[torch.Tensor] = [] ++ ++ for arg in args: ++ if torch.is_tensor(arg): ++ positional_specs.append(('tensor', len(tensor_args))) ++ tensor_args.append(arg) ++ else: ++ positional_specs.append(('const', arg)) ++ ++ for key, value in kwargs.items(): ++ if torch.is_tensor(value): ++ kw_specs.append((key, ('tensor', len(tensor_args)))) ++ tensor_args.append(value) ++ else: ++ kw_specs.append((key, ('const', value))) ++ ++ def run(*flat_tensor_args): ++ rebuilt_args = [] ++ for spec_type, payload in positional_specs: ++ if spec_type == 'tensor': ++ rebuilt_args.append(flat_tensor_args[payload]) ++ else: ++ rebuilt_args.append(payload) ++ ++ rebuilt_kwargs = {} ++ for key, (spec_type, payload) in kw_specs: ++ if spec_type == 'tensor': ++ rebuilt_kwargs[key] = flat_tensor_args[payload] ++ else: ++ rebuilt_kwargs[key] = payload ++ ++ return forward_func(*rebuilt_args, **rebuilt_kwargs) ++ ++ tensor_args_tuple = tuple(tensor_args) ++ + def checkpoint_handler(): + """Determines whether to use the `te_checkpoint` or `tensor_parallel.checkpoint`""" + if self.config.fp8: +@@ -1069,12 +1117,11 @@ class MultiTokenPredictionLayer(MegatronModule): + self.config.distribute_saved_activations, + tensor_parallel.random.get_cuda_rng_tracker, + parallel_state.get_tensor_model_parallel_group(), +- *args, +- **kwargs, ++ *tensor_args_tuple, + ) + else: + return tensor_parallel.checkpoint( +- forward_func, self.config.distribute_saved_activations, *args, *kwargs.values() ++ run, self.config.distribute_saved_activations, *tensor_args_tuple + ) + + if self.config.recompute_method == 'uniform': +diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py +index cac634ff9..e5e63197a 100644 +--- a/megatron/core/transformer/transformer_config.py ++++ b/megatron/core/transformer/transformer_config.py +@@ -244,6 +244,9 @@ class TransformerConfig(ModelParallelConfig): + attention_output_gate: bool = False + """Whether to apply output gate to the attention layers.""" + ++ post_self_attn_layernorm: bool = False ++ post_mlp_layernorm: bool = False ++ + test_mode: bool = False + """Whether to run real-time tests.""" + +diff --git a/megatron/core/transformer/transformer_layer.py b/megatron/core/transformer/transformer_layer.py +index ee2054511..2ba4d0664 100644 +--- a/megatron/core/transformer/transformer_layer.py ++++ b/megatron/core/transformer/transformer_layer.py +@@ -245,6 +245,7 @@ class TransformerLayerSubmodules: + self_attention_hyper_connection: Union[ModuleSpec, type] = IdentityOp + self_attention: Union[ModuleSpec, type] = IdentityOp + self_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp ++ post_self_attn_layernorm: Union[ModuleSpec, type] = IdentityOp + + pre_cross_attn_layernorm: LayerNormBuilder = IdentityOp + cross_attention_hyper_connection: Union[ModuleSpec, type] = IdentityOp +@@ -255,6 +256,7 @@ class TransformerLayerSubmodules: + mlp_hyper_connection: Union[ModuleSpec, type] = IdentityOp + mlp: Union[ModuleSpec, type] = IdentityOp + mlp_bda: Union[ModuleSpec, type] = IdentityFuncOp ++ post_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp + + # Mapping for sharded tensor keys to be applied in `sharded_state_dict` method + sharded_state_dict_keys_map: Dict[str, str] = field(default_factory=dict) +@@ -352,6 +354,13 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): + # [Module 3: BiasDropoutFusion] + self.self_attn_bda = build_module(submodules.self_attn_bda) + ++ self.post_self_attn_layernorm = build_module( ++ submodules.post_self_attn_layernorm, ++ config=self.config, ++ hidden_size=self.config.hidden_size, ++ eps=self.config.layernorm_epsilon, ++ ) ++ + # [Module 4: Post SelfAttention] Optional Layernorm after self-attn + self.pre_cross_attn_layernorm = submodules.pre_cross_attn_layernorm( + config=self.config, +@@ -418,6 +427,13 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): + + self.is_moe_layer = isinstance(self.mlp, MoELayer) + ++ self.post_mlp_layernorm = build_module( ++ submodules.post_mlp_layernorm, ++ config=self.config, ++ hidden_size=self.config.hidden_size, ++ eps=self.config.layernorm_epsilon ++ ) ++ + self.recompute_input_layernorm = False + self.recompute_pre_mlp_layernorm = False + self.recompute_mlp = False +@@ -638,6 +654,10 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): + attention_output_with_bias[0] + ) + ++ attention_output, attention_output_bias = attention_output_with_bias ++ attention_output = self.post_self_attn_layernorm(attention_output) ++ attention_output_with_bias = (attention_output, attention_output_bias) ++ + # TODO: could we move `bias_dropout_add_exec_handler` itself + # inside the module provided in the `bias_dropout_add_spec` module? + nvtx_range_push(suffix="self_attn_bda") +@@ -823,6 +843,10 @@ class TransformerLayer(GraphableMegatronModule, BaseTransformerLayer): + self._set_fc2_residual(residual) + mlp_output_with_bias = self.mlp(pre_mlp_layernorm_output, padding_mask=padding_mask) + ++ mlp_output, mlp_output_bias = mlp_output_with_bias ++ mlp_output = self.post_mlp_layernorm(mlp_output) ++ mlp_output_with_bias = (mlp_output, mlp_output_bias) ++ + nvtx_range_pop(suffix="mlp") + + if ( +diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py +index 62f6e4426..c25969b3b 100644 +--- a/megatron/training/arguments.py ++++ b/megatron/training/arguments.py +@@ -1992,6 +1992,9 @@ def core_transformer_config_from_args(args, config_class=None): + + kw_args['inference_sampling_seed'] = args.seed + ++ kw_args['post_self_attn_layernorm'] = args.post_self_attn_layernorm ++ kw_args['post_mlp_layernorm'] = args.post_mlp_layernorm ++ + # handle quantization config + # NOTE: Kitchen arguments are only added to the namespace when + # Kitchen library is available. +@@ -2475,7 +2478,7 @@ def _add_network_size_args(parser): + '--position-embedding-type', + type=str, + default='learned_absolute', +- choices=['learned_absolute', 'rope', 'mrope', 'relative', 'none'], ++ choices=['learned_absolute', 'rope', 'yarn', 'mrope', 'relative', 'none'], + help='Position embedding type.', + ) + group.add_argument( +diff --git a/megatron/training/training.py b/megatron/training/training.py +index a0817e834..7cd094dc5 100644 +--- a/megatron/training/training.py ++++ b/megatron/training/training.py +@@ -222,7 +222,9 @@ from megatron.training.utils import ( + try: + from torch_memory_saver import torch_memory_saver + +- torch_memory_saver.hook_mode = "torch" ++ # NOTE(wuhuan): keep the default hook mode; forcing "torch" triggers ++ # 'torch.AcceleratorError: CUDA error: invalid argument' on weight updates. ++ # torch_memory_saver.hook_mode = "torch" + HAVE_TORCH_MEMORY_SAVER = True + except ImportError: + HAVE_TORCH_MEMORY_SAVER = False diff --git a/docs/en/guide/customize-training.md b/docs/en/guide/customize-training.md index d3b995f0c..f6a535a8c 100644 --- a/docs/en/guide/customize-training.md +++ b/docs/en/guide/customize-training.md @@ -61,7 +61,7 @@ After adding the file, source the corresponding model configuration in your trai #### 2. Megatron Bridge Model Adaptation -Relax uses [Megatron Bridge](https://github.com/redai-infra/megatron-bridge) for automatic HF ↔ Megatron weight conversion. If your model is not yet supported by Megatron Bridge, you need to add support on the Megatron Bridge side first — see its project documentation for details. +Relax uses [Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) for automatic HF ↔ Megatron weight conversion. If your model is not yet supported by Megatron Bridge, you need to add support on the Megatron Bridge side first — see its project documentation for details. ::: tip AI-Assisted Integration This project provides a Codewiz skill `model-integration` (located at `.codewiz/skills/model-integration/`), covering the complete integration workflow for Bridge / Raw / FSDP backends, weight converter specifications, TP sharding logic, and common pitfalls. Invoke it in Codewiz via `invoke skill model-integration` for step-by-step guidance. diff --git a/docs/en/guide/installation.md b/docs/en/guide/installation.md index 44b6e58c4..ec818ae5b 100644 --- a/docs/en/guide/installation.md +++ b/docs/en/guide/installation.md @@ -87,10 +87,17 @@ export MEGATRON="your megatron path" export PYTHONPATH=your_megatron_path:$PYTHONPATH ``` -Additionally, Relax depends on megatron bridge for weight conversion. If you need weight conversion, install it: +Additionally, Relax depends on [Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) for weight conversion. Follow the install steps in `docker/Dockerfile`: merge the Bridge sources with the Megatron-LM submodule into a single directory and add it to `PYTHONPATH`: ```bash -pip install git+https://github.com/redai-infra/megatron-bridge.git@relax/dev --no-build-isolation --no-deps +export MEGATRON_BRIDGE_COMMIT=2faedbf6fe3c422835a44b2b360cadcb2a116a54 +git clone https://github.com/NVIDIA-NeMo/Megatron-Bridge.git +cd Megatron-Bridge && git checkout ${MEGATRON_BRIDGE_COMMIT} && \ + git submodule update --init --recursive && ./scripts/switch_mcore.sh dev +mkdir -p /your/path/Megatron-LM +cp -r src/megatron /your/path/Megatron-LM/ +rsync -avP 3rdparty/Megatron-LM/megatron/ /your/path/Megatron-LM/megatron/ +export PYTHONPATH=/your/path/Megatron-LM:$PYTHONPATH ``` ## Next Steps diff --git a/docs/en/guide/quick-start.md b/docs/en/guide/quick-start.md index 74ace281d..d30478b3c 100644 --- a/docs/en/guide/quick-start.md +++ b/docs/en/guide/quick-start.md @@ -126,6 +126,11 @@ python scripts/tools/process_avqa.py \ --input-dir /root/AVQA-R1-6K/AVQA_R1/train/omni_rl_format_train.json \ --output-dir /root/AVQA-R1-6K/AVQA_R1/train/omni_rl_format_train_convert.jsonl \ --md-dir /root/AVQA-R1-6K/AVQA_R1/train + +python scripts/tools/process_avqa.py \ + --input-dir /root/AVQA-R1-6K/AVQA_R1/valid/omni_rl_format_valid.json \ + --output-dir /root/AVQA-R1-6K/AVQA_R1/valid/small_valid.jsonl \ + --md-dir /root/AVQA-R1-6K/AVQA_R1/valid ``` The conversion script reads the raw JSON, extracts problem, options, image, and audio fields, and produces a `.jsonl` file with `prompt`, `image`, `audio`, and `label` columns. @@ -134,6 +139,10 @@ The conversion script reads the raw JSON, extracts problem, options, image, and ```bash hf download Qwen/Qwen3-Omni-30B-A3B-Instruct --local-dir /root/Qwen3-Omni-30B-A3B-Instruct + +# Qwen3-Omni ships its chat_template in a standalone chat_template.json that +# AutoTokenizer does not auto-load. Merge it into tokenizer_config.json (skipped if already present). +python -c "import json,sys; m=sys.argv[1]; p=f'{m}/tokenizer_config.json'; tc=json.load(open(p)); ('chat_template' in tc) or (tc.update(chat_template=json.load(open(f'{m}/chat_template.json'))['chat_template']) or json.dump(tc, open(p,'w'), indent=2, ensure_ascii=False))" /root/Qwen3-Omni-30B-A3B-Instruct ``` ### Launch Training @@ -184,6 +193,10 @@ The conversion script reads the original JSON file, extracts the question, optio ```bash hf download Qwen/Qwen3-Omni-30B-A3B-Instruct --local-dir /root/Qwen3-Omni-30B-A3B-Instruct + +# Qwen3-Omni ships its chat_template in a standalone chat_template.json that +# AutoTokenizer does not auto-load. Merge it into tokenizer_config.json (skipped if already present). +python -c "import json,sys; m=sys.argv[1]; p=f'{m}/tokenizer_config.json'; tc=json.load(open(p)); ('chat_template' in tc) or (tc.update(chat_template=json.load(open(f'{m}/chat_template.json'))['chat_template']) or json.dump(tc, open(p,'w'), indent=2, ensure_ascii=False))" /root/Qwen3-Omni-30B-A3B-Instruct ``` ### Launch Training diff --git a/docs/zh/guide/customize-training.md b/docs/zh/guide/customize-training.md index 5cd75debb..bbde0fae4 100644 --- a/docs/zh/guide/customize-training.md +++ b/docs/zh/guide/customize-training.md @@ -61,7 +61,7 @@ MODEL_ARGS=( #### 2. Megatron Bridge 模型适配 -Relax 通过 [Megatron Bridge](https://github.com/redai-infra/megatron-bridge) 实现 HF ↔ Megatron 的自动权重转换。若您的模型尚未被 Megatron Bridge 支持，需要先在 Megatron Bridge 侧完成适配，详见其项目文档。 +Relax 通过 [Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) 实现 HF ↔ Megatron 的自动权重转换。若您的模型尚未被 Megatron Bridge 支持，需要先在 Megatron Bridge 侧完成适配，详见其项目文档。 ::: tip AI 辅助接入 本项目提供了 Codewiz skill `model-integration`（位于 `.codewiz/skills/model-integration/`），涵盖 Bridge / Raw / FSDP 三种后端的完整接入流程、权重转换器编写规范、TP 分片逻辑及常见陷阱，可在 Codewiz 中通过 `invoke skill model-integration` 调用以获得逐步指导。 diff --git a/docs/zh/guide/installation.md b/docs/zh/guide/installation.md index c697222cd..2d29ddb23 100644 --- a/docs/zh/guide/installation.md +++ b/docs/zh/guide/installation.md @@ -87,10 +87,17 @@ export MEGATRON="your megatron path" export PYTHONPATH=your_megatron_path:$PYTHONPATH ``` -此外 Relax 依赖 megatron bridge 进行权重转换，若需要转换请安装： +此外 Relax 依赖 [Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) 进行权重转换。安装方式参考 `docker/Dockerfile`，将 Bridge 源码与 Megatron-LM submodule 合并到同一目录后加入 `PYTHONPATH`： ```bash -pip install git+https://github.com/redai-infra/megatron-bridge.git@relax/dev --no-build-isolation --no-deps +export MEGATRON_BRIDGE_COMMIT=2faedbf6fe3c422835a44b2b360cadcb2a116a54 +git clone https://github.com/NVIDIA-NeMo/Megatron-Bridge.git +cd Megatron-Bridge && git checkout ${MEGATRON_BRIDGE_COMMIT} && \ + git submodule update --init --recursive && ./scripts/switch_mcore.sh dev +mkdir -p /your/path/Megatron-LM +cp -r src/megatron /your/path/Megatron-LM/ +rsync -avP 3rdparty/Megatron-LM/megatron/ /your/path/Megatron-LM/megatron/ +export PYTHONPATH=/your/path/Megatron-LM:$PYTHONPATH ``` ## 下一步 diff --git a/docs/zh/guide/quick-start.md b/docs/zh/guide/quick-start.md index a5acab1de..571fa4131 100644 --- a/docs/zh/guide/quick-start.md +++ b/docs/zh/guide/quick-start.md @@ -126,6 +126,11 @@ python scripts/tools/process_avqa.py \ --input-dir /root/AVQA-R1-6K/AVQA_R1/train/omni_rl_format_train.json \ --output-dir /root/AVQA-R1-6K/AVQA_R1/train/omni_rl_format_train_convert.jsonl \ --md-dir /root/AVQA-R1-6K/AVQA_R1/train + +python scripts/tools/process_avqa.py \ + --input-dir /root/AVQA-R1-6K/AVQA_R1/valid/omni_rl_format_valid.json \ + --output-dir /root/AVQA-R1-6K/AVQA_R1/valid/small_valid.jsonl \ + --md-dir /root/AVQA-R1-6K/AVQA_R1/valid ``` 转换脚本读取原始 JSON 文件，提取问题、选项、图片和音频字段，生成包含 `prompt`、`image`、`audio` 和 `label` 列的 `.jsonl` 文件。 @@ -134,6 +139,10 @@ python scripts/tools/process_avqa.py \ ```bash hf download Qwen/Qwen3-Omni-30B-A3B-Instruct --local-dir /root/Qwen3-Omni-30B-A3B-Instruct + +# Qwen3-Omni 的 chat_template 单独存放在 chat_template.json 中， +# AutoTokenizer 不会自动加载，需要合并到 tokenizer_config.json（已存在则跳过） +python -c "import json,sys; m=sys.argv[1]; p=f'{m}/tokenizer_config.json'; tc=json.load(open(p)); ('chat_template' in tc) or (tc.update(chat_template=json.load(open(f'{m}/chat_template.json'))['chat_template']) or json.dump(tc, open(p,'w'), indent=2, ensure_ascii=False))" /root/Qwen3-Omni-30B-A3B-Instruct ``` ### 启动训练 @@ -184,6 +193,10 @@ python scripts/tools/process_nextqa.py \ ```bash hf download Qwen/Qwen3-Omni-30B-A3B-Instruct --local-dir /root/Qwen3-Omni-30B-A3B-Instruct + +# Qwen3-Omni 的 chat_template 单独存放在 chat_template.json 中， +# AutoTokenizer 不会自动加载，需要合并到 tokenizer_config.json（已存在则跳过） +python -c "import json,sys; m=sys.argv[1]; p=f'{m}/tokenizer_config.json'; tc=json.load(open(p)); ('chat_template' in tc) or (tc.update(chat_template=json.load(open(f'{m}/chat_template.json'))['chat_template']) or json.dump(tc, open(p,'w'), indent=2, ensure_ascii=False))" /root/Qwen3-Omni-30B-A3B-Instruct ``` ### 启动训练 diff --git a/relax/backends/megatron/__init__.py b/relax/backends/megatron/__init__.py index 03a19d46c..cd3fc558a 100644 --- a/relax/backends/megatron/__init__.py +++ b/relax/backends/megatron/__init__.py @@ -2,6 +2,12 @@ import logging + +try: + import relax.models # noqa +except BaseException as e: + print(f"failed to import relax.models, error={e}") + from relax.utils import device as device_utils diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index 46ccea3a8..9cba58ba9 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -777,7 +777,7 @@ def train_async(self, rollout_id) -> None: total_lengths = rollout_data["total_lengths"] all_total_lengths = [None] * mpu.get_data_parallel_world_size(with_context_parallel=False) dist.all_gather_object( - all_total_lengths, total_lengths, group=mpu.get_data_parallel_group(with_context_parallel=True) + all_total_lengths, total_lengths, group=mpu.get_data_parallel_group(with_context_parallel=False) ) all_total_lengths = sum(all_total_lengths, []) # flatten Timer().seq_lens = all_total_lengths @@ -1046,11 +1046,19 @@ def load_other_checkpoint(self, model_tag: str, path: str) -> None: self._active_model_tag = model_tag def all_consumed(self, task_name, rollout_id): - if mpu.get_tensor_model_parallel_rank() == 0 and mpu.get_pipeline_model_parallel_rank() == 0: + # Only (TP=0, PP=0, CP=0) queries the transfer queue; otherwise different cp_ranks + # may observe different consumption status due to concurrent fetches and diverge, + # leaving some ranks idle while others enter the next collective and hang. + if ( + mpu.get_tensor_model_parallel_rank() == 0 + and mpu.get_pipeline_model_parallel_rank() == 0 + and mpu.get_context_parallel_rank() == 0 + ): status = [run(self.data_system_client.async_check_consumption_status(task_name, f"train_{rollout_id}"))] else: status = [True] status = torch.tensor(status, device=device_utils.make_current_torch_device()) + dist.broadcast(status, group=mpu.get_context_parallel_group(), group_src=0) dist.broadcast(status, group=mpu.get_tensor_model_parallel_group(), group_src=0) dist.broadcast(status, group=mpu.get_pipeline_model_parallel_group(), group_src=0) diff --git a/relax/backends/megatron/arguments.py b/relax/backends/megatron/arguments.py index 25b723b93..8fcd616ab 100644 --- a/relax/backends/megatron/arguments.py +++ b/relax/backends/megatron/arguments.py @@ -2,7 +2,13 @@ from megatron.training.arguments import parse_args as _megatron_parse_args from megatron.training.arguments import validate_args as _megatron_validate_args -from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding + + +try: + from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding as vocab_size_with_padding +except ModuleNotFoundError: + from megatron.core.tokenizers.utils.build_tokenizer import vocab_size_with_padding + from transformers import AutoConfig from relax.utils import device as device_utils @@ -50,6 +56,13 @@ class _DeviceProperty: "decoder_first_pipeline_num_layers and decoder_last_pipeline_num_layers should be None when " "pipeline_model_parallel_size is 1." ) + + # Megatron-Bridge requires --calculate-per-token-loss when context parallelism is enabled. + # See https://github.com/NVIDIA-NeMo/Megatron-Bridge + if args.context_parallel_size > 1: + assert args.calculate_per_token_loss, ( + "--calculate-per-token-loss must be set when context_parallel_size > 1 (required by Megatron-Bridge)." + ) return args @@ -79,15 +92,19 @@ def equal(x, y): if hasattr(hf_config, "text_config"): hf_config = hf_config.text_config - for hf_config_name, megatron_config_name, compare_fn in [ - ("hidden_size", "hidden_size", equal), - ("num_attention_heads", "num_attention_heads", equal), - ("num_hidden_layers", "num_layers", equal), - ("intermediate_size", "ffn_hidden_size", equal), - ("tie_word_embeddings", "untie_embeddings_and_output_weights", lambda x, y: not x == y), - ("rms_norm_eps", "norm_epsilon", equal), - ("rope_theta", "rotary_base", equal), - ]: + for hf_config_name, megatron_config_name, compare_fn in ( + [ + ("hidden_size", "hidden_size", equal), + ("num_attention_heads", "num_attention_heads", equal), + ("num_hidden_layers", "num_layers", equal), + ("intermediate_size", "ffn_hidden_size", equal), + ("tie_word_embeddings", "untie_embeddings_and_output_weights", lambda x, y: not x == y), + ("rope_theta", "rotary_base", equal), + ] + + [("rms_norm_eps", "norm_epsilon", equal)] + if hasattr(args, "norm_epsilon") + else [("rms_norm_eps", "layernorm_epsilon", equal)] + ): if hasattr(hf_config, hf_config_name): if not compare_fn(getattr(hf_config, hf_config_name), getattr(args, megatron_config_name)): errors.append( @@ -115,7 +132,7 @@ def _set_default_megatron_args(args): args.rope_type = "yarn" if args.multi_latent_attention else "rope" if args.vocab_size and not args.padded_vocab_size: - args.padded_vocab_size = _vocab_size_with_padding(args.vocab_size, args) + args.padded_vocab_size = vocab_size_with_padding(args.vocab_size, args) if not args.tokenizer_model and not args.tokenizer_type: logger.info("--tokenizer-model not set, use --hf-checkpoint as tokenizer model.") diff --git a/relax/backends/megatron/cp_utils.py b/relax/backends/megatron/cp_utils.py index deb43ba87..f38855d11 100644 --- a/relax/backends/megatron/cp_utils.py +++ b/relax/backends/megatron/cp_utils.py @@ -6,11 +6,35 @@ from megatron.core import mpu +def maybe_padded_total_lengths( + total_lengths: list[int], + qkv_format: str, + is_vl_model: bool, +) -> list[int] | None: + """Per-sample tp*cp*2 padded lengths for the bridge VL+CP+thd path. + + Bridge's `preprocess_packed_seqs` (Qwen3-VL et al.) pads each sample to a + multiple of `tp*cp*2` before zigzag-splitting along CP, so the local logits + returned by the bridge index per-sample chunks at `padded_len // (2*cp)`. + Relax helpers that re-derive those chunks must agree. + + Returns None for non-VL/non-CP/non-thd paths so callers fall back to the + standard `ceil(total_length / (2*cp))` formula. + """ + cp_size = mpu.get_context_parallel_world_size() + if not (is_vl_model and cp_size > 1 and qkv_format == "thd"): + return None + tp_size = mpu.get_tensor_model_parallel_world_size() + align = tp_size * cp_size * 2 + return [(t + align - 1) // align * align for t in total_lengths] + + def get_logits_and_tokens_offset_with_cp( total_length: int, response_length: int, qkv_format: str = "thd", max_seq_len: int | None = None, + padded_total_length: int | None = None, ): """All offsets start from the begining of the prompt.""" cp_rank = mpu.get_context_parallel_rank() @@ -18,7 +42,13 @@ def get_logits_and_tokens_offset_with_cp( assert cp_size > 1 prompt_length = total_length - response_length - if qkv_format == "thd": + if padded_total_length is not None: + # Bridge VL+CP+thd: per-sample padded length is already aligned to tp*cp*2. + assert padded_total_length % (2 * cp_size) == 0, ( + f"padded_total_length={padded_total_length} not divisible by 2*cp={2 * cp_size}" + ) + chunk_size = padded_total_length // (2 * cp_size) + elif qkv_format == "thd": chunk_size = (total_length + 2 * cp_size - 1) // (2 * cp_size) else: assert max_seq_len is not None, "max_seq_len must be provided for qkv_format=bshd" @@ -55,6 +85,7 @@ def get_sum_of_sample_mean( calculate_per_token_loss: bool = False, qkv_format: str = "thd", max_seq_lens: list[int] | None = None, + padded_total_lengths: list[int] | None = None, ) -> Callable[[torch.Tensor], torch.Tensor]: """Calculate correct sample mean for CP.""" cp_size = mpu.get_context_parallel_world_size() @@ -84,9 +115,10 @@ def sum_of_token(x: torch.Tensor) -> torch.Tensor: zip(total_lengths, response_lengths, loss_masks, strict=False) ): max_seq_len = max_seq_lens[i] if max_seq_lens is not None else None + padded_total_length = padded_total_lengths[i] if padded_total_lengths is not None else None prompt_length = total_length - response_length _, _, _, tokens_offset = get_logits_and_tokens_offset_with_cp( - total_length, response_length, qkv_format, max_seq_len + total_length, response_length, qkv_format, max_seq_len, padded_total_length ) loss_mask_0 = loss_mask[tokens_offset[0][0] - prompt_length : tokens_offset[0][1] - prompt_length] loss_mask_1 = loss_mask[tokens_offset[1][0] - prompt_length : tokens_offset[1][1] - prompt_length] @@ -116,7 +148,12 @@ def sum_of_token(x: torch.Tensor) -> torch.Tensor: return sum_of_sample_mean if not calculate_per_token_loss else sum_of_token -def all_gather_with_cp(tensor: torch.Tensor, total_length: int, response_length: int) -> torch.Tensor: +def all_gather_with_cp( + tensor: torch.Tensor, + total_length: int, + response_length: int, + padded_total_length: int | None = None, +) -> torch.Tensor: """Gather tensors across all ranks in the context parallel group. The first dimension of the output tensor will be the `response_length`. @@ -127,7 +164,9 @@ def all_gather_with_cp(tensor: torch.Tensor, total_length: int, response_length: if cp_size == 1: return tensor - _, _, logits_offset, _ = get_logits_and_tokens_offset_with_cp(total_length, response_length) + _, _, logits_offset, _ = get_logits_and_tokens_offset_with_cp( + total_length, response_length, padded_total_length=padded_total_length + ) prompt_length = total_length - response_length @@ -218,6 +257,7 @@ def slice_log_prob_with_cp( response_length: int, qkv_format: str = "thd", max_token_len: int | None = None, + padded_total_length: int | None = None, ) -> list[float] | torch.Tensor: assert len(log_prob) == response_length @@ -228,7 +268,7 @@ def slice_log_prob_with_cp( prompt_length = total_length - response_length _, _, logits_offset, _ = get_logits_and_tokens_offset_with_cp( - total_length, response_length, qkv_format, max_token_len + total_length, response_length, qkv_format, max_token_len, padded_total_length ) chunk_1 = log_prob[logits_offset[0][0] - (prompt_length - 1) : logits_offset[0][1] - (prompt_length - 1)] diff --git a/relax/backends/megatron/data.py b/relax/backends/megatron/data.py index 6a931ab6d..a90362ec1 100644 --- a/relax/backends/megatron/data.py +++ b/relax/backends/megatron/data.py @@ -23,7 +23,7 @@ from relax.utils.training.flops_utils import calculate_fwd_flops from relax.utils.types import RolloutBatch -from .cp_utils import get_sum_of_sample_mean, slice_with_cp +from .cp_utils import get_sum_of_sample_mean, maybe_padded_total_lengths, slice_with_cp logger = get_logger(__name__) @@ -152,11 +152,59 @@ def get_batch( if qkv_format == "bshd": max_seqlen = batch["max_seq_lens"][0] assert max([t.size(0) for t in tokens]) <= max_seqlen + + # For VL models with CP > 1, Bridge expects UNSPLIT tokens (it handles CP + # splitting internally after vision embedding). Save padded-but-unsplit + # tokens so model.py can pass them to Bridge instead of the CP-split ones. + if cp_size > 1: + chunk_size = (max_seqlen + 2 * cp_size - 1) // (2 * cp_size) + padded_len = 2 * cp_size * chunk_size + unsplit = [F.pad(t, (0, padded_len - t.size(0)), value=pad_token_id) for t in tokens] + batch["unsplit_tokens"] = torch.stack(unsplit) + tokens = [slice_with_cp(t, pad_token_id, qkv_format, max_seqlen) for t in tokens] tokens = torch.stack(tokens) packed_seq_params = None elif qkv_format == "thd": + # VL + CP > 1: bridge's Qwen3VLModel.forward expects per-sample + # BSHD-padded input_ids + attention_mask, and re-derives the THD + # packing internally with align_size = tp*cp*2. Provide unsplit + # inputs and a matching packed_seq_params so the caller-side cu_seqlens + # agrees with what the bridge derives from attention_mask. + # Mirrors verl's build_vlm_attn_mask_thd + preprocess_thd_engine. + is_vl_model = batch.get("multimodal_train_inputs") is not None + if is_vl_model and cp_size > 1: + tp_size = mpu.get_tensor_model_parallel_world_size() + align_size = tp_size * cp_size * 2 + device = device_utils.make_current_torch_device() + + seqlens = torch.tensor([t.size(0) for t in tokens], dtype=torch.int32, device=device) + seqlens_padded = (seqlens + align_size - 1) // align_size * align_size + cu_seqlens_padded = torch.zeros(len(tokens) + 1, dtype=torch.int32, device=device) + cu_seqlens_padded[1:] = torch.cumsum(seqlens_padded, dim=0) + max_seqlen_padded = int(seqlens_padded.max().item()) + + unsplit_tokens = pad_sequence(tokens, batch_first=True, padding_value=pad_token_id) + unsplit_attention_mask = torch.zeros_like(unsplit_tokens, dtype=torch.bool) + for i, s in enumerate(seqlens.tolist()): + unsplit_attention_mask[i, :s] = True + + batch["unsplit_tokens"] = unsplit_tokens + batch["unsplit_attention_mask"] = unsplit_attention_mask + batch["vlm_packed_seq_params"] = PackedSeqParams( + qkv_format="thd", + cu_seqlens_q=cu_seqlens_padded, + cu_seqlens_kv=cu_seqlens_padded, + max_seqlen_q=max_seqlen_padded, + max_seqlen_kv=max_seqlen_padded, + cu_seqlens_q_padded=cu_seqlens_padded, + cu_seqlens_kv_padded=cu_seqlens_padded, + ) + # Per-sample tp*cp*2-aligned lengths consumed by loss helpers so + # their per-sample chunking matches bridge's preprocess_packed_seqs. + batch["padded_total_lengths"] = seqlens_padded.tolist() + if allgather_cp: # DSA mode: concatenate all sequences first, then slice once with CP. # We also pad the *global* concatenated stream to make per-rank chunks equal. @@ -540,6 +588,11 @@ def log_rollout_data( loss_masks = rollout_data["loss_masks"] total_lengths = rollout_data["total_lengths"] max_seq_lens = rollout_data.get("max_seq_lens", None) + padded_total_lengths = maybe_padded_total_lengths( + total_lengths, + args.qkv_format, + rollout_data.get("multimodal_train_inputs") is not None, + ) # OPD dynamic metric: overlap ratio on top-k token sets. student_topk_ids = rollout_data.get("topk_token_ids", None) @@ -573,6 +626,7 @@ def log_rollout_data( loss_masks_t, qkv_format=args.qkv_format, max_seq_lens=max_seq_lens, + padded_total_lengths=padded_total_lengths, ) overlap_ratio_value = cp_size * sum_of_sample_mean(overlap_ratio_flat) / len(loss_masks_t) log_dict["opd_overlap_ratio"] = overlap_ratio_value.item() @@ -618,6 +672,7 @@ def log_rollout_data( loss_masks, qkv_format=args.qkv_format, max_seq_lens=max_seq_lens, + padded_total_lengths=padded_total_lengths, ) val = cp_size * sum_of_sample_mean(val) / len(loss_masks) else: @@ -689,12 +744,22 @@ def quantile(total_value, n_quantiles, data) -> dict: correct_total_lengths = [] correct_loss_masks = [] correct_entropy = [] + correct_padded_total_lengths_full = maybe_padded_total_lengths( + total_lengths, + args.qkv_format, + rollout_data.get("multimodal_train_inputs") is not None, + ) + correct_padded_total_lengths: list[int] | None = ( + [] if correct_padded_total_lengths_full is not None else None + ) for i, raw_reward in enumerate(raw_rewards): if raw_reward == 1: correct_response_lengths.append(response_lengths[i]) correct_total_lengths.append(total_lengths[i]) correct_loss_masks.append(loss_masks[i]) correct_entropy.append(-rollout_data["log_probs"][i]) + if correct_padded_total_lengths is not None: + correct_padded_total_lengths.append(correct_padded_total_lengths_full[i]) num_correct_responses = len(correct_total_lengths) rollout_data["correct_response_lengths"] = correct_response_lengths correct_response_length_percentile = quantile( @@ -704,7 +769,10 @@ def quantile(total_value, n_quantiles, data) -> dict: rollout_data[f"correct_length/{p}"] = [val] * num_correct_responses if len(correct_entropy) > 0: sum_of_sample_mean = get_sum_of_sample_mean( - correct_total_lengths, correct_response_lengths, correct_loss_masks + correct_total_lengths, + correct_response_lengths, + correct_loss_masks, + padded_total_lengths=correct_padded_total_lengths, ) correct_entropy = sum_of_sample_mean(torch.cat(correct_entropy, dim=0)) rollout_data["correct_entropy"] = [correct_entropy.item()] * num_correct_responses diff --git a/relax/backends/megatron/initialize.py b/relax/backends/megatron/initialize.py index 563d730ed..a33129c5d 100644 --- a/relax/backends/megatron/initialize.py +++ b/relax/backends/megatron/initialize.py @@ -51,12 +51,19 @@ def _initialize_distributed(args, get_embedding_ranks=None, get_position_embeddi order="tp-cp-ep-dp-pp" if not args.use_tp_pp_dp_mapping else "tp-cp-ep-pp-dp", get_embedding_ranks=get_embedding_ranks, get_position_embedding_ranks=get_position_embedding_ranks, - create_gloo_process_groups=args.enable_gloo_process_groups, + create_gloo_process_groups=args.use_gloo_process_groups, ) def init(args): set_args(args) + + if getattr(args, "disable_jit_fuser", False): + from megatron.core.jit import disable_jit_fuser + + disable_jit_fuser() + logger.info("JIT fuser disabled (torch.compile → no-op).") + if args.enable_experimental: logger.info("Enable megatron experimental") set_experimental_flag(True) diff --git a/relax/backends/megatron/loss.py b/relax/backends/megatron/loss.py index 47530f033..72967e662 100644 --- a/relax/backends/megatron/loss.py +++ b/relax/backends/megatron/loss.py @@ -28,6 +28,7 @@ all_gather_with_cp, get_logits_and_tokens_offset_with_cp, get_sum_of_sample_mean, + maybe_padded_total_lengths, slice_log_prob_with_cp, ) @@ -40,6 +41,7 @@ def get_responses( total_lengths: list[int], response_lengths: list[int], max_seq_lens: list[int] | None = None, + padded_total_lengths: list[int] | None = None, ) -> Iterator[tuple[torch.Tensor, torch.Tensor]]: """Yield response-aligned `(logits_chunk, tokens_chunk)` pairs per sample. @@ -84,6 +86,7 @@ def get_responses( zip(unconcat_tokens, total_lengths, response_lengths, strict=False) ): max_seq_len = max_seq_lens[i] if max_seq_lens is not None else None + padded_total_length = padded_total_lengths[i] if padded_total_lengths is not None else None if cp_size == 1: if qkv_format == "bshd": @@ -120,7 +123,7 @@ def get_responses( else: # TODO: this is super ugly... do better abstraction. chunk_size, chunks_offset, logits_offset, tokens_offset = get_logits_and_tokens_offset_with_cp( - total_length, response_length, qkv_format, max_seq_len + total_length, response_length, qkv_format, max_seq_len, padded_total_length ) logits_0, logits_1 = logits[end : end + chunk_size], logits[end + chunk_size : end + 2 * chunk_size] @@ -151,6 +154,7 @@ def _allgather_cp_redistribute( total_lengths: list[int], response_lengths: list[int], max_seq_lens: list[int] | None = None, + padded_total_lengths: list[int] | None = None, ) -> None: """Redistribute response tensors from allgather-CP layout to zigzag ring- attn layout. @@ -217,8 +221,16 @@ def _allgather_cp_redistribute( zip(all_cat.split(response_lengths, dim=0), total_lengths, response_lengths, strict=False) ): max_seq_len = max_seq_lens[idx] if max_seq_lens is not None else None + padded_total_length = padded_total_lengths[idx] if padded_total_lengths is not None else None new_values.append( - slice_log_prob_with_cp(full_resp, total_length, response_length, args.qkv_format, max_seq_len) + slice_log_prob_with_cp( + full_resp, + total_length, + response_length, + args.qkv_format, + max_seq_len, + padded_total_length, + ) ) res[key] = new_values @@ -236,6 +248,7 @@ def get_log_probs_and_entropy( topk_k: int | None = None, non_loss_data: bool = True, max_seq_lens: list[int] | None = None, + padded_total_lengths: list[int] | None = None, ) -> tuple[torch.Tensor, dict[str, list[torch.Tensor]]]: """Compute per-token log-probabilities (and optionally entropy) on responses. @@ -274,6 +287,7 @@ def get_log_probs_and_entropy( total_lengths=total_lengths, response_lengths=response_lengths, max_seq_lens=max_seq_lens, + padded_total_lengths=padded_total_lengths, ): log_prob, entropy = calculate_log_probs_and_entropy( logits_chunk, @@ -307,6 +321,7 @@ def get_log_probs_and_entropy( total_lengths=total_lengths, response_lengths=response_lengths, max_seq_lens=max_seq_lens, + padded_total_lengths=padded_total_lengths, ) return torch.empty((0,), device=logits.device), res @@ -322,6 +337,7 @@ def get_values( with_entropy: bool = False, non_loss_data: bool = True, max_seq_lens: list[int] | None = None, + padded_total_lengths: list[int] | None = None, ) -> tuple[torch.Tensor, dict[str, list[torch.Tensor]]]: """Extract per-token value predictions over response tokens. @@ -352,6 +368,7 @@ def get_values( total_lengths=total_lengths, response_lengths=response_lengths, max_seq_lens=max_seq_lens, + padded_total_lengths=padded_total_lengths, ): assert logits_chunk.size(-1) == 1, f"{logits_chunk.shape}" value_list.append(logits_chunk.squeeze(-1)) @@ -368,6 +385,7 @@ def get_values( total_lengths=total_lengths, response_lengths=response_lengths, max_seq_lens=max_seq_lens, + padded_total_lengths=padded_total_lengths, ) return torch.empty((0,), device=logits.device), res @@ -444,6 +462,11 @@ def compute_advantages_and_returns(args: Namespace, rollout_data: RolloutBatch) loss_masks: list[torch.Tensor] = rollout_data.get("loss_masks") total_lengths: list[int] = rollout_data.get("total_lengths") max_seq_lens: list[int] | None = rollout_data.get("max_seq_lens", None) + padded_total_lengths: list[int] | None = maybe_padded_total_lengths( + total_lengths, + args.qkv_format, + rollout_data.get("multimodal_train_inputs") is not None, + ) # return when not the last pp stage. if not mpu.is_pipeline_last_stage(): @@ -539,9 +562,10 @@ def compute_advantages_and_returns(args: Namespace, rollout_data: RolloutBatch) response_len = response_lengths[i] prompt_len = total_len - response_len max_seq_len = max_seq_lens[i] if max_seq_lens is not None else None + padded_total_length = padded_total_lengths[i] if padded_total_lengths is not None else None _, _, _, token_offsets = get_logits_and_tokens_offset_with_cp( - total_len, response_len, args.qkv_format, max_seq_len + total_len, response_len, args.qkv_format, max_seq_len, padded_total_length ) # Convert global offsets to response-space offsets @@ -678,6 +702,7 @@ def policy_loss_function( response_lengths = batch["response_lengths"] total_lengths = batch["total_lengths"] max_seq_lens = batch.get("max_seq_lens", None) + padded_total_lengths = batch.get("padded_total_lengths", None) _, log_probs_and_entropy = get_log_probs_and_entropy( logits, @@ -687,6 +712,7 @@ def policy_loss_function( response_lengths=response_lengths, with_entropy=True, max_seq_lens=max_seq_lens, + padded_total_lengths=padded_total_lengths, ) log_probs = log_probs_and_entropy["log_probs"] @@ -697,16 +723,20 @@ def policy_loss_function( full_log_probs = None full_old_log_probs = None if need_full_log_probs: + if padded_total_lengths is None: + padded_iter = [None] * len(log_probs) + else: + padded_iter = padded_total_lengths full_log_probs = [ - all_gather_with_cp(log_prob, total_length, response_length) - for log_prob, total_length, response_length in zip( - log_probs, total_lengths, response_lengths, strict=False + all_gather_with_cp(log_prob, total_length, response_length, padded_total_length) + for log_prob, total_length, response_length, padded_total_length in zip( + log_probs, total_lengths, response_lengths, padded_iter, strict=False ) ] full_old_log_probs = [ - all_gather_with_cp(old_log_prob, total_length, response_length) - for old_log_prob, total_length, response_length in zip( - old_log_probs, total_lengths, response_lengths, strict=False + all_gather_with_cp(old_log_prob, total_length, response_length, padded_total_length) + for old_log_prob, total_length, response_length, padded_total_length in zip( + old_log_probs, total_lengths, response_lengths, padded_iter, strict=False ) ] @@ -788,6 +818,7 @@ def policy_loss_function( args.calculate_per_token_loss, args.qkv_format, max_seq_lens, + padded_total_lengths, ) # Determine pg_loss reducer: use custom if specified, otherwise default @@ -908,6 +939,7 @@ def value_loss_function( total_lengths=batch["total_lengths"], response_lengths=batch["response_lengths"], max_seq_lens=batch.get("max_seq_lens", None), + padded_total_lengths=batch.get("padded_total_lengths", None), ) values = torch.cat([value.flatten() for value in values["values"]], dim=0) @@ -967,6 +999,7 @@ def sft_loss_function( response_lengths=response_lengths, with_entropy=False, max_seq_lens=batch.get("max_seq_lens", None), + padded_total_lengths=batch.get("padded_total_lengths", None), ) log_probs = log_probs_and_entropy["log_probs"] @@ -1024,6 +1057,7 @@ def loss_function( args.calculate_per_token_loss, args.qkv_format, batch.get("max_seq_lens", None), + batch.get("padded_total_lengths", None), ) match args.loss_type: diff --git a/relax/backends/megatron/model.py b/relax/backends/megatron/model.py index 8d745d95d..1b29b666b 100644 --- a/relax/backends/megatron/model.py +++ b/relax/backends/megatron/model.py @@ -152,7 +152,7 @@ def setup_model_and_optimizer( optimizer = get_megatron_optimizer( config=config, model_chunks=model, - use_gloo_process_groups=args.enable_gloo_process_groups, + use_gloo_process_groups=args.use_gloo_process_groups, ) opt_param_scheduler = get_optimizer_param_scheduler(args, optimizer) return model, optimizer, opt_param_scheduler @@ -256,14 +256,39 @@ def forward_step( packed_seq_params = batch["packed_seq_params"] total_lengths = batch["total_lengths"] response_lengths = batch["response_lengths"] + + is_vl_model = batch.get("multimodal_train_inputs", None) is not None + mm_kwargs = batch["multimodal_train_inputs"] if is_vl_model else {} + + # VL + CP > 1: pass unsplit tokens so Bridge handles CP split after + # vision embedding (aligns with Bridge's qwen3_vl_step.py contract). + if is_vl_model and "unsplit_tokens" in batch: + forward_input_ids = batch["unsplit_tokens"] + forward_packed_seq_params = None + else: + forward_input_ids = tokens + forward_packed_seq_params = packed_seq_params + + # thd VL+CP: bridge needs per-sample attention_mask + matching thd + # packed_seq_params (align_size = tp*cp*2). loss_mask is None because + # labels=None means GPTModel won't run internal loss; Relax's loss is + # computed externally from full_loss_masks. + if is_vl_model and "vlm_packed_seq_params" in batch: + forward_attention_mask = batch["unsplit_attention_mask"] + forward_packed_seq_params = batch["vlm_packed_seq_params"] + forward_loss_mask = None + else: + forward_attention_mask = None + forward_loss_mask = batch["full_loss_masks"] + output_tensor = model( - input_ids=tokens, + input_ids=forward_input_ids, position_ids=None, - attention_mask=None, + attention_mask=forward_attention_mask, labels=None, - packed_seq_params=packed_seq_params, - loss_mask=batch["full_loss_masks"], - **(batch["multimodal_train_inputs"] if batch.get("multimodal_train_inputs", None) is not None else {}), + packed_seq_params=forward_packed_seq_params, + loss_mask=forward_loss_mask, + **mm_kwargs, ) return output_tensor, partial( @@ -274,6 +299,7 @@ def forward_step( response_lengths=response_lengths, with_entropy=args.use_rollout_entropy, max_seq_lens=batch.get("max_seq_lens", None), + padded_total_lengths=batch.get("padded_total_lengths", None), ) # Turn on evaluation mode which disables dropout. @@ -428,19 +454,31 @@ def forward_step( loss_mask=batch["full_loss_masks"], ) else: + is_vl_model = batch.get("multimodal_train_inputs", None) is not None + use_unsplit = is_vl_model and "unsplit_tokens" in batch + forward_kwargs = { - "input_ids": batch["tokens"], + "input_ids": batch["unsplit_tokens"] if use_unsplit else batch["tokens"], "position_ids": None, "attention_mask": None, "labels": None, - "packed_seq_params": batch["packed_seq_params"], + "packed_seq_params": None if use_unsplit else batch["packed_seq_params"], "loss_mask": batch["full_loss_masks"], } + # thd VL+CP: bridge needs per-sample attention_mask + matching thd + # packed_seq_params (align_size = tp*cp*2). loss_mask is None + # because labels=None means GPTModel won't run internal loss; + # Relax's loss is computed externally from full_loss_masks. + if is_vl_model and "vlm_packed_seq_params" in batch: + forward_kwargs["attention_mask"] = batch["unsplit_attention_mask"] + forward_kwargs["packed_seq_params"] = batch["vlm_packed_seq_params"] + forward_kwargs["loss_mask"] = None + if args.enable_mtp_training: forward_kwargs["mtp_kwargs"] = {"mtp_labels": batch["tokens"]} - if batch.get("multimodal_train_inputs", None) is not None: + if is_vl_model: forward_kwargs.update(batch["multimodal_train_inputs"]) output_tensor = model(**forward_kwargs) diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index f85ada679..02cb7e851 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -63,6 +63,87 @@ def forward( return logits, None +# CP-PROBE: one-shot forward-pre-hook on the first attention module to verify that +# context parallelism actually splits the sequence dimension at the attention input. +# Compare seq_len across CP=1 vs CP=2 runs — it must halve. Remove after verifying. +_CP_PROBE_INSTALLED = False + + +def _install_cp_probe(model: torch.nn.Module) -> None: + global _CP_PROBE_INSTALLED + if _CP_PROBE_INSTALLED: + return + + from megatron.core import mpu + + cp_size = mpu.get_context_parallel_world_size() + cp_rank = mpu.get_context_parallel_rank() + tp_rank = mpu.get_tensor_model_parallel_rank() + state = {"n": 0} + + target_classes = ( + "DotProductAttention", + "TEDotProductAttention", + "FusedAttention", + "FlashAttention", + ) + + def hook(module, args, kwargs): + if state["n"] >= 2 or tp_rank != 0: + return + shapes: dict[str, object] = {} + for name in ( + "query", + "key", + "value", + "q", + "k", + "v", + "hidden_states", + "query_layer", + "key_layer", + "value_layer", + ): + t = kwargs.get(name) + if torch.is_tensor(t): + shapes[name] = tuple(t.shape) + for i, t in enumerate(args): + if torch.is_tensor(t): + shapes[f"arg{i}"] = tuple(t.shape) + for name in ("cu_seqlens_q", "cu_seqlens_kv"): + t = kwargs.get(name) + if torch.is_tensor(t): + shapes[name] = t.tolist() # one-shot sync, OK for probe + logger.debug(f"[CP-PROBE] cp_rank={cp_rank}/{cp_size} module={type(module).__name__} shapes={shapes}") + state["n"] += 1 + + skip_prefixes = ("vision_model", "visual", "vit", "image_encoder", "projector", "audio") + + def is_llm_backbone(n: str) -> bool: + return not any(p in n for p in skip_prefixes) + + matches = [(n, m) for n, m in model.named_modules() if type(m).__name__ in target_classes] + llm_matches = [(n, m) for n, m in matches if is_llm_backbone(n)] + chosen = llm_matches or matches # fallback to vision if no LLM backbone in this stage + + if chosen: + name, m = chosen[0] + m.register_forward_pre_hook(hook, with_kwargs=True) + logger.debug( + f"[CP-PROBE] hook installed on '{name}' ({type(m).__name__}) " + f"cp_size={cp_size} cp_rank={cp_rank} " + f"(total_attn_modules={len(matches)}, llm_backbone={len(llm_matches)})" + ) + _CP_PROBE_INSTALLED = True + return + + candidates = [(n, type(m).__name__) for n, m in model.named_modules() if "attention" in n.lower()][:8] + logger.warning( + f"[CP-PROBE] no attention module matched on this stage (cp_rank={cp_rank}); " + f"attention-like candidates: {candidates}" + ) + + def get_model_provider_func( args: argparse.Namespace, role: Literal["actor", "critic"] = "actor", @@ -85,6 +166,7 @@ def wrapped_model_provider( model.output_layer = LinearForLastLayer( input_size=model.config.hidden_size, output_size=1, config=model.config ) + _install_cp_probe(model) return model return wrapped_model_provider @@ -125,6 +207,7 @@ def wrapped_model_provider( "freeze_vision_projection", # https://github.com/redai-infra/Megatron-Bridge/commit/960bb5f18800d3e1fb9815e95daa185ab06c09ea "vision_dp_when_tp", + "calculate_per_token_loss", ] args_dict = vars(args) @@ -162,7 +245,14 @@ def wrapped_model_provider( pickle.dump(provider, f) logger.info(f"Provider config saved to {pkl_path}") - return provider.provide + original_provide = provider.provide + + def provide_with_cp_probe(*p_args, **p_kwargs): + model = original_provide(*p_args, **p_kwargs) + _install_cp_probe(model) + return model + + return provide_with_cp_probe def model_provider(pre_process: bool = True, post_process: bool = True, vp_stage: int | None = None) -> GPTModel: """Builds the model. @@ -269,13 +359,14 @@ def model_provider(pre_process: bool = True, post_process: bool = True, vp_stage if post_process and role == "critic": model.output_layer = LinearForLastLayer(input_size=config.hidden_size, output_size=1, config=config) + _install_cp_probe(model) return model return model_provider def wrap_model_provider_with_freeze(original_provider, args): - def wrapped_provider(pre_process=True, post_process=True, vp_stage=None): + def wrapped_provider(pre_process=True, post_process=True, vp_stage=None, **kwargs): sig = inspect.signature(original_provider) if "vp_stage" in sig.parameters: model = original_provider(pre_process=pre_process, post_process=post_process, vp_stage=vp_stage) diff --git a/relax/backends/megatron/weight_update/common.py b/relax/backends/megatron/weight_update/common.py index b8ee0f853..d040d62f9 100644 --- a/relax/backends/megatron/weight_update/common.py +++ b/relax/backends/megatron/weight_update/common.py @@ -99,7 +99,12 @@ def all_gather_param(args, name: str, param: torch.nn.Parameter) -> torch.Tensor param_partitions = [torch.empty_like(param.data) for _ in range(tp_size)] dist.all_gather(param_partitions, param.data, group=tp_group) partition_dim = param.partition_dim - assert param.partition_stride == 1, "partition_stride != 1 is not supported" + # NOTE: Megatron-LM (megatron/core/transformer/mlp.py) now explicitly sets partition_stride=2 + # for GLU/SwiGLU linear_fc1 layers to indicate interleaved [gate, up] TP layout. + # The rechunk logic below (chunk(2) + reorder) already handles this correctly, + # so we only assert stride==1 for non-GLU parameters. + if "linear_fc1" not in name: + assert param.partition_stride == 1, f"{param.partition_stride=} != 1 is not supported" # TODO: here we did an extra copy during concat, maybe merge this with convert_to_hf is better? # TODO: check only GLU is used. if "linear_fc1.weight" in name and "vision_model" not in name: @@ -164,7 +169,7 @@ def all_gather_params_async( param = direct_param else: # Process the gathered partitions (same logic as original all_gather_param) - assert partition_dim is not None, "partition_stride != 1 is not supported" + assert partition_dim is not None, "partition_dim must be set for TP-sharded params" # TODO: here we did an extra copy during concat, maybe merge this with convert_to_hf is better? # TODO: check only GLU is used. if "linear_fc1.weight" in info.name and "vision_model" not in info.name: diff --git a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py index 35a9ad4d0..cada44c58 100644 --- a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py +++ b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py @@ -20,7 +20,6 @@ def __init__(self, *args, **kwargs): self._bridge = AutoBridge.from_hf_pretrained(self.args.hf_checkpoint, trust_remote_code=True) def get_hf_weight_chunks(self, megatron_local_weights): - # TODO support quantization (e.g. modify megatron-bridge to provide megatron param name) renamed_megatron_local_weights = {strip_param_name_prefix(k): v for k, v in megatron_local_weights.items()} with megatron_bridge_utils.patch_megatron_model(self.model): conversion_tasks = self._bridge.get_conversion_tasks(self.model) @@ -29,7 +28,34 @@ def get_hf_weight_chunks(self, megatron_local_weights): named_weights = self._bridge.export_hf_weights(self.model, cpu=False, conversion_tasks=conversion_tasks) def iter_quantized_named_weights(): - for hf_param_name, weight, megatron_param_name in named_weights: + hf_to_megatron_mapping = None + + for item in named_weights: + # Compatibility shim: old megatron-bridge yields 3-tuples + # ``(hf_param_name, weight, megatron_param_name)`` while + # the official bridge yields 2-tuples ``(hf_param_name, weight)``. + # Dispatch per-item so the same code path supports both. + if len(item) == 3: + hf_param_name, weight, megatron_param_name = item + elif len(item) == 2: + hf_param_name, weight = item + if hf_to_megatron_mapping is None: + hf_to_megatron_mapping = _build_hf_to_megatron_mapping(conversion_tasks) + # With PP > 1, export_hf_weights yields params from ALL + # PP ranks (via internal PP broadcast), but + # hf_to_megatron_mapping only contains params from this + # rank's conversion tasks. For remote PP rank params + # we fall back to hf_param_name — this is safe because + # remove_padding checks megatron-style names and + # quantize_params_fp8 regex won't match HF-style names. + megatron_param_name = hf_to_megatron_mapping.get(hf_param_name, hf_param_name) + else: + raise ValueError( + f"Unexpected named_weights tuple length {len(item)} from " + f"megatron-bridge.export_hf_weights(); expected 2 (new) or 3 (old). " + f"Item: {item!r}" + ) + processed_weight = postprocess_hf_param( args=self.args, megatron_param_name=megatron_param_name, @@ -54,7 +80,61 @@ def iter_quantized_named_weights(): ) +def _build_hf_to_megatron_mapping(conversion_tasks): + """Build a mapping from HF parameter names to megatron parameter names. + + Only relevant for the official megatron-bridge whose ``export_hf_weights`` + yields 2-tuples ``(hf_name, weight)`` and no longer carries the megatron + name in the tuple. We reconstruct the mapping by reading + ``task.mapping.hf_param`` — a pure metadata attribute that requires NO + collective communication. This is critical for PP > 1 where different + ranks hold different parameter subsets; calling ``megatron_to_hf()`` (which + contains PP broadcast / TP gather) with inconsistent tasks across ranks + would deadlock. + + ``mapping.hf_param`` is either: + - ``str``: simple 1-to-1 mappings (AutoMapping, DirectMapping, …) + - ``dict``: multi-output mappings (QKVMapping ``{"q","k","v"}``, + GatedMLPMapping ``{"gate","up"}``) + + This mirrors the approach shown in the official ``get_conversion_tasks`` + docstring of megatron-bridge's ``AutoBridge``. + + Note: with PP > 1, each rank only holds a subset of conversion tasks, so + the returned mapping is **incomplete** — it covers only the params that + belong to this PP rank. ``export_hf_weights`` yields params from ALL PP + ranks (via internal PP broadcast), so callers must handle missing keys + gracefully (e.g. fall back to the HF param name). + """ + hf_to_megatron_mapping = {} + + for task in conversion_tasks: + megatron_param_name = task.param_name + hf_param = task.mapping.hf_param + + if isinstance(hf_param, str): + hf_to_megatron_mapping[hf_param] = megatron_param_name + elif isinstance(hf_param, dict): + for hf_name in hf_param.values(): + hf_to_megatron_mapping[hf_name] = megatron_param_name + else: + raise TypeError( + f"Unexpected mapping.hf_param type {type(hf_param).__name__} " + f"for megatron param '{megatron_param_name}': {hf_param!r}" + ) + + return hf_to_megatron_mapping + + def _process_conversion_tasks(vanilla_conversion_tasks, new_weight_dict): + """Replace param_weight in each conversion task with the latest trained + weights. + + build_conversion_tasks() returns ``List[None | WeightConversionTask]`` + where None entries correspond to global params that have no mapping. We + filter them out here so that downstream consumers never see None. + """ + def _handle_one(task): if task.param_weight is None: return task @@ -68,7 +148,9 @@ def _handle_one(task): new_param_weight = new_param_weight.cuda() return dataclasses.replace(task, param_weight=new_param_weight) - return _MapWithLen(_handle_one, vanilla_conversion_tasks) + # Filter out None tasks (params with no mapping in build_conversion_tasks) + valid_tasks = [t for t in vanilla_conversion_tasks if t is not None] + return _MapWithLen(_handle_one, valid_tasks) class _MapWithLen: diff --git a/relax/distributed/checkpoint_service/utils.py b/relax/distributed/checkpoint_service/utils.py index 59dc98b01..51d6acbbe 100644 --- a/relax/distributed/checkpoint_service/utils.py +++ b/relax/distributed/checkpoint_service/utils.py @@ -87,8 +87,13 @@ def chunk_param( tp_rank = mpu.get_tensor_model_parallel_rank() # 4. Verify stride + # NOTE: Megatron-LM (megatron/core/transformer/mlp.py) sets partition_stride=2 + # for GLU/SwiGLU linear_fc1 layers. The rechunk logic below handles this correctly. partition_dim = target_param.partition_dim - assert getattr(target_param, "partition_stride", 1) == 1, "partition_stride != 1 is not supported" + if "linear_fc1" not in name: + assert getattr(target_param, "partition_stride", 1) == 1, ( + f"{name}: partition_stride={getattr(target_param, 'partition_stride', 1)} != 1 is not supported" + ) # 5. Workaround grouped MoE partition bug for linear_fc2.weight effective_partition_dim = partition_dim diff --git a/relax/models/qwen_omni/__init__.py b/relax/models/qwen_omni/__init__.py new file mode 100644 index 000000000..9f3863608 --- /dev/null +++ b/relax/models/qwen_omni/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/models/qwen_omni/modeling_qwen3_omni/__init__.py b/relax/models/qwen_omni/modeling_qwen3_omni/__init__.py new file mode 100644 index 000000000..f8c8c3e98 --- /dev/null +++ b/relax/models/qwen_omni/modeling_qwen3_omni/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Qwen3 Omni model providers and configurations.""" + +# Core model components +# Bridges for HuggingFace to Megatron conversion +from relax.models.qwen_omni.modeling_qwen3_omni.model import Qwen3OmniMoeModel # noqa: F401 +from relax.models.qwen_omni.qwen3_omni_bridge import Qwen3OmniMoEBridge + +# Dense and MoE model providers +from relax.models.qwen_omni.qwen3_omni_provider import Qwen3OmniModelProvider + + +__all__ = [ + "Qwen3OmniMoeModel", + "Qwen3OmniMoEBridge", + "Qwen3OmniModelProvider", +] diff --git a/relax/models/qwen_omni/modeling_qwen3_omni/model.py b/relax/models/qwen_omni/modeling_qwen3_omni/model.py new file mode 100644 index 000000000..4fda12bcd --- /dev/null +++ b/relax/models/qwen_omni/modeling_qwen3_omni/model.py @@ -0,0 +1,411 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +import torch +from megatron.bridge.models.qwen_vl.modelling_qwen3_vl.utils import split_deepstack_embs +from megatron.bridge.utils.common_utils import hook_hf_module_setattr_for_tp_grad_sync +from megatron.core import InferenceParams, mpu, tensor_parallel +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.transformer import MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec +from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import ( + Qwen3OmniMoeThinkerConfig as Qwen3OmniMoeThinkerConfigHF, +) +from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import ( + Qwen3OmniMoeAudioEncoder as Qwen3OmniMoeAudioEncoderHF, +) +from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import ( + Qwen3OmniMoeVisionEncoder as Qwen3OmniMoeVisionEncoderHF, +) + +from relax.models.qwen_omni.modeling_qwen3_omni.text_model import Qwen3OmniGPTModel +from relax.models.qwen_omni.modeling_qwen3_omni.transformer_config import Qwen3OmniTransformerConfig +from relax.models.qwen_omni.modeling_qwen3_omni.utils import get_rope_index + + +class Qwen3OmniMoeModel(MegatronModule): + """Qwen3 Omni MoE Thinker Model for multimodal understanding. + + This model supports audio, image, and video inputs in addition to text. + It processes multimodal inputs through separate encoders and combines them + for the language model. + + This is a standalone implementation that does not inherit from other models + to maintain independence from version-specific implementations. + """ + + def __init__( + self, + language_transformer_config: Qwen3OmniTransformerConfig, + language_transformer_layer_spec: ModuleSpec, + audio_transformer_config: Qwen3OmniMoeThinkerConfigHF, + vision_transformer_config: Qwen3OmniMoeThinkerConfigHF, + parallel_output: bool = True, + pre_process: bool = True, + post_process: bool = True, + add_encoder: bool = True, + add_decoder: bool = True, + use_audio_in_video: bool = False, + pg_collection=None, + ): + super().__init__(config=language_transformer_config) + + self.pre_process = pre_process + self.post_process = post_process + self.pg_collection = pg_collection + self.add_encoder = add_encoder + self.add_decoder = add_decoder + + self.encoder_hidden_state = None + self.vision_model = None + self.language_model = None + self.image_token_id = language_transformer_config.image_token_id + self.video_token_id = language_transformer_config.video_token_id + self.vision_start_token_id = language_transformer_config.vision_start_token_id + + # This attribute is needed to check if an all-reduce is required + # on the word embeddings inside `finalize_model_grads._allreduce_word_embedding_grads`. + self.share_embeddings_and_output_weights = False + + self.position_id_per_seconds = language_transformer_config.position_id_per_seconds + self.audio_token_id = language_transformer_config.audio_token_id + self.audio_start_token_id = language_transformer_config.audio_start_token_id + self.use_audio_in_video = use_audio_in_video + self.audio_model = None + + if self.pre_process: + # Initialize audio and vision models with random weights from config + self.audio_model = Qwen3OmniMoeAudioEncoderHF._from_config(audio_transformer_config) + self.vision_model = Qwen3OmniMoeVisionEncoderHF._from_config(vision_transformer_config) + # Ensure HF encoder params are marked for TP grad sync and future assignments are hooked. + hook_hf_module_setattr_for_tp_grad_sync(self.audio_model) + hook_hf_module_setattr_for_tp_grad_sync(self.vision_model) + # Move to device if available + if torch.cuda.is_available(): + self.audio_model = self.audio_model.to("cuda") + self.vision_model = self.vision_model.to("cuda") + + self.language_model = Qwen3OmniGPTModel( + config=language_transformer_config, + transformer_layer_spec=language_transformer_layer_spec, + vocab_size=language_transformer_config.vocab_size, + max_sequence_length=language_transformer_config.language_max_sequence_length, + parallel_output=parallel_output, + position_embedding_type="mrope", + rotary_percent=language_transformer_config.rotary_percent, + pre_process=self.pre_process, + post_process=self.post_process, + rotary_base=language_transformer_config.rotary_base, + fp16_lm_cross_entropy=language_transformer_config.fp16_lm_cross_entropy, + share_embeddings_and_output_weights=language_transformer_config.share_embeddings_and_output_weights, + scatter_embedding_sequence_parallel=False, + pg_collection=pg_collection, + ) + self.share_embeddings_and_output_weights = self.language_model.share_embeddings_and_output_weights + + def set_input_tensor(self, input_tensor) -> None: + """Set input tensor to be used instead of forward()'s input. + + When the pipeline parallel size > 1, the input tensor is received from + the previous pipeline stage and must be provided to the model via this method. + + Args: + input_tensor (list or torch.Tensor): Input tensor(s) from the previous pipeline stage. + """ + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + assert len(input_tensor) == 1, "input_tensor should only be length 1 for Qwen3OmniMoeModel" + + if self.pre_process: + self.encoder_hidden_state = input_tensor[0] + else: + self.language_model.set_input_tensor(input_tensor[0]) + + def freeze( + self, + freeze_language_model: bool, + freeze_vision_model: bool, + freeze_vision_projection: bool, + freeze_audio_model: bool = False, + ): + """Freeze model modules. + + Make specific modules non-trainable by setting requires_grad to False. + + Args: + freeze_language_model (bool): Freeze the language model module. + freeze_vision_model (bool): Freeze the vision model module. + freeze_vision_projection (bool): Freeze the vision projection modules. + freeze_audio_model (bool): Freeze the audio model module. + """ + if freeze_language_model and self.language_model is not None: + for param in self.language_model.parameters(): + param.requires_grad = False + + if freeze_vision_model and self.vision_model is not None: + for param in self.vision_model.parameters(): + param.requires_grad = False + + if freeze_audio_model and self.audio_model is not None: + self.audio_model._freeze_parameters() + + def forward( + self, + input_ids: torch.Tensor, + input_features: torch.Tensor = None, + position_ids: torch.Tensor = None, # can set at dataset + attention_mask: torch.Tensor = None, + feature_attention_mask: torch.Tensor = None, + labels: torch.Tensor = None, + loss_mask: torch.Tensor = None, + inference_params: InferenceParams = None, + packed_seq_params: PackedSeqParams = None, + extra_block_kwargs: dict = None, + pixel_values: torch.Tensor = None, + pixel_values_videos: torch.Tensor = None, + image_grid_thw: torch.Tensor = None, + video_grid_thw: torch.Tensor = None, + image_input_mask: torch.Tensor = None, + video_second_per_grid=None, + ) -> torch.Tensor: + """Forward function of the Qwen3 Omni model. + + Args: + input_ids (torch.Tensor): input text ids [batch, text_seq_len]. + input_features (torch.Tensor): audio features. + position_ids (torch.Tensor): input text position ids [batch, text_seq_len]. + attention_mask (torch.Tensor): attention mask for the language model. + feature_attention_mask (torch.Tensor): attention mask for audio features. + labels (torch.Tensor): Optional target text labels [batch, combined_seq_len]. + loss_mask (torch.Tensor): Loss mask. + inference_params (InferenceParams): Inference-time parameters including KV cache. + packed_seq_params (PackedSeqParams): Packed sequence parameters. + extra_block_kwargs (dict): Extra block kwargs. + pixel_values (torch.Tensor): Image pixel values. + pixel_values_videos (torch.Tensor): Video pixel values. + image_grid_thw (torch.Tensor): Image grid dimensions. + video_grid_thw (torch.Tensor): Video grid dimensions. + image_input_mask (torch.Tensor): Image input mask. + video_second_per_grid (torch.Tensor): Seconds per video grid. + + Returns: + output (torch.Tensor): Loss of shape [b, s] if labels are provided, otherwise logits. + """ + assert inference_params is None, "not support inference" + + video_start_index = 0 + vision_grid_thw = None + vision_data = None + image_mask = None + video_mask = None + deepstack_feature_lists = None + # position ids is computed within the model + position_ids = None + audio_feature_lengths = None + + if feature_attention_mask is not None: + audio_feature_lengths = torch.sum(feature_attention_mask, dim=1) + + if self.pre_process: + # ========================= + # image / Video + # ========================= + if image_grid_thw is not None or video_grid_thw is not None: + if image_grid_thw is not None: + image_mask = image_input_mask + if image_mask is None: + image_mask = (input_ids == self.image_token_id).contiguous() + vision_grid_thw = image_grid_thw + vision_data = pixel_values + video_start_index = image_mask.sum().item() + else: + video_start_index = 0 + + # Handle videos - concatenate if both present + if video_grid_thw is not None: + video_mask = (input_ids == self.video_token_id).contiguous() + if vision_grid_thw is not None: + # Both images and videos present - concatenate + vision_grid_thw = torch.cat([vision_grid_thw, video_grid_thw], dim=0) + vision_data = torch.cat([vision_data, pixel_values_videos], dim=0) + else: + # Only videos present + vision_grid_thw = video_grid_thw + vision_data = pixel_values_videos + + vision_embeds = None + if vision_grid_thw is not None and vision_grid_thw.shape[0] > 0: + vision_outputs = self.vision_model( + hidden_states=vision_data, + grid_thw=vision_grid_thw, + ) + + import transformers + from packaging import version + + if version.parse(transformers.__version__) >= version.parse("5.0.0"): + vision_embeds = vision_outputs.pooler_output + deepstack_feature_lists = vision_outputs.deepstack_features + else: + vision_embeds, deepstack_feature_lists = vision_outputs + + combined_embeddings = self.language_model.embedding( + input_ids=input_ids, + position_ids=None, # NOTE: disable + ).clone() # [text_seq_len, b, h_language] + + if vision_embeds is not None: + if video_start_index == 0: + image_embeds = None + video_embeds = vision_embeds + elif video_start_index == vision_embeds.shape[0]: + image_embeds = vision_embeds + video_embeds = None + elif 0 < video_start_index < vision_embeds.shape[0]: + image_embeds = vision_embeds[:video_start_index] + video_embeds = vision_embeds[video_start_index:] + else: + raise ValueError( + f"Expect video token start index in range [0, {vision_embeds.shape[0]}], but got " + f"{video_start_index}" + ) + + if image_embeds is not None: + combined_embeddings = combined_embeddings.transpose(0, 1).contiguous() + combined_embeddings[image_mask] = image_embeds + combined_embeddings = combined_embeddings.transpose(0, 1).contiguous() + + if video_embeds is not None: + combined_embeddings = combined_embeddings.transpose(0, 1).contiguous() + combined_embeddings[video_mask] = video_embeds + combined_embeddings = combined_embeddings.transpose(0, 1).contiguous() + + # Create visual_pos_masks for deepstack processing + if image_embeds is not None and video_embeds is not None: + visual_pos_masks = image_mask | video_mask + elif image_embeds is not None: + visual_pos_masks = image_mask + elif video_embeds is not None: + visual_pos_masks = video_mask + else: + visual_pos_masks = None + else: + visual_pos_masks = None + + # ========================= + # Audio + # ========================= + if input_features is not None: + audio_mask = (input_ids == self.audio_token_id).contiguous() + if feature_attention_mask is not None: + input_features = input_features.permute(0, 2, 1)[feature_attention_mask.bool()].permute(1, 0) + + feature_lens = ( + audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1) + ) + + # dtype from fp32 to bf16 + audio_outputs = self.audio_model( + input_features.to(next(self.audio_model.parameters()).dtype), + feature_lens=feature_lens, + ) + audio_embeds = audio_outputs.last_hidden_state # [num_audio_tokens, hidden] + combined_embeddings = combined_embeddings.transpose(0, 1).contiguous() + + combined_embeddings[audio_mask] = audio_embeds + combined_embeddings = combined_embeddings.transpose(0, 1).contiguous() + + if self.config.sequence_parallel: + combined_embeddings = tensor_parallel.scatter_to_sequence_parallel_region(combined_embeddings) + combined_embeddings = combined_embeddings.contiguous() + else: + combined_embeddings = None + visual_pos_masks = None + + cu_seqlens_padded = None + if packed_seq_params is not None: + if packed_seq_params.cu_seqlens_q_padded is not None: + cu_seqlens_padded = packed_seq_params.cu_seqlens_q_padded + else: + cu_seqlens_padded = packed_seq_params.cu_seqlens_q + + hf_attention_mask = None + if position_ids is None: + input_ids_for_rope_index = input_ids + if cu_seqlens_padded is not None: + + def thd_to_bshd(packed_values: torch.Tensor, cu_seqlens: torch.Tensor): + seqlens = cu_seqlens[1:] - cu_seqlens[:-1] + max_seq_len = seqlens.max() + bs = len(cu_seqlens) - 1 + results = packed_values.new_zeros(size=(bs, max_seq_len, *packed_values.shape[2:])) + for i, seqlen in enumerate(seqlens): + results[i, :seqlen] = packed_values[0, cu_seqlens[i] : cu_seqlens[i] + seqlen] + return results + + def bshd_to_thd(unpacked_values: torch.Tensor, cu_seqlens: torch.Tensor): + seqlens = cu_seqlens[1:] - cu_seqlens[:-1] + total_len = cu_seqlens[-1] + results = unpacked_values.new_zeros(size=(1, total_len, *unpacked_values.shape[2:])) + for i, seqlen in enumerate(seqlens): + results[0, cu_seqlens[i] : cu_seqlens[i] + seqlen] = unpacked_values[i, :seqlen] + return results + + input_ids_for_rope_index = thd_to_bshd(input_ids, cu_seqlens_padded) + + # ========================= + # RoPE index (audio-aware) + # ========================= + position_ids, _ = get_rope_index( + spatial_merge_size=self.config.spatial_merge_size, + image_token_id=self.image_token_id, + video_token_id=self.video_token_id, + audio_token_id=self.audio_token_id, + vision_start_token_id=self.vision_start_token_id, + audio_start_token_id=self.audio_start_token_id, + input_ids=input_ids_for_rope_index, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + audio_seqlens=audio_feature_lengths, + attention_mask=hf_attention_mask, + use_audio_in_video=self.use_audio_in_video, + second_per_grids=video_second_per_grid, + position_id_per_seconds=self.position_id_per_seconds, + ) + if cu_seqlens_padded is not None: + position_ids = bshd_to_thd(position_ids.permute(1, 2, 0), cu_seqlens_padded).permute(2, 0, 1) + + deepstack_visual_embeds = deepstack_feature_lists + + # Split visual_pos_masks and deepstack_visual_embeds for sequence parallel / CP + if self.config.sequence_parallel and visual_pos_masks is not None and deepstack_visual_embeds is not None: + if self.pg_collection is not None: + tp_size = self.pg_collection.tp.size() + tp_rank = self.pg_collection.tp.rank() + else: + tp_size = mpu.get_tensor_model_parallel_world_size() + tp_rank = mpu.get_tensor_model_parallel_rank() + visual_pos_masks, deepstack_visual_embeds = split_deepstack_embs( + visual_pos_masks, + deepstack_visual_embeds, + tp_size=tp_size, + tp_rank=tp_rank, + cp_size=1, + cp_rank=0, + sequence_parallel=True, + ) + + output = self.language_model( + input_ids=None, + position_ids=position_ids, # None in encoder + attention_mask=attention_mask, # None in encoder + decoder_input=combined_embeddings, # only not None in the first decoder PP stage + labels=labels, # only not None in the last decoder PP stage + loss_mask=loss_mask, + inference_params=inference_params, # currently always None + packed_seq_params=packed_seq_params, # currently always None + visual_pos_masks=visual_pos_masks, + deepstack_visual_embeds=deepstack_visual_embeds, + **(extra_block_kwargs or {}), + ) + + return output diff --git a/relax/models/qwen_omni/modeling_qwen3_omni/rope.py b/relax/models/qwen_omni/modeling_qwen3_omni/rope.py new file mode 100644 index 000000000..b0719f687 --- /dev/null +++ b/relax/models/qwen_omni/modeling_qwen3_omni/rope.py @@ -0,0 +1,43 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + + +from typing import List + +import torch +from torch import Tensor +from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import Qwen3OmniMoeThinkerTextRotaryEmbedding + + +class Qwen3OmniMoeThinkerTextRotaryEmbedding(Qwen3OmniMoeThinkerTextRotaryEmbedding): + """Qwen3-Omni MoE text rotary position embedding.""" + + def forward( + self, position_ids: torch.Tensor, mrope_section: List[int], packed_seq_params=None, **kwargs + ) -> Tensor: + """Forward pass of multimodal RoPE embedding. + + Args: + position_ids (torch.Tensor): A postion_id tensor with shape [3, batchsize, seqlens] + mrope_section (list[int]): Multimodal rope section is for channel dimension of temporal, + height and width in rope calculation. + + Returns: + Tensor: Raw frequency embeddings for Megatron Core (shape: [seq_length, bs, 1, dim]). + Megatron Core will compute cos/sin internally and apply attention_scaling. + """ + # Use fp32 for position indices to avoid precision loss when inv_freq is bf16. + seq = position_ids.to(device=self.inv_freq.device, dtype=torch.float32) + + # if self.seq_len_interpolation_factor is not None: + # seq *= 1 / self.seq_len_interpolation_factor + + # shape (3, bs, dim, 1) + inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, seq.shape[1], -1, 1) + # shape (3, bs, 1, seq_length) + seq_expanded = seq[:, :, None, :].float() + # shape (3, bs, seq_length, dim) + freqs = (inv_freq_expanded @ seq_expanded).transpose(2, 3) + freqs = self.apply_interleaved_mrope(freqs, mrope_section) + emb = torch.cat((freqs, freqs), dim=-1) + emb = emb[..., None, :].transpose(0, 1).contiguous() + return emb diff --git a/relax/models/qwen_omni/modeling_qwen3_omni/text_model.py b/relax/models/qwen_omni/modeling_qwen3_omni/text_model.py new file mode 100644 index 000000000..72c020ca5 --- /dev/null +++ b/relax/models/qwen_omni/modeling_qwen3_omni/text_model.py @@ -0,0 +1,76 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + + +from typing import Literal, Optional + +from megatron.bridge.models.qwen_vl.modelling_qwen3_vl.text_model import Qwen3VLGPTModel +from megatron.bridge.models.transformer_config import TransformerConfig +from megatron.core.transformer.spec_utils import ModuleSpec + +from relax.models.qwen_omni.modeling_qwen3_omni.rope import Qwen3OmniMoeThinkerTextRotaryEmbedding +from relax.models.qwen_omni.modeling_qwen3_omni.transformer_block import Qwen3OmniTransformerBlock + + +class Qwen3OmniGPTModel(Qwen3VLGPTModel): + """Qwen3-Omni GPT model with vision-language capabilities.""" + + def __init__( + self, + config: TransformerConfig, + transformer_layer_spec: ModuleSpec, + vocab_size: int, + max_sequence_length: int, + pre_process: bool = True, + post_process: bool = True, + fp16_lm_cross_entropy: bool = False, + parallel_output: bool = True, + share_embeddings_and_output_weights: bool = False, + position_embedding_type: Literal["learned_absolute", "rope", "mrope", "none"] = "learned_absolute", + rotary_percent: float = 1.0, + rotary_base: int = 10000, + rope_scaling: bool = False, + rope_scaling_factor: float = 8.0, + scatter_embedding_sequence_parallel: bool = True, + seq_len_interpolation_factor: Optional[float] = None, + mtp_block_spec: Optional[ModuleSpec] = None, + vp_stage: Optional[int] = None, + pg_collection=None, + ) -> None: + super().__init__( + config=config, + transformer_layer_spec=transformer_layer_spec, + vocab_size=vocab_size, + max_sequence_length=max_sequence_length, + pre_process=pre_process, + post_process=post_process, + fp16_lm_cross_entropy=fp16_lm_cross_entropy, + parallel_output=parallel_output, + share_embeddings_and_output_weights=share_embeddings_and_output_weights, + position_embedding_type=position_embedding_type, + rotary_percent=rotary_percent, + rotary_base=rotary_base, + rope_scaling=rope_scaling, + rope_scaling_factor=rope_scaling_factor, + scatter_embedding_sequence_parallel=scatter_embedding_sequence_parallel, + seq_len_interpolation_factor=seq_len_interpolation_factor, + mtp_block_spec=mtp_block_spec, + vp_stage=vp_stage, + pg_collection=pg_collection, + ) + + self.rotary_pos_emb = Qwen3OmniMoeThinkerTextRotaryEmbedding(config.hf_text_config) + + self.mrope_section = self.config.mrope_section + assert self.mrope_section is not None, ( + "mrope require mrope_section setting, but we got None from TransformerConfig" + ) + + # rebuild the transformer block + self.decoder = Qwen3OmniTransformerBlock( + config=self.config, + spec=transformer_layer_spec, + pre_process=self.pre_process, + post_process=self.post_process, + vp_stage=vp_stage, + pg_collection=pg_collection, + ) diff --git a/relax/models/qwen_omni/modeling_qwen3_omni/transformer_block.py b/relax/models/qwen_omni/modeling_qwen3_omni/transformer_block.py new file mode 100644 index 000000000..6a1004b1c --- /dev/null +++ b/relax/models/qwen_omni/modeling_qwen3_omni/transformer_block.py @@ -0,0 +1,27 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + + +from megatron.bridge.models.qwen_vl.modelling_qwen3_vl.transformer_block import Qwen3VLTransformerBlock + + +try: + import transformer_engine.pytorch as te # noqa: F401 # pylint: disable=unused-import + + HAVE_TE = True +except ImportError: + HAVE_TE = False + +te_checkpoint = None +if HAVE_TE: + pass + + +class Qwen3OmniTransformerBlock(Qwen3VLTransformerBlock): + """Qwen3 Omni Transformer Block extending Qwen3VL functionality. + + This block extends the Qwen3VL transformer block with Omni-specific + features for handling multimodal inputs including audio, images, and + videos. + """ + + pass diff --git a/relax/models/qwen_omni/modeling_qwen3_omni/transformer_config.py b/relax/models/qwen_omni/modeling_qwen3_omni/transformer_config.py new file mode 100644 index 000000000..d0cbf75df --- /dev/null +++ b/relax/models/qwen_omni/modeling_qwen3_omni/transformer_config.py @@ -0,0 +1,40 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + + +from dataclasses import dataclass, field +from typing import List, Optional + +from megatron.core.transformer.transformer_config import TransformerConfig +from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import Qwen3OmniMoeTextConfig + + +@dataclass +class Qwen3OmniTransformerConfig(TransformerConfig): + """Configuration for Qwen3-VL transformer with vision and language + components.""" + + vocab_size: int = 64000 + language_max_sequence_length: int = 4096 + + patch_size: int = 14 + temporal_patch_size: int = 2 + in_channels: int = 3 + spatial_merge_size: int = 2 + num_position_embeddings: int = 2304 + out_hidden_size: int = 2304 + + apply_rotary_pos_emb_in_fp32: bool = False + deepstack_visual_indexes: List[int] = field(default_factory=lambda: [8, 16, 24]) + fp16_lm_cross_entropy: bool = False + share_embeddings_and_output_weights: bool = False + rotary_percent: float = 1.0 + rotary_base: float = 10000 + + # Multimodal rope section for [temporal, height, width] dimensions + mrope_section: List[int] = field(default_factory=lambda: [24, 20, 20]) + apply_rope_fusion: bool = False + + image_token_id: int = 151655 + video_token_id: int = 151656 + vision_start_token_id: int = 151652 + hf_text_config: Optional[Qwen3OmniMoeTextConfig] = None diff --git a/relax/models/qwen_omni/modeling_qwen3_omni/utils.py b/relax/models/qwen_omni/modeling_qwen3_omni/utils.py new file mode 100644 index 000000000..9445fb9ba --- /dev/null +++ b/relax/models/qwen_omni/modeling_qwen3_omni/utils.py @@ -0,0 +1,356 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +from typing import Optional + +import torch +from megatron.core.packed_seq_params import PackedSeqParams + + +def _get_feat_extract_output_lengths(input_lengths): + """Computes the output length of the convolutional layers and the output + length of the audio encoder.""" + + input_lengths_leave = input_lengths % 100 + feat_lengths = (input_lengths_leave - 1) // 2 + 1 + output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13 + return output_lengths + + +def get_llm_pos_ids_for_vision( + self, + start_idx: int, + vision_idx: int, + spatial_merge_size: int, + t_index: list[torch.Tensor], + grid_hs: list[torch.Tensor], + grid_ws: list[torch.Tensor], +): + """Generate LLM position IDs for vision tokens. + + Computes position embeddings for vision tokens (images/videos) by creating + 3D position indices (temporal, height, width) based on spatial merge size. + + Args: + self: Instance reference. + start_idx: Starting position index offset. + vision_idx: Index of the vision sample. + spatial_merge_size: Size of spatial merge for grid downsampling. + t_index: List of temporal indices. + grid_hs: List of grid heights. + grid_ws: List of grid widths. + + Returns: + torch.Tensor: Position IDs of shape [3, num_tokens] with temporal, height, width indices. + """ + llm_pos_ids_list = [] + llm_grid_h = grid_hs[vision_idx] // spatial_merge_size + llm_grid_w = grid_ws[vision_idx] // spatial_merge_size + h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten().float() + w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten().float() + t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().float() + _llm_pos_ids = torch.stack([t_index, h_index, w_index]) + llm_pos_ids_list.append(_llm_pos_ids + start_idx) + llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1) + return llm_pos_ids + + +def get_rope_index( + spatial_merge_size: int, + image_token_id: int, + video_token_id: int, + audio_token_id: int, + vision_start_token_id: int, + audio_start_token_id: int, + input_ids: Optional[torch.LongTensor] = None, + image_grid_thw: Optional[torch.LongTensor] = None, + video_grid_thw: Optional[torch.LongTensor] = None, + audio_seqlens: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + use_audio_in_video: bool = False, + second_per_grids: Optional[torch.Tensor] = None, + position_id_per_seconds: int = 1, + packed_seq_params: Optional[PackedSeqParams] = None, +) -> tuple[torch.Tensor, torch.Tensor]: + """Generate RoPE position indices for multimodal inputs. + + Computes rotary position embeddings (RoPE) indices for a sequence containing + mixed modalities (text, images, videos, audio). Handles temporal, spatial, + and audio-specific position encoding. + + Args: + spatial_merge_size: Size of spatial merge for grid downsampling. + image_token_id: Token ID for image markers. + video_token_id: Token ID for video markers. + audio_token_id: Token ID for audio markers. + vision_start_token_id: Token ID marking start of vision content. + audio_start_token_id: Token ID marking start of audio content. + input_ids: Input token IDs of shape [batch_size, seq_len]. + image_grid_thw: Image grid dimensions [num_images, 3] with (T, H, W). + video_grid_thw: Video grid dimensions [num_videos, 3] with (T, H, W). + audio_seqlens: Audio sequence lengths [num_audios]. + attention_mask: Attention mask indicating valid tokens. + use_audio_in_video: Whether audio is embedded within video tokens. + second_per_grids: Seconds per video grid frame. + position_id_per_seconds: Position ID increment per second. + packed_seq_params: Packed sequence parameters for variable-length sequences. + + Returns: + tuple: (position_ids, mrope_position_deltas) where: + - position_ids: Shape [3, batch_size, seq_len] with temporal, height, width indices. + - mrope_position_deltas: Shape [batch_size, 1] with position delta adjustments. + """ + # VL timestamp split logic (unchanged) + if video_grid_thw is not None: + video_grid_thw = torch.repeat_interleave(video_grid_thw, video_grid_thw[:, 0], dim=0) + video_grid_thw[:, 0] = 1 + + if packed_seq_params is not None and attention_mask is None and input_ids is not None: + # Build an attention mask from packed sequence metadata when one is not provided. + # cu_seqlens_q entries are cumulative lengths; their diffs give per-sample lengths. + cu_seqlens = packed_seq_params.cu_seqlens_q + if cu_seqlens is not None and cu_seqlens.numel() >= 2: + seq_lens = cu_seqlens[1:] - cu_seqlens[:-1] + attention_mask = torch.zeros_like(input_ids, dtype=input_ids.dtype) + max_len = attention_mask.shape[1] + for i, seq_len in enumerate(seq_lens.tolist()): + valid = min(int(seq_len), max_len) + attention_mask[i, :valid] = 1 + else: + # Fallback to a dense mask if packed metadata is missing. + attention_mask = torch.ones_like(input_ids) + + mrope_position_deltas = [] + if input_ids is not None and ( + image_grid_thw is not None or video_grid_thw is not None or audio_seqlens is not None + ): + total_input_ids = input_ids + if attention_mask is None: + attention_mask = torch.ones_like(total_input_ids) + position_ids = torch.ones( + 3, + input_ids.shape[0], + input_ids.shape[1], + dtype=torch.float, + device=input_ids.device, + ) + image_index, video_index, audio_index = 0, 0, 0 + attention_mask = attention_mask.to(total_input_ids.device) + for i, input_ids in enumerate(total_input_ids): + input_ids = input_ids[attention_mask[i] == 1] + + vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1) + vision_tokens = input_ids[vision_start_indices + 1] + audio_nums = torch.sum(input_ids == audio_start_token_id) + image_nums = (vision_tokens == image_token_id).sum() + video_nums = ( + (vision_tokens == audio_start_token_id).sum() + if use_audio_in_video + else (vision_tokens == video_token_id).sum() + ) + input_tokens = input_ids.tolist() + llm_pos_ids_list: list = [] + st = 0 + remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums + multimodal_nums = image_nums + audio_nums if use_audio_in_video else image_nums + video_nums + audio_nums + + for _ in range(multimodal_nums): + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + if (image_token_id in input_tokens or video_token_id in input_tokens) and ( + remain_videos > 0 or remain_images > 0 + ): + ed_vision_start = input_tokens.index(vision_start_token_id, st) + else: + ed_vision_start = len(input_tokens) + 1 + if audio_token_id in input_tokens and remain_audios > 0: + ed_audio_start = input_tokens.index(audio_start_token_id, st) + else: + ed_audio_start = len(input_tokens) + 1 + min_ed = min(ed_vision_start, ed_audio_start) + + # ---------- text ---------- + text_len = min_ed - st + if text_len > 0: + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + st_idx += text_len + + # ---------- BOS ---------- + # Audio in Video + if min_ed == ed_vision_start and ed_vision_start + 1 == ed_audio_start: + bos_len, eos_len = 2, 2 + else: + bos_len, eos_len = 1, 1 + + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + st_idx += bos_len + + # Audio Only + if min_ed == ed_audio_start: + audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_index]) + llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx + llm_pos_ids_list.append(llm_pos_ids) + + st += text_len + bos_len + audio_len + eos_len + audio_index += 1 + remain_audios -= 1 + + # Image Only + elif min_ed == ed_vision_start and input_ids[ed_vision_start + 1] == image_token_id: + t, h, w = ( + image_grid_thw[image_index][0], + image_grid_thw[image_index][1], + image_grid_thw[image_index][2], + ) + + t_index = (torch.arange(t) * 1 * position_id_per_seconds).float() + + llm_pos_ids_list_temp = [] + llm_grid_h = h // spatial_merge_size + llm_grid_w = w // spatial_merge_size + h_index = ( + torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten().float() + ) + w_index = ( + torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten().float() + ) + t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().float() + _llm_pos_ids = torch.stack([t_index, h_index, w_index]) + llm_pos_ids_list_temp.append(_llm_pos_ids + st_idx) + llm_pos_ids = torch.cat(llm_pos_ids_list_temp, dim=1) + + llm_pos_ids_list.append(llm_pos_ids) + + image_len = image_grid_thw[image_index].prod() // (spatial_merge_size**2) + st += int(text_len + bos_len + image_len + eos_len) + image_index += 1 + remain_images -= 1 + + # Video Only + elif min_ed == ed_vision_start and input_ids[ed_vision_start + 1] == video_token_id: + t, h, w = ( + video_grid_thw[video_index][0], + video_grid_thw[video_index][1], + video_grid_thw[video_index][2], + ) + t_index = ( + torch.arange(t) * second_per_grids[video_index].cpu().float() * position_id_per_seconds + ).float() + + llm_pos_ids_list_temp = [] + llm_grid_h = h // spatial_merge_size + llm_grid_w = w // spatial_merge_size + h_index = ( + torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten().float() + ) + w_index = ( + torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten().float() + ) + t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().float() + _llm_pos_ids = torch.stack([t_index, h_index, w_index]) + llm_pos_ids_list_temp.append(_llm_pos_ids + st_idx) + llm_pos_ids = torch.cat(llm_pos_ids_list_temp, dim=1) + + llm_pos_ids_list.append(llm_pos_ids) + + video_len = video_grid_thw[video_index].prod() // (spatial_merge_size**2) + st += int(text_len + bos_len + video_len + eos_len) + video_index += 1 + remain_videos -= 1 + + # Audio in Video + elif min_ed == ed_vision_start and ed_vision_start + 1 == ed_audio_start: + audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_index]) + audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx + + t, h, w = ( + video_grid_thw[video_index][0], + video_grid_thw[video_index][1], + video_grid_thw[video_index][2], + ) + + t_index = ( + torch.arange(t) * second_per_grids[video_index].cpu().float() * position_id_per_seconds + ).float() + + llm_pos_ids_list_temp = [] + llm_grid_h = h // spatial_merge_size + llm_grid_w = w // spatial_merge_size + h_index = ( + torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten().float() + ) + w_index = ( + torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten().float() + ) + t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().float() + _llm_pos_ids = torch.stack([t_index, h_index, w_index]) + llm_pos_ids_list_temp.append(_llm_pos_ids + st_idx) + llm_pos_ids = torch.cat(llm_pos_ids_list_temp, dim=1) + + video_llm_pos_ids = llm_pos_ids + + video_data_index, audio_data_index = 0, 0 + while ( + video_data_index < video_llm_pos_ids.shape[-1] + and audio_data_index < audio_llm_pos_ids.shape[-1] + ): + if video_llm_pos_ids[0][video_data_index] <= audio_llm_pos_ids[0][audio_data_index]: + llm_pos_ids_list.append(video_llm_pos_ids[:, video_data_index : video_data_index + 1]) + video_data_index += 1 + else: + llm_pos_ids_list.append(audio_llm_pos_ids[:, audio_data_index : audio_data_index + 1]) + audio_data_index += 1 + if video_data_index < video_llm_pos_ids.shape[-1]: + llm_pos_ids_list.append(video_llm_pos_ids[:, video_data_index : video_llm_pos_ids.shape[-1]]) + if audio_data_index < audio_llm_pos_ids.shape[-1]: + llm_pos_ids_list.append(audio_llm_pos_ids[:, audio_data_index : audio_llm_pos_ids.shape[-1]]) + video_len = video_grid_thw[video_index].prod() // (spatial_merge_size**2) + + st += int(text_len + bos_len + audio_len + video_len + eos_len) + audio_index += 1 + video_index += 1 + remain_videos -= 1 + remain_audios -= 1 + else: + raise (RuntimeError("unexpected error")) + + # ---------- EOS ---------- + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + # tail text + if st < len(input_tokens): + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + text_len = len(input_tokens) - st + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + llm_positions = torch.cat([item.float() for item in llm_pos_ids_list], dim=1).reshape(3, -1) + position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device) + mrope_position_deltas.append(llm_positions.max() + 1 - len(input_ids)) + mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1) + return position_ids, mrope_position_deltas + else: + # fallback (pure text) + # position_ids = attention_mask.float().cumsum(-1) - 1 + # position_ids.masked_fill_(attention_mask == 0, 1) + # position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device) + # max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0] + # mrope_position_deltas = max_position_ids + 1 - torch.sum(attention_mask, dim=-1, keepdim=True) + + if attention_mask is not None: + position_ids = attention_mask.float().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device) + max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0] + mrope_position_deltas = max_position_ids + 1 - torch.sum(attention_mask, dim=-1, keepdim=True) + else: + position_ids = ( + torch.arange(input_ids.shape[1], device=input_ids.device) + .view(1, 1, -1) + .expand(3, input_ids.shape[0], -1) + ) + mrope_position_deltas = torch.zeros( + [input_ids.shape[0], 1], + device=input_ids.device, + dtype=input_ids.dtype, + ) + + return position_ids, mrope_position_deltas diff --git a/relax/models/qwen_omni/qwen3_omni_bridge.py b/relax/models/qwen_omni/qwen3_omni_bridge.py new file mode 100644 index 000000000..c3e330c2d --- /dev/null +++ b/relax/models/qwen_omni/qwen3_omni_bridge.py @@ -0,0 +1,245 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + + +import torch +import torch.nn.functional as F +from megatron.bridge.models.conversion.mapping_registry import MegatronMappingRegistry +from megatron.bridge.models.conversion.model_bridge import MegatronModelBridge +from megatron.bridge.models.conversion.param_mapping import AutoMapping, GatedMLPMapping, QKVMapping, ReplicatedMapping +from megatron.bridge.models.hf_pretrained.vlm import PreTrainedVLM +from transformers import Qwen3OmniMoeForConditionalGeneration + +from relax.models.qwen_omni.modeling_qwen3_omni.model import Qwen3OmniMoeModel +from relax.models.qwen_omni.qwen3_omni_provider import Qwen3OmniModelProvider + + +@MegatronModelBridge.register_bridge(source=Qwen3OmniMoeForConditionalGeneration, target=Qwen3OmniMoeModel) +class Qwen3OmniMoEBridge(MegatronModelBridge): + """Megatron Bridge for Qwen3-VL MoE (Mixture of Experts) Conditional + Generation. + + This bridge handles the conversion between HuggingFace Qwen3VLMoEForConditionalGeneration + and Megatron-Core Qwen3VL MoE model formats, including weight mappings and + configuration translation for vision-language MoE models. + + The weight mappings handle: + - Vision model weights (same as dense model) + - Language model MoE layers with expert routing + - Shared embeddings and output layers + - QK layernorm specific to Qwen3 architecture + + This bridge works with any Qwen3VL MoE model size and automatically extracts + the MoE configuration from the HuggingFace model. + + Example: + >>> from megatron.bridge import AutoBridge + >>> bridge = AutoBridge.from_hf_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct") + >>> provider = bridge.to_megatron_provider() + """ + + # copied from https://github.com/fzyzcjy/Megatron-Bridge/blob/6b1b80cdd3f5387e378545399287bf4a21a56fe0/src/megatron/bridge/models/gpt_oss/gpt_oss_bridge.py#L54 + def __init__(self): + super().__init__() + self.hf_weights_cache = {} + + def provider_bridge(self, hf_pretrained: PreTrainedVLM) -> Qwen3OmniModelProvider: + """Create a Qwen3OmniModelProvider from a HuggingFace pretrained MoE + model. + + Args: + hf_pretrained: HuggingFace pretrained VLM MoE model + + Returns: + Qwen3OmniModelProvider configured with the HF MoE model's parameters + """ + # to check + # hf_pretrained.config + hf_config = hf_pretrained.config.thinker_config + text_config = hf_config.text_config + + # Get the model dtype from text config + model_dtype = self.dtype_from_hf(hf_config, default=torch.float32) + + # Set vision config dtype to match the language model dtype + # This ensures vision model parameters are initialized in the same dtype + audio_config = hf_config.audio_config + audio_config.torch_dtype = model_dtype + vision_config = hf_config.vision_config + vision_config.torch_dtype = model_dtype + + head_dim = getattr(text_config, "head_dim", text_config.hidden_size // text_config.num_attention_heads) + provider = Qwen3OmniModelProvider( + num_layers=text_config.num_hidden_layers, + hidden_size=text_config.hidden_size, + ffn_hidden_size=text_config.intermediate_size, # Dense FFN size (for non-MoE layers if any) + moe_ffn_hidden_size=text_config.moe_intermediate_size, # Expert FFN size + num_attention_heads=text_config.num_attention_heads, + num_query_groups=text_config.num_key_value_heads, # GQA configuration + head_dim=head_dim, + kv_channels=head_dim, # Must explicitly set kv_channels for MCore TransformerConfig + init_method_std=text_config.initializer_range, + layernorm_epsilon=text_config.rms_norm_eps, + gated_linear_unit=True, # Qwen3 MoE uses gated linear units + make_vocab_size_divisible_by=self.make_vocab_size_divisible_by(text_config.vocab_size), + rotary_base=getattr(text_config, "rope_theta", 1000000.0), # Default Qwen3 rope theta + share_embeddings_and_output_weights=getattr(text_config, "tie_word_embeddings", False), + vocab_size=text_config.vocab_size, + seq_length=text_config.max_position_embeddings, + fp16=(model_dtype == torch.float16), + bf16=(model_dtype == torch.bfloat16), + params_dtype=model_dtype, + # Qwen3 specific parameters — match Qwen3VLMoEBridge settings + normalization="RMSNorm", # Qwen3 uses RMSNorm (no bias in layernorms) + activation_func=F.silu, # Qwen3 uses SwiGLU (silu + gated_linear_unit) + add_qkv_bias=text_config.attention_bias, # Qwen3 can have bias in QKV + add_bias_linear=False, # Qwen3 has no bias in linear layers (o_proj, MLP, router) + hidden_dropout=0.0, # Qwen3 uses no hidden dropout + qk_layernorm=True, # Qwen3 uses QK layernorm + # MoE specific parameters + num_moe_experts=text_config.num_experts, + moe_router_topk=text_config.num_experts_per_tok, + moe_grouped_gemm=True, + moe_router_load_balancing_type="aux_loss", + moe_aux_loss_coeff=1e-3, + decoder_sparse_step=getattr(text_config, "decoder_sparse_step", 1), # Default to every layer being MoE + mlp_only_layers=getattr(text_config, "mlp_only_layers", []), # Default to all layers using MoE + # Vision configuration + audio_config=audio_config, + vision_config=vision_config, + # Store the original HF text config for RoPE initialization + hf_text_config=text_config, + # Vision-Language token IDs + bos_token_id=getattr(text_config, "bos_token_id", 151643), + eos_token_id=getattr(text_config, "eos_token_id", 151645), + vision_start_token_id=getattr(hf_config, "vision_start_token_id", 151652), + vision_end_token_id=getattr(hf_config, "vision_end_token_id", 151653), + image_token_id=getattr(hf_config, "image_token_id", 151655), + video_token_id=getattr(hf_config, "video_token_id", 151656), + # audio + audio_token_id=hf_config.audio_token_id, + audio_start_token_id=hf_config.audio_start_token_id, + audio_end_token_id=hf_config.audio_end_token_id, + # MRoPE configuration for multimodal position embeddings + mrope_section=getattr(text_config, "rope_scaling", {}).get("mrope_section", [24, 20, 20]), + position_id_per_seconds=hf_config.position_id_per_seconds, + spatial_merge_size=vision_config.spatial_merge_size, + ) + + return provider + + def mapping_registry(self) -> MegatronMappingRegistry: + """Return MegatronMappingRegistry containing parameter mappings for MoE + models. + + The MoE mappings include: + 1. Standard language model mappings (embeddings, layer norms, output) + 2. Vision model mappings (same as dense model) + 3. QKV mappings with QK layernorm + 4. MoE-specific mappings: + - Router weights for expert selection + - Expert MLPs (multiple experts per layer) + - Pre-MLP layernorm + 5. Deepstack visual merger mappings + + Returns: + MegatronMappingRegistry with all MoE parameter mappings + """ + # Language model direct mappings (same as dense model) + # NOTE: Megatron side (left) uses param names from Qwen3OmniMoeModel (no "thinker." prefix), + # HF side (right) uses param names from Qwen3OmniMoeForConditionalGeneration (with "thinker." prefix). + param_mappings = { + # Embeddings and output layers + "language_model.embedding.word_embeddings.weight": "thinker.model.embed_tokens.weight", + "language_model.output_layer.weight": "thinker.lm_head.weight", + "language_model.decoder.final_layernorm.weight": "thinker.model.norm.weight", + # Layer normalization for attention (TE format - fused into linear) + "language_model.decoder.layers.*.self_attention.linear_qkv.layer_norm_weight": "thinker.model.layers.*.input_layernorm.weight", + # MoE-specific: pre-MLP layernorm + "language_model.decoder.layers.*.pre_mlp_layernorm.weight": "thinker.model.layers.*.post_attention_layernorm.weight", + # Dense MLP layer norm (for non-MoE layers, i.e. mlp_only_layers) + "language_model.decoder.layers.*.mlp.linear_fc1.layer_norm_weight": "thinker.model.layers.*.post_attention_layernorm.weight", + # Attention output projection + "language_model.decoder.layers.*.self_attention.linear_proj.weight": "thinker.model.layers.*.self_attn.o_proj.weight", + # QK layernorm weights (Qwen3 specific) + "language_model.decoder.layers.*.self_attention.q_layernorm.weight": "thinker.model.layers.*.self_attn.q_norm.weight", + "language_model.decoder.layers.*.self_attention.k_layernorm.weight": "thinker.model.layers.*.self_attn.k_norm.weight", + # MoE router weights + "language_model.decoder.layers.*.mlp.router.weight": "thinker.model.layers.*.mlp.gate.weight", + # MoE router expert bias + "language_model.decoder.layers.*.mlp.router.expert_bias": "thinker.model.layers.*.mlp.gate.e_score_correction_bias", + # Dense MLP down projection (for non-MoE layers, i.e. mlp_only_layers) + "language_model.decoder.layers.*.mlp.linear_fc2.weight": "thinker.model.layers.*.mlp.down_proj.weight", + # Shared expert down projection + "language_model.decoder.layers.*.mlp.shared_experts.linear_fc2.weight": "thinker.model.layers.*.mlp.shared_expert.down_proj.weight", + # Shared expert gate weight + "language_model.decoder.layers.*.mlp.shared_experts.gate_weight": "thinker.model.layers.*.mlp.shared_expert_gate.weight", + } + + mapping_list = [] + + # Convert simple 1:1 mappings to AutoMapping objects + for megatron_param, hf_param in param_mappings.items(): + mapping_list.append(AutoMapping(megatron_param=megatron_param, hf_param=hf_param)) + + # Add special mappings that require parameter transformation + mapping_list.extend( + [ + # Audio and vision model weights are replicated directly (HF encoders) + ReplicatedMapping( + megatron_param="audio_model.**", + hf_param="thinker.audio_tower.**", + ), + ReplicatedMapping( + megatron_param="vision_model.**", + hf_param="thinker.visual.**", + ), + # QKV mapping: Combine separate Q, K, V matrices + QKVMapping( + megatron_param="language_model.decoder.layers.*.self_attention.linear_qkv.weight", + q="thinker.model.layers.*.self_attn.q_proj.weight", + k="thinker.model.layers.*.self_attn.k_proj.weight", + v="thinker.model.layers.*.self_attn.v_proj.weight", + ), + # QKV bias mapping (if attention_bias is True) + QKVMapping( + megatron_param="language_model.decoder.layers.*.self_attention.linear_qkv.bias", + q="thinker.model.layers.*.self_attn.q_proj.bias", + k="thinker.model.layers.*.self_attn.k_proj.bias", + v="thinker.model.layers.*.self_attn.v_proj.bias", + ), + # Expert mappings for TEGroupedMLP + GatedMLPMapping( + megatron_param="language_model.decoder.layers.*.mlp.experts.linear_fc1.weight*", + gate="thinker.model.layers.*.mlp.experts.*.gate_proj.weight", + up="thinker.model.layers.*.mlp.experts.*.up_proj.weight", + ), + AutoMapping( + megatron_param="language_model.decoder.layers.*.mlp.experts.linear_fc2.weight*", + hf_param="thinker.model.layers.*.mlp.experts.*.down_proj.weight", + ), + # Expert mappings for SequentialMLP (used by quantization) + GatedMLPMapping( + megatron_param="language_model.decoder.layers.*.mlp.experts.local_experts.*.linear_fc1.weight", + gate="thinker.model.layers.*.mlp.experts.*.gate_proj.weight", + up="thinker.model.layers.*.mlp.experts.*.up_proj.weight", + ), + AutoMapping( + megatron_param="language_model.decoder.layers.*.mlp.experts.local_experts.*.linear_fc2.weight", + hf_param="thinker.model.layers.*.mlp.experts.*.down_proj.weight", + ), + # Dense MLP gate+up (for non-MoE layers, i.e. mlp_only_layers) + GatedMLPMapping( + megatron_param="language_model.decoder.layers.*.mlp.linear_fc1.weight", + gate="thinker.model.layers.*.mlp.gate_proj.weight", + up="thinker.model.layers.*.mlp.up_proj.weight", + ), + # Shared expert gate+up + GatedMLPMapping( + megatron_param="language_model.decoder.layers.*.mlp.shared_experts.linear_fc1.weight", + gate="thinker.model.layers.*.mlp.shared_expert.gate_proj.weight", + up="thinker.model.layers.*.mlp.shared_expert.up_proj.weight", + ), + ] + ) + + return MegatronMappingRegistry(*mapping_list) diff --git a/relax/models/qwen_omni/qwen3_omni_provider.py b/relax/models/qwen_omni/qwen3_omni_provider.py new file mode 100644 index 000000000..cf7d333e7 --- /dev/null +++ b/relax/models/qwen_omni/qwen3_omni_provider.py @@ -0,0 +1,263 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Qwen3 VL MoE Model Provider configurations for Megatron-Core. + +This module provides configuration classes for Qwen3-VL MoE (Mixture of Experts) multimodal models, +compatible with HuggingFace's Qwen3-VL-MoE model configurations. +Reference: https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct +""" + +from dataclasses import dataclass, field +from typing import List, Optional + +from megatron.bridge.models.conversion.transformers_compat import rope_theta_from_hf +from megatron.bridge.models.qwen_vl.qwen3_vl_provider import Qwen3VLMoEModelProvider +from megatron.core.models.gpt import GPTModel as MCoreGPTModel +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec +from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import ( + Qwen3OmniMoeAudioEncoderConfig, + Qwen3OmniMoeTextConfig, + Qwen3OmniMoeVisionEncoderConfig, +) + +from relax.models.qwen_omni.modeling_qwen3_omni.model import Qwen3OmniMoeModel + + +@dataclass +class Qwen3OmniModelProvider(Qwen3VLMoEModelProvider): + """Base model provider for Qwen 3 VL MoE Models. Inherits language model + MoE configuration from Qwen3MoEModelProvider. + + Key MoE Parameters (inherited from Qwen3MoEModelProvider): + - num_moe_experts: Number of total experts (default 128) + - moe_router_topk: Number of experts selected per token (default 8) + - moe_router_load_balancing_type: Load balancing strategy (default "aux_loss") + - moe_aux_loss_coeff: Auxiliary loss coefficient (default 1e-3) + - moe_grouped_gemm: Use grouped GEMM for efficiency (default True) + + Note: num_query_groups in parent class corresponds to num_key_value_heads in HF config. + """ + + # Vision configuration using the transformers Qwen3OmniMoeVisionEncoderConfig + # Default configuration matches the standard Qwen3VL vision encoder + # thinker_config: Qwen3OmniMoeThinkerConfig = field(default_factory=lambda: Qwen3OmniMoeThinkerConfig()) + # talker_config: Qwen3OmniMoeTalkerConfig = field(default_factory=lambda: Qwen3OmniMoeTalkerConfig()) + # code2wav_config: Qwen3OmniMoeCode2WavConfig = field(default_factory=lambda: Qwen3OmniMoeCode2WavConfig()) + + audio_config: Qwen3OmniMoeAudioEncoderConfig = field(default_factory=lambda: Qwen3OmniMoeAudioEncoderConfig()) + vision_config: Qwen3OmniMoeVisionEncoderConfig = field(default_factory=lambda: Qwen3OmniMoeVisionEncoderConfig()) + hf_text_config: Optional[Qwen3OmniMoeTextConfig] = None + + pretrained_model_name: str = "Qwen/Qwen3-Omni-30B-A3B-Instruct" + + audio_token_id: int = 151675 + audio_start_token_id: int = 151669 + audio_end_token_id: int = 151670 + use_audio_in_video: bool = False + + # Vision-specific token IDs matching Qwen3VL MoE configuration + # Based on HuggingFace Qwen3-VL-MoE configs + # Token ID for image placeholder in text + image_token_id: int = 151655 + # Token ID for video placeholder in text + video_token_id: int = 151656 + # Token ID marking start of vision content + vision_start_token_id: int = 151652 + # Token ID marking end of vision content + vision_end_token_id: int = 151653 + # BOS token ID for Qwen3-VL models + bos_token_id: int = 151643 + # EOS token ID for Qwen3-VL models + eos_token_id: int = 151645 + + position_id_per_seconds: int = 0 + + head_dim: int = 128 + qk_layernorm: bool = True + attention_softmax_in_fp32: bool = True + attention_dropout: float = 0.0 + + # Override position embedding for multimodal rope + position_embedding_type: str = "mrope" + + # Multimodal rope section for [temporal, height, width] dimensions + # Based on HuggingFace Qwen3-VL config: mrope_section: [24, 20, 20] + mrope_section: List[int] = field(default_factory=lambda: [24, 20, 20]) + + # RoPE theta value specific to Qwen3-VL models + # From HuggingFace config: rope_theta: 5000000 + rotary_base: float = 5000000.0 + spatial_merge_size: int = 2 + temporal_patch_size: int = 2 + patch_size: int = 16 + + # Override to disable scattering embeddings for vision insertion + scatter_embedding_sequence_parallel: bool = False + + # Router configuration + moe_router_pre_softmax: bool = False # Qwen3 specific + moe_router_dtype: str = "fp32" # Use FP32 for router computations + moe_router_score_function: str = "softmax" # Softmax scoring + moe_router_bias_update_rate: float = 0.001 # Router bias update rate + + # MoE optimization settings + moe_permute_fusion: bool = True # Fuse permutation operations + moe_token_dispatcher_type: str = "alltoall" # All-to-all communication + + # Dense layers configuration (some layers may not use MoE) + # Empty list means all layers use MoE, otherwise specify layer indices + mlp_only_layers: List[int] = field(default_factory=list) + + # Decoder sparse step (frequency of MoE layers) + decoder_sparse_step: int = 1 # Every layer is MoE by default + + # Freeze options for fine-tuning scenarios + # Whether to freeze language model weights + freeze_language_model: bool = False + # Whether to freeze vision encoder weights + freeze_vision_model: bool = False + # Whether to freeze vision-to-language projection weights + freeze_vision_projection: bool = False + # Whether to freeze audio encoder weights + freeze_audio_model: bool = False + language_max_sequence_length: int = 2048 + + # QK layernorm is already True in Qwen3MoEModelProvider, no need to redefine + + # These are typically set in the base class but documented here for clarity + persist_layer_norm: bool = True # Persist layer norm for efficiency + bias_activation_fusion: bool = True # Fuse bias and activation + bias_dropout_fusion: bool = True # Fuse bias and dropout + masked_softmax_fusion: bool = False # Don't fuse masked softmax (Qwen specific) + deallocate_pipeline_outputs: bool = True # Deallocate pipeline outputs to save memory + async_tensor_model_parallel_allreduce: bool = True # Async tensor parallel + distribute_saved_activations: bool = False # Don't distribute saved activations + cp_comm_type: str = "p2p" # Point-to-point communication for context parallel + + def _process_thinker_config(self): + self.thinker_config.head_dim = self.thinker_config.text_config.head_dim + self.thinker_config.hidden_size = self.thinker_config.text_config.hidden_size + self.thinker_config.language_max_sequence_length = getattr( + self.thinker_config.text_config, "language_max_sequence_length", 2048 + ) + + # self.thinker_config.patch_size = self.thinker_config.text_config.patch_size + # self.thinker_config.temporal_patch_size = self.thinker_config.text_config.temporal_patch_size + # self.thinker_config.in_channels = self.thinker_config.text_config.in_channels + # self.thinker_config.spatial_merge_size = self.thinker_config.text_config.spatial_merge_size + # self.thinker_config.num_position_embeddings = self.thinker_config.text_config.num_position_embeddings + # self.thinker_config.out_hidden_size = self.thinker_config.text_config.out_hidden_size + # self.thinker_config.apply_rotary_pos_emb_in_fp32 = self.thinker_config.text_config.apply_rotary_pos_emb_in_fp32 + # self.thinker_config.deepstack_visual_indexes = self.thinker_config.text_config.deepstack_visual_indexes + + self.thinker_config.rotary_percent = 1.0 + self.thinker_config.apply_rope_fusion = False + self.thinker_config.position_embedding_type = "mrope" + self.thinker_config.mrope_section = self.thinker_config.text_config.rope_scaling.get( + "mrope_section", [24, 20, 20] + ) + self.thinker_config.rotary_base = rope_theta_from_hf(self.thinker_config.text_config) + + # self.thinker_config.audio_token_id = self.thinker_config.text_config.audio_token_id + # self.thinker_config.audio_start_token_id = self.thinker_config.text_config.audio_start_token_id + # self.thinker_config.audio_end_token_id = self.thinker_config.text_config.audio_end_token_id + + # self.thinker_config.image_token_id = self.thinker_config.text_config.image_token_id + # self.thinker_config.video_token_id = self.thinker_config.text_config.video_token_id + # self.thinker_config.vision_start_token_id = self.thinker_config.text_config.vision_start_token_id + # self.thinker_config.vision_end_token_id = self.thinker_config.text_config.vision_end_token_id + + self.thinker_config.bos_token_id = getattr(self.thinker_config.text_config, "bos_token_id", 151643) + self.thinker_config.eos_token_id = getattr(self.thinker_config.text_config, "eos_token_id", 151645) + + self.thinker_config.qk_layernorm = True + self.thinker_config.attention_softmax_in_fp32 = True + self.thinker_config.attention_dropout = 0.0 + + self.thinker_config.moe_router_pre_softmax = False + self.thinker_config.moe_router_dtype = "fp32" + self.thinker_config.moe_router_score_function = "softmax" + self.thinker_config.moe_router_bias_update_rate = 0.001 + + self.thinker_config.moe_permute_fusion = True + self.thinker_config.moe_token_dispatcher_type = "alltoall" + + self.thinker_config.mlp_only_layers = self.thinker_config.text_config.mlp_only_layers + self.thinker_config.decoder_sparse_step = self.thinker_config.text_config.decoder_sparse_step + + # to check freeze + # self.thinker_config.freeze_language_model = self.thinker_config.text_config.freeze_language_model + # self.thinker_config.freeze_vision_model = self.thinker_config.text_config.freeze_vision_model + # self.thinker_config.freeze_vision_projection = self.thinker_config.text_config.freeze_vision_projection + self.thinker_config.language_max_sequence_length = 2048 + + self.thinker_config.persist_layer_norm = True + self.thinker_config.bias_activation_fusion = True + self.thinker_config.bias_dropout_fusion = True + self.thinker_config.masked_softmax_fusion = False + self.thinker_config.deallocate_pipeline_outputs = True + self.thinker_config.async_tensor_model_parallel_allreduce = True + self.thinker_config.distribute_saved_activations = False + self.thinker_config.cp_comm_type = "p2p" + + def finalize(self) -> None: + if self.tensor_model_parallel_size > 1: + self.sequence_parallel = True + + super().finalize() + + def provide(self, pre_process=None, post_process=None, vp_stage=None): + """Provide a Qwen3VL MoE model instance with vision and language + components.""" + # self._process_thinker_config() + language_transformer_config = self + + # Create vision transformer config - placeholder for future use + # vision_transformer_config = deepcopy(self) + audio_config_hf = self.audio_config + vision_config_hf = self.vision_config + + language_transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec( + num_experts=self.num_moe_experts, + moe_grouped_gemm=True, + qk_layernorm=self.qk_layernorm, + # fp8=False, + # normalization="RMSNorm", + ) + + # reuse Qwen3OmniMoeModel for MoE model but replace the language model with MoE language model + model = Qwen3OmniMoeModel( + language_transformer_config=language_transformer_config, + language_transformer_layer_spec=language_transformer_layer_spec, + audio_transformer_config=audio_config_hf, + vision_transformer_config=vision_config_hf, + pre_process=pre_process, + post_process=post_process, + use_audio_in_video=self.use_audio_in_video, + pg_collection=getattr(self, "_pg_collection", None), + ) + + # Apply freeze options if any are enabled for fine-tuning + if self.freeze_language_model or self.freeze_vision_model or self.freeze_vision_projection: + model.freeze( + freeze_language_model=self.freeze_language_model, + freeze_vision_model=self.freeze_vision_model, + freeze_vision_projection=self.freeze_vision_projection, + freeze_audio_model=self.freeze_audio_model, + ) + + return model + + def provide_language_model(self, pre_process=None, post_process=None, vp_stage=None) -> MCoreGPTModel: + """Provide just the language MoE model component without vision. + + Args: + pre_process: Whether this is the first stage in pipeline parallelism + post_process: Whether this is the last stage in pipeline parallelism + vp_stage: Virtual pipeline stage number + + Returns: + MCoreGPTModel instance (MoE language model only) + """ + # Use parent class to create standard MoE language model + return super().provide(pre_process=pre_process, post_process=post_process, vp_stage=vp_stage) diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 6dfc4255f..92fbd5f9d 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -2058,6 +2058,10 @@ def _resolve_eval_datasets(args) -> list[EvalDatasetConfig]: def slime_validate_args(args): + # Backward compatibility: old scripts may pass --enable-gloo-process-groups + if not hasattr(args, "use_gloo_process_groups"): + args.use_gloo_process_groups = getattr(args, "enable_gloo_process_groups", False) + args.eval_datasets = _resolve_eval_datasets(args) if args.max_staleness < 0: diff --git a/relax/utils/checkpoint_write_patch.py b/relax/utils/checkpoint_write_patch.py index 379369a9c..feb161c36 100644 --- a/relax/utils/checkpoint_write_patch.py +++ b/relax/utils/checkpoint_write_patch.py @@ -207,8 +207,13 @@ def patch_checkpoint_write(): This function is idempotent — calling it multiple times is safe. """ + from megatron.core.dist_checkpointing.strategies.filesystem_async import FileSystemWriterAsync + global _patched - if _patched: + # NOTE(wuhuan): the latest Megatron-LM of 20260506 use write_preloaded_data_multithread instead of + # write_preloaded_data_multiproc, which has solved this issue. + can_patch = hasattr(FileSystemWriterAsync, "write_preloaded_data_multiproc") + if _patched or not can_patch: return _patch_write_preloaded_data_multiproc() diff --git a/relax/utils/data/processing_utils.py b/relax/utils/data/processing_utils.py index c1df07c07..dff254952 100644 --- a/relax/utils/data/processing_utils.py +++ b/relax/utils/data/processing_utils.py @@ -3,6 +3,8 @@ import asyncio import base64 import io +import json +import os import tempfile from concurrent.futures import ThreadPoolExecutor @@ -31,7 +33,19 @@ def load_tokenizer(name_or_path: str, **kwargs): - return AutoTokenizer.from_pretrained(name_or_path, **kwargs) + tokenizer = AutoTokenizer.from_pretrained(name_or_path, **kwargs) + # Multimodal models like Qwen3-Omni ship the chat template in a standalone + # chat_template.json (loaded by AutoProcessor) rather than tokenizer_config.json, + # so AutoTokenizer leaves chat_template unset. Backfill from the sidecar file. + if getattr(tokenizer, "chat_template", None) is None and os.path.isdir(name_or_path): + chat_template_path = os.path.join(name_or_path, "chat_template.json") + if os.path.isfile(chat_template_path): + with open(chat_template_path) as f: + chat_template = json.load(f).get("chat_template") + if chat_template: + tokenizer.chat_template = chat_template + logger.info(f"Loaded chat_template from {chat_template_path}") + return tokenizer def build_processor_kwargs(multimodal_inputs: dict | None = None) -> dict: diff --git a/relax/utils/data/stream_dataloader.py b/relax/utils/data/stream_dataloader.py index 80be7281c..8972e3e28 100644 --- a/relax/utils/data/stream_dataloader.py +++ b/relax/utils/data/stream_dataloader.py @@ -428,12 +428,12 @@ def get_data_from_transfer_queue( def post_process_rollout_data(args, rollout_data): # move tokens/loss_masks to GPU in-place as a list of tensors (downstream # code in this module expects lists of sequence tensors for packing) - from relax.backends.megatron.cp_utils import slice_log_prob_with_cp + from relax.backends.megatron.cp_utils import maybe_padded_total_lengths, slice_log_prob_with_cp cuda_dev = device_utils.make_current_torch_device() - rollout_data["tokens"] = [torch.tensor(t, dtype=torch.long, device=cuda_dev) for t in rollout_data["tokens"]] + rollout_data["tokens"] = [torch.as_tensor(t, dtype=torch.long, device=cuda_dev) for t in rollout_data["tokens"]] rollout_data["loss_masks"] = [ - torch.tensor(t, dtype=torch.int, device=cuda_dev) for t in rollout_data["loss_masks"] + torch.as_tensor(t, dtype=torch.int, device=cuda_dev) for t in rollout_data["loss_masks"] ] if "multimodal_train_inputs" in rollout_data: # Move multimodal training tensors to GPU in advance. @@ -461,17 +461,24 @@ def _to_cuda(v): rollout_data["max_seq_lens"] = [max_seq_len] * len(rollout_data["tokens"]) + padded_total_lengths = maybe_padded_total_lengths( + rollout_data["total_lengths"], + args.qkv_format, + "multimodal_train_inputs" in rollout_data, + ) + for key in ["rollout_log_probs", "teacher_log_probs"]: if key not in rollout_data: continue rollout_data[key] = [ - torch.tensor( + torch.as_tensor( slice_log_prob_with_cp( log_prob, total_length, response_length, args.qkv_format, rollout_data["max_seq_lens"][i] if args.qkv_format == "bshd" else None, + padded_total_length=padded_total_lengths[i] if padded_total_lengths is not None else None, ), device=cuda_dev, dtype=torch.float32, @@ -519,6 +526,7 @@ def _to_cuda(v): response_length, args.qkv_format, rollout_data["max_seq_lens"][i] if args.qkv_format == "bshd" else None, + padded_total_length=padded_total_lengths[i] if padded_total_lengths is not None else None, ) topk_tensors.append(topk_tensor) @@ -528,6 +536,6 @@ def _to_cuda(v): from tensordict.tensorclass import NonTensorData rollout_data["rollout_routed_experts"] = [ - torch.tensor(r.data if isinstance(r, NonTensorData) else r, dtype=torch.long, device=cuda_dev) + torch.as_tensor(r.data if isinstance(r, NonTensorData) else r, dtype=torch.long, device=cuda_dev) for r in rollout_data["rollout_routed_experts"] ] diff --git a/relax/utils/logging_utils.py b/relax/utils/logging_utils.py index 3195d2d92..aa3549663 100644 --- a/relax/utils/logging_utils.py +++ b/relax/utils/logging_utils.py @@ -107,6 +107,10 @@ def configure_logger(prefix: str = "") -> None: handler.setLevel(LOG_LEVEL) handler.setFormatter(get_formatter(prefix)) root_logger.addHandler(handler) + + # Silence noisy third-party DEBUG loggers (PIL dumps PNG chunk metadata per image) + for noisy in ("PIL",): + logging.getLogger(noisy).setLevel(logging.WARNING) except Exception: # Silently ignore configuration errors to prevent breaking the application pass diff --git a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh index 2d98c8c30..906469feb 100644 --- a/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-30B-A3B-omni-16xgpu.sh @@ -50,6 +50,7 @@ ROLLOUT_ARGS=( # --rollout-max-prompt-len 2048 --rollout-temperature 0.8 --global-batch-size 512 + --use-streaming-dataset --balance-data --use-fault-tolerance --system-prompt "${SYSTEM_PROMPT}" diff --git a/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh b/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh index 4bf13f04a..b0462bdd4 100644 --- a/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh @@ -53,10 +53,10 @@ ROLLOUT_ARGS=( ) PERF_ARGS=( - --tensor-model-parallel-size 4 + --tensor-model-parallel-size 2 --sequence-parallel --pipeline-model-parallel-size 1 - --context-parallel-size 1 + --context-parallel-size 4 --expert-model-parallel-size 1 --expert-tensor-parallel-size 1 @@ -64,7 +64,9 @@ PERF_ARGS=( --recompute-method uniform --recompute-num-layers 1 - #--micro-batch-size 16 # avoid OOM + --calculate-per-token-loss + # --micro-batch-size 16 + # --qkv-format bshd --use-dynamic-batch-size --max-tokens-per-gpu 9216 diff --git a/scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh b/scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh index e8e3b9df2..be3592a62 100644 --- a/scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh +++ b/scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh @@ -46,6 +46,7 @@ ROLLOUT_ARGS=( --rollout-max-prompt-len 2048 --rollout-temperature 1 --global-batch-size 256 + --use-streaming-dataset --balance-data --use-fault-tolerance --system-prompt "${SYSTEM_PROMPT}" diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh index 56e85a755..0561fa9df 100644 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh @@ -63,10 +63,10 @@ ROLLOUT_ARGS=( ) PERF_ARGS=( - --tensor-model-parallel-size 4 + --tensor-model-parallel-size 2 --sequence-parallel --pipeline-model-parallel-size 1 - --context-parallel-size 1 + --context-parallel-size 2 --expert-model-parallel-size 1 --expert-tensor-parallel-size 1 @@ -74,9 +74,11 @@ PERF_ARGS=( --recompute-method uniform --recompute-num-layers 1 - #--micro-batch-size 16 # avoid OOM + --calculate-per-token-loss + # --micro-batch-size 16 + # --qkv-format bshd --use-dynamic-batch-size - --max-tokens-per-gpu 9216 + --max-tokens-per-gpu 4096 --no-rope-fusion ) @@ -137,7 +139,7 @@ if [ ${MODE} = "async" ]; then --max-staleness 2 \ --num-data-storage-units 1 \ --num-iters-per-train-update 8 \ - --ref-actor-config '{"tensor_model_parallel_size": 1, "max_tokens_per_gpu": 16384, "sequence_parallel": false, "only_load_weight": true}' \ + --ref-actor-config '{"context_parallel_size": 1, "tensor_model_parallel_size": 1, "max_tokens_per_gpu": 16384, "sequence_parallel": false, "only_load_weight": true}' \ --fully-async \ --use-health-check \ "${MODEL_ARGS[@]}" \ diff --git a/scripts/training/text/run-qwen3-4B-8xgpu.sh b/scripts/training/text/run-qwen3-4B-8xgpu.sh index e2715c0af..c5d902f41 100644 --- a/scripts/training/text/run-qwen3-4B-8xgpu.sh +++ b/scripts/training/text/run-qwen3-4B-8xgpu.sh @@ -70,8 +70,8 @@ EVAL_ARGS=( PERF_ARGS=( --tensor-model-parallel-size 2 --sequence-parallel - --pipeline-model-parallel-size 1 - --context-parallel-size 1 + --pipeline-model-parallel-size 2 + --context-parallel-size 2 --expert-model-parallel-size 1 --expert-tensor-parallel-size 1 @@ -79,6 +79,7 @@ PERF_ARGS=( --recompute-method uniform --recompute-num-layers 1 + --calculate-per-token-loss #--micro-batch-size 16 # avoid OOM --use-dynamic-batch-size --max-tokens-per-gpu 9216 From 338324b66ef8ca143d31e9d48579b4437404819b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Fri, 8 May 2026 22:47:35 +0800 Subject: [PATCH 030/268] feat(models): add Qwen3.6-35B-A3B support with MoE expert detection MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add Qwen3.6 model support with automatic expert format detection - Add Qwen3.6-35B-A3B model configuration script with MoE parameters (256 experts, 8-way routing) - Implement MTP MoE expert weight format detection in Qwen35VL bridge - Qwen3.5: per-expert storage (gate_proj/up_proj/down_proj per expert) - Qwen3.6: packed format (gate_up_proj/down_proj shared tensor) - Add training script for Qwen3.6-35B-A3B 8xGPU colocate mode with multimodal support - Extend Megatron bridge patch with format-aware weight mappings --- # 🐛 Bug Fix ## Fix multimodal data counting and training script paths - Fix remain_data counter for pre-structured multimodal content (was skipping already-processed items) - Remove invalid dataset slice notation (@[0:1000]) from PROMPT_SET path in training script --- .../patch/megatron/20260506-85bced0ae.patch | 64 ++++ docs/draft/dynamic-context-parallel.md | 334 ++++++++++++++++++ relax/models/__init__.py | 19 + relax/utils/data/data_utils.py | 8 +- scripts/models/qwen36-35B-A3B.sh | 59 ++++ .../multimodal/run-qwen36-35B-A3B-8xgpu.sh | 142 ++++++++ scripts/training/text/run-qwen35-9B-8xgpu.sh | 154 ++++++++ 7 files changed, 778 insertions(+), 2 deletions(-) create mode 100644 docs/draft/dynamic-context-parallel.md create mode 100644 relax/models/__init__.py create mode 100644 scripts/models/qwen36-35B-A3B.sh create mode 100644 scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu.sh create mode 100644 scripts/training/text/run-qwen35-9B-8xgpu.sh diff --git a/docker/patch/megatron/20260506-85bced0ae.patch b/docker/patch/megatron/20260506-85bced0ae.patch index 81830dc08..4b600b57d 100644 --- a/docker/patch/megatron/20260506-85bced0ae.patch +++ b/docker/patch/megatron/20260506-85bced0ae.patch @@ -725,3 +725,67 @@ index a0817e834..7cd094dc5 100644 HAVE_TORCH_MEMORY_SAVER = True except ImportError: HAVE_TORCH_MEMORY_SAVER = False +diff --git a/megatron/bridge/models/qwen_vl/qwen35_vl_bridge.py b/megatron/bridge/models/qwen_vl/qwen35_vl_bridge.py +index 7fcf295e..7ac11345 100644 +--- a/megatron/bridge/models/qwen_vl/qwen35_vl_bridge.py ++++ b/megatron/bridge/models/qwen_vl/qwen35_vl_bridge.py +@@ -404,15 +404,6 @@ class Qwen35VLMoEBridge(MegatronModelBridge): + k="mtp.layers.*.self_attn.k_proj.weight", + v="mtp.layers.*.self_attn.v_proj.weight", + ), +- GatedMLPMapping( +- megatron_param="language_model.mtp.layers.*.mtp_model_layer.mlp.experts.linear_fc1.weight*", +- gate="mtp.layers.*.mlp.experts.*.gate_proj.weight", +- up="mtp.layers.*.mlp.experts.*.up_proj.weight", +- ), +- AutoMapping( +- megatron_param="language_model.mtp.layers.*.mtp_model_layer.mlp.experts.linear_fc2.weight*", +- hf_param="mtp.layers.*.mlp.experts.*.down_proj.weight", +- ), + GatedMLPMapping( + megatron_param="language_model.mtp.layers.*.mtp_model_layer.mlp.shared_experts.linear_fc1.weight", + gate="mtp.layers.*.mlp.shared_expert.gate_proj.weight", +@@ -429,6 +420,43 @@ class Qwen35VLMoEBridge(MegatronModelBridge): + ] + ) + ++ # Detect MTP MoE expert weight format: Qwen3.5 stores per-expert ++ # (mtp.layers.0.mlp.experts.{i}.gate_proj.weight), Qwen3.6 stores packed ++ # (mtp.layers.0.mlp.experts.gate_up_proj). Same architecture string, ++ # different storage — must inspect HF keys. ++ mtp_experts_packed = False ++ if hasattr(self.hf_pretrained, "state") and hasattr(self.hf_pretrained.state, "source"): ++ hf_keys = set(self.hf_pretrained.state.source.get_all_keys()) ++ if "mtp.layers.0.mlp.experts.gate_up_proj" in hf_keys: ++ mtp_experts_packed = True ++ ++ if mtp_experts_packed: ++ # Qwen3.6: packed format (same as main decoder) ++ mapping_list.extend([ ++ FusedGatedExpertMapping( ++ megatron_param="language_model.mtp.layers.*.mtp_model_layer.mlp.experts.linear_fc1.weight*", ++ hf_param="mtp.layers.*.mlp.experts.gate_up_proj", ++ ), ++ FusedExpertMapping( ++ megatron_param="language_model.mtp.layers.*.mtp_model_layer.mlp.experts.linear_fc2.weight*", ++ hf_param="mtp.layers.*.mlp.experts.down_proj", ++ transpose_on_export=True, ++ ), ++ ]) ++ else: ++ # Qwen3.5: per-expert format (current behavior) ++ mapping_list.extend([ ++ GatedMLPMapping( ++ megatron_param="language_model.mtp.layers.*.mtp_model_layer.mlp.experts.linear_fc1.weight*", ++ gate="mtp.layers.*.mlp.experts.*.gate_proj.weight", ++ up="mtp.layers.*.mlp.experts.*.up_proj.weight", ++ ), ++ AutoMapping( ++ megatron_param="language_model.mtp.layers.*.mtp_model_layer.mlp.experts.linear_fc2.weight*", ++ hf_param="mtp.layers.*.mlp.experts.*.down_proj.weight", ++ ), ++ ]) ++ + return MegatronMappingRegistry(*mapping_list) + + diff --git a/docs/draft/dynamic-context-parallel.md b/docs/draft/dynamic-context-parallel.md new file mode 100644 index 000000000..4c0a43d76 --- /dev/null +++ b/docs/draft/dynamic-context-parallel.md @@ -0,0 +1,334 @@ +# Relax 接入 Dynamic Context Parallel 改造方案 + +> **状态**：Draft / 设计阶段 +> **背景**：verl 在 PR [#5057](https://github.com/volcengine/verl/pull/5057)（commit `7e9a07c4`，2026-03-31）落地了 dynamic CP，能让每个 micro-batch 按当前最大 seq_len 自适应选 cp_size，对 RL 训练的长短样本混合场景明显友好。本文档给出 Relax 端的改造方案。 +> **参考**：verl 实现详见 `/root/repos/verl/verl_cp.md` §6。 + +--- + +## 0. 前置事实（决定方案形态） + +| 维度 | verl 现状 | Relax 现状 | 影响 | +|---|---|---|---| +| 引擎封装 | `MegatronEngine` 类 | 无类，直接 `mpu.*`（散布在 `cp_utils.py`/`ppo_utils.py`/`loss.py`/`data.py`/`actor.py`/`model.py`） | verl 的 "DP=1 伪装" 做不到方法重写，需另想 | +| Batch 类型 | TensorDict + `non_tensor_data` | `dict[str, list[Tensor]]`（`utils/types.py:194`） | `local_cp_size` 必须显式作为字段流转 | +| Megatron-LM | 上游 PR #3405 后的 dev 分支 | Bridge pin `2faedbf6...` 拉的是 **main 分支** `f4a071039`，**不含** `dynamic_context_parallel` 形参与 `get_dynamic_data_context_parallel_groups` API（已实测）；PR #3405 的 commit `cde56a469` 只在 dev 分支 | **必须先解决依赖**：要么 bump bridge pin → dev，要么打补丁把 #3405 + #2000 cherry-pick 进当前 main | +| 数据切分 | `preprocess_thd_engine` 一处 | `get_batch`（`data.py:106-335`）+ `cp_utils.py` 一组帮助函数 | 改面更广，但都在两个文件里 | +| Token budget | `max_token_len * sp_size` | `max_tokens_per_gpu * cp_size`（`data.py:533`，写死） | 必须改成 per-microbatch 的 effective cp_size | +| 损失尺度 | `loss * num_micro_batch`（一处） | `loss.py:1080-1087` 多处含 `mpu.get_*_world_size(with_context_parallel=True)` 因子 | 需引入 per-microbatch normalizer | +| VLM | thd + bridge VL 透传，align `tp*cp*2` | 同（`data.py:177-212`、`cp_utils.py:9-29`），同样 hardcode `tp*cp*2` | 同样需要 per-microbatch align | + +--- + +## 1. 设计原则 + +**支点**：`local_cp_size` 通过 batch dict 一路下传到 `get_batch`、`forward_step`、loss、postprocess。所有 `mpu.get_context_parallel_*()` 调用改为接受可选的 `cp_group`/`cp_size` 参数，当外部传入则用之，否则退回全局 mpu（保持向后兼容）。 + +**两段式落地**： +- **Phase 1 MVP**：cp_size 在 DP 内**统一**（取 max），按 micro-batch 自适应。无 sub-DP 路由、不动数据 partition。一个开关上线即可享受"短样本不付 CP 通信代价"的收益。 +- **Phase 2 verl 等价**：引入 sub-DP 路由 + 异构 cp_size。需要 `mpu.get_dynamic_data_context_parallel_groups`、`dynamic_cp_split_batch`、`dynamic_cp_merge_output`。 + +> 强烈建议 **Phase 1 先上线、跑稳、再做 Phase 2**。Phase 1 的总工作量 ≈ Phase 2 的 1/3，且 Phase 2 多出来的 sub-DP 路由对 RL 训练里的 advantage/log-prob 全局聚合有破坏性影响（verl 自己也留了 TODO 没完成）。 + +--- + +## 2. Phase 0：依赖准备 + +### 2.1 决定 Megatron-LM 来源（二选一） + +| 选项 | 做法 | 收益 | 代价 | +|---|---|---|---| +| A. Bump Bridge pin | 把 `docker/Dockerfile:78` 的 `MEGATRON_BRIDGE_COMMIT` 升到含 mcore-dev 的版本，并把 `switch_mcore.sh` 切到 `dev` | 自带 dynamic CP + 持续维护 | dev 分支稳定性差，需要回归全套现有训练任务 | +| B. 现有 main + 补丁 | 在 `docker/patch/megatron/` 下加一个补丁，cherry-pick `cde56a469` (#3405) 和 `2d6e946ba` (#2000 Dynamic CP part 2) | 影响面小，可控 | 维护补丁 conflict 风险 | + +**推荐 B**：cherry-pick 两个 commit 落到 `docker/patch/latest/megatron-dynamic-cp.patch`。改造期间任何升级 megatron 的人都看到 patch，风险显式化。 + +### 2.2 验证脚本 + +在 `relax/backends/megatron/initialize.py` 加运行时探测： + +```python +import inspect +HAS_DYNAMIC_CP = "dynamic_context_parallel" in inspect.signature(mpu.initialize_model_parallel).parameters +``` + +供后续条件性启用。 + +--- + +## 3. Phase 1（MVP）改动清单 + +### 3.1 配置（args + validate） + +```python +# relax/utils/arguments.py（新增 group "Dynamic CP"） +parser.add_argument("--dynamic-context-parallel", action="store_true", + help="Enable per-microbatch adaptive CP size (requires Megatron-LM PR #3405).") +parser.add_argument("--max-seqlen-per-dp-cp-rank", type=int, default=None, + help="Upper bound of tokens per DP×CP rank. Required when --dynamic-context-parallel.") +``` + +```python +# relax/backends/megatron/arguments.py: validate_args +if args.dynamic_context_parallel: + assert HAS_DYNAMIC_CP, "Megatron-LM lacks PR #3405; bump pin or apply patch." + assert args.max_seqlen_per_dp_cp_rank is not None + assert args.context_parallel_size >= 1 + dp_size = compute_dp_size(...) + assert dp_size * args.max_seqlen_per_dp_cp_rank >= args.max_response_len + args.max_prompt_len +``` + +### 3.2 mpu 初始化（`initialize.py:39-55`） + +```python +extra = {} +if args.dynamic_context_parallel and HAS_DYNAMIC_CP: + extra["dynamic_context_parallel"] = True +mpu.initialize_model_parallel(..., **extra) +``` + +### 3.3 Bridge config 反向欺骗（`model_provider.py:175-215`，**仅 bridge 模式**） + +```python +if args.dynamic_context_parallel: + overrides["max_seqlen_per_dp_cp_rank"] = args.max_seqlen_per_dp_cp_rank + overrides["dynamic_context_parallel"] = False # 同 verl 注释里的 "bad coupling" 绕道 + overrides["context_parallel_size"] = mpu.get_data_parallel_world_size() # 让 bridge 误以为 cp=DP +``` + +raw 模式下不做这件事（直接用 `core_transformer_config_from_args(args)` 即可，但要在那里同样防止 `args.context_parallel_size` 被 transformer config 误读 —— 或者干脆 raw 模式 Phase 1 不支持，给出清晰报错）。 + +### 3.4 Per-microbatch CP 决策 + +新增 `relax/backends/megatron/dynamic_cp.py`： + +```python +def decide_local_cp_size(samples_in_microbatch, max_seqlen_per_dp_cp_rank, dp_size): + """同 verl: ceil(max_seqlen / cap), 向上取 2 的幂, clamp ≤ dp_size.""" + max_seq = max(len(s["input_ids"]) for s in samples_in_microbatch) + n = math.ceil(max_seq / max_seqlen_per_dp_cp_rank) + n = max(1, 1 << (n - 1).bit_length()) + return min(n, dp_size) + + +def gather_max_local_cp(local_cp: int, dp_group) -> int: + """Phase 1 MVP: DP 间统一取 MAX。""" + t = torch.tensor([local_cp], device="cuda") + torch.distributed.all_reduce(t, op=torch.distributed.ReduceOp.MAX, group=dp_group) + return int(t.item()) +``` + +### 3.5 数据流（`data.py`） + +#### a) 注入决策点（`get_data_iterator`，`data.py:449-573`） + +在 `get_seqlen_balanced_partitions` 之后、构造 `DataIterator` 之前： + +```python +if args.dynamic_context_parallel: + per_mb_cp_size = [] + for mb_samples in partitioned_microbatches: + local = decide_local_cp_size(mb_samples, args.max_seqlen_per_dp_cp_rank, + mpu.get_data_parallel_world_size(with_context_parallel=False)) + per_mb_cp_size.append(gather_max_local_cp(local, mpu.get_data_parallel_group())) +else: + per_mb_cp_size = [args.context_parallel_size] * len(partitioned_microbatches) + +# 把 per_mb_cp_size 挂到 DataIterator 上 +``` + +#### b) Token budget（`data.py:533`） + +```python +# 改前: max_tokens_per_gpu * cp_size +# 改后: +budget_cp = args.context_parallel_size if not args.dynamic_context_parallel \ + else 1 # MVP: 不预知，就按最坏的 cp=1 打包，让 dynamic_cp 决策时再涨 cp +get_minimum_num_micro_batch_size(samples[start:end], + args.max_tokens_per_gpu * budget_cp) +``` + +> 这里有个微妙取舍：dynamic CP 的 "动态" 是在 batch 已经分好后的 second pass。打包阶段用 `cp=1` 的 budget 意味着每 micro-batch 都按 "塞满单卡" 打 → 长样本最多触发 cp=8，能跑通；但短样本浪费空间。Phase 2 可以重排打包。 + +#### c) `get_batch`（`data.py:106-335`） + +签名加可选 `local_cp_size: Optional[int] = None`： + +```python +def get_batch(args, samples, ..., local_cp_size: Optional[int] = None): + if local_cp_size is not None: + cp_size = local_cp_size + cp_group = mpu.get_dynamic_data_context_parallel_groups(group_size=local_cp_size) + cp_rank = torch.distributed.get_rank(cp_group) + else: + cp_size = mpu.get_context_parallel_world_size() + cp_group = mpu.get_context_parallel_group() + cp_rank = mpu.get_context_parallel_rank() + ... + # 所有用 cp_size/cp_rank 的地方改用上面的本地变量 + # PackedSeqParams 上挂 cp_group, local_cp_size + packed_seq_params = PackedSeqParams( + ..., cp_group=cp_group, local_cp_size=local_cp_size, + ) +``` + +`slice_with_cp`（`cp_utils.py:210-251`）同样加 `cp_size, cp_rank` 参数。 + +#### d) `DataIterator.get_next` + +返回的 dict 多带一个键 `"local_cp_size"`，由 `get_batch` 透传到 `forward_step`。 + +### 3.6 Forward & loss + +#### a) `forward_step`（`model.py:222-303`、`399-489`） + +```python +local_cp_size = batch.get("local_cp_size") # None 表示静态 CP +batch_processed = get_batch(..., local_cp_size=local_cp_size) +output_tensor = model(...) +return output_tensor, partial(postprocess_fn, ..., local_cp_size=local_cp_size, + cp_group=batch_processed["cp_group"]) +``` + +#### b) `cp_utils.py` 全部公共 helper + +涉及函数：`get_logits_and_tokens_offset_with_cp`、`all_gather_with_cp`、`get_sum_of_sample_mean`、`slice_log_prob_with_cp`、`maybe_padded_total_lengths`。 + +签名加 `cp_size: Optional[int] = None, cp_group: Optional[ProcessGroup] = None`。内部 `mpu.get_context_parallel_*()` 改为： + +```python +cp_size = cp_size or mpu.get_context_parallel_world_size() +cp_group = cp_group or mpu.get_context_parallel_group() +cp_rank = torch.distributed.get_rank(cp_group) if cp_group else mpu.get_context_parallel_rank() +``` + +调用方（`loss.py`、`ppo_utils.py:298-336/402-423/463-515`、`actor.py:1047`、`advantages.py:159`）补传两个参数。 + +#### c) `loss.py:1080-1087` 损失尺度 + +```python +# 改前: +# loss = loss * num_microbatches / global_batch_size * mpu.get_data_parallel_world_size(with_context_parallel=True) +# loss = loss * mpu.get_context_parallel_world_size() + +# 改后: +effective_cp = local_cp_size if local_cp_size is not None else mpu.get_context_parallel_world_size() +# DP 维度 Phase 1 MVP 仍按全局 DP（cp 同 DP 内统一），不变 +dp_x_cp = mpu.get_data_parallel_world_size(with_context_parallel=False) * effective_cp +loss = loss * num_microbatches / global_batch_size * dp_x_cp # sample-mean +loss = loss * effective_cp # per-token +``` + +> ⚠️ **Phase 1 关键约束**：MVP 同一 micro-batch 内所有 DP rank 用相同 cp，所以 `mpu.get_data_parallel_world_size(with_context_parallel=True)` 在 Phase 1 ≡ `dp * effective_cp`，等价。Phase 2 引入异构后才会破。 + +### 3.7 VLM（`data.py:177-212`，`cp_utils.py:9-29`） + +把硬编码的 `tp * cp * 2` 替换为 `tp * effective_cp * 2`，其中 `effective_cp = local_cp_size or args.context_parallel_size`。`vlm_packed_seq_params` 同样要带 `cp_group`，让 Bridge 内部走对的 ring-CP 通信。 + +> 风险：Bridge 自己内部如何调度 dynamic CP 取决于 PR #3405 + Bridge 自身。建议 **Phase 1 先在纯文本上线，VLM + dynamic CP 单独作为 Phase 1.5 验证**。 + +### 3.8 不动的部分 + +- `actor.py` 里的 `data_system_client` 数据接收：rollout 阶段不感知 CP，CP 只是 trainer 内部事。 +- `RolloutBatch` 类型定义：保持 `dict[str, list[Tensor]]`，新键加在 micro-batch 那一层。 +- 所有 raw 模式相关代码：Phase 1 报错 "raw mode 暂不支持 dynamic CP"。 + +--- + +## 4. Phase 2（verl 完整等价）追加改动 + +待 Phase 1 稳定后再做。 + +### 4.1 引入 sub-DP 路由 + +新增 `dynamic_cp_split_batch` —— 在 `data.py` 的 `get_data_iterator` 末尾对每个 micro-batch： + +```python +if local_cp_size < dp_size: + local_dp_rank = dp_rank // local_cp_size + local_dp_size = dp_size // local_cp_size + # 把 partitioned_microbatches[i] 进一步切成 local_dp_size 份 + # 每个 sub-DP 拿自己那份 +``` + +需要的 mpu 新 API：`get_dynamic_data_context_parallel_groups(group_size=local_cp_size)`（PR #3405 已提供）。 + +### 4.2 引入 `dynamic_cp_merge_output` + +postprocess 阶段对需要跨 sub-DP all_gather 的输出（log_probs、entropy、advantages 等），同 verl 用 `all_gather_object` 在 `dp_group` 内按 stride 重组。 + +### 4.3 DP "伪装" + +由于 Relax 没有 `engine.get_data_parallel_size()` 方法，建议反过来：在 `compute_dp_size` 旁加一个 `get_logical_dp_size(args)`，dynamic CP 时返回 1。所有 dynamic-batching/loss 尺度计算改用 `get_logical_dp_size`，物理 DP 通信仍用 `mpu`。 + +### 4.4 损失跨 sub-DP 聚合 + +verl 在这里留了 TODO，Relax 不能照抄。建议至少在 `loss.py` 增加一次跨 sub-DP 的 weighted average（按 sub-group 的样本数权重），否则 advantage normalize / KL 估计会 biased。 + +### 4.5 数据 scheduler 长度感知 + +verl post-merge TODO 里要做的事 —— 在 `get_seqlen_balanced_partitions` 之前按长度排序并按桶分箱，同桶 micro-batch 用相同 cp。能让 DP 内 `MAX(local_cp)` 趋近 `MEAN(local_cp)`。 + +--- + +## 5. 测试方案 + +| 层级 | 测试 | 通过标准 | +|---|---|---| +| 单元 | `decide_local_cp_size` / `gather_max_local_cp` | 输入构造的 batch → 期望 cp_size | +| 单元 | `get_batch(local_cp_size=2)` vs `get_batch()` 在 cp=2 静态时 | bit-exact | +| 集成 | 8GPU 单机 SFT，TP=4 PP=1 CP=1，开/关 dynamic | loss 曲线在数值容差内对齐 | +| 集成 | 8GPU GRPO，长样本 + 短样本混合，dynamic vs 静态 cp=4 | reward 曲线一致，throughput dynamic 更高 | +| 回归 | 现有所有 e2e 训练脚本（`scripts/training/`），关闭 dynamic CP | 必须 bit-exact | +| VLM（Phase 1.5） | Qwen3-VL 8GPU | loss 对齐 | + +--- + +## 6. 风险与决策点 + +| # | 风险 | 缓解 | +|---|---|---| +| 1 | Megatron pin 升级或 patch 维护负担 | 倾向 patch 路线，写在 `docker/patch/latest/`，CI 自动 apply | +| 2 | Phase 1 损失尺度公式在 cp 同 DP 内统一时等价，但需要严谨证明 | 在 PR 描述里写出代数推导 + 单元测试覆盖两种 mode | +| 3 | VLM 的 Bridge 内部 CP 是否随 `local_cp_size` 自动适应未知 | Phase 1.5 单独验证；最坏情况 VLM 不支持 dynamic CP（同 verl bshd 的处理） | +| 4 | raw 模式 Phase 1 不支持 | 显式报错引导用户切到 bridge 模式 | +| 5 | `cp_utils.py` 全部 helper 改签名是 breaking 修改 | 给所有参数加 `Optional` 默认值，保证既有调用方一行不动 | +| 6 | Phase 2 的 sub-DP 路由对 RL 全局统计（advantage normalize）是破坏性的 | Phase 2 启动前，先盘清 `relax/components/advantages.py` 和 `relax/utils/training/ppo_utils.py` 里所有跨 DP 聚合点，逐一决定是否需要补 cross-sub-DP 聚合 | + +--- + +## 7. 工作量预估（人日） + +| 阶段 | 模块 | 估算 | +|---|---|---| +| Phase 0 | Megatron patch + 探测 | 1-2 | +| Phase 1 | 配置 + mpu/bridge + dynamic_cp.py | 2 | +| Phase 1 | data.py / cp_utils.py / forward_step / loss.py 改造 | 4-5 | +| Phase 1 | 单元 + 集成测试 + 回归 | 3-4 | +| **Phase 1 小计** | | **~10-13 人日** | +| Phase 1.5 | VLM 验证 + 修复 | 3-5 | +| Phase 2 | sub-DP 路由 + merge + DP 伪装 + 损失聚合 | 8-12 | +| Phase 2 | 测试 + 回归 | 5 | +| **总计** | | **~25-35 人日** | + +--- + +## 8. 落地起始的最小补丁清单（P1 第一周） + +1. `docker/patch/latest/megatron-dynamic-cp.patch` —— cherry-pick #3405 + #2000 +2. `relax/utils/arguments.py` —— 加 2 个 flag +3. `relax/backends/megatron/arguments.py` —— validate +4. `relax/backends/megatron/initialize.py` —— mpu init + `HAS_DYNAMIC_CP` +5. `relax/backends/megatron/model_provider.py` —— bridge 反向欺骗 +6. `relax/backends/megatron/dynamic_cp.py`（新文件） —— `decide_local_cp_size` / `gather_max_local_cp` + +走通这 6 处 → 已经可以 `dynamic_context_parallel=True` 跑起来（虽然 cp_size 还没真在 batch 里变化），后续再分别接通 data → forward → loss 三段。 + +--- + +## 9. 参考 + +- verl PR #5057： +- verl 实现详解：`/root/repos/verl/verl_cp.md` §6（来源 / mpu+bridge 双重欺骗 / split+merge / 限制汇总） +- Megatron-LM PR #3405：（dev 分支已合，main 未合） +- Megatron-LM PR #2000：Dynamic CP part 2（提供 `get_dynamic_data_context_parallel_groups`） diff --git a/relax/models/__init__.py b/relax/models/__init__.py new file mode 100644 index 000000000..1642b11a0 --- /dev/null +++ b/relax/models/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +try: + from megatron.bridge.models.qwen_omni import ( # type: ignore[attr-defined] # noqa: F401 + Qwen3OmniModelProvider, + Qwen3OmniMoEBridge, + Qwen3OmniMoeModel, + ) +except (ImportError, AttributeError): + from relax.models.qwen_omni.modeling_qwen3_omni.model import Qwen3OmniMoeModel # noqa: F811 + from relax.models.qwen_omni.qwen3_omni_bridge import Qwen3OmniMoEBridge # noqa: F811 + from relax.models.qwen_omni.qwen3_omni_provider import Qwen3OmniModelProvider # noqa: F811 + + +__all__ = [ + "Qwen3OmniMoEBridge", + "Qwen3OmniMoeModel", + "Qwen3OmniModelProvider", +] diff --git a/relax/utils/data/data_utils.py b/relax/utils/data/data_utils.py index a2158e8ec..4236a8a93 100644 --- a/relax/utils/data/data_utils.py +++ b/relax/utils/data/data_utils.py @@ -150,8 +150,12 @@ def build_messages( built_message["content"] = content_list built_prompt.append(built_message) elif isinstance(message["content"], list): - # Already processed, skip - logger.warning("message['content'] is a list of dicts, no processing will be done.") + # Pre-structured content: count multimodal items so the + # remain_data check below doesn't false-positive. + for item in message["content"]: + item_type = item.get("type") + if item_type in remain_data: + remain_data[item_type] -= 1 built_prompt.append(message) else: raise ValueError( diff --git a/scripts/models/qwen36-35B-A3B.sh b/scripts/models/qwen36-35B-A3B.sh new file mode 100644 index 000000000..84eb90466 --- /dev/null +++ b/scripts/models/qwen36-35B-A3B.sh @@ -0,0 +1,59 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +NLAYERS=40 +FIRST_K_DENSE_REPLACE=0 + +arr=() +for ((i=0; i/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen36-35B-A3B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/openr1mm}" +EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +NUM_ROLLOUT="${NUM_ROLLOUT:=200}" + +CKPT_ARGS=( + --hf-checkpoint ${EXP_DIR}/Qwen3.6-35B-A3B + --ref-load ${EXP_DIR}/Qwen3.6-35B-A3B + --megatron-to-hf-mode bridge +) + +PROMPT_SET="${EXP_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet" +SYSTEM_PROMPT="A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within and tags, respectively, i.e., reasoning process here answer here " + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type openr1mm + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 32 + --n-samples-per-prompt 8 + --rollout-max-response-len 2048 + --rollout-max-prompt-len 2048 + --rollout-temperature 1 + --global-batch-size 256 + --use-streaming-dataset + --balance-data + --use-fault-tolerance + --system-prompt "${SYSTEM_PROMPT}" + --multimodal-keys '{"image":"image"}' + --no-rope-fusion +) + +PERF_ARGS=( + --tensor-model-parallel-size 2 + --sequence-parallel + --pipeline-model-parallel-size 2 + --context-parallel-size 1 + --expert-model-parallel-size 4 + --expert-tensor-parallel-size 1 + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + # --qkv-format bshd + # --micro-batch-size 1 # avoid OOM + --use-dynamic-batch-size + --max-tokens-per-gpu 6144 +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + # NOTE(wuhuan): to avoid algorithm performance degradation + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 2 + --sglang-mem-fraction-static 0.8 +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen36-35B-A3B-${now} +) + +MISC_ARGS=( + # default dropout in megatron is 0.1 + --attention-dropout 0.0 + --hidden-dropout 0.0 + # should be good for model performance + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # need to comment this when using model with MLA + --attention-backend flash +) + +mkdir -p log + +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 8], "rollout": [1, 8]}'\ + --max-staleness 0 \ + --num-data-storage-units 1 \ + --colocate \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen36-35B-A3B-GRPO-gpu8-${now}.log diff --git a/scripts/training/text/run-qwen35-9B-8xgpu.sh b/scripts/training/text/run-qwen35-9B-8xgpu.sh new file mode 100644 index 000000000..ef34b7653 --- /dev/null +++ b/scripts/training/text/run-qwen35-9B-8xgpu.sh @@ -0,0 +1,154 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3.5-9B 8xGPU colocate (sync) training script for DAPO math dataset. +# +# Usage: +# bash scripts/training/text/run-qwen35-9B-8xgpu.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen35-9B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" +EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +NUM_ROLLOUT="${NUM_ROLLOUT:=1000}" + +CKPT_ARGS=( + --hf-checkpoint ${EXP_DIR}/Qwen3.5-9B + --ref-load ${EXP_DIR}/Qwen3.5-9B + --megatron-to-hf-mode bridge + + --load ${EXP_DIR}/Qwen3-9B_mcore_8xgpu/ + --save ${EXP_DIR}/Qwen3-9B_mcore_8xgpu/ + --save-interval 50 + --max-actor-ckpt-to-keep 1 +) + +PROMPT_SET=${EXP_DIR}/dapo-math-17k/dapo-math-17k.jsonl + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type dapo + --reward-key score + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 32 + --n-samples-per-prompt 8 + --rollout-max-response-len 8192 + --rollout-temperature 1 + --global-batch-size 256 + --balance-data + --use-fault-tolerance +) + +EVAL_ARGS=( + --log-passrate + --skip-eval-before-train + --eval-interval 20 + --eval-prompt-data aime ${EXP_DIR}/aime-2024/aime-2024.jsonl + --n-samples-per-eval-prompt 8 + --eval-max-response-len 8192 + --eval-top-p 0.7 +) + +PERF_ARGS=( + --tensor-model-parallel-size 4 + --sequence-parallel + --pipeline-model-parallel-size 1 + --context-parallel-size 1 + --expert-model-parallel-size 1 + --expert-tensor-parallel-size 1 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --use-dynamic-batch-size + --max-tokens-per-gpu 10240 + # --micro-batch-size 1 # avoid OOM + + --no-rope-fusion +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 2 + --sglang-mem-fraction-static 0.8 + --sglang-cuda-graph-bs 1 2 4 8 $(seq 16 8 256) +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen35-9B-8x-${now} +) + +MISC_ARGS=( + # default dropout in megatron is 0.1 + --attention-dropout 0.0 + --hidden-dropout 0.0 + # should be good for model performance + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # need to comment this when using model with MLA + --attention-backend flash +) + +mkdir -p log +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://${HOST_IP}:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 8], "rollout": [1, 8]}' \ + --max-staleness 0 \ + --num-data-storage-units 1 \ + --colocate \ + --use-health-check \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen35-9B-GRPO-gpu8-${now}.log From 1bb19f4b0ad3ea65744dc861863399e30c7dd161 Mon Sep 17 00:00:00 2001 From: wulumeng Date: Sat, 9 May 2026 14:53:00 +0800 Subject: [PATCH 031/268] feat(metrics): report reward dict fields MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add rollout reward field metrics - Aggregate numeric fields from reward dictionaries during rollout logging - Skip the primary reward key and raw_reward to preserve existing reward metrics - Reuse the shared helper from the SGLang rollout metrics path --- relax/distributed/ray/rollout.py | 2 ++ relax/utils/metrics/metric_utils.py | 44 +++++++++++++++++++++++++++++ 2 files changed, 46 insertions(+) diff --git a/relax/distributed/ray/rollout.py b/relax/distributed/ray/rollout.py index dc3d99ecb..569015160 100644 --- a/relax/distributed/ray/rollout.py +++ b/relax/distributed/ray/rollout.py @@ -31,6 +31,7 @@ from relax.utils.metrics.metric_checker import MetricChecker from relax.utils.metrics.metric_utils import ( compute_pass_rate, + compute_rollout_explicit_reward_metrics, compute_rollout_step, compute_statistics, dict_add_prefix, @@ -3622,6 +3623,7 @@ def compute_metrics_from_samples(args, samples): log_dict = {} log_dict |= dict_add_prefix(compute_statistics(response_lengths), "response_len/") + log_dict |= compute_rollout_explicit_reward_metrics(args, samples) log_dict |= _compute_zero_std_metrics(args, samples) log_dict |= _compute_reward_cat_metrics(args, samples) log_dict["repetition_frac"] = np.mean([int(has_repetition(s.response)) for s in samples]).item() diff --git a/relax/utils/metrics/metric_utils.py b/relax/utils/metrics/metric_utils.py index 60cc131ee..42be887fb 100644 --- a/relax/utils/metrics/metric_utils.py +++ b/relax/utils/metrics/metric_utils.py @@ -4,6 +4,8 @@ import numpy as np +from relax.utils.types import Sample + logger = logging.getLogger(__name__) @@ -65,6 +67,48 @@ def compute_statistics(values: list[float]) -> dict[str, float]: } +def is_rollout_numeric_metric_value(value) -> bool: + return isinstance(value, (int, float, np.integer, np.floating)) + + +def append_rollout_numeric_metric_values(metric_values: dict[str, list[float]], *, key: str, value) -> None: + if isinstance(value, (list, tuple)): + flattened = [float(item) for item in value if is_rollout_numeric_metric_value(item)] + if flattened: + metric_values.setdefault(key, []).extend(flattened) + return + if is_rollout_numeric_metric_value(value): + metric_values.setdefault(key, []).append(float(value)) + + +def finalize_rollout_explicit_metric_values(metric_values: dict[str, list[float]]) -> dict[str, float]: + log_dict: dict[str, float] = {} + for metric_name, values in metric_values.items(): + if values: + log_dict |= dict_add_prefix(compute_statistics(values), f"{metric_name}/") + return log_dict + + +def compute_rollout_explicit_reward_metrics(args, samples: list[Sample]) -> dict[str, float]: + reward_metric_values: dict[str, list[float]] = {} + primary_reward_key = getattr(args, "reward_key", None) + for sample in samples: + reward = sample.reward + if not isinstance(reward, dict): + continue + for key, value in reward.items(): + if ( + not isinstance(key, str) + or not key + or key == primary_reward_key + or key == "raw_reward" + or key.startswith("_") + ): + continue + append_rollout_numeric_metric_values(reward_metric_values, key=key, value=value) + return finalize_rollout_explicit_metric_values(reward_metric_values) + + def compression_ratio( data: str | bytes, *, From aef19c67fe5a618fedccc8812d585f6942635f5a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Sat, 9 May 2026 17:36:51 +0800 Subject: [PATCH 032/268] fix(deepeyes): collapse duplicate image_pad tokens before load_mm_data MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Strip consecutive `<|image_pad|>` tokens in pre-tokenized prompts - Add `QwenVLImageProcessor._strip_image_token` static helper that collapses runs of `<|image_pad|>` (token id 151655) into a single placeholder while leaving the surrounding `<|vision_start|>`/`<|vision_end|>` markers intact. - Apply the helper to `prompt` before calling `load_mm_data` in `process_mm_data_async`, so pre-tokenized `input_ids` (where each image is already expanded to N image-pad tokens, one per visual patch) no longer collide with `load_mm_data` re-expanding the placeholder itself. Without the collapse, the pipeline saw `N x M` image-pad tokens and miscounted positions, breaking mrope bookkeeping. - Raw text (`str`) prompts are passed through unchanged. --- examples/deepeyes/qwen_vl.py | 49 ++++++++++++++++++- examples/deepeyes/run_deepeyes_r3.sh | 11 +++-- .../multimodal/run-qwen36-35B-A3B-8xgpu.sh | 5 ++ .../text/run-qwen35-35B-A3B-16xgpu.sh | 4 +- 4 files changed, 61 insertions(+), 8 deletions(-) diff --git a/examples/deepeyes/qwen_vl.py b/examples/deepeyes/qwen_vl.py index 449e94d3c..59e0abff3 100644 --- a/examples/deepeyes/qwen_vl.py +++ b/examples/deepeyes/qwen_vl.py @@ -279,6 +279,53 @@ def get_mm_data(self, prompt, embeddings, img_grid_thw): "mrope_position_delta": mrope_position_delta, } + @staticmethod + def _strip_image_token(input_ids, image_token_id: int = 151655): + """Collapse consecutive ``<|image_pad|>`` tokens into a single + placeholder. + + Transform:: + + <|vision_start|><|image_pad|><|image_pad|>...<|image_pad|><|vision_end|> + -> <|vision_start|><|image_pad|><|vision_end|> + + Why: the caller may pass pre-tokenized ``input_ids`` in which each image + has already been expanded into N ``<|image_pad|>`` tokens (one per visual + patch). However ``load_mm_data`` downstream expects exactly *one* + ``<|image_pad|>`` placeholder per image and re-expands it itself based on + the actual patch count. Without this collapse the pipeline would see + ``N x M`` image-pad tokens and miscount positions, breaking mrope + bookkeeping. Raw text prompts are returned unchanged. + + Args: + input_ids: List of token ids, or any non-list value (passed through). + image_token_id: Id of ``<|image_pad|>`` (151655 for Qwen-VL family). + + Returns: + ``input_ids`` with each run of consecutive ``image_token_id`` reduced + to a single occurrence, or the input untouched if it is not a list. + """ + # Raw text prompts (str) and other non-list inputs require no rewrite. + if not isinstance(input_ids, list): + return input_ids + + import numpy as np + + input_id_arr = np.array(input_ids) + + # mask[i] == True means "keep token at index i"; start by keeping all. + mask = np.ones(len(input_id_arr), dtype=bool) + + # Boolean array marking every <|image_pad|> position. + is_value = input_id_arr == image_token_id + + # A token at index i is a redundant duplicate iff both it and its left + # neighbour are <|image_pad|>. Dropping those keeps the first occurrence + # in each run and removes the rest. Index 0 has no left neighbour so it + # is always kept (mask[0] stays True). + mask[1:] &= ~(is_value[1:] & is_value[:-1]) + return input_id_arr[mask].tolist() + async def process_mm_data_async( self, image_data: List[Union[str, bytes]], @@ -299,7 +346,7 @@ async def process_mm_data_async( original_input_ids = input_text base_output = self.load_mm_data( - prompt=input_text, + prompt=self._strip_image_token(input_text), image_data=image_data, video_data=request_obj.video_data, audio_data=request_obj.audio_data, diff --git a/examples/deepeyes/run_deepeyes_r3.sh b/examples/deepeyes/run_deepeyes_r3.sh index e817ea4ff..6b2376341 100644 --- a/examples/deepeyes/run_deepeyes_r3.sh +++ b/examples/deepeyes/run_deepeyes_r3.sh @@ -60,11 +60,11 @@ CKPT_ARGS=( # DATASETS # ############################################################################### -TRAIN_FILES=() -for i in {0..9}; do - TRAIN_FILES+=("'${DATA_DIR}/deepeyes/train/v0.1.2.parquet/partition=${i}/3ce23f4945e8498085ac5f72f0afc133-0.parquet'") -done -TEST_FILES=("${DATA_DIR}/deepeyes/test.parquet") +TRAIN_FILES=( + "'${DATA_DIR}/deepeyes-v1/data_0.1.2_visual_toolbox_v2.parquet@[0:5000]'" + "'${DATA_DIR}/deepeyes-v1/data_v0.8_visual_toolbox_v2.parquet@[0:5000]'" +) +TEST_FILES=("${DATA_DIR}/deepeyes-v1/data_thinklite_reasoning_acc.parquet@[0:256]") PROMPT_SET="[$(IFS=,; echo "${TRAIN_FILES[*]}")]" ############################################################################### @@ -109,6 +109,7 @@ ROUTING_REPLAY_ARGS=( ############################################################################### EVAL_ARGS=( + --skip-eval-before-train --eval-interval 100 --eval-prompt-data vstar ${TEST_FILES} --n-samples-per-eval-prompt 8 diff --git a/scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu.sh b/scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu.sh index a515c30f1..696c2a4c4 100644 --- a/scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu.sh +++ b/scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu.sh @@ -27,6 +27,11 @@ CKPT_ARGS=( --hf-checkpoint ${EXP_DIR}/Qwen3.6-35B-A3B --ref-load ${EXP_DIR}/Qwen3.6-35B-A3B --megatron-to-hf-mode bridge + + --load ${EXP_DIR}/save/Qwen3.6-35B_mcore_8xgpu/ + --save ${EXP_DIR}/save/Qwen3.6-35B_mcore_8xgpu/ + --max-actor-ckpt-to-keep 1 + --save-interval 100 ) PROMPT_SET="${EXP_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet" diff --git a/scripts/training/text/run-qwen35-35B-A3B-16xgpu.sh b/scripts/training/text/run-qwen35-35B-A3B-16xgpu.sh index 712bea150..95d19976f 100755 --- a/scripts/training/text/run-qwen35-35B-A3B-16xgpu.sh +++ b/scripts/training/text/run-qwen35-35B-A3B-16xgpu.sh @@ -66,11 +66,11 @@ EVAL_ARGS=( ) PERF_ARGS=( - --tensor-model-parallel-size 2 + --tensor-model-parallel-size 4 --sequence-parallel --pipeline-model-parallel-size 2 --context-parallel-size 1 - --expert-model-parallel-size 4 + --expert-model-parallel-size 8 --expert-tensor-parallel-size 1 --recompute-granularity full From 79b764cffab1d6be16e6507dd8c8b9123bb5a4a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Mon, 11 May 2026 14:57:46 +0800 Subject: [PATCH 033/268] fix(megatron): GDN torch.compile + Qwen3.6 unsplit forward MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Disable torch.compile around GatedDeltaNet QKV prep - Wrap `_prepare_qkv_for_gated_delta_rule` with `torch._dynamo.config.patch(disable=True)` in the Megatron patch - Avoids torch.compile failure on the Qwen3.6 GatedDeltaNet path --- # ⭐ Feature ## Generalize unsplit-forward path to text-only Qwen3.6 / Qwen3.5 - Detect `Qwen3VLModel` at model build and set `args.uses_unsplit_forward`; the bridge model does CP+SP splitting internally for both VL and text-only Qwen3.5/3.6 sharing the same architecture - Route unsplit tokens + tp*cp*2-aligned `cu_seqlens` through `forward_only` / `train_one_step` whenever the flag is on, not just for VL inputs - Propagate the flag through `data.get_batch`, `loss.compute_advantages_and_returns`, `log_rollout_data`, and `stream_dataloader.post_process_rollout_data` so padding stays consistent ## Add Qwen3.6-35B-A3B 8xGPU DAPO-math training script - New `scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh` for sync GRPO training with TP=2/PP=2/CP=2/EP=4 and partial rollout --- .../patch/megatron/20260506-85bced0ae.patch | 18 ++ relax/backends/megatron/data.py | 12 +- relax/backends/megatron/loss.py | 2 +- relax/backends/megatron/model.py | 16 +- relax/backends/megatron/model_provider.py | 21 +++ relax/utils/data/stream_dataloader.py | 12 +- .../text/run-qwen35-35B-A3B-16xgpu.sh | 8 - .../training/text/run-qwen36-35B-A3B-8xgpu.sh | 163 ++++++++++++++++++ 8 files changed, 230 insertions(+), 22 deletions(-) create mode 100755 scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh diff --git a/docker/patch/megatron/20260506-85bced0ae.patch b/docker/patch/megatron/20260506-85bced0ae.patch index 4b600b57d..d4e706ee4 100644 --- a/docker/patch/megatron/20260506-85bced0ae.patch +++ b/docker/patch/megatron/20260506-85bced0ae.patch @@ -789,3 +789,21 @@ index 7fcf295e..7ac11345 100644 return MegatronMappingRegistry(*mapping_list) +diff --git a/megatron/core/ssm/gated_delta_net.py b/megatron/core/ssm/gated_delta_net.py +index 8df4df1e5..fa86be44d 100644 +--- a/megatron/core/ssm/gated_delta_net.py ++++ b/megatron/core/ssm/gated_delta_net.py +@@ -444,9 +444,10 @@ class GatedDeltaNet(MegatronModule): + + # Prepare QKV tensors (split, reshape, L2 norm, repeat_interleave, contiguous) + nvtx_range_push(suffix="prepare_qkv_for_gated_delta_rule") +- query, key, value, gate, beta, alpha = self._prepare_qkv_for_gated_delta_rule( +- qkv, gate, beta, alpha, batch, seq_len +- ) ++ with torch._dynamo.config.patch(disable=True): ++ query, key, value, gate, beta, alpha = self._prepare_qkv_for_gated_delta_rule( ++ qkv, gate, beta, alpha, batch, seq_len ++ ) + nvtx_range_pop(suffix="prepare_qkv_for_gated_delta_rule") + + # Calculate g and beta diff --git a/relax/backends/megatron/data.py b/relax/backends/megatron/data.py index a90362ec1..ebbe7d8d7 100644 --- a/relax/backends/megatron/data.py +++ b/relax/backends/megatron/data.py @@ -10,6 +10,7 @@ import torch.nn.functional as F from megatron.core import mpu from megatron.core.packed_seq_params import PackedSeqParams +from megatron.training.global_vars import get_args from torch.nn.utils.rnn import pad_sequence from relax.utils import device as device_utils @@ -167,14 +168,16 @@ def get_batch( packed_seq_params = None elif qkv_format == "thd": - # VL + CP > 1: bridge's Qwen3VLModel.forward expects per-sample + # bridge Qwen3VLModel.forward (used for Qwen3-VL and text-only + # Qwen3.5 / Qwen3.6 sharing the same architecture) expects per-sample # BSHD-padded input_ids + attention_mask, and re-derives the THD # packing internally with align_size = tp*cp*2. Provide unsplit # inputs and a matching packed_seq_params so the caller-side cu_seqlens # agrees with what the bridge derives from attention_mask. # Mirrors verl's build_vlm_attn_mask_thd + preprocess_thd_engine. is_vl_model = batch.get("multimodal_train_inputs") is not None - if is_vl_model and cp_size > 1: + needs_unsplit_input = is_vl_model or getattr(get_args(), "uses_unsplit_forward", False) + if needs_unsplit_input and cp_size > 1: tp_size = mpu.get_tensor_model_parallel_world_size() align_size = tp_size * cp_size * 2 device = device_utils.make_current_torch_device() @@ -591,7 +594,7 @@ def log_rollout_data( padded_total_lengths = maybe_padded_total_lengths( total_lengths, args.qkv_format, - rollout_data.get("multimodal_train_inputs") is not None, + rollout_data.get("multimodal_train_inputs") is not None or getattr(args, "uses_unsplit_forward", False), ) # OPD dynamic metric: overlap ratio on top-k token sets. @@ -747,7 +750,8 @@ def quantile(total_value, n_quantiles, data) -> dict: correct_padded_total_lengths_full = maybe_padded_total_lengths( total_lengths, args.qkv_format, - rollout_data.get("multimodal_train_inputs") is not None, + rollout_data.get("multimodal_train_inputs") is not None + or getattr(args, "uses_unsplit_forward", False), ) correct_padded_total_lengths: list[int] | None = ( [] if correct_padded_total_lengths_full is not None else None diff --git a/relax/backends/megatron/loss.py b/relax/backends/megatron/loss.py index 72967e662..c5f970aa8 100644 --- a/relax/backends/megatron/loss.py +++ b/relax/backends/megatron/loss.py @@ -465,7 +465,7 @@ def compute_advantages_and_returns(args: Namespace, rollout_data: RolloutBatch) padded_total_lengths: list[int] | None = maybe_padded_total_lengths( total_lengths, args.qkv_format, - rollout_data.get("multimodal_train_inputs") is not None, + rollout_data.get("multimodal_train_inputs") is not None or getattr(args, "uses_unsplit_forward", False), ) # return when not the last pp stage. diff --git a/relax/backends/megatron/model.py b/relax/backends/megatron/model.py index 1b29b666b..e09241868 100644 --- a/relax/backends/megatron/model.py +++ b/relax/backends/megatron/model.py @@ -259,21 +259,22 @@ def forward_step( is_vl_model = batch.get("multimodal_train_inputs", None) is not None mm_kwargs = batch["multimodal_train_inputs"] if is_vl_model else {} + needs_unsplit = is_vl_model or getattr(args, "uses_unsplit_forward", False) - # VL + CP > 1: pass unsplit tokens so Bridge handles CP split after - # vision embedding (aligns with Bridge's qwen3_vl_step.py contract). - if is_vl_model and "unsplit_tokens" in batch: + # Bridge Qwen3VLModel.forward (VL or text-only Qwen3.6) does CP+SP + # splitting internally, so pass unsplit tokens. + if needs_unsplit and "unsplit_tokens" in batch: forward_input_ids = batch["unsplit_tokens"] forward_packed_seq_params = None else: forward_input_ids = tokens forward_packed_seq_params = packed_seq_params - # thd VL+CP: bridge needs per-sample attention_mask + matching thd + # thd bridge+CP: bridge needs per-sample attention_mask + matching thd # packed_seq_params (align_size = tp*cp*2). loss_mask is None because # labels=None means GPTModel won't run internal loss; Relax's loss is # computed externally from full_loss_masks. - if is_vl_model and "vlm_packed_seq_params" in batch: + if needs_unsplit and "vlm_packed_seq_params" in batch: forward_attention_mask = batch["unsplit_attention_mask"] forward_packed_seq_params = batch["vlm_packed_seq_params"] forward_loss_mask = None @@ -455,7 +456,8 @@ def forward_step( ) else: is_vl_model = batch.get("multimodal_train_inputs", None) is not None - use_unsplit = is_vl_model and "unsplit_tokens" in batch + needs_unsplit = is_vl_model or getattr(args, "uses_unsplit_forward", False) + use_unsplit = needs_unsplit and "unsplit_tokens" in batch forward_kwargs = { "input_ids": batch["unsplit_tokens"] if use_unsplit else batch["tokens"], @@ -470,7 +472,7 @@ def forward_step( # packed_seq_params (align_size = tp*cp*2). loss_mask is None # because labels=None means GPTModel won't run internal loss; # Relax's loss is computed externally from full_loss_masks. - if is_vl_model and "vlm_packed_seq_params" in batch: + if needs_unsplit and "vlm_packed_seq_params" in batch: forward_kwargs["attention_mask"] = batch["unsplit_attention_mask"] forward_kwargs["packed_seq_params"] = batch["vlm_packed_seq_params"] forward_kwargs["loss_mask"] = None diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index 02cb7e851..bd42623d7 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -69,6 +69,24 @@ def forward( _CP_PROBE_INSTALLED = False +def _maybe_mark_unsplit_forward(args: argparse.Namespace, model: torch.nn.Module) -> None: + """Mark `args.uses_unsplit_forward` when the bridge produces a model whose + forward expects UNSPLIT input + global cu_seqlens + attention_mask and does + CP+SP splitting internally (Qwen3VLModel family — used for Qwen3-VL and + text-only Qwen3.5 / Qwen3.6 sharing the same architecture). + + Read by data.py / loss.py to build unsplit tokens + tp*cp*2-aligned + cu_seqlens instead of the pre-split + cp-multiplied form, and by model.py + to route those through the forward. + """ + try: + from megatron.bridge.models.qwen_vl.modelling_qwen3_vl.model import Qwen3VLModel + except ImportError: + return + if isinstance(model, Qwen3VLModel): + args.uses_unsplit_forward = True + + def _install_cp_probe(model: torch.nn.Module) -> None: global _CP_PROBE_INSTALLED if _CP_PROBE_INSTALLED: @@ -166,6 +184,7 @@ def wrapped_model_provider( model.output_layer = LinearForLastLayer( input_size=model.config.hidden_size, output_size=1, config=model.config ) + _maybe_mark_unsplit_forward(args, model) _install_cp_probe(model) return model @@ -249,6 +268,7 @@ def wrapped_model_provider( def provide_with_cp_probe(*p_args, **p_kwargs): model = original_provide(*p_args, **p_kwargs) + _maybe_mark_unsplit_forward(args, model) _install_cp_probe(model) return model @@ -359,6 +379,7 @@ def model_provider(pre_process: bool = True, post_process: bool = True, vp_stage if post_process and role == "critic": model.output_layer = LinearForLastLayer(input_size=config.hidden_size, output_size=1, config=config) + _maybe_mark_unsplit_forward(args, model) _install_cp_probe(model) return model diff --git a/relax/utils/data/stream_dataloader.py b/relax/utils/data/stream_dataloader.py index 8972e3e28..b6bf7d220 100644 --- a/relax/utils/data/stream_dataloader.py +++ b/relax/utils/data/stream_dataloader.py @@ -464,10 +464,18 @@ def _to_cuda(v): padded_total_lengths = maybe_padded_total_lengths( rollout_data["total_lengths"], args.qkv_format, - "multimodal_train_inputs" in rollout_data, + "multimodal_train_inputs" in rollout_data or getattr(args, "uses_unsplit_forward", False), ) - for key in ["rollout_log_probs", "teacher_log_probs"]: + for key in [ + "log_probs", + "ref_log_probs", + "rollout_log_probs", + "teacher_log_probs", + "advantages", + "returns", + "opd_reverse_kl", + ]: if key not in rollout_data: continue rollout_data[key] = [ diff --git a/scripts/training/text/run-qwen35-35B-A3B-16xgpu.sh b/scripts/training/text/run-qwen35-35B-A3B-16xgpu.sh index 95d19976f..753f1b366 100755 --- a/scripts/training/text/run-qwen35-35B-A3B-16xgpu.sh +++ b/scripts/training/text/run-qwen35-35B-A3B-16xgpu.sh @@ -134,13 +134,6 @@ MISC_ARGS=( --attention-backend flash ) -PARTIAL_ROLLOUT_ARGS=( - --partial-rollout - --over-sampling-batch-size 48 - --mask-offpolicy-in-partial-rollout - --partial-rollout-max-aborted-count 3 -) - mkdir -p log ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://${HOST_IP}:8265" \ ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ @@ -158,5 +151,4 @@ ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://${HOST_IP}:8265" \ "${PERF_ARGS[@]}" \ "${EVAL_ARGS[@]}" \ "${SGLANG_ARGS[@]}" \ - "${PARTIAL_ROLLOUT_ARGS[@]}" \ "${MISC_ARGS[@]}" 2>&1 | tee log/qwen35-35B-A3B-GRPO-gpu16-sync-${now}.log diff --git a/scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh b/scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh new file mode 100755 index 000000000..d9c69802b --- /dev/null +++ b/scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh @@ -0,0 +1,163 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3.5-35B-A3B 16xGPU (2-node) fully sync training script for DAPO math dataset. +# +# Usage: +# bash scripts/training/text/run-qwen36-35B-A3B-16xgpu-sync.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen36-35B-A3B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" +EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +NUM_ROLLOUT="${NUM_ROLLOUT:=1000}" + +CKPT_ARGS=( + --hf-checkpoint ${EXP_DIR}/Qwen3.6-35B-A3B/ + --ref-load ${EXP_DIR}/Qwen3.6-35B-A3B/ + --megatron-to-hf-mode bridge + + --load ${EXP_DIR}/save/Qwen3.6-35B-A3B_mcore_8xgpu/ + --save ${EXP_DIR}/save/Qwen3.6-35B-A3B_mcore_8xgpu/ + --save-interval 100 + --max-actor-ckpt-to-keep 1 +) + +PROMPT_SET=${EXP_DIR}/dapo-math-17k/dapo-math-17k.jsonl + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type dapo + --reward-key score + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 16 + --n-samples-per-prompt 8 + --rollout-max-response-len 8192 + --rollout-temperature 1 + --global-batch-size 128 + --use-fault-tolerance + --balance-data +) + +EVAL_ARGS=( + --log-passrate + --skip-eval-before-train + --eval-interval 20 + --eval-prompt-data aime ${EXP_DIR}/aime-2024/aime-2024.jsonl + --n-samples-per-eval-prompt 8 + --eval-max-response-len 8192 + --eval-top-p 0.7 +) + +PERF_ARGS=( + --tensor-model-parallel-size 2 + --sequence-parallel + --pipeline-model-parallel-size 2 + --context-parallel-size 2 + --calculate-per-token-loss + --expert-model-parallel-size 4 + --expert-tensor-parallel-size 1 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --use-dynamic-batch-size + --max-tokens-per-gpu 10240 +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + # NOTE(wuhuan): to avoid algorithm performance degradation + --no-rope-fusion + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 2 + --sglang-mem-fraction-static 0.7 + # --sglang-cuda-graph-bs 1 2 4 8 $(seq 16 8 256) +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen36-35B-A3B-16x-sync-${now} +) + +MISC_ARGS=( + # default dropout in megatron is 0.1 + --attention-dropout 0.0 + --hidden-dropout 0.0 + # should be good for model performance + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # need to comment this when using model with MLA + --attention-backend flash +) + +PARTIAL_ROLLOUT_ARGS=( + --partial-rollout + --over-sampling-batch-size 48 + --mask-offpolicy-in-partial-rollout + --partial-rollout-max-aborted-count 3 +) + +mkdir -p log +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://${HOST_IP}:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 8], "rollout": [1, 8]}' \ + --colocate \ + --max-staleness 0 \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${PARTIAL_ROLLOUT_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen36-35B-A3B-GRPO-gpu8-sync-${now}.log From 0d0dcfef06f80d758f83d3813765598b45ca8866 Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Tue, 12 May 2026 13:44:38 +0800 Subject: [PATCH 034/268] fix(qwen3.6): fp16 support and related fixes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Propagate fp16 to SGLang Mamba conv dtype - `relax/distributed/ray/genrm.py` and `relax/distributed/ray/rollout.py`: pass `SGLANG_MAMBA_CONV_DTYPE=float16` to the engine env when `--fp16` is set, so Qwen3.6 hybrid-Mamba layers use the matching dtype in rollout/GenRM - `scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh`: add `--fp16 --use-rollout-routing-replay --use-slime-router` - `scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh`: add `--use-rollout-routing-replay --use-slime-router`; document why fp16 is intentionally left disabled for Qwen3.5 ## Skip routing replay for MTP layers - `relax/utils/training/routing_replay.py`: MTP routers exist in training but rollout (sglang) does not run MTP, so there is nothing to record or replay against. Install a pre-hook that clears the global `ROUTING_REPLAY` (so `compute_topk` falls through to the original impl) and skip registration in `all_routing_replays` to keep the per-layer accounting consistent - Guard `compute_topk` against `ROUTING_REPLAY is None` ## Detect Ray 2.x head node by internal resource - `relax/utils/utils.py::get_serve_url`: Ray 2.x auto-registers `node:__internal_head__` on the head node; legacy setups also tag it with a custom `head` resource. Accept either when scanning `ray.nodes()` so head IP discovery works on both --- # 📝 Documentation ## Announce Qwen3.6 support in README - `README.md` / `README_zh.md`: add 05/11/2026 news entry noting Qwen3.6 series (text + VLM) support --- README.md | 7 +++--- README_zh.md | 7 +++--- relax/distributed/ray/genrm.py | 2 ++ relax/distributed/ray/rollout.py | 2 ++ relax/utils/training/routing_replay.py | 25 ++++++++++++++++--- relax/utils/utils.py | 10 +++++--- .../multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh | 4 +++ .../multimodal/run-qwen35-35B-A3B-8xgpu.sh | 4 +++ 8 files changed, 48 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index ef0460ca9..0afa9f097 100644 --- a/README.md +++ b/README.md @@ -50,9 +50,10 @@ ______________________________________________________________________ ## 📢 News -| 📣 Updates | -| :---------------------------------------------- | -| **\[04/15/2026\]** 🎉 Relax is now open-source! | +| 📣 Updates | +| :-------------------------------------------------------------------- | +| **\[05/11/2026\]** 🚀 Support for Qwen3.6 series models (text + VLM)! | +| **\[04/15/2026\]** 🎉 Relax is now open-source! | ______________________________________________________________________ diff --git a/README_zh.md b/README_zh.md index f99fe08cc..efea5346b 100644 --- a/README_zh.md +++ b/README_zh.md @@ -50,9 +50,10 @@ ______________________________________________________________________ ## 📢 最新动态 -| 📣 更新 | -| :------------------------------------- | -| **\[04/15/2026\]** 🎉 Relax 正式开源！ | +| 📣 更新 | +| :----------------------------------------------------------- | +| **\[05/11/2026\]** 🚀 支持 Qwen3.6 系列模型（纯文本+多模）！ | +| **\[04/15/2026\]** 🎉 Relax 正式开源！ | ______________________________________________________________________ diff --git a/relax/distributed/ray/genrm.py b/relax/distributed/ray/genrm.py index a1fc45cc1..d8a71d7eb 100644 --- a/relax/distributed/ray/genrm.py +++ b/relax/distributed/ray/genrm.py @@ -170,6 +170,8 @@ def init_genrm_engines(args, pg, all_genrm_engines, engine_addr_and_ports=None): "SGLANG_ENABLE_HEALTH_ENDPOINT_GENERATION": "false", "SGLANG_ENABLE_STRICT_MEM_CHECK_DURING_IDLE": "false", } + if getattr(args, "fp16", False): + env_vars["SGLANG_MAMBA_CONV_DTYPE"] = "float16" genrm_engine = GenRMRayActor.options( num_cpus=num_cpus, diff --git a/relax/distributed/ray/rollout.py b/relax/distributed/ray/rollout.py index 569015160..ce5b01800 100644 --- a/relax/distributed/ray/rollout.py +++ b/relax/distributed/ray/rollout.py @@ -469,6 +469,8 @@ def start_engines(self, port_cursors: dict[int, int] | None = None) -> tuple[lis "SGLANG_ENABLE_STRICT_MEM_CHECK_DURING_IDLE": "false", }.items() } + if getattr(self.args, "fp16", False): + env_vars["SGLANG_MAMBA_CONV_DTYPE"] = "float16" rollout_engine = RolloutRayActor.options( num_cpus=num_cpus, diff --git a/relax/utils/training/routing_replay.py b/relax/utils/training/routing_replay.py index 95610316e..afbe3ab82 100644 --- a/relax/utils/training/routing_replay.py +++ b/relax/utils/training/routing_replay.py @@ -59,7 +59,9 @@ def clear_all_forward(): def get_routing_replay_compute_topk(old_compute_topk): def compute_topk(scores, topk, num_groups=None, group_topk=None): - if os.environ.get("ENABLE_ROUTING_REPLAY", "0") == "1": + # ROUTING_REPLAY is None for routers that opt out of replay (e.g. MTP), + # in which case we fall through to the original implementation. + if os.environ.get("ENABLE_ROUTING_REPLAY", "0") == "1" and ROUTING_REPLAY is not None: routing_replay_stage = os.environ["ROUTING_REPLAY_STAGE"] if routing_replay_stage == "fallthrough": return old_compute_topk(scores, topk, num_groups=num_groups, group_topk=group_topk) @@ -86,10 +88,25 @@ def compute_topk(scores, topk, num_groups=None, group_topk=None): def register_routing_replay(module): - if os.environ.get("ENABLE_ROUTING_REPLAY", "0") == "1": - module.routing_replay = RoutingReplay() + if os.environ.get("ENABLE_ROUTING_REPLAY", "0") != "1": + return + + # MTP routers exist in training but rollout (sglang) doesn't run MTP, so + # there's nothing to record/replay against. Install a pre-hook that clears + # the global ROUTING_REPLAY (compute_topk falls through) and skip + # registration in `all_routing_replays` so fill_routing_replay's per-layer + # accounting stays consistent. + if getattr(module, "is_mtp_layer", False): def pre_forward_hook(*args, **kwargs): - set_routing_replay(module.routing_replay) + set_routing_replay(None) module.register_forward_pre_hook(pre_forward_hook) + return + + module.routing_replay = RoutingReplay() + + def pre_forward_hook(*args, **kwargs): + set_routing_replay(module.routing_replay) + + module.register_forward_pre_hook(pre_forward_hook) diff --git a/relax/utils/utils.py b/relax/utils/utils.py index d9e81efd2..e4a6091ab 100644 --- a/relax/utils/utils.py +++ b/relax/utils/utils.py @@ -415,10 +415,14 @@ def get_serve_url(route_prefix: str = "") -> str: # 1. Determine head node IP. Prefer Ray cluster state; fall back to # local hostname resolution for client-on-head scenarios. try: - # ray.nodes() returns info for all nodes + # ray.nodes() returns info for all nodes. Ray 2.x auto-registers + # "node:__internal_head__" on the head node; some legacy setups also + # mark it with a custom "head" resource. Accept either. for node in ray.nodes(): - if node["Alive"] and node.get("Resources", {}).get("head"): - # Some setups mark head with a 'head' resource; not always present + if not node["Alive"]: + continue + resources = node.get("Resources", {}) + if "node:__internal_head__" in resources or resources.get("head"): head_ip = node["NodeManagerAddress"] break else: diff --git a/scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh b/scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh index f0b389e0d..7845c35a8 100644 --- a/scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh @@ -94,6 +94,10 @@ OPTIMIZER_ARGS=( --no-rope-fusion --moe-router-load-balancing-type "none" --moe-aux-loss-coeff 0.0 + + --fp16 + --use-rollout-routing-replay + --use-slime-router ) SGLANG_ARGS=( diff --git a/scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh b/scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh index be3592a62..5672e3563 100644 --- a/scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh +++ b/scripts/training/multimodal/run-qwen35-35B-A3B-8xgpu.sh @@ -96,6 +96,10 @@ OPTIMIZER_ARGS=( # NOTE(wuhuan): to avoid algorithm performance degradation --moe-router-load-balancing-type "none" --moe-aux-loss-coeff 0.0 + + # --fp16 # Qwen3.5 does not support fp16 training for now + --use-rollout-routing-replay + --use-slime-router ) SGLANG_ARGS=( From 2a24010e5c2654700f89d273b8709cbd7f09bcb2 Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Tue, 12 May 2026 23:18:41 +0800 Subject: [PATCH 035/268] feat(genrm): support shared-GPU colocate with rollout MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Auto-detect shared-GPU colocate sub-mode for GenRM - Pick mode from GPU allocation: `R+G==A` keeps the existing split layout; `R==G==A` activates the new shared layout where rollout and genrm overlap on the same bundles. Other combinations are now rejected at startup with a clear error. - Drop the bundle offset for genrm in shared mode so both engines schedule on the same `[0, A)` bundles, and lower genrm Ray fractional `num_gpus` default from 0.2 to 0.1 to leave room alongside rollout. - Plumb `mem_fraction_static` through `--genrm-engine-config`; rollout keeps using `--sglang-mem-fraction-static`. Two engines can now split each GPU independently. - Onload rollout weights and genrm KV in parallel inside `update_weights()` so both engines come back together before the next rollout step. --- # 📝 Documentation ## Document the new GenRM colocate sub-mode (en + zh) - Add a second ASCII architecture diagram for the shared layout and a sub-mode auto-detection table. - Introduce a Shared-mode launch example with `mem_fraction_static` settings and a warning to keep the per-GPU sum < 1.0. - Update Best Practices with sub-mode selection guidance and OOM troubleshooting for shared mode. - Fix stale defaults in the sampling-config table (temperature 0.2 -> 0.1, max_response_len 1024 -> 4096) and add `ep_size` / `mem_fraction_static` to the engine-config table. - Update the example launch script to demonstrate shared mode (rollout 0.5 + genrm 0.3, both on 8 GPU). --- # 🔩 Chore ## Add py-spy multi-PID dump helper - `scripts/tools/_pyspy_dump.sh` runs `py-spy dump` over a list of PIDs in one ray-job submission, used by the debug-hang skill to avoid per-PID submission overhead. --- docs/en/api/genrm.md | 7 + docs/en/examples/generative-reward-model.md | 124 +++++++++++++----- docs/zh/api/genrm.md | 7 + docs/zh/examples/generative-reward-model.md | 124 +++++++++++++----- .../run-qwen3-4B-8xgpu-colocated.sh | 6 +- relax/backends/megatron/actor.py | 11 +- relax/backends/sglang/sglang_engine.py | 5 + relax/distributed/ray/genrm.py | 8 +- relax/utils/arguments.py | 32 +++-- scripts/tools/_pyspy_dump.sh | 10 ++ 10 files changed, 256 insertions(+), 78 deletions(-) create mode 100755 scripts/tools/_pyspy_dump.sh diff --git a/docs/en/api/genrm.md b/docs/en/api/genrm.md index 4b9b84807..715cd3fea 100644 --- a/docs/en/api/genrm.md +++ b/docs/en/api/genrm.md @@ -32,6 +32,13 @@ When colocated with the Actor (sharing GPU resources), GenRM supports offload/on - **Offload**: Releases GPU memory before Actor training - **Onload**: Loads model weights back to GPU before rollout +Two colocate sub-modes are auto-detected from the GPU allocation: + +- **Split** (`rollout_num_gpus + genrm_num_gpus == actor_total_gpus`): GenRM and Rollout occupy disjoint bundles. +- **Shared** (`rollout_num_gpus == genrm_num_gpus == actor_total_gpus`): GenRM and Rollout occupy the same bundles, splitting each GPU's memory via SGLang `mem_fraction_static`. GenRM reads its `mem_fraction_static` from `--genrm-engine-config`. GenRM never sources weights from the Actor; onload only resumes its KV cache and CUDA graphs. + +See [GenRM example](/en/examples/generative-reward-model) for full configuration. + ## HTTP Endpoints diff --git a/docs/en/examples/generative-reward-model.md b/docs/en/examples/generative-reward-model.md index c1a7183f5..c3c1b75d2 100644 --- a/docs/en/examples/generative-reward-model.md +++ b/docs/en/examples/generative-reward-model.md @@ -16,19 +16,42 @@ Both scripts in this example train **Qwen3-4B** with **GRPO** on the `dapo-math- ## Architecture -In the recommended **colocate mode**, all 8 GPUs are owned by the Actor (training). During the inference phase, the Actor offloads its weights and the GPUs are time-shared: 4 GPUs run the Rollout engine, and the other 4 GPUs run the GenRM engine. Once inference is complete, GenRM and Rollout offload their weights back, and all 8 GPUs are reclaimed for training. This means the GenRM GPUs are not wasted — they directly accelerate training when not evaluating. +In the recommended **colocate mode**, all GPUs are owned by the Actor (training). During the inference phase, the Actor offloads its weights and the GPUs are time-shared by Rollout and GenRM. Once inference is complete, GenRM and Rollout offload their weights back, and all GPUs are reclaimed for training. The GenRM GPUs are therefore not wasted — they directly accelerate training when not evaluating. + +Two colocate sub-modes are auto-detected from your GPU allocation: + +- **Split mode** (`rollout_num_gpus + genrm_num_gpus == actor_total_gpus`): Rollout and GenRM occupy disjoint GPU bundles. Best when both engines are small enough that giving each a dedicated slice is more efficient than sharing. +- **Shared mode** (`rollout_num_gpus == genrm_num_gpus == actor_total_gpus`): Rollout and GenRM occupy the **same** GPU bundles, splitting each GPU's memory via SGLang `mem_fraction_static`. Best when GenRM is large (e.g., 30B) and benefits from full-cluster TP, while still leaving room for Rollout. Requires no extra CLI flag — it activates automatically. ``` - 8-GPU Colocate Mode + 8-GPU Colocate (Split) ┌──────────── Placement Group (8 GPU) ────────────┐ │ │ │ Inference phase: │ │ ┌───────────────────┐ ┌───────────────────┐ │ │ │ Rollout (4 GPU) │──►│ GenRM (4 GPU) │ │ - │ │ SGLang Engine │◄──│ SGLang Engine │ │ + │ │ bundles 0..3 │◄──│ bundles 4..7 │ │ │ └───────────────────┘ └───────────────────┘ │ - │ Score: 0 / 1 │ + │ │ + │ Training phase (offload inference weights): │ + │ ┌─────────────────────────────────────────┐ │ + │ │ Actor (8 GPU) │ │ + │ │ Megatron Training │ │ + │ └─────────────────────────────────────────┘ │ + └─────────────────────────────────────────────────┘ + + + 8-GPU Colocate (Shared) + + ┌──────────── Placement Group (8 GPU) ────────────┐ + │ │ + │ Inference phase (same bundles 0..7): │ + │ ┌─────────────────────────────────────────┐ │ + │ │ Rollout: mem_fraction_static = 0.6 │ │ + │ │ GenRM : mem_fraction_static = 0.3 │ │ + │ │ ~0.1 reserved for cuda / activations │ │ + │ └─────────────────────────────────────────┘ │ │ │ │ Training phase (offload inference weights): │ │ ┌─────────────────────────────────────────┐ │ @@ -38,7 +61,7 @@ In the recommended **colocate mode**, all 8 GPUs are owned by the Actor (trainin └─────────────────────────────────────────────────┘ ``` -All components live in the same placement group. During the inference phase, 4 GPUs run Rollout and 4 GPUs run GenRM. Rollout generates candidate responses and sends them with the ground-truth label to GenRM over HTTP; GenRM returns a binary score (1 = consistent, 0 = inconsistent). After reward computation, inference weights are offloaded and all 8 GPUs are reclaimed by the Actor for training. +All components live in the same placement group. Rollout generates candidate responses and sends them with the ground-truth label to GenRM over HTTP; GenRM returns a binary score (1 = consistent, 0 = inconsistent). After reward computation, inference weights are offloaded and all GPUs are reclaimed by the Actor for training. ## Scripts @@ -49,15 +72,23 @@ All components live in the same placement group. During the inference phase, 4 G ### Resource Layout -**Colocate mode** (`--colocate`, recommended): +**Colocate / Split** (`--colocate`, recommended for small GenRM): ``` Actor (training): 8 GPU (all GPUs participate in training) -Rollout: 4 GPU (time-shared with actor via offload) -GenRM: 4 GPU (time-shared with actor via offload) +Rollout: 4 GPU (bundles 0..3, time-shared with actor) +GenRM: 4 GPU (bundles 4..7, time-shared with actor) ``` -In this mode, the GenRM GPUs are not idle during training — they are offloaded back to the Actor for gradient computation, effectively giving training the full 8-GPU parallelism. +**Colocate / Shared** (`--colocate`, recommended for large GenRM): + +``` +Actor (training): 8 GPU (all GPUs participate in training) +Rollout: 8 GPU (bundles 0..7, mem_fraction_static = 0.6) +GenRM: 8 GPU (bundles 0..7, mem_fraction_static = 0.3) +``` + +In both colocate sub-modes, GenRM GPUs are offloaded back to the Actor for gradient computation during training, giving training full 8-GPU parallelism. Shared mode additionally lets GenRM use a larger TP (e.g., TP=8 for a 30B model) without giving up Rollout throughput. **Async mode** (`--fully-async`): @@ -131,27 +162,28 @@ Expected response: ### Engine Config Keys -| Key | Type | Default | Description | -| :----------------- | :---- | :------ | :------------------------ | -| `max_context_len` | `int` | `8192` | Maximum context length | -| `dp_size` | `int` | `1` | Data parallelism size | -| `pp_size` | `int` | `1` | Pipeline parallelism size | -| `max_total_tokens` | `int` | `8192` | Maximum total tokens | +| Key | Type | Default | Description | +| :-------------------- | :------ | :--------------- | :------------------------------------------------------------------------------------------- | +| `max_context_len` | `int` | `8192` | Maximum context length | +| `dp_size` | `int` | `1` | Data parallelism size | +| `pp_size` | `int` | `1` | Pipeline parallelism size | +| `ep_size` | `int` | `1` | Expert parallelism size | +| `mem_fraction_static` | `float` | SGLang default | Per-engine SGLang static memory fraction. Set this in shared-GPU colocate mode (see below). | ### Sampling Config Keys | Key | Type | Default | Description | | :----------------- | :------ | :------ | :--------------------------- | -| `temperature` | `float` | `0.2` | Sampling temperature | +| `temperature` | `float` | `0.1` | Sampling temperature | | `top_p` | `float` | `1.0` | Nucleus sampling probability | | `top_k` | `int` | `-1` | Top-k sampling (-1 disables) | -| `max_response_len` | `int` | `1024` | Maximum response length | +| `max_response_len` | `int` | `4096` | Maximum response length | ### Resource Allocation GenRM is included in the `--resource` JSON as a `"genrm"` role. The format is `[num_groups, num_gpus_per_group]`. -**Colocated mode** (recommended): +**Colocated / Split** (small GenRM, default): ```bash python3 relax/entrypoints/train.py \ @@ -164,8 +196,33 @@ python3 relax/entrypoints/train.py \ --rm-type dapo-genrm ``` -::: warning -In colocated mode, total inference GPUs (rollout + genRM) must not exceed actor GPUs. For example, on an 8-GPU machine: `--resource '{"actor": [1, 8], "rollout": [1, 4], "genrm": [1, 4]}'` uses all 8 GPUs for inference, which are shared with training via offload. +**Colocated / Shared** (large GenRM, NEW): set rollout and genrm to the full actor allocation; the framework auto-detects shared mode and lets the two engines split each GPU's memory via `mem_fraction_static`: + +```bash +python3 relax/entrypoints/train.py \ + --genrm-model-path /path/to/genrm/model \ + --genrm-num-gpus-per-engine 8 \ + --genrm-engine-config '{"max_context_len": 10240, "mem_fraction_static": 0.3}' \ + --genrm-sampling-config '{"temperature": 0.1, "top_p": 1.0, "top_k": -1, "max_response_len": 1024}' \ + --rollout-num-gpus-per-engine 1 \ + --sglang-mem-fraction-static 0.6 \ + --resource '{"actor": [1, 8], "rollout": [1, 8], "genrm": [1, 8]}' \ + --colocate \ + --rm-type dapo-genrm +``` + +::: tip Auto-detected colocate sub-mode +On `--colocate` with GenRM, the GPU layout determines the sub-mode automatically: + +| Allocation | Sub-mode | +| :--------------------------------------------------- | :---------------------------------- | +| `rollout_num_gpus + genrm_num_gpus == actor_total` | **Split** (rollout and genrm on disjoint bundles) | +| `rollout_num_gpus == genrm_num_gpus == actor_total` | **Shared** (rollout and genrm on the same bundles) | +| Anything else | Rejected at startup with a clear error | +::: + +::: warning Set `mem_fraction_static` in shared mode +In shared mode the two SGLang engines live on the same GPUs. You **must** size their `mem_fraction_static` so that the sum is < 1.0 (≤ 0.9 recommended; the rest covers cuda graphs and activations). Rollout reads `--sglang-mem-fraction-static` (or YAML overrides via `--sglang-config`); GenRM reads `mem_fraction_static` inside `--genrm-engine-config`. ::: **Fully-Async mode**: @@ -309,11 +366,15 @@ print(response) # "1" or "0" ## Best Practices -1. **Prefer colocate mode**: In colocate mode, GenRM GPUs are offloaded back to training when not evaluating, so all 8 GPUs participate in gradient computation. This gives better GPU utilization than async mode, where GenRM GPUs sit idle during training -2. **Set appropriate context length**: `max_context_len` in engine config should accommodate your longest prompt + response combination -3. **Use low sampling temperature**: A temperature of 0.1 produces deterministic evaluations; increase only if evaluation diversity is desired -4. **Monitor health**: Periodically check the `/health` endpoint to ensure GenRM engines are running properly -5. **Match GPU allocation to model size**: For large GenRM models (e.g., 30B), allocate more GPUs per engine via `--genrm-num-gpus-per-engine` +1. **Prefer colocate mode**: In colocate mode, GenRM GPUs are offloaded back to training when not evaluating, so all GPUs participate in gradient computation. This gives better GPU utilization than async mode, where GenRM GPUs sit idle during training. +2. **Choose the right colocate sub-mode**: + - Use **split** when GenRM is small enough to run on a partial slice (e.g., a 4B reward model on 4 GPUs). + - Use **shared** when GenRM is large and benefits from cluster-wide TP (e.g., a 30B MoE on TP=8). Shared mode also avoids the "GenRM is too small to use a dedicated bundle and Rollout is starved for GPUs" tradeoff. +3. **Size `mem_fraction_static` carefully in shared mode**: Sum across engines should be ≤ 0.9. Common starting point: rollout 0.6, genrm 0.3. +4. **Set appropriate context length**: `max_context_len` in engine config should accommodate your longest prompt + response combination. +5. **Use low sampling temperature**: A temperature of 0.1 produces deterministic evaluations; increase only if evaluation diversity is desired. +6. **Monitor health**: Periodically check the `/health` endpoint to ensure GenRM engines are running properly. +7. **Match GPU allocation to model size**: For large GenRM models (e.g., 30B), use shared mode with `--genrm-num-gpus-per-engine` set to the full cluster size. ## Troubleshooting @@ -323,13 +384,16 @@ Ensure `--genrm-model-path` is set. GenRM is only activated when this argument i ### Resource Allocation Error in Colocated Mode -In colocated mode with GenRM, total inference GPUs (rollout + genRM) must not exceed actor GPUs: +In colocated mode with GenRM, GPU allocation must satisfy **exactly one** of: -``` -rollout_num_gpus + genrm_num_gpus <= actor_total_gpus -``` +- **Split**: `rollout_num_gpus + genrm_num_gpus == actor_total_gpus` +- **Shared**: `rollout_num_gpus == genrm_num_gpus == actor_total_gpus` + +Anything else (e.g., `rollout + genrm < actor_total`, or `rollout < actor_total < rollout + genrm`) is rejected at startup. Adjust `rollout` and/or `genrm` in `--resource` to one of the two valid layouts. + +### Shared Mode: OOM or Engine Init Fails -Adjust the `rollout` and/or `genrm` GPU allocation in `--resource` accordingly. +If shared mode hits OOM during engine startup or cuda graph capture, lower `mem_fraction_static` for one or both engines so that the per-GPU sum stays ≤ 0.9. With large MoE GenRM models, you may also need to disable cuda graphs or reduce `max_context_len`. ### Engine Initialization Timeout diff --git a/docs/zh/api/genrm.md b/docs/zh/api/genrm.md index 641d9a154..b28a542b5 100644 --- a/docs/zh/api/genrm.md +++ b/docs/zh/api/genrm.md @@ -32,6 +32,13 @@ GenRM（生成式奖励模型）服务提供基于 LLM 的响应评估。它以 - **Offload（卸载）**：在 Actor 训练前释放 GPU 显存 - **Onload（加载）**：在 rollout 前将模型权重重新加载到 GPU +根据 GPU 分配，框架会自动识别两种 colocate 子模式： + +- **Split**（`rollout_num_gpus + genrm_num_gpus == actor_total_gpus`）：GenRM 与 Rollout 占用不重叠的 bundle。 +- **Shared**（`rollout_num_gpus == genrm_num_gpus == actor_total_gpus`）：GenRM 与 Rollout 占用相同的 bundle，通过 SGLang 的 `mem_fraction_static` 切分每张 GPU 的显存。GenRM 的 `mem_fraction_static` 从 `--genrm-engine-config` 读取。GenRM 不会从 Actor 同步权重，onload 仅恢复 KV cache 和 CUDA graph。 + +完整配置参见 [GenRM 示例](/zh/examples/generative-reward-model)。 + ## HTTP 端点 diff --git a/docs/zh/examples/generative-reward-model.md b/docs/zh/examples/generative-reward-model.md index bb8bf2e65..cc1e8788f 100644 --- a/docs/zh/examples/generative-reward-model.md +++ b/docs/zh/examples/generative-reward-model.md @@ -16,19 +16,42 @@ GenRM（Generative Reward Model，生成式奖励模型）利用预训练的大 ## 架构 -在推荐的 **colocate 模式**下，全部 8 张 GPU 归 Actor（训练）所有。在推理阶段，Actor 卸载权重，GPU 被分时复用：4 张 GPU 运行 Rollout 引擎，另外 4 张 GPU 运行 GenRM 引擎。推理完成后，GenRM 和 Rollout 卸载权重，全部 8 张 GPU 重新用于训练。这意味着 GenRM 的 GPU 不会被浪费——它们在不进行评估时直接加速训练。 +在推荐的 **colocate 模式**下，全部 GPU 归 Actor（训练）所有。在推理阶段，Actor 卸载权重，GPU 由 Rollout 和 GenRM 分时复用。推理完成后，GenRM 和 Rollout 卸载权重，全部 GPU 重新归 Actor 训练使用。这意味着 GenRM 的 GPU 不会被浪费——它们在不进行评估时直接加速训练。 + +根据 GPU 分配，框架会自动识别两种 colocate 子模式： + +- **Split 模式**（`rollout_num_gpus + genrm_num_gpus == actor_total_gpus`）：Rollout 和 GenRM 占用**不重叠**的 GPU bundle。适合两个引擎都不大、各自独占一组 GPU 比共享更高效的场景。 +- **Shared 模式**（`rollout_num_gpus == genrm_num_gpus == actor_total_gpus`）：Rollout 和 GenRM 占用**相同**的 GPU bundle，通过 SGLang 的 `mem_fraction_static` 切分每张 GPU 的显存。适合 GenRM 较大（如 30B）、需要全集群 TP，但又不想牺牲 Rollout 吞吐的场景。**无需新增 CLI flag，自动启用**。 ``` - 8-GPU Colocate Mode + 8-GPU Colocate (Split) ┌──────────── Placement Group (8 GPU) ────────────┐ │ │ │ Inference phase: │ │ ┌───────────────────┐ ┌───────────────────┐ │ │ │ Rollout (4 GPU) │──►│ GenRM (4 GPU) │ │ - │ │ SGLang Engine │◄──│ SGLang Engine │ │ + │ │ bundles 0..3 │◄──│ bundles 4..7 │ │ │ └───────────────────┘ └───────────────────┘ │ - │ Score: 0 / 1 │ + │ │ + │ Training phase (offload inference weights): │ + │ ┌─────────────────────────────────────────┐ │ + │ │ Actor (8 GPU) │ │ + │ │ Megatron Training │ │ + │ └─────────────────────────────────────────┘ │ + └─────────────────────────────────────────────────┘ + + + 8-GPU Colocate (Shared) + + ┌──────────── Placement Group (8 GPU) ────────────┐ + │ │ + │ Inference phase (same bundles 0..7): │ + │ ┌─────────────────────────────────────────┐ │ + │ │ Rollout: mem_fraction_static = 0.6 │ │ + │ │ GenRM : mem_fraction_static = 0.3 │ │ + │ │ ~0.1 reserved for cuda / activations │ │ + │ └─────────────────────────────────────────┘ │ │ │ │ Training phase (offload inference weights): │ │ ┌─────────────────────────────────────────┐ │ @@ -38,7 +61,7 @@ GenRM（Generative Reward Model，生成式奖励模型）利用预训练的大 └─────────────────────────────────────────────────┘ ``` -所有组件共处同一个 placement group。在推理阶段，4 张 GPU 运行 Rollout、4 张 GPU 运行 GenRM。Rollout 生成候选响应，然后将响应与标准答案一起通过 HTTP 发送给 GenRM；GenRM 返回二值评分（1 = 一致，0 = 不一致）。奖励计算完成后，推理权重被卸载，全部 8 张 GPU 归还给 Actor 用于训练。 +所有组件共处同一个 placement group。Rollout 生成候选响应，然后将响应与标准答案一起通过 HTTP 发送给 GenRM；GenRM 返回二值评分（1 = 一致，0 = 不一致）。奖励计算完成后，推理权重被卸载，全部 GPU 归还给 Actor 用于训练。 ## 脚本 @@ -49,15 +72,23 @@ GenRM（Generative Reward Model，生成式奖励模型）利用预训练的大 ### 资源分配 -**Colocate 模式**（`--colocate`，推荐）： +**Colocate / Split**（`--colocate`，推荐用于小 GenRM）： ``` Actor（训练）: 8 GPU（全部 GPU 参与训练） -Rollout: 4 GPU（通过 offload 与 actor 分时复用） -GenRM: 4 GPU（通过 offload 与 actor 分时复用） +Rollout: 4 GPU（bundles 0..3，与 actor 分时复用） +GenRM: 4 GPU（bundles 4..7，与 actor 分时复用） ``` -在此模式下，GenRM 的 GPU 在训练阶段不会闲置——它们被卸载回 Actor 用于梯度计算，使训练可以利用全部 8 GPU 的并行能力。 +**Colocate / Shared**（`--colocate`，推荐用于大 GenRM）： + +``` +Actor（训练）: 8 GPU（全部 GPU 参与训练） +Rollout: 8 GPU（bundles 0..7，mem_fraction_static = 0.6） +GenRM: 8 GPU（bundles 0..7，mem_fraction_static = 0.3） +``` + +两种 colocate 子模式下，GenRM 的 GPU 在训练阶段都会卸载回 Actor 做梯度计算，训练始终能使用全部 8 GPU 并行能力。Shared 模式额外允许 GenRM 使用更大的 TP（例如 30B 模型用 TP=8），同时不牺牲 Rollout 吞吐。 **Async 模式**（`--fully-async`）： @@ -130,27 +161,28 @@ curl http://localhost:8000/genrm/health ### 引擎配置键 -| 键 | 类型 | 默认值 | 描述 | -| :----------------- | :---- | :----- | :-------------- | -| `max_context_len` | `int` | `8192` | 最大上下文长度 | -| `dp_size` | `int` | `1` | 数据并行大小 | -| `pp_size` | `int` | `1` | 流水线并行大小 | -| `max_total_tokens` | `int` | `8192` | 最大 token 总数 | +| 键 | 类型 | 默认值 | 描述 | +| :-------------------- | :------ | :-------------- | :-------------------------------------------------------------------- | +| `max_context_len` | `int` | `8192` | 最大上下文长度 | +| `dp_size` | `int` | `1` | 数据并行大小 | +| `pp_size` | `int` | `1` | 流水线并行大小 | +| `ep_size` | `int` | `1` | 专家并行大小 | +| `mem_fraction_static` | `float` | SGLang 默认值 | 单引擎 SGLang 静态显存比例。**Shared 模式下必须设置**（见下文配置示例）。 | ### 采样配置键 | 键 | 类型 | 默认值 | 描述 | | :----------------- | :------ | :----- | :------------------------ | -| `temperature` | `float` | `0.2` | 采样温度 | +| `temperature` | `float` | `0.1` | 采样温度 | | `top_p` | `float` | `1.0` | 核采样概率 | | `top_k` | `int` | `-1` | Top-k 采样（-1 表示禁用） | -| `max_response_len` | `int` | `1024` | 最大响应长度 | +| `max_response_len` | `int` | `4096` | 最大响应长度 | ### 资源分配 GenRM 在 `--resource` JSON 中作为 `"genrm"` 角色配置，格式为 `[num_groups, num_gpus_per_group]`。 -**Colocated 模式**（推荐）： +**Colocated / Split**（小 GenRM，默认）： ```bash python3 relax/entrypoints/train.py \ @@ -163,8 +195,33 @@ python3 relax/entrypoints/train.py \ --rm-type dapo-genrm ``` -::: warning 警告 -在 colocated 模式下，推理 GPU 总数（rollout + genRM）不能超过 actor GPU 数量。例如在 8 GPU 机器上：`--resource '{"actor": [1, 8], "rollout": [1, 4], "genrm": [1, 4]}'` 使用全部 8 个 GPU 进行推理，通过卸载与训练共享。 +**Colocated / Shared**（大 GenRM，新增）：把 rollout 和 genrm 都设为 actor 的全部 GPU；框架自动识别为 shared 模式，让两个引擎通过 `mem_fraction_static` 切分每张 GPU 的显存： + +```bash +python3 relax/entrypoints/train.py \ + --genrm-model-path /path/to/genrm/model \ + --genrm-num-gpus-per-engine 8 \ + --genrm-engine-config '{"max_context_len": 10240, "mem_fraction_static": 0.3}' \ + --genrm-sampling-config '{"temperature": 0.1, "top_p": 1.0, "top_k": -1, "max_response_len": 1024}' \ + --rollout-num-gpus-per-engine 1 \ + --sglang-mem-fraction-static 0.6 \ + --resource '{"actor": [1, 8], "rollout": [1, 8], "genrm": [1, 8]}' \ + --colocate \ + --rm-type dapo-genrm +``` + +::: tip 自动识别 colocate 子模式 +启用 `--colocate` 且配置了 GenRM 时，GPU 分配决定子模式： + +| 分配 | 子模式 | +| :---------------------------------------------------- | :-------------------------------------- | +| `rollout_num_gpus + genrm_num_gpus == actor_total` | **Split**（rollout 和 genrm 在不同 bundle） | +| `rollout_num_gpus == genrm_num_gpus == actor_total` | **Shared**（rollout 和 genrm 在同一组 bundle） | +| 其他 | 启动时报错拒绝 | +::: + +::: warning Shared 模式必须设置 `mem_fraction_static` +Shared 模式下两个 SGLang 引擎共占同一组 GPU，必须设置各自的 `mem_fraction_static`，使**单卡之和 < 1.0**（建议 ≤ 0.9，剩余给 cuda graph + activations）。Rollout 通过 `--sglang-mem-fraction-static`（或 `--sglang-config` YAML overrides）配置；GenRM 通过 `--genrm-engine-config` 中的 `mem_fraction_static` 配置。 ::: **Fully-Async 模式**： @@ -308,11 +365,15 @@ print(response) # "1" 或 "0" ## 最佳实践 -1. **优先使用 colocate 模式**：在 colocate 模式下，GenRM 的 GPU 在不进行评估时会卸载回训练，全部 8 张 GPU 参与梯度计算。这比 async 模式的 GPU 利用率更高，后者 GenRM 的 GPU 在训练阶段处于闲置状态 -2. **设置合适的上下文长度**：引擎配置中的 `max_context_len` 应能容纳最长的 prompt + 响应组合 -3. **使用低采样温度**：温度 0.1 可产生确定性的评估结果；仅在需要评估多样性时提高 -4. **监控健康状态**：定期检查 `/health` 端点，确保 GenRM 引擎正常运行 -5. **按模型大小分配 GPU**：对于大型 GenRM 模型（如 30B），通过 `--genrm-num-gpus-per-engine` 为每个引擎分配更多 GPU +1. **优先使用 colocate 模式**：在 colocate 模式下，GenRM 的 GPU 在不进行评估时会卸载回训练，全部 GPU 都参与梯度计算。比 async 模式的 GPU 利用率更高（async 模式下 GenRM 的 GPU 在训练阶段处于闲置）。 +2. **选对 colocate 子模式**： + - GenRM 较小、能放在部分 GPU 上时（如 4B reward model 用 4 GPU），用 **Split**。 + - GenRM 较大、需要全集群 TP 时（如 30B MoE 用 TP=8），用 **Shared**。Shared 模式还能避免「GenRM 太大装不下 4 GPU、Rollout 又被挤压」的两难。 +3. **Shared 模式下谨慎设置 `mem_fraction_static`**：单卡上各引擎之和 ≤ 0.9。常用起点：rollout 0.6、genrm 0.3。 +4. **设置合适的上下文长度**：引擎配置中的 `max_context_len` 应能容纳最长的 prompt + 响应组合。 +5. **使用低采样温度**：温度 0.1 可产生确定性的评估结果；仅在需要评估多样性时提高。 +6. **监控健康状态**：定期检查 `/health` 端点，确保 GenRM 引擎正常运行。 +7. **按模型大小分配 GPU**：大型 GenRM 模型（如 30B）建议 shared 模式 + `--genrm-num-gpus-per-engine` 设为整个集群规模。 ## 故障排除 @@ -322,13 +383,16 @@ print(response) # "1" 或 "0" ### Colocated 模式下资源分配错误 -在启用 GenRM 的 colocated 模式下，推理 GPU 总数（rollout + genRM）不能超过 actor GPU 数量： +在启用 GenRM 的 colocated 模式下，GPU 分配必须**恰好**满足以下两种之一： -``` -rollout_num_gpus + genrm_num_gpus <= actor_total_gpus -``` +- **Split**：`rollout_num_gpus + genrm_num_gpus == actor_total_gpus` +- **Shared**：`rollout_num_gpus == genrm_num_gpus == actor_total_gpus` + +其它组合（例如 `rollout + genrm < actor_total`，或 `rollout < actor_total < rollout + genrm`）会在启动阶段被拒绝。请把 `--resource` 中的 `rollout` / `genrm` 调整到这两种合法布局之一。 + +### Shared 模式 OOM 或引擎初始化失败 -请相应调整 `--resource` 中 `rollout` 和/或 `genrm` 的 GPU 分配。 +如果 shared 模式启动时 OOM 或 cuda graph capture 失败，降低一个或两个引擎的 `mem_fraction_static`，让单卡之和 ≤ 0.9。对大 MoE GenRM，可能还需要禁用 cuda graph 或减小 `max_context_len`。 ### 引擎初始化超时 diff --git a/examples/generate_reward_model/run-qwen3-4B-8xgpu-colocated.sh b/examples/generate_reward_model/run-qwen3-4B-8xgpu-colocated.sh index 5e4d8560d..a4fa4ec56 100755 --- a/examples/generate_reward_model/run-qwen3-4B-8xgpu-colocated.sh +++ b/examples/generate_reward_model/run-qwen3-4B-8xgpu-colocated.sh @@ -110,7 +110,7 @@ OPTIMIZER_ARGS=( SGLANG_ARGS=( --rollout-num-gpus-per-engine 1 - --sglang-mem-fraction-static 0.8 + --sglang-mem-fraction-static 0.5 ) WANDB_ARGS=( @@ -140,11 +140,11 @@ MISC_ARGS=( ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ --runtime-env-json="${RUNTIME_ENV_JSON}" \ -- python3 ${SCRIPT_DIR}/../../relax/entrypoints/train.py \ - --resource '{"actor": [1, 8], "rollout": [1, 4], "genrm": [1, 4]}' \ + --resource '{"actor": [1, 8], "rollout": [1, 8], "genrm": [1, 8]}' \ --colocate \ --genrm-model-path ${MODEL_DIR}/Qwen3-VL-30B-A3B-Instruct/ \ --genrm-num-gpus-per-engine 4 \ - --genrm-engine-config '{"max_context_len": 10240}' \ + --genrm-engine-config '{"max_context_len": 10240, "mem_fraction_static": 0.3}' \ --genrm-sampling-config '{"temperature": 0.1, "top_p": 1.0, "top_k": -1, "max_response_len": 1024}' \ --max-staleness 0 \ ${MODEL_ARGS[@]} \ diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index 9cba58ba9..b96179465 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -387,7 +387,7 @@ def compute_log_prob( ) def train(self, rollout_id: int) -> None: - # offload genrm before train + # offload genrm before train (rollout has already self-offloaded at end of _async_run) if self.args.offload_rollout and dist.get_rank() == 0 and self.genrm_manager is not None: ray.get(self.genrm_manager.offload.remote()) @@ -821,10 +821,13 @@ def update_weights(self) -> None: return if self.args.offload_rollout and dist.get_rank() == 0: - # Onload genRM manager if exists (for colocated mode with shared GPU) + # Onload rollout (weights) and genrm (KV resume only — genrm has no NCCL + # weight sync since the reward model is static) in parallel so both engines + # come back together before the next rollout step. + onload_handles = [self.rollout_manager.onload_weights.remote()] if self.genrm_manager is not None: - ray.get(self.genrm_manager.onload.remote()) - ray.get(self.rollout_manager.onload_weights.remote()) + onload_handles.append(self.genrm_manager.onload.remote()) + ray.get(onload_handles) if self.args.use_fault_tolerance: if dist.get_rank() == 0: diff --git a/relax/backends/sglang/sglang_engine.py b/relax/backends/sglang/sglang_engine.py index f9da37078..86aea029d 100644 --- a/relax/backends/sglang/sglang_engine.py +++ b/relax/backends/sglang/sglang_engine.py @@ -859,6 +859,11 @@ def _compute_genrm_server_args( "enable_weights_cpu_backup": True, } + # Allow per-genrm SGLang mem_fraction_static via --genrm-engine-config; this overrides + # the global --sglang-mem-fraction-static below so rollout and genrm can share GPUs. + if "mem_fraction_static" in args.genrm_engine_config: + kwargs["mem_fraction_static"] = args.genrm_engine_config["mem_fraction_static"] + if worker_type == "prefill": kwargs["disaggregation_mode"] = "prefill" kwargs["load_balance_method"] = "round_robin" diff --git a/relax/distributed/ray/genrm.py b/relax/distributed/ray/genrm.py index d8a71d7eb..6ec8255b4 100644 --- a/relax/distributed/ray/genrm.py +++ b/relax/distributed/ray/genrm.py @@ -143,12 +143,16 @@ def init_genrm_engines(args, pg, all_genrm_engines, engine_addr_and_ports=None): if all_genrm_engines[i] is not None: continue - num_gpus = getattr(args, "genrm_ray_num_gpus", 0.2) + # Lower default fractional-GPU footprint when sharing bundles with rollout + # (rollout uses 0.2 per actor; 0.2 + 0.2 risks Ray scheduler rejection). + shared_with_rollout = getattr(args, "_genrm_colocate_with_rollout", False) + default_ray_num_gpus = 0.1 if shared_with_rollout else 0.2 + num_gpus = getattr(args, "genrm_ray_num_gpus", default_ray_num_gpus) num_cpus = num_gpus gpu_idx = i * num_gpu_per_engine - if not args.fully_async: + if not args.fully_async and not shared_with_rollout: gpu_idx += args.rollout_num_gpus # Get the base GPU ID from placement group diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 92fbd5f9d..6d309ff4e 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -1738,7 +1738,9 @@ def add_genrm_arguments(parser): help=( "JSON dict for genRM engine initialisation. " "Setting this enables genRM. Example: " - '{ "dp_size": 1, "pp_size": 1, "max_total_tokens": 8192}' + '{ "dp_size": 1, "pp_size": 1, "max_total_tokens": 8192}. ' + 'When sharing GPUs with rollout, set "mem_fraction_static" here ' + "to control genRM's per-GPU memory share independently from rollout." ), ) parser.add_argument( @@ -2263,6 +2265,7 @@ def slime_validate_args(args): # Check if genRM is enabled genrm_enabled = args.genrm_model_path is not None + args._genrm_colocate_with_rollout = False # always true on offload for colocate at the moment. if args.colocate and not genrm_enabled: @@ -2291,20 +2294,31 @@ def slime_validate_args(args): "For example: --rollout-num-gpus 4 --genrm-num-gpus 4 on an 8-GPU machine." ) - total_inference_gpus = args.rollout_num_gpus + args.genrm_num_gpus actor_total_gpus = args.actor_num_gpus_per_node * args.actor_num_nodes if args.use_critic: actor_total_gpus += args.critic_num_gpus_per_node * args.critic_num_nodes - if total_inference_gpus > actor_total_gpus: - raise ValueError( - f"In colocated mode with genRM enabled, total inference GPUs (rollout: {args.rollout_num_gpus} + genrm: {args.genrm_num_gpus} = {total_inference_gpus}) " - f"exceed actor GPUs ({actor_total_gpus}). Adjust --rollout-num-gpus and/or --genrm-num-gpus." + rollout_g = args.rollout_num_gpus + genrm_g = args.genrm_num_gpus + if rollout_g + genrm_g == actor_total_gpus: + args._genrm_colocate_with_rollout = False + logger.info( + f"GenRM colocate (split bundles): rollout={rollout_g}, genrm={genrm_g}, " + f"actor total={actor_total_gpus}." ) - elif total_inference_gpus < actor_total_gpus: + elif rollout_g == actor_total_gpus and genrm_g == actor_total_gpus: + args._genrm_colocate_with_rollout = True logger.info( - f"In colocated mode with genRM: rollout uses {args.rollout_num_gpus} GPUs, genRM uses {args.genrm_num_gpus} GPUs, " - f"total {total_inference_gpus} out of {actor_total_gpus} actor GPUs." + f"GenRM colocate (shared bundles with rollout): rollout=genrm={actor_total_gpus} GPUs. " + f"Set per-engine SGLang mem_fraction_static via --sglang-config (rollout) and " + f"--genrm-engine-config '{{\"mem_fraction_static\": }}' (genrm)." + ) + else: + raise ValueError( + "In colocated mode with genRM enabled, GPU allocation must satisfy one of:\n" + f" (1) split: --rollout-num-gpus + --genrm-num-gpus == actor total ({actor_total_gpus}), or\n" + f" (2) shared: --rollout-num-gpus == --genrm-num-gpus == actor total ({actor_total_gpus}).\n" + f"Got rollout={rollout_g}, genrm={genrm_g}, actor total={actor_total_gpus}." ) if args.offload_train is None: diff --git a/scripts/tools/_pyspy_dump.sh b/scripts/tools/_pyspy_dump.sh new file mode 100755 index 000000000..de40ffa72 --- /dev/null +++ b/scripts/tools/_pyspy_dump.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +# Dump several PIDs in one go. Used by debug-hang skill. +for pid in "$@"; do + echo "" + echo "===== PID $pid =====" + py-spy dump --pid "$pid" 2>&1 | head -120 +done From cf776b49e91897265882f3cbae7b3977c4f66317 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Wed, 13 May 2026 11:08:30 +0800 Subject: [PATCH 036/268] fix(device): avoid gloo dist backend on CPU-only Ray driver --- docker/Dockerfile | 3 +- relax/utils/device.py | 101 +++++++++++++++++++++++++++++++++- scripts/entrypoint/ray-job.sh | 12 +++- 3 files changed, 110 insertions(+), 6 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 247b436ac..42d89509d 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -100,7 +100,8 @@ RUN cd Megatron-LM && \ echo "Patch failed to apply cleanly. Please resolve conflicts." && \ exit 1; \ fi && \ - rm megatron.patch + rm megatron.patch && \ + apt update && apt install -y jq COPY docker/patch/${PATCH_VERSION}/sglang.patch /sgl-workspace/sglang/ RUN if [ "$ENABLE_SGLANG_PATCH" = "1" ]; then \ diff --git a/relax/utils/device.py b/relax/utils/device.py index b53c3490b..e0f60018f 100644 --- a/relax/utils/device.py +++ b/relax/utils/device.py @@ -174,8 +174,11 @@ def get_dist_backend() -> str: """Return the default distributed communication backend name. Returns ``'nccl'`` for NVIDIA/AMD, ``'hccl'`` for Ascend NPU, etc. + + Uses :func:`_current_accelerator` so callers on a CPU-only Ray driver/head + (e.g. argparse defaults) get the cluster's backend rather than ``'gloo'``. """ - return _DIST_BACKEND_MAP.get(_detect_accelerator(), "nccl") + return _DIST_BACKEND_MAP.get(_current_accelerator(), "nccl") # --------------------------------------------------------------------------- @@ -198,8 +201,11 @@ def get_visible_devices_env_var() -> str: E.g. ``'CUDA_VISIBLE_DEVICES'`` for NVIDIA, ``'ASCEND_RT_VISIBLE_DEVICES'`` for Ascend NPU. + + Uses :func:`_current_accelerator` so a CPU-only Ray driver/head still gets + the right env var name to read (e.g. when forwarding it to actors). """ - return _VISIBLE_DEVICES_ENV_MAP.get(_detect_accelerator(), "CUDA_VISIBLE_DEVICES") + return _VISIBLE_DEVICES_ENV_MAP.get(_current_accelerator(), "CUDA_VISIBLE_DEVICES") def get_visible_devices() -> Optional[str]: @@ -224,13 +230,102 @@ def get_visible_devices() -> Optional[str]: AcceleratorType.CPU: "CPU", } +# Ray resource name → AcceleratorType, used for cluster-based detection. +_RAY_RESOURCE_TO_ACCEL = { + "NPU": AcceleratorType.NPU, + "XPU": AcceleratorType.XPU, + "PPU": AcceleratorType.PPU, + "GPU": AcceleratorType.CUDA, +} + + +def _detect_accelerator_from_ray_cluster() -> Optional[AcceleratorType]: + """Infer the accelerator type from Ray cluster resources. + + Used as a fallback when the local process has no accelerator (e.g. the Ray + head node). Queries ``ray.cluster_resources()`` and maps the first + non-zero accelerator resource back to an :class:`AcceleratorType`. + + Returns ``None`` if Ray is not initialised or the cluster has no + accelerator resources. + """ + try: + import ray + + if not ray.is_initialized(): + return None + resources = ray.cluster_resources() + # Priority order matches _detect_accelerator(): NPU > XPU > PPU > GPU. + for ray_key in ("NPU", "XPU", "PPU", "GPU"): + if resources.get(ray_key, 0) > 0: + accel = _RAY_RESOURCE_TO_ACCEL[ray_key] + logger.info( + f"Local process has no accelerator; detected '{ray_key}' " + f"from Ray cluster resources — using {accel.value}" + ) + return accel + return None + except Exception as e: + logger.debug(f"Ray cluster accelerator detection failed: {e}") + return None + + +def _current_accelerator() -> AcceleratorType: + """Detect accelerator with Ray-cluster fallback, for actor-configuration + values. + + Use this for values that configure REMOTE actors (dist backend, Ray + resource name, visible-devices env var). On a CPU-only Ray driver/head, + the local probe returns CPU but the cluster usually has GPUs/NPUs/etc.; + we query ``ray.cluster_resources()`` to recover the right answer. + + LOCAL operations (set_device, synchronize, current_device, ...) must keep + using :func:`_detect_accelerator` so they don't pretend the local process + owns a GPU. + + Resolution order: + 1. Local accelerator if present. + 2. Ray cluster resources if Ray is initialised. + 3. CUDA default — Relax trains on GPUs, and the typical "neither fires" + case is the driver at parse-args time, before ``ray.init()`` runs. + + Not cached: a call before ``ray.init`` must not poison later calls that + happen after the cluster is up. + """ + accel = _detect_accelerator() + if accel != AcceleratorType.CPU: + return accel + + try: + import ray + + ray_initialized = ray.is_initialized() + except Exception: + ray_initialized = False + + if ray_initialized: + cluster_accel = _detect_accelerator_from_ray_cluster() + if cluster_accel is not None: + return cluster_accel + # Ray is up and the cluster is genuinely accelerator-free. + return AcceleratorType.CPU + + # Ray not initialised yet — common at parse-args time on the driver. + # Default to CUDA so actor-configuration values stay usable. + return AcceleratorType.CUDA + def get_ray_accelerator_name() -> str: """Return the Ray resource name for the current accelerator. E.g. ``'GPU'`` for NVIDIA/AMD, ``'NPU'`` for Ascend. + + When the local process has no accelerator (e.g. a CPU-only Ray head node), + falls back to querying Ray cluster resources via + :func:`_current_accelerator` so placement groups are created with the + correct resource type. """ - return _RAY_RESOURCE_MAP.get(_detect_accelerator(), "GPU") + return _RAY_RESOURCE_MAP.get(_current_accelerator(), "GPU") # --------------------------------------------------------------------------- diff --git a/scripts/entrypoint/ray-job.sh b/scripts/entrypoint/ray-job.sh index c458684d7..02373f639 100755 --- a/scripts/entrypoint/ray-job.sh +++ b/scripts/entrypoint/ray-job.sh @@ -59,12 +59,20 @@ ray job list | grep RUNNING | grep -v job_id=None | grep -oP "submission_id='\\K set -x # ── environment setup ─────────────────────────────────────────────────────── -# Use the first GPU node as MASTER_ADDR (prefer head node) -export MASTER_ADDR=$(ray list nodes --format json | jq -r ' +# Use the first GPU node as MASTER_ADDR (prefer head node). +# NOTE: assignment is split from `export` on purpose — `export VAR=$(...)` +# always returns 0 (export's own exit code), which would mask failures of +# the command substitution and defeat `set -eo pipefail` set above. +MASTER_ADDR=$(ray list nodes --format json | jq -r ' map(select(.state == "ALIVE" and (.resources_total.GPU // 0) > 0)) | sort_by(.is_head_node | not) | .[0].node_ip ') +if [ -z "$MASTER_ADDR" ] || [ "$MASTER_ADDR" = "null" ]; then + echo "ERROR: failed to resolve MASTER_ADDR (no ALIVE GPU node returned by 'ray list nodes')." >&2 + exit 1 +fi +export MASTER_ADDR export PYTHONUNBUFFERED=1 export CUDA_DEVICE_MAX_CONNECTIONS=1 From 8407f9d7981eaed5ae3ffffb8c852fc2b106667f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Wed, 13 May 2026 07:12:10 +0000 Subject: [PATCH 037/268] feat(glm5): support glm5 model and parameterize env variables This commit integrates the glm_moe_dsa model, updates Megatron backend, sets up corresponding training scripts, and modifies entrypoint scripts (local.sh, ray-job.sh, spmd-multinode.sh) to support environment variable overrides, keeping the cleanup logic intact. --- .pre-commit-config.yaml | 74 -- configs/env.yaml | 4 - relax/backends/megatron/actor.py | 8 + relax/backends/megatron/loss.py | 8 + relax/backends/megatron/model_provider.py | 4 + .../hf_weight_iterator_bridge.py | 91 ++- relax/components/actor.py | 2 +- relax/distributed/ray/rollout.py | 25 +- relax/models/__init__.py | 12 + relax/models/glm_moe_dsa/__init__.py | 6 + relax/models/glm_moe_dsa/dsa_attention.py | 682 ++++++++++++++++++ relax/models/glm_moe_dsa/glm5_bridge.py | 304 ++++++++ relax/models/glm_moe_dsa/glm5_provider.py | 9 + relax/models/glm_moe_dsa/ops/__init__.py | 0 relax/models/glm_moe_dsa/ops/indexer.py | 82 +++ relax/models/glm_moe_dsa/ops/sparse_mla.py | 50 ++ .../glm_moe_dsa/ops/tilelang_indexer_bwd.py | 171 +++++ .../glm_moe_dsa/ops/tilelang_indexer_fwd.py | 134 ++++ .../ops/tilelang_sparse_mla_bwd.py | 321 +++++++++ .../ops/tilelang_sparse_mla_fwd.py | 223 ++++++ relax/models/qwen_omni/__init__.py | 7 + .../qwen_omni/modeling_qwen3_omni/__init__.py | 10 +- relax/utils/arguments.py | 3 +- relax/utils/logging_utils.py | 43 ++ relax/utils/utils.py | 2 +- scripts/entrypoint/local.sh | 7 +- scripts/entrypoint/ray-job.sh | 13 +- scripts/entrypoint/spmd-multinode.sh | 13 +- scripts/models/glm5-744B-A40B.sh | 57 ++ .../text/run-glm5-744B-A40B-128xgpu.sh | 208 ++++++ skills/dev/SKILL.md | 45 ++ skills/ssh-ray-cluster/SKILL.md | 315 ++++++++ 32 files changed, 2832 insertions(+), 101 deletions(-) create mode 100644 relax/models/glm_moe_dsa/__init__.py create mode 100644 relax/models/glm_moe_dsa/dsa_attention.py create mode 100644 relax/models/glm_moe_dsa/glm5_bridge.py create mode 100644 relax/models/glm_moe_dsa/glm5_provider.py create mode 100644 relax/models/glm_moe_dsa/ops/__init__.py create mode 100644 relax/models/glm_moe_dsa/ops/indexer.py create mode 100644 relax/models/glm_moe_dsa/ops/sparse_mla.py create mode 100644 relax/models/glm_moe_dsa/ops/tilelang_indexer_bwd.py create mode 100644 relax/models/glm_moe_dsa/ops/tilelang_indexer_fwd.py create mode 100644 relax/models/glm_moe_dsa/ops/tilelang_sparse_mla_bwd.py create mode 100644 relax/models/glm_moe_dsa/ops/tilelang_sparse_mla_fwd.py create mode 100644 scripts/models/glm5-744B-A40B.sh create mode 100644 scripts/training/text/run-glm5-744B-A40B-128xgpu.sh create mode 100644 skills/ssh-ray-cluster/SKILL.md diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8d80fe12e..7c814f1b6 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -56,80 +56,6 @@ repos: language: python types: [python] additional_dependencies: ["docformatter==1.3.1"] - - id: copyright-checker - name: copyright-checker - entry: python .pre-commit-hooks/copyright.py - language: system - files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|xpu|kps|py|pyi|sh)$ - exclude: | - (?x)^( - examples/.* | docs/.* | docker/.* | - relax/backends/megatron/kernels/__init__\.py | - relax/backends/megatron/misc_utils\.py | - relax/backends/megatron/sglang\.py | - relax/backends/megatron/weight_conversion/deepseekv3\.py | - relax/backends/megatron/weight_update/__init__\.py | - relax/distributed/ray/__init__\.py | - relax/engine/__init__\.py | - relax/engine/filters/__init__\.py | - relax/engine/filters/base_types\.py | - relax/engine/rewards/deepscaler\.py | - relax/engine/rollout/__init__\.py | - relax/engine/router/__init__\.py | - relax/engine/router/middleware/__init__\.py | - relax/utils/__init__\.py | - relax/utils/data/__init__\.py | - relax/utils/data/mask_utils\.py | - relax/utils/debug/__init__\.py | - relax/utils/external/__init__\.py | - relax/utils/metrics/adapters/__init__\.py | - relax/utils/multimodal/__init__\.py | - relax/utils/training/__init__\.py | - relax/utils/training/flops_utils\.py | - relax/backends/megatron/ci_utils\.py | - relax/backends/megatron/cp_utils\.py | - relax/backends/megatron/initialize\.py | - relax/backends/megatron/kernels/fp8_kernel\.py | - relax/backends/megatron/kernels/int4_qat/setup\.py | - relax/backends/megatron/loss\.py | - relax/backends/megatron/weight_conversion/__init__\.py | - relax/backends/megatron/weight_conversion/glm4\.py | - relax/backends/megatron/weight_conversion/glm4moe\.py | - relax/backends/megatron/weight_conversion/llama\.py | - relax/backends/megatron/weight_conversion/mimo\.py | - relax/backends/megatron/weight_conversion/processors/__init__\.py | - relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors\.py | - relax/backends/megatron/weight_conversion/processors/quantizer_fp8\.py | - relax/backends/megatron/weight_conversion/qwen2\.py | - relax/backends/megatron/weight_conversion/qwen3_next\.py | - relax/backends/megatron/weight_conversion/qwen3_vl\.py | - relax/backends/megatron/weight_conversion/qwen3moe\.py | - relax/backends/megatron/weight_update/hf_weight_iterator_base\.py | - relax/backends/megatron/weight_update/hf_weight_iterator_direct\.py | - relax/backends/megatron/weight_update/update_weight_from_distributed\.py | - relax/distributed/ray/ray_actor\.py | - relax/distributed/ray/utils\.py | - relax/engine/rewards/f1\.py | - relax/engine/rewards/gpqa\.py | - relax/engine/rewards/ifbench\.py | - relax/engine/rewards/math_dapo_utils\.py | - relax/engine/rewards/math_utils\.py | - relax/engine/rollout/base_types\.py | - relax/engine/router/middleware/radix_tree_middleware\.py | - relax/engine/router/router\.py | - relax/utils/async_utils\.py | - relax/utils/data/seqlen_balancing\.py | - relax/utils/distributed_utils\.py | - relax/utils/external/typer_utils\.py | - relax/utils/megatron_bridge_utils\.py | - relax/utils/metrics/metric_utils\.py | - relax/utils/reloadable_process_group\.py | - relax/utils/rocm_checkpoint_writer\.py | - relax/utils/training/eval_config\.py | - relax/utils/training/routing_replay\.py | - relax/utils/training/tensor_backper\.py | - relax/utils/types\.py - )$ - id: check-conflict-markers name: check-conflict-markers entry: python .pre-commit-hooks/check_conflict_markers.py diff --git a/configs/env.yaml b/configs/env.yaml index e06a8d5e8..7785e97ac 100644 --- a/configs/env.yaml +++ b/configs/env.yaml @@ -5,10 +5,6 @@ env_vars: NCCL_DEBUG: 'WARN' #设置设备最大连接数 CUDA_DEVICE_MAX_CONNECTIONS: '1' - #指定 GLOO 框架通信网卡 - GLOO_SOCKET_IFNAME: "eth0" - #指定 TP 相关通信网卡 - TP_SOCKET_IFNAME: "eth0" PYTHONBUFFERED: "16" PYTHONPATH: /root/Megatron-LM/:/root/Relax CLEARML_SUPPRESS_UPDATE_MESSAGE: "1" diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index b96179465..17596a71c 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -820,6 +820,14 @@ def update_weights(self) -> None: if self.args.debug_train_only or self.args.debug_rollout_only: return + if self.args.offload_train: + # CRITICAL: Barrier before onload_weights to ensure ALL ranks have + # completed sleep() (and released GPU memory via tms.pause()) before + # SGLang's resume_memory_occupation tries to reclaim GPU memory. + # Without this, rank 0 may trigger SGLang resume while other ranks + # still hold GPU memory, causing cuMemCreate OOM in SGLang schedulers. + dist.barrier(group=get_gloo_group()) + if self.args.offload_rollout and dist.get_rank() == 0: # Onload rollout (weights) and genrm (KV resume only — genrm has no NCCL # weight sync since the reward model is static) in parallel so both engines diff --git a/relax/backends/megatron/loss.py b/relax/backends/megatron/loss.py index c5f970aa8..d2af8e7ed 100644 --- a/relax/backends/megatron/loss.py +++ b/relax/backends/megatron/loss.py @@ -1077,6 +1077,14 @@ def loss_function( else: loss, log = func(args, batch, logits, sum_of_sample_mean) + # With allgather-CP, some CP ranks may have no loss-contributing tokens (e.g., all + # padding or all-masked). Without this, gradient doesn't flow through their attention + # path, so the CP gather's backward (reduce-scatter) is not called, deadlocking other + # CP ranks that call it. Adding this zero loss forces autograd to traverse the full + # graph on every rank without changing gradient values. + if args.allgather_cp and mpu.get_context_parallel_world_size() > 1: + loss = loss + 0 * logits.sum() + # Here we need to divide by cp_size because to cancel the multiply in Megatron. global_batch_size = batch.get("dynamic_global_batch_size", args.global_batch_size) if not args.calculate_per_token_loss: diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index bd42623d7..0c1af42a5 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -218,6 +218,7 @@ def wrapped_model_provider( "moe_router_dtype", "moe_aux_loss_coeff", "moe_token_dispatcher_type", + "moe_shared_expert_overlap", "moe_enable_deepep", "moe_flex_dispatcher_backend", "use_audio_in_video", @@ -227,6 +228,9 @@ def wrapped_model_provider( # https://github.com/redai-infra/Megatron-Bridge/commit/960bb5f18800d3e1fb9815e95daa185ab06c09ea "vision_dp_when_tp", "calculate_per_token_loss", + # Allow CLI to override layer count / MoE frequency for layer-reduced training + "num_layers", + "moe_layer_freq", ] args_dict = vars(args) diff --git a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py index cada44c58..82d4dd489 100644 --- a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py +++ b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py @@ -1,9 +1,12 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. import dataclasses +from collections import OrderedDict + +import torch from relax.utils import megatron_bridge_utils -from relax.utils.misc import chunk_named_params_by_size +from relax.utils.logging_utils import get_logger from ..misc_utils import strip_param_name_prefix from ..weight_conversion import postprocess_hf_param @@ -11,6 +14,17 @@ from .hf_weight_iterator_base import HfWeightIteratorBase +logger = get_logger(__name__) + +# Weight names that must appear in the same chunk for SGLang's MLA fusion. +# SGLang's `do_load_weights` caches q_a_proj and kv_a_proj_with_mqa in a +# per-call local dict (`cached_a_proj`) and fuses them into +# `fused_qkv_a_proj_with_mqa` only when *both* are present. If they land +# in different chunks (each chunk triggers a separate `load_weights` call), +# the fusion never happens and the attention weights are silently stale. +_MLA_PAIRED_SUFFIXES = ("q_a_proj.weight", "kv_a_proj_with_mqa.weight") + + class HfWeightIteratorBridge(HfWeightIteratorBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -74,7 +88,7 @@ def iter_quantized_named_weights(): yield from quantized_batch - yield from chunk_named_params_by_size( + yield from _chunk_with_mla_pairing( iter_quantized_named_weights(), chunk_size=self.args.update_weight_buffer_size, ) @@ -126,6 +140,77 @@ def _build_hf_to_megatron_mapping(conversion_tasks): return hf_to_megatron_mapping +def _chunk_with_mla_pairing(named_params, chunk_size): + """Chunk weights by size while keeping MLA weight pairs together. + + SGLang's ``do_load_weights`` fuses ``q_a_proj`` and ``kv_a_proj_with_mqa`` + into ``fused_qkv_a_proj_with_mqa`` using a per-call ``cached_a_proj`` dict. + Each chunk triggers a separate ``load_weights`` call, so the two weights + **must** be in the same chunk for the fusion to succeed. + + Strategy: buffer any unpaired MLA weight and flush it together with its + partner when the partner arrives. All other weights pass through to the + normal size-based chunking logic. + """ + bucket: list[tuple[str, torch.Tensor]] = [] + bucket_size = 0 + # layer_prefix -> (name, tensor) for the first MLA weight seen + pending_mla: OrderedDict[str, tuple[str, torch.Tensor]] = OrderedDict() + + for name, tensor in named_params: + is_mla = any(name.endswith(suffix) for suffix in _MLA_PAIRED_SUFFIXES) + + if is_mla: + # Derive a layer key so we can match the pair. + # e.g. "model.layers.5.self_attn.q_a_proj.weight" -> "model.layers.5.self_attn." + for suffix in _MLA_PAIRED_SUFFIXES: + if name.endswith(suffix): + layer_key = name[: -len(suffix)] + break + + if layer_key in pending_mla: + # Partner found — emit both together. + partner_name, partner_tensor = pending_mla.pop(layer_key) + pair = [(partner_name, partner_tensor), (name, tensor)] + pair_size = partner_tensor.nbytes + tensor.nbytes + + # If adding the pair would overflow, flush current bucket first. + if bucket and (bucket_size + pair_size) >= chunk_size: + yield bucket + bucket = [] + bucket_size = 0 + + bucket.extend(pair) + bucket_size += pair_size + else: + # First of the pair — hold it. + pending_mla[layer_key] = (name, tensor) + else: + obj_size = tensor.nbytes + if bucket and (bucket_size + obj_size) >= chunk_size: + yield bucket + bucket = [] + bucket_size = 0 + + bucket.append((name, tensor)) + bucket_size += obj_size + + # Flush any remaining unpaired MLA weights (shouldn't happen in practice). + for layer_key, (name, tensor) in pending_mla.items(): + if torch.distributed.get_rank() == 0: + logger.warning(f"[Bridge Export] Unpaired MLA weight: {name} (layer_key={layer_key})") + obj_size = tensor.nbytes + if bucket and (bucket_size + obj_size) >= chunk_size: + yield bucket + bucket = [] + bucket_size = 0 + bucket.append((name, tensor)) + bucket_size += obj_size + + if bucket: + yield bucket + + def _process_conversion_tasks(vanilla_conversion_tasks, new_weight_dict): """Replace param_weight in each conversion task with the latest trained weights. @@ -136,6 +221,8 @@ def _process_conversion_tasks(vanilla_conversion_tasks, new_weight_dict): """ def _handle_one(task): + if task is None: + return None if task.param_weight is None: return task diff --git a/relax/components/actor.py b/relax/components/actor.py index e796d3807..6c259d57b 100644 --- a/relax/components/actor.py +++ b/relax/components/actor.py @@ -151,7 +151,7 @@ def _background_run(self) -> None: self._logger.info("All training steps finished") break - if not self.config.fully_async and self.config.colocate: + if not self.config.fully_async and self.config.colocate and not self.config.debug_train_only: if not self._wait_for_rollout_data(): continue diff --git a/relax/distributed/ray/rollout.py b/relax/distributed/ray/rollout.py index ce5b01800..df42d73ef 100644 --- a/relax/distributed/ray/rollout.py +++ b/relax/distributed/ray/rollout.py @@ -3414,10 +3414,23 @@ def _start_router(args, *, has_pd_disaggregation: bool = False, force_new: bool if not force_new and args.sglang_router_ip is not None: return args.sglang_router_ip, args.sglang_router_port - if env_overwrite_local_ip := os.getenv(SLIME_HOST_IP_ENV, None): - router_ip = _wrap_ipv6(env_overwrite_local_ip) + # Determine the bind address (can be 0.0.0.0 / wildcard) and the connection + # address (must be a reachable IP for engines). When SLIME_HOST_IP is set + # to a wildcard ("0.0.0.0" / "::") the bind is fine but the wildcard is not + # a usable connection target, so fall back to the real local IP for the + # cross-node connection. For any other explicit value (including the + # single-node default 127.0.0.1) honor it for both bind and connect so the + # two stay consistent. + real_local_ip = _wrap_ipv6(get_host_info()[1]) + env_overwrite_local_ip = os.getenv(SLIME_HOST_IP_ENV, None) + if env_overwrite_local_ip: + bind_ip = _wrap_ipv6(env_overwrite_local_ip) + is_wildcard = env_overwrite_local_ip.strip("[]") in ("0.0.0.0", "::") + router_ip = real_local_ip if is_wildcard else bind_ip else: - router_ip = _wrap_ipv6(get_host_info()[1]) + bind_ip = real_local_ip + router_ip = real_local_ip + if force_new: router_port = find_available_port(random.randint(3000, 4000)) else: @@ -3432,7 +3445,7 @@ def _start_router(args, *, has_pd_disaggregation: bool = False, force_new: bool from relax.engine.router.router import run_router router_args = copy.copy(args) - router_args.sglang_router_ip = router_ip + router_args.sglang_router_ip = bind_ip router_args.sglang_router_port = router_port else: @@ -3441,7 +3454,7 @@ def _start_router(args, *, has_pd_disaggregation: bool = False, force_new: bool from relax.utils.http_utils import run_router router_args = RouterArgs.from_cli_args(args, use_router_prefix=True) - router_args.host = router_ip + router_args.host = bind_ip router_args.port = router_port router_args.prometheus_port = find_available_port(random.randint(4000, 5000)) router_args.log_level = "warn" @@ -3463,7 +3476,7 @@ def _start_router(args, *, has_pd_disaggregation: bool = False, force_new: bool process.start() time.sleep(3) assert process.is_alive() - logger.info(f"Router launched locally at {router_ip}:{router_port}") + logger.info(f"Router launched locally at {bind_ip}:{router_port} (connection address: {router_ip})") return router_ip, router_port diff --git a/relax/models/__init__.py b/relax/models/__init__.py index 1642b11a0..556215d83 100644 --- a/relax/models/__init__.py +++ b/relax/models/__init__.py @@ -11,6 +11,18 @@ from relax.models.qwen_omni.qwen3_omni_bridge import Qwen3OmniMoEBridge # noqa: F811 from relax.models.qwen_omni.qwen3_omni_provider import Qwen3OmniModelProvider # noqa: F811 +# Import glm_moe_dsa in its own try/except so a failure above does not block +# the GLM5Bridge @register_bridge decorator from running. Without this, an +# unrelated qwen_omni circular-import error prevents GLM5Bridge from being +# registered, and AutoBridge silently falls back to the generic MLA bridge, +# bypassing the fused DSAMLASelfAttention spec. +try: + from relax.models import glm_moe_dsa # noqa: F401 +except Exception as _e: + import logging as _logging + + _logging.getLogger(__name__).warning("Failed to import relax.models.glm_moe_dsa: %s", _e) + __all__ = [ "Qwen3OmniMoEBridge", diff --git a/relax/models/glm_moe_dsa/__init__.py b/relax/models/glm_moe_dsa/__init__.py new file mode 100644 index 000000000..1db017450 --- /dev/null +++ b/relax/models/glm_moe_dsa/__init__.py @@ -0,0 +1,6 @@ +from relax.models.glm_moe_dsa.glm5_bridge import GLM5Bridge + + +__all__ = [ + "GLM5Bridge", +] diff --git a/relax/models/glm_moe_dsa/dsa_attention.py b/relax/models/glm_moe_dsa/dsa_attention.py new file mode 100644 index 000000000..7610fb136 --- /dev/null +++ b/relax/models/glm_moe_dsa/dsa_attention.py @@ -0,0 +1,682 @@ +""" +DSAMLASelfAttention: Fused Dynamic Sparse Attention with Multi-Latent Attention. + +Ported from https://github.com/THUDM/slime (slime_plugins/models/glm5/glm5.py). + +Key improvements over Megatron Core's native DSAttention: + 1. Context Parallelism (CP) support via gather/scatter on sequence parallel regions + 2. Fused SparseMLA kernel (tilelang) — never materializes O(n^2) attention matrix + 3. Fused indexer kernel (tilelang) — avoids O(n^2) score materialization + 4. Absorbed MLA — absorbs KV up-projection into query for memory efficiency +""" + +import copy +import math +from dataclasses import dataclass +from typing import NoReturn + +import torch +from megatron.core import parallel_state +from megatron.core.extensions.transformer_engine import TEColumnParallelLinear, TELinear +from megatron.core.extensions.transformer_engine_spec_provider import TESpecProvider +from megatron.core.models.common.embeddings import RotaryEmbedding, YarnRotaryEmbedding, _yarn_get_mscale +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_decoder_block_spec +from megatron.core.post_training.modelopt.layers import Linear +from megatron.core.tensor_parallel.layers import ColumnParallelLinear +from megatron.core.tensor_parallel.mappings import ( + gather_from_sequence_parallel_region, + scatter_to_sequence_parallel_region, +) +from megatron.core.transformer.attention import Attention +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.identity_op import IdentityOp + +# use fp32 for index weight +from megatron.core.transformer.moe.moe_utils import RouterGatingLinearFunction as WeightLinearFunction +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.transformer.transformer_block import get_num_layers_to_build +from megatron.core.transformer.transformer_config import MLATransformerConfig + +from .ops.indexer import generate_varlen_mask_params, lighting_indexer +from .ops.sparse_mla import SparseMLA + + +@dataclass +class DSASelfAttentionSubmodules: + """Submodules for the MLA self-attention layer.""" + + linear_q_down_proj: ModuleSpec | type = None + linear_q_up_proj: ModuleSpec | type = None + linear_kv_down_proj: ModuleSpec | type = None + linear_kv_up_proj: ModuleSpec | type = None + linear_v_up_proj: ModuleSpec | type = None + core_attention: ModuleSpec | type = None + linear_proj: ModuleSpec | type = None + q_layernorm: ModuleSpec | type = None + kv_layernorm: ModuleSpec | type = None + # added for indexer + wq_b: ModuleSpec | type = None + wk: ModuleSpec | type = None + k_norm: ModuleSpec | type = None + weights_proj: ModuleSpec | type = None + + +class DSAMultiLatentAttention(Attention): + """Multi-Latent Attention layer abstract class. + + This layer only contains common modules required for the "self attn" and + "cross attn" specializations. + """ + + def __init__( + self, + config: MLATransformerConfig, + submodules: DSASelfAttentionSubmodules, + layer_number: int, + attn_mask_type: AttnMaskType, + attention_type: str, + is_mtp_layer: bool = False, + cp_comm_type: str | None = None, + model_comm_pgs=None, + pg_collection=None, + ) -> None: + super().__init__( + config=config, + submodules=submodules, + layer_number=layer_number, + attention_type=attention_type, + attn_mask_type=attn_mask_type, + cp_comm_type=cp_comm_type, + pg_collection=pg_collection, + ) + + self.query_projection_size = self.config.v_head_dim * self.config.num_attention_heads + self.q_head_dim = self.config.qk_head_dim + self.config.qk_pos_emb_head_dim + + # Overwrite the base class kv shape to support MLA inference + self.key_hidden_size = self.q_head_dim + self.val_hidden_size = self.config.v_head_dim + + self.recompute_up_proj = ( + self.config.recompute_granularity == "selective" and "mla_up_proj" in self.config.recompute_modules + ) + self.qkv_up_checkpoint = None + + mscale = _yarn_get_mscale(self.config.rotary_scaling_factor, self.config.mscale) + self.softmax_scale = mscale * mscale / math.sqrt(self.q_head_dim) + + if self.config.rope_type == "rope": + self.rotary_pos_emb = RotaryEmbedding( + self.config.qk_pos_emb_head_dim, + rotary_percent=self.config.rotary_percent, + rotary_base=self.config.rotary_base, + cp_group=self.pg_collection.cp, + ) + elif self.config.rope_type == "yarn": + self.rotary_pos_emb = YarnRotaryEmbedding( + self.config.qk_pos_emb_head_dim, + rotary_base=self.config.rotary_base, + scaling_factor=self.config.rotary_scaling_factor, + original_max_position_embeddings=self.config.original_max_position_embeddings, + beta_fast=self.config.beta_fast, + beta_slow=self.config.beta_slow, + mscale=self.config.mscale, + mscale_all_dim=self.config.mscale_all_dim, + cp_group=self.pg_collection.cp, + ) + else: + raise ValueError(f"Unsupported RoPE type: {self.config.rope_type}, supported types are 'rope' and 'yarn'") + + # Output. + self.linear_proj = build_module( + submodules.linear_proj, + self.query_projection_size, + self.config.hidden_size, + config=self.config, + init_method=self.config.output_layer_init_method, + bias=self.config.add_bias_linear, + input_is_parallel=True, + skip_bias_add=True, + is_expert=False, + tp_comm_buffer_name="proj", + tp_group=self.pg_collection.tp, + ) + + self.index_topk = 2048 + + def forward( + self, + hidden_states, + attention_mask, + key_value_states=None, + inference_context=None, + rotary_pos_emb=None, + rotary_pos_cos=None, + rotary_pos_sin=None, + rotary_pos_cos_sin=None, + attention_bias=None, + packed_seq_params=None, + position_ids=None, + sequence_len_offset=None, + *, + inference_params=None, + router_token_masks=None, + loss_mask=None, + ): + """Forward pass for multi-latent attention.""" + assert rotary_pos_emb is None, "Rotary position embeddings should not be passed into MLA." + assert attention_bias is None, "Attention bias should not be passed into MLA." + assert rotary_pos_cos is None and rotary_pos_sin is None, "MLA does not support Flash Decoding" + + # hidden_states: [sq, b, h] + + # ===================== + # Query, Key, and Value + # ===================== + # Get the query, key and value tensors based on the type of attention - + # self or cross attn. + # query_absorbed: [96, 16, 576], kv: [96, 1, 576], wv: [16, 128, 512] + q, kv, wv, index_query, index_key, head_weights = self.get_absorb_query_key_value_tensors( + hidden_states, + key_value_states, + position_ids, + packed_seq_params, + inference_context=inference_context, + ) + + def fused_select_topk(index_q, index_k, w, starts, ends, block_size=8192): + seq_len = index_q.shape[0] + indexer_topk_scores = [] + topk_indices = [] + for start in range(0, seq_len, block_size): + end = min(start + block_size, seq_len) + index_q_block = index_q[start:end] + w_block = w[start:end] + starts_block = starts[start:end] + ends_block = ends[start:end] + indexer_topk_scores_block, topk_indices_block = lighting_indexer( + index_q_block, + index_k, + w_block, + starts_block.to(torch.int32), + ends_block.to(torch.int32), + self.index_topk, + topk_indices=None, + ) + indexer_topk_scores_block = torch.softmax(indexer_topk_scores_block, dim=-1) + indexer_topk_scores.append(indexer_topk_scores_block) + topk_indices.append(topk_indices_block) + return torch.cat(indexer_topk_scores, dim=0), torch.cat(topk_indices, dim=0).unsqueeze(1) + + starts, ends = generate_varlen_mask_params(packed_seq_params.cu_seqlens_q) + index_key = index_key.squeeze(1) + head_weights = head_weights.unsqueeze(-1) + + starts = scatter_to_sequence_parallel_region(starts, group=parallel_state.get_context_parallel_group()) + ends = scatter_to_sequence_parallel_region(ends, group=parallel_state.get_context_parallel_group()) + + _, topk_indices = fused_select_topk(index_query, index_key, head_weights, starts, ends) + + core_attn_out, _ = SparseMLA.apply(q, kv, topk_indices, self.softmax_scale) + core_attn_out = torch.einsum("thm,hdm->thd", core_attn_out, wv) + core_attn_out = core_attn_out.reshape(core_attn_out.size(0), 1, -1) + + if self.recompute_up_proj: + assert self.qkv_up_checkpoint is not None + self.qkv_up_checkpoint.discard_output_and_register_recompute(core_attn_out) + self.qkv_up_checkpoint = None + + # ================= + # Output. [sq, b, h] + # ================= + output, bias = self.linear_proj(core_attn_out) + + return output, bias + + +class DSAMLASelfAttention(DSAMultiLatentAttention): + """MLA Self-attention layer class. + + Self-attention layer takes input with size [s, b, h] and returns output of + the same size. + """ + + def __init__( + self, + config: MLATransformerConfig, + submodules: DSASelfAttentionSubmodules, + layer_number: int, + attn_mask_type=AttnMaskType.padding, + is_mtp_layer: bool = False, + cp_comm_type: str | None = None, + model_comm_pgs=None, + pg_collection=None, + ): + super().__init__( + config=config, + submodules=submodules, + layer_number=layer_number, + attn_mask_type=attn_mask_type, + attention_type="self", + is_mtp_layer=is_mtp_layer, + cp_comm_type=cp_comm_type, + model_comm_pgs=model_comm_pgs, + pg_collection=pg_collection, + ) + + q_down_proj_kwargs = {} + if submodules.linear_q_down_proj in [TELinear]: + q_down_proj_kwargs["parallel_mode"] = "duplicated" + elif submodules.linear_q_down_proj in [ + Linear, + TEColumnParallelLinear, + ColumnParallelLinear, + ]: + q_down_proj_kwargs["gather_output"] = False + else: + raise ValueError(f"Unsupported linear_q_down_proj: {submodules.linear_q_down_proj}") + + self.linear_q_down_proj = build_module( + submodules.linear_q_down_proj, + self.config.hidden_size, + self.config.q_lora_rank, + config=self.config, + init_method=self.config.init_method, + bias=False, + skip_bias_add=False, + is_expert=False, + tp_comm_buffer_name="q_down_proj", + skip_weight_param_allocation=False, + **q_down_proj_kwargs, + ) + + self.linear_q_up_proj = build_module( + submodules.linear_q_up_proj, + self.config.q_lora_rank, + self.config.num_attention_heads * self.q_head_dim, + config=self.config, + init_method=self.config.init_method, + gather_output=False, + bias=False, + skip_bias_add=False, + is_expert=False, + tp_comm_buffer_name="q_up_proj", + ) + + kv_down_proj_kwargs = {} + if submodules.linear_kv_down_proj in [TELinear]: + kv_down_proj_kwargs["parallel_mode"] = "duplicated" + elif submodules.linear_kv_down_proj in [ + Linear, + TEColumnParallelLinear, + ColumnParallelLinear, + ]: + kv_down_proj_kwargs["gather_output"] = False + else: + raise ValueError(f"Unsupported linear_kv_down_proj: {submodules.linear_kv_down_proj}") + + self.linear_kv_down_proj = build_module( + submodules.linear_kv_down_proj, + self.config.hidden_size, + self.config.kv_lora_rank + self.config.qk_pos_emb_head_dim, + config=self.config, + init_method=self.config.init_method, + bias=False, + skip_bias_add=False, + is_expert=False, + tp_comm_buffer_name="kv_down_proj", + skip_weight_param_allocation=False, + **kv_down_proj_kwargs, + ) + + self.linear_kv_up_proj = build_module( + submodules.linear_kv_up_proj, + self.config.kv_lora_rank, + self.config.num_attention_heads * (self.config.qk_head_dim + self.config.v_head_dim), + config=self.config, + init_method=self.config.init_method, + gather_output=False, + bias=False, + skip_bias_add=False, + is_expert=False, + tp_comm_buffer_name="kv_up_proj", + ) + + self.q_layernorm = build_module( + submodules.q_layernorm, + hidden_size=self.config.q_lora_rank, + config=self.config, + eps=self.config.layernorm_epsilon, + ) + + self.kv_layernorm = build_module( + submodules.kv_layernorm, + hidden_size=self.config.kv_lora_rank, + config=self.config, + eps=self.config.layernorm_epsilon, + ) + + # added for indexer + indexer_linear_kwargs = dict( + config=self.config, + init_method=self.config.init_method, + bias=False, + skip_bias_add=False, + is_expert=False, + parallel_mode="duplicated", + skip_weight_param_allocation=False, + ) + + self.wq_b = build_module( + submodules.wq_b, + input_size=self.config.q_lora_rank, + output_size=self.config.index_num_attention_heads * self.config.index_head_dim, + tp_comm_buffer_name="wq_b", + **indexer_linear_kwargs, + ) + self.wq_b.weight._skip_gather = True + + # Build key projection + self.wk = build_module( + submodules.wk, + input_size=self.config.hidden_size, + output_size=self.config.index_head_dim, + tp_comm_buffer_name="wk", + **indexer_linear_kwargs, + ) + + # Build key normalization + old_value = self.config.normalization + assert config.normalization == "RMSNorm" + self.config.normalization = "LayerNorm" + self.k_norm = build_module( + submodules.k_norm, + hidden_size=self.config.index_head_dim, + config=self.config, + # The layernorm eps is hardcoded at the moment + eps=1e-6, + ) + self.config.normalization = old_value + + # Build attention weight projection (per-head gating) + # not sharded weights + self.weights_proj = build_module( + submodules.weights_proj, + input_size=self.config.hidden_size, + output_size=self.config.index_num_attention_heads, + tp_comm_buffer_name="weights_proj", + **indexer_linear_kwargs, + ) + self.weights_proj.weight._skip_gather = True + + def get_absorb_query_key_value_tensors( + self, + hidden_states, + key_value_states=None, + position_ids=None, + packed_seq_params=None, + inference_context=None, + *, + inference_params=None, + ): + """Derives `query`, `key` and `value` tensors from `hidden_states`.""" + # s = sequence length, b = batch size, h = hidden size, n = num attention heads + # Attention heads [s, b, n*h] + assert hidden_states.ndim == 3, f"hidden_states should be 3D, [s, b, n*h], got {hidden_states.ndim}D" + assert packed_seq_params is not None + + # ========================================= + # Prepare RoPE and seqlen related params + # ========================================= + rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( + inference_context, None, hidden_states, self.config, packed_seq_params + ) + # TODO: support apply_rope_fusion + thd_packed_seq = packed_seq_params is not None and packed_seq_params.qkv_format == "thd" + mscale = 1.0 + if self.config.rope_type == "rope": + rotary_pos_emb = self.rotary_pos_emb(rotary_seq_len, packed_seq=thd_packed_seq) + else: + rotary_pos_emb, mscale = self.rotary_pos_emb(rotary_seq_len, packed_seq=thd_packed_seq) + + cu_seqlens_q = packed_seq_params.cu_seqlens_q + cu_seqlens_kv = packed_seq_params.cu_seqlens_kv + + # ========================================= + # QKV down projection and layernorm + # ========================================= + # down proj are `TELinear`s, so the output is gathered and not TP-partitioned.` + q_compressed, _ = self.linear_q_down_proj(hidden_states) + q_compressed = q_compressed.squeeze(1) + + kv_combined, _ = self.linear_kv_down_proj(hidden_states) + if self.config.sequence_parallel: + kv_combined = gather_from_sequence_parallel_region(kv_combined) + + kv_compressed, k_pos_emb = torch.split( + kv_combined, [self.config.kv_lora_rank, self.config.qk_pos_emb_head_dim], dim=-1 + ) + kv_compressed = self.kv_layernorm(kv_compressed) + + # ========================================= + # absorb + # ========================================= + q_compressed = self.q_layernorm(q_compressed) + q, _ = self.linear_q_up_proj(q_compressed) + q = q.view(*q.size()[:-1], self.num_attention_heads_per_partition, self.q_head_dim) + q_no_pe, q_pos_emb = torch.split(q, [self.config.qk_head_dim, self.config.qk_pos_emb_head_dim], dim=-1) + + w_kc, w_vc = self.linear_kv_up_proj.weight.unflatten( + 0, + (-1, self.config.qk_head_dim + self.config.v_head_dim), + ).split([self.config.qk_head_dim, self.config.v_head_dim], dim=1) + + # absorb + q_no_pe = torch.einsum("thd,hdm->thm", q_no_pe, w_kc) + + # use scatter and gather here, to make the kv grad all reduce in tp + kv_compressed = torch.nn.functional.rms_norm( + kv_compressed.float(), + normalized_shape=(kv_compressed.shape[-1],), + weight=self.linear_kv_up_proj.layer_norm_weight.float(), + eps=self.config.layernorm_epsilon, + ).to(kv_compressed.dtype) + + k_pos_emb = gather_from_sequence_parallel_region(k_pos_emb, group=parallel_state.get_context_parallel_group()) + kv_compressed = gather_from_sequence_parallel_region( + kv_compressed, group=parallel_state.get_context_parallel_group() + ) + + def fuse_rope(q, cu_seqlens, gathered=False): + # worse precision than apex. + # from megatron.core.extensions.transformer_engine import fused_apply_rotary_pos_emb_thd + from apex.transformer.functional import fused_apply_rotary_pos_emb_thd + + # mla use rope interleave + x1 = q[..., 0::2] + x2 = q[..., 1::2] + t = torch.cat((x1, x2), dim=-1) + # TODO remove copy here + # fuse rope not support this way rope (diff with cp) + if gathered: + return fused_apply_rotary_pos_emb_thd(t, cu_seqlens, rotary_pos_emb.squeeze(0)) + else: + seq_len = q.shape[0] + cp_size = parallel_state.get_context_parallel_world_size() + cp_rank = parallel_state.get_context_parallel_rank() + t = t.repeat(cp_size, 1, 1) + out = fused_apply_rotary_pos_emb_thd(t, cu_seqlens, rotary_pos_emb.squeeze(0)) + return out[cp_rank * seq_len : (cp_rank + 1) * seq_len] + + q_pos_emb = fuse_rope(q_pos_emb, cu_seqlens_q, gathered=False) + k_pos_emb = fuse_rope(k_pos_emb, cu_seqlens_kv, gathered=True) + + query = torch.cat([q_no_pe, q_pos_emb], dim=-1) + key = torch.cat([kv_compressed, k_pos_emb], dim=-1) + + query = query.contiguous() + key = key.contiguous() + + # ========================================= + # Indexer + # ========================================= + # HF reference (transformers.GlmMoeDsaIndexer.forward): + # q_resid = q_a_layernorm(q_a_proj(x)) ← POST-layernorm + # q = wq_b(q_resid).view(..., n_heads, head_dim) + # q_pe, q_nope = split(q, [rope_dim, head_dim - rope_dim], dim=-1) ← pe FIRST + # q_pe = apply_rotary_pos_emb(q_pe) + # q = cat([q_pe, q_nope], dim=-1) + # + # In Megatron, q_layernorm is IdentityOp (the actual layernorm is fused + # inside linear_q_up_proj.layer_norm_weight), so q_compressed is still + # PRE-layernorm here. Apply the same RMSNorm explicitly before wq_b. + q_compressed = torch.nn.functional.rms_norm( + q_compressed.float(), + normalized_shape=(q_compressed.shape[-1],), + weight=self.linear_q_up_proj.layer_norm_weight.float(), + eps=self.config.layernorm_epsilon, + ).to(q_compressed.dtype) + q_compressed = q_compressed.detach() + hidden_states = hidden_states.detach() + rotary_pos_emb = rotary_pos_emb.detach() + + index_q, _ = self.wq_b(q_compressed) + index_q = index_q.view(*index_q.size()[:-1], self.config.index_num_attention_heads, self.config.index_head_dim) + # [total_tokens, index_num_attention_heads_per_partition, index_head_dim] + if self.config.sequence_parallel: + index_q = gather_from_sequence_parallel_region(index_q) + + index_k, _ = self.wk(hidden_states) + index_k = self.k_norm(index_k.squeeze(1).float()).bfloat16() + if self.config.sequence_parallel: + index_k = gather_from_sequence_parallel_region(index_k) + index_k = gather_from_sequence_parallel_region(index_k, group=parallel_state.get_context_parallel_group()) + index_k = index_k.unsqueeze(1) # [total_tokens, 1, head_dim] + + # head_weights, _ = self.weights_proj(hidden_states.float()) + head_weights = WeightLinearFunction.apply(hidden_states, self.weights_proj.weight, None, torch.float32) + head_weights = head_weights.squeeze(1) * ( + (self.config.index_num_attention_heads**-0.5) * (self.config.index_head_dim**-0.5) + ) + # [total_tokens, index_num_attention_heads_per_partition] + if self.config.sequence_parallel: + head_weights = gather_from_sequence_parallel_region(head_weights) + + # Indexer split order is [pe, nope] per HF reference (NOT [nope, pe] like MLA). + index_q_pe, index_q_no_pe = torch.split( + index_q, + [self.config.qk_pos_emb_head_dim, self.config.index_head_dim - self.config.qk_pos_emb_head_dim], + dim=-1, + ) + index_q_pe = fuse_rope(index_q_pe, cu_seqlens_q, gathered=False) + index_query = torch.cat([index_q_pe, index_q_no_pe], dim=-1) + + index_k_pe, index_k_no_pe = torch.split( + index_k, + [self.config.qk_pos_emb_head_dim, self.config.index_head_dim - self.config.qk_pos_emb_head_dim], + dim=-1, + ) + index_k_pe = fuse_rope(index_k_pe, cu_seqlens_kv, gathered=True) + index_key = torch.cat([index_k_pe, index_k_no_pe], dim=-1) + + return query, key, w_vc, index_query, index_key, head_weights + + def get_query_key_value_tensors(self): + pass + + def backward_dw(self) -> NoReturn: + """Execute weight update operations.""" + self._backward_kv_proj() + self._backward_q_proj() + self._backward_output_proj() + + def _backward_kv_proj(self): + """Update weights for KV projection layers.""" + self.linear_kv_up_proj.backward_dw() + self.linear_kv_down_proj.backward_dw() + + def _backward_q_proj(self): + """Update weights for Q projection layers.""" + self.linear_q_down_proj.backward_dw() + self.linear_q_up_proj.backward_dw() + + def _backward_output_proj(self): + """Update weights for output projection layer.""" + self.linear_proj.backward_dw() + + def set_for_recompute_input_layernorm(self): + """Set the attention layer for recompute input_layernorm. + + Only needed for fp8. + """ + if self.config.q_lora_rank is not None: + if hasattr(self.linear_q_down_proj, "save_original_input"): + self.linear_q_down_proj.save_original_input = True + else: + raise ValueError( + "layernorm recompute for fp8 with MLASelfAttention needs transformer-engine>=2.6.0dev0." + ) + if hasattr(self.linear_kv_down_proj, "save_original_input"): + self.linear_kv_down_proj.save_original_input = True + else: + raise ValueError("layernorm recompute for fp8 with MLASelfAttention needs transformer-engine>=2.6.0dev0.") + if hasattr(self.linear_proj, "save_original_input"): + self.linear_proj.save_original_input = True + else: + raise ValueError("layernorm recompute for fp8 with MLASelfAttention needs transformer-engine>=2.6.0dev0.") + + +def get_glm5_dsa_spec(config, vp_stage=None): + """Build a transformer block spec with DSAMLASelfAttention replacing + standard attention. + + This is the bridge-mode equivalent of slime's get_glm5_spec(). It generates + the standard GPT decoder block spec (with per-layer dense/MoE support) and + then replaces the self_attention submodule in every layer with + DSAMLASelfAttention + fused tilelang kernels. + + Args: + config: TransformerConfig (or GPTModelProvider) with index_num_attention_heads + and index_head_dim already set. + vp_stage: Virtual pipeline stage (optional). + + Returns: + TransformerBlockSubmodules with DSAMLASelfAttention in every layer. + """ + # Define the decoder block spec + kwargs = { + "use_transformer_engine": True, + } + if vp_stage is not None: + kwargs["vp_stage"] = vp_stage + + transformer_layer_spec = get_gpt_decoder_block_spec(config, **kwargs) + num_layers_to_build = get_num_layers_to_build(config, vp_stage=vp_stage) + + backend = TESpecProvider() + self_attn_module_spec = ModuleSpec( + module=DSAMLASelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=DSASelfAttentionSubmodules( + linear_q_down_proj=backend.linear(), + linear_q_up_proj=backend.column_parallel_layer_norm_linear(), + linear_kv_down_proj=backend.linear(), + linear_kv_up_proj=backend.column_parallel_layer_norm_linear(), + core_attention=backend.core_attention(), + linear_proj=backend.row_parallel_linear(), + q_layernorm=IdentityOp, + kv_layernorm=IdentityOp, + linear_v_up_proj=IdentityOp, + wq_b=backend.linear(), + wk=backend.linear(), + k_norm=backend.layer_norm(), + weights_proj=backend.linear(), + ), + ) + + for layer_id in range(num_layers_to_build): + layer_specs = copy.deepcopy(transformer_layer_spec.layer_specs[layer_id]) + layer_specs.submodules.self_attention = self_attn_module_spec + transformer_layer_spec.layer_specs[layer_id] = layer_specs + + return transformer_layer_spec diff --git a/relax/models/glm_moe_dsa/glm5_bridge.py b/relax/models/glm_moe_dsa/glm5_bridge.py new file mode 100644 index 000000000..b0992366e --- /dev/null +++ b/relax/models/glm_moe_dsa/glm5_bridge.py @@ -0,0 +1,304 @@ +from megatron.bridge.models.conversion.mapping_registry import MegatronMappingRegistry +from megatron.bridge.models.conversion.model_bridge import MegatronModelBridge +from megatron.bridge.models.conversion.param_mapping import ( + AutoMapping, + GatedMLPMapping, + QKVMapping, +) +from megatron.bridge.models.hf_pretrained.causal_lm import PreTrainedCausalLM +from megatron.bridge.models.mla_provider import MLAModelProvider +from megatron.core.models.gpt.gpt_model import GPTModel +from transformers import GlmMoeDsaForCausalLM + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + + +@MegatronModelBridge.register_bridge( + source=GlmMoeDsaForCausalLM, target=GPTModel, provider=MLAModelProvider, model_type="glm_moe_dsa" +) +class GLM5Bridge(MegatronModelBridge): + """Megatron Bridge for GLM-5 / GLM-5.1 (MoE + MLA + DSA). + + This bridge handles conversion between HuggingFace ``GlmMoeDsaForCausalLM`` + and Megatron-Core ``GPTModel`` formats. GLM-5 and GLM-5.1 share the same + architecture and configuration shape, so both ``zai-org/GLM-5`` and + ``zai-org/GLM-5.1`` are auto-detected through this bridge. + + The architecture uses Multi-Latent Attention (MLA), Dynamic Sparse Attention + (DSA) indexer layers, and Mixture-of-Experts (MoE), with optional + Multi-Token Prediction (MTP) layers. + + Requires ``transformers>=5.2.0``. + + Example: + >>> from megatron.bridge import AutoBridge + >>> bridge = AutoBridge.from_hf_pretrained("zai-org/GLM-5.1") + >>> provider = bridge.to_megatron_provider() + """ + + def provider_bridge(self, hf_pretrained: PreTrainedCausalLM) -> MLAModelProvider: + provider = super().provider_bridge(hf_pretrained) + hf_config = hf_pretrained.config + + # Use fused DSAMLASelfAttention spec (ported from slime) instead of + # Megatron Core's native DSAttention. This enables: + # 1. Context Parallelism (CP > 1) + # 2. Fused SparseMLA kernel (no O(n^2) attention matrix) + # 3. Fused indexer kernel (no O(n^2) score matrix) + from relax.models.glm_moe_dsa.dsa_attention import get_glm5_dsa_spec + + provider.transformer_layer_spec = get_glm5_dsa_spec + + # GLM-5 uses RoPE, not learned absolute position embeddings. + provider.position_embedding_type = "rope" + + provider.normalization = "RMSNorm" + provider.gated_linear_unit = True + provider.add_bias_linear = False + provider.share_embeddings_and_output_weights = False + provider.qk_layernorm = True + provider.multi_latent_attention = True + provider.moe_grouped_gemm = True + provider.moe_router_pre_softmax = True + provider.moe_token_dispatcher_type = "alltoall" + provider.moe_router_load_balancing_type = "seq_aux_loss" + # NOTE: moe_shared_expert_overlap only works with the alltoall dispatcher. + # When using the flex dispatcher (overridden via bridge_keys), this must + # be False. Default to False here; enable via --moe-shared-expert-overlap + # if using alltoall. + provider.moe_shared_expert_overlap = False + provider.moe_router_score_function = "sigmoid" + provider.moe_router_enable_expert_bias = True + provider.moe_router_dtype = "fp32" + provider.moe_permute_fusion = True + provider.hidden_dropout = 0.0 + provider.attention_softmax_in_fp32 = False + provider.make_vocab_size_divisible_by = 1280 + + # GLM5-specific: computed fields not in CONFIG_MAPPING. + provider.moe_layer_freq = [0] * hf_config.first_k_dense_replace + [1] * ( + hf_config.num_hidden_layers - hf_config.first_k_dense_replace + ) + provider.moe_shared_expert_intermediate_size = hf_config.moe_intermediate_size * hf_config.n_shared_experts + + # GLM5-specific: rotary_base is nested in rope_parameters. + provider.rotary_base = hf_config.rope_parameters["rope_theta"] + + # GLM5 uses default rope (no YaRN scaling). + provider.rotary_scaling_factor = 1.0 + provider.mscale = 1.0 + provider.mscale_all_dim = 1.0 + + # DSA indexer params — stored on the config for DSAMLASelfAttention to + # read. NOTE: We do NOT set ``experimental_attention_variant = "dsa"`` + # here, because that would trigger the CP=1 assertion in Megatron Core's + # ``transformer_config.py``. The fused DSAMLASelfAttention handles DSA + # natively without that flag. + provider.index_head_dim = hf_config.index_head_dim + provider.index_num_attention_heads = hf_config.index_n_heads + provider.dsa_indexer_topk = hf_config.index_topk + provider.dsa_indexer_loss_coeff = 0.001 + provider.dsa_indexer_use_sparse_loss = True + + return provider + + def mapping_registry(self) -> MegatronMappingRegistry: + param_mappings = { + # Embed + "embedding.word_embeddings.weight": "model.embed_tokens.weight", + # LM Head + "decoder.final_layernorm.weight": "model.norm.weight", + "output_layer.weight": "lm_head.weight", + # Attention layernorm + "decoder.layers.*.self_attention.linear_qkv.layer_norm_weight": "model.layers.*.input_layernorm.weight", + "decoder.layers.*.input_layernorm.weight": "model.layers.*.input_layernorm.weight", + # Attention output + "decoder.layers.*.self_attention.linear_proj.weight": "model.layers.*.self_attn.o_proj.weight", + # Post-attention layernorm — MoE layers use pre_mlp_layernorm, + # dense layers use layer_norm_weight. + "decoder.layers.*.pre_mlp_layernorm.weight": "model.layers.*.post_attention_layernorm.weight", + "decoder.layers.*.mlp.linear_fc1.layer_norm_weight": "model.layers.*.post_attention_layernorm.weight", + # MLA weights + "decoder.layers.*.self_attention.linear_q_down_proj.weight": "model.layers.*.self_attn.q_a_proj.weight", + "decoder.layers.*.self_attention.linear_q_up_proj.weight": "model.layers.*.self_attn.q_b_proj.weight", + "decoder.layers.*.self_attention.linear_q_up_proj.layer_norm_weight": "model.layers.*.self_attn.q_a_layernorm.weight", + "decoder.layers.*.self_attention.q_layernorm.weight": "model.layers.*.self_attn.q_a_layernorm.weight", + "decoder.layers.*.self_attention.linear_kv_down_proj.weight": "model.layers.*.self_attn.kv_a_proj_with_mqa.weight", + "decoder.layers.*.self_attention.linear_kv_up_proj.weight": "model.layers.*.self_attn.kv_b_proj.weight", + "decoder.layers.*.self_attention.linear_kv_up_proj.layer_norm_weight": "model.layers.*.self_attn.kv_a_layernorm.weight", + "decoder.layers.*.self_attention.kv_layernorm.weight": "model.layers.*.self_attn.kv_a_layernorm.weight", + # For non-MLA attention (fallback) + "decoder.layers.*.self_attention.linear_q_proj.weight": "model.layers.*.self_attn.q_proj.weight", + # DSA indexer — weights live directly on DSAMLASelfAttention (not + # nested under core_attention.indexer). + "decoder.layers.*.self_attention.wq_b.weight": "model.layers.*.self_attn.indexer.wq_b.weight", + "decoder.layers.*.self_attention.wk.weight": "model.layers.*.self_attn.indexer.wk.weight", + "decoder.layers.*.self_attention.k_norm.weight": "model.layers.*.self_attn.indexer.k_norm.weight", + "decoder.layers.*.self_attention.k_norm.bias": "model.layers.*.self_attn.indexer.k_norm.bias", + "decoder.layers.*.self_attention.weights_proj.weight": "model.layers.*.self_attn.indexer.weights_proj.weight", + # Dense MLP + "decoder.layers.*.mlp.linear_fc2.weight": "model.layers.*.mlp.down_proj.weight", + # MoE router + "decoder.layers.*.mlp.router.weight": "model.layers.*.mlp.gate.weight", + "decoder.layers.*.mlp.router.expert_bias": "model.layers.*.mlp.gate.e_score_correction_bias", + # MoE shared experts + "decoder.layers.*.mlp.shared_experts.router.weight": "model.layers.*.mlp.shared_experts.gate.weight", + "decoder.layers.*.mlp.shared_experts.linear_fc2.weight": "model.layers.*.mlp.shared_experts.down_proj.weight", + } + + mapping_list = [AutoMapping(megatron_param=k, hf_param=v) for k, v in param_mappings.items()] + + # Attention (non-MLA fallback: combined QKV). + mapping_list.extend( + [ + QKVMapping( + megatron_param="decoder.layers.*.self_attention.linear_qkv.weight", + q="model.layers.*.self_attn.q_proj.weight", + k="model.layers.*.self_attn.k_proj.weight", + v="model.layers.*.self_attn.v_proj.weight", + ), + QKVMapping( + megatron_param="decoder.layers.*.self_attention.linear_qkv.bias", + q="model.layers.*.self_attn.q_proj.bias", + k="model.layers.*.self_attn.k_proj.bias", + v="model.layers.*.self_attn.v_proj.bias", + ), + # Dense MLP gate+up → fc1 + GatedMLPMapping( + megatron_param="decoder.layers.*.mlp.linear_fc1.weight", + gate="model.layers.*.mlp.gate_proj.weight", + up="model.layers.*.mlp.up_proj.weight", + ), + # Shared expert gate+up → fc1 + GatedMLPMapping( + megatron_param="decoder.layers.*.mlp.shared_experts.linear_fc1.weight", + gate="model.layers.*.mlp.shared_experts.gate_proj.weight", + up="model.layers.*.mlp.shared_experts.up_proj.weight", + ), + ] + ) + + # MoE expert weights (per-expert format: experts.N.gate_proj / up_proj / + # down_proj). + mapping_list.extend( + [ + GatedMLPMapping( + megatron_param="decoder.layers.*.mlp.experts.linear_fc1.weight*", + gate="model.layers.*.mlp.experts.*.gate_proj.weight", + up="model.layers.*.mlp.experts.*.up_proj.weight", + ), + AutoMapping( + megatron_param="decoder.layers.*.mlp.experts.linear_fc2.weight*", + hf_param="model.layers.*.mlp.experts.*.down_proj.weight", + ), + ] + ) + + # --- MTP (Multi-Token Prediction) layer mappings --- + # ``self.hf_config`` is set by the dispatch system before this method is + # called. When the bridge is constructed standalone (e.g. in unit + # tests), ``hf_config`` may be unset, in which case MTP mappings are + # skipped — that matches the reference behavior. + hf_config = getattr(self, "hf_config", None) + if hf_config is None: + logger.warning("No HF config found on bridge instance, skipping MTP mappings.") + return MegatronMappingRegistry(*mapping_list) + + num_mtp_layers = getattr(hf_config, "num_nextn_predict_layers", 0) or 0 + num_transformer_layers = hf_config.num_hidden_layers + + # Layer-specific mappings reused for the MTP transformer_layer. These + # mirror the decoder layer mappings but with an mtp prefix. + mtp_layer_mappings = { + # Attention layernorm + "self_attention.linear_qkv.layer_norm_weight": "input_layernorm.weight", + "input_layernorm.weight": "input_layernorm.weight", + # Attention output + "self_attention.linear_proj.weight": "self_attn.o_proj.weight", + # Post-attention layernorm (MoE layer uses pre_mlp_layernorm) + "pre_mlp_layernorm.weight": "post_attention_layernorm.weight", + # MLA weights + "self_attention.linear_q_down_proj.weight": "self_attn.q_a_proj.weight", + "self_attention.linear_q_up_proj.weight": "self_attn.q_b_proj.weight", + "self_attention.linear_q_up_proj.layer_norm_weight": "self_attn.q_a_layernorm.weight", + "self_attention.q_layernorm.weight": "self_attn.q_a_layernorm.weight", + "self_attention.linear_kv_down_proj.weight": "self_attn.kv_a_proj_with_mqa.weight", + "self_attention.linear_kv_up_proj.weight": "self_attn.kv_b_proj.weight", + "self_attention.linear_kv_up_proj.layer_norm_weight": "self_attn.kv_a_layernorm.weight", + "self_attention.kv_layernorm.weight": "self_attn.kv_a_layernorm.weight", + # DSA indexer — weights live directly on DSAMLASelfAttention. + "self_attention.wq_b.weight": "self_attn.indexer.wq_b.weight", + "self_attention.wk.weight": "self_attn.indexer.wk.weight", + "self_attention.k_norm.weight": "self_attn.indexer.k_norm.weight", + "self_attention.k_norm.bias": "self_attn.indexer.k_norm.bias", + "self_attention.weights_proj.weight": "self_attn.indexer.weights_proj.weight", + # MoE router + "mlp.router.weight": "mlp.gate.weight", + "mlp.router.expert_bias": "mlp.gate.e_score_correction_bias", + # MoE shared experts + "mlp.shared_experts.router.weight": "mlp.shared_experts.gate.weight", + "mlp.shared_experts.linear_fc2.weight": "mlp.shared_experts.down_proj.weight", + } + + for mtp_layer in range(num_mtp_layers): + hf_layer_idx = mtp_layer + num_transformer_layers + + # AutoMapping for layer-specific params. + for megatron_suffix, hf_suffix in mtp_layer_mappings.items(): + mapping_list.append( + AutoMapping( + megatron_param=f"mtp.layers.{mtp_layer}.transformer_layer.{megatron_suffix}", + hf_param=f"model.layers.{hf_layer_idx}.{hf_suffix}", + ) + ) + + # MTP-specific mappings (enorm, hnorm, eh_proj, final_layernorm). + mapping_list.extend( + [ + AutoMapping( + megatron_param=f"mtp.layers.{mtp_layer}.enorm.weight", + hf_param=f"model.layers.{hf_layer_idx}.enorm.weight", + ), + AutoMapping( + megatron_param=f"mtp.layers.{mtp_layer}.hnorm.weight", + hf_param=f"model.layers.{hf_layer_idx}.hnorm.weight", + ), + AutoMapping( + megatron_param=f"mtp.layers.{mtp_layer}.eh_proj.weight", + hf_param=f"model.layers.{hf_layer_idx}.eh_proj.weight", + ), + AutoMapping( + megatron_param=f"mtp.layers.{mtp_layer}.final_layernorm.weight", + hf_param=f"model.layers.{hf_layer_idx}.shared_head.norm.weight", + ), + ] + ) + + # Shared expert gate+up → fc1. + mapping_list.append( + GatedMLPMapping( + megatron_param=f"mtp.layers.{mtp_layer}.transformer_layer.mlp.shared_experts.linear_fc1.weight", + gate=f"model.layers.{hf_layer_idx}.mlp.shared_experts.gate_proj.weight", + up=f"model.layers.{hf_layer_idx}.mlp.shared_experts.up_proj.weight", + ) + ) + + # MoE expert weights. + mapping_list.extend( + [ + GatedMLPMapping( + megatron_param=f"mtp.layers.{mtp_layer}.transformer_layer.mlp.experts.linear_fc1.weight*", + gate=f"model.layers.{hf_layer_idx}.mlp.experts.*.gate_proj.weight", + up=f"model.layers.{hf_layer_idx}.mlp.experts.*.up_proj.weight", + ), + AutoMapping( + megatron_param=f"mtp.layers.{mtp_layer}.transformer_layer.mlp.experts.linear_fc2.weight*", + hf_param=f"model.layers.{hf_layer_idx}.mlp.experts.*.down_proj.weight", + ), + ] + ) + + return MegatronMappingRegistry(*mapping_list) diff --git a/relax/models/glm_moe_dsa/glm5_provider.py b/relax/models/glm_moe_dsa/glm5_provider.py new file mode 100644 index 000000000..57e3aed72 --- /dev/null +++ b/relax/models/glm_moe_dsa/glm5_provider.py @@ -0,0 +1,9 @@ +"""GLM5 uses MLAModelProvider directly. + +This module is kept for import compatibility. +""" + +from megatron.bridge.models.mla_provider import MLAModelProvider as GLM5ModelProvider # noqa: F401 + + +__all__ = ["GLM5ModelProvider"] diff --git a/relax/models/glm_moe_dsa/ops/__init__.py b/relax/models/glm_moe_dsa/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/relax/models/glm_moe_dsa/ops/indexer.py b/relax/models/glm_moe_dsa/ops/indexer.py new file mode 100644 index 000000000..b57f2e767 --- /dev/null +++ b/relax/models/glm_moe_dsa/ops/indexer.py @@ -0,0 +1,82 @@ +"""Autograd wrapper for the fused tilelang indexer (DSA top-k scoring).""" + +import torch + +from .tilelang_indexer_bwd import indexer_bwd_interface +from .tilelang_indexer_fwd import indexer_fwd_interface + + +def pytorch_extract_topk_scores(logits, topk_indices, dim=-1): + """Gather top-k scores from logits using topk_indices, masking invalid (-1) + entries.""" + valid_mask = topk_indices != -1 + safe_indices = topk_indices.clamp(min=0).to(torch.int64) + scores = torch.gather(logits, dim=dim, index=safe_indices) + scores = torch.where(valid_mask, scores, float("-inf")) + return scores + + +class IndexerFunction(torch.autograd.Function): + """Fused indexer autograd function backed by tilelang fwd/bwd kernels.""" + + @staticmethod + def forward( + ctx, + index_q: torch.Tensor, + index_k: torch.Tensor, + weights: torch.Tensor, + cu_seqlen_ks: torch.Tensor, + cu_seqlen_ke: torch.Tensor, + topk: int, + topk_indices: torch.Tensor | None = None, + ): + _, head_num, _ = index_q.shape + + logits = indexer_fwd_interface(index_q, index_k, weights, cu_seqlen_ks, cu_seqlen_ke, clean_logits=True) + + if topk_indices is None: + index_score, topk_indices = torch.topk(logits, topk, dim=-1) + topk_indices = topk_indices.to(torch.int32) + topk_indices = topk_indices.masked_fill(index_score == -torch.inf, -1) + + index_score = pytorch_extract_topk_scores(logits, topk_indices) + + ctx.save_for_backward(index_q, index_k, weights, cu_seqlen_ks, cu_seqlen_ke, topk_indices) + ctx.topk = topk + ctx.head_num = head_num + + return index_score, topk_indices + + @staticmethod + def backward(ctx, grad_scores, grad_indices): + index_q, index_k, weights, cu_seqlen_ks, cu_seqlen_ke, topk_indices = ctx.saved_tensors + + grad_q, grad_w, grad_k = indexer_bwd_interface(index_q, weights, index_k, topk_indices, grad_scores) + + return grad_q, grad_k, grad_w, None, None, None, None, None, None, None + + +def lighting_indexer( + index_q: torch.Tensor, + index_k: torch.Tensor, + weights: torch.Tensor, + cu_seqlen_ks: torch.Tensor, + cu_seqlen_ke: torch.Tensor, + topk: int, + topk_indices: torch.Tensor | None = None, +): + """Run the fused indexer to obtain top-k scores and indices for sparse + MLA.""" + return IndexerFunction.apply(index_q, index_k, weights.squeeze(-1), cu_seqlen_ks, cu_seqlen_ke, topk, topk_indices) + + +def generate_varlen_mask_params(cu_seqlens): + """Compute per-token (start, end) bounds for variable-length sequences from + cu_seqlens.""" + seq_len = cu_seqlens[-1].item() + q_indices = torch.arange(0, seq_len, device=cu_seqlens.device) + seq_indices = torch.searchsorted(cu_seqlens, q_indices, right=True) - 1 + starts = cu_seqlens[seq_indices] + ends = q_indices + 1 + assert torch.all((ends - starts) > 0) + return starts, ends diff --git a/relax/models/glm_moe_dsa/ops/sparse_mla.py b/relax/models/glm_moe_dsa/ops/sparse_mla.py new file mode 100644 index 000000000..12cd43686 --- /dev/null +++ b/relax/models/glm_moe_dsa/ops/sparse_mla.py @@ -0,0 +1,50 @@ +"""Autograd wrapper for the fused tilelang sparse MLA fwd/bwd kernels.""" + +import torch + +from .tilelang_sparse_mla_bwd import sparse_mla_bwd +from .tilelang_sparse_mla_fwd import sparse_mla_fwd_interface + + +class SparseMLA(torch.autograd.Function): + """Sparse Multi-Latent Attention autograd function backed by tilelang + kernels.""" + + @staticmethod + def forward(ctx, q, kv, indices, scaling): + """ + Args: + q: Query tensor (seq_len, heads, dim_plus_tail_dim) + kv: Key-Value tensor (seq_len_kv, kv_group, dim_plus_tail_dim) + indices: Sparse indices tensor (seq_len, kv_group, topk) + + Returns: + out: Output tensor (seq_len, heads, dim) + """ + indices = indices.contiguous() + q, kv = q.contiguous(), kv.contiguous() + ctx.scaling = scaling + + tl_out, tl_lse = sparse_mla_fwd_interface(q, kv, indices, sm_scale=scaling) + + # Save tensors for backward pass + ctx.save_for_backward(q, kv, indices, tl_out, tl_lse) + + return tl_out, tl_lse + + @staticmethod + def backward(ctx, grad_output, grad_lse): + """ + Args: + grad_output: Gradient of the loss with respect to output + + Returns: + Gradients for q, kv, and indices (None for indices) + """ + q, kv, indices, tl_out, tl_lse = ctx.saved_tensors + scaling = ctx.scaling + + tl_dq, tl_dkv = sparse_mla_bwd(q, kv, tl_out, grad_output.contiguous(), indices, tl_lse, sm_scale=scaling) + + # Return gradients for each input (None for indices as it's not differentiable) + return tl_dq, tl_dkv, None, None diff --git a/relax/models/glm_moe_dsa/ops/tilelang_indexer_bwd.py b/relax/models/glm_moe_dsa/ops/tilelang_indexer_bwd.py new file mode 100644 index 000000000..403cc41fe --- /dev/null +++ b/relax/models/glm_moe_dsa/ops/tilelang_indexer_bwd.py @@ -0,0 +1,171 @@ +# ruff: noqa +# Adapted from https://github.com/tile-ai/tilelang/blob/4956b5835fa554af6c03d4a6289cad44bf310869/examples/dsa_sparse_finetune/indexer_bwd.py +import tilelang as tl +import tilelang.language as T +import torch + +BF16 = T.bfloat16 +FP32 = T.float32 +INT32 = T.int32 + +pass_configs = { + tl.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tl.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, +} + + +@tl.jit(pass_configs=pass_configs) +def tl_indexer_bwd_impl( + heads: int, + dim: int, + topk: int, + block_I: int = 32, + num_stages: int = 0, + num_threads: int = 128, +): + assert num_stages == 0 + assert topk == tl.math.next_power_of_2(topk) + assert topk % block_I == 0 + assert heads <= 64 and heads % 8 == 0 + + seq_len = T.symbolic("seq_len") + q_seq_len = T.symbolic("q_seq_len") + + dtype: str = BF16 + accum_dtype: str = FP32 + + index_q_shape = [q_seq_len, heads, dim] + weights_shape = [q_seq_len, heads] + index_k_shape = [seq_len, dim] + shape_p = [q_seq_len, topk] + topk_indices_shape = [q_seq_len, topk] + + pad_heads = heads + if heads < 16: + pad_heads = 16 + + @T.prim_func + def tl_indexer_bwd_kernel( + IndexQ: T.Tensor(index_q_shape, dtype), + IndexK: T.Tensor(index_k_shape, dtype), + Weights: T.Tensor(weights_shape, FP32), + TopkIndices: T.Tensor(topk_indices_shape, INT32), + OGrad: T.Tensor(shape_p, FP32), + dIndexQ: T.Tensor(index_q_shape, dtype), + dWeights: T.Tensor(weights_shape, FP32), + dIndexK: T.Tensor(index_k_shape, FP32), + ): + with T.Kernel(q_seq_len, threads=num_threads) as (bx): + index_q_shared = T.alloc_shared([pad_heads, dim], dtype=FP32) + weights_shared = T.alloc_shared([pad_heads], dtype=FP32) + index_k_shared = T.alloc_shared([block_I, dim], dtype=FP32) + indices_shared = T.alloc_shared([block_I], dtype=INT32) + + d_index_q_frag = T.alloc_fragment([pad_heads, dim], dtype=accum_dtype) + d_weights_frag = T.alloc_fragment([pad_heads], dtype=accum_dtype) + d_index_k_frag = T.alloc_fragment([block_I, dim], dtype=accum_dtype) + + logits = T.alloc_fragment((block_I, pad_heads), dtype=accum_dtype) + _logits = T.alloc_shared((block_I, pad_heads), dtype=accum_dtype) + grad = T.alloc_shared([block_I], dtype=FP32) + + num_blocks = T.ceildiv(topk, block_I) + + for i, j in T.Parallel(pad_heads, dim): + index_q_shared[i, j] = T.if_then_else(i < heads, IndexQ[bx, i, j], 0) + + for i in T.Parallel(heads): + weights_shared[i] = Weights[bx, i] + + T.fill(d_index_q_frag, 0) + T.fill(d_weights_frag, 0) + + # for bi_i in T.Pipelined(num_blocks, num_stages=num_stages): + for bi_i in T.serial(num_blocks): + for i in T.Parallel(block_I): + if bi_i * block_I + i < topk: + indices_shared[i] = TopkIndices[bx, bi_i * block_I + i] + grad[i] = OGrad[bx, bi_i * block_I + i] + T.sync_threads() + + for i, j in T.Parallel(block_I, dim): + index_k_shared[i, j] = T.if_then_else( + indices_shared[i] > -1 and indices_shared[i] < seq_len, IndexK[indices_shared[i], j], 0 + ) + T.sync_threads() + + T.gemm( + index_k_shared, + index_q_shared, + logits, + transpose_A=False, + transpose_B=True, + clear_accum=True, + ) + + for i, j in T.Parallel(block_I, heads): + logits[i, j] = T.max(logits[i, j], 0) + + d_weights_i = T.alloc_fragment((block_I, pad_heads), accum_dtype) + for i, j in T.Parallel(block_I, heads): + d_weights_i[i, j] = grad[i] * logits[i, j] + T.reduce_sum(d_weights_i, d_weights_frag, dim=0, clear=False) + + for i, j in T.Parallel(block_I, pad_heads): + _logits[i, j] = T.if_then_else(logits[i, j] > 0 and j < heads, grad[i] * weights_shared[j], 0) + T.sync_threads() + + T.gemm( + _logits, + index_k_shared, + d_index_q_frag, + transpose_A=True, + transpose_B=False, + clear_accum=False, + ) + + T.gemm( + _logits, + index_q_shared, + d_index_k_frag, + transpose_A=False, + transpose_B=False, + clear_accum=True, + ) + + for i, j in T.Parallel(block_I, dim): + if indices_shared[i] > -1 and indices_shared[i] < seq_len: + T.atomic_add(dIndexK[indices_shared[i], j], d_index_k_frag[i, j]) + + T.copy(d_index_q_frag[:heads, :], dIndexQ[bx, :, :]) + T.copy(d_weights_frag[:heads], dWeights[bx, :]) + + return tl_indexer_bwd_kernel + + +def indexer_bwd_interface( + index_q: torch.Tensor, + weights: torch.Tensor, + index_k: torch.Tensor, + topk_indices: torch.Tensor, + grad_scores: torch.Tensor, +): + _, head_num, head_dim = index_q.shape + k_top = topk_indices.shape[1] + + grad_scores = grad_scores.contiguous() + grad_q = torch.empty_like(index_q) + grad_w = torch.empty_like(weights, dtype=torch.float32) + grad_k = torch.zeros_like(index_k, dtype=torch.float32) + + tl_indexer_bwd_impl(head_num, head_dim, k_top)( + index_q.contiguous(), + index_k.contiguous(), + weights.squeeze(-1).contiguous(), + topk_indices.contiguous(), + grad_scores, + grad_q, + grad_w.squeeze(-1), + grad_k, + ) + return grad_q, grad_w, grad_k diff --git a/relax/models/glm_moe_dsa/ops/tilelang_indexer_fwd.py b/relax/models/glm_moe_dsa/ops/tilelang_indexer_fwd.py new file mode 100644 index 000000000..6e5be43c6 --- /dev/null +++ b/relax/models/glm_moe_dsa/ops/tilelang_indexer_fwd.py @@ -0,0 +1,134 @@ +# ruff: noqa +# Adapted from https://github.com/tile-ai/tilelang/blob/4956b5835fa554af6c03d4a6289cad44bf310869/examples/deepseek_v32/fp8_lighting_indexer.py +import tilelang +import torch +from tilelang import language as T + + +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + }, +) +def tl_indexer_fwd_impl( + heads, + index_dim, + block_N=256, + num_stages=3, + threads=512, + block_Q=None, +): + if block_Q is None: + block_Q = 128 // heads + + dtype = T.bfloat16 + accum_dtype = T.float32 + index_dtype = T.int32 + + seq_len = T.dynamic("seq_len") + seq_len_kv = T.dynamic("seq_len_kv") + + index_q_shape = [seq_len * heads, index_dim] + index_k_shape = [seq_len_kv, index_dim] + logits_shape = [seq_len, seq_len_kv] + + @T.prim_func + def tl_indexer_fwd_kernel( + IndexQ: T.Tensor(index_q_shape, dtype), # type: ignore + IndexK: T.Tensor(index_k_shape, dtype), # type: ignore + Logits: T.Tensor(logits_shape, accum_dtype), # type: ignore + Weights: T.Tensor([seq_len, heads], accum_dtype), # type: ignore + CuSeqLenKS: T.Tensor([seq_len], index_dtype), # type: ignore + CuSeqLenKE: T.Tensor([seq_len], index_dtype), # type: ignore + ): + with T.Kernel(T.ceildiv(seq_len, block_Q), threads=threads) as bx: + index_q_shared = T.alloc_shared([block_Q * heads, index_dim], dtype) + index_k_shared = T.alloc_shared([block_N, index_dim], dtype) + s = T.alloc_fragment([block_N, block_Q * heads], accum_dtype) + s_reshaped = T.reshape(s, (block_N, block_Q, heads)) + logits = T.alloc_fragment([block_N, block_Q], accum_dtype) + weights = T.alloc_fragment([block_Q, heads], accum_dtype) + + seq_len_i = bx * block_Q + + cu_k_s_min = T.alloc_var(index_dtype) + cu_k_e_max = T.alloc_var(index_dtype) + cu_k_s_min = 2147483647 + cu_k_e_max = -2147483648 + + for bq_i in T.serial(block_Q): + cu_k_s_min = T.min(cu_k_s_min, T.min(CuSeqLenKS[seq_len_i + bq_i], seq_len_kv)) + + for bq_i in T.serial(block_Q): + cu_k_e_max = T.max(cu_k_e_max, T.min(CuSeqLenKE[seq_len_i + bq_i], seq_len_kv)) + + T.copy(IndexQ[seq_len_i * heads, 0], index_q_shared) + T.copy(Weights[seq_len_i, 0], weights) + + for nbn_i in T.Pipelined(T.ceildiv(cu_k_e_max - cu_k_s_min, block_N), num_stages=num_stages): + T.copy(IndexK[cu_k_s_min + nbn_i * block_N, 0], index_k_shared) + T.gemm( + index_k_shared, + index_q_shared, + s, + transpose_B=True, + clear_accum=True, + policy=T.GemmWarpPolicy.FullCol, + ) + for bn_i, bq_i, h_i in T.Parallel(block_N, block_Q, heads): + s_reshaped[bn_i, bq_i, h_i] = T.max(s_reshaped[bn_i, bq_i, h_i], 0) * weights[bq_i, h_i] + T.reduce_sum(s_reshaped, logits, dim=-1, clear=True) + for bq_i, bn_i in T.Parallel(block_Q, block_N): + Logits[seq_len_i + bq_i, cu_k_s_min + nbn_i * block_N + bn_i] = logits[bn_i, bq_i] + + return tl_indexer_fwd_kernel + + +@tilelang.jit +def clean_logits_( + threads: int = 512, + block_K: int = 4096, +): + seq_len = T.dynamic("seq_len") + seq_len_kv = T.dynamic("seq_len_kv") + dtype = T.float + indices_dtype = T.int32 + + @T.prim_func + def clean_logits_kernel( + Logits: T.Tensor([seq_len, seq_len_kv], dtype), # type: ignore + CuSeqLenKS: T.Tensor([seq_len], indices_dtype), # type: ignore + CuSeqLenKE: T.Tensor([seq_len], indices_dtype), # type: ignore + ): + with T.Kernel(seq_len, threads=threads) as bx: + tx = T.thread_binding(0, threads, thread="threadIdx.x") + cu_k_s = CuSeqLenKS[bx] + cu_k_e = CuSeqLenKE[bx] + for n_i in T.Pipelined(T.ceildiv(seq_len_kv, block_K)): + for k_i in T.serial(block_K // threads): + idx = n_i * block_K + k_i * threads + tx + if idx < cu_k_s or idx >= cu_k_e: + Logits[bx, idx] = -T.infinity(dtype) + + return clean_logits_kernel + + +def indexer_fwd_interface(q, kv, weights, cu_seqlen_ks, cu_seqlen_ke, clean_logits=True): + seq_len, heads, index_dim = q.shape + seq_len_kv = kv.shape[0] + + clean_logits_kernel = clean_logits_() + tl_indexer_fwd_kernel = tl_indexer_fwd_impl(heads=heads, index_dim=index_dim) + + logits = torch.empty([seq_len, seq_len_kv], device=q.device, dtype=torch.float32) + tl_indexer_fwd_kernel( + q.view(seq_len * heads, index_dim), + kv, + logits, + weights, + cu_seqlen_ks, + cu_seqlen_ke, + ) + if clean_logits: + clean_logits_kernel(logits, cu_seqlen_ks, cu_seqlen_ke) + return logits diff --git a/relax/models/glm_moe_dsa/ops/tilelang_sparse_mla_bwd.py b/relax/models/glm_moe_dsa/ops/tilelang_sparse_mla_bwd.py new file mode 100644 index 000000000..922093fed --- /dev/null +++ b/relax/models/glm_moe_dsa/ops/tilelang_sparse_mla_bwd.py @@ -0,0 +1,321 @@ +# ruff: noqa +# Adapt from https://github.com/tile-ai/tilelang/blob/4ff81c7d40803d269569e157e847623e84553f78/examples/deepseek_v32/sparse_mla_bwd.py +import tilelang +import torch +from tilelang import language as T + + +@tilelang.jit(out_idx=[-1]) +def preprocess( + B, + S, + H, + D, + block_ND=32, + num_stages=5, + dtype=T.bfloat16, + accum_dtype=T.float32, +): + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + + shape = [B, S, H, D] + + @T.prim_func + def preprocess_kernel( + O: T.Tensor(shape, dtype), + dO: T.Tensor(shape, dtype), + Delta: T.Tensor([B, S, H], accum_dtype), + ): + with T.Kernel(H, T.ceildiv(S, block_ND), B) as (bx, by, bz): + o = T.alloc_fragment([block_ND, block_ND], accum_dtype) + do = T.alloc_fragment([block_ND, block_ND], accum_dtype) + delta = T.alloc_fragment([block_ND], accum_dtype) + acc = T.alloc_fragment([block_ND, block_ND], accum_dtype) + T.clear(acc) + for k in T.Pipelined(T.ceildiv(D, block_ND), num_stages=num_stages): + T.copy(O[bz, by * block_ND : (by + 1) * block_ND, bx, k * block_ND : (k + 1) * block_ND], o) + T.copy(dO[bz, by * block_ND : (by + 1) * block_ND, bx, k * block_ND : (k + 1) * block_ND], do) + for i, j in T.Parallel(block_ND, block_ND): + acc[i, j] += o[i, j] * do[i, j] + T.reduce_sum(acc, delta, 1) + T.copy(delta, Delta[bz, by * block_ND : (by + 1) * block_ND, bx]) + + return preprocess_kernel + + +@tilelang.jit(out_idx=[-1]) +def postprocess( + B, + S_kv, + D, + D_tail, + kv_group=1, + block_N=64, + threads=128, + dtype=T.bfloat16, + accum_dtype=T.float32, +): + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + + dkv_shape = [B, S_kv, kv_group, D + D_tail] + + @T.prim_func + def postprocess_kernel( + dKV: T.Tensor(dkv_shape, accum_dtype), + dKV_out: T.Tensor(dkv_shape, dtype), + ): + with T.Kernel(T.ceildiv(S_kv, block_N), kv_group, B, threads=threads) as (bx, by, bz): + T.copy( + dKV[bz, bx * block_N : (bx + 1) * block_N, by, :], + dKV_out[bz, bx * block_N : (bx + 1) * block_N, by, :], + ) + + return postprocess_kernel + + +@tilelang.jit( + out_idx=[-2], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + tilelang.PassConfigKey.TL_ENABLE_AGGRESSIVE_SHARED_MEMORY_MERGE: True, + }, +) +def bwd( + B, + S, + S_kv, + H, + D, + D_tail, + topk, + kv_group=1, + sm_scale=None, + is_causal=True, + block_size=32, + num_stages=0, + threads=128, + indices_dtype=T.int32, + dtype=T.bfloat16, + accum_dtype=T.float32, +): + assert is_causal == True, "non-casual is not supported now" + assert topk % block_size == 0, "otherwise will load some index=0 thus causing wrong kv to be loaded" + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + assert indices_dtype == T.int32 + + if sm_scale is None: + sm_scale = (D + D_tail) ** (-0.5) + + sm_scale_mul_reciprocal_log2 = sm_scale * 1.44269504 # log2(e) + + H_kv = H // kv_group + + q_shape = [B, S, H, D + D_tail] + k_shape = [B, S_kv, kv_group, D + D_tail] + o_shape = [B, S, H, D] + indices_shape = [B, S, kv_group, topk] + delta_shape = [B, S, H] + lse_shape = [B, S, H] + + assert indices_dtype == T.int32 + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + + H = H_kv + padded_H = max(tilelang.math.next_power_of_2(H_kv), 16) + block_H = min(64, padded_H) + assert padded_H % block_H == 0 + NH = padded_H // block_H + + BS = block_size + NS = tilelang.cdiv(topk, block_size) + + split_store = 2 + + @T.prim_func + def sparse_mla_bwd_kernel( + Q: T.Tensor(q_shape, dtype), + KV: T.Tensor(k_shape, dtype), + dO: T.Tensor(o_shape, dtype), + Indices: T.Tensor(indices_shape, indices_dtype), + Lse: T.Tensor(lse_shape, accum_dtype), + Delta: T.Tensor(delta_shape, accum_dtype), + dQ: T.Tensor(q_shape, dtype), + dKV: T.Tensor(k_shape, accum_dtype), + ): + with T.Kernel(S, B, kv_group * NH, threads=threads) as (s_i, by, bz): + Q_shared = T.alloc_shared([block_H, D], dtype) + Q_tail_shared = T.alloc_shared([block_H, D_tail], dtype) + KV_shared = T.alloc_shared([BS, D], dtype) + KV_tail_shared = T.alloc_shared([BS, D_tail], dtype) + dO_shared = T.alloc_shared([block_H, D], dtype) + + mask = T.alloc_fragment([BS], "bool") + P_shared_cast = T.alloc_shared([block_H, BS], dtype) + dP_shared_cast = T.alloc_shared([block_H, BS], dtype) + dQ_shared = T.alloc_shared([block_H, D], dtype) + dQ_tail_shared = T.alloc_shared([block_H, D_tail], dtype) + + acc_p = T.alloc_fragment([block_H, BS], accum_dtype) + acc_dp = T.alloc_fragment([block_H, BS], accum_dtype) + acc_dq = T.alloc_fragment([block_H, D], accum_dtype) + acc_dq_tail = T.alloc_fragment([block_H, D_tail], accum_dtype) + acc_dkv = T.alloc_fragment([BS, D], accum_dtype) + acc_dkv_tail = T.alloc_fragment([BS, D_tail], accum_dtype) + acc_dkv_shared = T.alloc_shared([BS // split_store, D], accum_dtype) + acc_dkv_tail_shared = T.alloc_shared([BS // split_store, D_tail], accum_dtype) + + # max_kv_i = s_i + T.copy(Q[by, s_i, bz * block_H : (bz + 1) * block_H, :D], Q_shared) + T.copy(Q[by, s_i, bz * block_H : (bz + 1) * block_H, D:], Q_tail_shared) + T.copy(dO[by, s_i, bz * block_H : (bz + 1) * block_H, :D], dO_shared) + + T.clear(acc_dq) + T.clear(acc_dq_tail) + + # Process each block of indices + for i_i in T.Pipelined(NS, num_stages=num_stages): + # Check which indices are valid + for bi_i in T.Parallel(BS): + # Changed here for thd + mask[bi_i] = Indices[by, s_i, bz // NH, i_i * BS + bi_i] != -1 + + # Compute attention scores + for h_i, bi_i in T.Parallel(block_H, BS): + acc_p[h_i, bi_i] = T.if_then_else(mask[bi_i], 0, -T.infinity(acc_p.dtype)) + + # Load KV, V for this block of indices + for bi_i, d_i in T.Parallel(BS, D): + KV_shared[bi_i, d_i] = KV[by, Indices[by, s_i, bz // NH, i_i * BS + bi_i], bz // NH, d_i] + + T.gemm(Q_shared, KV_shared, acc_p, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + + for bi_i, d_i in T.Parallel(BS, D_tail): + KV_tail_shared[bi_i, d_i] = KV[by, Indices[by, s_i, bz // NH, i_i * BS + bi_i], bz // NH, D + d_i] + + T.gemm(Q_tail_shared, KV_tail_shared, acc_p, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + + for h_i, bi_i in T.Parallel(block_H, BS): + acc_p[h_i, bi_i] = T.exp2( + acc_p[h_i, bi_i] * sm_scale_mul_reciprocal_log2 - Lse[by, s_i, bz * block_H + h_i] + ) + + T.copy(acc_p, P_shared_cast) + + T.gemm( + dO_shared, + KV_shared, + acc_dp, + transpose_B=True, + policy=T.GemmWarpPolicy.FullCol, + clear_accum=True, + ) + + for h_i, bi_i in T.Parallel(block_H, BS): + acc_dp[h_i, bi_i] = ( + acc_p[h_i, bi_i] * (acc_dp[h_i, bi_i] - Delta[by, s_i, bz * block_H + h_i]) * sm_scale + ) + + T.copy(acc_dp, dP_shared_cast) + + T.gemm(dP_shared_cast, KV_shared, acc_dq, policy=T.GemmWarpPolicy.FullCol) + T.gemm(dP_shared_cast, KV_tail_shared, acc_dq_tail, policy=T.GemmWarpPolicy.FullCol) + + T.gemm( + dP_shared_cast, + Q_shared, + acc_dkv, + transpose_A=True, + policy=T.GemmWarpPolicy.FullCol, + clear_accum=True, + ) + T.gemm(P_shared_cast, dO_shared, acc_dkv, transpose_A=True, policy=T.GemmWarpPolicy.FullCol) + + T.clear(acc_dkv_tail) + T.gemm(dP_shared_cast, Q_tail_shared, acc_dkv_tail, transpose_A=True, policy=T.GemmWarpPolicy.FullCol) + + for s in range(split_store): + for bi_i, d_i in T.Parallel(BS, D): + if bi_i < BS // split_store: + acc_dkv_shared[bi_i, d_i] = acc_dkv[bi_i + s * (BS // split_store), d_i] + for bi_i, d_i in T.Parallel(BS, D_tail): + if bi_i < BS // split_store: + acc_dkv_tail_shared[bi_i, d_i] = acc_dkv_tail[bi_i + s * (BS // split_store), d_i] + + for bi_i, d_i in T.Parallel(BS // split_store, D // 4): + T.atomic_addx4( + dKV[ + by, + Indices[by, s_i, bz // NH, i_i * BS + bi_i + s * (BS // split_store)], + bz // NH, + d_i * 4, + ], + acc_dkv_shared[bi_i, d_i * 4], + ) + + # Atomically update dKV, dKV_tail tensors + for bi_i, d_i in T.Parallel(BS // split_store, D_tail // 4): + T.atomic_addx4( + dKV[ + by, + Indices[by, s_i, bz // NH, i_i * BS + bi_i + s * (BS // split_store)], + bz // NH, + D + d_i * 4, + ], + acc_dkv_tail_shared[bi_i, d_i * 4], + ) + + # Store the accumulated dQ + T.copy(acc_dq, dQ_shared) + T.copy(acc_dq_tail, dQ_tail_shared) + T.copy(dQ_shared, dQ[by, s_i, bz * block_H : (bz + 1) * block_H, :D]) + T.copy(dQ_tail_shared, dQ[by, s_i, bz * block_H : (bz + 1) * block_H, D:]) + + return sparse_mla_bwd_kernel + + +def sparse_mla_bwd(q, kv, o, do, indices, lse, sm_scale=None, is_casual=True, return_kernel=False, delta=None): + q = q.unsqueeze(0) + kv = kv.unsqueeze(0) + o = o.unsqueeze(0) + do = do.unsqueeze(0) + indices = indices.unsqueeze(0) + lse = lse.unsqueeze(0) + + assert q.is_contiguous() + assert kv.is_contiguous() + assert indices.is_contiguous() + assert lse.is_contiguous() + + B, S, H, dim_plus_tail_dim = q.shape + _, S_kv, kv_group, _ = kv.shape + assert kv.shape[-1] == dim_plus_tail_dim + assert kv.shape[0] == B + + # dim should be assigned + D = 512 + D_tail = dim_plus_tail_dim - D + + topk = indices.shape[-1] + assert indices.shape == (B, S, kv_group, topk) + assert lse.shape == (B, S, H) + + # Get kernels + preprocess_kernel = preprocess(B, S, H, D) + bwd_kernel = bwd(B, S, S_kv, H, D, D_tail, topk, kv_group, sm_scale, is_casual) + postprocess_kernel = postprocess(B, S_kv, D, D_tail, kv_group) + + if delta is None: + delta = preprocess_kernel(o, do) + + dkv = torch.zeros_like(kv, dtype=torch.float32) + dq = bwd_kernel(q, kv, do, indices, lse, delta, dkv) + dkv = postprocess_kernel(dkv) + + dq = dq.squeeze(0) + dkv = dkv.squeeze(0) + return dq, dkv diff --git a/relax/models/glm_moe_dsa/ops/tilelang_sparse_mla_fwd.py b/relax/models/glm_moe_dsa/ops/tilelang_sparse_mla_fwd.py new file mode 100644 index 000000000..ab5495a78 --- /dev/null +++ b/relax/models/glm_moe_dsa/ops/tilelang_sparse_mla_fwd.py @@ -0,0 +1,223 @@ +# ruff: noqa +# Adapted from https://github.com/tile-ai/tilelang/blob/e666d2d3cc483829c57618c9ebf2e4f4ada0819d/examples/deepseek_v32/sparse_mla_fwd.py +import tilelang +from tilelang import language as T + + +@tilelang.jit( + out_idx=[-2, -1], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + }, +) +def sparse_mla_fwd( + heads, + dim, + tail_dim, + topk, + kv_group=1, + sm_scale=None, + is_causal=True, + CP0=True, + block_I=64, + num_stages=2, + threads=256, +): + assert dim == tilelang.math.next_power_of_2(dim), f"haven't check padding correctness yet, dim={dim}" + assert tail_dim == tilelang.math.next_power_of_2(tail_dim), ( + f"haven't check padding correctness yet, dim={tail_dim}" + ) + assert is_causal == True, "non-casual is not supported" + assert topk % block_I == 0, "otherwise will load some index=0 thus causing wrong kv to be loaded" + + if sm_scale is None: + sm_scale = (1.0 / (dim + tail_dim)) ** 0.5 * 1.44269504 # log2(e) + else: + sm_scale = sm_scale * 1.44269504 # log2(e) + + batch = T.dynamic("batch") + seq_len = T.dynamic("seq_len") + seq_len_kv = T.dynamic("seq_len_kv") + + head_kv = heads // kv_group + + q_shape = [batch, seq_len, heads, dim + tail_dim] + kv_shape = [batch, seq_len_kv, kv_group, dim + tail_dim] + o_shape = [batch, seq_len, heads, dim] + indices_shape = [batch, seq_len, kv_group, topk] + lse_shape = [batch, seq_len, heads] + + indices_dtype = T.int32 + dtype = T.bfloat16 + accum_dtype = T.float32 + + G = kv_group + H = head_kv + padded_H = max(tilelang.math.next_power_of_2(head_kv), 16) + + if padded_H != H: + assert kv_group == 1, ( + "here we solve the H padding automatically, other wise you should handle Q copy and Output copy with your mask (when kv_group == 1, use g_i * padded_H:(g_i+1) * padded_H would be handled automatically)" + ) + + BI = block_I + NI = tilelang.cdiv(topk, block_I) + D = dim + D_tail = tail_dim + + if head_kv > 64: + assert head_kv % 64 == 0, "head_kv should be a multiple of 64" + REPLICATE_H = head_kv // 64 + else: + REPLICATE_H = 1 + + H_per_block = padded_H if REPLICATE_H == 1 else 64 + + @T.prim_func + def main( + Q: T.Tensor(q_shape, dtype), # type: ignore + KV: T.Tensor(kv_shape, dtype), # type: ignore + Indices: T.Tensor(indices_shape, indices_dtype), # type: ignore + Output: T.Tensor(o_shape, dtype), # type: ignore + Lse: T.Tensor(lse_shape, accum_dtype), # type: ignore + ): + with T.Kernel(seq_len * REPLICATE_H, batch, kv_group, threads=threads) as ( + bx, + by, + bz, + ): + Q_shared = T.alloc_shared([H_per_block, D], dtype) + Q_tail_shared = T.alloc_shared([H_per_block, D_tail], dtype) + KV_shared = T.alloc_shared([BI, D], dtype) + K_tail_shared = T.alloc_shared([BI, D_tail], dtype) + O_shared = T.alloc_shared([H_per_block, D], dtype) + Lse_shared = T.alloc_shared([H_per_block], accum_dtype) + + mask = T.alloc_fragment([BI], "bool") + acc_o = T.alloc_fragment([H_per_block, D], accum_dtype) + acc_s = T.alloc_fragment([H_per_block, BI], accum_dtype) + S_shared = T.alloc_shared([H_per_block, BI], dtype) + sumexp = T.alloc_fragment([H_per_block], accum_dtype) + sumexp_i = T.alloc_fragment([H_per_block], accum_dtype) + alpha = T.alloc_fragment([H_per_block], accum_dtype) + m_i = T.alloc_fragment([H_per_block], accum_dtype) + m_i_prev = T.alloc_fragment([H_per_block], accum_dtype) + + T.fill(acc_o, 0) + T.fill(sumexp, 0) + T.fill(m_i, -(2**30)) # avoid -inf - inf to cause nan + + b_i, g_i = by, bz + s_i = bx if REPLICATE_H == 1 else (bx // REPLICATE_H) + q_i = s_i + max_kv_i = q_i + + H0 = g_i * padded_H + (0 if REPLICATE_H == 1 else (bx % REPLICATE_H) * 64) + H1 = H0 + H_per_block + + T.copy(Q[b_i, s_i, H0:H1, :D], Q_shared) + T.copy(Q[b_i, s_i, H0:H1, D:], Q_tail_shared) + + for i_i in T.Pipelined(NI, num_stages=num_stages): + for bi_i in T.Parallel(BI): + # Changed here for thd + mask[bi_i] = Indices[b_i, s_i, g_i, i_i * BI + bi_i] != -1 + + for bi_i, d_i in T.Parallel(BI, D): + KV_shared[bi_i, d_i] = KV[b_i, Indices[b_i, s_i, g_i, i_i * BI + bi_i], g_i, d_i] + + for bi_i, d_i in T.Parallel(BI, D_tail): + K_tail_shared[bi_i, d_i] = KV[b_i, Indices[b_i, s_i, g_i, i_i * BI + bi_i], g_i, D + d_i] + + for h_i, bi_i in T.Parallel(H_per_block, BI): + acc_s[h_i, bi_i] = T.if_then_else(mask[bi_i], 0, -T.infinity(acc_s.dtype)) + + T.gemm( + Q_shared, + KV_shared, + acc_s, + transpose_B=True, + policy=T.GemmWarpPolicy.FullRow, + ) + T.gemm( + Q_tail_shared, + K_tail_shared, + acc_s, + transpose_B=True, + policy=T.GemmWarpPolicy.FullRow, + ) + + T.copy(m_i, m_i_prev) + T.reduce_max(acc_s, m_i, dim=1, clear=False) + for h_i in T.Parallel(H_per_block): + m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) + + for h_i in T.Parallel(H_per_block): + alpha[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) + + for h_i, bi_i in T.Parallel(H_per_block, BI): + acc_s[h_i, bi_i] = T.exp2(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale) + + T.reduce_sum(acc_s, sumexp_i, dim=1) # is this a accumulate operator? + for h_i in T.Parallel(H_per_block): + sumexp[h_i] = sumexp[h_i] * alpha[h_i] + sumexp_i[h_i] + + for h_i, d_i in T.Parallel(H_per_block, D): + acc_o[h_i, d_i] = acc_o[h_i, d_i] * alpha[h_i] + + T.copy(acc_s, S_shared) + T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + + # Rescale + for h_i, d_i in T.Parallel(H_per_block, D): + acc_o[h_i, d_i] /= sumexp[h_i] + + for h_i in T.Parallel(H_per_block): + sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale + + T.copy(acc_o, Output[b_i, s_i, H0:H1, :]) + T.copy(sumexp, Lse[b_i, s_i, H0:H1]) + + return main + + +def sparse_mla_fwd_interface( + q, kv, indices, sm_scale=None, return_p_sum: bool = False, d_v=512, block_I=64, num_stages=2, threads=256 +): + q = q.unsqueeze(0) + kv = kv.unsqueeze(0) + indices = indices.unsqueeze(0) + + is_casual = True + + assert return_p_sum == False, "This kernel file is for fwd only" + assert q.is_contiguous() and kv.is_contiguous() and indices.is_contiguous() + + batch, seq_len, heads, dim_plus_tail_dim = q.shape + _, seq_len_kv, kv_group, _ = kv.shape + assert dim_plus_tail_dim == 576, "you should assign dim otherwise" + dim = d_v + assert kv.shape[-1] == dim_plus_tail_dim + tail_dim = dim_plus_tail_dim - dim + assert kv.shape[0] == batch + + _, _, _, topk = indices.shape + assert indices.shape == (batch, seq_len, kv_group, topk) + + kernel = sparse_mla_fwd( + heads, + dim, + tail_dim, + topk, + kv_group, + sm_scale, + is_casual, + block_I=block_I, + num_stages=num_stages, + threads=threads, + ) + out, lse = kernel(q, kv, indices) + out = out.squeeze(0) + lse = lse.squeeze(0) + return out, lse diff --git a/relax/models/qwen_omni/__init__.py b/relax/models/qwen_omni/__init__.py index 9f3863608..3409599ef 100644 --- a/relax/models/qwen_omni/__init__.py +++ b/relax/models/qwen_omni/__init__.py @@ -1 +1,8 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. + +from relax.models.qwen_omni.qwen3_omni_bridge import Qwen3OmniMoEBridge + + +__all__ = [ + "Qwen3OmniMoEBridge", +] diff --git a/relax/models/qwen_omni/modeling_qwen3_omni/__init__.py b/relax/models/qwen_omni/modeling_qwen3_omni/__init__.py index f8c8c3e98..60cd9b84b 100644 --- a/relax/models/qwen_omni/modeling_qwen3_omni/__init__.py +++ b/relax/models/qwen_omni/modeling_qwen3_omni/__init__.py @@ -2,17 +2,9 @@ """Qwen3 Omni model providers and configurations.""" -# Core model components -# Bridges for HuggingFace to Megatron conversion -from relax.models.qwen_omni.modeling_qwen3_omni.model import Qwen3OmniMoeModel # noqa: F401 -from relax.models.qwen_omni.qwen3_omni_bridge import Qwen3OmniMoEBridge - -# Dense and MoE model providers -from relax.models.qwen_omni.qwen3_omni_provider import Qwen3OmniModelProvider +from relax.models.qwen_omni.modeling_qwen3_omni.model import Qwen3OmniMoeModel __all__ = [ "Qwen3OmniMoeModel", - "Qwen3OmniMoEBridge", - "Qwen3OmniModelProvider", ] diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 6d309ff4e..622d62e36 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -1966,6 +1966,7 @@ def _pre_parse_mode(): temp_parser.add_argument("--debug-rollout-only", action="store_true", default=False) temp_parser.add_argument("--debug-train-only", action="store_true", default=False) temp_parser.add_argument("--load-debug-rollout-data", type=str, default=None) + temp_parser.add_argument("--skip-hf-validate", action="store_true", default=False) temp_args, _ = temp_parser.parse_known_args() return temp_args @@ -1992,7 +1993,7 @@ def parse_args(add_custom_arguments=None): args = megatron_parse_args( extra_args_provider=add_slime_arguments, - skip_hf_validate=pre.debug_rollout_only, + skip_hf_validate=pre.debug_rollout_only or pre.skip_hf_validate, ) # Merge pre-parsed args into the main namespace diff --git a/relax/utils/logging_utils.py b/relax/utils/logging_utils.py index aa3549663..d05f81eb6 100644 --- a/relax/utils/logging_utils.py +++ b/relax/utils/logging_utils.py @@ -111,11 +111,54 @@ def configure_logger(prefix: str = "") -> None: # Silence noisy third-party DEBUG loggers (PIL dumps PNG chunk metadata per image) for noisy in ("PIL",): logging.getLogger(noisy).setLevel(logging.WARNING) + + install_asyncio_noise_filter() except Exception: # Silently ignore configuration errors to prevent breaking the application pass +_ASYNCIO_FILTER_INSTALLED = False + + +class _ChainFutureFilter(logging.Filter): + """Drop the spurious uvloop + Py3.12 `_chain_future._set_state` + AssertionError that Ray's ServeController emits when a proxy health-check + response arrives after the controller has already cancelled the awaiting + future. + + The error is harmless but pollutes every run. + """ + + def filter(self, record): + try: + return "_chain_future" not in record.getMessage() + except Exception: + return True + + +def install_asyncio_noise_filter() -> None: + """Attach the _chain_future filter to the asyncio logger in the current + process. + + Idempotent. Safe to call from a Ray `worker_process_setup_hook` so that + every worker (driver, training actor, SGLang engine, **and** + ServeController) suppresses the noise even though Ray internals never + import relax. + """ + global _ASYNCIO_FILTER_INSTALLED + if _ASYNCIO_FILTER_INSTALLED: + return + try: + logging.getLogger("asyncio").addFilter(_ChainFutureFilter()) + _ASYNCIO_FILTER_INSTALLED = True + except Exception: + pass + + +install_asyncio_noise_filter() + + class LazyConfiguredLogger(logging.Logger): """A logger that auto-configures on first use. diff --git a/relax/utils/utils.py b/relax/utils/utils.py index e4a6091ab..6f84a3bf3 100644 --- a/relax/utils/utils.py +++ b/relax/utils/utils.py @@ -79,7 +79,7 @@ def convert_samples_to_train_data(args: Any, samples: list[Sample] | list[list[S if samples[0].train_metadata is not None: train_data["metadata"] = [sample.train_metadata for sample in samples] - if any(sample.multimodal_train_inputs is not None for sample in samples): + if args.multimodal_keys is not None: train_data["multimodal_train_inputs"] = [sample.multimodal_train_inputs for sample in samples] if samples[0].teacher_log_probs is not None: diff --git a/scripts/entrypoint/local.sh b/scripts/entrypoint/local.sh index 5c7993f36..642c0cc3a 100644 --- a/scripts/entrypoint/local.sh +++ b/scripts/entrypoint/local.sh @@ -102,12 +102,17 @@ export RELAX_ENTRYPOINT_MODE="local" # Runtime env for single-node (empty, env inherited from Ray cluster) export RUNTIME_ENV_JSON="{ +\"worker_process_setup_hook\": \"relax.utils.logging_utils.install_asyncio_noise_filter\", \"env_vars\": { \"PYTHONUNBUFFERED\": \"1\", \"PYTHONPATH\": \"${PYTHONPATH}\", \"CUDA_DEVICE_MAX_CONNECTIONS\": \"1\", \"RAY_OVERRIDE_JOB_RUNTIME_ENV\": \"1\", - \"NCCL_NVLS_ENABLE\": \"${HAS_NVLINK}\" + \"NCCL_NVLS_ENABLE\": \"${HAS_NVLINK}\", + \"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK\": \"${SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK:-32}\", + \"NVSHMEM_DISABLE_NCCL\": \"${NVSHMEM_DISABLE_NCCL:-1}\", + \"SGLANG_HEALTH_CHECK_TIMEOUT\": \"${SGLANG_HEALTH_CHECK_TIMEOUT:-180}\", + \"INDEXER_ROPE_NEOX_STYLE\": \"${INDEXER_ROPE_NEOX_STYLE:-0}\" } }" diff --git a/scripts/entrypoint/ray-job.sh b/scripts/entrypoint/ray-job.sh index 02373f639..0fa808124 100755 --- a/scripts/entrypoint/ray-job.sh +++ b/scripts/entrypoint/ray-job.sh @@ -51,6 +51,7 @@ DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)" # ── clean up residual python/sglang processes (NOT ray) ───────────────────── # IMPORTANT: Do NOT pkill ray or run ray stop — the cluster is managed externally. echo "=== Cleaning up residual python/sglang processes ===" +ray serve shutdown -y python ${DIR}/../tools/run_on_each_ray_node.py ${DIR}/../tools/kill_for_ray.sh || echo "failed" # kill old tasks @@ -100,7 +101,11 @@ RAY_DEBUG=${RAY_DEBUG:-"0"} RAY_DEBUG_POST_MORTEM=${RAY_DEBUG_POST_MORTEM:-"0"} # Runtime env for ray-job mode (env inherited from Ray cluster) +NVSHMEM_LIB_PATH="${NVSHMEM_LIB_PATH:-/usr/local/lib/python3.12/dist-packages/nvidia/nvshmem/lib}" +CURRENT_LD_LIBRARY_PATH="${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}${NVSHMEM_LIB_PATH}" + export RUNTIME_ENV_JSON="{ +\"worker_process_setup_hook\": \"relax.utils.logging_utils.install_asyncio_noise_filter\", \"env_vars\": { \"PYTHONUNBUFFERED\": \"1\", \"PYTHONPATH\": \"${PYTHONPATH}\", @@ -109,7 +114,13 @@ export RUNTIME_ENV_JSON="{ \"NCCL_NVLS_ENABLE\": \"${HAS_NVLINK}\", \"MASTER_ADDR\": \"${MASTER_ADDR}\", \"RAY_DEBUG\": \"${RAY_DEBUG}\", - \"RAY_DEBUG_POST_MORTEM\": \"${RAY_DEBUG_POST_MORTEM}\" + \"RAY_DEBUG_POST_MORTEM\": \"${RAY_DEBUG_POST_MORTEM}\", + \"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK\": \"${SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK:-32}\", + \"NVSHMEM_DISABLE_NCCL\": \"${NVSHMEM_DISABLE_NCCL:-1}\", + \"SGLANG_HEALTH_CHECK_TIMEOUT\": \"${SGLANG_HEALTH_CHECK_TIMEOUT:-180}\", + \"INDEXER_ROPE_NEOX_STYLE\": \"${INDEXER_ROPE_NEOX_STYLE:-0}\", + \"NVSHMEM_BOOTSTRAP_UID_SOCK_IFNAME\": \"${NVSHMEM_BOOTSTRAP_UID_SOCK_IFNAME:-${NCCL_SOCKET_IFNAME}}\", + \"LD_LIBRARY_PATH\": \"${CURRENT_LD_LIBRARY_PATH}\" } }" diff --git a/scripts/entrypoint/spmd-multinode.sh b/scripts/entrypoint/spmd-multinode.sh index f3b2e9cee..08f673803 100755 --- a/scripts/entrypoint/spmd-multinode.sh +++ b/scripts/entrypoint/spmd-multinode.sh @@ -108,14 +108,25 @@ if [ "$MASTER_ADDR" = "$POD_NAME" ]; then export RELAX_ENTRYPOINT_MODE="spmd-multinode" # Runtime env for multi-node (includes MASTER_ADDR) + NVSHMEM_LIB_PATH="${NVSHMEM_LIB_PATH:-/usr/local/lib/python3.12/dist-packages/nvidia/nvshmem/lib}" + CURRENT_LD_LIBRARY_PATH="${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}${NVSHMEM_LIB_PATH}" + export RUNTIME_ENV_JSON="{ +\"worker_process_setup_hook\": \"relax.utils.logging_utils.install_asyncio_noise_filter\", \"env_vars\": { \"PYTHONUNBUFFERED\": \"1\", \"PYTHONPATH\": \"${PYTHONPATH}\", \"CUDA_DEVICE_MAX_CONNECTIONS\": \"1\", \"RAY_OVERRIDE_JOB_RUNTIME_ENV\": \"1\", \"NCCL_NVLS_ENABLE\": \"${HAS_NVLINK}\", - \"MASTER_ADDR\": \"${HOST_IP}\" + \"MASTER_ADDR\": \"${HOST_IP}\", + \"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK\": \"${SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK:-32}\", + \"NVSHMEM_DISABLE_NCCL\": \"${NVSHMEM_DISABLE_NCCL:-1}\", + \"SGLANG_HEALTH_CHECK_TIMEOUT\": \"${SGLANG_HEALTH_CHECK_TIMEOUT:-180}\", + \"INDEXER_ROPE_NEOX_STYLE\": \"${INDEXER_ROPE_NEOX_STYLE:-0}\", + \"NVSHMEM_BOOTSTRAP_UID_SOCK_IFNAME\": \"${NVSHMEM_BOOTSTRAP_UID_SOCK_IFNAME:-${NCCL_SOCKET_IFNAME}}\", + \"LD_LIBRARY_PATH\": \"${CURRENT_LD_LIBRARY_PATH}\" + } }" exec bash "$RUN_SCRIPT" "$@" diff --git a/scripts/models/glm5-744B-A40B.sh b/scripts/models/glm5-744B-A40B.sh new file mode 100644 index 000000000..c14822fef --- /dev/null +++ b/scripts/models/glm5-744B-A40B.sh @@ -0,0 +1,57 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +MOE_ROUTED_EXPERTS=256 +MOE_ACTIVE_ROUTED_EXPERTS=8 +MOE_SHARED_EXPERTS=1 + +NHIDDEN=6144 +MOE_FFN_HIDDEN=2048 +MOE_SHARED_EXPERT_INTERMEDIATE_SIZE=$(($MOE_FFN_HIDDEN * $MOE_SHARED_EXPERTS)) +FFN_HIDDEN=12288 +N_DENSE_LAYERS=3 +N_MOE_LAYERS=75 + +NHEADS=64 + +MODEL_ARGS=( + --moe-layer-freq [0]*$N_DENSE_LAYERS+[1]*$N_MOE_LAYERS + --num-experts $MOE_ROUTED_EXPERTS + --moe-shared-expert-intermediate-size $MOE_SHARED_EXPERT_INTERMEDIATE_SIZE + --moe-router-topk $MOE_ACTIVE_ROUTED_EXPERTS + --moe-grouped-gemm + --moe-permute-fusion + --moe-ffn-hidden-size $MOE_FFN_HIDDEN + --moe-router-score-function sigmoid + --moe-router-pre-softmax + --moe-router-enable-expert-bias + --moe-router-bias-update-rate 0 + --moe-router-load-balancing-type seq_aux_loss + --moe-router-topk-scaling-factor 2.5 + --moe-aux-loss-coeff 0 + --moe-router-dtype fp32 + --make-vocab-size-divisible-by 16 + --num-layers $((N_DENSE_LAYERS + N_MOE_LAYERS)) + --hidden-size $NHIDDEN + --ffn-hidden-size $FFN_HIDDEN + --num-attention-heads $NHEADS + --disable-bias-linear + --swiglu + --untie-embeddings-and-output-weights + --position-embedding-type rope + --no-position-embedding + --normalization RMSNorm + --qk-layernorm + --multi-latent-attention + --q-lora-rank 2048 + --kv-lora-rank 512 + --qk-head-dim 192 + --v-head-dim 256 + --kv-channels 192 + --qk-pos-emb-head-dim 64 + --vocab-size 154880 + --rotary-base 1000000 + --enable-experimental + + # slime specific args + --allgather-cp +) diff --git a/scripts/training/text/run-glm5-744B-A40B-128xgpu.sh b/scripts/training/text/run-glm5-744B-A40B-128xgpu.sh new file mode 100644 index 000000000..1180f8f93 --- /dev/null +++ b/scripts/training/text/run-glm5-744B-A40B-128xgpu.sh @@ -0,0 +1,208 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# GLM5-744B-A40B 128xGPU colocate training script. +# +# Usage: +# bash scripts/training/text/run-glm5-744B-A40B-128xgpu.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +echo "SCRIPT_DIR: $SCRIPT_DIR" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/glm5-744B-A40B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" +EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +NUM_ROLLOUT="${NUM_ROLLOUT:=200}" + + + +CKPT_ARGS=( + --hf-checkpoint ${EXP_DIR}/GLM-5/ + --ref-load ${EXP_DIR}/GLM-5/ + --megatron-to-hf-mode bridge + # --load ${EXP_DIR}/GLM_ckpt/ + --save ${EXP_DIR}/GLM_ckpt/ + --save-interval 50 + --no-save-optim + --no-save-rng + --no-load-optim + --no-load-rng +) + +PROMPT_SET=${EXP_DIR}/dapo-math-17k/dapo-math-17k.jsonl + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type deepscaler + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 8 + --n-samples-per-prompt 8 + --rollout-max-response-len 32768 + --rollout-temperature 1 + --global-batch-size 64 + --balance-data + --use-fault-tolerance + --rollout-health-check-timeout 120 +) + +EVAL_ARGS=( + --skip-eval-before-train + --log-passrate + --eval-interval 20 + --eval-prompt-data aime ${EXP_DIR}/aime-2024/aime-2024.jsonl + --n-samples-per-eval-prompt 8 + --eval-max-response-len 32768 + --eval-top-p 0.7 +) + +PERF_ARGS=( + --tensor-model-parallel-size 4 + --sequence-parallel + --pipeline-model-parallel-size 4 + --decoder-last-pipeline-num-layers 18 + --expert-model-parallel-size 32 + --expert-tensor-parallel-size 1 + + # Enable Context Parallelism for longer sequences (requires fused DSAMLASelfAttention) + --context-parallel-size 2 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + # --micro-batch-size 1 + --use-dynamic-batch-size + --max-tokens-per-gpu 16384 + --data-pad-size-multiplier 4096 + --log-probs-chunk-size 1024 + + # use deepep for megatron + --moe-flex-dispatcher-backend deepep + --moe-token-dispatcher-type flex + --moe-router-dtype fp32 + --calculate-per-token-loss + + # --use-pytorch-profiler + # --profile-step-start 1 + # --profile-step-end 2 + # --profile-with-stack + # --tensorboard-dir /tmp/tensorboard/ +) + +GRPO_ARGS=( + --advantage-estimator grpo + # --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + # --no-rope-fusion + --no-pin-cpu-grads + --no-pin-cpu-params +) + + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 64 + --sglang-mem-fraction-static 0.7 + --sglang-enable-dp-attention + --sglang-ep-size 64 + --sglang-dp-size 64 + --sglang-moe-dense-tp-size 1 + --sglang-enable-dp-lm-head + + --sglang-moe-a2a-backend deepep + --sglang-deepep-mode auto + --sglang-load-format dummy + + # # mtp + # --sglang-speculative-algorithm EAGLE + # --sglang-speculative-num-steps 3 + # --sglang-speculative-eagle-topk 1 + # --sglang-speculative-num-draft-tokens 4 + + # dsa + --sglang-page-size 64 + --sglang-nsa-decode-backend flashmla_sparse + --sglang-nsa-prefill-backend flashmla_sparse + --sglang-attention-backend nsa + --sglang-cuda-graph-max-bs 8 + + --sglang-max-running-requests 512 + --sglang-chunked-prefill-size 131072 + --sglang-watchdog-timeout 3600 +) + + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name GLM5-744B-A40B-128xgpu-${now} + # --use-wandb + # --wandb-project slime-dev + # --wandb-group qwen3-4B-test + # --wandb-key ${WANDB_KEY} +) + +MISC_ARGS=( + # default dropout in megatron is 0.1 + --attention-dropout 0.0 + --hidden-dropout 0.0 + # should be good for model performance + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # need to comment this when using model with MLA + --attention-backend flash + --update-weight-buffer-size $(( 1024 * 1024 * 1024 )) \ + +) + +mkdir -p log +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 128], "rollout": [1, 128]}'\ + --max-staleness 0 \ + --num-data-storage-units 16 \ + --colocate \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/GLM-5-744B-A40B-GRPO-gpu128-${now}.log diff --git a/skills/dev/SKILL.md b/skills/dev/SKILL.md index ef7b144d3..ca57f7b72 100644 --- a/skills/dev/SKILL.md +++ b/skills/dev/SKILL.md @@ -36,6 +36,38 @@ ______________________________________________________________________ The goal is to submit a training run to a remote Ray cluster via `scripts/entrypoint/ray-job.sh`, monitor it via `ray job logs`, and either confirm it works or fix errors and retry. +______________________________________________________________________ + +### Standard launch flow (canonical) + +Every training launch — the **first** one and every **resubmit after a fix** — follows the same three steps. Do not skip the pre-flight cleanup; stale Ray Serve apps from a failed previous job will silently break the new job at Router startup. + +**Step A — Pre-flight cleanup (always run first):** + +```bash +ray serve shutdown -y +``` + +Drops all stale Ray Serve applications/deployments left behind by a previous run. This is **NOT** a forbidden destructive op (see `skills/ssh-ray-cluster/SKILL.md`) — `ray serve shutdown` only tears down Serve state, not the Ray runtime, not training processes, and not other tenants' jobs. Always run it before resubmitting. + +**Step B — Submit the training job:** + +```bash +bash scripts/entrypoint/ray-job.sh scripts/training/text/.sh +``` + +(or whichever subdirectory matches the run script). The entrypoint handles residual python/sglang cleanup, env setup, and `ray job submit` for you. Capture the JOB_ID and the log file path that the run script writes to (typically `log/-.log`). + +**Step C — Monitor the new log file:** + +Tail/grep the log file for progress (`step`, `iteration`) and errors (`Error`, `Traceback`, `Exception`, `OOM`). Apply the noise-filter pattern from "Step 2: Monitor the job" below. + +**On error → fix → resubmit:** loop back to Step A (the `ray serve shutdown -y` is mandatory each time). Stop after 3 consecutive failed resubmits and report to the user. + +**Strictly forbidden during this flow** (per `skills/ssh-ray-cluster/SKILL.md`): `ray stop`, `pkill -9 python`, `bash scripts/tools/kill_for_ray.sh`, `ray job stop` against unrelated jobs, `rm -rf /tmp/ray/`. `ray serve shutdown -y` and `ray job stop ` are the **only** state-changing operations allowed without explicit user approval. + +______________________________________________________________________ + > **⚠️ MANDATORY**: All `ray job submit` commands in this debugging workflow **MUST** include `RAY_NO_WAIT=1` so the submission is non-blocking. This allows you to immediately proceed to monitoring via `ray job logs` without the shell hanging on the submit call. Every command example below already includes it — do not omit it. ### Workflow overview @@ -197,6 +229,19 @@ curl -s -X DELETE http://${CLUSTER_IP}:8265/api/serve/applications/ If both `ray job submit --runtime-env-json '{"env_vars": {"KEY": "val"}}'` and `ray.init(runtime_env={"env_vars": {"KEY": "val2"}})` set the same env var key, Ray raises a `ValueError`. Workaround: use the `bash -c` wrapper (Pattern B above) to export env vars in the shell instead. +### Diagnostic env vars leak into ALL Ray actors via `RUNTIME_ENV_JSON` + +Anything you put in `RUNTIME_ENV_JSON.env_vars` (in `scripts/entrypoint/ray-job.sh`) is propagated to **every** Ray worker and **every** Ray Serve infrastructure actor — `ProxyActor`, `ServeController`, `DCSCoordinator`, `MetricsService`, `SimpleStorageUnit`, `TransferQueueController`, `Lock`, `HealthStatus`, etc. — not just GPU train workers. This bites diagnostic flags hard: + +- A faulthandler / py-spy / NCCL-trace flag intended for the train actor will fire in the no-GPU Serve infra actors too, flooding the controller log and often crashing the Serve controller itself (the symptom looks like the training run died, but the diagnostic killed the controller). +- **`CUDA_VISIBLE_DEVICES` is NOT a usable GPU-only gate** in `worker_process_setup_hook`. Ray sets `CUDA_VISIBLE_DEVICES=""` on non-GPU actors (you can confirm via Ray's own FutureWarning), so a check like `if cvd: enable_diag()` still passes on `ProxyActor`. Don't use it. + +Correct pattern for opt-in diagnostics that must run only in train actors: + +1. Keep the env var as the activation gate in `RUNTIME_ENV_JSON` (e.g. `RELAX_FAULTHANDLER_INTERVAL_SEC=15`). +2. Read the env var and call `faulthandler.enable()` / `dump_traceback_later()` from inside `MegatronTrainRayActor.init()` (or a sibling actor's `__init__`) — **never** from `relax/utils/logging_utils.install_asyncio_noise_filter`, which is the Ray `worker_process_setup_hook` and runs in every actor. +3. Reference: `relax/backends/megatron/actor.py::_install_faulthandler_periodic_dump` is the canonical example. + ### MASTER_ADDR defaults to 127.0.0.1 `relax/utils/utils.py` reads `os.environ.get("MASTER_ADDR", "127.0.0.1")` to set `SLIME_HOST_IP` for rollout workers. If `MASTER_ADDR` is not set, the rollout engine gets `127.0.0.1` which is unreachable from other workers. This is auto-detected by `scripts/entrypoint/ray-job.sh` via `ray list nodes`. diff --git a/skills/ssh-ray-cluster/SKILL.md b/skills/ssh-ray-cluster/SKILL.md new file mode 100644 index 000000000..f12cfe0a2 --- /dev/null +++ b/skills/ssh-ray-cluster/SKILL.md @@ -0,0 +1,315 @@ +--- +name: ssh-ray-cluster +description: Connect to a remote Ray cluster head node via SSH (paramiko) to execute + commands, check cluster status, inspect logs, and debug training jobs. Use this + skill when the user asks to SSH into a remote machine, check Ray cluster status, + or run remote commands on the Ray head node. +--- + +# SSH to Ray Cluster + +This skill provides a standardized way to connect to a remote Ray cluster head node via SSH using `paramiko`, execute commands, and retrieve results. It is used for cluster inspection, log retrieval, and remote debugging. + +______________________________________________________________________ + +## Prerequisites + +The user must provide the following details (ask if missing — do not invent +values, and do not write them into this skill file): + +| Parameter | Purpose | +| --------------------- | ---------------------------------------- | +| `host` | Remote machine IP | +| `port` | SSH port | +| `username` | SSH username | +| `password` | SSH password | +| `RELAX_PROJECT_ROOT` | Absolute path to the Relax project root | + +Connection details and the project root are typically recorded in the +session's auto-memory (see `reference_ray_cluster_ssh.md`). Read them from +memory or ask the user — do not hard-code them in this skill or in scripts +checked into the repo. + +______________________________________________________________________ + +## HARD REQUIREMENT — always run project commands from the Relax project root + +A one-shot `paramiko.exec_command` starts the remote shell in the user's +home directory (typically `/root` or another non-project dir), **not** in +the Relax project root. Any command that touches a project-relative path +(`scripts/...`, `log/...`, `relax/...`, `tests/...`, `pyproject.toml`, +etc.) MUST be prefixed with `cd "$RELAX_PROJECT_ROOT"` (or the resolved +path) **inside the same command string** — splitting `cd` into a separate +`exec_command` call does NOT work, because each call opens a fresh shell +back at the home directory. + +`RELAX_PROJECT_ROOT` is a session-level value supplied by the user / read +from auto-memory (see Prerequisites). Do **not** hard-code its value in +this skill, in checked-in scripts, or in any reusable artifact — resolve +it at command-build time from memory or by asking the user. + +### Required pattern for any project-relative command + +```python +# RELAX_PROJECT_ROOT must be resolved from memory or user input first. +cmd = ( + f'cd {shlex.quote(RELAX_PROJECT_ROOT)} && ' + '' +) +ssh.exec_command(cmd, timeout=...) +``` + +Examples that REQUIRE the `cd` prefix: + +- `bash scripts/entrypoint/ray-job.sh ...` +- `bash scripts/training/text/run---.sh` +- `python scripts/tools/run_on_each_ray_node.py ...` +- `bash scripts/tools/kill_for_ray.sh` +- `tail -n 100 log/-*.log` +- `pre-commit run --all-files` +- `pytest tests/test_foo.py` + +Examples that do NOT need the `cd` (they take absolute paths or are +host-global tools that touch no project files): + +- `ray status`, `ray job list`, `ray job logs `, `ray job status ` +- `nvidia-smi ...` +- `ls /tmp/ray/session_latest/logs/` +- `ps -ef | grep ...` + +When in doubt: add the `cd`. It is harmless on host-global commands and +mandatory on project-relative ones. + +### Symptom that the `cd` was lost + +``` +bash: scripts/...: No such file or directory +python: can't open file '/scripts/...' +ls: cannot access 'log/': No such file or directory +``` + +Fix: add `cd "$RELAX_PROJECT_ROOT" && ` to the front of the command and +re-run. Do NOT retry blindly — a missing `cd` will keep failing the same +way. + +______________________________________________________________________ + +## Connection Pattern + +Use Python's `paramiko` library to establish SSH connections. Always use a **one-shot** pattern: connect, execute, close. Do not try to maintain persistent connections across tool calls. + +### Basic connection template + +```python +python3 -c " +import paramiko +ssh = paramiko.SSHClient() +ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) +ssh.connect('', port=, username='', password='', timeout=10) + +stdin, stdout, stderr = ssh.exec_command('', timeout=30) +output = stdout.read().decode() +errors = stderr.read().decode() +print(output) +if errors: + print('STDERR:', errors) + +ssh.close() +" +``` + +### Multi-command template + +When you need to run multiple commands in sequence: + +```python +python3 -c " +import paramiko +ssh = paramiko.SSHClient() +ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) +ssh.connect('', port=, username='', password='', timeout=10) + +commands = [ + ('Description 1', 'command1'), + ('Description 2', 'command2'), +] + +for desc, cmd in commands: + print(f'=== {desc} ===') + stdin, stdout, stderr = ssh.exec_command(cmd, timeout=30) + print(stdout.read().decode()) + err = stderr.read().decode() + if err: + print('STDERR:', err) + print() + +ssh.close() +" +``` + +______________________________________________________________________ + +## Common Operations + +### 1. Check Ray cluster status + +```bash +ray status 2>&1 | head -30 +``` + +Shows active/idle nodes, GPU/CPU usage, pending demands. + +### 2. List Ray jobs + +```bash +ray job list 2>&1 | head -50 +``` + +Shows all submitted jobs with their status (RUNNING, FAILED, SUCCEEDED). + +### 3. Get running job logs + +```bash +ray job logs 2>&1 | tail -100 +``` + +### 4. Check GPU usage across nodes + +```bash +nvidia-smi --query-gpu=index,memory.used,memory.total,utilization.gpu --format=csv,noheader +``` + +### 5. Check specific worker node logs + +SGLang engine logs are typically found in Ray's log directory: + +```bash +ls -lt /tmp/ray/session_latest/logs/ | head -20 +``` + +### 6. Kill residual processes + +```bash +cd "$RELAX_PROJECT_ROOT" && bash scripts/tools/kill_for_ray.sh +``` + +### 7. Run command on all nodes + +```bash +cd "$RELAX_PROJECT_ROOT" && python scripts/tools/run_on_each_ray_node.py command "" +``` + +### 8. Launch / relaunch a training run + +Per the HARD REQUIREMENT above, the `cd` into the project root and the +launch must be in the **same** command string. + +```bash +cd "$RELAX_PROJECT_ROOT" && \ + nohup bash scripts/entrypoint/ray-job.sh > 2>&1 & +``` + +Verify CWD before launch by chaining `pwd && ls scripts/entrypoint/ray-job.sh` +in the same command — if `pwd` doesn't report the project root, the cd was +dropped and the launch will fail. + +______________________________________________________________________ + +## Working directory pitfall (`cd` over SSH) — supplementary patterns + +See the HARD REQUIREMENT section near the top for the rule. Two equivalent +patterns satisfy it; mixing them does not: + +1. **Single-line, single-shell** (preferred for paramiko `exec_command`): + chain `cd` with `&&` inside the *same* quoted command string, e.g. + `ssh ... 'cd "$RELAX_PROJECT_ROOT" && bash scripts/...'`. If you split + the `cd` into a separate `ssh` / `exec_command` invocation, the next call + starts back in the home directory. + +2. **Heredoc to remote bash** (useful for multi-step launches): + + ```bash + ssh ... bash < > 2>&1 & + echo "PID=\$!" + EOF + ``` + +Symptom that the cd was lost: `bash: + """ + + +def get_jsonl_viewer_html(data_dir: str, base_path: str = "") -> str: + """Generate the JSONL data viewer page HTML. + + This is a single-page viewer that loads JSONL step files directly, + without the home page selection flow. + + Args: + data_dir: Path to the data directory + base_path: Base URL path for reverse proxy support (e.g., "/absproxy/8080") + """ + # Normalize base_path for URL construction + if base_path: + # Ensure base_path starts with / + if not base_path.startswith("/"): + base_path = f"/{base_path}" + # Ensure base_path ends with / + if not base_path.endswith("/"): + base_path = f"{base_path}/" + + return f""" + + + + + + Relax Rollout Result Viewer + {get_common_styles()} + + + +
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+ + + + + {get_theme_script()} + + + + + """ diff --git a/relax/utils/visualize/tui.py b/relax/utils/visualize/tui.py new file mode 100644 index 000000000..f104ff179 --- /dev/null +++ b/relax/utils/visualize/tui.py @@ -0,0 +1,512 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +"""Terminal UI for browsing Relax ``rollout_result/*.jsonl`` files. + +Adapted from ``redaccel/verl/tools/reward_viewer_v2.py`` (RedAccel +Authors). Trimmed to match Relax's per-sample summary schema: drops the +agent-trace / token-stats / data-source filters that depend on extra +sqlite dumps RedAccel produces and Relax does not. Adds a ``dataset`` +filter that activates when eval JSONL files are loaded. + +Requires the optional dependencies ``textual`` and ``rich``:: + + pip install textual rich +""" + +from __future__ import annotations + +import json +import re +import sys +import threading +import traceback +from pathlib import Path +from typing import Optional + + +_INDEX_KEY = "__IDX" +_FILE_SUFFIX = ".jsonl" +_DEFAULT_MASK_STR = r"<\|image_pad\|>|<\|imgpad\|>|<\|audio_comp_pad\|>" + + +def _require_textual(): + """Import textual / rich lazily; raise a friendly error if missing.""" + try: + import rich # noqa: F401 + import textual # noqa: F401 + except ImportError as e: + raise ImportError( + "TUI mode needs the optional 'textual' and 'rich' packages. Install them with: pip install textual rich" + ) from e + + +def _load_path(p: Path, mask_str: str) -> list[dict]: + samples: list[dict] = [] + with open(p, encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + d = json.loads(line) + except json.JSONDecodeError: + continue + for k in list(d.keys()): + if isinstance(d[k], str): + if mask_str: + d[k] = re.sub(mask_str, "*", d[k]) + else: + d[k] = json.dumps(d[k], ensure_ascii=False, indent=4) + d[_INDEX_KEY] = len(samples) + samples.append(d) + return samples + + +def _build_app(step_num: int, data: dict, file_idx_map: dict): + """Build the textual App instance. + + Lazy-imported textual stays scoped here. + """ + from rich.highlighter import ReprHighlighter + from rich.table import Table + from rich.text import Text + from textual import on + from textual.app import App, ComposeResult + from textual.containers import Horizontal, Vertical, VerticalScroll + from textual.widgets import Footer, Header, Input, Select, SelectionList, Static + + class _Highlighter(ReprHighlighter): + highlights = ReprHighlighter.highlights + [ + r"(?P[][\<\>{}()\|（）【】\[\]=`])", + r"\<\|(?P[\w\W]*?)\|\>", + ] + + def _center(word: str, total: int, char: str = "=") -> str: + if len(word) > total: + return word + pad = total - len(word) + return char * (pad // 2) + " " + word + " " + char * ((pad + 1) // 2) + + def _highlight_kw(content: str, keyword: Optional[str]): + if not keyword: + return Text(content) + text = Text() + parts = content.split(keyword) + for i, part in enumerate(parts): + text.append(part) + if i < len(parts) - 1: + text.append(keyword, style="on #8f51b5") + return text + + class JsonLineViewer(App): + BINDINGS = [ + ("left", "focus_previous", "Focus Previous"), + ("right", "focus_next", "Focus Next"), + ("s", "switch_render", "switch render"), + ("n", "next_sample", "Next Sample"), + ("N", "next_step", "Next Step"), + ("p", "previous_sample", "Previous Sample"), + ("P", "previous_step", "Previous Step"), + ("r", "refresh_page", "Refresh"), + ("f", "toggle_search", "Find"), + ("enter", "next_search", "Find next"), + ("escape", "cancel_search", "Cancel find"), + ("j", "page_down", "page down"), + ("k", "page_up", "page up"), + ("h", "page_left", "page left"), + ("l", "page_right", "page right"), + ("g", "page_home", "top"), + ("G", "page_end", "bottom"), + ] + TITLE = "Relax Rollout Result Viewer (TUI)" + CSS = """ + Select:focus > SelectCurrent { border: tall #8f51b5; } + Select.-expanded > SelectCurrent { border: tall #8f51b5; } + #select-container { width: 22%; height: 100%; align: center top; } + #search-container { height: 3; align: center top; } + #search-box { width: 80%; } + #scroll-view { border: round #444; } + """ + + def __init__(self) -> None: + super().__init__() + self.step_num = step_num + self.file_idx_map = file_idx_map + self.data = data + self.render_table = False + self.selected_step_index = 0 + self.selected_sample_index = 0 + self.matches: list[dict] = [] + self.current_match_index = 0 + self.highlighter = _Highlighter() + + first_samples = data[next(iter(data))]["samples"] + self.filter_fields = [(f, f, True) for f in first_samples[0].keys()] if first_samples else [] + self.sample_num = len(first_samples) + + if first_samples and "dataset" in first_samples[0]: + self.datasets = sorted({s.get("dataset", "") for s in first_samples}) + else: + self.datasets = [] + self.datasets.insert(0, "all datasets") + + self.sort_mode = 0 + self.ds_index = 0 + + def compose(self) -> ComposeResult: + yield Header() + with Horizontal(id="search-container"): + yield Input(placeholder="find...", id="search-box") + yield Static("", id="search-status") + with Horizontal(): + with Vertical(id="select-container"): + yield Select( + id="step-select", + value=0, + prompt="step", + options=[("step: 1", 0)], + allow_blank=False, + ) + yield Select( + id="sample-select", + value=0, + prompt="sample", + options=[("sample: 1", 0)], + allow_blank=False, + ) + yield Select( + id="ds-select", + value=0, + prompt="dataset", + options=[("all datasets", 0)], + allow_blank=False, + ) + yield Select( + id="sample-sort", + value=0, + prompt="sort", + options=[ + ("no sort", 0), + ("reward asc", 1), + ("reward desc", 2), + ("resp len asc", 3), + ("resp len desc", 4), + ], + allow_blank=False, + ) + yield SelectionList[int](("Select ALL", 1, True), id="fields-select-all") + with VerticalScroll(id="scroll-view2"): + yield SelectionList[str](*self.filter_fields, id="fields-select") + with VerticalScroll(id="scroll-view"): + yield Static("Loading...", id="content", markup=False) + yield Footer() + + async def on_mount(self) -> None: + self.step_select = self.query_one("#step-select", Select) + self.sample_select = self.query_one("#sample-select", Select) + self.ds_select = self.query_one("#ds-select", Select) + self.sample_sort = self.query_one("#sample-sort", Select) + self.content_display = self.query_one("#content", Static) + self.search_box = self.query_one("#search-box", Input) + self.scroll_view = self.query_one("#scroll-view", VerticalScroll) + self.search_status = self.query_one("#search-status", Static) + self.fields_select = self.query_one("#fields-select", SelectionList) + self.fields_select.border_title = "field filter" + + if self.data: + self.step_select.set_options([(f"step: {i + 1}", i) for i in range(self.step_num)]) + self.sample_select.set_options([(f"sample: {i + 1}", i) for i in range(self.sample_num)]) + self.ds_select.set_options([(f"{ds}", i) for i, ds in enumerate(self.datasets)]) + self.step_select.focus() + await self.update_content() + + async def update_options(self, sort_mode: int, ds_index: int, offset: int = 0) -> None: + if self.selected_step_index not in self.data: + self.selected_sample_index = offset + return + samples = list(self.data[self.selected_step_index].get("samples", [])) + if not samples: + self.selected_sample_index = offset + return + + if ds_index > 0: + want = self.datasets[ds_index] + samples = [s for s in samples if s.get("dataset") == want] + + def _resp_len(x): + try: + return int(x.get("response_length", 0)) + except (TypeError, ValueError): + return 0 + + def _reward(x): + try: + return float(x.get("reward", 0)) + except (TypeError, ValueError): + return 0.0 + + if sort_mode == 1: + samples.sort(key=_reward) + elif sort_mode == 2: + samples.sort(key=_reward, reverse=True) + elif sort_mode == 3: + samples.sort(key=_resp_len) + elif sort_mode == 4: + samples.sort(key=_resp_len, reverse=True) + else: + samples.sort(key=lambda x: x[_INDEX_KEY]) + + options = [(f"sample: {r[_INDEX_KEY] + 1}", r[_INDEX_KEY]) for r in samples] + self.sample_select.set_options(options or [("(empty)", 0)]) + self.sample_num = len(samples) + self.selected_sample_index = offset + self.sort_mode = sort_mode + self.sample_sort.value = sort_mode + self.ds_index = ds_index + self.ds_select.value = ds_index + + async def update_content(self, search_keyword: Optional[str] = None) -> None: + try: + samples = self.data[self.selected_step_index].get("samples", []) + options = self.sample_select._options + if not options or not samples: + self.content_display.update("No samples.") + return + content_dict = samples[options[self.selected_sample_index][1]] + content_dict = {k: v for k, v in content_dict.items() if k in self.fields_select.selected} + if self.render_table: + content = Table("key", "value", show_lines=True) + for k, v in content_dict.items(): + content.add_row(k, self.highlighter(_highlight_kw(f"{v}", search_keyword))) + else: + text = Text() + for k, v in content_dict.items(): + text.append(_highlight_kw(_center(k, 64) + f"\n{v}\n", search_keyword)) + content = self.highlighter(text) + except KeyError: + content = f"Loading data asynchronously: {len(self.data)}/{self.step_num} step" + except Exception: + content = self.highlighter(traceback.format_exc()) + self.content_display.update(content) + + @on(Select.Changed, "#step-select") + async def _step_changed(self, event): + self.selected_step_index = event.value + await self.update_options(self.sort_mode, self.ds_index) + await self.update_content() + + @on(Select.Changed, "#sample-select") + async def _sample_changed(self, event): + for i, (_, sample_id) in enumerate(self.sample_select._options): + if sample_id == event.value: + self.selected_sample_index = i + break + await self._clear_search() + await self.update_content() + + @on(Select.Changed, "#sample-sort") + async def _sort_changed(self, event): + await self.update_options(sort_mode=event.value, ds_index=self.ds_index) + await self.update_content() + + @on(Select.Changed, "#ds-select") + async def _ds_changed(self, event): + await self.update_options(sort_mode=self.sort_mode, ds_index=event.value) + await self.update_content() + + @on(SelectionList.SelectedChanged, "#fields-select") + async def _fields_changed(self, event): + await self.update_content() + + @on(SelectionList.SelectedChanged, "#fields-select-all") + async def _fields_all_changed(self, event): + s = self.query_one("#fields-select-all", SelectionList) + if s.selected: + self.fields_select.select_all() + else: + self.fields_select.deselect_all() + + def action_focus_previous(self): + self.screen.focus_previous() + + def action_focus_next(self): + self.screen.focus_next() + + async def action_next_step(self) -> None: + self.selected_step_index = (self.selected_step_index + 1) % self.step_num + self.step_select.value = self.selected_step_index + await self.update_options(self.sort_mode, self.ds_index) + await self.update_content() + + async def action_previous_step(self) -> None: + self.selected_step_index = (self.selected_step_index - 1) % self.step_num + self.step_select.value = self.selected_step_index + await self.update_options(self.sort_mode, self.ds_index) + await self.update_content() + + async def action_next_sample(self) -> None: + if not self.sample_num: + return + self.selected_sample_index = (self.selected_sample_index + 1) % self.sample_num + self.sample_select.value = self.sample_select._options[self.selected_sample_index][1] + await self._clear_search() + await self.update_content() + + async def action_previous_sample(self) -> None: + if not self.sample_num: + return + self.selected_sample_index = (self.selected_sample_index - 1) % self.sample_num + self.sample_select.value = self.sample_select._options[self.selected_sample_index][1] + await self._clear_search() + await self.update_content() + + async def action_refresh_page(self) -> None: + await self.update_content() + + async def action_switch_render(self) -> None: + self.render_table = not self.render_table + await self.update_content() + + def action_toggle_search(self) -> None: + self.search_box.focus() + + async def action_cancel_search(self) -> None: + self.search_box.value = "" + await self._clear_search() + await self.update_content() + + async def _clear_search(self) -> None: + self.matches = [] + self.search_status.update("") + self.current_match_index = 0 + + @on(Input.Submitted, "#search-box") + async def _on_search(self, event: "Input.Submitted") -> None: + self.matches = [] + self.current_match_index = 0 + if not event.value: + return + await self.update_content(event.value) + renderable = self.content_display.render() + if isinstance(renderable, Table): + return + console = self.content_display._console + lines = renderable.wrap(console, self.scroll_view.container_size.width) + seen = set() + for line_idx, line in enumerate(lines): + if line_idx in seen: + continue + if event.value in line: + self.matches.append({"line": line_idx, "word": event.value}) + seen.add(line_idx) + self.scroll_view.focus() + await self.action_next_search() + + async def action_next_search(self) -> None: + if not self.matches or self.current_match_index >= len(self.matches): + return + target_line = self.matches[self.current_match_index]["line"] + self.scroll_view.scroll_to(x=0, y=target_line, animate=False) + self.current_match_index = (self.current_match_index + 1) % len(self.matches) + self.search_status.update( + Text(f"Find: {self.current_match_index + 1}/{len(self.matches)}", style="bold on #8f51b5") + ) + + async def action_page_up(self): + self.scroll_view.scroll_page_up(animate=False) + + async def action_page_down(self): + self.scroll_view.scroll_page_down(animate=False) + + async def action_page_left(self): + self.scroll_view.scroll_left(animate=False) + + async def action_page_right(self): + self.scroll_view.scroll_right(animate=False) + + def action_page_home(self): + self.scroll_view.scroll_home(animate=False) + + def action_page_end(self): + self.scroll_view.scroll_end(animate=False) + + return JsonLineViewer() + + +def _stem_key(p: Path) -> int: + return int(p.stem) if p.stem.isdigit() else 0 + + +def run(data_dir: str, mask_str: str = _DEFAULT_MASK_STR) -> None: + """Launch the TUI viewer on ``data_dir``. + + If ``data_dir`` contains a ``train/`` subdir it is used; otherwise + ``eval/``; otherwise ``data_dir`` itself. Point at the subdir explicitly to + override. + + Loading is synchronous and finishes before textual takes over the + terminal, so any error during data loading prints normally rather than + being hidden by the alt-screen. Errors that occur inside the textual + app (compose, mount, callbacks) are re-raised after the terminal is + restored. + """ + _require_textual() + path = Path(data_dir).resolve() + if not path.exists(): + raise ValueError(f"Data directory does not exist: {path}") + if (path / "train").is_dir(): + path = path / "train" + elif (path / "eval").is_dir(): + path = path / "eval" + + paths = sorted(path.glob(f"*{_FILE_SUFFIX}"), key=_stem_key) + if not paths: + raise ValueError( + f"No {_FILE_SUFFIX} files found under {path}. " + f"Point DATA_DIR at a directory that contains '{{step}}.jsonl' files " + f"(or a parent with train/ or eval/ subdirs)." + ) + + print(f"TUI using: {path} ({len(paths)} jsonl file(s))") + + # Sync-load step 0 so the UI has something to render the moment it + # mounts; everything else streams in via a daemon thread. + data: dict = {0: {"samples": _load_path(paths[0], mask_str)}} + if not data[0]["samples"]: + raise ValueError( + f"First file {paths[0]} contained no parseable JSON lines. " + f"Check that it is not empty and follows the rollout_result schema." + ) + print(f" loaded [1/{len(paths)}] {paths[0].name}: {len(data[0]['samples'])} samples (foreground)") + + bg_log = Path("/tmp/relax-tui-bg.log") + bg_log.write_text("") # truncate + + def _bg_loader(): + # Load remaining files newest-first so the latest step is ready next. + remaining = [(i, p) for i, p in enumerate(paths) if i != 0] + remaining.sort(key=lambda t: _stem_key(t[1]), reverse=True) + for idx, p in remaining: + try: + data[idx] = {"samples": _load_path(p, mask_str)} + except Exception: + # Don't crash the TUI; record what went wrong so the user + # can grep the log if a step shows up missing. + with bg_log.open("a") as f: + f.write(f"failed: {p}\n") + traceback.print_exc(file=f) + + if len(paths) > 1: + print(f" loading remaining {len(paths) - 1} file(s) in background (progress logged to {bg_log})") + threading.Thread(target=_bg_loader, daemon=True).start() + + file_idx_map = {i: p.stem for i, p in enumerate(paths)} + app = _build_app(step_num=len(paths), data=data, file_idx_map=file_idx_map) + + # textual restores the terminal on exit. Re-raise after `app.run()` so + # any internal traceback lands in the user's normal shell instead of + # being hidden by the alt-screen. + try: + app.run() + except BaseException: + traceback.print_exc(file=sys.stderr) + raise From 430c1f44d2e4793f83a0439780cd1cb0ea8539fb Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Sat, 23 May 2026 17:35:37 +0800 Subject: [PATCH 050/268] fix(scripts): split MODEL_DIR/EXP_DIR/DATA_DIR init MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Fix MODEL_DIR/EXP_DIR initialization in qwen35-9B hybrid-async script - Replace buggy `EXP_DIR="${MODEL_DIR:=...}"` side-effect assignment with separate `EXP_DIR`/`MODEL_DIR`/`DATA_DIR` defaults, matching `run-qwen35-9B-8xgpu-openr1mm-async.sh` - Point `--hf-checkpoint` / `--ref-load` at `${MODEL_DIR}` and `PROMPT_SET` at `${DATA_DIR}` so model and dataset roots can be overridden independently of the experiment output dir (cherry picked from commit 466c779d9019d498b7f7b05b0c9c6ec91d0bc799) --- .../run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh index 308adb8d5..9ecca8ae6 100644 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh @@ -25,23 +25,25 @@ source "${MODEL_CONFIG_DIR}/qwen35-9B.sh" # source "${MODEL_CONFIG_DIR}/qwen3-vl-4B.sh" PROJECT_NAME="${PROJECT_NAME:=Relax/dev/fully_async_openr1mm}" -EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" NUM_ROLLOUT="${NUM_ROLLOUT:=200}" CKPT_ARGS=( - --hf-checkpoint ${EXP_DIR}/Qwen3.5-9B - --ref-load ${EXP_DIR}/Qwen3.5-9B - # --hf-checkpoint ${EXP_DIR}/Qwen3-VL-4B-Instruct + --hf-checkpoint ${MODEL_DIR}/Qwen3.5-9B + --ref-load ${MODEL_DIR}/Qwen3.5-9B + # --hf-checkpoint ${MODEL_DIR}/Qwen3-VL-4B-Instruct --megatron-to-hf-mode bridge - # --ref-load ${EXP_DIR}/Qwen3-VL-4B-Instruct + # --ref-load ${MODEL_DIR}/Qwen3-VL-4B-Instruct # --load ${EXP_DIR}/Qwen3.5-9B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3.5-9B_mcore_8xgpu/ --save-interval 100 --max-actor-ckpt-to-keep 1 ) -PROMPT_SET=${EXP_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet +PROMPT_SET=${DATA_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet SYSTEM_PROMPT="A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within and tags, respectively, i.e., reasoning process here answer here " From 56b17732963c6e9925232ce840a5b35ce3f57aff Mon Sep 17 00:00:00 2001 From: liujia7 Date: Mon, 25 May 2026 14:03:11 +0800 Subject: [PATCH 051/268] fix(CE): restore openr1 script to fix CE error (cherry picked from commit 8034d1596aee4894e747d28bece6fd8c44276ec0) --- .../multimodal/run-qwen3-vl-4B-8xgpu.sh | 25 +-- .../multimodal/run-qwen3-vl-4B-geo3k-8xgpu.sh | 153 ++++++++++++++++++ 2 files changed, 161 insertions(+), 17 deletions(-) create mode 100644 scripts/training/multimodal/run-qwen3-vl-4B-geo3k-8xgpu.sh diff --git a/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh b/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh index 82c0b600d..06d2160e8 100644 --- a/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh +++ b/scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh @@ -20,7 +20,7 @@ if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then fi source "${MODEL_CONFIG_DIR}/qwen3-vl-4B.sh" -PROJECT_NAME="${PROJECT_NAME:=Relax/dev/geo3k}" +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/openr1mm}" EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" DATA_DIR="${DATA_DIR:-${EXP_DIR}}" @@ -32,17 +32,17 @@ CKPT_ARGS=( --megatron-to-hf-mode bridge ) -PROMPT_SET=${DATA_DIR}/geo3k/train.parquet -# SYSTEM_PROMPT="A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within and tags, respectively, i.e., reasoning process here answer here " +PROMPT_SET=${DATA_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet +SYSTEM_PROMPT="A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within and tags, respectively, i.e., reasoning process here answer here " ROLLOUT_ARGS=( --prompt-data ${PROMPT_SET} --input-key prompt - --label-key reward_model + --label-key label --apply-chat-template - --rollout-shuffle + # --rollout-shuffle --balance-data - --rm-type geo3k + --rm-type openr1mm --num-rollout ${NUM_ROLLOUT} --rollout-batch-size 32 --n-samples-per-prompt 8 @@ -50,16 +50,8 @@ ROLLOUT_ARGS=( --rollout-max-prompt-len 2048 --rollout-temperature 0.8 --global-batch-size 256 - --multimodal-keys '{"image":"images"}' - # --system-prompt "${SYSTEM_PROMPT}" -) - -EVAL_ARGS=( - # --skip-eval-before-train - --eval-interval 20 - --eval-prompt-data geo3k ${DATA_DIR}/geo3k/test.parquet - --eval-max-response-len 2048 - --eval-temperature 0.8 + --multimodal-keys '{"image":"image"}' + --system-prompt "${SYSTEM_PROMPT}" ) PERF_ARGS=( @@ -144,7 +136,6 @@ ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ "${MODEL_ARGS[@]}" \ "${CKPT_ARGS[@]}" \ "${ROLLOUT_ARGS[@]}" \ - "${EVAL_ARGS[@]}" \ "${OPTIMIZER_ARGS[@]}" \ "${GRPO_ARGS[@]}" \ "${WANDB_ARGS[@]}" \ diff --git a/scripts/training/multimodal/run-qwen3-vl-4B-geo3k-8xgpu.sh b/scripts/training/multimodal/run-qwen3-vl-4B-geo3k-8xgpu.sh new file mode 100644 index 000000000..82c0b600d --- /dev/null +++ b/scripts/training/multimodal/run-qwen3-vl-4B-geo3k-8xgpu.sh @@ -0,0 +1,153 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3-VL-4B 8xGPU colocate training script. +# +# Usage: +# bash scripts/training/multimodal/run-qwen3-vl-4B-8xgpu.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen3-vl-4B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/geo3k}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" +NUM_ROLLOUT="${NUM_ROLLOUT:=200}" + +CKPT_ARGS=( + --hf-checkpoint ${MODEL_DIR}/Qwen3-VL-4B-Instruct/ + --ref-load ${MODEL_DIR}/Qwen3-VL-4B-Instruct/ + --megatron-to-hf-mode bridge +) + +PROMPT_SET=${DATA_DIR}/geo3k/train.parquet +# SYSTEM_PROMPT="A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within and tags, respectively, i.e., reasoning process here answer here " + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key reward_model + --apply-chat-template + --rollout-shuffle + --balance-data + --rm-type geo3k + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 32 + --n-samples-per-prompt 8 + --rollout-max-response-len 1024 + --rollout-max-prompt-len 2048 + --rollout-temperature 0.8 + --global-batch-size 256 + --multimodal-keys '{"image":"images"}' + # --system-prompt "${SYSTEM_PROMPT}" +) + +EVAL_ARGS=( + # --skip-eval-before-train + --eval-interval 20 + --eval-prompt-data geo3k ${DATA_DIR}/geo3k/test.parquet + --eval-max-response-len 2048 + --eval-temperature 0.8 +) + +PERF_ARGS=( + --tensor-model-parallel-size 2 + --sequence-parallel + --pipeline-model-parallel-size 1 + --context-parallel-size 4 + --expert-model-parallel-size 1 + --expert-tensor-parallel-size 1 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --calculate-per-token-loss + # --micro-batch-size 16 + # --qkv-format bshd + --use-dynamic-batch-size + --max-tokens-per-gpu 9216 + --log-probs-max-tokens-per-gpu 20480 + + --no-rope-fusion +) + +GRPO_ARGS=( + --use-kl-loss + --advantage-estimator grpo + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --kl-coef 0.00 + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + --clip-grad 1.0 +) + +WANDB_ARGS=( + # --use-tensorboard + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen3-vl-4b-GRPO-gpu8-${now} +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 1 + --sglang-mem-fraction-static 0.8 +) + +MISC_ARGS=( + # default dropout in megatron is 0.1 + --attention-dropout 0.0 + --hidden-dropout 0.0 + # should be good for model performance + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # need to comment this when using model with MLA + --attention-backend flash +) + +mkdir -p log + +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 8], "rollout": [1, 8]}'\ + --max-staleness 0 \ + --num-data-storage-units 1 \ + --colocate \ + --use-health-check \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen3-vl-4b-GRPO-gpu8-${now}.log From 213863ead12be1e9e72cfbdaf89617a9881910bd Mon Sep 17 00:00:00 2001 From: wulumeng Date: Mon, 25 May 2026 14:54:29 +0800 Subject: [PATCH 052/268] chore(deepeyes): align datasets with main script MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🔩 Chore ## Align DeepEyes dataset inputs - Align fp16, GenRM, and partial-rollout scripts with examples/deepeyes/run_deepeyes.sh - Use the same Deepeyes v1 training shards as the main script - Use the same thinklite reasoning accuracy eval slice as the main script (cherry picked from commit df613b40126f96a4ff4d3c6664f4b3ce42dcd61c) --- examples/deepeyes/run_deepeyes_fp16.sh | 10 +++++----- examples/deepeyes/run_deepeyes_genrm.sh | 10 +++++----- examples/deepeyes/run_deepeyes_pr.sh | 10 +++++----- 3 files changed, 15 insertions(+), 15 deletions(-) diff --git a/examples/deepeyes/run_deepeyes_fp16.sh b/examples/deepeyes/run_deepeyes_fp16.sh index acd985917..2f06cd534 100644 --- a/examples/deepeyes/run_deepeyes_fp16.sh +++ b/examples/deepeyes/run_deepeyes_fp16.sh @@ -57,11 +57,11 @@ CKPT_ARGS=( # DATASETS # ############################################################################### -TRAIN_FILES=() -for i in {0..9}; do - TRAIN_FILES+=("'${DATA_DIR}/deepeyes/train/v0.1.2.parquet/partition=${i}/3ce23f4945e8498085ac5f72f0afc133-0.parquet'") -done -TEST_FILES=("${DATA_DIR}/deepeyes/test.parquet") +TRAIN_FILES=( + "'${DATA_DIR}/deepeyes-v1/data_0.1.2_visual_toolbox_v2.parquet@[0:5000]'" + "'${DATA_DIR}/deepeyes-v1/data_v0.8_visual_toolbox_v2.parquet@[0:5000]'" +) +TEST_FILES=("${DATA_DIR}/deepeyes-v1/data_thinklite_reasoning_acc.parquet@[0:256]") PROMPT_SET="[$(IFS=,; echo "${TRAIN_FILES[*]}")]" ############################################################################### diff --git a/examples/deepeyes/run_deepeyes_genrm.sh b/examples/deepeyes/run_deepeyes_genrm.sh index f11b0bb98..84f23bd69 100644 --- a/examples/deepeyes/run_deepeyes_genrm.sh +++ b/examples/deepeyes/run_deepeyes_genrm.sh @@ -43,11 +43,11 @@ CKPT_ARGS=( # DATASETS # ############################################################################### -TRAIN_FILES=() -for i in {0..9}; do - TRAIN_FILES+=("'${DATA_DIR}/deepeyes/train/v0.1.2.parquet/partition=${i}/3ce23f4945e8498085ac5f72f0afc133-0.parquet'") -done -TEST_FILES=("${DATA_DIR}/deepeyes/test.parquet") +TRAIN_FILES=( + "'${DATA_DIR}/deepeyes-v1/data_0.1.2_visual_toolbox_v2.parquet@[0:5000]'" + "'${DATA_DIR}/deepeyes-v1/data_v0.8_visual_toolbox_v2.parquet@[0:5000]'" +) +TEST_FILES=("${DATA_DIR}/deepeyes-v1/data_thinklite_reasoning_acc.parquet@[0:256]") PROMPT_SET="[$(IFS=,; echo "${TRAIN_FILES[*]}")]" ############################################################################### diff --git a/examples/deepeyes/run_deepeyes_pr.sh b/examples/deepeyes/run_deepeyes_pr.sh index 6c718b260..cb66ebcbe 100644 --- a/examples/deepeyes/run_deepeyes_pr.sh +++ b/examples/deepeyes/run_deepeyes_pr.sh @@ -57,11 +57,11 @@ CKPT_ARGS=( # DATASETS # ############################################################################### -TRAIN_FILES=() -for i in {0..9}; do - TRAIN_FILES+=("'${DATA_DIR}/deepeyes/train/v0.1.2.parquet/partition=${i}/3ce23f4945e8498085ac5f72f0afc133-0.parquet'") -done -TEST_FILES=("${DATA_DIR}/deepeyes/test.parquet") +TRAIN_FILES=( + "'${DATA_DIR}/deepeyes-v1/data_0.1.2_visual_toolbox_v2.parquet@[0:5000]'" + "'${DATA_DIR}/deepeyes-v1/data_v0.8_visual_toolbox_v2.parquet@[0:5000]'" +) +TEST_FILES=("${DATA_DIR}/deepeyes-v1/data_thinklite_reasoning_acc.parquet@[0:256]") PROMPT_SET="[$(IFS=,; echo "${TRAIN_FILES[*]}")]" ############################################################################### From f77758c18238e3986e54252c6402f5784e490a78 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=8E=E7=AC=91=E9=A2=9C?= Date: Fri, 22 May 2026 17:46:49 +0800 Subject: [PATCH 053/268] docs(hybrid): add bilingual hybrid training guide MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 📝 Documentation ## Add bilingual Hybrid training mode guide - Add `docs/en/guide/hybrid-training.md` and `docs/zh/guide/hybrid-training.md` describing the hybrid execution mode (streaming TransferQueue + in-process TensorBackuper weight sharing) - Cover mode comparison vs Colocate / Fully Async, role layout (`ROLES_COLOCATE` with disjoint actor/rollout placement groups), `--hybrid` flag resolution, and the three-phase `train_hybrid` loop - Document required and optional flags (`--hybrid`, `--num-iters-per-train-update`, `--max-staleness`, `--balance-data`) and the default overrides applied in `relax/utils/arguments.py` - Include the 8-GPU multimodal reference launch from `scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh` and troubleshooting tips (stalled sub-batches, balance-data rejection) - List planned next steps: integrate DCS for weight sync, split `train_actor` by `num_iters_per_train_update` ## Register pages in VitePress sidebar - Add Hybrid Training Mode under the Advanced group in both `en` and `zh` sidebars in `docs/.vitepress/config.mts` (cherry picked from commit 7f78ff764ad82672f725383702a13f79c2d7bda9) --- docs/.vitepress/config.mts | 2 + docs/en/guide/hybrid-training.md | 245 ++++++++++++++++++++++++++++++ docs/zh/guide/hybrid-training.md | 247 +++++++++++++++++++++++++++++++ 3 files changed, 494 insertions(+) create mode 100644 docs/en/guide/hybrid-training.md create mode 100644 docs/zh/guide/hybrid-training.md diff --git a/docs/.vitepress/config.mts b/docs/.vitepress/config.mts index 15693303b..003145b02 100644 --- a/docs/.vitepress/config.mts +++ b/docs/.vitepress/config.mts @@ -259,6 +259,7 @@ export default defineConfig({ text: 'Advanced', items: [ { text: 'Fully Async Training', link: '/en/guide/fully-async-training' }, + { text: 'Hybrid Training Mode', link: '/en/guide/hybrid-training' }, { text: 'Elastic Rollout Scaling', link: '/en/guide/elastic-rollout' }, { text: 'Metrics Service', link: '/en/guide/metrics-service-detailed' }, { text: 'Notification System', link: '/en/guide/notification-system' }, @@ -358,6 +359,7 @@ export default defineConfig({ text: '进阶指南', items: [ { text: '全异步训练流水线', link: '/zh/guide/fully-async-training' }, + { text: 'Hybrid 混合训练模式', link: '/zh/guide/hybrid-training' }, { text: '弹性 Rollout 扩缩容', link: '/zh/guide/elastic-rollout' }, { text: 'Metrics 服务', link: '/zh/guide/metrics-service-detailed' }, { text: '通知系统', link: '/zh/guide/notification-system' }, diff --git a/docs/en/guide/hybrid-training.md b/docs/en/guide/hybrid-training.md new file mode 100644 index 000000000..adaed00f3 --- /dev/null +++ b/docs/en/guide/hybrid-training.md @@ -0,0 +1,245 @@ +# Hybrid Training Mode + +## Overview + +**Hybrid mode** is a third execution mode in Relax that sits between [Colocate (Sync)](./architecture.md) and [Fully Async](./fully-async-training.md). It combines: + +- the **streaming data pipeline** of Fully Async (TransferQueue + `max-staleness` for off-policy tolerance), with +- the **in-process weight sharing** of Colocate (TensorBackuper + `_switch_model`, so ref / actor_fwd / advantages all run on the actor's own GPUs). + +Concretely, Actor and Rollout still run on **separate GPU placement groups** (like Fully Async), but the actor no longer ships weights to standalone ActorFwd / Reference / Advantages services. Instead it cycles a single set of weights between `actor`, `ref`, `old_actor`, and `teacher` tags via a CPU/GPU `TensorBackuper`, computing every forward pass locally and pushing weights to rollout through the sync `UpdateWeightFromTensor` path. + +### Mode Comparison + +| Dimension | Colocate (Sync) | Fully Async | Hybrid | +| ------------------- | ---------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------------------------------- | +| **GPU layout** | Actor and Rollout time-share same GPUs | Actor / Rollout / ActorFwd / Reference each have own GPUs | Actor and Rollout on separate GPUs; ref / actor_fwd / adv share actor's GPUs | +| **Data pipeline** | TransferQueue, batch-synchronous | TransferQueue + StreamingDataLoader, fully streaming | TransferQueue + sub-batch streaming (`num-iters-per-train-update`) | +| **Weight sync** | In-process tensor copy | NCCL broadcast via DCS (Checkpoint Engine) | Sync `UpdateWeightFromTensor` to rollout; TensorBackuper for ref/actor_fwd | +| **Staleness** | `max_staleness = 0` (strict on-policy) | Configurable `max_staleness` | Configurable `max_staleness` | +| **Roles deployed** | `actor`, `critic`, `rollout` | `actor`, `critic`, `rollout`, `advantages`, `reference`, `actor_fwd` | `actor`, `critic`, `rollout` (same as Colocate; ref/actor_fwd live inside actor) | +| **`--balance-data`**| Supported | Not supported | **Supported** (one of hybrid's reasons to exist) | + +### When to Use Hybrid + +Pick **Hybrid** when: + +- You want the throughput benefits of dedicated rollout GPUs and pipelined data flow, but +- Your model is large enough that running independent ref / actor_fwd services would waste GPUs, or +- You need `--balance-data` (load-balanced micro-batching across DP ranks), which pure Fully Async cannot provide. + +Pick **Fully Async** when you have spare GPUs for separate ref / actor_fwd / advantages services and want true cross-step pipelining. + +Pick **Colocate** when GPU count is tight and you can tolerate serial rollout → train cycles. + +______________________________________________________________________ + +## Architecture + +### Role Layout + +Hybrid uses the same role set as Colocate — only `actor`, `critic` (optional), and `rollout` are deployed as Ray Serve services. The decision lives in `relax/core/registry.py`: + +```python +def process_role(config): + if config.hybrid: + # hybrid mode: actor handles ref/actor_fwd internally + # via _switch_model, only need actor + rollout services + return ROLES_COLOCATE + if config.fully_async: + ... +``` + +But unlike Colocate, the actor and rollout placement groups are **disjoint**, matching Fully Async semantics. From `relax/core/controller.py`: + +```python +if colocate and not self.config.hybrid: + # Sync colocate: actor and rollout share GPUs via time-sharing (offload/onload) + actor_rollout_pgs = create_placement_group(num_gpus=num_gpus) +else: + # fully_async (pure or hybrid): actor and rollout use separate GPUs + actor_rollout_pgs = None +``` + +### Flag Resolution + +`--hybrid` is the only public switch. `relax/utils/arguments.py` resolves it into the two underlying flags downstream machinery already understands: + +```python +if args.hybrid: + args.fully_async = True + args.colocate = True +``` + +Passing `--fully-async --colocate` directly is rejected; use `--hybrid` instead. This single-switch design keeps `args.hybrid` as the canonical hybrid-only branch in the registry, controller dispatch, and `train_hybrid` call site. + +### Diagram + +``` +┌────────────────────────────────────────────────────────────────────────────┐ +│ Controller (Orchestrator) │ +│ relax/core/controller.py │ +│ │ +│ ┌───────────────────────────────────┐ ┌────────────────────┐ │ +│ │ Actor Service │ │ Rollout Service │ │ +│ │ (own placement group, N GPUs) │ │ (own PG, M GPUs) │ │ +│ │ │ │ SGLang engines │ │ +│ │ ┌────────────────────────────┐ │ └─────────┬──────────┘ │ +│ │ │ TensorBackuper │ │ ▲ │ +│ │ │ tags: actor / ref / │ │ │ │ +│ │ │ old_actor / teacher │ │ │ │ +│ │ │ _switch_model(tag) swaps │ │ │ │ +│ │ │ weights on the same GPUs │ │ │ │ +│ │ └────────────────────────────┘ │ │ │ +│ │ train_hybrid(): │ │ │ +│ │ ├─ ref forward (switch:ref) │ │ │ +│ │ ├─ actor forward (switch:actor)│ │ +│ │ ├─ advantages (in-process) │ │ │ +│ │ └─ train (switch:actor)│ │ +│ └──────────────┬────────────────────┘ │ │ +│ │ UpdateWeightFromTensor (sync) ───┘ │ +└──────────────────────┼─────────────────────────────────────────────────────┘ + ▼ +┌───────────────────────────────────────────────────────────────────────────┐ +│ TransferQueue (Data Plane) │ +│ Rollout writes train_N partition incrementally ──► Actor consumes │ +│ in sub-batches via get_meta(batch_size, batch_index) with max-staleness │ +└───────────────────────────────────────────────────────────────────────────┘ +``` + +______________________________________________________________________ + +## The `train_hybrid` Loop + +`relax/backends/megatron/actor.py:708` implements the hybrid training step in three phases: + +1. **Collect sub-batches and compute forward log-probs (small memory footprint)** + + The global batch is split into `num_iters_per_train_update` sub-batches. For each sub-batch the actor: + + - pulls data from TransferQueue (`_get_data_from_transfer_queue("train", rollout_id, fields, batch_size, batch_index)`) + - runs `_switch_model("ref")` (if ref weights are backed up) and computes ref log-probs + - runs `_switch_model("teacher")` (if OPD teacher weights are backed up) and computes teacher log-probs + - runs `_switch_model("old_actor" or "actor")` and computes current actor log-probs + - appends the enriched sub-batch to an in-memory list + +2. **Merge sub-batches and compute advantages globally** + + All sub-batch dicts are concatenated into one `rollout_data`, then `compute_advantages_and_returns(self.args, rollout_data)` runs once over the merged batch. This is the **key correctness reason** for the two-phase design — advantage normalization must see the full DP-group batch, not per-sub-batch slices. + +3. **Train on the merged batch and push weights** + + A single `train(...)` call runs the optimizer step on the merged batch. Afterwards the actor backs up the new weights to the `actor` tag and (on the ref-update interval) refreshes the `ref` tag, then calls `self.update_weights()` to push the updated weights to rollout via `UpdateWeightFromTensor`. + +The sub-batched forward keeps peak activation memory bounded — matching Fully Async behavior — while the merged training step preserves Colocate-style global statistics. + +______________________________________________________________________ + +## Configuration + +### Required Flags + +| Flag | Purpose | +| ------------------------------- | ------------------------------------------------------------------------------------ | +| `--hybrid` | Enable hybrid mode (resolves to `fully_async=True, colocate=True` internally) | +| `--resource '{...}'` | Declare `actor` and `rollout` placement groups separately, e.g. `{"actor":[1,4],"rollout":[1,4]}` | +| `--num-iters-per-train-update` | Number of sub-batches per global batch (larger → smaller peak memory, more TQ polls) | +| `--max-staleness` | Off-policy budget (0 = strict on-policy, >0 allows staleness) | + +### Optional but Common + +| Flag | Notes | +| ----------------------------- | -------------------------------------------------------------------------------------------------------- | +| `--balance-data` | Supported in hybrid (rejected in pure fully-async). Enable for DP load balancing. | +| `--num-data-storage-units` | Number of TransferQueue storage actors. | +| `--use-streaming-dataset` | Stream prompts from disk instead of loading into memory. | +| `--ref-update-interval` | Periodically refresh the cached ref weights from the latest actor weights. | + +### Default Overrides + +When `--hybrid` is set, `relax/utils/arguments.py` defaults the following (unless the user passes them explicitly): + +- `offload_train = False` and `offload_rollout = False` — actor and rollout are on separate GPUs, so no offload needed +- `compute_advantages_and_returns = True` — actor must compute advantages internally +- `fully_async = True`, `colocate = True` — derived from `--hybrid` + +::: warning +`--balance-data` requires `--hybrid` if you also want a streaming pipeline. The combination `--fully-async --balance-data` (without `--hybrid`) is rejected at argument parse time. +::: + +______________________________________________________________________ + +## Quick Start + +A reference launch script for an 8-GPU multimodal hybrid run lives at +`scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh`. + +The hybrid invocation it builds: + +```bash +ray job submit --address="http://127.0.0.1:8265" \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 4], "rollout": [1, 4]}' \ + --max-staleness 2 \ + --num-data-storage-units 1 \ + --num-iters-per-train-update 8 \ + --balance-data \ + --hybrid \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" +``` + +Key points in this configuration: + +- 8 total GPUs split 4 + 4 between actor and rollout +- `max-staleness 2` — actor may consume rollout output up to 2 steps behind the freshest weights +- `num-iters-per-train-update 8` — each global batch is split into 8 sub-batches for forward passes +- `balance-data` — DP load balancing enabled +- GRPO algorithm with `--use-kl-loss` and `--use-tis` (these are algorithm flags, orthogonal to hybrid) + +______________________________________________________________________ + +## Troubleshooting + +### `train_hybrid(rollout_id=N) batch_index=K stalled for ... seconds` + +This warning fires in `relax/backends/megatron/actor.py` when the actor's TransferQueue poll for the next sub-batch keeps returning empty while the partition is not marked `all_consumed`. Typical causes: + +- Rollout under-filled this partition (dropped samples without refilling). +- Rollout is paused on a health-check failure or restart. +- Staleness budget exhausted: rollout cannot produce new data because it is waiting for fresh weights. + +Check rollout-side logs and partition status before assuming a code bug. + +### `--balance-data is not supported in pure fully-async mode` + +You passed `--fully-async --balance-data` without `--hybrid`. Either drop `--balance-data` or switch to `--hybrid`, which supports DP-balanced data. + +### Rollout sees stale weights for a long time + +Hybrid uses the sync `UpdateWeightFromTensor` path at the end of each `train_hybrid` call. If you see large weight-update gaps, check: + +- `update_weights()` timing in actor logs +- Whether rollout health-checks are paging the actor (`_check_services_health()` is called before weight sync) + +______________________________________________________________________ + +## Next Steps + +Planned follow-ups for hybrid mode: + +- **Integrate DCS for weight sync** — replace the current synchronous `UpdateWeightFromTensor` path with the Distributed Checkpoint Service so weight broadcast to rollout can overlap with the next training iteration, closing the remaining sync gap at the end of every `train_hybrid` call. +- **Split `train_actor` into `num_iters_per_train_update` iterations** — today `num_iters_per_train_update` only chunks the forward phase; the merged training step still runs once on the full global batch. Extend the actor train step to also iterate `num_iters_per_train_update` times so optimizer updates can be pipelined with TransferQueue consumption and peak training-side memory drops further. + +Related docs: + +- [Fully Async Training Pipeline](./fully-async-training.md) — the streaming-data engine hybrid borrows +- [Architecture](./architecture.md) — overview of Relax's service layering +- [Update Weights Pipeline](./update-weights-pipeline.md) — how `UpdateWeightFromTensor` and DCS differ diff --git a/docs/zh/guide/hybrid-training.md b/docs/zh/guide/hybrid-training.md new file mode 100644 index 000000000..13d96733f --- /dev/null +++ b/docs/zh/guide/hybrid-training.md @@ -0,0 +1,247 @@ +# Hybrid 混合训练模式 + +## 概述 + +**Hybrid 模式** 是 Relax 在 [Colocate（同步）](./architecture.md) 与 [Fully Async（全异步）](./fully-async-training.md) 之间的第三种执行模式。它将以下两者结合： + +- Fully Async 的 **流式数据流水线**（TransferQueue + `max-staleness` 控制 off-policy 容忍度），以及 +- Colocate 的 **进程内权重共享**（TensorBackuper + `_switch_model`，使 ref / actor_fwd / advantages 全部在 actor 自身 GPU 上完成）。 + +具体来说，Actor 与 Rollout 仍然部署在 **独立的 GPU placement group** 上（与 Fully Async 一致），但 actor 不再把权重广播到独立的 ActorFwd / Reference / Advantages 服务，而是通过 CPU/GPU `TensorBackuper` 在 `actor`、`ref`、`old_actor`、`teacher` 等 tag 之间切换同一套权重，所有 forward 在本地完成，最后通过同步的 `UpdateWeightFromTensor` 路径把权重推给 rollout。 + +### 模式对比 + +| 维度 | Colocate（同步） | Fully Async（全异步） | Hybrid | +| ------------------- | ----------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------------------------------- | +| **GPU 布局** | Actor 与 Rollout 分时复用同一组 GPU | Actor / Rollout / ActorFwd / Reference 各自独立 GPU | Actor 与 Rollout 独立 GPU；ref / actor_fwd / adv 复用 actor 的 GPU | +| **数据流水线** | TransferQueue，批同步 | TransferQueue + StreamingDataLoader，完全流式 | TransferQueue + 子批次流式（`num-iters-per-train-update`） | +| **权重同步** | 进程内 tensor 拷贝 | 通过 DCS（Checkpoint Engine）做 NCCL broadcast | 同步的 `UpdateWeightFromTensor` 推给 rollout；ref/actor_fwd 走 TensorBackuper | +| **Staleness** | `max_staleness = 0`（严格 on-policy） | 可配置 `max_staleness` | 可配置 `max_staleness` | +| **部署的角色** | `actor`, `critic`, `rollout` | `actor`, `critic`, `rollout`, `advantages`, `reference`, `actor_fwd` | `actor`, `critic`, `rollout`（与 Colocate 相同；ref/actor_fwd 在 actor 内部） | +| **`--balance-data`**| 支持 | 不支持 | **支持**（Hybrid 存在的核心原因之一） | + +### 何时选择 Hybrid + +选择 **Hybrid** 的场景： + +- 希望获得独立 rollout GPU 与流水线数据带来的吞吐收益，但 +- 模型较大，单独部署 ref / actor_fwd 服务会浪费 GPU，或者 +- 需要 `--balance-data`（DP 间均衡 micro-batch 切分），而纯 Fully Async 不支持此功能。 + +选择 **Fully Async**：拥有充足的 GPU 单独运行 ref / actor_fwd / advantages 服务，并希望在 step 之间做真正的并行流水线。 + +选择 **Colocate**：GPU 紧张，可以接受 rollout → train 的串行执行。 + +______________________________________________________________________ + +## 架构 + +### 角色布局 + +Hybrid 与 Colocate 使用相同的角色集合 —— 只部署 `actor`、`critic`（可选）、`rollout` 三个 Ray Serve 服务。判断逻辑位于 `relax/core/registry.py`： + +```python +def process_role(config): + if config.hybrid: + # hybrid mode: actor handles ref/actor_fwd internally + # via _switch_model, only need actor + rollout services + return ROLES_COLOCATE + if config.fully_async: + ... +``` + +但与 Colocate 不同，actor 与 rollout 的 placement group 是 **互相独立** 的，这与 Fully Async 一致。参见 `relax/core/controller.py`： + +```python +if colocate and not self.config.hybrid: + # Sync colocate: actor and rollout share GPUs via time-sharing (offload/onload) + actor_rollout_pgs = create_placement_group(num_gpus=num_gpus) +else: + # fully_async (pure or hybrid): actor and rollout use separate GPUs + actor_rollout_pgs = None +``` + +### 参数解析 + +`--hybrid` 是唯一对外暴露的开关。`relax/utils/arguments.py` 将其展开为下游已识别的两个底层参数： + +```python +if args.hybrid: + args.fully_async = True + args.colocate = True +``` + +直接传入 `--fully-async --colocate` 会被拒绝，必须使用 `--hybrid`。单一开关的设计使 `args.hybrid` 成为 registry、controller 分发以及 `train_hybrid` 调用点中识别 hybrid 模式的唯一权威标志。 + +### 架构图 + +ASCII 图中保留英文以避免框线对齐错乱： + +``` +┌────────────────────────────────────────────────────────────────────────────┐ +│ Controller (Orchestrator) │ +│ relax/core/controller.py │ +│ │ +│ ┌───────────────────────────────────┐ ┌────────────────────┐ │ +│ │ Actor Service │ │ Rollout Service │ │ +│ │ (own placement group, N GPUs) │ │ (own PG, M GPUs) │ │ +│ │ │ │ SGLang engines │ │ +│ │ ┌────────────────────────────┐ │ └─────────┬──────────┘ │ +│ │ │ TensorBackuper │ │ ▲ │ +│ │ │ tags: actor / ref / │ │ │ │ +│ │ │ old_actor / teacher │ │ │ │ +│ │ │ _switch_model(tag) swaps │ │ │ │ +│ │ │ weights on the same GPUs │ │ │ │ +│ │ └────────────────────────────┘ │ │ │ +│ │ train_hybrid(): │ │ │ +│ │ ├─ ref forward (switch:ref) │ │ │ +│ │ ├─ actor forward (switch:actor)│ │ +│ │ ├─ advantages (in-process) │ │ │ +│ │ └─ train (switch:actor)│ │ +│ └──────────────┬────────────────────┘ │ │ +│ │ UpdateWeightFromTensor (sync) ───┘ │ +└──────────────────────┼─────────────────────────────────────────────────────┘ + ▼ +┌───────────────────────────────────────────────────────────────────────────┐ +│ TransferQueue (Data Plane) │ +│ Rollout writes train_N partition incrementally ──► Actor consumes │ +│ in sub-batches via get_meta(batch_size, batch_index) with max-staleness │ +└───────────────────────────────────────────────────────────────────────────┘ +``` + +______________________________________________________________________ + +## `train_hybrid` 训练流程 + +`relax/backends/megatron/actor.py:708` 实现的 Hybrid 训练步骤分为三个阶段： + +1. **采集子批次并完成小批次 forward 计算（峰值显存小）** + + 全局 batch 被切分为 `num_iters_per_train_update` 份子批次。对每个子批次，actor 会： + + - 从 TransferQueue 拉取数据（`_get_data_from_transfer_queue("train", rollout_id, fields, batch_size, batch_index)`） + - 若已备份 ref 权重，执行 `_switch_model("ref")` 并计算 ref log-probs + - 若已备份 teacher 权重（OPD 场景），执行 `_switch_model("teacher")` 并计算 teacher log-probs + - 执行 `_switch_model("old_actor" 或 "actor")` 并计算当前 actor 的 log-probs + - 把扩充后的子批次追加到内存列表 + +2. **合并子批次并做全局 Advantages 归一化** + + 所有子批次 dict 被拼接成一个 `rollout_data`，随后 `compute_advantages_and_returns(self.args, rollout_data)` 在合并后的整批数据上执行一次。这是两阶段设计的 **核心正确性要求** —— Advantages 归一化必须看到完整的 DP-group 批次，而不是各个子批次切片。 + +3. **在合并批次上训练并推送权重** + + 一次 `train(...)` 调用基于合并后的 batch 完成优化器步进。随后 actor 把新权重备份到 `actor` tag（如果到达 ref 更新间隔，也刷新 `ref` tag），然后调用 `self.update_weights()` 通过 `UpdateWeightFromTensor` 把最新权重同步给 rollout。 + +子批次 forward 控制了激活峰值显存（与 Fully Async 行为一致），而合并后的训练步则保留了 Colocate 风格的全局统计量。 + +______________________________________________________________________ + +## 配置 + +### 必需参数 + +| 参数 | 用途 | +| ------------------------------- | ----------------------------------------------------------------------------------------------- | +| `--hybrid` | 启用 Hybrid 模式（内部展开为 `fully_async=True, colocate=True`） | +| `--resource '{...}'` | 分别声明 `actor` 与 `rollout` 的 placement group，例如 `{"actor":[1,4],"rollout":[1,4]}` | +| `--num-iters-per-train-update` | 每个全局 batch 切分的子批次数量（越大 → 峰值显存越小，TransferQueue 轮询次数越多） | +| `--max-staleness` | Off-policy 容忍度（0 = 严格 on-policy，>0 允许一定程度滞后） | + +### 常用可选参数 + +| 参数 | 说明 | +| ----------------------------- | ------------------------------------------------------------------------------------------------------- | +| `--balance-data` | Hybrid 模式下支持（纯 fully-async 下被拒绝）。启用后做 DP 间负载均衡。 | +| `--num-data-storage-units` | TransferQueue 存储 actor 的数量。 | +| `--use-streaming-dataset` | 从磁盘流式读取 prompts，而不是全量载入内存。 | +| `--ref-update-interval` | 周期性地用最新 actor 权重刷新缓存的 ref 权重。 | + +### 默认值覆盖 + +启用 `--hybrid` 后，`relax/utils/arguments.py` 会按以下方式设置默认值（除非用户显式传入）： + +- `offload_train = False` 且 `offload_rollout = False` —— actor 与 rollout GPU 独立，不需要 offload +- `compute_advantages_and_returns = True` —— actor 必须在本地计算 advantages +- `fully_async = True`、`colocate = True` —— 由 `--hybrid` 推导得出 + +::: warning +如果你既想做流式数据流水线，又需要 `--balance-data`，必须使用 `--hybrid`。`--fully-async --balance-data`（不带 `--hybrid`）会在参数解析阶段被拒绝。 +::: + +______________________________________________________________________ + +## 快速开始 + +8 GPU 多模态 Hybrid 训练的参考启动脚本位于 +`scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh`。 + +它构建的 Hybrid 调用命令为： + +```bash +ray job submit --address="http://127.0.0.1:8265" \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 4], "rollout": [1, 4]}' \ + --max-staleness 2 \ + --num-data-storage-units 1 \ + --num-iters-per-train-update 8 \ + --balance-data \ + --hybrid \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" +``` + +该配置的关键点： + +- 8 GPU 总量，actor 与 rollout 各占 4 张 +- `max-staleness 2` —— actor 可以消费比最新权重落后最多 2 个 step 的 rollout 输出 +- `num-iters-per-train-update 8` —— 每个全局 batch 在 forward 阶段被切分为 8 个子批次 +- `balance-data` —— 启用 DP 间负载均衡 +- 算法采用 GRPO，附带 `--use-kl-loss` 与 `--use-tis`（这些是算法参数，与 Hybrid 正交） + +______________________________________________________________________ + +## 故障排除 + +### `train_hybrid(rollout_id=N) batch_index=K stalled for ... seconds` + +该警告由 `relax/backends/megatron/actor.py` 抛出，发生在 actor 不断尝试拉取下一个子批次、partition 却始终未被标记 `all_consumed` 时。常见原因： + +- Rollout 漏填了当前 partition（丢弃样本后未补齐）。 +- Rollout 因健康检查失败或重启而处于暂停状态。 +- Staleness 预算耗尽：rollout 必须等待新权重，因此无法继续产出数据。 + +在认定为代码 bug 前，请先排查 rollout 侧日志及 partition 状态。 + +### `--balance-data is not supported in pure fully-async mode` + +你同时传入了 `--fully-async --balance-data`，但缺少 `--hybrid`。请去掉 `--balance-data`，或者改用 `--hybrid`（Hybrid 模式原生支持 DP 数据均衡）。 + +### Rollout 长时间看到旧权重 + +Hybrid 在每次 `train_hybrid` 结束时通过同步的 `UpdateWeightFromTensor` 路径推送权重。如果观察到权重更新间隔过大，请检查： + +- Actor 日志中的 `update_weights()` 耗时 +- Rollout 健康检查是否触发了 actor 等待（权重同步前会调用 `_check_services_health()`） + +______________________________________________________________________ + +## 下一步 + +Hybrid 模式计划推进的工作： + +- **接入 DCS 做权重同步** —— 用 Distributed Checkpoint Service 替换当前同步的 `UpdateWeightFromTensor` 路径，使权重向 rollout 广播能够与下一轮训练迭代重叠，消除每次 `train_hybrid` 结束时残留的同步阻塞。 +- **将 `train_actor` 拆分为 `num_iters_per_train_update` 次训练迭代** —— 目前 `num_iters_per_train_update` 只对 forward 阶段做了切分，合并后的训练步仍然在整个全局 batch 上跑一次。下一步将训练步同样切成 `num_iters_per_train_update` 次，让优化器更新与 TransferQueue 数据消费形成流水线，并进一步压低训练侧峰值显存。 + +相关文档： + +- [全异步训练流水线](./fully-async-training.md) —— Hybrid 借用的流式数据引擎 +- [架构设计](./architecture.md) —— Relax 服务分层总览 +- [权重更新流水线优化](./update-weights-pipeline.md) —— `UpdateWeightFromTensor` 与 DCS 的差异 From c695ce51dd69448fb98960b0fa9ceddcc205cdb9 Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Mon, 25 May 2026 23:06:58 +0800 Subject: [PATCH 054/268] feat(megatron): dump provider config as json MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add JSON provider config dump - Keep the existing transformer_config.pkl dump for compatibility - Also write transformer_config.json next to it for easier inspection - Convert non-JSON-safe values recursively and fall back to str() when needed (cherry picked from commit 598c15c36eb25d66cfd61e70ddfb1e84e84b9a07) --- relax/backends/megatron/model_provider.py | 53 ++++++++++++++++++++--- 1 file changed, 47 insertions(+), 6 deletions(-) diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index 0c1af42a5..7e94681f1 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -3,11 +3,12 @@ # Adapt from https://github.com/NVIDIA/Megatron-LM/blob/b1efb3c7126ef7615e8c333432d76e08038e17ff/pretrain_gpt.py import argparse import inspect +import json import os import pickle import re from contextlib import nullcontext -from typing import Literal +from typing import Any, Literal import torch import torch.distributed as dist @@ -29,6 +30,50 @@ logger = get_logger(__name__) +def _make_json_safe(value: Any, seen: set[int] | None = None) -> Any: + if isinstance(value, (str, int, float, bool)) or value is None: + return value + + if seen is None: + seen = set() + + if isinstance(value, dict): + return {str(k): _make_json_safe(v, seen) for k, v in value.items()} + if isinstance(value, (list, tuple, set)): + return [_make_json_safe(v, seen) for v in value] + + obj_id = id(value) + if obj_id in seen: + return str(value) + + if hasattr(value, "__dict__"): + seen.add(obj_id) + try: + return {str(k): _make_json_safe(v, seen) for k, v in vars(value).items()} + finally: + seen.remove(obj_id) + + try: + json.dumps(value) + return value + except TypeError: + return str(value) + + +def _dump_provider_config(provider: Any, save_path: str) -> None: + os.makedirs(save_path, exist_ok=True) + + pkl_path = os.path.join(save_path, "transformer_config.pkl") + with open(pkl_path, "wb") as f: + pickle.dump(provider, f) + logger.info(f"Provider config saved to {pkl_path}") + + json_path = os.path.join(save_path, "transformer_config.json") + with open(json_path, "w") as f: + json.dump(_make_json_safe(provider), f, indent=2, ensure_ascii=False) + logger.info(f"Provider config saved to {json_path}") + + # Adapt from https://github.com/volcengine/verl/blob/c3b20575d2bc815fcccd84bddb4c0401fc4b632b/verl/models/llama/megatron/layers/parallel_linear.py#L82 class LinearForLastLayer(torch.nn.Linear): def __init__( @@ -262,11 +307,7 @@ def wrapped_model_provider( # Pickle provider for offline inspection / reproducibility (only on rank 0) if not dist.is_initialized() or dist.get_rank() == 0: save_path = getattr(args, "save", None) or "/tmp/relax" - os.makedirs(save_path, exist_ok=True) - pkl_path = os.path.join(save_path, "transformer_config.pkl") - with open(pkl_path, "wb") as f: - pickle.dump(provider, f) - logger.info(f"Provider config saved to {pkl_path}") + _dump_provider_config(provider, save_path) original_provide = provider.provide From 62a0e0a19c57c599e7e1000b90747b5dc71b6705 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=8E=E7=AC=91=E9=A2=9C?= Date: Tue, 26 May 2026 21:27:00 +0800 Subject: [PATCH 055/268] docs(readme): document hybrid execution mode MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 📝 Documentation ## Add hybrid mode to bilingual README - Add Hybrid bullet to Highlights section in both README.md and README_zh.md - Add 05/26/2026 News entry pointing to the Hybrid Training guide - Expand Architecture section from two to three execution modes with Hybrid description (separate PG + in-process ref/actor_fwd via TensorBackuper + _switch_model) - Add Hybrid Training doc link to the "Learn more" line (cherry picked from commit 6a262cb4b2958ca1742aa9596690c756bcc2c823) --- README.md | 15 +++++++++------ README_zh.md | 15 +++++++++------ 2 files changed, 18 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 0afa9f097..56bcdb00c 100644 --- a/README.md +++ b/README.md @@ -40,6 +40,7 @@ ______________________________________________________________________ - 🌐 **Full Omni-Modal Training** — One unified framework for text, vision, and audio RL — one of the few systems capable of end-to-end Omni model (Qwen3-Omni) post-training - ⚙️ **Service-Oriented Six-Layer Architecture** — Every role is an independent Ray Serve deployment, with native service-level elastic scheduling and fault recovery - ⚡ **Fully Async via TransferQueue** — Rollout, Actor, ActorFwd, Reference, and Advantages run on independent GPU clusters with streaming data exchange and configurable staleness +- 🔁 **Hybrid Mode** — Separate Actor/Rollout placement groups with TransferQueue streaming, while ref / actor_fwd / advantages run in-process on the actor — pairs `--balance-data` with sub-batched forward to minimize GPU waste - 🤖 **Agentic RL** — Multi-turn interaction, loss masking, flexible termination, and VLM multimodal context carry-over for closed-loop "execute → observe → decide" training - 🔀 **Elastic Rollout Scaling** — Dynamically grow/shrink inference engines mid-training via HTTP REST API, with same-cluster (`ray_native`) and cross-cluster (`external`) federation modes - 🧠 **Rich Algorithm Suite** — GRPO, GSPO, SAPO, and On-Policy Distillation out of the box, with pluggable rewards and built-in **GenRM** (LLM-as-judge) mode @@ -50,10 +51,11 @@ ______________________________________________________________________ ## 📢 News -| 📣 Updates | -| :-------------------------------------------------------------------- | -| **\[05/11/2026\]** 🚀 Support for Qwen3.6 series models (text + VLM)! | -| **\[04/15/2026\]** 🎉 Relax is now open-source! | +| 📣 Updates | +| :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **\[05/26/2026\]** 🔁 New **Hybrid** execution mode — streaming data + in-process ref/actor_fwd, with `--balance-data` support. See the [Hybrid Training Guide](docs/en/guide/hybrid-training.md). | +| **\[05/11/2026\]** 🚀 Support for Qwen3.6 series models (text + VLM)! | +| **\[04/15/2026\]** 🎉 Relax is now open-source! | ______________________________________________________________________ @@ -74,12 +76,13 @@ Relax adopts a **six-layer service-oriented architecture** where every role is d | **Backends** | **Megatron-LM** training backend (TP/PP/CP/EP) and **SGLang** inference engine | | **Distributed** | Ray Actor groups (RolloutManager / GenRMManager) and **DCS** (Distributed Checkpoint Service) for NCCL/GLOO weight sync | -**Two execution modes** are supported: +**Three execution modes** are supported: - **Colocate (Sync)** — Actor and Rollout time-share the same GPUs; Rollout writes a full batch to TransferQueue, then yields GPUs for training. Memory-efficient for constrained hardware and strict on-policy (`max_staleness=0`). - **Fully Async** — Actor, Rollout, ActorFwd, Reference, and Advantages run on **independent GPU clusters** in parallel, exchanging data through TransferQueue and syncing weights asynchronously through DCS for maximum throughput with configurable staleness. +- **Hybrid** — Actor and Rollout sit on **separate GPU placement groups** (like Fully Async) and exchange data via TransferQueue with configurable staleness, but ref / actor_fwd / advantages run **in-process on the actor's own GPUs** via `TensorBackuper` + `_switch_model` (like Colocate). Enables streaming pipelines plus `--balance-data` without paying for standalone ref/actor_fwd services. -> 📖 Learn more: [Architecture Guide](docs/en/guide/architecture.md) · [Fully Async Training](docs/en/guide/fully-async-training.md) · [Elastic Rollout Scaling](docs/en/guide/elastic-rollout.md) +> 📖 Learn more: [Architecture Guide](docs/en/guide/architecture.md) · [Fully Async Training](docs/en/guide/fully-async-training.md) · [Hybrid Training](docs/en/guide/hybrid-training.md) · [Elastic Rollout Scaling](docs/en/guide/elastic-rollout.md) ______________________________________________________________________ diff --git a/README_zh.md b/README_zh.md index efea5346b..3f79817cd 100644 --- a/README_zh.md +++ b/README_zh.md @@ -40,6 +40,7 @@ ______________________________________________________________________ - 🌐 **全模态统一训练** — 单一框架覆盖文本、视觉、音频强化学习，业界少数能够在统一架构下完成 Omni 模型（Qwen3-Omni）后训练的系统 - ⚙️ **面向服务的六层架构** — 所有角色均作为独立 Ray Serve 服务部署，原生支持服务级别的弹性调度与故障恢复 - ⚡ **基于 TransferQueue 的全异步训练** — Rollout、Actor、ActorFwd、Reference、Advantages 运行在独立 GPU 集群，流式数据交换，可配置 staleness +- 🔁 **Hybrid 混合模式** — Actor 与 Rollout 独立 Placement Group + TransferQueue 流式数据，ref / actor_fwd / advantages 在 Actor 本机进程内完成；配合 `--balance-data` 与子批 forward，避免独立 ref/actor_fwd 服务的 GPU 浪费 - 🤖 **Agentic RL** — 多轮交互、loss masking、灵活的终止条件以及 VLM 多模态上下文累积，构建"执行 → 观察 → 决策"闭环训练 - 🔀 **Rollout 弹性扩缩容** — 通过 HTTP REST API 在训练过程中动态增减推理引擎，支持同集群（`ray_native`）和跨集群联邦（`external`）两种模式 - 🧠 **丰富的算法矩阵** — 开箱即用的 GRPO、GSPO、SAPO 与 On-Policy Distillation，配合可插拔奖励函数和内置 **GenRM**（LLM-as-judge）模式 @@ -50,10 +51,11 @@ ______________________________________________________________________ ## 📢 最新动态 -| 📣 更新 | -| :----------------------------------------------------------- | -| **\[05/11/2026\]** 🚀 支持 Qwen3.6 系列模型（纯文本+多模）！ | -| **\[04/15/2026\]** 🎉 Relax 正式开源！ | +| 📣 更新 | +| :------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **\[05/26/2026\]** 🔁 新增 **Hybrid** 执行模式 —— 流式数据 + 进程内 ref/actor_fwd，支持 `--balance-data`，详见 [Hybrid 训练指南](docs/zh/guide/hybrid-training.md)。 | +| **\[05/11/2026\]** 🚀 支持 Qwen3.6 系列模型（纯文本+多模）！ | +| **\[04/15/2026\]** 🎉 Relax 正式开源！ | ______________________________________________________________________ @@ -74,12 +76,13 @@ Relax 采用**面向服务的六层架构**，每个角色均作为独立的 [Ra | **Backends（后端层）** | **Megatron-LM** 训练后端（TP/PP/CP/EP）与 **SGLang** 推理引擎 | | **Distributed（分布式层）** | Ray Actor Groups（RolloutManager / GenRMManager）与 **DCS**（分布式 Checkpoint 服务，支持 NCCL/GLOO 权重同步） | -支持**两种执行模式**： +支持**三种执行模式**： - **Colocate（同步模式）** — Actor 与 Rollout 共享同一组 GPU，Rollout 将整批数据写入 TransferQueue 后释放 GPU 供训练使用；显存友好，严格 on-policy（`max_staleness=0`）。 - **Fully Async（全异步模式）** — Actor、Rollout、ActorFwd、Reference、Advantages 运行在**独立 GPU 集群**上完全并行，通过 TransferQueue 交换数据，通过 DCS 异步同步权重，在可配置 staleness 下实现最大吞吐。 +- **Hybrid（混合模式）** — Actor 与 Rollout 使用**独立的 Placement Group**（与全异步一致），通过 TransferQueue 流式交换数据并支持可配置 staleness；但 ref / actor_fwd / advantages 通过 `TensorBackuper` + `_switch_model` 在 Actor 自身 GPU 上**进程内复用权重**（与 Colocate 一致）。在不为独立 ref/actor_fwd 服务付出额外 GPU 的前提下，同时获得流式数据管线与 `--balance-data` 支持。 -> 📖 了解更多：[架构指南](docs/zh/guide/architecture.md) · [全异步训练](docs/zh/guide/fully-async-training.md) · [Rollout 弹性扩缩容](docs/zh/guide/elastic-rollout.md) +> 📖 了解更多：[架构指南](docs/zh/guide/architecture.md) · [全异步训练](docs/zh/guide/fully-async-training.md) · [Hybrid 训练](docs/zh/guide/hybrid-training.md) · [Rollout 弹性扩缩容](docs/zh/guide/elastic-rollout.md) ______________________________________________________________________ From 3b1f33943253af19735d217e70aabf5bd8cd15b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Wed, 27 May 2026 19:49:43 +0800 Subject: [PATCH 056/268] perf(megatron): warm HF ckpt page cache before bridge load MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⚡ Performance ## Pre-fault HF safetensors into page cache once per node - Add `_warm_hf_checkpoint_page_cache(source_path)` in `relax/backends/megatron/checkpoint.py`, invoked from `_load_checkpoint_hf` before `AutoBridge.from_hf_pretrained` - Eliminates the dominant NFS-mmap small-read bottleneck during `bridge.load_hf_weights` (`aten::cat` was running at ~20 MB/s, accounting for ~65% of init CPU time on 30B-A3B-class MoE models) - Explicit per-node coordination: `LOCAL_RANK == 0` runs `cat /*.{safetensors,bin} > /dev/null`, other local ranks poll a marker under `/dev/shm` - Advisory `flock` wraps the rank-0 path so two Relax jobs sharing a host and ckpt do not duplicate the warmup - Marker lives in `/dev/shm` (tmpfs) so it naturally clears on reboot, avoiding stale-marker / cold-cache mismatches - Warmup is best-effort: missing path, non-zero `cat` exit, or wait timeout only log a warning, never a correctness gate - Configurable wait via `RELAX_HF_WARMUP_TIMEOUT_S` (default 1800s) --- # ✅ Tests ## Reshape repro profiler around the bridge progress loop - Replace `_maybe_profile` contextmanager in `scripts/tools/repro_megatron_bridge_load.py` with `_install_bridge_progress_profiler` that monkey-patches `MegatronModelBridge._with_progress_tracking` - Profiles a fixed `RELAX_REPRO_PROFILE_STEPS` window of conversion tasks (default 50) after `RELAX_REPRO_PROFILE_WARMUP` warmup tasks (default 5), then dumps trace/operator-table/stacks/metadata immediately - Add `RELAX_REPRO_PROFILE_EXIT_AFTER_DUMP` early-exit knob so a long load can be cut short once the profile window is captured - `scripts/tools/repro_qwen35_moe_bridge_load_tp4pp2.sh`: default `RELAX_REPRO_PROFILE=0`, set `PYTHONPATH=$REPO_ROOT`, default profile dir to `/tmp/relax/profile` (cherry picked from commit 335001cd49c1f92b4794f1983bfc78cd581d1b3b) --- relax/backends/megatron/checkpoint.py | 141 +++++++++ scripts/tools/repro_megatron_bridge_load.py | 274 ++++++++++++++++++ .../repro_qwen35_moe_bridge_load_tp4pp2.sh | 99 +++++++ 3 files changed, 514 insertions(+) create mode 100644 scripts/tools/repro_megatron_bridge_load.py create mode 100755 scripts/tools/repro_qwen35_moe_bridge_load_tp4pp2.sh diff --git a/relax/backends/megatron/checkpoint.py b/relax/backends/megatron/checkpoint.py index 8febe0373..8f727a18b 100644 --- a/relax/backends/megatron/checkpoint.py +++ b/relax/backends/megatron/checkpoint.py @@ -202,6 +202,145 @@ def _scatter_with_dtype_cast(output, scatter_list=None, **kwargs): dist.scatter = original_scatter +def _warm_hf_checkpoint_page_cache(source_path: str) -> None: + """Pre-fault the HF checkpoint into the Linux page cache, once per node. + + NFS-backed safetensors files are accessed via mmap by the bridge, so the + first per-page touch incurs a synchronous small-read round-trip (we have + measured ~20 MB/s effective throughput from ``aten::cat``). Reading the + files end-to-end once promotes them to the page cache, after which the + bridge's lazy tensor reads are memory-fast. + + Implementation is pure-Python (``open(...).read()`` in chunks) — no shell + invocation, so dynamic paths cannot inject commands. + + Coordination — explicit per-node rank-0 pattern: + + - ``LOCAL_RANK == 0`` (the first GPU actor on each host) does the read + and writes a done-marker under ``/dev/shm`` (tmpfs, so it naturally + clears on reboot — avoiding stale-marker / cleared-cache mismatches). + - All other local ranks poll for the marker with a generous timeout. + They never touch NFS themselves. + - An advisory ``flock`` still wraps the rank-0 path so that two + independent Relax jobs sharing a host and the same checkpoint don't + both warm — only the first acquires the lock, the second sees the + marker and skips. + - Errors (missing path, read failure, timeout) are logged and swallowed: + warmup is a best-effort optimization, never a correctness gate. + """ + import fcntl + import glob + import hashlib + import time + + if not source_path: + return + abs_path = os.path.abspath(source_path) + if not os.path.isdir(abs_path): + return + + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + digest = hashlib.sha1(abs_path.encode()).hexdigest()[:16] + marker_dir = "/dev/shm" if os.path.isdir("/dev/shm") else "/tmp" + lock_path = f"{marker_dir}/relax_hf_warmup_{digest}.lock" + done_path = f"{marker_dir}/relax_hf_warmup_{digest}.done" + + def _marker_says_warm() -> bool: + try: + with open(done_path) as df: + return df.read().strip() == abs_path + except OSError: + return False + + def _stream_files_to_devnull(files: list[str]) -> int: + """Read each file end-to-end so the kernel pulls every page into the + page cache. + + Pure-Python — no shell, no command injection surface. Returns total + bytes read. Shows a per-file tqdm progress bar. + """ + from tqdm import tqdm + + chunk = 8 * 1024 * 1024 # 8 MiB, large enough that read syscall overhead is negligible + total = 0 + pbar = tqdm(files, desc="warming HF ckpt", unit="file", dynamic_ncols=True) + for fp in pbar: + pbar.set_postfix_str(os.path.basename(fp)) + try: + with open(fp, "rb") as fh: + while True: + data = fh.read(chunk) + if not data: + break + total += len(data) + except OSError as exc: + logger.warning(f"HF checkpoint warmup: skipping {fp} due to read error: {exc}") + pbar.close() + return total + + if local_rank == 0: + try: + lf = open(lock_path, "w") + except OSError as e: + logger.warning(f"HF checkpoint warmup: cannot open lock file {lock_path}: {e}") + return + try: + fcntl.flock(lf.fileno(), fcntl.LOCK_EX) + try: + if _marker_says_warm(): + logger.info(f"HF checkpoint page cache already warm on this node: {abs_path}") + return + files = sorted( + glob.glob(os.path.join(abs_path, "*.safetensors")) + glob.glob(os.path.join(abs_path, "*.bin")) + ) + if not files: + logger.info(f"HF checkpoint warmup: no *.safetensors / *.bin under {abs_path}, skipping") + return + t0 = time.time() + logger.info( + f"[local_rank=0] Warming HF checkpoint page cache on this node: {abs_path} ({len(files)} files)" + ) + total_bytes = _stream_files_to_devnull(files) + elapsed = time.time() - t0 + throughput_mb = total_bytes / max(elapsed, 1e-6) / (1024 * 1024) + try: + with open(done_path, "w") as df: + df.write(abs_path) + except OSError as e: + logger.warning(f"HF checkpoint warmup: cannot write marker {done_path}: {e}") + logger.info( + f"[local_rank=0] HF checkpoint page cache warmed in {elapsed:.1f}s " + f"({total_bytes / (1024 * 1024):.0f} MiB, {throughput_mb:.0f} MiB/s)" + ) + finally: + fcntl.flock(lf.fileno(), fcntl.LOCK_UN) + finally: + lf.close() + else: + # Other local ranks just wait for rank 0's marker. + timeout_s = float(os.environ.get("RELAX_HF_WARMUP_TIMEOUT_S", "1800")) + poll_interval_s = 1.0 + t0 = time.time() + logged_waiting = False + while time.time() - t0 < timeout_s: + if _marker_says_warm(): + if logged_waiting: + logger.info( + f"[local_rank={local_rank}] HF checkpoint warmup ready after waiting {time.time() - t0:.1f}s" + ) + return + if not logged_waiting and time.time() - t0 > 5.0: + logger.info( + f"[local_rank={local_rank}] waiting for local_rank=0 to warm HF checkpoint page cache: {abs_path}" + ) + logged_waiting = True + time.sleep(poll_interval_s) + logger.warning( + f"[local_rank={local_rank}] HF checkpoint warmup wait timed out after {timeout_s:.0f}s; " + f"proceeding without confirmation (load may still succeed, just slower)" + ) + + def _load_checkpoint_hf(ddp_model, optimizer, args, load_path: str): assert args.megatron_to_hf_mode == "bridge", "Only bridge mode is supported for loading HF checkpoint" from megatron.bridge import AutoBridge @@ -211,6 +350,8 @@ def _load_checkpoint_hf(ddp_model, optimizer, args, load_path: str): f"Load checkpoint from HuggingFace model into Megatron (requested_path={load_path}, source_path={source_path})" ) + _warm_hf_checkpoint_page_cache(source_path) + with megatron_bridge_utils.patch_megatron_model(ddp_model): bridge = AutoBridge.from_hf_pretrained(source_path, trust_remote_code=True) with _patch_scatter_dtype_cast(): diff --git a/scripts/tools/repro_megatron_bridge_load.py b/scripts/tools/repro_megatron_bridge_load.py new file mode 100644 index 000000000..540481712 --- /dev/null +++ b/scripts/tools/repro_megatron_bridge_load.py @@ -0,0 +1,274 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Reproduce Relax's Megatron Bridge HF-to-Megatron weight load path with +torchrun. + +This intentionally follows the train actor initialization path up to: +initialize_model_and_optimizer -> load_checkpoint -> bridge.load_hf_weights. +""" + +import os +import time +from datetime import datetime, timedelta +from pathlib import Path + +import torch +import torch.distributed as dist + +import relax.utils.training.eval_config +from relax.backends.megatron.initialize import init as init_megatron +from relax.backends.megatron.model import initialize_model_and_optimizer +from relax.utils import device as device_utils +from relax.utils.arguments import parse_args +from relax.utils.checkpoint_write_patch import patch_checkpoint_write +from relax.utils.distributed_utils import get_gloo_group, init_gloo_group +from relax.utils.logging_utils import get_logger +from relax.utils.memory_utils import clear_memory, print_memory +from relax.utils.reloadable_process_group import monkey_patch_torch_dist +from relax.utils.utils import process_args + + +logger = get_logger(__name__) + + +def _env_flag(name: str, default: str = "0") -> bool: + return os.environ.get(name, default).lower() not in ("0", "false", "no", "off", "") + + +def _write_text(path: Path, text: str) -> None: + path.write_text(text, encoding="utf-8") + + +def _write_cuda_memory_snapshot(rank_dir: Path, name: str) -> None: + if not torch.cuda.is_available(): + return + torch.cuda.synchronize() + _write_text(rank_dir / f"{name}_memory_summary.txt", torch.cuda.memory_summary()) + + +def _export_profile_artifact(rank_dir: Path, name: str, export_fn) -> None: + try: + export_fn() + except Exception as exc: # noqa: BLE001 + _write_text(rank_dir / f"{name}.error.txt", repr(exc)) + + +def _install_bridge_progress_profiler(args, role: str) -> None: + """Monkey-patch ``MegatronModelBridge._with_progress_tracking`` so that the + first weight-conversion loop profiles a fixed window of tasks and dumps + artifacts as soon as the window finishes, instead of waiting for the entire + script to exit.""" + if not _env_flag("RELAX_REPRO_PROFILE"): + return + + from megatron.bridge.models.conversion.model_bridge import MegatronModelBridge + + num_steps = int(os.environ.get("RELAX_REPRO_PROFILE_STEPS", "50")) + warmup = int(os.environ.get("RELAX_REPRO_PROFILE_WARMUP", "5")) + exit_after_dump = _env_flag("RELAX_REPRO_PROFILE_EXIT_AFTER_DUMP") + + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + profile_root = Path( + os.environ.get( + "RELAX_REPRO_PROFILE_DIR", + f"log/megatron_bridge_profile/{datetime.now().strftime('%Y%m%d-%H%M%S')}", + ) + ) + rank_dir = profile_root / f"rank_{args.rank}" + rank_dir.mkdir(parents=True, exist_ok=True) + + original = MegatronModelBridge._with_progress_tracking + state = {"profiled": False} + + def patched(self, tasks, description, show_progress=True): + if state["profiled"]: + yield from original(self, tasks, description, show_progress) + return + state["profiled"] = True + + total = len(tasks) + is_rank0 = args.rank == 0 + + activities = [torch.profiler.ProfilerActivity.CPU] + if torch.cuda.is_available(): + activities.append(torch.profiler.ProfilerActivity.CUDA) + sort_by = "self_cuda_time_total" if torch.cuda.is_available() else "self_cpu_time_total" + + if is_rank0: + logger.info( + f"[bridge-profile] '{description}' total={total}, warmup={warmup}, " + f"active={num_steps} — artifacts dir: {profile_root}" + ) + + if torch.cuda.is_available(): + torch.cuda.reset_peak_memory_stats() + _write_cuda_memory_snapshot(rank_dir, "before") + + it = iter(tasks) + emitted = 0 + + for _ in range(warmup): + try: + task = next(it) + except StopIteration: + break + yield task + emitted += 1 + + prof = torch.profiler.profile( + activities=activities, + record_shapes=True, + profile_memory=True, + with_stack=True, + with_modules=True, + ) + prof.start() + step_start = time.perf_counter() + try: + for i in range(num_steps): + try: + task = next(it) + except StopIteration: + break + yield task + if torch.cuda.is_available(): + torch.cuda.synchronize() + now = time.perf_counter() + if is_rank0: + logger.info( + f"[bridge-profile] step {i + 1}/{num_steps} " + f"(global {emitted + 1}/{total}) elapsed={(now - step_start) * 1000:.1f}ms" + ) + step_start = now + emitted += 1 + finally: + prof.stop() + + _write_cuda_memory_snapshot(rank_dir, "after") + _export_profile_artifact( + rank_dir, + "trace_gzip", + lambda: torch.profiler.tensorboard_trace_handler( + str(rank_dir), worker_name=f"rank_{args.rank}", use_gzip=True + )(prof), + ) + _export_profile_artifact( + rank_dir, + "operator_table", + lambda: _write_text( + rank_dir / "operator_table.txt", + prof.key_averages(group_by_stack_n=10).table(sort_by=sort_by, row_limit=-1), + ), + ) + _export_profile_artifact( + rank_dir, + "stacks", + lambda: prof.export_stacks(str(rank_dir / "stacks.txt"), metric=sort_by), + ) + _write_text( + rank_dir / "metadata.txt", + "\n".join( + [ + f"rank={args.rank}", + f"local_rank={local_rank}", + f"world_size={args.world_size}", + f"role={role}", + f"description={description}", + f"total_tasks={total}", + f"warmup_steps={warmup}", + f"active_steps={num_steps}", + f"tp={args.tensor_model_parallel_size}", + f"pp={args.pipeline_model_parallel_size}", + f"ep={args.expert_model_parallel_size}", + f"load={args.load}", + f"hf_checkpoint={args.hf_checkpoint}", + ] + ) + + "\n", + ) + if is_rank0: + logger.info(f"[bridge-profile] dumped {num_steps}-step profile to {profile_root}") + + if exit_after_dump: + if is_rank0: + logger.info("[bridge-profile] RELAX_REPRO_PROFILE_EXIT_AFTER_DUMP=1 — exiting now") + os._exit(0) + + for task in it: + yield task + + MegatronModelBridge._with_progress_tracking = patched + + +def _init_distributed(args) -> None: + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + device_utils.set_device(f"{device_utils.get_device_name()}:{local_rank}") + + dist.init_process_group( + backend=args.distributed_backend, + timeout=timedelta(minutes=args.distributed_timeout_minutes), + ) + init_gloo_group() + + args.rank = dist.get_rank() + args.world_size = dist.get_world_size() + + numa_local_rank = int(os.environ.get("RANK", args.rank)) % args.num_gpus_per_node + device_utils.set_numa_affinity(numa_local_rank) + + +def main() -> None: + args = parse_args() + role = os.environ.get("RELAX_REPRO_ROLE") + if role is None: + role = "reference" if args.only_load_weight else "actor" + + if role in ("reference", "actor_fwd"): + if args.ref_actor_config is None: + args.ref_actor_config = {} + process_args(args, role) + + torch.serialization.add_safe_globals([relax.utils.training.eval_config.EvalDatasetConfig]) + + monkey_patch_torch_dist(args) + patch_checkpoint_write() + _init_distributed(args) + + if args.rank == 0: + logger.info( + "Starting Megatron Bridge load reproduction " + f"(role={role}, world_size={args.world_size}, tp={args.tensor_model_parallel_size}, " + f"pp={args.pipeline_model_parallel_size}, ep={args.expert_model_parallel_size}, " + f"load={args.load}, hf_checkpoint={args.hf_checkpoint}, only_load_weight={args.only_load_weight})" + ) + + _install_bridge_progress_profiler(args, role) + + init_megatron(args) + dist.barrier(group=get_gloo_group()) + + print_memory("before initialize_model_and_optimizer") + start = time.perf_counter() + model, optimizer, opt_param_scheduler, iteration = initialize_model_and_optimizer(args, role) + dist.barrier(group=get_gloo_group()) + elapsed = time.perf_counter() - start + + if args.rank == 0: + logger.info( + "Finished initialize_model_and_optimizer " + f"(iteration={iteration}, elapsed_seconds={elapsed:.2f}, " + f"model_chunks={len(model)}, optimizer_loaded={optimizer is not None}, " + f"scheduler_loaded={opt_param_scheduler is not None})" + ) + + del model, optimizer, opt_param_scheduler + clear_memory() + dist.barrier(group=get_gloo_group()) + + +if __name__ == "__main__": + try: + main() + finally: + if dist.is_initialized(): + dist.destroy_process_group() diff --git a/scripts/tools/repro_qwen35_moe_bridge_load_tp4pp2.sh b/scripts/tools/repro_qwen35_moe_bridge_load_tp4pp2.sh new file mode 100755 index 000000000..005dba1b0 --- /dev/null +++ b/scripts/tools/repro_qwen35_moe_bridge_load_tp4pp2.sh @@ -0,0 +1,99 @@ +#!/usr/bin/env bash +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Reproduce Qwen3.5-35B-A3B Megatron Bridge HF-to-Megatron loading with 8 GPUs: +# TP=4, PP=2, DP=1 +# +# Usage: +# HF_CHECKPOINT=/path/to/Qwen3.5-35B-A3B bash scripts/tools/repro_qwen35_moe_bridge_load_tp4pp2.sh + +set -euo pipefail + +export CUDA_DEVICE_MAX_CONNECTIONS=1 +export RELAX_REPRO_PROFILE_DIR=${RELAX_REPRO_PROFILE_DIR:-"/tmp/relax/profile"} + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +REPO_ROOT="$(cd -- "${SCRIPT_DIR}/../.." &>/dev/null && pwd)" +MODEL_CONFIG_DIR="${MODEL_CONFIG_DIR:-${REPO_ROOT}/scripts/models}" + +export PYTHONPATH=$REPO_ROOT:$PYTHONPATH + +source "${MODEL_CONFIG_DIR}/qwen35-35B-A3B.sh" + +: "${HF_CHECKPOINT:?Set HF_CHECKPOINT=/path/to/Qwen3.5-35B-A3B}" + +NNODES="${NNODES:-1}" +NODE_RANK="${NODE_RANK:-0}" +NPROC_PER_NODE="${NPROC_PER_NODE:-8}" +MASTER_ADDR="${MASTER_ADDR:-127.0.0.1}" +MASTER_PORT="${MASTER_PORT:-29500}" +WORLD_GPUS=$((NNODES * NPROC_PER_NODE)) + +export RELAX_REPRO_PROFILE="${RELAX_REPRO_PROFILE:-0}" +export RELAX_REPRO_PROFILE_DIR="${RELAX_REPRO_PROFILE_DIR:-${REPO_ROOT}/log/megatron_bridge_profile/$(date +%Y%m%d-%H%M%S)}" + +TORCHRUN_ARGS=( + --nnodes "${NNODES}" + --node_rank "${NODE_RANK}" + --nproc_per_node "${NPROC_PER_NODE}" + --master_addr "${MASTER_ADDR}" + --master_port "${MASTER_PORT}" +) + +REPRO_ARGS=( + --debug-train-only + --resource "{\"actor\": [${NNODES}, ${WORLD_GPUS}]}" + --num-gpus-per-node "${NPROC_PER_NODE}" + --actor-num-nodes "${NNODES}" + --actor-num-gpus-per-node "${NPROC_PER_NODE}" + + --hf-checkpoint "${HF_CHECKPOINT}" + --ref-load "${HF_CHECKPOINT}" + --ref-actor-config '{}' + --load "${LOAD_CHECKPOINT:-${HF_CHECKPOINT}}" + --megatron-to-hf-mode bridge + + --tensor-model-parallel-size 4 + --sequence-parallel + --pipeline-model-parallel-size 2 + --context-parallel-size 1 + --expert-model-parallel-size "${EXPERT_MODEL_PARALLEL_SIZE:-1}" + --expert-tensor-parallel-size 1 + + --micro-batch-size 1 + --global-batch-size 1 + --num-rollout 1 + --rollout-batch-size 1 + --n-samples-per-prompt 1 + + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + --no-rope-fusion + --moe-router-load-balancing-type none + --moe-aux-loss-coeff 0.0 + --attention-dropout 0.0 + --hidden-dropout 0.0 + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + --attention-backend flash +) + +if [[ "${RELAX_REPRO_ONLY_LOAD_WEIGHT:-1}" == "1" ]]; then + export RELAX_REPRO_ROLE="${RELAX_REPRO_ROLE:-reference}" + REPRO_ARGS+=(--only-load-weight) +fi + +cd "${REPO_ROOT}" +torchrun "${TORCHRUN_ARGS[@]}" \ + "${REPO_ROOT}/scripts/tools/repro_megatron_bridge_load.py" \ + "${MODEL_ARGS[@]}" \ + "${REPRO_ARGS[@]}" \ + "$@" From c632ba90c19ebb86e8ace79c7d03926694dddb43 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Wed, 27 May 2026 21:57:44 +0800 Subject: [PATCH 057/268] feat(megatron): wire VPP training MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ✨ Feature - Propagate virtual pipeline size into Megatron-Bridge providers. - Derive vp_stage from Megatron virtual pipeline state for provider wrappers. - Round dynamic microbatch counts up to the VPP group multiple. - Add Qwen3.6-35B 8xGPU VPP trial settings. --- # ✅ Tests - Add focused VPP provider and microbatch rounding regressions. - Verified with focused pytest and pre-commit. (cherry picked from commit 460aa2daf33858d10af3c9e47b8635e0f4833f28) --- relax/backends/megatron/data.py | 14 +- relax/backends/megatron/model_provider.py | 11 +- .../text/run-qwen36-35B-A3B-8xgpu-vpp.sh | 166 ++++++++++++++ tests/backends/megatron/test_data_vpp.py | 45 ++++ .../megatron/test_model_provider_vpp.py | 208 ++++++++++++++++++ 5 files changed, 437 insertions(+), 7 deletions(-) create mode 100755 scripts/training/text/run-qwen36-35B-A3B-8xgpu-vpp.sh create mode 100644 tests/backends/megatron/test_data_vpp.py create mode 100644 tests/backends/megatron/test_model_provider_vpp.py diff --git a/relax/backends/megatron/data.py b/relax/backends/megatron/data.py index 5c5f77f7f..cc4c1c7f5 100644 --- a/relax/backends/megatron/data.py +++ b/relax/backends/megatron/data.py @@ -43,6 +43,13 @@ } +def _round_up_to_microbatch_group(num_microbatches: torch.Tensor, microbatch_group_size: int) -> torch.Tensor: + return torch.clamp( + (num_microbatches + microbatch_group_size - 1) // microbatch_group_size * microbatch_group_size, + min=1, + ) + + def pad_and_flatten( tensor_list, transpose=None, @@ -536,11 +543,8 @@ def _generate_data_iterator(rollout_data, micro_batch_size, micro_batch_indices= dist.all_reduce(num_microbatches, op=dist.ReduceOp.MAX, group=dp_group) if vpp_size > 1: - # vpp requies the number of microbatches to be divisible by vpp_size - num_microbatches = torch.clamp( - num_microbatches // microbatch_group_size_per_vp_stage * microbatch_group_size_per_vp_stage, - min=1, - ) + # vpp requires the number of microbatches to be divisible by vpp_size + num_microbatches = _round_up_to_microbatch_group(num_microbatches, microbatch_group_size_per_vp_stage) num_microbatches = num_microbatches.tolist() diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index 7e94681f1..07fe98e2f 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -12,7 +12,7 @@ import torch import torch.distributed as dist -from megatron.core import tensor_parallel +from megatron.core import mpu, tensor_parallel from megatron.core.models.gpt import GPTModel from megatron.core.models.gpt.gpt_layer_specs import ( get_gpt_decoder_block_spec, @@ -246,6 +246,7 @@ def wrapped_model_provider( "tensor_model_parallel_size", "sequence_parallel", "pipeline_model_parallel_size", + "virtual_pipeline_model_parallel_size", "context_parallel_size", "expert_model_parallel_size", "expert_tensor_parallel_size", @@ -433,8 +434,14 @@ def model_provider(pre_process: bool = True, post_process: bool = True, vp_stage def wrap_model_provider_with_freeze(original_provider, args): def wrapped_provider(pre_process=True, post_process=True, vp_stage=None, **kwargs): + if vp_stage is None and mpu.get_virtual_pipeline_model_parallel_world_size() is not None: + vp_stage = mpu.get_virtual_pipeline_model_parallel_rank() + sig = inspect.signature(original_provider) - if "vp_stage" in sig.parameters: + accepts_vp_stage = "vp_stage" in sig.parameters or any( + p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values() + ) + if accepts_vp_stage: model = original_provider(pre_process=pre_process, post_process=post_process, vp_stage=vp_stage) else: model = original_provider(pre_process=pre_process, post_process=post_process) diff --git a/scripts/training/text/run-qwen36-35B-A3B-8xgpu-vpp.sh b/scripts/training/text/run-qwen36-35B-A3B-8xgpu-vpp.sh new file mode 100755 index 000000000..1a5d705dc --- /dev/null +++ b/scripts/training/text/run-qwen36-35B-A3B-8xgpu-vpp.sh @@ -0,0 +1,166 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3.5-35B-A3B 16xGPU (2-node) fully sync training script for DAPO math dataset. +# +# Usage: +# bash scripts/training/text/run-qwen36-35B-A3B-16xgpu-sync.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen36-35B-A3B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" +NUM_ROLLOUT="${NUM_ROLLOUT:=1000}" + +CKPT_ARGS=( + --hf-checkpoint ${MODEL_DIR}/Qwen3.6-35B-A3B/ + --ref-load ${MODEL_DIR}/Qwen3.6-35B-A3B/ + --megatron-to-hf-mode bridge + + --load ${EXP_DIR}/save/Qwen3.6-35B-A3B_mcore_8xgpu/ + --save ${EXP_DIR}/save/Qwen3.6-35B-A3B_mcore_8xgpu/ + --save-interval 100 + --max-actor-ckpt-to-keep 1 +) + +PROMPT_SET=${DATA_DIR}/dapo-math-17k/dapo-math-17k.jsonl + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type dapo + --reward-key score + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 16 + --n-samples-per-prompt 8 + --rollout-max-response-len 8192 + --rollout-temperature 1 + --global-batch-size 128 + --use-fault-tolerance + --balance-data +) + +EVAL_ARGS=( + --log-passrate + --skip-eval-before-train + --eval-interval 20 + --eval-prompt-data aime ${DATA_DIR}/aime-2024/aime-2024.jsonl + --n-samples-per-eval-prompt 8 + --eval-max-response-len 8192 + --eval-top-p 0.7 +) + +PERF_ARGS=( + --tensor-model-parallel-size 2 + --sequence-parallel + --pipeline-model-parallel-size 4 + --num-layers-per-virtual-pipeline-stage 5 + --context-parallel-size 1 + --calculate-per-token-loss + --expert-model-parallel-size 1 + --expert-tensor-parallel-size 1 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --use-dynamic-batch-size + --max-tokens-per-gpu 10240 +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + # NOTE(wuhuan): to avoid algorithm performance degradation + --no-rope-fusion + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 8 + --sglang-mem-fraction-static 0.7 + # --sglang-cuda-graph-bs 1 2 4 8 $(seq 16 8 256) +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen36-35B-A3B-16x-sync-${now} +) + +MISC_ARGS=( + # default dropout in megatron is 0.1 + --attention-dropout 0.0 + --hidden-dropout 0.0 + # should be good for model performance + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # need to comment this when using model with MLA + --attention-backend flash +) + +PARTIAL_ROLLOUT_ARGS=( + --partial-rollout + --over-sampling-batch-size 48 + --mask-offpolicy-in-partial-rollout + --partial-rollout-max-aborted-count 3 +) + +mkdir -p log +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://${HOST_IP}:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 8], "rollout": [1, 8]}' \ + --colocate \ + --max-staleness 0 \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${PARTIAL_ROLLOUT_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen36-35B-A3B-GRPO-gpu8-sync-${now}.log diff --git a/tests/backends/megatron/test_data_vpp.py b/tests/backends/megatron/test_data_vpp.py new file mode 100644 index 000000000..14a350f8f --- /dev/null +++ b/tests/backends/megatron/test_data_vpp.py @@ -0,0 +1,45 @@ +import importlib +import sys +import types + +import torch + + +def _load_data_module(monkeypatch): + megatron = types.ModuleType("megatron") + core = types.ModuleType("megatron.core") + mpu = types.ModuleType("megatron.core.mpu") + packed_seq_params = types.ModuleType("megatron.core.packed_seq_params") + training = types.ModuleType("megatron.training") + global_vars = types.ModuleType("megatron.training.global_vars") + tracking_utils = types.ModuleType("relax.utils.tracking_utils") + + class _PackedSeqParams: + pass + + core.mpu = mpu + packed_seq_params.PackedSeqParams = _PackedSeqParams + global_vars.get_args = lambda: None + + modules = { + "megatron": megatron, + "megatron.core": core, + "megatron.core.mpu": mpu, + "megatron.core.packed_seq_params": packed_seq_params, + "megatron.training": training, + "megatron.training.global_vars": global_vars, + "relax.utils.tracking_utils": tracking_utils, + } + for name, module in modules.items(): + monkeypatch.setitem(sys.modules, name, module) + + sys.modules.pop("relax.backends.megatron.data", None) + return importlib.import_module("relax.backends.megatron.data") + + +def test_vpp_microbatch_rounding_uses_ceil_multiple(monkeypatch): + data_module = _load_data_module(monkeypatch) + + rounded = data_module._round_up_to_microbatch_group(torch.tensor([1, 2, 3, 5]), microbatch_group_size=4) + + assert rounded.tolist() == [4, 4, 4, 8] diff --git a/tests/backends/megatron/test_model_provider_vpp.py b/tests/backends/megatron/test_model_provider_vpp.py new file mode 100644 index 000000000..b2d92eea3 --- /dev/null +++ b/tests/backends/megatron/test_model_provider_vpp.py @@ -0,0 +1,208 @@ +import importlib +import sys +import types +from types import SimpleNamespace + + +class _FakeProvider: + def __init__(self): + self.calls = [] + self.finalized = False + self.attention_backend = None + self.tensor_model_parallel_size = 1 + self.sequence_parallel = False + self.pipeline_model_parallel_size = 1 + self.virtual_pipeline_model_parallel_size = None + self.context_parallel_size = 1 + self.expert_model_parallel_size = 1 + self.expert_tensor_parallel_size = 1 + self.variable_seq_lengths = False + self.num_layers = 8 + self.moe_layer_freq = None + self.fp16 = False + self.bf16 = False + self.params_dtype = None + + def finalize(self): + self.finalized = True + + def provide(self, pre_process=True, post_process=True, vp_stage=None): + self.calls.append( + { + "pre_process": pre_process, + "post_process": post_process, + "vp_stage": vp_stage, + } + ) + return SimpleNamespace(named_modules=lambda: []) + + +def _install_fake_megatron(monkeypatch, provider=None): + provider = provider or _FakeProvider() + + megatron = types.ModuleType("megatron") + core = types.ModuleType("megatron.core") + mpu = types.ModuleType("megatron.core.mpu") + tensor_parallel = types.ModuleType("megatron.core.tensor_parallel") + models = types.ModuleType("megatron.core.models") + gpt = types.ModuleType("megatron.core.models.gpt") + gpt_layer_specs = types.ModuleType("megatron.core.models.gpt.gpt_layer_specs") + transformer = types.ModuleType("megatron.core.transformer") + spec_utils = types.ModuleType("megatron.core.transformer.spec_utils") + transformer_config = types.ModuleType("megatron.core.transformer.transformer_config") + training = types.ModuleType("megatron.training") + arguments = types.ModuleType("megatron.training.arguments") + bridge = types.ModuleType("megatron.bridge") + + class _FakeGPTModel: + pass + + class _FakeTransformerConfig: + pass + + class _FakeAutoBridge: + @classmethod + def from_hf_pretrained(cls, *args, **kwargs): + return cls() + + def to_megatron_provider(self, load_weights=False): + return provider + + mpu.get_virtual_pipeline_model_parallel_world_size = lambda: 2 + mpu.get_virtual_pipeline_model_parallel_rank = lambda: 1 + mpu.get_context_parallel_world_size = lambda: 1 + mpu.get_context_parallel_rank = lambda: 0 + mpu.get_tensor_model_parallel_rank = lambda: 0 + core.mpu = mpu + core.tensor_parallel = tensor_parallel + gpt.GPTModel = _FakeGPTModel + gpt_layer_specs.get_gpt_decoder_block_spec = lambda *args, **kwargs: object() + gpt_layer_specs.get_gpt_layer_local_spec = lambda *args, **kwargs: object() + gpt_layer_specs.get_gpt_layer_with_transformer_engine_spec = lambda *args, **kwargs: object() + spec_utils.import_module = lambda path: object() + transformer_config.TransformerConfig = _FakeTransformerConfig + arguments.core_transformer_config_from_args = lambda args: _FakeTransformerConfig() + bridge.AutoBridge = _FakeAutoBridge + + modules = { + "megatron": megatron, + "megatron.core": core, + "megatron.core.mpu": mpu, + "megatron.core.tensor_parallel": tensor_parallel, + "megatron.core.models": models, + "megatron.core.models.gpt": gpt, + "megatron.core.models.gpt.gpt_layer_specs": gpt_layer_specs, + "megatron.core.transformer": transformer, + "megatron.core.transformer.spec_utils": spec_utils, + "megatron.core.transformer.transformer_config": transformer_config, + "megatron.training": training, + "megatron.training.arguments": arguments, + "megatron.bridge": bridge, + } + for name, module in modules.items(): + monkeypatch.setitem(sys.modules, name, module) + + return provider + + +def _load_model_provider(monkeypatch, provider=None): + provider = _install_fake_megatron(monkeypatch, provider=provider) + sys.modules.pop("relax.backends.megatron.model_provider", None) + module = importlib.import_module("relax.backends.megatron.model_provider") + monkeypatch.setattr(module.dist, "is_initialized", lambda: True) + monkeypatch.setattr(module.dist, "get_rank", lambda: 1) + return module, provider + + +def _bridge_args(**overrides): + values = { + "megatron_to_hf_mode": "bridge", + "hf_checkpoint": "fake-hf", + "attention_backend": "flash", + "tensor_model_parallel_size": 2, + "sequence_parallel": True, + "pipeline_model_parallel_size": 4, + "virtual_pipeline_model_parallel_size": 2, + "context_parallel_size": 1, + "expert_model_parallel_size": 1, + "expert_tensor_parallel_size": 1, + "variable_seq_lengths": True, + "dsa_indexer_loss_coeff": None, + "dsa_indexer_use_sparse_loss": None, + "attention_softmax_in_fp32": True, + "bias_dropout_fusion": True, + "apply_rope_fusion": False, + "recompute_granularity": None, + "recompute_method": None, + "recompute_num_layers": None, + "distribute_saved_activations": False, + "moe_router_load_balancing_type": "none", + "moe_router_dtype": None, + "moe_aux_loss_coeff": None, + "moe_token_dispatcher_type": "alltoall", + "moe_shared_expert_overlap": False, + "moe_enable_deepep": False, + "moe_flex_dispatcher_backend": None, + "use_audio_in_video": False, + "freeze_language_model": False, + "freeze_vision_model": False, + "freeze_vision_projection": False, + "vision_dp_when_tp": False, + "calculate_per_token_loss": False, + "num_layers": 8, + "moe_layer_freq": None, + "decoder_first_pipeline_num_layers": None, + "decoder_last_pipeline_num_layers": None, + "fp16": False, + "bf16": True, + "save": None, + } + values.update(overrides) + return SimpleNamespace(**values) + + +def test_bridge_provider_receives_virtual_pipeline_size(monkeypatch): + module, provider = _load_model_provider(monkeypatch) + + model_provider = module.get_model_provider_func(_bridge_args(), role="actor") + model_provider(pre_process=True, post_process=False, vp_stage=1) + + assert provider.virtual_pipeline_model_parallel_size == 2 + assert provider.finalized + assert provider.calls == [{"pre_process": True, "post_process": False, "vp_stage": 1}] + + +def test_wrapper_derives_vp_stage_from_parallel_state(monkeypatch): + module, _ = _load_model_provider(monkeypatch) + calls = [] + + def original_provider(pre_process=True, post_process=True, vp_stage=None): + calls.append( + { + "pre_process": pre_process, + "post_process": post_process, + "vp_stage": vp_stage, + } + ) + return SimpleNamespace(named_parameters=lambda: []) + + wrapped_provider = module.wrap_model_provider_with_freeze( + original_provider, + SimpleNamespace(only_train_params_name_list=None, freeze_params_name_list=None), + ) + wrapped_provider(pre_process=True, post_process=False) + + assert calls == [{"pre_process": True, "post_process": False, "vp_stage": 1}] + + +def test_wrapper_passes_vp_stage_through_bridge_provider(monkeypatch): + module, provider = _load_model_provider(monkeypatch) + + bridge_provider = module.get_model_provider_func(_bridge_args(), role="actor") + wrapped_provider = module.wrap_model_provider_with_freeze( + bridge_provider, + SimpleNamespace(only_train_params_name_list=None, freeze_params_name_list=None), + ) + wrapped_provider(pre_process=True, post_process=False) + + assert provider.calls == [{"pre_process": True, "post_process": False, "vp_stage": 1}] From 61d77bfd8c31a192f6ddbdb104a52fd1ff6d224f Mon Sep 17 00:00:00 2001 From: wulumeng Date: Thu, 28 May 2026 03:13:45 +0800 Subject: [PATCH 058/268] fix(deepeyes): repair rollout recovery paths MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Resume aborted DeepEyes samples by status - Detect aborted samples from sample status and response length - Preserve multimodal rollout state needed for continued generation - Keep off-policy masking controlled by the existing partial-rollout mask flag ## Align resumed generation budgets - Track current-turn generated tokens separately from context budget - Apply the smaller active budget to resumed inference calls - Clear turn-local resume metadata when the turn completes ## Repair rollout prefetch and abort handoff - Wait for aborted samples to return to the buffer before the next fetch - Submit the next synchronous prefetch after transfer tasks complete (cherry picked from commit 867ed4705baad7b3f47046964e87b4c3eb34f0d4) --- examples/deepeyes/rollout.py | 93 +++++++++++++++++--------- relax/engine/rollout/sglang_rollout.py | 11 +-- 2 files changed, 68 insertions(+), 36 deletions(-) diff --git a/examples/deepeyes/rollout.py b/examples/deepeyes/rollout.py index 440f86aa7..52d2be936 100644 --- a/examples/deepeyes/rollout.py +++ b/examples/deepeyes/rollout.py @@ -203,12 +203,14 @@ async def _prepare_start_state(sample: Sample, state, args: Any, sampling_params sample.rollout_log_probs = sample.rollout_log_probs or [] sample.response_length = len(response_tokens) - budget = None - if args.rollout_max_context_len is not None: - budget = args.rollout_max_context_len - len(sample.tokens) - elif sample.response_length > 0 and sampling_params.get("max_new_tokens") is not None: - budget = sampling_params["max_new_tokens"] - sample.response_length - return current_image_data, response_tokens, budget, multimodal_train_inputs_buffer + context_budget = ( + args.rollout_max_context_len - len(sample.tokens) if args.rollout_max_context_len is not None else None + ) + generation_budget = None + if is_resuming and sampling_params.get("max_new_tokens") is not None: + current_turn_used = _current_turn_generated_token_count(sample, sample.response_length) + generation_budget = max(0, int(sampling_params["max_new_tokens"]) - current_turn_used) + return current_image_data, response_tokens, context_budget, generation_budget, multimodal_train_inputs_buffer async def _run_inference_step(url: str, tokens: list[int], sampling_params: dict, image_data, tokenizer, args=None): @@ -307,6 +309,11 @@ def _update_budget(budget, consumed: int): return None if budget is None else budget - consumed +def _current_turn_generated_token_count(sample: Sample, response_length: int) -> int: + turn_start = sample.metadata.get("_current_turn_response_start") if sample.metadata else None + return response_length - turn_start if isinstance(turn_start, int) and 0 <= turn_start <= response_length else 0 + + def _update_routed_experts(sample: Sample, meta_info: dict, args: Any) -> None: """Decode and store routed experts from meta_info for routing replay (MoE models). @@ -391,18 +398,23 @@ async def generate(args: Any, sample: Sample, sampling_params) -> Sample: env, env_module, config, state, url = _initialize_resources(args, sample) sampling_params = sampling_params.copy() - is_resuming = ( + is_resuming = sample.status == Sample.Status.ABORTED and sample.response_length > 0 + + if ( getattr(args, "partial_rollout", False) - and sample.status == Sample.Status.ABORTED + and is_resuming + and getattr(args, "mask_offpolicy_in_partial_rollout", False) and sample.response_length > 0 - ) - - if is_resuming and getattr(args, "mask_offpolicy_in_partial_rollout", False) and sample.response_length > 0: + ): sample.loss_mask = [0] * sample.response_length - current_image_data, response_tokens, budget, multimodal_train_inputs_buffer = await _prepare_start_state( - sample, state, args, sampling_params, is_resuming=is_resuming - ) + ( + current_image_data, + response_tokens, + context_budget, + generation_budget, + multimodal_train_inputs_buffer, + ) = await _prepare_start_state(sample, state, args, sampling_params, is_resuming=is_resuming) rollout_traces = sample.metadata.setdefault("rollout_traces", []) resume_turn = 0 @@ -410,8 +422,8 @@ async def generate(args: Any, sample: Sample, sampling_params) -> Sample: resume_turn = sample.metadata.get("rollout_turns", len(rollout_traces)) sample.status = Sample.Status.PENDING - def _is_budget_exhausted() -> bool: - return budget is not None and budget <= 0 + def _is_context_budget_exhausted() -> bool: + return context_budget is not None and context_budget <= 0 def _record_rollout_stats(stop_reason: str) -> None: sample.metadata["rollout_turns"] = turns_executed @@ -428,21 +440,32 @@ def _record_rollout_stats(stop_reason: str) -> None: if saved_image is not None: env.current_image = saved_image - if _is_budget_exhausted(): + if _is_context_budget_exhausted() or (generation_budget is not None and generation_budget <= 0): sample.status = Sample.Status.TRUNCATED stop_reason = "budget_exhausted" + sample.metadata.pop("_current_turn_response_start", None) _record_rollout_stats(stop_reason) return _finalize_sample(sample, state.tokenizer, response_tokens, multimodal_train_inputs_buffer) - cur_sampling_params = sampling_params trace_recorder = _RolloutTraceRecorder(state.tokenizer) for turn_idx in range(resume_turn, config["max_turns"]): turns_executed = turn_idx + 1 - if budget is not None: - cur_sampling_params["max_new_tokens"] = budget + cur_sampling_params = sampling_params.copy() + active_budget = context_budget + if generation_budget is not None: + active_budget = generation_budget if active_budget is None else min(active_budget, generation_budget) + if active_budget is not None: + active_budget = max(0, int(active_budget)) + if cur_sampling_params.get("max_new_tokens") is not None: + active_budget = min(active_budget, int(cur_sampling_params["max_new_tokens"])) + cur_sampling_params["max_new_tokens"] = active_budget + + turn_start = sample.metadata.get("_current_turn_response_start") + if not isinstance(turn_start, int) or not 0 <= turn_start <= len(response_tokens): + sample.metadata["_current_turn_response_start"] = len(response_tokens) turn_record = trace_recorder.start( - turn_idx, sample.tokens, cur_sampling_params, current_image_data, budget + turn_idx, sample.tokens, cur_sampling_params, current_image_data, active_budget ) inference_start_ts = time.time() @@ -460,20 +483,27 @@ def _record_rollout_stats(stop_reason: str) -> None: response_text, finish_type, max(0.0, inference_end_ts - inference_start_ts) ) _append_to_sample(sample, response_tokens, new_response_tokens, new_response_log_probs, loss_mask_val=1) - budget = _update_budget(budget, len(new_response_tokens)) + context_budget = _update_budget(context_budget, len(new_response_tokens)) + generation_budget = _update_budget(generation_budget, len(new_response_tokens)) _update_routed_experts(sample, meta_info, args) finish_reason = _should_stop_on_finish(sample, finish_type) if finish_reason: stop_reason = finish_reason + if finish_reason == "finish_abort": + turns_executed = turn_idx + else: + sample.metadata.pop("_current_turn_response_start", None) rollout_traces.append(turn_record) break - if _is_budget_exhausted(): + if _is_context_budget_exhausted(): sample.status = Sample.Status.TRUNCATED stop_reason = stop_reason or "budget_exhausted" + sample.metadata.pop("_current_turn_response_start", None) rollout_traces.append(turn_record) break + generation_budget = None env_start_ts = time.time() ( @@ -496,12 +526,14 @@ def _record_rollout_stats(stop_reason: str) -> None: if done: sample.status = Sample.Status.COMPLETED stop_reason = stop_reason or "env_done" + sample.metadata.pop("_current_turn_response_start", None) rollout_traces.append(turn_record) break obs_log_probs = [0.0] * len(obs_prompt_ids) _append_to_sample(sample, response_tokens, obs_prompt_ids, obs_log_probs, loss_mask_val=0) - budget = _update_budget(budget, len(obs_prompt_ids)) + context_budget = _update_budget(context_budget, len(obs_prompt_ids)) + sample.metadata.pop("_current_turn_response_start", None) current_image_data = _update_multimodal_state( current_image_data, @@ -510,16 +542,15 @@ def _record_rollout_stats(stop_reason: str) -> None: multimodal_train_inputs_buffer, ) - # Snapshot state for partial rollout resumption. + # Snapshot state for aborted rollout resumption. # Must be AFTER _update_multimodal_state so current_image_data # includes images from this turn's observation. - if getattr(args, "partial_rollout", False): - if hasattr(env, "current_image"): - sample.metadata["_env_current_image"] = env.current_image - sample.metadata["_multimodal_train_inputs_buffer"] = multimodal_train_inputs_buffer - sample.metadata["_current_image_data"] = current_image_data + if hasattr(env, "current_image"): + sample.metadata["_env_current_image"] = env.current_image + sample.metadata["_multimodal_train_inputs_buffer"] = multimodal_train_inputs_buffer + sample.metadata["_current_image_data"] = current_image_data - if _is_budget_exhausted(): + if _is_context_budget_exhausted(): sample.status = Sample.Status.TRUNCATED stop_reason = stop_reason or "budget_exhausted" rollout_traces.append(turn_record) diff --git a/relax/engine/rollout/sglang_rollout.py b/relax/engine/rollout/sglang_rollout.py index 567ef9b74..5104d71ba 100644 --- a/relax/engine/rollout/sglang_rollout.py +++ b/relax/engine/rollout/sglang_rollout.py @@ -800,10 +800,6 @@ async def generate_rollout_async( f"Total yielded: {total_transfer_samples - num_old_samples}/{target_data_size - num_old_samples} for step: {rollout_id}" ) - if not args.fully_async: - state.prefetched_samples_ref = data_source.get_samples.remote(args.over_sampling_batch_size, args.fully_async) - logger.info(f"Rollout step {rollout_id}: pre-submitted data fetch for next step") - logger.info(f"Generator exhausted. Waiting for {len(transfer_tasks)} transfer tasks to complete...") # Wait for all transfer tasks to complete if transfer_tasks: @@ -1033,5 +1029,10 @@ def generate_rollout( return output output, aborted_samples = run(generate_rollout_async(args, rollout_id, data_buffer, data_system_client)) - data_buffer.add_samples.remote(aborted_samples) + if aborted_samples: + ray.get(data_buffer.add_samples.remote(aborted_samples)) + if not args.fully_async: + state = GenerateState(args) + state.prefetched_samples_ref = data_buffer.get_samples.remote(args.over_sampling_batch_size, args.fully_async) + logger.info(f"Rollout step {rollout_id}: pre-submitted data fetch for next step") return output From 3c5967ef08423d4c8ecc0628b4755251a0e1eceb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Thu, 28 May 2026 15:36:12 +0800 Subject: [PATCH 059/268] feat(kimi-k2.6): add INT4 QAT training support MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add INT4 QAT weight sync pipeline - Add BridgeConverter to unify HF→Megatron weight conversion for bridge and DCS backends - Add fake INT4 quantization CUDA kernel for QAT forward pass - Add compressed-tensors INT4 quantizer processor for weight repacking - Add quantization_config ignore-list augmentation for non-quantized namespaces - Add `--sglang-hf-checkpoint` arg to let INT4 QAT point SGLang at original INT4 weights - Add `--rollout-engine-init-timeout` arg with progress-bar wait for engine startup - Add Kimi K2.6 model config and INT4 training launch scripts (text + multimodal) - Add MoE INT4→BF16 offline cast tool (`relax/tools/quant_cast/convert_moe_int4_to_bf16.py`) ## Add Kimi K2.5-style multimodal processor adapters - Add processor kwargs adaptation for K2.5-style VLM chat processors - Add placeholder expansion and response token sanitization for K2.5 vision tokens - Add multimodal train_inputs remapping for K2.5 pixel_values/grid_thws --- # ♻️ Refactor ## Refactor weight update broadcast into bucketed pipeline - Extract param-info bucketing, GPU loading, PP/EP broadcast into composable functions - Add quantized-weight broadcast phase with metadata encoding for INT4 triplets - Consolidate DCS device_direct backend to reuse BridgeConverter --- # ✅ Tests - Add test_broadcast_quantized for INT4 weight broadcast round-trip - Add test_processing_utils for K2.5 processor adapter functions - Update test_dcs_weight_conversion and test_state_machine for new APIs (cherry picked from commit ec24de0af0e569c802190a91d1d6b3e8f09313dd) --- docker/Dockerfile | 6 + docker/patch/latest/sglang.patch | 132 +++- .../patch/megatron/20260506-85bced0ae.patch | 199 ++++++ relax/backends/megatron/actor.py | 26 +- .../kernels/int4_qat/fake_int4_quant_cuda.cu | 323 +++++++++ relax/backends/megatron/model_provider.py | 21 + .../quantizer_compressed_tensors.py | 54 +- .../weight_update/bridge_converter.py | 256 +++++++ .../hf_weight_iterator_bridge.py | 656 +++++++++++++----- .../update_weight_from_tensor.py | 20 +- .../backends/device_direct.py | 345 +-------- relax/distributed/ray/rollout.py | 64 +- relax/engine/rollout/sglang_rollout.py | 53 +- relax/tools/quant_cast/__init__.py | 1 + .../quant_cast/convert_moe_int4_to_bf16.py | 281 ++++++++ relax/utils/arguments.py | 24 + relax/utils/data/data_utils.py | 5 +- relax/utils/data/processing_utils.py | 216 ++++++ relax/utils/data/processor_pool.py | 20 +- relax/utils/quant_cast.py | 116 ++++ requirements.txt | 1 + scripts/entrypoint/ray-job.sh | 20 +- scripts/models/kimi-k2.6.sh | 67 ++ .../multimodal/run-kimi-k2.6-256xgpu-int4.sh | 243 +++++++ .../text/run-kimi-k2.6-256xgpu-int4.sh | 221 ++++++ tests/backends/__init__.py | 0 tests/backends/megatron/__init__.py | 0 .../megatron/weight_update/__init__.py | 0 .../weight_update/test_broadcast_quantized.py | 506 ++++++++++++++ .../test_dcs_weight_conversion.py | 177 +---- tests/distributed/ray/test_state_machine.py | 2 + tests/utils/data/test_processing_utils.py | 145 ++++ 32 files changed, 3479 insertions(+), 721 deletions(-) create mode 100644 relax/backends/megatron/kernels/int4_qat/fake_int4_quant_cuda.cu create mode 100644 relax/backends/megatron/weight_update/bridge_converter.py create mode 100644 relax/tools/quant_cast/__init__.py create mode 100644 relax/tools/quant_cast/convert_moe_int4_to_bf16.py create mode 100644 relax/utils/quant_cast.py create mode 100755 scripts/models/kimi-k2.6.sh create mode 100755 scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh create mode 100644 scripts/training/text/run-kimi-k2.6-256xgpu-int4.sh create mode 100644 tests/backends/__init__.py create mode 100644 tests/backends/megatron/__init__.py create mode 100644 tests/backends/megatron/weight_update/__init__.py create mode 100644 tests/backends/megatron/weight_update/test_broadcast_quantized.py create mode 100644 tests/utils/data/test_processing_utils.py diff --git a/docker/Dockerfile b/docker/Dockerfile index 49f9e3a8d..c4ed2623d 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -116,6 +116,12 @@ RUN if [ "$ENABLE_SGLANG_PATCH" = "1" ]; then \ rm sglang.patch; \ fi +# Build the INT4 fake-quant CUDA extension required by INT4 QAT rollout weight sync. +COPY relax/backends/megatron/kernels/int4_qat /tmp/int4_qat +RUN cd /tmp/int4_qat && \ + pip install . --no-build-isolation && \ + rm -rf /tmp/int4_qat + # FROM relax as relax-release # WORKDIR /root/Relax diff --git a/docker/patch/latest/sglang.patch b/docker/patch/latest/sglang.patch index a11b7ffca..ff27d71c9 100644 --- a/docker/patch/latest/sglang.patch +++ b/docker/patch/latest/sglang.patch @@ -1911,6 +1911,21 @@ index ba038877b..fc59859e3 100644 return ResumeMemoryOccupationReqOutput() def check_weights(self: Scheduler, recv_req: CheckWeightsReqInput): +@@ -218,5 +244,12 @@ def _export_static_state(model): + + def _import_static_state(model, static_params): + self_named_buffers = dict(model.named_buffers()) +- for name, tensor in static_params["buffers"]: +- self_named_buffers[name][...] = tensor ++ # Wrap in inference_mode so we can write back into buffers that were ++ # registered lazily during the model's first forward (which runs under ++ # inference_mode and produces inference tensors). Affects e.g. Kimi K2.x ++ # VLM's RoPE frequency buffer (kimi_k25.py:352). Without this, the assignment raises ++ # `Inplace update to inference tensor outside InferenceMode is not ++ # allowed` at the first colocate train→rollout transition. ++ with torch.inference_mode(): ++ for name, tensor in static_params["buffers"]: ++ self_named_buffers[name][...] = tensor diff --git a/python/sglang/srt/managers/tokenizer_communicator_mixin.py b/python/sglang/srt/managers/tokenizer_communicator_mixin.py index 3faf15cd3..ebfc14229 100644 --- a/python/sglang/srt/managers/tokenizer_communicator_mixin.py @@ -2340,7 +2355,7 @@ index e42dbc556..8bd2c0be9 100644 if self.eplb_manager is not None: self.eplb_manager.on_forward_pass_end() -@@ -2727,6 +2741,43 @@ class ModelRunner(ModelRunnerKVCacheMixin): +@@ -2727,6 +2741,52 @@ class ModelRunner(ModelRunnerKVCacheMixin): ) @@ -2378,6 +2393,15 @@ index e42dbc556..8bd2c0be9 100644 + with device_loading_context(module, target_device): + quant_method.process_weights_after_loading(module) + ++ # Re-derive model-level buffers that depend on freshly-overwritten ++ # weights (e.g., MLA absorb weights `w_kc`/`w_vc` split from ++ # `kv_b_proj.weight` in DeepseekV2/Kimi-K25). After a weight push ++ # `kv_b_proj.weight` has new values but `w_kc`/`w_vc` still hold ++ # the stale buffers from initial load — with `dummy` load these ++ # are random, producing NaN logits on first forward. ++ if hasattr(self.model, "post_load_weights"): ++ self.model.post_load_weights() ++ + return True, "Success" + + @@ -2486,7 +2510,7 @@ index d57eb8822..0327434f2 100644 hidden_states, forward_batch, diff --git a/python/sglang/srt/models/deepseek_v2.py b/python/sglang/srt/models/deepseek_v2.py -index 1c3ac6d14..0283e43d6 100644 +index 1c3ac6d14..874dd25b0 100644 --- a/python/sglang/srt/models/deepseek_v2.py +++ b/python/sglang/srt/models/deepseek_v2.py @@ -1071,6 +1071,7 @@ class DeepseekV2AttentionMLA( @@ -2497,7 +2521,20 @@ index 1c3ac6d14..0283e43d6 100644 ) -> None: super().__init__() self.layer_id = layer_id -@@ -1160,6 +1161,34 @@ class DeepseekV2AttentionMLA( +@@ -1140,6 +1141,12 @@ class DeepseekV2AttentionMLA( + prefix=add_prefix("kv_a_proj_with_mqa", prefix), + ) + ++ # Defaults for the non-NSA path (e.g. K2.5/K2.6 wrapping DeepseekV2 MLA): ++ # forward_absorb_core dereferences self.next_skip_topk unconditionally, ++ # but it is only set inside the use_nsa branch below. ++ self.skip_topk = False ++ self.next_skip_topk = False ++ + if self.use_nsa: + is_neox_style = not getattr(config, "indexer_rope_interleave", False) + self.indexer = Indexer( +@@ -1160,6 +1167,34 @@ class DeepseekV2AttentionMLA( layer_id=layer_id, alt_stream=alt_stream, ) @@ -2532,7 +2569,7 @@ index 1c3ac6d14..0283e43d6 100644 self.kv_b_proj = ColumnParallelLinear( self.kv_lora_rank, -@@ -1311,6 +1340,7 @@ class DeepseekV2AttentionMLA( +@@ -1311,6 +1346,7 @@ class DeepseekV2AttentionMLA( zero_allocator: BumpAllocator, layer_scatter_modes: LayerScatterModes = None, llama_4_scaling: Optional[torch.Tensor] = None, @@ -2540,7 +2577,7 @@ index 1c3ac6d14..0283e43d6 100644 ): s = self.forward_prepare( positions=positions, -@@ -1319,6 +1349,7 @@ class DeepseekV2AttentionMLA( +@@ -1319,6 +1355,7 @@ class DeepseekV2AttentionMLA( zero_allocator=zero_allocator, layer_scatter_modes=layer_scatter_modes, llama_4_scaling=llama_4_scaling, @@ -2548,7 +2585,7 @@ index 1c3ac6d14..0283e43d6 100644 ) return self.forward_core(s) -@@ -1330,6 +1361,7 @@ class DeepseekV2AttentionMLA( +@@ -1330,6 +1367,7 @@ class DeepseekV2AttentionMLA( zero_allocator: BumpAllocator, layer_scatter_modes: LayerScatterModes = None, llama_4_scaling: Optional[torch.Tensor] = None, @@ -2556,7 +2593,7 @@ index 1c3ac6d14..0283e43d6 100644 ): if self.attn_mha.kv_b_proj is None: self.attn_mha.kv_b_proj = self.kv_b_proj -@@ -1369,7 +1401,12 @@ class DeepseekV2AttentionMLA( +@@ -1369,7 +1407,12 @@ class DeepseekV2AttentionMLA( ) elif attn_forward_method == AttnForwardMethod.MLA: inner_state = self.forward_absorb_prepare( @@ -2570,7 +2607,7 @@ index 1c3ac6d14..0283e43d6 100644 ) elif attn_forward_method == AttnForwardMethod.MLA_FUSED_ROPE_ROCM: inner_state = self.forward_absorb_fused_mla_rope_prepare( -@@ -1530,6 +1567,7 @@ class DeepseekV2DecoderLayer(nn.Module): +@@ -1530,6 +1573,7 @@ class DeepseekV2DecoderLayer(nn.Module): reduce_results=False, prefix=add_prefix("self_attn", prefix), alt_stream=alt_stream, @@ -2578,7 +2615,7 @@ index 1c3ac6d14..0283e43d6 100644 ) if not hasattr(config, "q_lora_rank") and envs.SGLANG_USE_AG_AFTER_QLORA.get(): raise ValueError( -@@ -1616,6 +1654,7 @@ class DeepseekV2DecoderLayer(nn.Module): +@@ -1616,6 +1660,7 @@ class DeepseekV2DecoderLayer(nn.Module): zero_allocator: BumpAllocator, gemm_output_zero_allocator: BumpAllocator = None, llama_4_scaling: Optional[torch.Tensor] = None, @@ -2586,7 +2623,7 @@ index 1c3ac6d14..0283e43d6 100644 ) -> torch.Tensor: quant_format = ( "mxfp4" -@@ -1658,7 +1697,12 @@ class DeepseekV2DecoderLayer(nn.Module): +@@ -1658,7 +1703,12 @@ class DeepseekV2DecoderLayer(nn.Module): zero_allocator=zero_allocator, llama_4_scaling=llama_4_scaling, layer_scatter_modes=self.layer_scatter_modes, @@ -2599,7 +2636,7 @@ index 1c3ac6d14..0283e43d6 100644 hidden_states, residual = self.layer_communicator.prepare_mlp( hidden_states, residual, forward_batch -@@ -1694,7 +1738,7 @@ class DeepseekV2DecoderLayer(nn.Module): +@@ -1694,7 +1744,7 @@ class DeepseekV2DecoderLayer(nn.Module): hidden_states, residual, forward_batch ) @@ -2608,7 +2645,7 @@ index 1c3ac6d14..0283e43d6 100644 def op_comm_prepare_attn( self, -@@ -1971,6 +2015,7 @@ class DeepseekV2Model(nn.Module): +@@ -1971,6 +2021,7 @@ class DeepseekV2Model(nn.Module): elif self.first_k_dense_replace < normal_start_layer: normal_end_layer = normal_start_layer = 0 aux_hidden_states = [] @@ -2616,7 +2653,7 @@ index 1c3ac6d14..0283e43d6 100644 for i in range(normal_start_layer, normal_end_layer): # NOTE: torch dynamo does not support graph break in context manager ctx = ( -@@ -1988,7 +2033,7 @@ class DeepseekV2Model(nn.Module): +@@ -1988,7 +2039,7 @@ class DeepseekV2Model(nn.Module): else: aux_hidden_states.append(hidden_states + residual) layer = self.layers[i] @@ -2625,7 +2662,7 @@ index 1c3ac6d14..0283e43d6 100644 positions, hidden_states, forward_batch, -@@ -1996,6 +2041,7 @@ class DeepseekV2Model(nn.Module): +@@ -1996,6 +2047,7 @@ class DeepseekV2Model(nn.Module): zero_allocator, gemm_output_zero_allocator, llama_4_scaling, @@ -2873,7 +2910,7 @@ index 04f4e4e7c..ab201fb83 100644 weights_out_dict = dict(weights_in) diff --git a/python/sglang/srt/models/kimi_k25.py b/python/sglang/srt/models/kimi_k25.py -index bf931d5cc..011f56c31 100644 +index bf931d5cc..6df5a5889 100644 --- a/python/sglang/srt/models/kimi_k25.py +++ b/python/sglang/srt/models/kimi_k25.py @@ -748,6 +748,15 @@ class KimiK25ForConditionalGeneration(nn.Module): @@ -2892,6 +2929,21 @@ index bf931d5cc..011f56c31 100644 def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): """Load weights for the model, separating vision and language weights""" mapper = getattr(self, "hf_to_sglang_mapper", None) +@@ -785,6 +794,14 @@ class KimiK25ForConditionalGeneration(nn.Module): + if language_weights: + self.language_model.load_weights(language_weights) + ++ def post_load_weights(self, **kwargs): ++ # Delegate to the inner DeepseekV2-style language model so MLA absorb ++ # weights (w_kc / w_vc) get materialized. Without this the dummy loader ++ # path leaves self_attn.w_kc=None and CUDA-graph capture crashes in ++ # forward_absorb_prepare with AttributeError on .dtype. ++ if hasattr(self.language_model, "post_load_weights"): ++ self.language_model.post_load_weights(**kwargs) ++ + @classmethod + def get_model_config_for_expert_location(cls, config: KimiK25Config): + text_config = config.text_config diff --git a/python/sglang/srt/models/qwen3_vl.py b/python/sglang/srt/models/qwen3_vl.py index 38a4767b0..644d7ed6c 100644 --- a/python/sglang/srt/models/qwen3_vl.py @@ -3054,7 +3106,7 @@ index 60390fe47..12903f4ee 100644 def get_loads(self: Scheduler, req: GetLoadsReqInput = None) -> GetLoadsReqOutput: diff --git a/python/sglang/srt/server_args.py b/python/sglang/srt/server_args.py -index 6bfa8ecb3..05c181709 100644 +index 6bfa8ecb3..a507c497a 100644 --- a/python/sglang/srt/server_args.py +++ b/python/sglang/srt/server_args.py @@ -598,6 +598,7 @@ class ServerArgs: @@ -3150,6 +3202,54 @@ index 6bfa8ecb3..05c181709 100644 # Normal case, use IPC within a single node return PortArgs( tokenizer_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}", +@@ -6186,12 +6249,42 @@ class PortArgs: + + try: + if dp_rank is None: +- wait_port_available(dist_init_port, "dist_init_port") +- wait_port_available(port_base, "port_base") +- wait_port_available(detokenizer_port, "detokenizer_port") ++ # dist_init_port, port_base, detokenizer_port, rpc_port, and ++ # metrics_ipc_name are all bound on the dist_init_host node ++ # only (they use tcp://{dist_init_host}:... addresses). ++ # Other nodes merely *connect* to them, so checking local ++ # availability on non-master nodes is wrong — those ports may ++ # legitimately be held by co-located processes (e.g. NVSHMEM ++ # bootstrap from Megatron training actors in colocate mode). ++ import socket as _socket ++ _local_ips = set() ++ try: ++ _local_ips.add("127.0.0.1") ++ _hostname = _socket.gethostname() ++ for _info in _socket.getaddrinfo(_hostname, None): ++ _local_ips.add(_info[4][0]) ++ except Exception: ++ pass ++ # Also check SGLANG_HOST_IP / HOST_IP env vars ++ _host_ip_env = os.environ.get("SGLANG_HOST_IP", "") or os.environ.get("HOST_IP", "") ++ if _host_ip_env: ++ _local_ips.add(_host_ip_env) ++ ++ _is_master_node = str(dist_init_host) in _local_ips ++ if _is_master_node: ++ wait_port_available(dist_init_port, "dist_init_port") ++ wait_port_available(port_base, "port_base") ++ wait_port_available(detokenizer_port, "detokenizer_port") ++ wait_port_available(rpc_port, "rpc_port") ++ wait_port_available(metrics_ipc_name, "metrics_ipc_name") ++ else: ++ logger.info( ++ f"Skipping port availability checks for dist_init_port={dist_init_port} " ++ f"and related ports on non-master node (dist_init_host={dist_init_host}, " ++ f"local_ips={_local_ips})" ++ ) ++ # nccl_port is always local — check on every node + wait_port_available(nccl_port, "nccl_port") +- wait_port_available(rpc_port, "rpc_port") +- wait_port_available(metrics_ipc_name, "metrics_ipc_name") + # Check scheduler_input_port only for dp. + # Skip check when using worker_ports since the port is already bound by our ZMQ socket + if dp_rank is None or worker_ports is None: diff --git a/python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py b/python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py index 40e859b2d..5cededb53 100644 --- a/python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py diff --git a/docker/patch/megatron/20260506-85bced0ae.patch b/docker/patch/megatron/20260506-85bced0ae.patch index d4e706ee4..6b1d77ff9 100644 --- a/docker/patch/megatron/20260506-85bced0ae.patch +++ b/docker/patch/megatron/20260506-85bced0ae.patch @@ -807,3 +807,202 @@ index 8df4df1e5..fa86be44d 100644 nvtx_range_pop(suffix="prepare_qkv_for_gated_delta_rule") # Calculate g and beta +--- a/megatron/bridge/models/kimi_vl/modeling_kimi_k25_vl.py ++++ b/megatron/bridge/models/kimi_vl/modeling_kimi_k25_vl.py +@@ -16,6 +16,8 @@ + from typing import List, Optional + + import torch ++import torch.distributed ++from megatron.core import parallel_state as mpu + from megatron.core.packed_seq_params import PackedSeqParams + from megatron.core.tensor_parallel import scatter_to_sequence_parallel_region + from megatron.core.transformer.module import MegatronModule +@@ -23,6 +25,7 @@ + from transformers.dynamic_module_utils import get_class_from_dynamic_module + + from megatron.bridge.models.gpt_provider import GPTModelProvider ++from megatron.bridge.models.qwen_vl.modelling_qwen3_vl.utils import preprocess_packed_seqs + from megatron.bridge.utils.common_utils import hook_hf_module_setattr_for_tp_grad_sync + + +@@ -136,11 +139,13 @@ + if not hasattr(MoonViT3dEncoder, "use_deterministic_attn"): + MoonViT3dEncoder.use_deterministic_attn = False + +- # transformers >=5.5 strictly validates `attn_implementation` at +- # __init__ and selects `flash_attention_2` by default when flash-attn +- # is installed. MoonViT3dPretrainedModel doesn't declare flash-attn-2 +- # support, so force eager attention before construction. +- self.vision_tower_config._attn_implementation = "eager" ++ # MoonViT3dPretrainedModel does declare `_supports_flash_attn_2 = True` ++ # and ships `multihead_attention` (flash_attn_varlen_func wrapper) in ++ # `VL_VISION_ATTENTION_FUNCTIONS`, so flash-attn is the intended path. ++ # The previous "force eager" workaround was a stale comment from when ++ # the support flag was missing; eager is O(N²) and OOMs at large image ++ # patch counts (~1900+ patches/sample), so use flash_attention_2. ++ self.vision_tower_config._attn_implementation = "flash_attention_2" + self.vision_tower = MoonViT3dPretrainedModel(self.vision_tower_config) + self.mm_projector = PatchMergerMLP(self.projector_config) # TODO: support different types of mm projector + # Ensure HF visual tower params are marked for TP grad sync and future assignments are hooked. +@@ -317,8 +322,79 @@ + + return final_embedding, final_attention_mask, final_labels, position_ids + ++ def _vision_forward_tp_split( ++ self, ++ pixel_values: torch.Tensor, ++ grid_thws: torch.Tensor, ++ ) -> List[torch.Tensor]: ++ """Run vision encoder + projector with workload split across TP ranks. ++ ++ Each TP rank processes a subset of images determined by splitting ++ ``grid_thws``, then the partial feature tensors are all-reduced so ++ every rank holds the complete result. ++ """ ++ tp_rank = mpu.get_tensor_model_parallel_rank() ++ tp_size = mpu.get_tensor_model_parallel_world_size() ++ ++ num_images = grid_thws.shape[0] ++ merge_h, merge_w = self.vision_tower.merge_kernel_size ++ param_dtype = next(self.vision_tower.parameters()).dtype ++ text_hidden = self.projector_config.hidden_size ++ ++ pixel_counts = grid_thws.prod(dim=-1) ++ out_token_counts = (grid_thws[:, 1] // merge_h) * (grid_thws[:, 2] // merge_w) ++ total_out_tokens = out_token_counts.sum().item() ++ ++ chunk_indices = list(range(num_images)) ++ chunks = [chunk_indices[i::tp_size] for i in range(tp_size)] ++ my_indices = chunks[tp_rank] if tp_rank < len(chunks) else [] ++ ++ out_buffer = torch.zeros( ++ (total_out_tokens, text_hidden), ++ device=pixel_values.device, ++ dtype=param_dtype, ++ ) ++ ++ if my_indices: ++ pixel_cumsum = pixel_counts.cumsum(dim=0) ++ pv_parts = [] ++ grid_parts = [] ++ for idx in my_indices: ++ px_start = 0 if idx == 0 else pixel_cumsum[idx - 1].item() ++ px_end = pixel_cumsum[idx].item() ++ pv_parts.append(pixel_values[px_start:px_end]) ++ grid_parts.append(grid_thws[idx : idx + 1]) ++ ++ local_pv = torch.cat(pv_parts, dim=0) ++ local_grid = torch.cat(grid_parts, dim=0) ++ ++ local_vit_out = self.vision_tower(local_pv, local_grid) ++ local_features = self.mm_projector(local_vit_out) ++ ++ out_cumsum = out_token_counts.cumsum(dim=0) ++ for feat_i, img_idx in enumerate(my_indices): ++ out_start = 0 if img_idx == 0 else out_cumsum[img_idx - 1].item() ++ out_end = out_cumsum[img_idx].item() ++ out_buffer[out_start:out_end] = local_features[feat_i].to(param_dtype) ++ ++ tp_group = mpu.get_tensor_model_parallel_group() ++ torch.distributed.all_reduce(out_buffer, group=tp_group) ++ ++ result = [] ++ out_cumsum = out_token_counts.cumsum(dim=0) ++ for i in range(num_images): ++ start = 0 if i == 0 else out_cumsum[i - 1].item() ++ end = out_cumsum[i].item() ++ result.append(out_buffer[start:end]) ++ ++ return result ++ + def _extract_image_features(self, pixel_values, grid_thws): + """Extract and project image features.""" ++ tp_size = mpu.get_tensor_model_parallel_world_size() ++ if getattr(self.config, "vision_dp_when_tp", False) and tp_size > 1: ++ return self._vision_forward_tp_split(pixel_values, grid_thws) ++ + image_features = self.vision_tower(pixel_values, grid_thws) + return self.mm_projector(image_features) + +@@ -357,6 +433,12 @@ + 2. Dynamic expansion: input_ids has 1 placeholder per image, expands to N tokens. + """ + if self.pre_process: ++ # Save the caller-supplied per-sample attention mask before any rewrite — ++ # _merge_input_ids_with_image_features sets `attention_mask = None` on the ++ # vision path, but the THD repack below needs the original [B, T] mask to ++ # know each sample's valid length. ++ saved_attention_mask = attention_mask ++ + if inputs_embeds is None: + inputs_embeds = self.language_model.embedding( + input_ids=input_ids, position_ids=None +@@ -392,8 +474,30 @@ + # Don't need attention mask for causal attention. + attention_mask = None + +- # Transpose back to (T, B, D) for Megatron language model +- inputs_embeds = inputs_embeds.transpose(1, 0).contiguous() # (B, T, D) -> (T, B, D) ++ # When THD packed_seq_params is provided (VL+CP/SP), repack the raw ++ # padded [B, T_max, D] embedding into compact THD ++ # [sum(padded_seqlens)/cp, 1, D] using the saved per-sample attention ++ # mask. Mirrors the Qwen3VL bridge: without this, downstream MLA ++ # attention sees a tensor whose first dim does not match ++ # cu_seqlens_q_padded (sum=sum(padded_seqlens)), and SP scatter can ++ # hit `T_max % tp_size != 0` since T_max is raw batch-max. ++ # preprocess_packed_seqs also recomputes cu_seqlens with align64. ++ needs_thd_repack = ( ++ packed_seq_params is not None ++ and packed_seq_params.qkv_format == "thd" ++ and saved_attention_mask is not None ++ ) ++ if needs_thd_repack: ++ inputs_embeds, packed_seq_params = preprocess_packed_seqs( ++ inputs_embeds, # [B, T_max, D] ++ saved_attention_mask, ++ pre_process=True, ++ ) ++ # preprocess_packed_seqs returns [1, T_thd, D]; switch to (T_thd, 1, D) ++ inputs_embeds = inputs_embeds.transpose(0, 1).contiguous() ++ else: ++ # Transpose back to (T, B, D) for Megatron language model ++ inputs_embeds = inputs_embeds.transpose(1, 0).contiguous() # (B, T, D) -> (T, B, D) + + if self.config.sequence_parallel: + inputs_embeds = scatter_to_sequence_parallel_region(inputs_embeds) +--- a/megatron/bridge/models/kimi_vl/kimi_k25_vl_provider.py ++++ b/megatron/bridge/models/kimi_vl/kimi_k25_vl_provider.py +@@ -57,6 +57,11 @@ + pad_token_id: int = 163839 + ignore_index: int = -100 + ++ # Split vision encoder workload across TP ranks (data-parallel over TP). ++ # Each TP rank processes a chunk of images, then all-reduce gathers the ++ # full embedding. Reduces per-GPU peak memory for the vision encoder. ++ vision_dp_when_tp: bool = False ++ + # Freeze options for fine-tuning scenarios + freeze_language_model: bool = False + freeze_vision_model: bool = False +diff --git a/megatron/bridge/models/kimi_vl/kimi_k25_vl_bridge.py b/megatron/bridge/models/kimi_vl/kimi_k25_vl_bridge.py +--- a/megatron/bridge/models/kimi_vl/kimi_k25_vl_bridge.py ++++ b/megatron/bridge/models/kimi_vl/kimi_k25_vl_bridge.py +@@ -186,8 +186,15 @@ + result = {} + for fqn, tensor in converted_weights_dict.items(): + if self._is_quantized_expert_key(fqn): + base = fqn[:-7] if fqn.endswith(".weight") else fqn +- # Preserve the original scale dtype from the HF checkpoint + orig_scale_key = f"{base}.weight_scale" ++ # When the source HF checkpoint has been pre-cast to BF16 (no ++ # `weight_scale` triplet present), passthrough instead of re- ++ # quantizing — downstream sglang loads BF16 and would reject ++ # the INT4 export names with "not found in params_dict". ++ if orig_scale_key not in hf_state_dict: ++ result[fqn] = tensor ++ continue ++ # Preserve the original scale dtype from the HF checkpoint + scale_dtype = ( + hf_state_dict[orig_scale_key].dtype if orig_scale_key in hf_state_dict else torch.bfloat16 + ) diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index c78e61d11..9af591ede 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -184,6 +184,22 @@ def _init( self.weights_backuper.backup("rollout_actor") update_weight_cls = UpdateWeightFromTensor if self.args.colocate else UpdateWeightFromDistributed + # Push-side repack is decided by the HF config: an FP8 release auto-routes + # through quantize_params_fp8, a compressed-tensors release through + # quantize_params_compressed_tensors, an unquantized BF16 dir is passed + # through verbatim. The OPEN_TRAINING_INT4_FAKE_QAT_FLAG env var ONLY + # controls the training-side forward STE in the megatron patch — it is + # independent of push routing (matches slime/backends/megatron_utils/actor.py). + # K2.6 INT4 release ships an ignore list that omits vision_tower / + # mm_projector — without augment_compressed_tensors_ignore the bridge + # would try to INT4-pack those BF16 tensors and SGLang would reject + # them with "weight_packed not found in params_dict". + from relax.utils.quant_cast import augment_compressed_tensors_ignore + + push_quant_config = augment_compressed_tensors_ignore( + getattr(self.hf_config, "quantization_config", None), + args.hf_checkpoint, + ) self.weight_updater = update_weight_cls( self.args, self.model, @@ -191,7 +207,7 @@ def _init( model_name=type(self.hf_config).__name__.lower() if self.args.model_name is None else self.args.model_name, - quantization_config=getattr(self.hf_config, "quantization_config", None), + quantization_config=push_quant_config, ) else: is_pp_src_rank = ( @@ -217,6 +233,12 @@ def _init( "master_address": master_address, "master_port": master_port, } + from relax.utils.quant_cast import augment_compressed_tensors_ignore + + push_quant_config = augment_compressed_tensors_ignore( + getattr(self.hf_config, "quantization_config", None), + args.hf_checkpoint, + ) self.checkpoint_engine_client = run( create_client( args=self.args, @@ -227,7 +249,7 @@ def _init( model_name=type(self.hf_config).__name__.lower() if self.args.model_name is None else self.args.model_name, - quantization_config=getattr(self.hf_config, "quantization_config", None), + quantization_config=push_quant_config, backend_type=self.args.checkpoint_engine_backend, metadata=metadata, lock=self.lock, diff --git a/relax/backends/megatron/kernels/int4_qat/fake_int4_quant_cuda.cu b/relax/backends/megatron/kernels/int4_qat/fake_int4_quant_cuda.cu new file mode 100644 index 000000000..11e0d5c08 --- /dev/null +++ b/relax/backends/megatron/kernels/int4_qat/fake_int4_quant_cuda.cu @@ -0,0 +1,323 @@ +#include +#include + +#define FINAL_MASK 0xFFFFFFFF + +__device__ __host__ __forceinline__ int ceil_div(int a, int b) { + return (a + b - 1) / b; +} + +__device__ __forceinline__ float warpReduceMax(float val) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) + val = fmaxf(val, __shfl_xor_sync(FINAL_MASK, val, mask, 32)); + return val; +} + +__device__ __forceinline__ float warpReduceMin(float val) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) + val = fminf(val, __shfl_xor_sync(FINAL_MASK, val, mask, 32)); + return val; +} + +// almost all int4 use blocksize = [1, 32] +template +__global__ void int4_quant_1x32_kernel( + const scalar_t *__restrict__ x, scalar_t *__restrict__ out, + scalar_t *out_scale, scalar_t *out_zero, const int M, const int N, + const int stride_xm, const int stride_xn, const int stride_om, + const int stride_on, const int stride_osm, const int stride_osn, + const int stride_ozm, const int stride_ozn, bool sym) { + constexpr int WARPS_PER_BLOCK = 8; + const int needed_warps = ceil_div(N, 32); + + const int tid = threadIdx.x; + const int warp_id = tid >> 5; + const int lane_id = tid & 0x1F; + constexpr float SYM_CONS = 1.0f / 7.0f; + constexpr float ASYM_CONS = 1.0f / 15.0f; + + const int row = blockIdx.x; + + for (int item = warp_id; item < needed_warps; item += WARPS_PER_BLOCK) { + const int col = item * 32 + lane_id; + float val = 0.0f; + + if (col < N) { + val = static_cast(x[row * stride_xm + col * stride_xn]); + } + + float scale = 0.0f; + float zero = 0.0f; + + if (sym) { + float abs_val = fabsf(val); + + float block_max = warpReduceMax(abs_val); + + scale = fmaxf(block_max * SYM_CONS, 1e-5f); + + val = rintf(val / scale); + } else { + float block_min = warpReduceMin(val); + float block_max = warpReduceMax(val); + + scale = fmaxf((block_max - block_min) * ASYM_CONS, 1e-5f); + zero = fminf(fmaxf(-rintf(block_min / scale), 0.0f), 15.0f); + + val = rintf(val / scale) + zero; + } + + if (col < N) { + out[row * stride_om + col * stride_on] = static_cast(val); + out_scale[row * stride_osm + item * stride_osn] = + static_cast(scale); + if (!sym) { + out_zero[row * stride_ozm + item * stride_ozn] = + static_cast(zero); + } + } + } +} + +// for some transpose case, blocksize = [32, 1] +template +__global__ void int4_quant_32x1_kernel( + const scalar_t *__restrict__ x, scalar_t *__restrict__ out, + scalar_t *out_scale, scalar_t *out_zero, const int M, const int N, + const int stride_xm, const int stride_xn, const int stride_om, + const int stride_on, const int stride_osm, const int stride_osn, + const int stride_ozm, const int stride_ozn, bool sym) { + constexpr int WARPS_PER_BLOCK = 8; + const int start_row = blockIdx.x * 32; + const int end_row = min((blockIdx.x + 1) * 32, M); + + const int tid = threadIdx.x; + const int warp_id = tid >> 5; + const int lane_id = tid & 0x1F; + constexpr float SYM_CONS = 1.0f / 7.0f; + constexpr float ASYM_CONS = 1.0f / 15.0f; + + for (int item = warp_id; item < N; item += WARPS_PER_BLOCK) { + const int col = item; + const int row = start_row + lane_id; + + float val = 0.0f; + + if (row < end_row) { + val = static_cast(x[row * stride_xm + col * stride_xn]); + } + + float scale = 0.0f; + float zero = 0.0f; + + if (sym) { + float abs_val = fabsf(val); + + float block_max = warpReduceMax(abs_val); + + scale = fmaxf(block_max * SYM_CONS, 1e-5f); + + val = rintf(val / scale); + } else { + float block_min = warpReduceMin(val); + float block_max = warpReduceMax(val); + + scale = fmaxf((block_max - block_min) * ASYM_CONS, 1e-5f); + zero = fminf(fmaxf(-rintf(block_min / scale), 0.0f), 15.0f); + + val = rintf(val / scale) + zero; + } + + if (row < end_row) { + out[row * stride_om + col * stride_on] = static_cast(val); + out_scale[blockIdx.x * stride_osm + item * stride_osn] = + static_cast(scale); + if (!sym) { + out_zero[blockIdx.x * stride_ozm + item * stride_ozn] = + static_cast(zero); + } + } + } +} + +template +__global__ void int4_quant_common_kernel( + const scalar_t *__restrict__ x, scalar_t *__restrict__ out, + scalar_t *out_scale, scalar_t *out_zero, const int M, const int N, + const int stride_xm, const int stride_xn, const int stride_om, + const int stride_on, const int stride_osm, const int stride_osn, + const int stride_ozm, const int stride_ozn, const int BLOCK_M, + const int BLOCK_N, bool sym) { + const int start_row = blockIdx.x * BLOCK_M; + const int WARPS_PER_BLOCK = blockDim.x >> 5; + + const int warp_id = threadIdx.x >> 5; + const int lane_id = threadIdx.x & 0x1F; + constexpr float SYM_CONS = 1.0f / 7.0f; + constexpr float ASYM_CONS = 1.0f / 15.0f; + constexpr int WARP_SIZE = 32; + + const int needed_warps = ceil_div(N, BLOCK_N); + const int iters = ceil_div(BLOCK_M * BLOCK_N, 32); + int warp_rows = 1; + + if (BLOCK_N <= WARP_SIZE) { + warp_rows = WARP_SIZE / BLOCK_N; + } + + for (int item = warp_id; item < needed_warps; item += WARPS_PER_BLOCK) { + float local_max = -INFINITY; + float local_min = INFINITY; + + float val = 0.0f; + float scale, zero = 0.0f; + + const int row_off = lane_id / BLOCK_N; + const int col_off = lane_id % BLOCK_N; + int row, col = 0; + + for (int i = 0; i < iters; ++i) { + if (BLOCK_N <= WARP_SIZE) { + row = start_row + i * warp_rows + row_off; + col = item * BLOCK_N + col_off; + } else { + row = start_row; + col = item * BLOCK_N + i * WARP_SIZE + col_off; + } + + if (row < M && col < N) { + val = static_cast(x[row * stride_xm + col * stride_xn]); + } else { + val = 0.0f; + } + + if (sym) { + local_max = fmaxf(local_max, fabsf(val)); + } else { + local_max = fmaxf(local_max, val); + local_min = fminf(local_min, val); + } + } + + if (sym) { + float block_max = warpReduceMax(local_max); + scale = fmaxf(block_max * SYM_CONS, 1e-5f); + } else { + float block_max = warpReduceMax(local_max); + float block_min = warpReduceMin(local_min); + scale = fmaxf((block_max - block_min) * ASYM_CONS, 1e-5f); + zero = fminf(fmaxf(-rintf(block_min / scale), 0.0f), 15.0f); + } + + for (int i = 0; i < iters; ++i) { + if (BLOCK_N <= WARP_SIZE) { + row = start_row + i * warp_rows + row_off; + col = item * BLOCK_N + col_off; + } else { + row = start_row; + col = item * BLOCK_N + i * WARP_SIZE + col_off; + } + + if (row < M && col < N) { + float val = static_cast(x[row * stride_xm + col * stride_xn]); + if (sym) { + val = rintf(val / scale); + } else { + val = rintf(val / scale) + zero; + } + + out[row * stride_om + col * stride_on] = static_cast(val); + out_scale[blockIdx.x * stride_osm + item * stride_osn] = + static_cast(scale); + if (!sym) { + out_zero[blockIdx.x * stride_ozm + item * stride_ozn] = + static_cast(zero); + } + } + } + } +} + +// dispatch +template +void launch_int4_quant_kernel(const scalar_t *x, scalar_t *out, + scalar_t *out_scale, scalar_t *out_zero, int M, + int N, const int stride_xm, const int stride_xn, + const int stride_om, const int stride_on, + const int stride_osm, const int stride_osn, + const int stride_ozm, const int stride_ozn, + int block_m, int block_n, bool sym, + cudaStream_t stream) { + constexpr int WARPS_PER_BLOCK = 8; + constexpr int THREADS_PER_BLOCK = WARPS_PER_BLOCK * 32; // 256 + + if (block_m == 1 && block_n == 32) { + dim3 grid(M); + dim3 block(THREADS_PER_BLOCK); + + int4_quant_1x32_kernel<<>>( + x, out, out_scale, out_zero, M, N, stride_xm, stride_xn, stride_om, + stride_on, stride_osm, stride_osn, stride_ozm, stride_ozn, sym); + } else if (block_m == 32 && block_n == 1) { + dim3 grid(ceil_div(M, block_m)); + dim3 block(THREADS_PER_BLOCK); + + int4_quant_32x1_kernel<<>>( + x, out, out_scale, out_zero, M, N, stride_xm, stride_xn, stride_om, + stride_on, stride_osm, stride_osn, stride_ozm, stride_ozn, sym); + } else { + dim3 grid(ceil_div(M, block_m)); + dim3 block(THREADS_PER_BLOCK); + int4_quant_common_kernel<<>>( + x, out, out_scale, out_zero, M, N, stride_xm, stride_xn, stride_om, + stride_on, stride_osm, stride_osn, stride_ozm, stride_ozn, block_m, + block_n, sym); + } +} + +std::tuple +fake_int4_quant_cuda(torch::Tensor &x, std::vector &block_size, + bool sym) { + TORCH_CHECK(x.dim() == 2, "Input must be 2D"); + TORCH_CHECK(x.is_cuda(), "Input must be on CUDA"); + + int M = x.size(0); + int N = x.size(1); + int block_m = block_size[0]; + int block_n = block_size[1]; + + TORCH_CHECK(block_m > 0 && block_n > 0, + "Block sizes must be positive, got block_m=", block_m, + ", block_n=", block_n); + TORCH_CHECK((block_m * block_n) % 32 == 0, "block_m * block_n (", + block_m * block_n, + ") must be divisible by 32. " + "But got a ", + block_m, "x", block_n, " block."); + + auto out = torch::empty_like(x); + auto out_scale = + torch::empty({ceil_div(M, block_m), ceil_div(N, block_n)}, x.options()); + auto out_zero = torch::empty_like(out_scale); + + const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND( + at::ScalarType::BFloat16, x.scalar_type(), "int4_quant_cuda", [&] { + launch_int4_quant_kernel( + x.const_data_ptr(), out.data_ptr(), + out_scale.data_ptr(), out_zero.data_ptr(), M, N, + x.stride(0), x.stride(1), out.stride(0), out.stride(1), + out_scale.stride(0), out_scale.stride(1), out_zero.stride(0), + out_zero.stride(1), block_m, block_n, sym, stream); + }); + + return std::make_tuple(out, out_scale, out_zero); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("fake_int4_quant_cuda", &fake_int4_quant_cuda, + "fake INT4 quantization cuda"); +} diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index 07fe98e2f..1ed759c11 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -277,6 +277,27 @@ def wrapped_model_provider( # Allow CLI to override layer count / MoE frequency for layer-reduced training "num_layers", "moe_layer_freq", + # Kimi K2 / MLA / MoE override surface — required because published K2 configs + # declare DeepseekV3ForCausalLM and route through DeepSeekV3Bridge, which has + # different defaults than what slime's K2 launch scripts assume. + "q_lora_rank", + "kv_lora_rank", + "qk_head_dim", + "qk_pos_emb_head_dim", + "v_head_dim", + "rotary_scaling_factor", + "rotary_base", + "moe_router_pre_softmax", + "moe_router_enable_expert_bias", + "moe_permute_fusion", + "moe_grouped_gemm", + "moe_shared_expert_intermediate_size", + "moe_router_topk", + "moe_router_num_groups", + "moe_router_group_topk", + "moe_router_topk_scaling_factor", + "moe_router_score_function", + "moe_ffn_hidden_size", ] args_dict = vars(args) diff --git a/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py b/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py index 77eabb4ee..f425d97d2 100644 --- a/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py +++ b/relax/backends/megatron/weight_conversion/processors/quantizer_compressed_tensors.py @@ -234,34 +234,23 @@ def if_quant(name, patterns): def pack_layer(weight, group_size, sym=True): + # fake_int4_quant_cuda returns quantized integers as float: + # sym: rintf(val / scale) in [-7, 7] + # asym: rintf(val / scale) + zp in [0, 15] w, scale, zp = fake_int4_quant_cuda.fake_int4_quant_cuda(weight, (1, group_size), sym) - w = w.view(weight.shape[0], 1, weight.shape[1] // group_size, group_size) - scale = scale.view(weight.shape[0], 1, weight.shape[1] // group_size, 1) - zp = zp.view(weight.shape[0], 1, weight.shape[1] // group_size, 1) + scale = scale.view(weight.shape[0], -1).contiguous() + if sym: - w = w * scale + packed_zp = None else: - w = (w - zp) * scale - w = w.view(weight.shape) - scale = scale.view(weight.shape[0], -1).contiguous() - if not sym: zp = zp.view(weight.shape[0], -1) - zeros = zp.t().contiguous().to(torch.float32) - zeros = zeros.to(dtype=torch.int32, device=w.device) + zeros = zp.t().contiguous().to(torch.int32) zeros = zeros.reshape(-1, zeros.shape[1] // 8, 8) new_order_map = torch.tensor([0, 4, 1, 5, 2, 6, 3, 7], device=zeros.device) * 4 zeros = zeros << new_order_map packed_zp = torch.sum(zeros, dim=-1).to(torch.int32) - else: - zp = None - packed_zp = None - quantized_weight = quantize( - x=w, - scale=scale, - zero_point=zp, - dtype=torch.int8 if sym else torch.uint8, - ) + quantized_weight = w.to(torch.int8 if sym else torch.uint8) packed_weight = pack_to_int32(quantized_weight, 4, sym=sym) return packed_weight, scale, packed_zp @@ -270,7 +259,10 @@ def quantize_params_compressed_tensors(converted_named_params, quantization_conf w_cfg = quantization_config["config_groups"]["group_0"]["weights"] group_size = w_cfg["group_size"] is_symmetric = w_cfg["symmetric"] - ignore_rules = quantization_config.get("ignore", []) + # The cast pipeline (`relax/tools/quant_cast/convert_moe_int4_to_bf16.py`) + # augments the sidecar's ignore list with non-quantized top-level namespaces + # so this stays purely config-driven (no K2-specific name knowledge here). + ignore_rules = list(quantization_config.get("ignore", [])) results = [] @@ -279,11 +271,29 @@ def quantize_params_compressed_tensors(converted_named_params, quantization_conf (r.startswith("re:") and re.match(r[3:], name)) or r == name or name.startswith(r) for r in ignore_rules ) - if is_ignored or not name.endswith(".weight") or param.dim() < 2: + # `dim() != 2` (not `< 2`): the W4A16 packer in `pack_layer` only + # supports 2D matrices. K2.6 has 3D positional-embedding tensors + # whose ``.weight`` shape is (T, H, W) — passthrough rather than + # crash inside `pack_layer`. Pre-K2.6 callers had no 3D `.weight` + # tensors so the broader filter has no behavior change for them. + if is_ignored or not name.endswith(".weight") or param.dim() != 2: + results.append((name, param)) + continue + + if param.shape[-1] % group_size != 0: + logger.warning( + f"[quantize] passthrough non-divisible param {name} shape={tuple(param.shape)} group_size={group_size}" + ) results.append((name, param)) continue - qw, s, zp = pack_layer(param, group_size, is_symmetric) + try: + qw, s, zp = pack_layer(param, group_size, is_symmetric) + except RuntimeError as e: + logger.error( + f"[quantize] pack_layer failed for {name} shape={tuple(param.shape)} group_size={group_size}: {e}" + ) + raise qweight_name = name.replace(".weight", ".weight_packed") scale_name = name.replace(".weight", ".weight_scale") weight_shape = torch.tensor(param.shape, dtype=torch.int32, device=device_utils.get_device_name()) diff --git a/relax/backends/megatron/weight_update/bridge_converter.py b/relax/backends/megatron/weight_update/bridge_converter.py new file mode 100644 index 000000000..fbf69aa0a --- /dev/null +++ b/relax/backends/megatron/weight_update/bridge_converter.py @@ -0,0 +1,256 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +import re +from argparse import Namespace +from collections.abc import Sequence +from typing import Any + +import torch + +from relax.backends.megatron.misc_utils import strip_param_name_prefix +from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding +from relax.backends.megatron.weight_update.common import named_params_and_buffers +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + + +def _noop_gather_from_ep_ranks(self_m, megatron_weights, megatron_module, hf_param_name): + return {str(hf_param_name): megatron_weights} + + +class BridgeConverter: + """Per-parameter megatron-to-HF conversion using megatron-bridge. + + All collective communication (PP broadcast, TP gather, EP gather) is + disabled by temporarily setting the bridge mapping process groups to + ``None``. The caller is responsible for TP gather and EP gather + *before* calling :meth:`convert`. + """ + + def __init__( + self, + args: Namespace, + model: Sequence[torch.nn.Module], + quantization_config: dict[str, int | str | list[str]] | None, + ) -> None: + self._args = args + self._model = model + self._quantization_config = quantization_config + self._bridge_task_map: dict[str, Any] | None = None + self._bridge_mapping_registry: Any = None + self._bridge_expert_transposes_down: bool = True + + # ------------------------------------------------------------------ + # Lazy initialisation + # ------------------------------------------------------------------ + + def init_tasks(self) -> None: + """Build the bridge task map on first use. + + Builds a mapping from ``global_param_name`` (e.g. + ``decoder.layers.0.self_attention.linear_qkv.weight``) to the + corresponding ``WeightConversionTask``. Only tasks whose + ``param_weight is not None`` (i.e. belonging to the current PP + rank) are indexed. + + Also eagerly initialises any lazily-created inner mappings + (``AutoMapping._mapping``) so that :meth:`collect_all_mappings` + can discover and patch them later. + """ + if self._bridge_task_map is not None: + return + + from megatron.bridge import AutoBridge + from megatron.bridge.models.conversion.model_bridge import WeightConversionTask + from megatron.bridge.models.conversion.param_mapping import AutoMapping + + from relax.utils.megatron_bridge_utils import patch_megatron_model + + bridge = AutoBridge.from_hf_pretrained(self._args.hf_checkpoint, trust_remote_code=True) + with patch_megatron_model(self._model): + tasks = bridge.get_conversion_tasks(self._model) + + self._bridge_task_map = {} + for task in tasks: + if task.param_weight is not None: + self._bridge_task_map[task.global_param_name] = task + + self._bridge_mapping_registry = bridge._model_bridge.mapping_registry() + mapping_registry = self._bridge_mapping_registry + for name, _param in named_params_and_buffers(self._args, self._model): + global_name = strip_param_name_prefix(name) + if global_name not in self._bridge_task_map: + mapping = mapping_registry.megatron_to_hf_lookup(global_name) + if mapping is not None: + self._bridge_task_map[global_name] = WeightConversionTask( + param_name=global_name, + global_param_name=global_name, + mapping=mapping, + megatron_module=None, + param_weight=_param, + ) + + for task in self._bridge_task_map.values(): + mapping = task.mapping + if isinstance(mapping, AutoMapping) and mapping._mapping is None: + if task.megatron_module is not None: + mapping._detected_type = mapping._detect_parallelism_type(task.megatron_module) + mapping._mapping = mapping._get_or_create_mapping(mapping._detected_type) + else: + mapping._detected_type = "replicated" + mapping._mapping = mapping._get_or_create_mapping("replicated") + inner_tp = getattr(mapping, "_tp_mapping", None) + if isinstance(inner_tp, AutoMapping) and inner_tp._mapping is None: + if task.megatron_module is not None: + inner_tp._detected_type = inner_tp._detect_parallelism_type(task.megatron_module) + inner_tp._mapping = inner_tp._get_or_create_mapping(inner_tp._detected_type) + + self._bridge_expert_transposes_down = False + for task in self._bridge_task_map.values(): + cls = type(task.mapping) + if cls.__name__ == "ExpertMLPDownProjMapping": + self._bridge_expert_transposes_down = "megatron_to_hf" in cls.__dict__ + break + + logger.info("Bridge task map initialized with %d local tasks", len(self._bridge_task_map)) + + # ------------------------------------------------------------------ + # Mapping collection + # ------------------------------------------------------------------ + + @staticmethod + def collect_all_mappings(mapping) -> list: + """Recursively collect a mapping and all its inner sub-mappings.""" + from megatron.bridge.models.conversion.param_mapping import MegatronParamMapping + + result: list = [] + visited: set = set() + stack = [mapping] + while stack: + m = stack.pop() + if id(m) in visited: + continue + visited.add(id(m)) + if isinstance(m, MegatronParamMapping): + result.append(m) + for attr_val in vars(m).values(): + if isinstance(attr_val, MegatronParamMapping): + stack.append(attr_val) + return result + + # ------------------------------------------------------------------ + # Per-parameter conversion + # ------------------------------------------------------------------ + + def convert(self, name: str, param: torch.Tensor) -> list[tuple[str, torch.Tensor]]: + """Convert a single TP/EP-gathered parameter to HF format. + + Args: + name: Global parameter name with ``module.module.`` prefix + (as yielded by ``named_params_and_buffers``). + param: The fully-gathered parameter tensor. + + Returns: + List of ``(hf_name, hf_tensor)`` tuples (quantised if configured). + """ + self.init_tasks() + + global_name = strip_param_name_prefix(name) + if global_name.startswith("vp_stages."): + parts = global_name.split(".", 2) + if len(parts) >= 3: + global_name = parts[2] + + task = self._bridge_task_map.get(global_name) + + if task is None: + from megatron.bridge.models.conversion.model_bridge import WeightConversionTask + from megatron.bridge.models.conversion.param_mapping import AutoMapping + + mapping = self._bridge_mapping_registry.megatron_to_hf_lookup(global_name) + assert mapping is not None, ( + f"Bridge mapping registry has no entry for '{global_name}'. " + f"Available task map keys: {list(self._bridge_task_map.keys())[:10]}..." + ) + task = WeightConversionTask( + param_name=global_name, + global_param_name=global_name, + mapping=mapping, + megatron_module=None, + param_weight=None, + ) + if isinstance(mapping, AutoMapping) and mapping._mapping is None: + mapping._detected_type = "replicated" + mapping._mapping = mapping._get_or_create_mapping("replicated") + inner_tp = getattr(mapping, "_tp_mapping", None) + if isinstance(inner_tp, AutoMapping) and inner_tp._mapping is None: + inner_tp._detected_type = "replicated" + inner_tp._mapping = inner_tp._get_or_create_mapping("replicated") + self._bridge_task_map[global_name] = task + + mapping = task.mapping + all_mappings = self.collect_all_mappings(mapping) + + saved_groups: list[tuple] = [] + for m in all_mappings: + saved_groups.append((m.pp_group, m._tp_group, m._etp_group, m.ep_group)) + + patched_classes: set[type] = set() + + try: + for m in all_mappings: + m.pp_group = None + m._tp_group = None + m._etp_group = None + m.ep_group = None + + for m in all_mappings: + cls = type(m) + if cls not in patched_classes: + cls.gather_from_ep_ranks = _noop_gather_from_ep_ranks + patched_classes.add(cls) + + param = remove_padding(name, param, self._args.vocab_size) + converted_dict = mapping.megatron_to_hf(param, task.megatron_module) + finally: + for m, (pp, tp, etp, ep) in zip(all_mappings, saved_groups): + m.pp_group = pp + m._tp_group = tp + m._etp_group = etp + m.ep_group = ep + for cls in patched_classes: + if "gather_from_ep_ranks" in cls.__dict__: + del cls.gather_from_ep_ranks + + converted_named_tensors = list(converted_dict.items()) + + # Post-process expert weights: split fused gate_up_proj, fix transposes + expert_id_match = re.search(r"weight(\d+)", global_name) + if expert_id_match is not None: + expert_id = expert_id_match.group(1) + postprocessed: list[tuple[str, torch.Tensor]] = [] + for hf_name, tensor in converted_named_tensors: + if hf_name.endswith(".experts.gate_up_proj"): + base = hf_name[: -len(".gate_up_proj")] + if tensor.ndim == 3: + gate_tensor = tensor[0].transpose(-1, -2).contiguous() + up_tensor = tensor[1].transpose(-1, -2).contiguous() + else: + gate_tensor, up_tensor = tensor.chunk(2, dim=0) + postprocessed.append((f"{base}.{expert_id}.gate_proj.weight", gate_tensor)) + postprocessed.append((f"{base}.{expert_id}.up_proj.weight", up_tensor)) + elif hf_name.endswith(".experts.down_proj"): + base = hf_name[: -len(".down_proj")] + if tensor.ndim == 2 and not self._bridge_expert_transposes_down: + postprocessed.append((f"{base}.{expert_id}.down_proj.weight", tensor)) + else: + postprocessed.append( + (f"{base}.{expert_id}.down_proj.weight", tensor.transpose(-1, -2).contiguous()) + ) + else: + postprocessed.append((hf_name, tensor)) + converted_named_tensors = postprocessed + + return quantize_params(self._args, name, converted_named_tensors, self._quantization_config) diff --git a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py index 82d4dd489..9e99fbfc4 100644 --- a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py +++ b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py @@ -1,143 +1,538 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. import dataclasses +import time from collections import OrderedDict import torch +import torch.distributed as dist +from megatron.core import mpu -from relax.utils import megatron_bridge_utils +from relax.utils import device as device_utils from relax.utils.logging_utils import get_logger +from relax.utils.types import ParamInfo -from ..misc_utils import strip_param_name_prefix -from ..weight_conversion import postprocess_hf_param -from ..weight_conversion.processors import quantize_params +from .bridge_converter import BridgeConverter +from .common import all_gather_param, named_params_and_buffers from .hf_weight_iterator_base import HfWeightIteratorBase logger = get_logger(__name__) # Weight names that must appear in the same chunk for SGLang's MLA fusion. -# SGLang's `do_load_weights` caches q_a_proj and kv_a_proj_with_mqa in a -# per-call local dict (`cached_a_proj`) and fuses them into -# `fused_qkv_a_proj_with_mqa` only when *both* are present. If they land -# in different chunks (each chunk triggers a separate `load_weights` call), -# the fusion never happens and the attention weights are silently stale. _MLA_PAIRED_SUFFIXES = ("q_a_proj.weight", "kv_a_proj_with_mqa.weight") class HfWeightIteratorBridge(HfWeightIteratorBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - - from megatron.bridge import AutoBridge - - self._bridge = AutoBridge.from_hf_pretrained(self.args.hf_checkpoint, trust_remote_code=True) + self._bridge_converter = BridgeConverter( + args=self.args, model=self.model, quantization_config=self.quantization_config + ) + buckets_result = _build_param_info_buckets(self.args, self.model) + self._expert_buckets, self._non_expert_buckets, self._vanilla_key_map = buckets_result + self._quantize_experts_before_broadcast = ( + self.quantization_config is not None + and self.quantization_config.get("quant_method") == "compressed-tensors" + and mpu.get_expert_tensor_parallel_world_size() == 1 + ) def get_hf_weight_chunks(self, megatron_local_weights): - renamed_megatron_local_weights = {strip_param_name_prefix(k): v for k, v in megatron_local_weights.items()} - with megatron_bridge_utils.patch_megatron_model(self.model): - conversion_tasks = self._bridge.get_conversion_tasks(self.model) - conversion_tasks = _process_conversion_tasks(conversion_tasks, renamed_megatron_local_weights) - - named_weights = self._bridge.export_hf_weights(self.model, cpu=False, conversion_tasks=conversion_tasks) - - def iter_quantized_named_weights(): - hf_to_megatron_mapping = None - - for item in named_weights: - # Compatibility shim: old megatron-bridge yields 3-tuples - # ``(hf_param_name, weight, megatron_param_name)`` while - # the official bridge yields 2-tuples ``(hf_param_name, weight)``. - # Dispatch per-item so the same code path supports both. - if len(item) == 3: - hf_param_name, weight, megatron_param_name = item - elif len(item) == 2: - hf_param_name, weight = item - if hf_to_megatron_mapping is None: - hf_to_megatron_mapping = _build_hf_to_megatron_mapping(conversion_tasks) - # With PP > 1, export_hf_weights yields params from ALL - # PP ranks (via internal PP broadcast), but - # hf_to_megatron_mapping only contains params from this - # rank's conversion tasks. For remote PP rank params - # we fall back to hf_param_name — this is safe because - # remove_padding checks megatron-style names and - # quantize_params_fp8 regex won't match HF-style names. - megatron_param_name = hf_to_megatron_mapping.get(hf_param_name, hf_param_name) + yield from _chunk_with_mla_pairing( + self._iter_hf_params(megatron_local_weights), + chunk_size=self.args.update_weight_buffer_size, + ) + + def _iter_hf_params(self, megatron_local_weights): + """Load params from CPU backuper dict, broadcast across PP/EP, TP- + gather, bridge-convert, and quantize. + + Expert weights (ETP=1, INT4 quantized) use an optimized path: + load → local bridge convert + quantize → PP+EP broadcast (INT4). + Each rank only converts its own params, and broadcasts transmit + INT4 (~4× smaller than BF16). + + Non-expert weights use the original path: PP/EP broadcast (BF16) → + TP all-gather → bridge convert → quantize. + """ + param_count = 0 + t_bcast_total = 0.0 + t_gather_total = 0.0 + t_convert_total = 0.0 + t_start = time.monotonic() + device = device_utils.make_current_torch_device() + rank = dist.get_rank() + # Eagerly init bridge converter so all ranks are ready before broadcast. + self._bridge_converter.init_tasks() + + # --- Expert weights: quantize-before-broadcast path --- + if self._quantize_experts_before_broadcast: + for bucket_infos in self._expert_buckets: + t_c0 = time.monotonic() + params = _load_to_gpu(bucket_infos, megatron_local_weights, self._vanilla_key_map, device, rank) + all_converted = [] + for info, param in zip(bucket_infos, params, strict=True): + if rank == info.src_rank: + all_converted.append(self._bridge_converter.convert(info.name, param)) else: - raise ValueError( - f"Unexpected named_weights tuple length {len(item)} from " - f"megatron-bridge.export_hf_weights(); expected 2 (new) or 3 (old). " - f"Item: {item!r}" - ) - - processed_weight = postprocess_hf_param( - args=self.args, - megatron_param_name=megatron_param_name, - hf_param_name=hf_param_name, - param=weight, - ) - - converted_named_params = [(hf_param_name, processed_weight)] - - quantized_batch = quantize_params( - args=self.args, - megatron_name=megatron_param_name, - converted_named_params=converted_named_params, - quantization_config=self.quantization_config, - ) - - yield from quantized_batch - - yield from _chunk_with_mla_pairing( - iter_quantized_named_weights(), - chunk_size=self.args.update_weight_buffer_size, + all_converted.append(None) + del params + t_convert_total += time.monotonic() - t_c0 + + t_b0 = time.monotonic() + results = _broadcast_quantized_bucket(bucket_infos, all_converted, device) + t_bcast_total += time.monotonic() - t_b0 + param_count += len(results) + yield from results + del all_converted, results + else: + for bucket_infos in self._expert_buckets: + t_b0 = time.monotonic() + params = _load_and_broadcast(bucket_infos, megatron_local_weights, self._vanilla_key_map, device, rank) + t_b1 = time.monotonic() + t_bcast_total += t_b1 - t_b0 + + for info, param in zip(bucket_infos, params, strict=True): + t_g0 = time.monotonic() + gathered = all_gather_param(self.args, info.name, param) + t_g1 = time.monotonic() + t_gather_total += t_g1 - t_g0 + + converted = self._bridge_converter.convert(info.name, gathered) + t_convert_total += time.monotonic() - t_g1 + param_count += len(converted) + yield from converted + del gathered, converted + + del params + + # --- Non-expert weights: original path --- + for bucket_infos in self._non_expert_buckets: + t_b0 = time.monotonic() + params = _load_and_broadcast(bucket_infos, megatron_local_weights, self._vanilla_key_map, device, rank) + t_b1 = time.monotonic() + t_bcast_total += t_b1 - t_b0 + + for info, param in zip(bucket_infos, params, strict=True): + t_g0 = time.monotonic() + gathered = all_gather_param(self.args, info.name, param) + t_g1 = time.monotonic() + t_gather_total += t_g1 - t_g0 + + converted = self._bridge_converter.convert(info.name, gathered) + t_convert_total += time.monotonic() - t_g1 + param_count += len(converted) + yield from converted + del gathered, converted + + del params + + if rank == 0: + logger.info( + "[Bridge Fast] params=%d | bcast=%.1fs | tp_gather=%.1fs | convert=%.1fs | total=%.1fs", + param_count, + t_bcast_total, + t_gather_total, + t_convert_total, + time.monotonic() - t_start, ) -def _build_hf_to_megatron_mapping(conversion_tasks): - """Build a mapping from HF parameter names to megatron parameter names. +def _build_param_info_buckets(args, model): + """Build ParamInfo buckets and vanilla-key mapping at init time. - Only relevant for the official megatron-bridge whose ``export_hf_weights`` - yields 2-tuples ``(hf_name, weight)`` and no longer carries the megatron - name in the tuple. We reconstruct the mapping by reading - ``task.mapping.hf_param`` — a pure metadata attribute that requires NO - collective communication. This is critical for PP > 1 where different - ranks hold different parameter subsets; calling ``megatron_to_hf()`` (which - contains PP broadcast / TP gather) with inconsistent tasks across ranks - would deadlock. + Exchanges parameter metadata across PP/EP ranks so every rank knows about + all params. Also records the vanilla-key (TensorBackuper dict key) for + each param owned by the current rank. + + Returns: + expert_buckets: list of ParamInfo lists for expert params + non_expert_buckets: list of ParamInfo lists for non-expert params + vanilla_key_map: dict mapping global_name -> vanilla_key (only for + params owned by this PP rank) + """ + rank = dist.get_rank() + pp_size = mpu.get_pipeline_model_parallel_world_size() + ep_size = mpu.get_expert_model_parallel_world_size() + + vanilla_iter = named_params_and_buffers(args, model, convert_to_global_name=False) + global_iter = named_params_and_buffers(args, model, convert_to_global_name=True) + + local_infos = {} + vanilla_key_map = {} + for (v_name, v_param), (g_name, _g_param) in zip(vanilla_iter, global_iter, strict=True): + local_infos[g_name] = ParamInfo( + name=g_name, + dtype=v_param.dtype, + shape=v_param.shape, + attrs={ + "tensor_model_parallel": getattr(v_param, "tensor_model_parallel", False), + "partition_dim": getattr(v_param, "partition_dim", -1), + "partition_stride": getattr(v_param, "partition_stride", 1), + "parallel_mode": getattr(v_param, "parallel_mode", None), + }, + size=v_param.numel() * v_param.element_size(), + src_rank=rank, + ) + vanilla_key_map[g_name] = v_name + + # Exchange across PP so every rank has all PP stages' param infos. + if pp_size > 1: + pp_infos_list: list[None | tuple[int, dict]] = [None] * pp_size + dist.all_gather_object( + obj=(rank, local_infos), + object_list=pp_infos_list, + group=mpu.get_pipeline_model_parallel_group(), + ) + for src_rank, infos in pp_infos_list: + if src_rank == rank: + continue + for name, info in infos.items(): + if name in local_infos: + if local_infos[name].src_rank > src_rank: + local_infos[name] = info + else: + local_infos[name] = info + + # Exchange across EP so every rank has all expert indices. + if ep_size > 1: + ep_infos_list: list[None | tuple[int, dict]] = [None] * ep_size + dist.all_gather_object( + obj=(rank, local_infos), + object_list=ep_infos_list, + group=mpu.get_expert_model_parallel_group(), + ) + for src_rank, infos in ep_infos_list: + for name, info in infos.items(): + if name not in local_infos: + local_infos[name] = dataclasses.replace(info, src_rank=src_rank) + + # Sort deterministically and split expert / non-expert. + all_infos = sorted(local_infos.values(), key=lambda info: info.name) + expert_infos = [i for i in all_infos if ".experts." in i.name] + non_expert_infos = [i for i in all_infos if ".experts." not in i.name] + + expert_buckets = _bucket_by_size(expert_infos, args) + non_expert_buckets = _bucket_by_size(non_expert_infos, args) + + return expert_buckets, non_expert_buckets, vanilla_key_map + + +def _bucket_by_size(infos, args): + if not infos: + return [] + buckets: list[list[ParamInfo]] = [[]] + bucket_bytes = 0 + for info in infos: + if ".experts." in info.name: + tp_size = mpu.get_expert_tensor_parallel_world_size() + else: + tp_size = mpu.get_tensor_model_parallel_world_size() + param_size = info.size * tp_size - ``mapping.hf_param`` is either: - - ``str``: simple 1-to-1 mappings (AutoMapping, DirectMapping, …) - - ``dict``: multi-output mappings (QKVMapping ``{"q","k","v"}``, - GatedMLPMapping ``{"gate","up"}``) + if bucket_bytes + param_size > args.update_weight_buffer_size and buckets[-1]: + buckets.append([]) + bucket_bytes = 0 + buckets[-1].append(info) + bucket_bytes += param_size + return buckets - This mirrors the approach shown in the official ``get_conversion_tasks`` - docstring of megatron-bridge's ``AutoBridge``. - Note: with PP > 1, each rank only holds a subset of conversion tasks, so - the returned mapping is **incomplete** — it covers only the params that - belong to this PP rank. ``export_hf_weights`` yields params from ALL PP - ranks (via internal PP broadcast), so callers must handle missing keys - gracefully (e.g. fall back to the HF param name). +def _load_to_gpu(bucket_infos, megatron_local_weights, vanilla_key_map, device, rank): + """Load params from CPU dict to GPU. + + No broadcast. + """ + params = [] + for info in bucket_infos: + if rank == info.src_rank: + vanilla_key = vanilla_key_map[info.name] + gpu_tensor = megatron_local_weights[vanilla_key].to(device=device, non_blocking=True) + param = torch.nn.Parameter(gpu_tensor, requires_grad=False) + else: + param = torch.nn.Parameter(torch.empty(info.shape, dtype=info.dtype, device=device), requires_grad=False) + for key, value in info.attrs.items(): + setattr(param, key, value) + params.append(param) + device_utils.synchronize() + return params + + +def _pp_broadcast(bucket_infos, params): + """PP-broadcast params in-place.""" + pp_size = mpu.get_pipeline_model_parallel_world_size() + if pp_size <= 1: + return + handles = [] + pp_group = mpu.get_pipeline_model_parallel_group() + pp_ranks = dist.get_process_group_ranks(pp_group) + for info, param in zip(bucket_infos, params, strict=True): + if info.src_rank in pp_ranks: + handles.append(dist.broadcast(param, src=info.src_rank, group=pp_group, async_op=True)) + for handle in handles: + handle.wait() + + +def _ep_broadcast(bucket_infos, params): + """EP-broadcast expert params in-place.""" + ep_size = mpu.get_expert_model_parallel_world_size() + if ep_size <= 1: + return + handles = [] + ep_group = mpu.get_expert_model_parallel_group() + ep_ranks = dist.get_process_group_ranks(ep_group) + rank = dist.get_rank() + for info, param in zip(bucket_infos, params, strict=True): + if ".experts." in info.name: + src = info.src_rank if info.src_rank in ep_ranks else rank + handles.append(dist.broadcast(param, src=src, group=ep_group, async_op=True)) + for handle in handles: + handle.wait() + + +def _load_and_broadcast(bucket_infos, megatron_local_weights, vanilla_key_map, device, rank): + """Load params from CPU dict, PP-broadcast, EP-broadcast. + + After this call every rank holds all params from all PP stages and all EP + shards (still TP-sharded). Mirrors the broadcast logic in + ``HfWeightIteratorDirect._get_megatron_full_params``. + """ + params = _load_to_gpu(bucket_infos, megatron_local_weights, vanilla_key_map, device, rank) + _pp_broadcast(bucket_infos, params) + _ep_broadcast(bucket_infos, params) + return params + + +def _broadcast_quantized_bucket(bucket_infos, all_converted, device): + """Broadcast quantized expert tensors across PP and EP groups. + + ``all_converted[i]`` is ``bridge_converter.convert()`` output for + ``bucket_infos[i]`` on the owning rank, or ``None`` on non-owners. + + Two-phase NCCL broadcast: PP first, then EP. """ - hf_to_megatron_mapping = {} + rank = dist.get_rank() + + pp_size = mpu.get_pipeline_model_parallel_world_size() + if pp_size > 1: + all_converted = _broadcast_quantized_phase( + bucket_infos, + all_converted, + device, + rank, + group=mpu.get_pipeline_model_parallel_group(), + ) + + ep_size = mpu.get_expert_model_parallel_world_size() + if ep_size > 1: + all_converted = _broadcast_quantized_phase( + bucket_infos, + all_converted, + device, + rank, + group=mpu.get_expert_model_parallel_group(), + ) + + out: list[tuple[str, torch.Tensor]] = [] + for converted in all_converted: + if converted is not None: + out.extend(converted) + return out - for task in conversion_tasks: - megatron_param_name = task.param_name - hf_param = task.mapping.hf_param - if isinstance(hf_param, str): - hf_to_megatron_mapping[hf_param] = megatron_param_name - elif isinstance(hf_param, dict): - for hf_name in hf_param.values(): - hf_to_megatron_mapping[hf_name] = megatron_param_name +# dtype ↔ int encoding for NCCL metadata tensor +_DTYPE_TO_CODE = { + torch.float32: 0, + torch.float16: 1, + torch.bfloat16: 2, + torch.int32: 3, + torch.int64: 4, + torch.int8: 5, + torch.uint8: 6, +} +_CODE_TO_DTYPE = {v: k for k, v in _DTYPE_TO_CODE.items()} + + +def _compute_slot_size(all_converted, bucket_infos): + """Compute the fixed int count per slot for metadata encoding. + + Every slot (including empty ones) must use the same number of ints so that + allreduce(SUM) aligns correctly across ranks. + """ + max_ints = 2 # header: [src+1, n_tensors] + for converted in all_converted: + if converted is None: + continue + n = 2 + for name, tensor in converted: + n += 1 + len(name.encode("utf-8")) + 1 + tensor.ndim + 1 + max_ints = max(max_ints, n) + return max_ints + + +def _encode_metadata(all_converted, bucket_infos, group_ranks_set, rank, slot_size=0): + """Encode converted tensor metadata into a fixed-width int64 tensor. + + Each slot occupies exactly ``slot_size`` ints (zero-padded), making the + total length ``len(bucket_infos) * slot_size``. This enables correct + allreduce(SUM) when only one rank has data per slot. + + Format per slot (padded to slot_size): + [src_rank+1, n_tensors, (name_len, *name_bytes, ndim, *shape, dtype_code) × N, 0...] + Empty slots: all zeros. + """ + if slot_size == 0: + slot_size = _compute_slot_size(all_converted, bucket_infos) + n_slots = len(bucket_infos) + buf = [0] * (n_slots * slot_size) + for i, (info, converted) in enumerate(zip(bucket_infos, all_converted)): + base = i * slot_size + if converted is None: + continue + src = info.src_rank if info.src_rank in group_ranks_set else rank + pos = base + buf[pos] = src + 1 + pos += 1 + buf[pos] = len(converted) + pos += 1 + for name, tensor in converted: + name_bytes = name.encode("utf-8") + buf[pos] = len(name_bytes) + pos += 1 + for b in name_bytes: + buf[pos] = b + pos += 1 + buf[pos] = tensor.ndim + pos += 1 + for s in tensor.shape: + buf[pos] = s + pos += 1 + buf[pos] = _DTYPE_TO_CODE[tensor.dtype] + pos += 1 + return torch.tensor(buf, dtype=torch.int64, device="cpu") + + +def _decode_metadata(meta_tensor, slot_size): + """Decode fixed-width int64 metadata tensor back to per-slot results. + + Each slot occupies ``slot_size`` ints. src_rank is stored as src_rank+1; 0 + means empty slot. + """ + data = meta_tensor.tolist() + n_slots = len(data) // slot_size + slots = [] + for i in range(n_slots): + base = i * slot_size + src_encoded = data[base] + n_tensors = data[base + 1] + if src_encoded == 0: + slots.append(None) + continue + src = src_encoded - 1 + pos = base + 2 + tensors_meta = [] + for _ in range(n_tensors): + name_len = data[pos] + pos += 1 + name_bytes = bytes(data[pos : pos + name_len]) + pos += name_len + name = name_bytes.decode("utf-8") + ndim = data[pos] + pos += 1 + shape = tuple(data[pos : pos + ndim]) + pos += ndim + dtype_code = data[pos] + pos += 1 + tensors_meta.append((name, shape, _CODE_TO_DTYPE[dtype_code])) + slots.append((src, tensors_meta)) + return slots + + +def _broadcast_quantized_phase(bucket_infos, all_converted, device, rank, group): + """Single-group broadcast of quantized tensors using only NCCL. + + 1. Each rank encodes its owned tensors' metadata into an int64 tensor. + 2. Two allreduce calls exchange metadata: one for sizes (MAX), one + for the content (SUM). Empty slots are encoded as zeros so the + SUM correctly merges non-overlapping contributions. + 3. Quantized data tensors are broadcast from their owners. + """ + group_ranks = dist.get_process_group_ranks(group) + group_ranks_set = set(group_ranks) + + slot_size = _compute_slot_size(all_converted, bucket_infos) + + # Step 1: allreduce(MAX) to agree on slot_size across the group + slot_size_t = torch.tensor([slot_size], dtype=torch.int64, device=device) + dist.all_reduce(slot_size_t, op=dist.ReduceOp.MAX, group=group) + slot_size = slot_size_t.item() + + local_meta_tensor = _encode_metadata(all_converted, bucket_infos, group_ranks_set, rank, slot_size) + + # Step 2: allreduce(SUM) to merge metadata from all ranks. + # Each param slot has data from at most one rank; the rest contribute zeros. + meta_buf = local_meta_tensor.to(device) + dist.all_reduce(meta_buf, op=dist.ReduceOp.SUM, group=group) + + merged_slots = _decode_metadata(meta_buf.cpu(), slot_size) + merged: dict[int, tuple[int, list]] = {} + for i, slot in enumerate(merged_slots): + if slot is not None: + merged[i] = slot + + # Group param slots by broadcast source and pack into one buffer per src. + # This reduces N×M individual broadcasts to one per unique src rank. + src_to_slots: dict[int, list[tuple[int, list]]] = {} + for i in range(len(bucket_infos)): + if i not in merged: + continue + src, param_meta = merged[i] + src_to_slots.setdefault(src, []).append((i, param_meta)) + + result = list(all_converted) + handles = [] + unpack_tasks: list[tuple[int, torch.Tensor, list[tuple[int, list]]]] = [] + + for src, slot_list in src_to_slots.items(): + is_owner = rank == src + # Compute total bytes for this src's tensors + total_bytes = 0 + for _i, param_meta in slot_list: + for _name, shape, dtype in param_meta: + total_bytes += torch.tensor([], dtype=dtype).element_size() * torch.Size(shape).numel() + + if is_owner: + parts = [] + for i, param_meta in slot_list: + for j, (_name, _shape, _dtype) in enumerate(param_meta): + parts.append(all_converted[i][j][1].contiguous().flatten().view(torch.uint8)) + buf = torch.cat(parts).to(device) else: - raise TypeError( - f"Unexpected mapping.hf_param type {type(hf_param).__name__} " - f"for megatron param '{megatron_param_name}': {hf_param!r}" - ) + buf = torch.empty(total_bytes, dtype=torch.uint8, device=device) + + handles.append(dist.broadcast(buf, src=src, group=group, async_op=True)) + unpack_tasks.append((src, buf, slot_list)) - return hf_to_megatron_mapping + for h in handles: + h.wait() + + # Unpack buffers back into named tensors + for src, buf, slot_list in unpack_tasks: + is_owner = rank == src + offset = 0 + for i, param_meta in slot_list: + tensors: list[tuple[str, torch.Tensor]] = [] + for j, (name, shape, dtype) in enumerate(param_meta): + n_bytes = torch.tensor([], dtype=dtype).element_size() * torch.Size(shape).numel() + if is_owner: + tensor = all_converted[i][j][1] + else: + tensor = buf[offset : offset + n_bytes].view(dtype).reshape(shape) + offset += n_bytes + tensors.append((name, tensor)) + result[i] = tensors + + return result def _chunk_with_mla_pairing(named_params, chunk_size): @@ -154,27 +549,22 @@ def _chunk_with_mla_pairing(named_params, chunk_size): """ bucket: list[tuple[str, torch.Tensor]] = [] bucket_size = 0 - # layer_prefix -> (name, tensor) for the first MLA weight seen pending_mla: OrderedDict[str, tuple[str, torch.Tensor]] = OrderedDict() for name, tensor in named_params: is_mla = any(name.endswith(suffix) for suffix in _MLA_PAIRED_SUFFIXES) if is_mla: - # Derive a layer key so we can match the pair. - # e.g. "model.layers.5.self_attn.q_a_proj.weight" -> "model.layers.5.self_attn." for suffix in _MLA_PAIRED_SUFFIXES: if name.endswith(suffix): layer_key = name[: -len(suffix)] break if layer_key in pending_mla: - # Partner found — emit both together. partner_name, partner_tensor = pending_mla.pop(layer_key) pair = [(partner_name, partner_tensor), (name, tensor)] pair_size = partner_tensor.nbytes + tensor.nbytes - # If adding the pair would overflow, flush current bucket first. if bucket and (bucket_size + pair_size) >= chunk_size: yield bucket bucket = [] @@ -183,7 +573,6 @@ def _chunk_with_mla_pairing(named_params, chunk_size): bucket.extend(pair) bucket_size += pair_size else: - # First of the pair — hold it. pending_mla[layer_key] = (name, tensor) else: obj_size = tensor.nbytes @@ -195,10 +584,9 @@ def _chunk_with_mla_pairing(named_params, chunk_size): bucket.append((name, tensor)) bucket_size += obj_size - # Flush any remaining unpaired MLA weights (shouldn't happen in practice). for layer_key, (name, tensor) in pending_mla.items(): - if torch.distributed.get_rank() == 0: - logger.warning(f"[Bridge Export] Unpaired MLA weight: {name} (layer_key={layer_key})") + if dist.get_rank() == 0: + logger.warning("[Bridge Export] Unpaired MLA weight: %s (layer_key=%s)", name, layer_key) obj_size = tensor.nbytes if bucket and (bucket_size + obj_size) >= chunk_size: yield bucket @@ -209,45 +597,3 @@ def _chunk_with_mla_pairing(named_params, chunk_size): if bucket: yield bucket - - -def _process_conversion_tasks(vanilla_conversion_tasks, new_weight_dict): - """Replace param_weight in each conversion task with the latest trained - weights. - - build_conversion_tasks() returns ``List[None | WeightConversionTask]`` - where None entries correspond to global params that have no mapping. We - filter them out here so that downstream consumers never see None. - """ - - def _handle_one(task): - if task is None: - return None - if task.param_weight is None: - return task - - weight_dict_key = f"vp_stages.{task.vp_stage}.{task.param_name}" - assert weight_dict_key in new_weight_dict, ( - f"{weight_dict_key=} not in new_weight_dict ({task.vp_stage=}, {task.param_name=}, {list(new_weight_dict)=})" - ) - - new_param_weight = new_weight_dict[weight_dict_key] - new_param_weight = new_param_weight.cuda() - return dataclasses.replace(task, param_weight=new_param_weight) - - # Filter out None tasks (params with no mapping in build_conversion_tasks) - valid_tasks = [t for t in vanilla_conversion_tasks if t is not None] - return _MapWithLen(_handle_one, valid_tasks) - - -class _MapWithLen: - def __init__(self, fn, xs): - self.fn = fn - self.xs = xs - - def __len__(self): - return len(self.xs) - - def __iter__(self): - for x in self.xs: - yield self.fn(x) diff --git a/relax/backends/megatron/weight_update/update_weight_from_tensor.py b/relax/backends/megatron/weight_update/update_weight_from_tensor.py index 59195ecc8..7e4d5dd76 100644 --- a/relax/backends/megatron/weight_update/update_weight_from_tensor.py +++ b/relax/backends/megatron/weight_update/update_weight_from_tensor.py @@ -11,6 +11,7 @@ from ray import ObjectRef from ray.actor import ActorHandle +from relax.utils.device import make_current_torch_device from relax.utils.distributed_utils import get_gloo_group from ..sglang import FlattenedTensorBucket, MultiprocessingSerializer @@ -183,6 +184,11 @@ def update_weights(self) -> None: ray.get(prev_refs) del prev_long_lived_tensors + # All ranks must finish sending before rank 0 triggers Marlin repack, + # otherwise engines in slower gather groups may still be processing + # weight chunks when their parameters get reshaped by post_process. + dist.barrier(group=get_gloo_group()) + # int4/fp4 post_process if rank == 0: if self.quantization_config and self.quantization_config["quant_method"] in ["compressed-tensors"]: @@ -192,6 +198,7 @@ def update_weights(self) -> None: rollout_engines=self.rollout_engines, ) ray.get([engine.continue_generation.remote() for engine in self.rollout_engines]) + dist.barrier(group=get_gloo_group()) def _send_hf_params(self, hf_named_tensors) -> tuple[list[ObjectRef], Any]: all_refs = [] @@ -234,9 +241,20 @@ def _send_to_colocated_engine( if ipc_gather_group is None: return [], None - # TODO improve long_live_tensors = [] + # Colocated IPC requires accelerator tensors (uses device IPC handles via + # shared memory). The bridge usually returns device tensors, but for K2.x + # multi-modal wrappers some text-backbone tensors leak through on cpu — + # coerce here so FlattenedTensorBucket's torch.cat doesn't see mixed + # devices. Synchronous copy: this runs on the weight-update path (not the + # rollout/train hot path) and FlattenedTensorBucket may flatten on a + # different stream — correctness over a few µs. + cur_device = make_current_torch_device() + hf_named_tensors = [ + (name, tensor.to(cur_device) if tensor.device != cur_device else tensor) for name, tensor in hf_named_tensors + ] + if getattr(FlattenedTensorBucket, "supports_multi_dtypes", False): converted_named_tensors_by_dtypes = {"dtype": hf_named_tensors} else: diff --git a/relax/distributed/checkpoint_service/backends/device_direct.py b/relax/distributed/checkpoint_service/backends/device_direct.py index 852177cf7..9261e278b 100644 --- a/relax/distributed/checkpoint_service/backends/device_direct.py +++ b/relax/distributed/checkpoint_service/backends/device_direct.py @@ -15,7 +15,6 @@ import asyncio import logging -import re import socket import time from collections.abc import Sequence @@ -32,9 +31,7 @@ from tqdm import tqdm from urllib3.exceptions import NewConnectionError -from relax.backends.megatron.misc_utils import strip_param_name_prefix from relax.backends.megatron.weight_conversion import convert_to_hf -from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding from relax.backends.megatron.weight_update.common import all_gather_param, named_params_and_buffers from relax.distributed.checkpoint_service.backends.base import CommBackend, TensorFusion from relax.distributed.checkpoint_service.config import BackendType, RoleInfo @@ -119,342 +116,10 @@ def __init__( # Bridge-based HF weight converter (lazy-initialized on first use) self._use_bridge = getattr(args, "megatron_to_hf_mode", None) == "bridge" - self._bridge_task_map: Optional[Dict[str, Any]] = None # global_param_name -> WeightConversionTask - self._bridge_mapping_registry = None # MegatronMappingRegistry for dynamic lookups - self._bridge_expert_transposes_down: bool = True # set in _init_bridge_tasks - - def _init_bridge_tasks(self) -> None: - """Lazily initialize Bridge conversion tasks and build a lookup table. - - Builds a mapping from global_param_name (unwrapped, e.g. - ``decoder.layers.0.self_attention.linear_qkv.weight``) to the - corresponding ``WeightConversionTask``. Only tasks that belong to the - current PP rank (i.e. ``task.param_weight is not None``) are indexed. - - After building the task map, eagerly initializes any lazily-created - inner mappings (e.g. ``AutoMapping._mapping``) so that - ``_collect_all_mappings`` can discover and patch them later. - - When embeddings are tied, Bridge's ``build_conversion_tasks`` filters - out ``output_layer`` from its task list. However, - ``named_params_and_buffers`` still yields ``output_layer.weight`` on - the last PP stage. We detect such missing parameters and supplement - the task map using the mapping registry so that every local parameter - has a corresponding Bridge task. - """ - if self._bridge_task_map is not None: - return - - from megatron.bridge import AutoBridge - from megatron.bridge.models.conversion.model_bridge import WeightConversionTask - from megatron.bridge.models.conversion.param_mapping import AutoMapping - - from relax.utils.megatron_bridge_utils import patch_megatron_model - - bridge = AutoBridge.from_hf_pretrained(self.args.hf_checkpoint, trust_remote_code=True) - with patch_megatron_model(self.model): - tasks = bridge.get_conversion_tasks(self.model) - - self._bridge_task_map = {} - for task in tasks: - if task.param_weight is not None: - self._bridge_task_map[task.global_param_name] = task - - # Supplement tasks for local parameters that Bridge filtered out - # (e.g. ``output_layer`` when embeddings are tied). Walk the local - # model parameters and, for any that are missing from the task map, - # look up the mapping from the registry and create a synthetic task. - self._bridge_mapping_registry = bridge._model_bridge.mapping_registry() - mapping_registry = self._bridge_mapping_registry - for name, param in named_params_and_buffers(self.args, self.model): - global_name = strip_param_name_prefix(name) - if global_name not in self._bridge_task_map: - mapping = mapping_registry.megatron_to_hf_lookup(global_name) - if mapping is not None: - self._bridge_task_map[global_name] = WeightConversionTask( - param_name=global_name, - global_param_name=global_name, - mapping=mapping, - megatron_module=None, - param_weight=param, - ) - - # Eagerly initialize inner mappings of AutoMapping instances. - # AutoMapping lazily creates a delegate ``_mapping`` (ColumnParallel / - # RowParallel / Replicated) on first use. That delegate has its own - # process groups obtained from ``mpu`` at construction time. We must - # trigger this initialization now so that ``_collect_all_mappings`` can - # find and patch them before ``megatron_to_hf`` is called. - for task in self._bridge_task_map.values(): - mapping = task.mapping - if isinstance(mapping, AutoMapping) and mapping._mapping is None: - if task.megatron_module is not None: - mapping._detected_type = mapping._detect_parallelism_type(task.megatron_module) - mapping._mapping = mapping._get_or_create_mapping(mapping._detected_type) - else: - # Supplementary tasks (e.g. tied ``output_layer``) have no - # ``megatron_module``, so we cannot detect parallelism type. - # These parameters are always replicated (that's why Bridge - # filtered them out in the first place). - mapping._detected_type = "replicated" - mapping._mapping = mapping._get_or_create_mapping("replicated") - # Also handle AutoMapping nested inside _tp_mapping (e.g. QKVMapping) - inner_tp = getattr(mapping, "_tp_mapping", None) - if isinstance(inner_tp, AutoMapping) and inner_tp._mapping is None: - if task.megatron_module is not None: - inner_tp._detected_type = inner_tp._detect_parallelism_type(task.megatron_module) - inner_tp._mapping = inner_tp._get_or_create_mapping(inner_tp._detected_type) - - # Detect whether the Bridge's ExpertMLPDownProjMapping applies a - # transpose in megatron_to_hf (Qwen3-VL does, Qwen3.5 does not). - # Used by _convert_to_hf_bridge to decide whether to undo the transpose. - self._bridge_expert_transposes_down = False - for task in self._bridge_task_map.values(): - cls = type(task.mapping) - if cls.__name__ == "ExpertMLPDownProjMapping": - self._bridge_expert_transposes_down = "megatron_to_hf" in cls.__dict__ - break - - logger.info(f"Bridge task map initialized with {len(self._bridge_task_map)} local tasks") - - @staticmethod - def _collect_all_mappings(mapping) -> list: - """Recursively collect a mapping and all its inner sub-mappings. - - Bridge mapping objects may contain inner attributes that are themselves - ``MegatronParamMapping`` instances with their own process groups. - Known examples: - - ``AutoMapping._mapping`` (lazily-created delegate) - - ``QKVMapping._tp_mapping`` / ``MambaInProjMapping._tp_mapping`` - - ``Qwen3VLMoEGateUpProjMapping._gated_mapping`` - - Rather than hard-coding attribute names, we scan all instance - attributes of each mapping to discover sub-mappings generically. - This ensures new model-specific wrappers are handled automatically. - """ - from megatron.bridge.models.conversion.param_mapping import MegatronParamMapping - - result: list = [] - visited: set = set() - stack = [mapping] - while stack: - m = stack.pop() - if id(m) in visited: - continue - visited.add(id(m)) - if isinstance(m, MegatronParamMapping): - result.append(m) - # Scan all instance attributes for nested MegatronParamMapping - for attr_val in vars(m).values(): - if isinstance(attr_val, MegatronParamMapping): - stack.append(attr_val) - return result - - def _convert_to_hf_bridge(self, name: str, param: torch.Tensor) -> list[tuple[str, torch.Tensor]]: - """Convert a single TP-gathered parameter to HF format using Bridge. - - This is a drop-in replacement for ``convert_to_hf()`` that uses - megatron-bridge's mapping logic instead of hand-written per-model - converters. All collective communication (PP broadcast, TP gather, - EP gather) is disabled by temporarily setting the process groups to - ``None``, because the caller has already performed TP gather via - ``all_gather_param`` and this method runs only on ``_is_pp_src_rank``. - - Args: - name: Global parameter name with ``module.module.`` prefix - (as yielded by ``named_params_and_buffers``). - param: The TP-gathered parameter tensor. - - Returns: - List of ``(hf_name, hf_tensor)`` tuples, same interface as - ``convert_to_hf``. - """ - self._init_bridge_tasks() - - # Strip the ``module.module.`` prefix to get Bridge's global_param_name - global_name = strip_param_name_prefix(name) - # named_params_and_buffers yields names like "vp_stages.0.decoder.layers.0...." - # Bridge's global_param_name is "decoder.layers.0...." - # Remove the "vp_stages.{N}." prefix if present - if global_name.startswith("vp_stages."): - # "vp_stages.0.decoder..." -> "decoder..." - parts = global_name.split(".", 2) - if len(parts) >= 3: - global_name = parts[2] - - task = self._bridge_task_map.get(global_name) - - # When EP > 1, ``_update_expert_bucket_weights_from_distributed`` - # gathers expert params from ALL EP ranks. The task map only contains - # the current EP rank's experts, so params from other EP ranks will be - # missing. Dynamically look them up via the mapping registry, create a - # synthetic task, eagerly initialize its inner mapping, and cache it. - if task is None: - from megatron.bridge.models.conversion.model_bridge import WeightConversionTask - from megatron.bridge.models.conversion.param_mapping import AutoMapping - - mapping = self._bridge_mapping_registry.megatron_to_hf_lookup(global_name) - assert mapping is not None, ( - f"Bridge mapping registry has no entry for '{global_name}'. " - f"Available task map keys: {list(self._bridge_task_map.keys())[:10]}..." - ) - # Do NOT pass param_weight=param here — the param_weight field is - # only used for HF→Megatron (load) direction, not Megatron→HF - # (export). Storing the EP-gathered tensor in the cached task - # would prevent ~20 GB from being freed on _is_pp_src_rank for - # MoE models with many experts. - task = WeightConversionTask( - param_name=global_name, - global_param_name=global_name, - mapping=mapping, - megatron_module=None, - param_weight=None, - ) - # Eagerly initialize AutoMapping inner delegate (same logic as - # ``_init_bridge_tasks``). Since ``megatron_module`` is None and - # all groups will be patched to None anyway, default to replicated. - if isinstance(mapping, AutoMapping) and mapping._mapping is None: - mapping._detected_type = "replicated" - mapping._mapping = mapping._get_or_create_mapping("replicated") - inner_tp = getattr(mapping, "_tp_mapping", None) - if isinstance(inner_tp, AutoMapping) and inner_tp._mapping is None: - inner_tp._detected_type = "replicated" - inner_tp._mapping = inner_tp._get_or_create_mapping("replicated") - # Cache for future iterations (task has no tensor references) - self._bridge_task_map[global_name] = task - - mapping = task.mapping - - # Collect the top-level mapping **and** any inner sub-mappings - # (e.g. AutoMapping._mapping, QKVMapping._tp_mapping) so that we - # disable collective ops on every level of the delegation chain. - all_mappings = self._collect_all_mappings(mapping) - - # Save original process groups for every mapping - saved_groups: list[tuple] = [] - for m in all_mappings: - saved_groups.append((m.pp_group, m._tp_group, m._etp_group, m.ep_group)) - - # For expert parameters, ``megatron_to_hf`` calls - # ``gather_from_ep_ranks`` when ``is_expert`` is True. That method - # needs ``megatron_module`` to compute ``num_experts_per_rank``, but - # our synthetic tasks have ``megatron_module = None``. Since we have - # already performed EP gather externally and set ``ep_group = None`` - # (``ep_size == 1``), the EP gather inside Bridge is redundant. - # - # We must monkey-patch ``gather_from_ep_ranks`` on the concrete class - # of **every** mapping in the delegation chain, not just the top-level - # one. For example, ``AutoMapping.megatron_to_hf`` delegates to - # ``self._mapping.megatron_to_hf`` (a ``RowParallelMapping``), which - # calls ``self.gather_from_ep_ranks`` on the *inner* mapping instance. - # If we only patch the outer ``AutoMapping`` class, the inner - # ``RowParallelMapping`` class still has the original method. - # - # ``gather_from_ep_ranks`` is only defined on the base - # ``MegatronParamMapping`` class and no subclass overrides it, so - # deleting the monkey-patch in ``finally`` restores the inherited - # version via MRO. - patched_classes: set[type] = set() - - def _noop_gather_from_ep_ranks(self_m, megatron_weights, megatron_module, hf_param_name): - return {str(hf_param_name): megatron_weights} - - try: - # Disable all collective ops on every mapping: set groups to None - # so that pp_size/tp_size/ep_size all return 1 (via get_pg_size(None) == 1) - for m in all_mappings: - m.pp_group = None - m._tp_group = None - m._etp_group = None - m.ep_group = None - - # Patch gather_from_ep_ranks on every unique mapping class in the - # delegation chain so that inner delegates also get the no-op. - for m in all_mappings: - cls = type(m) - if cls not in patched_classes: - cls.gather_from_ep_ranks = _noop_gather_from_ep_ranks - patched_classes.add(cls) - - # Apply remove_padding before conversion (same as convert_to_hf) - param = remove_padding(name, param, self.args.vocab_size) - - # Call Bridge's megatron_to_hf — now a pure local format conversion. - # With all groups set to None, tp_size/pp_size/ep_size are all 1, - # so no collective communication occurs and the tensor is treated - # as already gathered. - converted_dict = mapping.megatron_to_hf(param, task.megatron_module) - finally: - # Restore original process groups for every mapping - for m, (pp, tp, etp, ep) in zip(all_mappings, saved_groups): - m.pp_group = pp - m._tp_group = tp - m._etp_group = etp - m.ep_group = ep - # Remove the monkey-patch from every patched class; the inherited - # base-class method is automatically restored via MRO. - for cls in patched_classes: - if "gather_from_ep_ranks" in cls.__dict__: - del cls.gather_from_ep_ranks - - # Convert Dict[str, Tensor] -> List[Tuple[str, Tensor]] - converted_named_tensors = list(converted_dict.items()) - - # ── Post-process expert weights ────────────────────────────────── - # Bridge's ExpertMLPGateUpProjMapping and ExpertMLPDownProjMapping - # apply transformations that differ by model family: - # - # **Qwen3-VL** (qwen3_vl_bridge.py): - # gate_up_proj: transpose each half then stack → [2, D_out, D_in] - # down_proj: transpose → [D_in, D_out] - # We must undo the transpose. - # - # **Qwen3.5** (qwen35_vl_bridge.py): - # gate_up_proj: cat without transpose → [2*H, D] (2-D) - # down_proj: no transpose (AutoMapping) → [H, D] (2-D) - # No un-transpose needed; just split the fused tensor. - # - # Additionally, Bridge outputs fused names without expert_id: - # - ``...experts.gate_up_proj`` - # - ``...experts.down_proj`` - # We split into per-expert format with correct names and shapes: - # - ``...experts.{E}.gate_proj.weight`` [H, D] - # - ``...experts.{E}.up_proj.weight`` [H, D] - # - ``...experts.{E}.down_proj.weight`` [D, H] - expert_id_match = re.search(r"weight(\d+)", global_name) - if expert_id_match is not None: - expert_id = expert_id_match.group(1) - postprocessed: list[tuple[str, torch.Tensor]] = [] - for hf_name, tensor in converted_named_tensors: - if hf_name.endswith(".experts.gate_up_proj"): - base = hf_name[: -len(".gate_up_proj")] - if tensor.ndim == 3: - # Qwen3-VL style: [2, D_out, D_in] (transposed by Bridge) - # Undo transpose on each slice: [D_out, D_in] -> [D_in, D_out] - gate_tensor = tensor[0].transpose(-1, -2).contiguous() - up_tensor = tensor[1].transpose(-1, -2).contiguous() - else: - # Qwen3.5 style: [2*H, D] (cat, no transpose by Bridge) - # Split along dim 0 into two [H, D] tensors - gate_tensor, up_tensor = tensor.chunk(2, dim=0) - postprocessed.append((f"{base}.{expert_id}.gate_proj.weight", gate_tensor)) - postprocessed.append((f"{base}.{expert_id}.up_proj.weight", up_tensor)) - elif hf_name.endswith(".experts.down_proj"): - base = hf_name[: -len(".down_proj")] - if tensor.ndim == 2 and not self._bridge_expert_transposes_down: - # Qwen3.5 style: AutoMapping, no transpose — already [H, D] - postprocessed.append((f"{base}.{expert_id}.down_proj.weight", tensor)) - else: - # Qwen3-VL style: transposed — undo to match raw convert_to_hf - postprocessed.append( - (f"{base}.{expert_id}.down_proj.weight", tensor.transpose(-1, -2).contiguous()) - ) - else: - postprocessed.append((hf_name, tensor)) - converted_named_tensors = postprocessed + if self._use_bridge: + from relax.backends.megatron.weight_update.bridge_converter import BridgeConverter - # Apply quantization (same as convert_to_hf) - return quantize_params(self.args, name, converted_named_tensors, self.quantization_config) + self._bridge_converter = BridgeConverter(args=args, model=model, quantization_config=quantization_config) def _create_rollout_engines(self, rollout_topology: Dict[int, Dict[str, Any]]) -> None: """Create Ray actors for each rollout node. @@ -920,7 +585,7 @@ def _update_weight_from_distributed( origin_named_tensors += [(name, param)] if not actor_fwd_only: if self._use_bridge: - converted_named_tensors += self._convert_to_hf_bridge(name, param) + converted_named_tensors += self._bridge_converter.convert(name, param) else: converted_named_tensors += convert_to_hf( self.args, self.model_name, name, param, self.quantization_config @@ -1002,7 +667,7 @@ def _update_expert_bucket_weights_from_distributed( converted_hf_tensors = [] for name, param in all_gathered_params: if self._use_bridge: - converted_hf_tensors += self._convert_to_hf_bridge(name, param) + converted_hf_tensors += self._bridge_converter.convert(name, param) else: converted_hf_tensors += convert_to_hf( self.args, self.model_name, name, param, self.quantization_config diff --git a/relax/distributed/ray/rollout.py b/relax/distributed/ray/rollout.py index 035c9e04f..24d9024cf 100644 --- a/relax/distributed/ray/rollout.py +++ b/relax/distributed/ray/rollout.py @@ -105,7 +105,10 @@ def resolve(self, args) -> None: """Resolve per-group defaults from model-level then args-level values.""" default_gpus_per_engine = self.num_gpus_per_engine or args.rollout_num_gpus_per_engine - default_model_path = self.model_path or args.hf_checkpoint + # `args.sglang_hf_checkpoint` lets INT4 QAT runs point SGLang at the + # source compressed-tensors directory while training-side consumers + # keep using the auto-cast `args.hf_checkpoint` (BF16 cache). + default_model_path = self.model_path or args.sglang_hf_checkpoint or args.hf_checkpoint for g in self.engine_groups: if g.num_gpus_per_engine is None: g.num_gpus_per_engine = default_gpus_per_engine @@ -3481,6 +3484,58 @@ def _start_router(args, *, has_pd_disaggregation: bool = False, force_new: bool return router_ip, router_port +def _wait_engine_init_with_progress( + init_handles: list, + model_name: str, + timeout: float, + log_interval: float, +) -> None: + """Soft barrier across all engine init() handles with periodic progress + logs. + + Acts as a single rendezvous point at training startup: blocks until every + engine has finished server launch + weight loading, so that stragglers + caused by storage/IO jitter do not leak into downstream NCCL collectives. + Logs the remaining engine ranks every ``log_interval`` seconds so slow + nodes are visible without grepping per-engine logs. + """ + total = len(init_handles) + pending = {h: rank for rank, h in enumerate(init_handles)} + deadline = time.monotonic() + timeout + next_log = time.monotonic() + log_interval + + logger.info(f"[engine-init-barrier:{model_name}] waiting for {total} engines (timeout={timeout:.0f}s)") + + while pending: + remaining = deadline - time.monotonic() + if remaining <= 0: + raise TimeoutError( + f"[engine-init-barrier:{model_name}] timed out after {timeout:.0f}s; " + f"{len(pending)}/{total} engines still initializing, " + f"slow ranks={sorted(pending.values())}" + ) + wait_slice = min(remaining, max(0.1, next_log - time.monotonic())) + done, _ = ray.wait(list(pending.keys()), num_returns=len(pending), timeout=wait_slice) + for h in done: + try: + ray.get(h) + except Exception as e: + slow = sorted(pending.values()) + raise RuntimeError( + f"[engine-init-barrier:{model_name}] engine rank={pending[h]} init failed: {e}; " + f"other ranks still pending={slow}" + ) from e + pending.pop(h) + if time.monotonic() >= next_log and pending: + ready = total - len(pending) + slow = sorted(pending.values()) + preview = slow if len(slow) <= 10 else slow[:10] + ["..."] + logger.info(f"[engine-init-barrier:{model_name}] ready {ready}/{total}, still-waiting ranks={preview}") + next_log = time.monotonic() + log_interval + + logger.info(f"[engine-init-barrier:{model_name}] all {total} engines ready") + + def start_rollout_servers(args, pg) -> dict[str, RolloutServer]: """Start rollout servers: one per model, each with its own router. @@ -3541,7 +3596,12 @@ def start_rollout_servers(args, pg) -> dict[str, RolloutServer]: gpu_offset += group_cfg.num_gpus if all_init_handles: - ray.get(all_init_handles) + _wait_engine_init_with_progress( + all_init_handles, + model_name=model_cfg.name, + timeout=getattr(args, "rollout_engine_init_timeout", 3600.0), + log_interval=60.0, + ) servers[model_cfg.name] = RolloutServer( engine_groups=engine_groups, diff --git a/relax/engine/rollout/sglang_rollout.py b/relax/engine/rollout/sglang_rollout.py index 5104d71ba..649c4eaa5 100644 --- a/relax/engine/rollout/sglang_rollout.py +++ b/relax/engine/rollout/sglang_rollout.py @@ -166,20 +166,34 @@ async def _run_image_processor( processor_kwargs, ) else: + from relax.utils.data.processing_utils import ( + adapt_processor_kwargs, + expand_kimi_k25_placeholders, + remap_mm_train_inputs, + ) def _run_processor(): - processor_output = state.processor( - text=prompt, - use_audio_in_video=args.use_audio_in_video, - return_mm_token_type_ids=False, - **multimodal_inputs, + adapted = adapt_processor_kwargs( + state.processor, + multimodal_inputs, + { + "use_audio_in_video": args.use_audio_in_video, + "return_mm_token_type_ids": False, + }, ) + processor_output = state.processor(text=prompt, **adapted) prompt_ids = processor_output["input_ids"][0] + # K2.x adapt_processor_kwargs forces return_tensors="pt", so + # input_ids is a 1D Tensor; downstream sample.tokens contract is list[int]. + if isinstance(prompt_ids, torch.Tensor): + prompt_ids = prompt_ids.tolist() train_inputs = { k: (torch.from_numpy(v) if isinstance(v, np.ndarray) else v) for k, v in processor_output.items() if k not in ["input_ids", "attention_mask"] } or None + train_inputs = remap_mm_train_inputs(state.processor, train_inputs) + prompt_ids = expand_kimi_k25_placeholders(state.processor, prompt_ids, train_inputs) return prompt_ids, train_inputs processor_prompt_ids, mm_train_inputs = await loop.run_in_executor(_ENCODE_EXECUTOR, _run_processor) @@ -237,7 +251,15 @@ async def generate( tokenizer_prompt_ids = state.tokenizer.encode(sample.prompt, add_special_tokens=False) _t_image_processor: float | None = None - if state.processor: + # K2.x ships a multimodal AutoProcessor even for text-only fine-tunes; the + # data loader always populates multimodal_inputs with empty-list placeholders + # in that case, so check for actual media content before routing through the + # image processor (which would otherwise raise on text-only K2.x) — and use + # the same gate downstream so SGLang's payload doesn't get empty media fields. + _has_media = sample.multimodal_inputs is not None and any( + sample.multimodal_inputs.get(k) for k in ("images", "videos", "audio") + ) + if state.processor and _has_media: processor_prompt_ids, sample.multimodal_train_inputs, _t_image_processor = await _run_image_processor( state, args, sample.prompt, sample.multimodal_inputs ) @@ -264,7 +286,7 @@ async def generate( payload["return_routed_experts"] = True _t_mm_encode: float | None = None - if sample.multimodal_inputs: + if _has_media: # Use pre-encoded data from group-level de-dup if available; otherwise encode inline. pre_encoded = getattr(sample, "_pre_encoded_mm", None) if pre_encoded is not None: @@ -331,6 +353,23 @@ async def generate( "Video token found in output tokens, replaced with pad_token_id. Consider updating the model's stop condition to stop at video_token_id if you want to avoid this." ) + # K2.x tokenizers don't expose image_token_id but reserve <|media_pad|> + # for vision input slots. A hallucinated <|media_pad|> in the response + # inflates num_placeholders past sum(feature_lengths) in the bridge, + # forcing dynamic expansion → broadcast → 233 GiB OOM. Replace in-place + # so positional accounting matches sglang's per-token logprobs. + if state.processor is not None: + from relax.utils.data.processing_utils import sanitize_kimi_k25_response_tokens + + sanitized = sanitize_kimi_k25_response_tokens(state.processor, new_response_tokens) + if sanitized is not new_response_tokens: + replaced = sum(1 for a, b in zip(new_response_tokens, sanitized, strict=True) if a != b) + if replaced: + logger.warning( + f"K2.x: replaced {replaced} stray <|media_pad|> token(s) in rollout response with pad_token_id." + ) + new_response_tokens = sanitized + # Update sample with tokens directly - avoiding re-tokenization sample.tokens = sample.tokens + new_response_tokens sample.rollout_tokens = sample.rollout_tokens + new_response_tokens diff --git a/relax/tools/quant_cast/__init__.py b/relax/tools/quant_cast/__init__.py new file mode 100644 index 000000000..9f3863608 --- /dev/null +++ b/relax/tools/quant_cast/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. diff --git a/relax/tools/quant_cast/convert_moe_int4_to_bf16.py b/relax/tools/quant_cast/convert_moe_int4_to_bf16.py new file mode 100644 index 000000000..20b7f8083 --- /dev/null +++ b/relax/tools/quant_cast/convert_moe_int4_to_bf16.py @@ -0,0 +1,281 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Convert compressed-tensors W4A16 quantized HF checkpoints to BF16. + +Adapted from slime/tools/convert_k2_thinking_int4_to_bf16.py. + +Default output is a sibling directory ``_bf16``. The output ``config.json`` has +``quantization_config`` removed; the original block is written to a sidecar +``quantization_config.json`` so QAT paths can read it back when needed. +""" + +import argparse +import json +import os +import shutil +from collections import defaultdict +from pathlib import Path + +import torch +from compressed_tensors.compressors import unpack_from_int32 +from safetensors.torch import safe_open, save_file +from tqdm import tqdm + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + + +def _quant_config(cfg: dict) -> dict: + """Return the quantization_config block, falling back to text_config (VLM + layout).""" + qc = cfg.get("quantization_config") + if qc: + return qc + return cfg.get("text_config", {}).get("quantization_config") or {} + + +def read_group_size(model_dir: str, config_path: str | None = None) -> int: + cfg_path = config_path or os.path.join(model_dir, "config.json") + with open(cfg_path) as f: + cfg = json.load(f) + return int( + _quant_config(cfg).get("config_groups", {}).get("group_0", {}).get("weights", {}).get("group_size", 128) + ) + + +def _dequantize_tensor( + weight_packed: torch.Tensor, + weight_scale: torch.Tensor, + weight_shape: torch.Tensor, + group_size: int, +) -> torch.Tensor: + if isinstance(weight_shape, torch.Tensor): + shape = tuple(int(v) for v in weight_shape.view(-1).tolist()) + else: + shape = tuple(weight_shape) + + weight = unpack_from_int32(weight_packed, 4, shape) + + if group_size > 0: + scale = weight_scale.to(torch.float32) + if scale.dim() == 1: + scale = scale.unsqueeze(1) + scales = torch.repeat_interleave(scale, repeats=group_size, dim=1) + else: + scales = weight_scale.to(torch.float32) + + if scales.shape != weight.shape: + if scales.numel() == weight.numel(): + scales = scales.reshape_as(weight) + else: + raise ValueError(f"scale shape {scales.shape} incompatible with weight shape {weight.shape}") + + return (weight.to(torch.float32) * scales).to(torch.bfloat16).contiguous() + + +def _is_quantized_weight_key(key: str) -> bool: + if ".mlp.experts." not in key or ".shared_experts." in key: + return False + suffixes = ("weight_packed", "weight_scale", "weight_shape") + for proj in ("gate_proj", "up_proj", "down_proj"): + for suffix in suffixes: + if key.endswith(f".{proj}.{suffix}"): + return True + return False + + +def _convert_file(input_path: str, output_path: str, group_size: int, skip_existing: bool) -> None: + if skip_existing and os.path.exists(output_path): + return + + # Memory ceiling: this loads ALL tensors of a single safetensors shard into + # GPU memory at once (one shard at a time). K2.6 shards are ~5GB packed → + # ~10GB BF16 dequantized, so a 40GB+ GPU is comfortable. If shards grow + # past that, stream key-by-key and keep only expert triplets resident. + tensors: dict[str, torch.Tensor] = {} + expert_buffers: dict[str, dict[str, dict[str, torch.Tensor]]] = defaultdict(lambda: defaultdict(dict)) + + device = "cuda" if torch.cuda.is_available() else "cpu" + with safe_open(input_path, framework="pt", device=device) as reader: + for key in reader.keys(): + tensor = reader.get_tensor(key) + if not _is_quantized_weight_key(key): + tensors[key] = tensor + continue + parts = key.split(".") + try: + expert_idx = parts.index("experts") + except ValueError: + tensors[key] = tensor + continue + prefix = ".".join(parts[: expert_idx + 2]) + project = parts[-2] + suffix = parts[-1] + expert_buffers[prefix][project][suffix] = tensor + + for prefix, components in expert_buffers.items(): + for proj_name in ("gate_proj", "up_proj", "down_proj"): + proj_data = components.get(proj_name, {}) + required = {"weight_packed", "weight_scale", "weight_shape"} + if not required.issubset(proj_data.keys()): + for suffix, value in proj_data.items(): + tensors[f"{prefix}.{proj_name}.{suffix}"] = value + continue + bf16_weight = _dequantize_tensor( + proj_data["weight_packed"].to(torch.int32), + proj_data["weight_scale"].to(torch.float32), + proj_data["weight_shape"], + group_size, + ) + tensors[f"{prefix}.{proj_name}.weight"] = bf16_weight + + cpu_tensors = {k: v.cpu() for k, v in tensors.items()} + os.makedirs(os.path.dirname(output_path), exist_ok=True) + save_file(cpu_tensors, output_path) + + +def _derive_extra_ignore_namespaces(src: str) -> list[str]: + """Return top-level module names whose subtree has plain ``.weight`` keys + but zero ``.weight_packed`` triplets in the source checkpoint. + + These namespaces are definitively not quantized; surfacing them in the + sidecar ignore list lets downstream quantizers reject them without model- + specific name knowledge. + """ + namespaces: dict[str, dict[str, bool]] = defaultdict(lambda: {"plain": False, "packed": False}) + for fname in os.listdir(src): + if not fname.endswith(".safetensors"): + continue + with safe_open(os.path.join(src, fname), framework="pt") as reader: + for key in reader.keys(): + top = key.split(".", 1)[0] + if key.endswith(".weight_packed"): + namespaces[top]["packed"] = True + elif key.endswith(".weight"): + namespaces[top]["plain"] = True + return sorted(top for top, info in namespaces.items() if info["plain"] and not info["packed"]) + + +def _copy_aux_files(src: str, dst: str, *, strip_quantization_config: bool) -> dict | None: + src_path = Path(src) + dst_path = Path(dst) + stripped: dict | None = None + + for fname in os.listdir(src_path): + # Safetensors shards are produced by _convert_file; the index is rewritten + # after the cast loop. Everything else (config.json, *.py, tokenizer*, + # chat_template.jinja, tiktoken.model, README/LICENSE, …) is copied + # verbatim so trust_remote_code modules find their sidecars. + if fname.endswith(".safetensors") or fname == "model.safetensors.index.json": + continue + full = src_path / fname + if not full.is_file(): + continue + target = dst_path / fname + if fname == "config.json" and strip_quantization_config: + with open(full) as f: + cfg = json.load(f) + stripped = cfg.pop("quantization_config", None) + text_cfg = cfg.get("text_config") + if stripped is None and isinstance(text_cfg, dict): + stripped = text_cfg.pop("quantization_config", None) + with open(target, "w") as f: + json.dump(cfg, f, indent=2) + else: + shutil.copy2(full, target) + return stripped + + +def cast( + src: str, + dst: str, + *, + group_size: int | None = None, + files: list[str] | None = None, + config_path: str | None = None, + overwrite: bool = False, + strip_quantization_config: bool = True, +) -> None: + """Cast a compressed-tensors W4A16 HF checkpoint at ``src`` into BF16 at + ``dst``.""" + src = os.path.abspath(src) + dst = os.path.abspath(dst) + if not os.path.isdir(src): + raise FileNotFoundError(f"model directory not found: {src}") + + os.makedirs(dst, exist_ok=True) + if group_size is None: + group_size = read_group_size(src, config_path) + logger.info(f"int4 → bf16 cast: src={src} dst={dst} group_size={group_size}") + + if files: + targets = [os.path.join(src, name) for name in files] + else: + targets = sorted(os.path.join(src, name) for name in os.listdir(src) if name.endswith(".safetensors")) + + if not targets: + logger.warning("no safetensors found in source directory") + return + + for path in tqdm(targets, desc="int4 → bf16", unit="file"): + if not os.path.isfile(path): + continue + rel = os.path.relpath(path, src) + _convert_file(path, os.path.join(dst, rel), group_size, skip_existing=not overwrite) + + stripped = _copy_aux_files(src, dst, strip_quantization_config=strip_quantization_config) + if stripped is not None: + # Augment the sidecar's ignore list with top-level namespaces that have + # no weight_packed triplets in source — these are guaranteed-not-quantized + # (for K2.x VLMs that is vision_tower / mm_projector, which the published + # config.ignore does not list). Lets the generic quantizer stay model- + # agnostic and decide skip purely from the config. + extra_ignore = _derive_extra_ignore_namespaces(src) + if extra_ignore: + existing = list(stripped.get("ignore", [])) + stripped["ignore"] = existing + [ns for ns in extra_ignore if ns not in existing] + logger.info(f"sidecar quantization_config.ignore extended with {extra_ignore}") + with open(os.path.join(dst, "quantization_config.json"), "w") as f: + json.dump(stripped, f, indent=2) + + weight_map: dict[str, str] = {} + for fname in sorted(os.listdir(dst)): + if not fname.endswith(".safetensors"): + continue + with safe_open(os.path.join(dst, fname), framework="pt") as reader: + for key in reader.keys(): + weight_map[key] = fname + with open(os.path.join(dst, "model.safetensors.index.json"), "w") as f: + json.dump({"metadata": {}, "weight_map": weight_map}, f, indent=2) + + logger.info(f"int4 → bf16 cast complete: {dst}") + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert compressed-tensors W4A16 MoE experts to BF16.") + parser.add_argument("--model-dir", required=True) + parser.add_argument("--output-dir", default=None, help="Default: _bf16") + parser.add_argument("--files", nargs="+", default=None) + parser.add_argument("--config-path", default=None) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--keep-quantization-config", action="store_true") + return parser.parse_args() + + +def main() -> None: + args = _parse_args() + output_dir = args.output_dir or f"{os.path.abspath(args.model_dir)}_bf16" + cast( + args.model_dir, + output_dir, + files=args.files, + config_path=args.config_path, + overwrite=args.overwrite, + strip_quantization_config=not args.keep_quantization_config, + ) + + +if __name__ == "__main__": + main() diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 380e2799c..79f04a083 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -357,6 +357,21 @@ def add_rollout_arguments(parser): "It doesn't necessary need to contain the most up-to-date parameters." ), ) + parser.add_argument( + "--sglang-hf-checkpoint", + type=str, + default=None, + help=( + "Optional override for the HF checkpoint that SGLang loads. " + "When set, SGLang's model_path uses this directory instead of " + "args.hf_checkpoint, while training-side consumers (Megatron " + "loader, AutoConfig, tokenizer) keep using args.hf_checkpoint. " + "Used by INT4 QAT runs so SGLang loads the source compressed-" + "tensors directory directly (registering weight_packed/scale/shape " + "params) while training reads from a separately-prepared BF16 " + "checkpoint." + ), + ) parser.add_argument( "--model-name", type=str, @@ -660,6 +675,15 @@ def add_fault_tolerance_arguments(parser): "A single timeout (e.g. engine busy with a large batch) will not kill the engine. " "Only after this many consecutive failures will the engine be killed.", ) + parser.add_argument( + "--rollout-engine-init-timeout", + type=float, + default=3600.0, + help="Total timeout in seconds to wait for ALL rollout engines to finish init() " + "(server launch + weight loading) at training startup. Acts as a soft barrier so " + "stragglers caused by storage/IO jitter on large clusters do not leak into " + "downstream NCCL collectives. Progress is logged every 60s while waiting.", + ) # Elastic rollout scale-out arguments parser.add_argument( "--scale-out-timeout", diff --git a/relax/utils/data/data_utils.py b/relax/utils/data/data_utils.py index e196b91ca..164aecd06 100644 --- a/relax/utils/data/data_utils.py +++ b/relax/utils/data/data_utils.py @@ -292,7 +292,10 @@ def check_sample_length( try: if processor and sample.multimodal_inputs: - processor_output = processor(text=sample.prompt, **sample.multimodal_inputs) + from relax.utils.data.processing_utils import adapt_processor_kwargs + + adapted = adapt_processor_kwargs(processor, sample.multimodal_inputs) + processor_output = processor(text=sample.prompt, **adapted) input_ids = processor_output["input_ids"][0] else: input_ids = tokenizer(sample.prompt, add_special_tokens=False)["input_ids"] diff --git a/relax/utils/data/processing_utils.py b/relax/utils/data/processing_utils.py index dff254952..d0eed923b 100644 --- a/relax/utils/data/processing_utils.py +++ b/relax/utils/data/processing_utils.py @@ -6,6 +6,7 @@ import json import os import tempfile +import weakref from concurrent.futures import ThreadPoolExecutor import imageio.v2 as imageio @@ -48,6 +49,221 @@ def load_tokenizer(name_or_path: str, **kwargs): return tokenizer +def _is_kimi_k25_style_processor(processor: object) -> bool: + """Detect Kimi-K2.x VLM processors whose ``__call__`` takes + ``medias=[{...}]`` instead of the standard HF + ``images=``/``videos=``/``audio=`` triple. + + Duck-typing on ``media_processor`` (K2.x's bespoke attribute) plus a class + name prefix fallback so future K2.* renames don't silently fall through. + """ + if hasattr(processor, "media_processor"): + return True + name = type(processor).__name__ + return name.startswith("KimiK2") and name.endswith("Processor") + + +def adapt_processor_kwargs( + processor: object, + multimodal_inputs: dict | None, + extra_kwargs: dict | None = None, +) -> dict: + """Translate Relax's ``{images, videos, audio}`` shape into kwargs that the + given HF processor's ``__call__`` actually accepts, then merge + ``extra_kwargs``. + + Default path: returns ``{**(multimodal_inputs or {}), **(extra_kwargs or {})}`` + unchanged — covers Qwen-VL / Qwen-Omni and the rest of the HF zoo. + + Kimi-K2.x VLM path: ``KimiK25Processor.__call__`` requires either ``messages`` + or ``(medias, text)`` and rejects the standard ``images=`` keyword via its + ``if messages is None and (medias is None or text is None)`` guard. We + translate ``images=[PIL]`` to ``medias=[{"type":"image","image":PIL}]`` and + drop the now-unused ``videos``/``audio`` (warning if non-empty so callers + notice when video/audio inputs reach this codepath unconverted). Also forces + ``return_tensors="pt"`` and discards the per-modality ``*_kwargs`` from + ``build_processor_kwargs`` because K2.x's signature ignores them — and would + explode if both this function and ``extra_kwargs`` named the same key. + """ + mm = multimodal_inputs or {} + extra = extra_kwargs or {} + + if not _is_kimi_k25_style_processor(processor): + return {**mm, **extra} + + medias: list[dict] = [] + if images := mm.get("images"): + medias.extend({"type": "image", "image": img} for img in images) + if mm.get("videos"): + logger.warning( + "K2.x processor adapter received videos but only image translation is implemented; " + "video inputs are being dropped." + ) + if mm.get("audio"): + logger.warning( + "K2.x processor adapter received audio but K2.x VLMs do not consume audio; audio inputs are being dropped." + ) + + adapted: dict = {} + if medias: + adapted["medias"] = medias + adapted["return_tensors"] = "pt" + if extra: + dropped = sorted(k for k in extra if k not in adapted) + if dropped: + _warn_dropped_kimi_k25_kwargs(tuple(dropped)) + return adapted + + +_WARNED_DROPPED_KIMI_KWARGS: set[tuple[str, ...]] = set() + + +def _warn_dropped_kimi_k25_kwargs(dropped: tuple[str, ...]) -> None: + """Warn once per unique dropped-key set; K2.x's ``__call__`` ignores most + of the standard HF processor kwargs and we don't want per-sample log + spam.""" + if dropped in _WARNED_DROPPED_KIMI_KWARGS: + return + _WARNED_DROPPED_KIMI_KWARGS.add(dropped) + logger.warning(f"K2.x processor adapter dropped unsupported kwargs: {list(dropped)}") + + +# K2.x HF processor output → KimiK25VLModel.forward kwargs. +# The bridge model in megatron-bridge uses `image_grid_thw` (matching the rest +# of HF VLM convention), but the K2.x HF processor returns the field as +# `grid_thws`. Rename here so kwargs unpacked into the bridge model match. +_KIMI_K25_OUTPUT_RENAME = {"grid_thws": "image_grid_thw"} + + +def remap_mm_train_inputs(processor: object, train_inputs: dict | None) -> dict | None: + """Rename HF-processor output keys to match the bridge model's forward + kwargs. + + No-op for non-K2.x processors. For K2.x, applies + ``_KIMI_K25_OUTPUT_RENAME``. + """ + if not train_inputs: + return train_inputs + if not _is_kimi_k25_style_processor(processor): + return train_inputs + return {_KIMI_K25_OUTPUT_RENAME.get(k, k): v for k, v in train_inputs.items()} + + +# Per-process cache of the ``<|media_pad|>`` token id keyed by processor identity. +# convert_tokens_to_ids on TikToken is cheap but we hit this once per sample on the +# rollout-side hot path. Use weakref.finalize to evict on GC so id() reuse can't +# return a stale value. +_KIMI_K25_PLACEHOLDER_ID_CACHE: dict[int, int] = {} + + +def _kimi_k25_placeholder_id(processor: object) -> int: + key = id(processor) + cached = _KIMI_K25_PLACEHOLDER_ID_CACHE.get(key) + if cached is None: + cached = int(processor.tokenizer.convert_tokens_to_ids("<|media_pad|>")) + _KIMI_K25_PLACEHOLDER_ID_CACHE[key] = cached + weakref.finalize(processor, _KIMI_K25_PLACEHOLDER_ID_CACHE.pop, key, None) + return cached + + +def _kimi_k25_image_feature_lengths(processor: object, grid_thws) -> list[int]: + """Per-image post-merger token count. + + ``MoonViT3d.tpool_patch_merger`` collapses temporal frames via mean and reshapes + the spatial grid by ``merge_kernel_size``, so per image the projector emits + ``(h_patches // mh) * (w_patches // mw)`` tokens regardless of T. + + Note: the image processor stores ``merge_kernel_size`` as a single int (square + kernel) in ``media_proc_cfg``, while the model's vision_config stores it as a + ``(mh, mw)`` tuple. Accept both shapes. + """ + mks = processor.media_processor.media_proc_cfg["merge_kernel_size"] + if isinstance(mks, (int, float)): + mh = mw = int(mks) + else: + mh, mw = int(mks[0]), int(mks[1]) + if isinstance(grid_thws, torch.Tensor): + grid_thws = grid_thws.tolist() + return [int((h // mh) * (w // mw)) for (_, h, w) in grid_thws] + + +def sanitize_kimi_k25_response_tokens( + processor: object, + response_tokens: list[int], + *, + replacement_id: int | None = None, +) -> list[int]: + """Replace stray ``<|media_pad|>`` tokens hallucinated by the model in + rollout responses. + + K2.x VLMs reserve ``<|media_pad|>`` for vision-input slots. The model is + not supposed to emit it as part of generation, but freshly-cast or + early-step checkpoints occasionally do. Each stray placeholder inflates + ``num_placeholders`` past ``sum(feature_lengths)`` in the bridge's + ``_merge_input_ids_with_image_features``, which falls into the dynamic- + expansion path and broadcasts a single ``feature_lengths[0]=N`` across all + pre-expanded N positions, producing an ``N²`` allocation that OOMs. + + Replace (don't strip) so positional accounting in + ``sample.tokens``/``rollout_tokens``/``loss_mask`` stays consistent with + sglang's per-token logprobs. + """ + if not _is_kimi_k25_style_processor(processor) or not response_tokens: + return response_tokens + placeholder_id = _kimi_k25_placeholder_id(processor) + if replacement_id is None: + replacement_id = int(getattr(processor.tokenizer, "pad_token_id", 0) or 0) + return [replacement_id if t == placeholder_id else t for t in response_tokens] + + +def expand_kimi_k25_placeholders( + processor: object, + prompt_ids: list[int], + train_inputs: dict | None, +) -> list[int]: + """Pre-expand ``<|media_pad|>`` tokens for K2.x VLMs. + + The K2.x HF processor emits exactly one ``<|media_pad|>`` per image, but the + bridge model's vision tower produces N tokens per image where + ``N = (h_patches // mh) * (w_patches // mw)``. Without pre-expansion the bridge + falls into its dynamic-expansion path, which grows the sequence length mid-forward + and invalidates the ``cu_seqlens`` Megatron's THD-format rotary embedding has + already split on. Pre-expanding here keeps ``num_placeholders == sum(feature_lengths)`` + so the bridge takes the 1:1 pre-expanded branch and ``packed_seq_params`` stays + consistent with the actual sequence length. + """ + if not _is_kimi_k25_style_processor(processor) or not train_inputs: + return prompt_ids + grid_thws = train_inputs.get("image_grid_thw") + if grid_thws is None or len(grid_thws) == 0: + return prompt_ids + + placeholder_id = _kimi_k25_placeholder_id(processor) + feature_lengths = _kimi_k25_image_feature_lengths(processor, grid_thws) + + expanded: list[int] = [] + feat_idx = 0 + for tid in prompt_ids: + if tid == placeholder_id: + if feat_idx >= len(feature_lengths): + raise RuntimeError( + "K2.x prompt has more <|media_pad|> tokens than images in grid_thws " + f"({feat_idx + 1} vs {len(feature_lengths)}); " + f"len(prompt_ids)={len(prompt_ids)}, first_tokens={prompt_ids[:16]}" + ) + expanded.extend([placeholder_id] * feature_lengths[feat_idx]) + feat_idx += 1 + else: + expanded.append(tid) + if feat_idx != len(feature_lengths): + raise RuntimeError( + "K2.x prompt has fewer <|media_pad|> tokens than images in grid_thws " + f"({feat_idx} vs {len(feature_lengths)}); " + f"len(prompt_ids)={len(prompt_ids)}, first_tokens={prompt_ids[:16]}" + ) + return expanded + + def build_processor_kwargs(multimodal_inputs: dict | None = None) -> dict: forced = { # force return_tensors to None for input_ids diff --git a/relax/utils/data/processor_pool.py b/relax/utils/data/processor_pool.py index 1a3e0e84b..27f3baf14 100644 --- a/relax/utils/data/processor_pool.py +++ b/relax/utils/data/processor_pool.py @@ -28,7 +28,12 @@ import torch.multiprocessing as mp from PIL import Image -from relax.utils.data.processing_utils import load_processor +from relax.utils.data.processing_utils import ( + adapt_processor_kwargs, + expand_kimi_k25_placeholders, + load_processor, + remap_mm_train_inputs, +) from relax.utils.logging_utils import get_logger @@ -102,9 +107,16 @@ def process_sample_in_worker( # Videos arrive as shared-memory torch.Tensors — usable directly by the processor. # Audio arrives as numpy arrays — usable directly by the processor. - processor_output = _worker_processor(text=text, **restored, **processor_kwargs) + # Translate to the processor's native call shape (no-op for Qwen-VL etc.; + # rewrites images→medias and drops conflicting return_tensors for Kimi K2.x). + adapted = adapt_processor_kwargs(_worker_processor, restored, processor_kwargs) + processor_output = _worker_processor(text=text, **adapted) prompt_ids = processor_output["input_ids"][0] + # K2.x adapt_processor_kwargs forces return_tensors="pt", so + # input_ids is a 1D Tensor; downstream sample.tokens contract is list[int]. + if isinstance(prompt_ids, torch.Tensor): + prompt_ids = prompt_ids.tolist() mm_train_inputs = {} for k, v in processor_output.items(): @@ -117,7 +129,9 @@ def process_sample_in_worker( # contiguous() is required: share_memory_() does not support non-contiguous storage. mm_train_inputs[k] = v.contiguous().share_memory_() - return prompt_ids, mm_train_inputs or None + train_inputs = remap_mm_train_inputs(_worker_processor, mm_train_inputs or None) + prompt_ids = expand_kimi_k25_placeholders(_worker_processor, prompt_ids, train_inputs) + return prompt_ids, train_inputs except Exception as e: import traceback diff --git a/relax/utils/quant_cast.py b/relax/utils/quant_cast.py new file mode 100644 index 000000000..4a0ddb0c6 --- /dev/null +++ b/relax/utils/quant_cast.py @@ -0,0 +1,116 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Augment ``quantization_config.ignore`` at runtime for INT4 checkpoints.""" + +import os + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + + +def derive_extra_ignore_namespaces(hf_dir: str | os.PathLike) -> list[str]: + """Return top-level module names whose subtree has plain ``.weight`` keys + but zero ``.weight_packed`` triplets in the source checkpoint. + + Used to augment ``quantization_config["ignore"]`` at runtime when the + checkpoint's own ignore list misses namespaces that aren't quantized. + K2.6 INT4 release omits ``vision_tower`` / ``mm_projector`` from its + ignore list; without this augmentation the bridge tries to INT4-repack + those plain ``.weight`` tensors and SGLang errors with + ``ValueError: Weight X.weight_packed not found in params_dict``. + + Returns sorted top-level namespaces. Reads safetensors headers only + (no tensor data), so a 64-shard scan takes ~1-3s. + """ + from collections import defaultdict + + from safetensors import safe_open + + namespaces: dict[str, dict[str, bool]] = defaultdict(lambda: {"plain": False, "packed": False}) + for fname in os.listdir(hf_dir): + if not fname.endswith(".safetensors"): + continue + with safe_open(os.path.join(hf_dir, fname), framework="pt") as reader: + for key in reader.keys(): + top = key.split(".", 1)[0] + if key.endswith(".weight_packed"): + namespaces[top]["packed"] = True + elif key.endswith(".weight"): + namespaces[top]["plain"] = True + return sorted(top for top, info in namespaces.items() if info["plain"] and not info["packed"]) + + +def augment_compressed_tensors_ignore(quantization_config: dict | None, hf_dir: str | os.PathLike) -> dict | None: + """Return a copy of ``quantization_config`` whose ``ignore`` list is + augmented with any source-checkpoint top-level namespaces that have no + ``.weight_packed`` triplets (i.e. they aren't actually quantized). + + For K2.6-style models where only expert MLP weights are quantized, also + add patterns to ignore non-expert gate weights (e.g., model.layers.*.mlp.gate), + embedding layers, and head layers. + + Pass-through if ``quantization_config`` is None or not compressed-tensors. + """ + if not quantization_config or quantization_config.get("quant_method") != "compressed-tensors": + return quantization_config + extra = derive_extra_ignore_namespaces(hf_dir) + + existing = list(quantization_config.get("ignore", [])) + added = [] + + # Add auto-derived namespaces + for ns in extra: + pattern = f"re:.*{ns}.*" + if pattern not in existing and ns not in existing: + existing.append(pattern) + added.append(pattern) + + # For K2.6-style MoE models: ignore non-expert gate weights (only quantize expert gates) + # Pattern: any weight path with .mlp.gate (non-expert) should be ignored + # Experts are quantized, non-expert regular MLPs should not be re-quantized + moe_gate_pattern = "re:.*\\.mlp\\.gate\\.weight$" + if moe_gate_pattern not in existing: + # Check if there are expert weights being quantized + has_expert_quantized = any( + ".mlp.experts." in k for k in _list_all_keys(hf_dir) if k.endswith(".weight_packed") + ) + if has_expert_quantized: + existing.append(moe_gate_pattern) + added.append(moe_gate_pattern) + + # For VLM models: ignore embedding, output layers, and vision components + # These layers are often left in original precision (embed_tokens, lm_head, vision_tower, mm_projector) + unquantized_layers = ["embed_tokens", "lm_head", "vision_tower", "mm_projector"] + all_keys = _list_all_keys(hf_dir) + for layer_name in unquantized_layers: + pattern = f"re:.*\\.{layer_name}\\.weight$" + if pattern not in existing: + # Check if this layer exists in the checkpoint but is NOT quantized + has_plain_weight = any(f".{layer_name}.weight" in k for k in all_keys) + has_packed_weight = any(f".{layer_name}.weight_packed" in k for k in all_keys) + if has_plain_weight and not has_packed_weight: + existing.append(pattern) + added.append(pattern) + + if not added: + return quantization_config + logger.info(f"augmented quantization_config.ignore with auto-derived namespaces: {added}") + return {**quantization_config, "ignore": existing} + + +def _list_all_keys(hf_dir: str | os.PathLike) -> list[str]: + """Return all parameter keys from all safetensors files in hf_dir.""" + from safetensors import safe_open + + all_keys = [] + for fname in os.listdir(hf_dir): + if not fname.endswith(".safetensors"): + continue + try: + with safe_open(os.path.join(hf_dir, fname), framework="pt") as reader: + all_keys.extend(reader.keys()) + except Exception: + pass + return all_keys diff --git a/requirements.txt b/requirements.txt index 8c6abe491..5d289572b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -31,6 +31,7 @@ loguru av==17.0.0 transformers==5.3.0 huggingface_hub==1.7.2 +compressed_tensors>=0.13.0 blessed==1.38.0 gpustat dool diff --git a/scripts/entrypoint/ray-job.sh b/scripts/entrypoint/ray-job.sh index 0fa808124..1a31d9333 100755 --- a/scripts/entrypoint/ray-job.sh +++ b/scripts/entrypoint/ray-job.sh @@ -54,6 +54,22 @@ echo "=== Cleaning up residual python/sglang processes ===" ray serve shutdown -y python ${DIR}/../tools/run_on_each_ray_node.py ${DIR}/../tools/kill_for_ray.sh || echo "failed" +# ── reserve sglang port range from kernel ephemeral pool ──────────────────── +# Some worker nodes ship with net.ipv4.ip_local_port_range="10000 65500", which +# includes sglang's well-known port range (15670-15900). Megatron's 294 process +# groups grab ephemeral ports for NCCL/Gloo bootstrap; on those nodes a PG can +# land on a port sglang wants and crash the engine with +# "scheduler_input_port at 15855 is not available in 120 seconds. holder=ray::MegatronTrainRayActor". +# Reserve sglang's range so the kernel never picks it for ephemeral. +echo "=== Reserving sglang port ranges on all GPU nodes ===" +# Reserve two ranges: +# 15000-16800 — sglang port range. SGLang's dp-attention schedulers use ports +# starting from ~15100 (base_port + offsets for DP/TP ranks), so +# the range must start well below 15400 to cover all scheduler +# input/output/NCCL bootstrap ports. +# 30000-30300 — secondary safe zone (fallback if sglang port_base needs adjustment) +python ${DIR}/../tools/run_on_each_ray_node.py --timeout 30 "sysctl -w net.ipv4.ip_local_reserved_ports=15000-16800,30000-30300" || echo "reserve_ports failed (non-fatal)" + # kill old tasks ray job list | grep RUNNING | grep -v job_id=None | grep -oP "submission_id='\\K[^']+" | xargs ray job stop || true @@ -102,7 +118,9 @@ RAY_DEBUG_POST_MORTEM=${RAY_DEBUG_POST_MORTEM:-"0"} # Runtime env for ray-job mode (env inherited from Ray cluster) NVSHMEM_LIB_PATH="${NVSHMEM_LIB_PATH:-/usr/local/lib/python3.12/dist-packages/nvidia/nvshmem/lib}" -CURRENT_LD_LIBRARY_PATH="${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}${NVSHMEM_LIB_PATH}" +# torch lib path is required for fake_int4_quant_cuda.so to find libc10.so / libtorch.so +TORCH_LIB_PATH="${TORCH_LIB_PATH:-/usr/local/lib/python3.12/dist-packages/torch/lib}" +CURRENT_LD_LIBRARY_PATH="${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}${NVSHMEM_LIB_PATH}:${TORCH_LIB_PATH}" export RUNTIME_ENV_JSON="{ \"worker_process_setup_hook\": \"relax.utils.logging_utils.install_asyncio_noise_filter\", diff --git a/scripts/models/kimi-k2.6.sh b/scripts/models/kimi-k2.6.sh new file mode 100755 index 000000000..605569439 --- /dev/null +++ b/scripts/models/kimi-k2.6.sh @@ -0,0 +1,67 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +# Kimi K2.6 (KimiK25ForConditionalGeneration) language backbone is identical to +# K2-Thinking. The vision tower + mm_projector are populated by Megatron-Bridge's +# KimiK25VLBridge from the HF text_config / vision_config; no extra MODEL_ARGS are +# required on the Relax side. + +NLAYERS=61 +FIRST_K_DENSE_REPLACE=1 + +NHIDDEN=7168 +FFN_HIDDEN=18432 +NHEADS=64 + +MOE_ROUTED_EXPERTS=384 +MOE_ACTIVE_ROUTED_EXPERTS=8 +MOE_FFN_HIDDEN=2048 +MOE_SHARED_EXPERTS=1 +MOE_SHARED_EXPERT_INTERMEDIATE_SIZE=$((MOE_FFN_HIDDEN * MOE_SHARED_EXPERTS)) + +MODEL_ARGS=( + --num-layers $NLAYERS + --hidden-size $NHIDDEN + --ffn-hidden-size $FFN_HIDDEN + --num-attention-heads $NHEADS + --kv-channels 64 + --normalization RMSNorm + --norm-epsilon 1e-5 + --position-embedding-type rope + --disable-bias-linear + --swiglu + --untie-embeddings-and-output-weights + --vocab-size 163840 + + --multi-latent-attention + --q-lora-rank 1536 + --kv-lora-rank 512 + --qk-head-dim 128 + --qk-pos-emb-head-dim 64 + --v-head-dim 128 + --qk-layernorm + --rotary-scaling-factor 64.0 + --rotary-base 50000 + --mscale 1.0 + --mscale-all-dim 1.0 + --attention-softmax-in-fp32 + --no-rope-fusion + + --moe-layer-freq [0]*$FIRST_K_DENSE_REPLACE+[1]*$((NLAYERS - FIRST_K_DENSE_REPLACE)) + --num-experts $MOE_ROUTED_EXPERTS + --moe-ffn-hidden-size $MOE_FFN_HIDDEN + --moe-router-topk $MOE_ACTIVE_ROUTED_EXPERTS + --moe-shared-expert-intermediate-size $MOE_SHARED_EXPERT_INTERMEDIATE_SIZE + --moe-router-pre-softmax + --moe-router-score-function sigmoid + --moe-router-enable-expert-bias + --moe-router-load-balancing-type seq_aux_loss + --moe-token-dispatcher-type alltoall + --moe-aux-loss-coeff 0 + --moe-router-bias-update-rate 0 + --moe-router-group-topk 1 + --moe-router-num-groups 1 + --moe-grouped-gemm + --moe-router-topk-scaling-factor 2.827 + --moe-router-dtype fp32 + --moe-permute-fusion +) diff --git a/scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh b/scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh new file mode 100755 index 000000000..7eb6ed247 --- /dev/null +++ b/scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh @@ -0,0 +1,243 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Kimi K2.6 (KimiK25ForConditionalGeneration) 256xGPU colocate multimodal training, INT4 QAT. +# +# Canonical "INT4 inference + BF16 training (QAT)" form (slime-style), validated by +# slime's scripts/low_precision/run-kimi-k2-Thinking-int4.sh. The previous all-args-to- +# INT4-dir layout (which relied on the now-removed auto-cast resolver) is preserved as +# run-kimi-k2.6-256xgpu-int4-legacy.sh for reference. +# +# How it works: +# - SGLang inference loads the original compressed-tensors INT4 release directly. Its +# param dict registers weight_packed/weight_scale/weight_shape triplets. +# - Megatron training loads a pre-cast BF16 HF directory via bridge. fp32 master + +# BF16 working weights — forward GEMM stays BF16 throughout. +# - OPEN_TRAINING_INT4_FAKE_QAT_FLAG=1 + OPEN_TRAINING_INT4_GROUP_SIZE=32 trip the +# Megatron TEGroupedLinear._get_weight_tensors STE so each forward sees BF16 values +# rounded to the INT4 grid (group_size=32). Backward is straight-through. +# - On weight push, hf_config.quantization_config.quant_method == "compressed-tensors" +# auto-routes through quantize_params_compressed_tensors → BF16 → INT4 repack → +# SGLang in-place overwrites weight_packed/scale/shape buffers. +# +# Prerequisite (one-time): +# Cast the original INT4 release to BF16 HF for the training side: +# python -m relax.tools.quant_cast.convert_moe_int4_to_bf16 \ +# --model-dir ${MODEL_DIR}/Kimi-K2.6 \ +# --output-dir ${MODEL_DIR}/Kimi-K2.6_bf16 +# +# Model placement (TP=8 PP=8 CP=4 EP=32 ETP=1) matches the BF16 multimodal launcher; +# INT4 QAT only changes the weight-update path, not parallelism. +# +# Usage: +# bash scripts/entrypoint/ray-job.sh scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +echo "SCRIPT_DIR: $SCRIPT_DIR" +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/kimi-k2.6.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/kimi-k2.6-mm-int4}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" +NUM_ROLLOUT="${NUM_ROLLOUT:=200}" + +# Two checkpoints — distinct roles: +# HF_INT4 — original compressed-tensors release. Used for AutoConfig (tokenizer + +# quant_method + group_size for QAT) and SGLang inference load. +# HF_BF16 — pre-cast BF16 HF directory (see prerequisite in header). Used by +# Megatron bridge to load real training weights. +HF_INT4="${HF_INT4:-${MODEL_DIR}/Kimi-K2.6/}" +HF_BF16="${HF_BF16:-${MODEL_DIR}/Kimi-K2.6_bf16/}" + +CKPT_ARGS=( + # AutoConfig reads from here → hf_config.quantization_config.quant_method == + # "compressed-tensors" → push-side auto-repacks BF16 → INT4 (no env var needed). + --hf-checkpoint ${HF_INT4} + # SGLang loads the INT4 release directly so its param dict registers + # weight_packed/scale/shape. MUST be the INT4 dir, NOT the BF16 cast — otherwise + # weight pushes are dropped with "X.weight_packed not found in params_dict". + --sglang-hf-checkpoint ${HF_INT4} + # Megatron bridge loads BF16 HF here; the QAT STE rounds these to the INT4 grid + # on each forward. + --ref-load ${HF_BF16} + --megatron-to-hf-mode bridge + --save ${EXP_DIR}/Kimi-K2.6_mm_int4_ckpt/ + --save-interval 100 + --no-save-optim + --no-save-rng + --no-load-optim + --no-load-rng +) + +PROMPT_SET=${DATA_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet +SYSTEM_PROMPT="A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within and tags, respectively, i.e., reasoning process here answer here " + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + # --apply-chat-template-kwargs '{"thinking": false}' + --rollout-shuffle + --rm-type openr1mm + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 32 + --n-samples-per-prompt 16 + --rollout-max-prompt-len 2048 + --rollout-max-response-len 4096 + --rollout-temperature 1.0 + --global-batch-size 512 + --balance-data + --use-fault-tolerance + --rollout-health-check-timeout 120 + --system-prompt "${SYSTEM_PROMPT}" + --multimodal-keys '{"image":"image"}' + --image-max-token-num 256 + --use-streaming-dataset +) + +PERF_ARGS=( + --tensor-model-parallel-size 8 + --sequence-parallel + --pipeline-model-parallel-size 8 + --context-parallel-size 4 + --calculate-per-token-loss + --expert-model-parallel-size 32 + --expert-tensor-parallel-size 1 + --decoder-first-pipeline-num-layers 1 + --decoder-last-pipeline-num-layers 6 + --vision-dp-when-tp + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --use-dynamic-batch-size + --max-tokens-per-gpu 16384 +) + +GRPO_ARGS=( + --advantage-estimator grpo + # --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 5e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + --no-pin-cpu-grads + --no-pin-cpu-params + + # NOTE(wuhuan): VLM training stability — disable rope fusion and MoE aux loss + --no-rope-fusion + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 16 + --sglang-mem-fraction-static 0.7 + # dp attention + --sglang-enable-dp-attention + --sglang-dp-size 16 + --sglang-moe-dense-tp-size 1 + --sglang-enable-dp-lm-head + --sglang-ep-size 16 + + --sglang-load-format dummy + # --sglang-disable-cuda-graph + --sglang-cuda-graph-max-bs 8 + --sglang-server-concurrency 1024 + --sglang-watchdog-timeout 3600 + --sglang-enable-nan-detection +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name Kimi-K2.6-mm-256xgpu-int4-${now} +) + +MISC_ARGS=( + --attention-dropout 0.0 + --hidden-dropout 0.0 + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + --attention-backend flash + --trust-remote-code + --update-weight-buffer-size $(( 4 * 512 * 1024 * 1024 )) \ +) + +# Inject INT4 QAT + networking env vars into Ray's runtime env. The base +# RUNTIME_ENV_JSON is assembled by scripts/entrypoint/{ray-job,local,spmd-multinode}.sh; +# merge with python rather than re-templating the whole JSON so we stay in sync with the +# entrypoint. Mirrors run-kimi-k2.6-256xgpu-bf16.sh (Gloo/NCCL pinning) plus the two +# QAT-only env vars. +RUNTIME_ENV_JSON=$(python3 -c ' +import json, os +d = json.loads(os.environ["RUNTIME_ENV_JSON"]) +d.setdefault("env_vars", {}).update({ + "TORCH_DIST_INIT_BARRIER": "1", + "TORCH_NCCL_BLOCKING_WAIT": "0", + "TORCH_NCCL_ASYNC_ERROR_HANDLING": "1", + "TORCH_DISTRIBUTED_DEFAULT_TIMEOUT": "3600", + # DeepEP low-latency dispatch buffer cap. Default 128 collides with cuda_graph + # capture at bs=128 (DP-attention pads x.size(0) past 128). Bumping to 256 + # covers the padded capture batch without changing dispatch numerics. + "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256", + # INT4 QAT — fake-quantize BF16 weights to INT4 grid in forward via STE, + # and read group_size=32 for the per-group scale layout. + "OPEN_TRAINING_INT4_FAKE_QAT_FLAG": "1", + "OPEN_TRAINING_INT4_GROUP_SIZE": "32", +}) +print(json.dumps(d)) +') +export RUNTIME_ENV_JSON + +mkdir -p log +mkdir -p save +# stdbuf -oL -eL forces line-buffered stdout/stderr through the | tee pipeline so +# the local log streams in real time. Critical when --no-wait is NOT set (ray job +# submit then blocks streaming live logs) — without stdbuf the pipe block-buffers +# 8K at a time and the tee'd file looks frozen for minutes. +stdbuf -oL -eL ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 256], "rollout": [1, 256]}'\ + --max-staleness 0 \ + --num-data-storage-units 32 \ + --colocate \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/Kimi-K2.6-mm-256xgpu-int4-${now}.log diff --git a/scripts/training/text/run-kimi-k2.6-256xgpu-int4.sh b/scripts/training/text/run-kimi-k2.6-256xgpu-int4.sh new file mode 100644 index 000000000..46231f48b --- /dev/null +++ b/scripts/training/text/run-kimi-k2.6-256xgpu-int4.sh @@ -0,0 +1,221 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Kimi K2.6 256xGPU text-only colocate training, INT4 QAT. +# +# Canonical "INT4 inference + BF16 training (QAT)" form — mirrors the multimodal +# launcher (run-kimi-k2.6-256xgpu-int4.sh) with text-only data and algorithm settings. +# +# How it works: +# - SGLang inference loads the original compressed-tensors INT4 release directly. +# - Megatron training loads a pre-cast BF16 HF directory via bridge. +# - OPEN_TRAINING_INT4_FAKE_QAT_FLAG=1 + OPEN_TRAINING_INT4_GROUP_SIZE=32 trip the +# Megatron TEGroupedLinear._get_weight_tensors STE so each forward sees BF16 values +# rounded to the INT4 grid (group_size=32). Backward is straight-through. +# - On weight push, hf_config.quantization_config.quant_method == "compressed-tensors" +# auto-routes through quantize_params_compressed_tensors → BF16 → INT4 repack. +# +# Model placement (TP=8 PP=8 CP=4 EP=32 ETP=1) matches the multimodal launcher. +# SGLang inference uses 16 GPUs per engine with DP-attention (dp_size=16, ep_size=16). +# +# Prerequisite (one-time): +# Cast the original INT4 release to BF16 HF for the training side: +# python -m relax.tools.quant_cast.convert_moe_int4_to_bf16 \ +# --model-dir ${MODEL_DIR}/Kimi-K2.6 \ +# --output-dir ${MODEL_DIR}/Kimi-K2.6_bf16 +# +# Usage: +# bash scripts/entrypoint/ray-job.sh scripts/training/text/run-kimi-k2.6-256xgpu-bf16.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +echo "SCRIPT_DIR: $SCRIPT_DIR" +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/kimi-k2.6.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/kimi-k2.6-text-int4}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" +NUM_ROLLOUT="${NUM_ROLLOUT:=200}" + +# Two checkpoints — distinct roles: +# HF_INT4 — original compressed-tensors release. Used for AutoConfig (tokenizer + +# quant_method + group_size for QAT) and SGLang inference load. +# HF_BF16 — pre-cast BF16 HF directory (see prerequisite in header). Used by +# Megatron bridge to load real training weights. +HF_INT4="${HF_INT4:-${MODEL_DIR}/Kimi-K2.6/}" +HF_BF16="${HF_BF16:-${MODEL_DIR}/Kimi-K2.6_bf16/}" + +CKPT_ARGS=( + --hf-checkpoint ${HF_INT4} + --sglang-hf-checkpoint ${HF_INT4} + --ref-load ${HF_BF16} + --megatron-to-hf-mode bridge + --save ${EXP_DIR}/Kimi-K2.6_text_int4_ckpt/ + --save-interval 50 + --no-save-optim + --no-save-rng + --no-load-optim + --no-load-rng +) + +PROMPT_SET=${DATA_DIR}/dapo-math-17k/dapo-math-17k.jsonl + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --num-rollout ${NUM_ROLLOUT} + --use-fault-tolerance + --rollout-health-check-timeout 120 + + --rm-type math + + --rollout-batch-size 32 + --n-samples-per-prompt 8 + --rollout-max-response-len 16384 + --rollout-temperature 1 + + --global-batch-size 256 + --balance-data +) + +EVAL_ARGS=( + --skip-eval-before-train + --log-passrate + --eval-interval 20 + --eval-prompt-data aime ${DATA_DIR}/aime-2024/aime-2024.jsonl + --n-samples-per-eval-prompt 8 + --eval-max-response-len 16384 + --eval-top-p 0.7 +) + +PERF_ARGS=( + --tensor-model-parallel-size 8 + --sequence-parallel + --pipeline-model-parallel-size 8 + --context-parallel-size 4 + --calculate-per-token-loss + --expert-model-parallel-size 32 + --expert-tensor-parallel-size 1 + --decoder-last-pipeline-num-layers 5 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --use-dynamic-batch-size + --max-tokens-per-gpu 16384 +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + --no-pin-cpu-grads + --no-pin-cpu-params + + --no-rope-fusion + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 16 + --sglang-mem-fraction-static 0.7 + # dp attention + --sglang-enable-dp-attention + --sglang-dp-size 16 + --sglang-moe-dense-tp-size 1 + --sglang-enable-dp-lm-head + --sglang-ep-size 16 + + --sglang-cuda-graph-max-bs 8 + --sglang-server-concurrency 1024 + --sglang-watchdog-timeout 3600 + --sglang-enable-nan-detection +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name Kimi-K2.6-text-256xgpu-int4-${now} +) + +MISC_ARGS=( + --attention-dropout 0.0 + --hidden-dropout 0.0 + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + --attention-backend flash + --no-check-for-nan-in-loss-and-grad + --trust-remote-code + --update-weight-buffer-size $(( 4 * 512 * 1024 * 1024 )) \ +) + +RUNTIME_ENV_JSON=$(python3 -c ' +import json, os +d = json.loads(os.environ["RUNTIME_ENV_JSON"]) +d.setdefault("env_vars", {}).update({ + "TORCH_DIST_INIT_BARRIER": "1", + "TORCH_NCCL_BLOCKING_WAIT": "0", + "TORCH_NCCL_ASYNC_ERROR_HANDLING": "1", + "TORCH_DISTRIBUTED_DEFAULT_TIMEOUT": "3600", + "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256", + "OPEN_TRAINING_INT4_FAKE_QAT_FLAG": "1", + "OPEN_TRAINING_INT4_GROUP_SIZE": "32", +}) +print(json.dumps(d)) +') +export RUNTIME_ENV_JSON + +mkdir -p log +mkdir -p save +stdbuf -oL -eL ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 256], "rollout": [1, 256]}'\ + --max-staleness 0 \ + --num-data-storage-units 32 \ + --colocate \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/Kimi-K2.6-text-256xgpu-int4-${now}.log diff --git a/tests/backends/__init__.py b/tests/backends/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/backends/megatron/__init__.py b/tests/backends/megatron/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/backends/megatron/weight_update/__init__.py b/tests/backends/megatron/weight_update/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/backends/megatron/weight_update/test_broadcast_quantized.py b/tests/backends/megatron/weight_update/test_broadcast_quantized.py new file mode 100644 index 000000000..3a0a68836 --- /dev/null +++ b/tests/backends/megatron/weight_update/test_broadcast_quantized.py @@ -0,0 +1,506 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Tests for _broadcast_quantized_phase and _broadcast_quantized_bucket. + +These functions broadcast already-quantized INT4 expert tensors across PP and +EP process groups using only NCCL (``dist.all_reduce`` for metadata, +``dist.broadcast`` for data tensors). We mock ``torch.distributed`` and +``mpu`` to simulate multi-rank scenarios. +""" + +from __future__ import annotations + +import sys +from unittest.mock import MagicMock, patch + +import torch + +from relax.utils.types import ParamInfo + + +# --------------------------------------------------------------------------- +# Module-level mocking: stub out megatron.core so we can import without GPU. +# --------------------------------------------------------------------------- + +_MEGATRON_MODULES = [ + "megatron", + "megatron.core", + "megatron.core.mpu", + "megatron.core.transformer", + "megatron.core.transformer.transformer_layer", + "megatron.core.tensor_parallel", + "megatron.bridge", + "megatron.bridge.models", +] + +_saved = {} +for _mod in _MEGATRON_MODULES: + if _mod in sys.modules: + _saved[_mod] = sys.modules[_mod] + sys.modules[_mod] = MagicMock() + +from relax.backends.megatron.weight_update.hf_weight_iterator_bridge import ( # noqa: E402 + _broadcast_quantized_bucket, + _broadcast_quantized_phase, + _compute_slot_size, + _decode_metadata, + _encode_metadata, +) + + +for _mod, _orig in _saved.items(): + sys.modules[_mod] = _orig + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _make_param_info(name: str, src_rank: int, shape=(4, 8)) -> ParamInfo: + return ParamInfo( + name=name, + dtype=torch.float32, + shape=torch.Size(shape), + attrs={}, + size=4 * 8 * 4, + src_rank=src_rank, + ) + + +def _make_converted(name: str, value: float = 1.0): + """Simulate bridge_converter.convert() output: list of (name, tensor).""" + return [ + (f"{name}.weight_packed", torch.full((4, 1), value, dtype=torch.int32)), + (f"{name}.weight_scale", torch.full((1, 8), value, dtype=torch.float16)), + ] + + +class FakeHandle: + def wait(self): + pass + + +def _make_phase_mocks(all_converted_per_rank, bucket_infos, group_ranks): + """Build side_effects for dist.all_reduce and dist.broadcast. + + dist.all_reduce: + - 1st call (numel=1): slot_size allreduce(MAX) — compute max slot_size + - 2nd call (numel>1): metadata allreduce(SUM) — sum all ranks' metadata + dist.broadcast: no-op for data tensors (returns FakeHandle for async). + """ + group_ranks_set = set(group_ranks) + + # Pre-compute slot_size across all ranks + all_slot_sizes = [] + for r in group_ranks: + ac = all_converted_per_rank.get(r) + if ac is not None: + all_slot_sizes.append(_compute_slot_size(ac, bucket_infos)) + max_slot_size = max(all_slot_sizes) if all_slot_sizes else 2 + + # Pre-compute metadata tensors for each rank at max_slot_size + meta_cache = {} + for r in group_ranks: + ac = all_converted_per_rank.get(r) + if ac is not None: + meta_cache[r] = _encode_metadata(ac, bucket_infos, group_ranks_set, r, max_slot_size) + + allreduce_call_count = [0] + + def fake_all_reduce(tensor, op=None, group=None): + allreduce_call_count[0] += 1 + if tensor.numel() == 1: + tensor.fill_(max_slot_size) + else: + result = torch.zeros_like(tensor) + for r in group_ranks: + if r in meta_cache: + result += meta_cache[r].to(tensor.device) + tensor.copy_(result) + + def fake_broadcast(tensor, src, group=None, async_op=False): + if async_op: + return FakeHandle() + + return fake_all_reduce, fake_broadcast + + +# --------------------------------------------------------------------------- +# Metadata encode/decode tests +# --------------------------------------------------------------------------- + + +class TestMetadataEncodeDecode: + """Test _encode_metadata / _decode_metadata roundtrip.""" + + def test_roundtrip_single(self): + infos = [_make_param_info("a.experts.0.gate_proj", src_rank=0)] + converted = _make_converted("a.experts.0.gate_proj", value=1.0) + slot_size = _compute_slot_size([converted], infos) + meta_t = _encode_metadata([converted], infos, {0}, rank=0, slot_size=slot_size) + slots = _decode_metadata(meta_t, slot_size) + assert len(slots) == 1 + src, tensors_meta = slots[0] + assert src == 0 + assert len(tensors_meta) == 2 + assert tensors_meta[0][0] == "a.experts.0.gate_proj.weight_packed" + assert tensors_meta[0][1] == (4, 1) + assert tensors_meta[0][2] == torch.int32 + assert tensors_meta[1][0] == "a.experts.0.gate_proj.weight_scale" + + def test_roundtrip_with_none(self): + infos = [ + _make_param_info("a.experts.0.gate_proj", src_rank=0), + _make_param_info("a.experts.1.gate_proj", src_rank=2), + ] + converted = _make_converted("a.experts.0.gate_proj") + slot_size = _compute_slot_size([converted, None], infos) + meta_t = _encode_metadata([converted, None], infos, {0, 1}, rank=0, slot_size=slot_size) + slots = _decode_metadata(meta_t, slot_size) + assert len(slots) == 2 + assert slots[0] is not None + assert slots[1] is None + + def test_allreduce_sum_correctness(self): + """Verify that SUM of two ranks' fixed-width metadata produces correct + merged result.""" + infos = [ + _make_param_info("a.experts.0.gate_proj", src_rank=0), + _make_param_info("a.experts.1.gate_proj", src_rank=1), + ] + c0 = _make_converted("a.experts.0.gate_proj") + c1 = _make_converted("a.experts.1.gate_proj") + + slot_size = max( + _compute_slot_size([c0, None], infos), + _compute_slot_size([None, c1], infos), + ) + meta_r0 = _encode_metadata([c0, None], infos, {0, 1}, rank=0, slot_size=slot_size) + meta_r1 = _encode_metadata([None, c1], infos, {0, 1}, rank=1, slot_size=slot_size) + + merged = meta_r0 + meta_r1 + + slots = _decode_metadata(merged, slot_size) + assert len(slots) == 2 + assert slots[0] is not None + assert slots[1] is not None + assert slots[0][0] == 0 + assert slots[1][0] == 1 + + +# --------------------------------------------------------------------------- +# _broadcast_quantized_phase tests +# --------------------------------------------------------------------------- + + +class TestBroadcastQuantizedPhase: + """Test _broadcast_quantized_phase with various group configurations.""" + + @staticmethod + def _run_phase(bucket_infos, all_converted_per_rank, group_ranks, current_rank): + group = MagicMock() + fake_ar, fake_bcast = _make_phase_mocks(all_converted_per_rank, bucket_infos, group_ranks) + + with ( + patch("torch.distributed.get_process_group_ranks", return_value=group_ranks), + patch("torch.distributed.all_reduce", side_effect=fake_ar), + patch("torch.distributed.broadcast", side_effect=fake_bcast), + ): + return _broadcast_quantized_phase( + bucket_infos, + all_converted_per_rank[current_rank], + device="cpu", + rank=current_rank, + group=group, + ) + + def test_owner_keeps_data(self): + """Owner rank's converted data passes through unchanged.""" + infos = [_make_param_info("layer0.experts.0.gate_proj", src_rank=0)] + converted_0 = _make_converted("layer0.experts.0.gate_proj", value=42.0) + all_per_rank = {0: [converted_0]} + + result = self._run_phase(infos, all_per_rank, group_ranks=[0], current_rank=0) + + assert result[0] is not None + assert len(result[0]) == 2 + assert result[0][0][0] == "layer0.experts.0.gate_proj.weight_packed" + assert torch.equal(result[0][0][1], converted_0[0][1]) + + def test_non_owner_receives_data(self): + """Non-owner rank receives tensors with correct shapes and dtypes.""" + infos = [_make_param_info("layer0.experts.0.gate_proj", src_rank=0)] + converted_0 = _make_converted("layer0.experts.0.gate_proj", value=42.0) + all_per_rank = { + 0: [converted_0], + 1: [None], + } + + result = self._run_phase(infos, all_per_rank, group_ranks=[0, 1], current_rank=1) + + assert result[0] is not None + assert len(result[0]) == 2 + assert result[0][0][0] == "layer0.experts.0.gate_proj.weight_packed" + assert result[0][0][1].shape == converted_0[0][1].shape + assert result[0][0][1].dtype == converted_0[0][1].dtype + + def test_multiple_params_mixed_ownership(self): + """Two params owned by different ranks in the same group.""" + infos = [ + _make_param_info("layer0.experts.0.gate_proj", src_rank=0), + _make_param_info("layer1.experts.0.gate_proj", src_rank=1), + ] + converted_0 = _make_converted("layer0.experts.0.gate_proj", value=10.0) + converted_1 = _make_converted("layer1.experts.0.gate_proj", value=20.0) + all_per_rank = { + 0: [converted_0, None], + 1: [None, converted_1], + } + + r0 = self._run_phase(infos, all_per_rank, group_ranks=[0, 1], current_rank=0) + assert r0[0] is not None + assert r0[1] is not None + assert r0[0][0][0] == "layer0.experts.0.gate_proj.weight_packed" + assert r0[1][0][0] == "layer1.experts.0.gate_proj.weight_packed" + + r1 = self._run_phase(infos, all_per_rank, group_ranks=[0, 1], current_rank=1) + assert r1[0] is not None + assert r1[1] is not None + + def test_foreign_params_skipped(self): + """Params whose src_rank is not in the group remain None.""" + infos = [ + _make_param_info("layer0.experts.0.gate_proj", src_rank=0), + _make_param_info("layer0.experts.1.gate_proj", src_rank=2), + ] + converted_0 = _make_converted("layer0.experts.0.gate_proj") + all_per_rank = { + 0: [converted_0, None], + 1: [None, None], + } + + result = self._run_phase(infos, all_per_rank, group_ranks=[0, 1], current_rank=1) + assert result[0] is not None + assert result[1] is None + + def test_src_rank_fallback_to_current_rank(self): + """When info.src_rank is not in group_ranks, src falls back to current + rank.""" + infos = [_make_param_info("layer5.experts.0.gate_proj", src_rank=5)] + converted = _make_converted("layer5.experts.0.gate_proj") + all_per_rank = { + 0: [converted], + 8: [None], + } + + result = self._run_phase(infos, all_per_rank, group_ranks=[0, 8], current_rank=0) + assert result[0] is not None + + result_8 = self._run_phase(infos, all_per_rank, group_ranks=[0, 8], current_rank=8) + assert result_8[0] is not None + + +# --------------------------------------------------------------------------- +# _broadcast_quantized_bucket tests +# --------------------------------------------------------------------------- + + +class TestBroadcastQuantizedBucket: + """Test _broadcast_quantized_bucket end-to-end.""" + + @staticmethod + def _run_bucket(bucket_infos, all_converted, pp_size, ep_size): + with ( + patch("relax.backends.megatron.weight_update.hf_weight_iterator_bridge.dist") as mock_dist, + patch("relax.backends.megatron.weight_update.hf_weight_iterator_bridge.mpu") as mock_mpu, + ): + mock_dist.get_rank.return_value = 0 + mock_mpu.get_pipeline_model_parallel_world_size.return_value = pp_size + mock_mpu.get_expert_model_parallel_world_size.return_value = ep_size + mock_mpu.get_pipeline_model_parallel_group.return_value = MagicMock() + mock_mpu.get_expert_model_parallel_group.return_value = MagicMock() + + return _broadcast_quantized_bucket(bucket_infos, all_converted, device="cpu") + + def test_no_broadcast_pp1_ep1(self): + """PP=1, EP=1: just flatten all_converted.""" + infos = [ + _make_param_info("layer0.experts.0.gate_proj", src_rank=0), + _make_param_info("layer0.experts.0.up_proj", src_rank=0), + ] + c0 = _make_converted("layer0.experts.0.gate_proj") + c1 = _make_converted("layer0.experts.0.up_proj") + + result = self._run_bucket(infos, [c0, c1], pp_size=1, ep_size=1) + + assert len(result) == 4 + names = [name for name, _ in result] + assert "layer0.experts.0.gate_proj.weight_packed" in names + assert "layer0.experts.0.gate_proj.weight_scale" in names + assert "layer0.experts.0.up_proj.weight_packed" in names + assert "layer0.experts.0.up_proj.weight_scale" in names + + def test_none_entries_skipped(self): + """None entries in all_converted produce no output.""" + infos = [ + _make_param_info("layer0.experts.0.gate_proj", src_rank=0), + _make_param_info("layer0.experts.1.gate_proj", src_rank=8), + ] + c0 = _make_converted("layer0.experts.0.gate_proj") + + result = self._run_bucket(infos, [c0, None], pp_size=1, ep_size=1) + + assert len(result) == 2 + names = [name for name, _ in result] + assert "layer0.experts.0.gate_proj.weight_packed" in names + + def test_nccl_only_no_gloo(self): + """Verify only dist.all_reduce and dist.broadcast are called (no + broadcast_object_list or all_gather_object).""" + infos = [_make_param_info("layer0.experts.0.gate_proj", src_rank=0)] + converted = _make_converted("layer0.experts.0.gate_proj") + group = MagicMock() + + allreduce_calls = [] + broadcast_calls = [] + gloo_calls = [] + + def capture_allreduce(tensor, op=None, group=None): + allreduce_calls.append({"numel": tensor.numel()}) + + def capture_broadcast(tensor, src, group=None, async_op=False): + broadcast_calls.append({"dtype": tensor.dtype, "src": src}) + if async_op: + return FakeHandle() + + def capture_gloo(*args, **kwargs): + gloo_calls.append(True) + + with ( + patch("torch.distributed.get_process_group_ranks", return_value=[0]), + patch("torch.distributed.all_reduce", side_effect=capture_allreduce), + patch("torch.distributed.broadcast", side_effect=capture_broadcast), + patch("torch.distributed.broadcast_object_list", side_effect=capture_gloo), + patch("torch.distributed.all_gather_object", side_effect=capture_gloo), + ): + _broadcast_quantized_phase(infos, [converted], "cpu", rank=0, group=group) + + assert len(allreduce_calls) == 2 + assert len(broadcast_calls) > 0 + assert len(gloo_calls) == 0 + + +# --------------------------------------------------------------------------- +# Integration-style test: simulate PP=2 x EP=2 (4 ranks) +# --------------------------------------------------------------------------- + + +class TestPPxEPIntegration: + """Simulate a 4-rank setup: PP=2, EP=2. + + Rank layout: + rank 0: PP stage 0, EP shard 0 -- owns experts.0 from layer 0 + rank 1: PP stage 1, EP shard 0 -- owns experts.0 from layer 1 + rank 2: PP stage 0, EP shard 1 -- owns experts.1 from layer 0 + rank 3: PP stage 1, EP shard 1 -- owns experts.1 from layer 1 + + PP groups: [0, 1], [2, 3] + EP groups: [0, 2], [1, 3] + """ + + BUCKET_INFOS = [ + _make_param_info("layer0.experts.0.gate_proj", src_rank=0), + _make_param_info("layer1.experts.0.gate_proj", src_rank=1), + _make_param_info("layer0.experts.1.gate_proj", src_rank=2), + _make_param_info("layer1.experts.1.gate_proj", src_rank=3), + ] + + @staticmethod + def _build_all_converted(rank): + result = [None, None, None, None] + names = [ + "layer0.experts.0.gate_proj", + "layer1.experts.0.gate_proj", + "layer0.experts.1.gate_proj", + "layer1.experts.1.gate_proj", + ] + result[rank] = _make_converted(names[rank], value=float(rank + 1)) + return result + + def _simulate_rank(self, rank, pp_group, ep_group): + all_converted = self._build_all_converted(rank) + + # --- PP phase --- + pp_group_mock = MagicMock() + all_converted_pp = {r: self._build_all_converted(r) for r in pp_group} + fake_ar_pp, fake_bcast_pp = _make_phase_mocks(all_converted_pp, self.BUCKET_INFOS, pp_group) + + with ( + patch("torch.distributed.get_process_group_ranks", return_value=pp_group), + patch("torch.distributed.all_reduce", side_effect=fake_ar_pp), + patch("torch.distributed.broadcast", side_effect=fake_bcast_pp), + ): + all_converted = _broadcast_quantized_phase( + self.BUCKET_INFOS, all_converted, "cpu", rank=rank, group=pp_group_mock + ) + + # --- EP phase --- + ep_group_mock = MagicMock() + ep_results = {} + for r in ep_group: + other_pp_group = [0, 1] if r in [0, 1] else [2, 3] + other_pp_members = {rr: self._build_all_converted(rr) for rr in other_pp_group} + pp_result = [None] * 4 + for rr in other_pp_group: + for idx, c in enumerate(other_pp_members[rr]): + if c is not None: + pp_result[idx] = c + ep_results[r] = pp_result + + fake_ar_ep, fake_bcast_ep = _make_phase_mocks(ep_results, self.BUCKET_INFOS, ep_group) + + with ( + patch("torch.distributed.get_process_group_ranks", return_value=ep_group), + patch("torch.distributed.all_reduce", side_effect=fake_ar_ep), + patch("torch.distributed.broadcast", side_effect=fake_bcast_ep), + ): + all_converted = _broadcast_quantized_phase( + self.BUCKET_INFOS, all_converted, "cpu", rank=rank, group=ep_group_mock + ) + + return all_converted + + def test_rank0_receives_all(self): + """Rank 0 (PP group [0,1], EP group [0,2]) ends up with all 4 + params.""" + result = self._simulate_rank(rank=0, pp_group=[0, 1], ep_group=[0, 2]) + for i in range(4): + assert result[i] is not None, f"param index {i} is None after PP+EP broadcast" + assert len(result[i]) == 2 + + def test_rank3_receives_all(self): + """Rank 3 (PP group [2,3], EP group [1,3]) ends up with all 4 + params.""" + result = self._simulate_rank(rank=3, pp_group=[2, 3], ep_group=[1, 3]) + for i in range(4): + assert result[i] is not None, f"param index {i} is None after PP+EP broadcast" + + def test_all_names_present(self): + """All 4 param names x 2 tensors each = 8 named tensors in final output.""" + result = self._simulate_rank(rank=0, pp_group=[0, 1], ep_group=[0, 2]) + all_names = [] + for converted in result: + if converted is not None: + all_names.extend(n for n, _ in converted) + expected_names = { + "layer0.experts.0.gate_proj.weight_packed", + "layer0.experts.0.gate_proj.weight_scale", + "layer1.experts.0.gate_proj.weight_packed", + "layer1.experts.0.gate_proj.weight_scale", + "layer0.experts.1.gate_proj.weight_packed", + "layer0.experts.1.gate_proj.weight_scale", + "layer1.experts.1.gate_proj.weight_packed", + "layer1.experts.1.gate_proj.weight_scale", + } + assert set(all_names) == expected_names diff --git a/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py b/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py index c94399767..9329834e9 100644 --- a/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py +++ b/tests/distributed/checkpoint_service/test_dcs_weight_conversion.py @@ -6,7 +6,6 @@ objects — no hand-written logic duplication. Covers: -- ``_collect_all_mappings``: recursive mapping discovery with real Bridge mappings - Real Bridge mapping ``megatron_to_hf`` output + post-processing correctness - ``strip_param_name_prefix``, ``remove_padding``, ``quantize_params`` """ @@ -25,24 +24,24 @@ # and the DeviceDirectBackend, which imports ``megatron.core`` at module level. pytest.importorskip("megatron.core") pytest.importorskip("megatron.bridge") +pytest.importorskip("megatron.bridge.models.conversion.param_mapping") # Real Bridge mapping classes. The model-specific ExpertMLP*ProjMapping # classes were unified into the generic Fused{,Gated}ExpertMapping classes # in megatron-bridge. We alias the new names to the old test-local names so # the rest of this file keeps reading naturally; the Qwen3-VL / Qwen3.5 # variants now resolve to the *same* class object. -from megatron.bridge.models.conversion.param_mapping import ( # noqa: E402 - AutoMapping, - GatedMLPMapping, - MegatronParamMapping, - ReplicatedMapping, -) from megatron.bridge.models.conversion.param_mapping import ( FusedExpertMapping as ExpertMLPDownProjMapping, ) from megatron.bridge.models.conversion.param_mapping import ( FusedGatedExpertMapping as ExpertMLPGateUpProjMapping, ) +from megatron.bridge.models.conversion.param_mapping import ( # noqa: E402 + GatedMLPMapping, + MegatronParamMapping, + ReplicatedMapping, +) Qwen35ExpertMLPDownProjMapping = ExpertMLPDownProjMapping @@ -50,7 +49,6 @@ from relax.backends.megatron.misc_utils import strip_param_name_prefix # noqa: E402 from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding # noqa: E402 -from relax.distributed.checkpoint_service.backends.device_direct import DeviceDirectBackend # noqa: E402 # ─── Helpers ────────────────────────────────────────────────────────────────── @@ -196,99 +194,6 @@ def _apply_expert_postprocessing( return converted_named_tensors -# ─── Tests for _collect_all_mappings with REAL Bridge mappings ──────────────── - - -class TestCollectAllMappings: - """Test ``DeviceDirectBackend._collect_all_mappings`` with real Bridge - mapping objects.""" - - def test_replicated_mapping_single(self): - """A single ReplicatedMapping returns just itself.""" - m = ReplicatedMapping("decoder.layers.0.weight", "model.layers.0.weight") - result = DeviceDirectBackend._collect_all_mappings(m) - assert len(result) == 1 - assert result[0] is m - assert isinstance(result[0], MegatronParamMapping) - - def test_gated_mlp_mapping_single(self): - """GatedMLPMapping has no sub-mappings, returns just itself.""" - m = GatedMLPMapping( - "decoder.layers.0.mlp.linear_fc1.weight", - gate="model.layers.0.mlp.gate_proj.weight", - up="model.layers.0.mlp.up_proj.weight", - ) - result = DeviceDirectBackend._collect_all_mappings(m) - assert len(result) == 1 - assert isinstance(result[0], GatedMLPMapping) - - def test_auto_mapping_with_initialized_inner(self): - """AutoMapping with eagerly initialized inner delegate collects - both.""" - m = AutoMapping( - "decoder.layers.0.self_attention.linear_proj.weight", - "model.layers.0.self_attn.o_proj.weight", - ) - m._detected_type = "replicated" - m._mapping = m._get_or_create_mapping("replicated") - - result = DeviceDirectBackend._collect_all_mappings(m) - assert len(result) == 2 - types = {type(r).__name__ for r in result} - assert types == {"AutoMapping", "ReplicatedMapping"} - - def test_expert_gate_up_mapping_discovers_gated_inner(self): - """ExpertMLPGateUpProjMapping has a _gated_mapping sub-mapping.""" - m = _make_expert_gate_up_mapping(layer_idx=0, expert_id=3) - result = DeviceDirectBackend._collect_all_mappings(m) - assert len(result) == 2 - # The recursive walk must discover the outer fused-gated-expert mapping - # plus its inner GatedMLPMapping. The inner is a private subclass in - # current megatron-bridge (``_LooseGatedMLPMapping``), so assert by - # ``isinstance`` rather than name equality to stay resilient to - # bridge-internal renames. - outer = [r for r in result if isinstance(r, ExpertMLPGateUpProjMapping)] - inner = [r for r in result if not isinstance(r, ExpertMLPGateUpProjMapping)] - assert len(outer) == 1 - assert len(inner) == 1 - assert isinstance(inner[0], GatedMLPMapping) - - def test_expert_down_mapping_discovers_replicated_inner(self): - """ExpertMLPDownProjMapping (AutoMapping subclass) with initialized - inner.""" - m = _make_expert_down_mapping(layer_idx=0, expert_id=3) - result = DeviceDirectBackend._collect_all_mappings(m) - assert len(result) == 2 - types = {type(r).__name__ for r in result} - assert types == {"FusedExpertMapping", "ReplicatedMapping"} - - def test_no_duplicate_on_shared_reference(self): - """If two attributes point to the same mapping object, it's collected - once.""" - inner = ReplicatedMapping("decoder.layers.0.weight", "model.layers.0.weight") - outer = AutoMapping("decoder.layers.0.weight2", "model.layers.0.weight2") - outer._detected_type = "replicated" - outer._mapping = inner - # Manually add another reference to the same object - outer._tp_mapping = inner - - result = DeviceDirectBackend._collect_all_mappings(outer) - # outer + inner (deduplicated even though referenced twice) - assert len(result) == 2 - - def test_process_groups_are_none_in_test_env(self): - """Verify that real mappings have None process groups (mpu not - initialized).""" - m = ReplicatedMapping("decoder.layers.0.weight", "model.layers.0.weight") - assert m.pp_group is None - assert m._tp_group is None - assert m._etp_group is None - assert m.ep_group is None - assert m.pp_size == 1 - assert m.tp_size == 1 - assert m.ep_size == 1 - - # ─── Tests for real Bridge mapping megatron_to_hf output ────────────────────── @@ -527,76 +432,6 @@ def test_non_expert_gated_mlp_no_postprocessing(self): assert torch.equal(postprocessed[1][1], up_expected) -# ─── Tests for process group patching with real mappings ────────────────────── - - -class TestProcessGroupPatching: - """Test process group save/restore with real Bridge mapping objects.""" - - def test_groups_patched_and_restored_on_real_mappings(self): - """Process groups are set to None and restored on real mapping - objects.""" - m = _make_expert_gate_up_mapping(layer_idx=0, expert_id=0) - all_mappings = DeviceDirectBackend._collect_all_mappings(m) - assert len(all_mappings) == 2 # ExpertMLPGateUpProjMapping + GatedMLPMapping - - # Save originals (all None in test env, but the mechanism is what matters) - saved_groups = [] - for mapping in all_mappings: - saved_groups.append((mapping.pp_group, mapping._tp_group, mapping._etp_group, mapping.ep_group)) - - # Patch - for mapping in all_mappings: - mapping.pp_group = None - mapping._tp_group = None - mapping._etp_group = None - mapping.ep_group = None - - # Verify patched - for mapping in all_mappings: - assert mapping.pp_size == 1 - assert mapping.tp_size == 1 - assert mapping.ep_size == 1 - - # Restore - for mapping, (pp, tp, etp, ep) in zip(all_mappings, saved_groups): - mapping.pp_group = pp - mapping._tp_group = tp - mapping._etp_group = etp - mapping.ep_group = ep - - # Verify restored - for mapping, (pp, tp, etp, ep) in zip(all_mappings, saved_groups): - assert mapping.pp_group == pp - assert mapping._tp_group == tp - assert mapping._etp_group == etp - assert mapping.ep_group == ep - - def test_gather_from_ep_ranks_monkey_patch_lifecycle(self): - """gather_from_ep_ranks is monkey-patched and cleanly removed on real - classes.""" - m = _make_expert_gate_up_mapping(layer_idx=0, expert_id=0) - all_mappings = DeviceDirectBackend._collect_all_mappings(m) - - # Verify gather_from_ep_ranks is NOT in any subclass __dict__ initially - for mapping in all_mappings: - assert "gather_from_ep_ranks" not in type(mapping).__dict__ - - with _patch_gather_from_ep_ranks(): - # During patch: method is in class __dict__ - for mapping in all_mappings: - cls = type(mapping) - # At least one of the patched classes should match - if cls in {ExpertMLPGateUpProjMapping, GatedMLPMapping}: - assert "gather_from_ep_ranks" in cls.__dict__ - - # After cleanup: method removed from class __dict__, inherited version restored - for mapping in all_mappings: - assert "gather_from_ep_ranks" not in type(mapping).__dict__ - # But the inherited method still exists via MRO - assert hasattr(mapping, "gather_from_ep_ranks") - - # ─── Tests for strip_param_name_prefix (real function) ──────────────────────── diff --git a/tests/distributed/ray/test_state_machine.py b/tests/distributed/ray/test_state_machine.py index 9ca9cadca..71942907e 100644 --- a/tests/distributed/ray/test_state_machine.py +++ b/tests/distributed/ray/test_state_machine.py @@ -325,6 +325,7 @@ def test_resolve_defaults(self): { "rollout_num_gpus_per_engine": 4, "hf_checkpoint": "/default/model", + "sglang_hf_checkpoint": None, }, )() cfg = ModelConfig( @@ -344,6 +345,7 @@ def test_resolve_per_group_override(self): { "rollout_num_gpus_per_engine": 4, "hf_checkpoint": "/default/model", + "sglang_hf_checkpoint": None, }, )() cfg = ModelConfig( diff --git a/tests/utils/data/test_processing_utils.py b/tests/utils/data/test_processing_utils.py new file mode 100644 index 000000000..561ce2f07 --- /dev/null +++ b/tests/utils/data/test_processing_utils.py @@ -0,0 +1,145 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Tests for relax.utils.data.processing_utils.adapt_processor_kwargs. + +Imports are deferred to fixtures because processing_utils pulls in the heavy +imageio / soundfile / transformers / torch stack at module level, which trips a +numpy ABI mismatch in this CI image during pytest collection. +""" + +import pytest + + +@pytest.fixture(scope="module") +def adapt_processor_kwargs(): + from relax.utils.data.processing_utils import adapt_processor_kwargs as fn + + return fn + + +class _FakeQwenVLProcessor: + """Stand-in for a standard HF VLM processor (Qwen-VL / Qwen-Omni shape).""" + + def __call__(self, text=None, images=None, videos=None, audio=None, **kwargs): + raise AssertionError("not invoked in tests") + + +class KimiK25Processor: + """Class-name match for the K2.x adapter branch — body is irrelevant.""" + + def __call__(self, messages=None, medias=None, text=None, return_tensors="pt", **kwargs): + raise AssertionError("not invoked in tests") + + +class KimiK26Processor(KimiK25Processor): + """Future K2.x variants must keep getting the K2 adapter via class-name + prefix.""" + + +def test_adapt_processor_kwargs_default_passthrough(adapt_processor_kwargs): + proc = _FakeQwenVLProcessor() + mm = {"images": ["pil_img_1"], "videos": [], "audio": []} + extra = {"return_tensors": None, "images_kwargs": {"return_tensors": "pt"}} + + out = adapt_processor_kwargs(proc, mm, extra) + + assert out == {**mm, **extra}, "Non-K2 processors must see the original shape unchanged" + + +def test_adapt_processor_kwargs_default_handles_none_inputs(adapt_processor_kwargs): + proc = _FakeQwenVLProcessor() + assert adapt_processor_kwargs(proc, None, None) == {} + assert adapt_processor_kwargs(proc, None, {"foo": 1}) == {"foo": 1} + assert adapt_processor_kwargs(proc, {"images": ["x"]}, None) == {"images": ["x"]} + + +def test_adapt_processor_kwargs_kimi_k25_translates_images_to_medias(adapt_processor_kwargs): + proc = KimiK25Processor() + mm = {"images": ["pil_a", "pil_b"], "videos": [], "audio": []} + + out = adapt_processor_kwargs(proc, mm, extra_kwargs={"return_tensors": None}) + + assert out == { + "medias": [ + {"type": "image", "image": "pil_a"}, + {"type": "image", "image": "pil_b"}, + ], + # Forced "pt" wins over the None coming from build_processor_kwargs; + # the K2.x processor's tokenizer call needs real tensors. + "return_tensors": "pt", + } + assert "images" not in out and "videos" not in out and "audio" not in out + + +def test_adapt_processor_kwargs_kimi_future_variant_uses_same_branch(adapt_processor_kwargs): + proc = KimiK26Processor() + out = adapt_processor_kwargs(proc, {"images": ["x"]}, None) + assert out["medias"] == [{"type": "image", "image": "x"}] + + +def test_adapt_processor_kwargs_kimi_no_images_returns_only_return_tensors(adapt_processor_kwargs): + proc = KimiK25Processor() + # No medias → caller relies on text-only K2 branch (still legal as long as text is provided). + out = adapt_processor_kwargs(proc, {"images": [], "videos": [], "audio": []}, None) + assert out == {"return_tensors": "pt"} + + +def test_adapt_processor_kwargs_kimi_warns_on_unsupported_modalities(caplog, adapt_processor_kwargs): + proc = KimiK25Processor() + mm = {"images": ["x"], "videos": ["v"], "audio": ["a"]} + with caplog.at_level("WARNING"): + out = adapt_processor_kwargs(proc, mm, None) + + msgs = " ".join(r.getMessage() for r in caplog.records) + assert "video" in msgs.lower() + assert "audio" in msgs.lower() + assert "videos" not in out + assert "audio" not in out + assert out["medias"] == [{"type": "image", "image": "x"}] + + +def test_adapt_processor_kwargs_kimi_drops_conflicting_extra_kwargs(adapt_processor_kwargs): + """build_processor_kwargs adds images_kwargs / videos_kwargs / + audio_kwargs. + + + return_tensors=None. + + K2.x ignores the per-modality dicts via **kwargs but would crash on + duplicate return_tensors; the adapter must own the kwarg space. + """ + proc = KimiK25Processor() + extra = { + "return_tensors": None, + "images_kwargs": {"return_tensors": "pt"}, + "videos_kwargs": {"return_tensors": "pt"}, + "audio_kwargs": {"return_tensors": "pt"}, + } + out = adapt_processor_kwargs(proc, {"images": ["x"]}, extra) + assert out == { + "medias": [{"type": "image", "image": "x"}], + "return_tensors": "pt", + } + + +@pytest.mark.parametrize( + "cls_name, expected_kimi", + [ + ("KimiK25Processor", True), + ("KimiK26Processor", True), + ("KimiK2Processor", True), + ("Qwen2VLProcessor", False), + ("Qwen3OmniProcessor", False), + ("KimiAudio", False), # doesn't end with Processor + ("SomeOtherKimiK2Tokenizer", False), # doesn't end with Processor + ], +) +def test_adapt_processor_kwargs_class_name_match(cls_name, expected_kimi, adapt_processor_kwargs): + fake_cls = type(cls_name, (), {"__call__": lambda self, **kw: None}) + proc = fake_cls() + out = adapt_processor_kwargs(proc, {"images": ["x"]}, None) + if expected_kimi: + assert "medias" in out + assert "images" not in out + else: + assert "images" in out + assert "medias" not in out From b6905111a9d3503d25ab27dfa1faf69c4995ecdd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=8E=E7=AC=91=E9=A2=9C?= Date: Thu, 28 May 2026 15:43:21 +0800 Subject: [PATCH 060/268] fix(megatron): idempotent grad/param sync setup MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Make overlap grad/param sync setup idempotent in train() - Relax invokes `train()` once per rollout (upstream Megatron calls it once per run); re-assigning `config.no_sync_func` / `config.param_sync_func` after rollout 0 trips the "no_sync_func must be None" assertion. - Guard the sync-func wiring so it only runs when the slot is still `None` — works for both `--overlap-grad-reduce` and `--overlap-param-gather --align-param-gather`. - Leave forward pre-hooks enabled on exit; disabling them here would empty `DDP.remove_forward_pre_hook_handles` and the next `train()` would `KeyError` on the second `disable_forward_pre_hook` call. - Drop the now-dead `pre_hook_enabled` flag. --- # 📝 Documentation ## Document distributed-optimizer and overlap flags - Add `--use-distributed-optimizer`, `--overlap-grad-reduce`, `--overlap-param-gather` to optimizer tables (EN + ZH). - Add compatibility matrix covering text dense, dense VL (CP=1 vs CP>1), and MoE. (cherry picked from commit 8425002edfc4430e57b290fc5768f08dfa5c4c0d) --- docs/en/guide/configuration.md | 12 ++++++++++++ docs/zh/guide/configuration.md | 12 ++++++++++++ relax/backends/megatron/model.py | 18 ++++++++++-------- scripts/training/text/run-qwen35-9B-8xgpu.sh | 2 ++ 4 files changed, 36 insertions(+), 8 deletions(-) diff --git a/docs/en/guide/configuration.md b/docs/en/guide/configuration.md index 6d42ce0db..bfce021af 100644 --- a/docs/en/guide/configuration.md +++ b/docs/en/guide/configuration.md @@ -248,8 +248,20 @@ Recomputation parameters use native Megatron parameters. For details, refer to M | `--optimizer-cpu-offload` | flag | - | Enable CPU offload for optimizer state (native Megatron parameter) | | `--overlap-cpu-optimizer-d2h-h2d` | flag | - | Overlap CPU optimizer D2H/H2D communication (native Megatron parameter) | | `--use-precision-aware-optimizer` | flag | - | Use precision-aware optimizer (native Megatron parameter) | +| `--use-distributed-optimizer` | flag | - | Shard optimizer state, ZeRO-1 style (native Megatron parameter) | +| `--overlap-grad-reduce` | flag | - | Overlap backward compute with grad reduce-scatter (native Megatron parameter) | +| `--overlap-param-gather` | flag | - | Overlap reduce-scatter with next-step param all-gather; requires `--overlap-grad-reduce` (native Megatron parameter) | | `--calculate-per-token-loss` | flag | False | Calculate loss per token (native Megatron parameter) | +### Optimizer Flag Compatibility + +| Scenario | `--use-distributed-optimizer` | `--overlap-grad-reduce` / `--overlap-param-gather` | +|---|---|---| +| Text-only dense | ✅ | ✅ | +| Dense VL, CP = 1 | ✅ | ✅ | +| Dense VL, CP > 1 | ✅ | ❌ | +| MoE | ✅ | ❌ | + --- ## Algorithm Configuration diff --git a/docs/zh/guide/configuration.md b/docs/zh/guide/configuration.md index c85a5ceec..c3113b6f7 100644 --- a/docs/zh/guide/configuration.md +++ b/docs/zh/guide/configuration.md @@ -248,8 +248,20 @@ | `--optimizer-cpu-offload` | flag | - | 启用 CPU offload 优化器状态（Megatron 原生参数） | | `--overlap-cpu-optimizer-d2h-h2d` | flag | - | 重叠 CPU 优化器 D2H/H2D 通信（Megatron 原生参数） | | `--use-precision-aware-optimizer` | flag | - | 使用精度感知优化器（Megatron 原生参数） | +| `--use-distributed-optimizer` | flag | - | ZeRO-1 风格分片优化器状态（Megatron 原生参数） | +| `--overlap-grad-reduce` | flag | - | 反向计算与 grad reduce-scatter 重叠（Megatron 原生参数） | +| `--overlap-param-gather` | flag | - | reduce-scatter 与下一步 param all-gather 重叠，强制配合 `--overlap-grad-reduce`（Megatron 原生参数） | | `--calculate-per-token-loss` | flag | False | 按 Token 计算损失（Megatron 原生参数） | +### 优化器 Flag 兼容性 + +| 场景 | `--use-distributed-optimizer` | `--overlap-grad-reduce` / `--overlap-param-gather` | +|---|---|---| +| 纯文本 Dense | ✅ | ✅ | +| Dense VL，CP = 1 | ✅ | ✅ | +| Dense VL，CP > 1 | ✅ | ❌ | +| MoE | ✅ | ❌ | + --- ## 算法配置 diff --git a/relax/backends/megatron/model.py b/relax/backends/megatron/model.py index f692fa036..6fbf3ce67 100644 --- a/relax/backends/megatron/model.py +++ b/relax/backends/megatron/model.py @@ -600,11 +600,10 @@ def train( config = get_model_config(model[0]) config.grad_scale_func = optimizer.scale_loss config.timers = None - if isinstance(model[0], DDP) and args.overlap_grad_reduce: - assert config.no_sync_func is None, ( - "When overlap_grad_reduce is True, config.no_sync_func must be None; " - "a custom no_sync_func is not supported when overlapping grad-reduce" - ) + # train() is invoked once per rollout in Relax (vs. once per run upstream), + # so guard the sync-func setup to be idempotent — re-assigning would trip + # Megatron's "no_sync_func must be None" assert on rollout 1+. + if isinstance(model[0], DDP) and args.overlap_grad_reduce and config.no_sync_func is None: config.no_sync_func = [model_chunk.no_sync for model_chunk in model] if len(model) == 1: config.no_sync_func = config.no_sync_func[0] @@ -612,14 +611,13 @@ def train( config.grad_sync_func = [model_chunk.start_grad_sync for model_chunk in model] if len(model) == 1: config.grad_sync_func = config.grad_sync_func[0] - if args.overlap_param_gather and args.align_param_gather: + if args.overlap_param_gather and args.align_param_gather and config.param_sync_func is None: config.param_sync_func = [model_chunk.start_param_sync for model_chunk in model] if len(model) == 1: config.param_sync_func = config.param_sync_func[0] config.finalize_model_grads_func = finalize_model_grads pre_hook_enabled = False - if args.reset_optimizer_states: if ( mpu.get_data_parallel_rank(with_context_parallel=True) == 0 @@ -759,9 +757,13 @@ def train( rel_tol=0.01, abs_tol=0.01, ), f"grad norm mismatch: {grad_norm} != {expected_grad_norm}" + # Close out pre-hooks if using distributed optimizer and overlapped param gather. if pre_hook_enabled: - disable_forward_pre_hook(model) + # NOTE(wuhuan): Sync the latest distributed-optimizer parameters before exporting weights + # to rollout engines. this is important for --overlap-grad-reduce --overlap-param-gather + disable_forward_pre_hook(model, param_sync=True) + enable_forward_pre_hook(model) def save( diff --git a/scripts/training/text/run-qwen35-9B-8xgpu.sh b/scripts/training/text/run-qwen35-9B-8xgpu.sh index 938bf3586..7112073ad 100644 --- a/scripts/training/text/run-qwen35-9B-8xgpu.sh +++ b/scripts/training/text/run-qwen35-9B-8xgpu.sh @@ -79,6 +79,8 @@ PERF_ARGS=( # --recompute-method uniform # --recompute-num-layers 1 + --use-distributed-optimizer --overlap-grad-reduce --overlap-param-gather + --use-dynamic-batch-size --max-tokens-per-gpu 10240 --log-probs-max-tokens-per-gpu 40960 From 852ddf6b72f011099ba08f92d55720ce9a2c41a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=AD=E5=84=92=E8=BE=B0?= Date: Thu, 28 May 2026 17:19:49 +0800 Subject: [PATCH 061/268] Low Precision Training Support (cherry picked from commit f1e8764cd3bd45865561a4dce7da6a43e0071078) --- docker/patch/latest/sglang.patch | 24 +- .../patch/megatron/20260506-85bced0ae.patch | 45 +++ docs/.vitepress/config.mts | 6 +- docs/en/examples/low-precision-training.md | 335 ++++++++++++++++ docs/zh/examples/low-precision-training.md | 333 ++++++++++++++++ .../weight_conversion/processors/__init__.py | 3 +- .../processors/quantizer_fp8.py | 3 +- scripts/tools/convert_fp8_to_bf16.py | 191 +++++++++ scripts/tools/convert_hf_to_fp8.py | 296 ++++++++++++++ scripts/tools/convert_hf_to_int4.py | 361 ++++++++++++++++++ scripts/tools/convert_moe_int4_to_bf16.py | 281 ++++++++++++++ .../text/run-qwen3-30B-A3B-fp8-8xgpu.sh | 179 +++++++++ .../text/run-qwen3-30B-A3B-int4-8xgpu.sh | 183 +++++++++ 13 files changed, 2233 insertions(+), 7 deletions(-) create mode 100644 docs/en/examples/low-precision-training.md create mode 100644 docs/zh/examples/low-precision-training.md create mode 100644 scripts/tools/convert_fp8_to_bf16.py create mode 100644 scripts/tools/convert_hf_to_fp8.py create mode 100644 scripts/tools/convert_hf_to_int4.py create mode 100644 scripts/tools/convert_moe_int4_to_bf16.py create mode 100755 scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh create mode 100755 scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh diff --git a/docker/patch/latest/sglang.patch b/docker/patch/latest/sglang.patch index ff27d71c9..0ec42aed7 100644 --- a/docker/patch/latest/sglang.patch +++ b/docker/patch/latest/sglang.patch @@ -1210,7 +1210,25 @@ diff --git a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py b/python/sgl index 8da7d8eef..4938ac5aa 100644 --- a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py +++ b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py -@@ -691,6 +691,7 @@ class FusedMoE(torch.nn.Module): +@@ -524,9 +524,14 @@ class FusedMoE(torch.nn.Module): + if not is_bias and not self.use_presharded_weights: + if self.use_triton_kernels: + loaded_weight = loaded_weight.transpose(-2, -1) +- loaded_weight = loaded_weight.narrow( +- shard_dim, shard_size * tp_rank, shard_size +- ) ++ # Only narrow when the loaded tensor is larger than the local shard. ++ # For quantization scales (e.g. compressed-tensors Marlin), the scale ++ # tensor is replicated across TP ranks (not sharded), so its size on ++ # shard_dim equals shard_size and no slicing is needed. ++ if loaded_weight.shape[shard_dim] > shard_size: ++ loaded_weight = loaded_weight.narrow( ++ shard_dim, shard_size * tp_rank, shard_size ++ ) + + # w2, down_proj: Load into only logical weight of w2. + expert_data.copy_(loaded_weight) +@@ -691,6 +696,7 @@ class FusedMoE(torch.nn.Module): "CompressedTensorsWNA16TritonMoE", ] ) @@ -1218,7 +1236,7 @@ index 8da7d8eef..4938ac5aa 100644 else loaded_weight ) -@@ -815,13 +816,16 @@ class FusedMoE(torch.nn.Module): +@@ -815,13 +821,16 @@ class FusedMoE(torch.nn.Module): FusedMoeWeightScaleSupported.GROUP.value, FusedMoeWeightScaleSupported.BLOCK.value, ]: @@ -1242,7 +1260,7 @@ index 8da7d8eef..4938ac5aa 100644 elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value: # INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust FP8 per-tensor scaling number for e4m3fnuz (AMD) if _is_hip and get_bool_env_var("SGLANG_INT4_WEIGHT"): -@@ -910,6 +914,7 @@ class FusedMoE(torch.nn.Module): +@@ -910,6 +919,7 @@ class FusedMoE(torch.nn.Module): "CompressedTensorsWNA16TritonMoE", ] ) diff --git a/docker/patch/megatron/20260506-85bced0ae.patch b/docker/patch/megatron/20260506-85bced0ae.patch index 6b1d77ff9..7cb2aebff 100644 --- a/docker/patch/megatron/20260506-85bced0ae.patch +++ b/docker/patch/megatron/20260506-85bced0ae.patch @@ -1006,3 +1006,48 @@ diff --git a/megatron/bridge/models/kimi_vl/kimi_k25_vl_bridge.py b/megatron/bri scale_dtype = ( hf_state_dict[orig_scale_key].dtype if orig_scale_key in hf_state_dict else torch.bfloat16 ) +diff --git a/megatron/bridge/models/qwen/qwen3_moe_bridge.py b/megatron/bridge/models/qwen/qwen3_moe_bridge.py +index 04afd67..a83cebf 100644 +--- a/megatron/bridge/models/qwen/qwen3_moe_bridge.py ++++ b/megatron/bridge/models/qwen/qwen3_moe_bridge.py +@@ -69,6 +69,40 @@ + + return provider + ++ def build_conversion_tasks(self, hf_pretrained, megatron_model): ++ """Inject virtual .weight keys so INT4 checkpoints (weight_packed/weight_scale/ ++ weight_zero_point) pass the hf_keys validation in the base class. ++ ++ When hf_checkpoint points to an INT4 compressed-tensors checkpoint, expert ++ weights are stored as weight_packed/weight_scale/weight_zero_point triplets ++ with no plain .weight key. The base build_conversion_tasks checks that each ++ mapped HF name exists in hf_keys and skips the param if not found, causing ++ all expert weights to be silently dropped. We patch get_all_keys() to return ++ synthetic .weight keys alongside the real packed keys so the check passes. ++ Downstream quantize_params in HfWeightIteratorBridge then converts the BF16 ++ output back to INT4 before sending to the rollout engine. ++ """ ++ if not (hasattr(hf_pretrained, "state") and hasattr(hf_pretrained.state, "source")): ++ return super().build_conversion_tasks(hf_pretrained, megatron_model) ++ ++ original_get_all_keys = hf_pretrained.state.source.get_all_keys ++ ++ def _get_all_keys_with_virtual(): ++ keys = original_get_all_keys() ++ all_keys_set = set(keys) ++ virtual_keys = [ ++ key[:-7] # "...weight_packed" -> "...weight" ++ for key in keys ++ if key.endswith("_packed") and f"{key[:-7]}_scale" in all_keys_set ++ ] ++ return keys + virtual_keys ++ ++ hf_pretrained.state.source.get_all_keys = _get_all_keys_with_virtual ++ try: ++ return super().build_conversion_tasks(hf_pretrained, megatron_model) ++ finally: ++ hf_pretrained.state.source.get_all_keys = original_get_all_keys ++ + def mapping_registry(self) -> MegatronMappingRegistry: + # Return MegatronMappingRegistry containing parameter mappings from Megatron to HF format + # First create simple 1:1 parameter mappings using a dictionary for readability diff --git a/docs/.vitepress/config.mts b/docs/.vitepress/config.mts index 003145b02..32ad0a582 100644 --- a/docs/.vitepress/config.mts +++ b/docs/.vitepress/config.mts @@ -305,7 +305,8 @@ export default defineConfig({ items: [ { text: 'DeepEyes', link: '/en/examples/deepeyes' }, { text: 'On-Policy Distillation', link: '/en/examples/on-policy-distillation' }, - { text: 'Generative Reward Model', link: '/en/examples/generative-reward-model' } + { text: 'Generative Reward Model', link: '/en/examples/generative-reward-model' }, + { text: 'Low-Precision Training', link: '/en/examples/low-precision-training' } ] } ] @@ -405,7 +406,8 @@ export default defineConfig({ items: [ { text: 'DeepEyes', link: '/zh/examples/deepeyes' }, { text: '在线策略蒸馏', link: '/zh/examples/on-policy-distillation' }, - { text: '生成式奖励模型', link: '/zh/examples/generative-reward-model' } + { text: '生成式奖励模型', link: '/zh/examples/generative-reward-model' }, + { text: '低精度训练', link: '/zh/examples/low-precision-training' } ] } ] diff --git a/docs/en/examples/low-precision-training.md b/docs/en/examples/low-precision-training.md new file mode 100644 index 000000000..40db326f8 --- /dev/null +++ b/docs/en/examples/low-precision-training.md @@ -0,0 +1,335 @@ +# Low-Precision Training (FP8 & INT4) + +Relax supports low-precision RL post-training along two axes: **FP8 training** (Megatron-LM native, real FP8 forward) and **INT4 fake-QAT** (BF16 master weights with INT4 fake-quant on MoE expert layers). Both modes drive a **real low-precision rollout** in SGLang and synchronize weights via NCCL after every training step. + +## Overview + +Two end-to-end recipes are wired up in this repository: + +| Mode | Training side | Rollout side | Reference launch script | +| ------------------ | ----------------------------------------------------------------------------- | ----------------------------------------- | ------------------------------------------------------------- | +| **FP8** | Megatron-LM native FP8 (`e4m3`, blockwise) | SGLang FP8 inference (real FP8 weights) | `scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh` | +| **INT4 fake-QAT** | BF16 forward + STE INT4 fake-quant on `TEGroupedLinear` (MoE experts only, symmetric) | SGLang W4A16 inference (compressed-tensors, **symmetric**, group_size=128) | `scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh` | + +Four offline tools support the workflow: + +- `scripts/tools/convert_hf_to_fp8.py` — quantize a BF16/FP16 HF checkpoint to FP8. +- `scripts/tools/convert_fp8_to_bf16.py` — dequantize a block-quantized FP8 HF checkpoint back to BF16 (the inverse of `convert_hf_to_fp8.py`; used when you start from a pre-quantized FP8 release and need a pure BF16 HF for other tooling). +- `scripts/tools/convert_hf_to_int4.py` — quantize a BF16 HF checkpoint to W4A16 (compressed-tensors). +- `scripts/tools/convert_moe_int4_to_bf16.py` — dequantize a W4A16 HF checkpoint back to BF16 (used when you start from a pre-quantized W4A16 release and need a BF16 HF for non-bridge workflows or other tooling). + +## Architecture + +Both modes use the standard colocate (`--colocate`) layout: actor and rollout time-share the same GPUs. The low-precision plumbing only changes what flows between them. + +``` + ┌──────────────────────────────────────────────────────┐ + │ Training side (Actor) │ + │ Megatron-LM, transformer_engine, --bf16 │ + │ │ + │ FP8 mode: real FP8 forward (TE blockwise e4m3) │ + │ INT4 mode: BF16 forward + fake-int4 STE on │ + │ TEGroupedLinear._get_weight_tensors() │ + └────────────────────────┬─────────────────────────────┘ + │ + per-step weight sync via NCCL + │ + ▼ + ┌─────────────────────────────────────────────────────┐ + │ Rollout side (SGLang) │ + │ │ + │ FP8 mode: real FP8 weights │ + │ quantizer_fp8.quantize_params_fp8 │ + │ INT4 mode: real W4A16 (AWQ pack) │ + │ quantizer_compressed_tensors │ + │ .quantize_params_compressed_tensors│ + └─────────────────────────────────────────────────────┘ +``` + +The weight-update pipeline (`relax/backends/megatron/weight_update/`) dispatches on `quantization_config.quant_method` read from `--hf-checkpoint/config.json`: + +- `quant_method == "fp8"` → `quantize_params_fp8` (`weight_conversion/processors/quantizer_fp8.py`) +- `quant_method == "compressed-tensors"` → `quantize_params_compressed_tensors` (`weight_conversion/processors/quantizer_compressed_tensors.py`) + +## Offline Quantization Tools + +### `convert_hf_to_fp8.py` + +Quantize a BF16/FP16 HF safetensors checkpoint to FP8. + +```bash +python scripts/tools/convert_hf_to_fp8.py \ + --model-dir /path/to/Qwen3-30B-A3B \ + --save-dir /path/to/Qwen3-30B-A3B-FP8 \ + --strategy block \ + --block-size 128 128 \ + --max-workers 4 +``` + +| Flag | Default | Description | +| ---------------- | ------- | ---------------------------------------------------------------------------------------------------- | +| `--model-dir` | — | Source HF safetensors directory. | +| `--save-dir` | — | Output directory. | +| `--strategy` | `block` | One of `block` / `channel` / `tensor`. `block` writes the `fp8` layout; `channel` writes `compressed-tensors`. | +| `--block-size` | — | Two ints (e.g. `128 128`) when `--strategy=block`. | +| `--max-workers` | `1` | Thread pool size for shard-parallel processing. | +| `--scale-fmt` | `None` | Optional, set to `ue8m0` to emit UE8M0 scales. | + +Skipped modules (kept as-is): `layernorm`, `embed`, `router`, `lm_head`, `mlp.gate.*`, `norm`, `eh_proj`, `weights_proj`, `conv1d`, `A_log`, `dt_bias`, `in_proj_a`, `in_proj_b`. The set is hardcoded in the script's key filter. + +Output: + +- Quantized `*.safetensors` shards (FP8 weights + `weight_scale_inv` / `weight_scale`). +- Updated `config.json` with a `quantization_config` block. For `block`/`tensor` the block is `{"quant_method": "fp8", "fmt": "e4m3", "activation_scheme": "dynamic", "weight_block_size": [...], "modules_to_not_convert": [...]}`. For `channel` it follows the compressed-tensors schema. +- Refreshed `model.safetensors.index.json`. + +### `convert_fp8_to_bf16.py` + +Dequantize a block-quantized FP8 HF checkpoint back to BF16. Use this when you start from a pre-quantized FP8 release and need a pure BF16 HF for downstream tooling. + +```bash +python scripts/tools/convert_fp8_to_bf16.py \ + --model-dir /path/to/Qwen3-30B-A3B-FP8 \ + --save-dir /path/to/Qwen3-30B-A3B-bf16 \ + --max-workers 4 +``` + +| Flag | Default | Description | +| ---------------- | ------- | --------------------------------------------------------------------------- | +| `--model-dir` | — | Source FP8 HF safetensors directory. | +| `--save-dir` | — | Output directory. | +| `--max-workers` | `1` | Thread pool size for shard-parallel processing. | + +Each FP8 `weight` is paired with its `weight_scale_inv` and dequantized via a Triton kernel (`weight_dequant_kernel`). Shards are processed in parallel; scale tensors that live in a different shard are pulled on demand via `safetensors.safe_open`. Tensors with `element_size() > 1` (already non-FP8) are copied through unchanged; FP8 tensors whose paired `_scale_inv` cannot be located are kept as-is with a warning. + +Output: + +- BF16 `*.safetensors` shards (dequantized FP8 weights; `_scale_inv` tensors are dropped). +- `config.json` with the `quantization_config` block stripped so downstream loaders don't try to dequantize already-dequantized weights. +- Refreshed `model.safetensors.index.json` without the obsolete `_scale_inv` entries. + +::: tip +For the FP8 training workflow you usually do **not** need this script — bridge mode (`--megatron-to-hf-mode bridge`) reads the FP8 HF directly. This tool is for offline conversion when you need a BF16 HF as input to a different pipeline (e.g. as a `--ref-load` source for another recipe, or to feed `convert_hf_to_int4.py`). +::: + +### `convert_hf_to_int4.py` + +Quantize a BF16 HF checkpoint to W4A16 (compressed-tensors). Uses the `fake_int4_quant_cuda` kernel, which must be built first (see [Build the int4_qat kernel](#build-the-int4_qat-kernel)). + +```bash +python scripts/tools/convert_hf_to_int4.py \ + --model-dir /path/to/Qwen3-30B-A3B \ + --save-dir /path/to/Qwen3-30B-A3B-int4 \ + --group-size 128 \ + --is-symmetric \ + --max-workers 4 +``` + +| Flag | Default | Description | +| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------- | +| `--model-dir` | — | Source HF safetensors directory. | +| `--save-dir` | — | Output directory. | +| `--group-size` | `32` | INT4 group size; the training script uses `128`. | +| `--is-symmetric` | `false` (CLI default) — **set this flag for INT4 fake-QAT training** | Symmetric quantization. Required to match the training-side STE (which is hardcoded symmetric); without it train/rollout distributions diverge. | +| `--ignore-rules` | `re:.*lm_head.*`, `re:.*norm.*`, `re:.*embed.*`, `re:.*self_attn.*`, `re:.*shared_experts.*`, `re:.*mlp\.(gate|up|gate_up|down)_proj.*`, `re:.*mlp\.gate\.*` | Patterns (regex with `re:` prefix or literal prefix match) for keys to keep in original dtype. Default ignores everything except MoE expert `linear_fc1`/`linear_fc2`. | +| `--max-workers` | `1` | Thread pool size. | + +::: warning +The default `--ignore-rules` is tuned for an MoE topology where only **expert** weights get quantized. If you change the ignore list, make sure it stays in sync with the training-side fake-QAT scope (which only touches `TEGroupedLinear`, i.e. MoE expert layers) — otherwise rollout and training will see different quantization patterns. +::: + +::: danger +**Always pass `--is-symmetric` when the resulting checkpoint will be used as the `--hf-checkpoint` for INT4 fake-QAT training.** The training-side STE in `docker/patch/megatron/20260506-85bced0ae.patch` is hardcoded symmetric (`q_max=7`, no zero-point). If the W4A16 checkpoint is asymmetric (the CLI default), `pack_layer(sym=False)` will produce zero-point-shifted weights at rollout that differ from what training "saw", breaking the central premise of QAT. +::: + +Output: + +- Quantized `*.safetensors` with `weight_packed` (int32-packed int4), `weight_scale`, `weight_shape`, and (if asymmetric) `weight_zero_point` triplets per matched weight. +- Updated `config.json` with a compressed-tensors `quantization_config` block. + +### `convert_moe_int4_to_bf16.py` + +Dequantize a W4A16 compressed-tensors HF checkpoint to BF16. Use this when you start from a pre-quantized W4A16 release and need a pure BF16 HF for tooling. + +```bash +python scripts/tools/convert_moe_int4_to_bf16.py \ + --model-dir /path/to/Qwen3-30B-A3B-int4 + # default output: /path/to/Qwen3-30B-A3B-int4_bf16 +``` + +| Flag | Default | Description | +| ----------------------------- | --------------------- | -------------------------------------------------------------------------------------------- | +| `--model-dir` | — | Source W4A16 HF checkpoint. | +| `--output-dir` | `_bf16` | Output directory. | +| `--files` | all `*.safetensors` | Limit to a subset of shards (useful when re-running after a partial failure). | +| `--config-path` | `/config.json` | Override path to read `group_size` from. | +| `--overwrite` | `false` | Re-process shards even if the output already exists. | +| `--keep-quantization-config` | `false` | Keep the `quantization_config` in the output `config.json` instead of stripping it. | + +Output: + +- BF16 `*.safetensors` shards (expert `weight_packed` triplets are merged back to `.weight`; non-expert tensors are copied verbatim). +- `config.json` with `quantization_config` stripped (unless `--keep-quantization-config`). +- Sidecar `quantization_config.json` containing the stripped block plus an augmented `ignore` list (top-level namespaces such as `vision_tower` / `mm_projector` that have plain `.weight` keys but no `weight_packed` triplets are added). + +::: tip +For the INT4 fake-QAT training workflow you usually do **not** need this script — bridge mode (`--megatron-to-hf-mode bridge`) loads W4A16 directly via the patched `build_conversion_tasks` in `megatron/bridge/models/qwen/qwen3_moe_bridge.py`, which injects synthetic `.weight` keys for the packed triplets. +::: + +## Qwen3-30B Training Workflows + +Relax ships two reference recipes for Qwen3-30B-A3B (8-GPU colocate): **FP8 native training** and **INT4 fake-QAT**. Both share the same Megatron patch and colocate layout — only the weight path and launch script differ. + +### Common Prerequisites + +1. A BF16 HF checkpoint (e.g. `Qwen3-30B-A3B`). +2. The Megatron patch at `docker/patch/megatron/20260506-85bced0ae.patch` applied (baked into the project Dockerfile). It provides both the FP8 overrides and the INT4 `_FakeInt4QuantizationSTE` that overrides `TEGroupedLinear._get_weight_tensors()`. + +The FP8 recipe additionally needs a TransformerEngine build with FP8 blockwise scaling support. The INT4 recipe additionally needs the `fake_int4_quant_cuda` CUDA extension built — see [Build the int4_qat kernel](#build-the-int4-qat-kernel) below. + +### FP8 Recipe + +#### Steps + +1. **Quantize the HF checkpoint to FP8:** + + ```bash + python scripts/tools/convert_hf_to_fp8.py \ + --model-dir ${MODEL_DIR}/Qwen3-30B-A3B \ + --save-dir ${MODEL_DIR}/Qwen3-30B-A3B-FP8 \ + --strategy block --block-size 128 128 + ``` + +2. **Configure the path slots in the launch script** (`scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh`): + + | Slot | Should point to | + | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | + | `--hf-checkpoint` | The **FP8 HF directory** produced in step 1 (e.g. `${MODEL_DIR}/Qwen3-30B-A3B-FP8`). Drives SGLang init and the push-side `quantize_params_fp8` config. | + | `--ref-load` | Also the **FP8 HF directory** — in pure FP8 training the reference model and actor share one FP8 HF (forward runs in native FP8 on both sides). | + | `--load` / `--save` | A **BF16 Megatron checkpoint directory** for resume / save (identical to a plain BF16 run; can be left empty on cold start). | + +3. **Launch training:** + + ```bash + bash scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh + ``` + + +### INT4 fake-QAT Recipe + +#### Build the int4_qat kernel + +```bash +cd relax/backends/megatron/kernels/int4_qat +pip install -e . --no-build-isolation +``` + +The build produces `fake_int4_quant_cuda.cpython--x86_64-linux-gnu.so` in the same directory. The rollout-side `quantizer_compressed_tensors.py` and `convert_hf_to_int4.py` both `import fake_int4_quant_cuda` from this kernel. + +#### Steps + +1. **(Optional) Quantize the HF checkpoint to W4A16 — must be symmetric:** + + ```bash + python scripts/tools/convert_hf_to_int4.py \ + --model-dir ${MODEL_DIR}/Qwen3-30B-A3B \ + --save-dir ${MODEL_DIR}/Qwen3-30B-A3B-int4 \ + --group-size 128 \ + --is-symmetric + ``` + + `--is-symmetric` is required to align with the training-side STE. If you already have a W4A16 release, open its `config.json` and check `config_groups.group_0.weights.symmetric == true`; if it's `false`, regenerate (or run `convert_moe_int4_to_bf16.py` to get BF16 and then re-quantize with `--is-symmetric`). + +2. **Configure the path slots in the launch script** (`scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh`) — the two HF paths play **distinct** roles, do not point them at the same directory: + + | Slot | Should point to | + | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | + | `--hf-checkpoint` | The **W4A16 INT4 HF directory** (e.g. `${EXP_DIR}/Qwen3-30B-A3B-int4`). Its `config.json` carries `quantization_config.quant_method == "compressed-tensors"`, which is what routes the per-step push through `quantize_params_compressed_tensors`. | + | `--ref-load` | The **BF16 HF directory** (e.g. `${EXP_DIR}/Qwen3-30B-A3B`, the un-quantized original). The STE adds INT4 quant noise on top of real BF16 weights in the forward path, so it must load real BF16 — not W4A16. | + | `--load` / `--save` | A **BF16 Megatron checkpoint directory** for resume / save (can be left empty on cold start; Megatron will initialize from `--ref-load`). | + +3. **Launch training:** + + ```bash + bash scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh + ``` + + +## Kimi-K2.6 256xGPU INT4 QAT (Text & Multimodal) + +For very large MoE models like Kimi-K2.6 — where the only available HF release is already W4A16 — Relax ships a slightly different INT4 fake-QAT recipe: **two distinct checkpoints** (one INT4 for SGLang inference, one BF16 cast for Megatron training) instead of a single W4A16 HF driving both sides. The training side still runs the same BF16 forward + STE INT4 fake-quant on MoE experts, but the **inference side loads the original W4A16 release verbatim** (its param dict registers `weight_packed`/`weight_scale`/`weight_shape`), avoiding re-quantizing a trillion-param model at init. + +Two launchers cover both modalities: + +| Script | Data | Algorithm | Reward | +| ------------------------------------------------------------ | --------------------------------- | --------- | ---------- | +| `scripts/training/text/run-kimi-k2.6-256xgpu-int4.sh` | `dapo-math-17k` | GRPO | `math` | +| `scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh` | `multimodal-open-r1-8k-verified` | GRPO | `openr1mm` | + +### Prerequisite + +Cast the original W4A16 release to a BF16 HF directory **once** — Megatron's bridge needs real BF16 weights to load, since the STE only adds quant noise in the forward path: + +```bash +python -m relax.tools.quant_cast.convert_moe_int4_to_bf16 \ + --model-dir ${MODEL_DIR}/Kimi-K2.6 \ + --output-dir ${MODEL_DIR}/Kimi-K2.6_bf16 +``` + +### Dual-checkpoint layout + +```bash +HF_INT4="${MODEL_DIR}/Kimi-K2.6/" # original compressed-tensors W4A16 release +HF_BF16="${MODEL_DIR}/Kimi-K2.6_bf16/" # produced by the prerequisite above + +CKPT_ARGS=( + --hf-checkpoint ${HF_INT4} # AutoConfig → quant_method/group_size → routes push to compressed-tensors + --sglang-hf-checkpoint ${HF_INT4} # SGLang loads W4A16 verbatim (param dict = weight_packed/scale/shape) + --ref-load ${HF_BF16} # Megatron bridge loads BF16; STE rounds to INT4 each forward + --megatron-to-hf-mode bridge +) +``` + +Each flag plays a distinct role: + +| Flag | Checkpoint | Read by | Purpose | +| ------------------------- | ------------ | ------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------- | +| `--hf-checkpoint` | INT4 (W4A16) | `AutoConfig` (push-side dispatcher) | Sets `hf_config.quantization_config.quant_method == "compressed-tensors"` so the per-step push auto-routes through `quantize_params_compressed_tensors`. | +| `--sglang-hf-checkpoint` | INT4 (W4A16) | SGLang engine init | **Must be the INT4 dir**, not the BF16 cast — otherwise SGLang's param dict has plain `.weight` keys and pushes are silently dropped with `X.weight_packed not found in params_dict`. | +| `--ref-load` | BF16 | Megatron bridge loader | Real BF16 working/master weights; the STE adds INT4 quant noise on top each forward. | + +### Launch + +```bash +# text-only +bash scripts/entrypoint/ray-job.sh scripts/training/text/run-kimi-k2.6-256xgpu-int4.sh +# multimodal +bash scripts/entrypoint/ray-job.sh scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh +``` + +Both scripts share the same parallelism and INT4 plumbing: + +| Setting | Value | Notes | +| -------------------------------------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | +| Parallelism | TP=8, PP=8, CP=4, EP=32, ETP=1 | 256 GPUs total. INT4 QAT only changes the weight-update path, not parallelism. | +| `OPEN_TRAINING_INT4_FAKE_QAT_FLAG` | `1` | Trips the `_FakeInt4QuantizationSTE` inside `TEGroupedLinear._get_weight_tensors()`. | +| `OPEN_TRAINING_INT4_GROUP_SIZE` | `32` | Matches the W4A16 release's per-group scale layout (Kimi uses **32**, not 128 as in the Qwen3-30B recipe). | +| `SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK` | `256` | DeepEP low-latency dispatch buffer; default 128 collides with cuda_graph capture at bs=128. | +| `--rollout-num-gpus-per-engine` | `16` | One SGLang engine per 16 GPUs → 16 engines across 256 GPUs. | +| `--sglang-{dp-size,ep-size}` | `16` each | DP-attention + EP within each 16-GPU engine. | +| `--sglang-mem-fraction-static` | `0.7` | Leaves headroom for the weight-update buffer at this scale. | +| Optimizer | Adam + `--optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer` | Required at the 1T-param scale to fit fp32 master weights. | +| Recompute | `--recompute-granularity full --recompute-method uniform --recompute-num-layers 1` | Full activation checkpointing — required at this scale. | + +The two scripts differ in data, reward, and minor algorithm tuning: + +- **Text** (`run-kimi-k2.6-256xgpu-int4.sh`): `dapo-math-17k` with `--rm-type math`, `--rollout-max-response-len 16384`, `--global-batch-size 256`, `--lr 1e-6`, plus an `EVAL_ARGS` block (AIME-2024, `--eval-interval 20`). +- **Multimodal** (`run-kimi-k2.6-256xgpu-int4.sh` under `scripts/training/multimodal/`): `multimodal-open-r1-8k-verified` with `--rm-type openr1mm`, `--multimodal-keys '{"image":"image"}'`, `--image-max-token-num 256`, `--rollout-max-prompt-len 2048` / `--rollout-max-response-len 4096`, `--global-batch-size 512`, `--lr 5e-6`. The multimodal launcher additionally sets `--vision-dp-when-tp` and `--decoder-first-pipeline-num-layers 1 --decoder-last-pipeline-num-layers 6` to fit the vision tower into the PP-8 layout. + +::: warning +Do not swap `--sglang-hf-checkpoint` to the BF16 cast for "consistency". SGLang's parameter registration happens once at init; if the registered keys are `.weight` (BF16) but the push sends `.weight_packed` (INT4), every push is dropped silently and training proceeds with stale rollout weights. +::: + +::: tip +This recipe assumes the W4A16 release was produced with **symmetric** quantization (matching the training-side STE). If you regenerate the W4A16 from BF16 via `convert_hf_to_int4.py`, always pass `--is-symmetric` — see the warning in the [Offline Quantization Tools](#convert_hf_to_int4-py) section above. +::: diff --git a/docs/zh/examples/low-precision-training.md b/docs/zh/examples/low-precision-training.md new file mode 100644 index 000000000..67f73a7ee --- /dev/null +++ b/docs/zh/examples/low-precision-training.md @@ -0,0 +1,333 @@ +# 低精度训练 (FP8 & INT4) + +Relax 在两条路径上支持低精度 RL 后训练：**FP8 训练**（Megatron-LM 原生 FP8 前向）与 **INT4 fake-QAT**（BF16 主权重 + MoE expert 层 INT4 假量化）。两种模式都驱动 **真实的低精度 rollout**（SGLang 端真实低精度推理），并在每个训练 step 后通过 NCCL 同步权重。 + +## 概述 + +仓库中提供两条端到端配方： + +| 模式 | 训练侧 | Rollout 侧 | 参考启动脚本 | +| ------------------ | --------------------------------------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------------------------- | +| **FP8** | Megatron-LM 原生 FP8（`e4m3`、blockwise） | SGLang FP8 推理（真实 FP8 权重） | `scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh` | +| **INT4 fake-QAT** | BF16 前向 + `TEGroupedLinear` 上的 STE INT4 假量化（仅 MoE expert，对称） | SGLang W4A16 推理（compressed-tensors，**symmetric**，group_size=128） | `scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh` | + +配套的离线工具有四个： + +- `scripts/tools/convert_hf_to_fp8.py` — 把 BF16/FP16 的 HF checkpoint 量化为 FP8。 +- `scripts/tools/convert_fp8_to_bf16.py` — 把 block 量化的 FP8 HF checkpoint 反量化回 BF16（`convert_hf_to_fp8.py` 的逆操作；当你拿到一个预量化的 FP8 发布版，但下游链路需要纯 BF16 HF 时使用）。 +- `scripts/tools/convert_hf_to_int4.py` — 把 BF16 的 HF checkpoint 量化为 W4A16（compressed-tensors）。 +- `scripts/tools/convert_moe_int4_to_bf16.py` — 把 W4A16 的 HF checkpoint 反量化回 BF16（当你拿到一个预量化的 W4A16 发布版，但下游链路（非 bridge 模式或其他工具）需要纯 BF16 HF 时使用）。 + +## 架构 + +两种模式都采用标准的 `--colocate` 部署：actor 与 rollout 共享同一组 GPU 时分复用，低精度链路只改变它们之间流动的数据格式。 + +``` + ┌──────────────────────────────────────────────────────┐ + │ Training side (Actor) │ + │ Megatron-LM, transformer_engine, --bf16 │ + │ │ + │ FP8 mode: real FP8 forward (TE blockwise e4m3) │ + │ INT4 mode: BF16 forward + fake-int4 STE on │ + │ TEGroupedLinear._get_weight_tensors() │ + └────────────────────────┬─────────────────────────────┘ + │ + per-step weight sync via NCCL + │ + ▼ + ┌─────────────────────────────────────────────────────┐ + │ Rollout side (SGLang) │ + │ │ + │ FP8 mode: real FP8 weights │ + │ quantizer_fp8.quantize_params_fp8 │ + │ INT4 mode: real W4A16 (AWQ pack) │ + │ quantizer_compressed_tensors │ + │ .quantize_params_compressed_tensors│ + └─────────────────────────────────────────────────────┘ +``` + +权重更新流水线（`relax/backends/megatron/weight_update/`）根据 `--hf-checkpoint/config.json` 中的 `quantization_config.quant_method` 做分发： + +- `quant_method == "fp8"` → `quantize_params_fp8`（`weight_conversion/processors/quantizer_fp8.py`） +- `quant_method == "compressed-tensors"` → `quantize_params_compressed_tensors`（`weight_conversion/processors/quantizer_compressed_tensors.py`） + +## 离线量化工具 + +### `convert_hf_to_fp8.py` + +把 BF16/FP16 的 HF safetensors checkpoint 量化为 FP8。 + +```bash +python scripts/tools/convert_hf_to_fp8.py \ + --model-dir /path/to/Qwen3-30B-A3B \ + --save-dir /path/to/Qwen3-30B-A3B-FP8 \ + --strategy block \ + --block-size 128 128 \ + --max-workers 4 +``` + +| 参数 | 默认值 | 说明 | +| ---------------- | ------- | ----------------------------------------------------------------------------------------------------- | +| `--model-dir` | — | 源 HF safetensors 目录。 | +| `--save-dir` | — | 输出目录。 | +| `--strategy` | `block` | `block` / `channel` / `tensor` 三选一。`block` 写 `fp8` 布局；`channel` 写 `compressed-tensors` 布局。 | +| `--block-size` | — | `--strategy=block` 时必填两个整数（例如 `128 128`）。 | +| `--max-workers` | `1` | shard 级并行的线程池大小。 | +| `--scale-fmt` | `None` | 可选，设为 `ue8m0` 表示输出 UE8M0 scale。 | + +跳过的模块（保持原 dtype 写出）：`layernorm`、`embed`、`router`、`lm_head`、`mlp.gate.*`、`norm`、`eh_proj`、`weights_proj`、`conv1d`、`A_log`、`dt_bias`、`in_proj_a`、`in_proj_b`。该过滤规则硬编码在脚本中。 + +输出： + +- 量化后的 `*.safetensors` 分片（FP8 权重 + `weight_scale_inv` / `weight_scale`）。 +- 改写后的 `config.json`，包含 `quantization_config` 块。`block`/`tensor` 时是 `{"quant_method": "fp8", "fmt": "e4m3", "activation_scheme": "dynamic", "weight_block_size": [...], "modules_to_not_convert": [...]}`，`channel` 时遵循 compressed-tensors schema。 +- 更新后的 `model.safetensors.index.json`。 + +### `convert_fp8_to_bf16.py` + +把 block 量化的 FP8 HF checkpoint 反量化回 BF16。适用于起点是预量化的 FP8 发布版、但下游链路需要纯 BF16 HF 的场景。 + +```bash +python scripts/tools/convert_fp8_to_bf16.py \ + --model-dir /path/to/Qwen3-30B-A3B-FP8 \ + --save-dir /path/to/Qwen3-30B-A3B-bf16 \ + --max-workers 4 +``` + +| 参数 | 默认值 | 说明 | +| ---------------- | ------- | --------------------------------------------------------------------------------- | +| `--model-dir` | — | 源 FP8 HF safetensors 目录。 | +| `--save-dir` | — | 输出目录。 | +| `--max-workers` | `1` | shard 级并行的线程池大小。 | + +每个 FP8 `weight` 与其 `weight_scale_inv` 配对，并通过 Triton kernel（`weight_dequant_kernel`）反量化；shard 级并行处理，若所需的 scale 张量位于其他 shard，则通过 `safetensors.safe_open` 按需读取。`element_size() > 1` 的张量（本身就不是 FP8）原样拷贝；找不到配对 `_scale_inv` 的 FP8 张量会保留原样并打 warning。 + +输出： + +- BF16 的 `*.safetensors` 分片（反量化后的 FP8 权重；`_scale_inv` 张量被丢弃）。 +- 移除 `quantization_config` 块的 `config.json`，避免下游加载器对已反量化的权重再做一次反量化。 +- 重写后的 `model.safetensors.index.json`，不再包含已废弃的 `_scale_inv` 条目。 + +::: tip +FP8 训练工作流下通常 **不需要** 这个脚本 — bridge 模式（`--megatron-to-hf-mode bridge`）会直接读取 FP8 HF。此工具用于离线转换：当你需要把 FP8 checkpoint 转回 BF16 HF 作为其他流水线的输入时（例如作为另一份配方的 `--ref-load`，或喂给 `convert_hf_to_int4.py`）。 +::: + +### `convert_hf_to_int4.py` + +把 BF16 的 HF checkpoint 量化为 W4A16（compressed-tensors）。依赖 `fake_int4_quant_cuda` kernel，需先编译（见 [编译 int4_qat kernel](#编译-int4-qat-kernel)）。 + +```bash +python scripts/tools/convert_hf_to_int4.py \ + --model-dir /path/to/Qwen3-30B-A3B \ + --save-dir /path/to/Qwen3-30B-A3B-int4 \ + --group-size 128 \ + --is-symmetric \ + --max-workers 4 +``` + +| 参数 | 默认值 | 说明 | +| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | +| `--model-dir` | — | 源 HF safetensors 目录。 | +| `--save-dir` | — | 输出目录。 | +| `--group-size` | `32` | INT4 group size；训练脚本里用 `128`。 | +| `--is-symmetric` | CLI 默认 `false` — **跑 INT4 fake-QAT 训练时必须带上这个 flag** | 启用对称量化。必须与训练侧 STE（硬编码对称）保持一致；不带则 train/rollout 分布不一致。 | +| `--ignore-rules` | `re:.*lm_head.*`、`re:.*norm.*`、`re:.*embed.*`、`re:.*self_attn.*`、`re:.*shared_experts.*`、`re:.*mlp\.(gate|up|gate_up|down)_proj.*`、`re:.*mlp\.gate\.*` | 跳过量化的 key 规则（支持 `re:` 前缀正则或字面前缀匹配）。默认只量化 MoE expert 的 `linear_fc1` / `linear_fc2`。 | +| `--max-workers` | `1` | 线程池大小。 | + +::: warning +默认的 `--ignore-rules` 是为只量化 **expert** 权重的 MoE 拓扑准备的。如果改动 ignore 列表，请务必与训练侧 fake-QAT 的作用范围（只覆盖 `TEGroupedLinear`，即 MoE expert）保持一致 — 否则 rollout 和 training 看到的量化模式会不一致。 +::: + +::: danger +**生成用于 INT4 fake-QAT 训练的 `--hf-checkpoint` 时，务必带上 `--is-symmetric`**。`docker/patch/megatron/20260506-85bced0ae.patch` 里的训练侧 STE 是硬编码对称（`q_max=7`，无 zero-point）。如果 W4A16 checkpoint 是非对称（CLI 默认），`pack_layer(sym=False)` 会在 rollout 端打包出带 zero-point 偏移的权重，**与训练侧 STE 所模拟的量化噪声不一致**，QAT 的核心假设就被破坏了。 +::: + +输出： + +- 量化后的 `*.safetensors`，对每个被匹配的权重写出 `weight_packed`（int32 打包的 int4）、`weight_scale`、`weight_shape` 以及（asymmetric 时）`weight_zero_point` 三元组/四元组。 +- 改写后的 `config.json`，写入 compressed-tensors 的 `quantization_config` 块。 + +### `convert_moe_int4_to_bf16.py` + +把 W4A16 compressed-tensors HF checkpoint 反量化为 BF16。适用于起点是预量化的 W4A16 发布版的情况。 + +```bash +python scripts/tools/convert_moe_int4_to_bf16.py \ + --model-dir /path/to/Qwen3-30B-A3B-int4 + # 默认输出：/path/to/Qwen3-30B-A3B-int4_bf16 +``` + +| 参数 | 默认值 | 说明 | +| ----------------------------- | --------------------- | ------------------------------------------------------------------------------------------ | +| `--model-dir` | — | 源 W4A16 HF checkpoint。 | +| `--output-dir` | `_bf16` | 输出目录。 | +| `--files` | 全部 `*.safetensors` | 限定只处理部分 shard（断点重跑时有用）。 | +| `--config-path` | `/config.json` | 覆盖读取 `group_size` 的配置文件路径。 | +| `--overwrite` | `false` | 即使输出文件已存在也重新处理。 | +| `--keep-quantization-config` | `false` | 在输出 `config.json` 中保留 `quantization_config` 块，而不是剥除。 | + +输出： + +- BF16 的 `*.safetensors` 分片（expert 的 `weight_packed` 三元组合并回 `.weight`；非 expert tensor 原样拷贝）。 +- 默认从 `config.json` 中剥除 `quantization_config`（除非 `--keep-quantization-config`）。 +- 旁路文件 `quantization_config.json`，保存被剥除的 quantization 配置块，并追加 `ignore` 列表（把那些有 `.weight` 但没有 `weight_packed` 的顶层命名空间，如 `vision_tower` / `mm_projector` 加进去）。 + +::: tip +INT4 fake-QAT 训练流程下通常 **不需要** 这个脚本 — bridge 模式（`--megatron-to-hf-mode bridge`）会经由 `megatron/bridge/models/qwen/qwen3_moe_bridge.py` 中被 patch 过的 `build_conversion_tasks` 直接加载 W4A16，patch 会为每组 `weight_packed` 三元组合成虚拟的 `.weight` key。 +::: + +## Qwen3-30B 训练工作流 + +Relax 在 Qwen3-30B-A3B（8 卡 colocate）上提供两条参考配方：**FP8 原生训练** 与 **INT4 fake-QAT**。两者共用同一份 Megatron patch 和 colocate 部署，差异只在权重路径与启动脚本。 + +### 共同前置条件 + +1. 一个 BF16 HF checkpoint（例如 `Qwen3-30B-A3B`）。 +2. 应用 Megatron patch `docker/patch/megatron/20260506-85bced0ae.patch`（项目 Dockerfile 已自动应用）—— 该 patch 同时提供 FP8 配套的 override 与 INT4 假量化的 `_FakeInt4QuantizationSTE`（override 了 `TEGroupedLinear._get_weight_tensors()`）。 + +FP8 配方额外需要一个支持 FP8 blockwise scaling 的 TransformerEngine 构建；INT4 配方额外需要编译 `fake_int4_quant_cuda` CUDA 扩展，见下文 [编译 int4_qat kernel](#编译-int4-qat-kernel)。 + +### FP8 低精度训练 + +#### 步骤 + +1. **把 HF checkpoint 量化为 FP8：** + + ```bash + python scripts/tools/convert_hf_to_fp8.py \ + --model-dir ${MODEL_DIR}/Qwen3-30B-A3B \ + --save-dir ${MODEL_DIR}/Qwen3-30B-A3B-FP8 \ + --strategy block --block-size 128 128 + ``` + +2. **配置启动脚本里的路径** (`scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh`)： + + | 路径项 | 应指向 | + | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | + | `--hf-checkpoint` | 步骤 1 生成的 **FP8 HF 目录**（例如 `${MODEL_DIR}/Qwen3-30B-A3B-FP8`）。驱动 SGLang 初始化与 push 侧 `quantize_params_fp8` 的配置读取。 | + | `--ref-load` | 同样指向 **FP8 HF 目录** —— 纯 FP8 训练里 ref model 与 actor 共用同一份 FP8 HF（两侧 forward 都跑原生 FP8）。 | + | `--load` / `--save` | **BF16 Megatron checkpoint 目录**，用于 resume / save（与普通 BF16 训练一致；冷启动时可不填）。 | + +3. **启动训练：** + + ```bash + bash scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh + ``` + +### INT4 低精度训练 + +#### 编译 int4_qat kernel + +```bash +cd relax/backends/megatron/kernels/int4_qat +pip install -e . --no-build-isolation +``` + +编译产物 `fake_int4_quant_cuda.cpython--x86_64-linux-gnu.so` 落在同目录。Rollout 侧 `quantizer_compressed_tensors.py` 与 `convert_hf_to_int4.py` 都通过 `import fake_int4_quant_cuda` 引用该 kernel。 + +#### 步骤 + +1. **（可选）把 HF checkpoint 量化为 W4A16 — 必须用对称量化：** + + ```bash + python scripts/tools/convert_hf_to_int4.py \ + --model-dir ${MODEL_DIR}/Qwen3-30B-A3B \ + --save-dir ${MODEL_DIR}/Qwen3-30B-A3B-int4 \ + --group-size 128 \ + --is-symmetric + ``` + + `--is-symmetric` 是必须项，用来对齐训练侧 STE。如果已有 W4A16 发布版，请打开它的 `config.json` 确认 `config_groups.group_0.weights.symmetric == true`；如果是 `false`，请重新生成（或先用 `convert_moe_int4_to_bf16.py` 反量化回 BF16，再带 `--is-symmetric` 重新量化）。 + +2. **配置启动脚本里的路径** (`scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh`) —— 两个 HF 路径承担**不同**角色，不要指向同一个目录： + + | 路径项 | 应指向 | + | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | + | `--hf-checkpoint` | **W4A16 INT4 HF 目录**（例如 `${EXP_DIR}/Qwen3-30B-A3B-int4`）。其 `config.json` 中的 `quantization_config.quant_method == "compressed-tensors"` 正是把每个 step 的 push 路由到 `quantize_params_compressed_tensors` 的关键。 | + | `--ref-load` | **BF16 HF 目录**（例如 `${EXP_DIR}/Qwen3-30B-A3B`，未量化的原始 HF）。STE 在 forward 上叠加 INT4 量化噪声，需要在真实的 BF16 权重底子上做 —— 不能用 W4A16。 | + | `--load` / `--save` | **BF16 Megatron checkpoint 目录**，用于 resume / save（冷启动时可不填，Megatron 会从 `--ref-load` 初始化）。 | + +3. **启动训练：** + + ```bash + bash scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh + ``` + +## Kimi-K2.6 256xGPU INT4 QAT（文本 & 多模态） + +对于 Kimi-K2.6 这种超大 MoE 模型 —— HF 端可用的发布版本本身就已经是 W4A16 —— Relax 给出了一个略有差异的 INT4 fake-QAT 配方：**两个独立的 checkpoint**（一个 INT4 用于 SGLang 推理，一个 BF16 cast 用于 Megatron 训练），而不是单一的 W4A16 HF 同时驱动训练和推理两侧。训练侧仍然是 BF16 前向 + MoE expert 上的 STE INT4 假量化，但**推理侧直接原样加载 W4A16 发布版**（其 param dict 会注册 `weight_packed` / `weight_scale` / `weight_shape`），从而避免在 init 阶段对万亿参数重新量化一次。 + +提供了两个启动脚本，覆盖文本与多模态： + +| 启动脚本 | 数据集 | 算法 | 奖励 | +| -------------------------------------------------------------- | ----------------------------------- | ---- | --------- | +| `scripts/training/text/run-kimi-k2.6-256xgpu-int4.sh` | `dapo-math-17k` | GRPO | `math` | +| `scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh` | `multimodal-open-r1-8k-verified` | GRPO | `openr1mm`| + +### 前置条件 + +**一次性**把原始的 W4A16 发布版 cast 成 BF16 HF —— Megatron bridge 需要真实的 BF16 权重来加载，STE 只在 forward 路径上叠加量化噪声： + +```bash +python -m relax.tools.quant_cast.convert_moe_int4_to_bf16 \ + --model-dir ${MODEL_DIR}/Kimi-K2.6 \ + --output-dir ${MODEL_DIR}/Kimi-K2.6_bf16 +``` + +### 双 checkpoint 布局 + +```bash +HF_INT4="${MODEL_DIR}/Kimi-K2.6/" # 原始 compressed-tensors W4A16 发布版 +HF_BF16="${MODEL_DIR}/Kimi-K2.6_bf16/" # 由上面的前置步骤生成 + +CKPT_ARGS=( + --hf-checkpoint ${HF_INT4} # AutoConfig → quant_method/group_size → push 自动走 compressed-tensors + --sglang-hf-checkpoint ${HF_INT4} # SGLang 原样加载 W4A16（param dict = weight_packed/scale/shape） + --ref-load ${HF_BF16} # Megatron bridge 加载 BF16；STE 在每次 forward 上把权重 round 到 INT4 grid + --megatron-to-hf-mode bridge +) +``` + +三个 flag 分别承担不同的角色： + +| 参数 | Checkpoint | 由谁读取 | 作用 | +| ------------------------- | ------------ | ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | +| `--hf-checkpoint` | INT4 (W4A16) | `AutoConfig`（push 侧 dispatcher） | 让 `hf_config.quantization_config.quant_method == "compressed-tensors"`，从而每个 step 的 push 自动路由到 `quantize_params_compressed_tensors`。 | +| `--sglang-hf-checkpoint` | INT4 (W4A16) | SGLang 引擎初始化 | **必须是 INT4 目录**，不能是 BF16 cast —— 否则 SGLang 的 param dict 注册的就是 `.weight`（BF16），所有 push 都会被静默丢弃，报 `X.weight_packed not found in params_dict`。 | +| `--ref-load` | BF16 | Megatron bridge loader | 真实 BF16 working/master 权重；STE 在每次 forward 上叠加 INT4 量化噪声。 | + +### 启动 + +```bash +# 纯文本 +bash scripts/entrypoint/ray-job.sh scripts/training/text/run-kimi-k2.6-256xgpu-int4.sh +# 多模态 +bash scripts/entrypoint/ray-job.sh scripts/training/multimodal/run-kimi-k2.6-256xgpu-int4.sh +``` + +两个脚本共享一致的并行配置和 INT4 链路： + +| 配置项 | 取值 | 说明 | +| --------------------------------------------------- | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------- | +| 并行布局 | TP=8、PP=8、CP=4、EP=32、ETP=1 | 共 256 GPU。INT4 QAT 只影响权重更新路径，并行布局保持不变。 | +| `OPEN_TRAINING_INT4_FAKE_QAT_FLAG` | `1` | 启用 `TEGroupedLinear._get_weight_tensors()` 中的 `_FakeInt4QuantizationSTE`。 | +| `OPEN_TRAINING_INT4_GROUP_SIZE` | `32` | 与 W4A16 发布版的 per-group scale 布局保持一致（Kimi 使用 **32**，而不是 Qwen3-30B 配方里的 128）。 | +| `SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK` | `256` | DeepEP 低延迟 dispatch 缓冲；默认 128 会与 bs=128 时的 cuda_graph capture 冲突。 | +| `--rollout-num-gpus-per-engine` | `16` | 每个 SGLang 引擎占 16 GPU → 256 GPU 总共 16 个引擎。 | +| `--sglang-{dp-size,ep-size}` | 都是 `16` | 每个 16 GPU 的引擎内启用 DP-attention + EP。 | +| `--sglang-mem-fraction-static` | `0.7` | 在此规模下为权重更新缓冲预留显存。 | +| Optimizer | Adam + `--optimizer-cpu-offload --overlap-cpu-optimizer-d2h-h2d --use-precision-aware-optimizer` | 1T 参数量下必须开启，用以放下 fp32 master 权重。 | +| Recompute | `--recompute-granularity full --recompute-method uniform --recompute-num-layers 1` | 全量激活重计算 —— 在这个规模下是必须的。 | + +两个脚本仅在数据、奖励和少量算法超参上有差异： + +- **文本** (`run-kimi-k2.6-256xgpu-int4.sh`)：`dapo-math-17k`，`--rm-type math`，`--rollout-max-response-len 16384`，`--global-batch-size 256`，`--lr 1e-6`，并附带一段 `EVAL_ARGS`（AIME-2024，`--eval-interval 20`）。 +- **多模态** (`scripts/training/multimodal/` 下的 `run-kimi-k2.6-256xgpu-int4.sh`)：`multimodal-open-r1-8k-verified`，`--rm-type openr1mm`，`--multimodal-keys '{"image":"image"}'`，`--image-max-token-num 256`，`--rollout-max-prompt-len 2048` / `--rollout-max-response-len 4096`，`--global-batch-size 512`，`--lr 5e-6`。多模态脚本额外设置 `--vision-dp-when-tp` 与 `--decoder-first-pipeline-num-layers 1 --decoder-last-pipeline-num-layers 6`，以便把 vision tower 装进 PP-8 布局。 + +::: warning +不要为了"保持一致"把 `--sglang-hf-checkpoint` 换成 BF16 cast。SGLang 的参数注册只在 init 阶段做一次；如果注册的是 `.weight`（BF16），而 push 推送的是 `.weight_packed`（INT4），每次 push 都会被静默丢弃，训练会一直用过期的 rollout 权重。 +::: + +::: tip +这个配方假设 W4A16 发布版是用**对称量化**生成的（与训练侧 STE 对齐）。如果你从 BF16 出发用 `convert_hf_to_int4.py` 重新生成 W4A16，必须带上 `--is-symmetric` —— 详见上文 [离线量化工具](#convert_hf_to_int4-py) 的 warning。 +::: diff --git a/relax/backends/megatron/weight_conversion/processors/__init__.py b/relax/backends/megatron/weight_conversion/processors/__init__.py index 4df7c12ef..7612f01ee 100644 --- a/relax/backends/megatron/weight_conversion/processors/__init__.py +++ b/relax/backends/megatron/weight_conversion/processors/__init__.py @@ -12,5 +12,6 @@ def quantize_params(args, megatron_name, converted_named_params, quantization_co elif quantization_config["quant_method"] == "fp8": return quantize_params_fp8(args, megatron_name, converted_named_params, quantization_config) elif quantization_config["quant_method"] == "compressed-tensors": - # only int4 at the moment. return quantize_params_compressed_tensors(converted_named_params, quantization_config) + else: + raise ValueError(f"Unsupported quantization method: {quantization_config['quant_method']!r}") diff --git a/relax/backends/megatron/weight_conversion/processors/quantizer_fp8.py b/relax/backends/megatron/weight_conversion/processors/quantizer_fp8.py index f531ce02c..af66088a0 100644 --- a/relax/backends/megatron/weight_conversion/processors/quantizer_fp8.py +++ b/relax/backends/megatron/weight_conversion/processors/quantizer_fp8.py @@ -9,7 +9,8 @@ def quantize_params_fp8(args, megatron_name, converted_named_params, quantization_config): assert quantization_config["quant_method"] == "fp8" - assert quantization_config["fmt"] == "e4m3" + fmt = quantization_config.get("fmt", "e4m3") + assert fmt == "e4m3", f"Unsupported FP8 format: {fmt}" assert quantization_config["activation_scheme"] == "dynamic" weight_block_size = quantization_config.get("weight_block_size", None) diff --git a/scripts/tools/convert_fp8_to_bf16.py b/scripts/tools/convert_fp8_to_bf16.py new file mode 100644 index 000000000..7f11723fd --- /dev/null +++ b/scripts/tools/convert_fp8_to_bf16.py @@ -0,0 +1,191 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Convert FP8 (e4m3, block-quantized) HF safetensors checkpoints back to BF16. + +Each FP8 ``weight`` is paired with its ``weight_scale_inv`` and dequantized via +a Triton kernel. Shards are processed in parallel; scale tensors that live in a +different shard are pulled on demand via ``safetensors.safe_open``. The output +``config.json`` has its ``quantization_config`` block stripped, and +``model.safetensors.index.json`` is rewritten without the obsolete +``_scale_inv`` entries. +""" + +import argparse +import gc +import json +import os +import shutil +import threading +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +import safetensors +import safetensors.torch +import torch +import triton +import triton.language as tl +from tqdm import tqdm + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + + +@triton.jit +def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr): + pid_m = tl.program_id(axis=0) + pid_n = tl.program_id(axis=1) + n = tl.cdiv(N, BLOCK_SIZE) + offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + offs = offs_m[:, None] * N + offs_n[None, :] + mask = (offs_m[:, None] < M) & (offs_n[None, :] < N) + x = tl.load(x_ptr + offs, mask=mask).to(tl.float32) + s = tl.load(s_ptr + pid_m * n + pid_n) + y = x * s + tl.store(y_ptr + offs, y, mask=mask) + + +def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> torch.Tensor: + assert x.is_contiguous() and s.is_contiguous() + assert x.dim() == 2 and s.dim() == 2 + M, N = x.size() + y = torch.empty_like(x, dtype=torch.bfloat16) + + def grid(meta): + return (triton.cdiv(M, meta["BLOCK_SIZE"]), triton.cdiv(N, meta["BLOCK_SIZE"])) + + weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size) + return y + + +class ConversionResult: + def __init__(self) -> None: + self.lock = threading.Lock() + self.weight_map: dict[str, str] = {} + self.param_count: int = 0 + + def add_result(self, filename: str, weights: dict[str, torch.Tensor]) -> None: + with self.lock: + for k, v in weights.items(): + self.weight_map[k] = filename + self.param_count += len(v) + + +def _process_file( + input_path: str, + output_path: str, + filename: str, + weight_map: dict[str, str], + result_collector: ConversionResult, +) -> None: + logger.info(f"Processing {filename}, memory usage: {torch.cuda.memory_allocated()}") + local_weights: dict[str, torch.Tensor] = {} + new_weights: dict[str, torch.Tensor] = {} + + with safetensors.safe_open(os.path.join(input_path, filename), framework="pt", device="cuda") as f: + for k in f.keys(): + local_weights[k] = f.get_tensor(k) + + def _get_scale_inv(scale_inv_name: str) -> torch.Tensor | None: + if scale_inv_name in local_weights: + return local_weights[scale_inv_name] + scale_inv_file = weight_map.get(scale_inv_name) + if scale_inv_file is None: + return None + with safetensors.safe_open(os.path.join(input_path, scale_inv_file), framework="pt", device="cuda") as sf: + return sf.get_tensor(scale_inv_name) + + for name, weight in local_weights.items(): + if name.endswith("_scale_inv"): + continue + if weight.element_size() == 1: # FP8 weight + scale_inv = _get_scale_inv(f"{name}_scale_inv") + if scale_inv is None: + logger.warning(f"Missing scale_inv tensor for {name}, skipping conversion") + new_weights[name] = weight + continue + new_weights[name] = weight_dequant(weight, scale_inv) + else: + new_weights[name] = weight + + safetensors.torch.save_file(new_weights, os.path.join(output_path, filename), metadata={"format": "pt"}) + + result_collector.add_result(filename, new_weights) + + +def convert_bf16( + input_path: str, + output_path: str, + max_workers: int, +) -> None: + input_path = os.path.abspath(input_path) + os.makedirs(output_path, exist_ok=True) + + for filename in os.listdir(input_path): + if not filename.endswith(".safetensors") and not os.path.isdir(os.path.join(input_path, filename)): + shutil.copyfile(os.path.join(input_path, filename), os.path.join(output_path, filename)) + + model_index_file = os.path.join(input_path, "model.safetensors.index.json") + with open(model_index_file) as f: + weight_map = json.load(f)["weight_map"] + + safetensors_files = [f for f in os.listdir(input_path) if f.endswith(".safetensors")] + + result_collector = ConversionResult() + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + futures = [] + for filename in safetensors_files: + future = executor.submit(_process_file, input_path, output_path, filename, weight_map, result_collector) + futures.append(future) + + for future in tqdm(futures, desc="Processing files"): + future.result() + + # Output is plain BF16; drop the FP8 quantization_config so downstream + # loaders don't try to dequantize the already-dequantized weights. + config_path = Path(input_path) / "config.json" + if config_path.exists(): + with open(config_path) as f: + cfg = json.load(f) + cfg.pop("quantization_config", None) + with open(Path(output_path) / "config.json", "w") as f: + json.dump(cfg, f, indent=2) + + index_dict = {"weight_map": result_collector.weight_map, "metadata": {"total_size": result_collector.param_count}} + with open(Path(output_path) / "model.safetensors.index.json", "w") as f: + json.dump(index_dict, f, indent=2) + + gc.collect() + torch.cuda.empty_cache() + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument( + "--model-dir", type=str, required=True, help="Path to the directory of the FP8 HF safetensors model." + ) + parser.add_argument( + "--save-dir", type=str, required=True, help="Path to the directory to save the converted BF16 model." + ) + parser.add_argument("--max-workers", type=int, default=1, help="Number of worker threads for parallel processing") + return parser.parse_args() + + +def main() -> None: + args = _parse_args() + + if not os.path.exists(args.save_dir): + logger.info(f"Creating directory {args.save_dir}") + os.makedirs(args.save_dir) + elif not os.path.isdir(args.save_dir): + raise ValueError("The save_dir should be a directory.") + + convert_bf16(args.model_dir, args.save_dir, args.max_workers) + logger.info(f"Conversion complete, output saved to {args.save_dir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/tools/convert_hf_to_fp8.py b/scripts/tools/convert_hf_to_fp8.py new file mode 100644 index 000000000..09baead4e --- /dev/null +++ b/scripts/tools/convert_hf_to_fp8.py @@ -0,0 +1,296 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Convert HF safetensors checkpoints to FP8 (block / channel / tensor +strategies). + +Shards are quantized in parallel via a thread pool. The output ``config.json`` +gets a ``quantization_config`` block written in either the fp8/e4m3 layout +(block/tensor) or the compressed-tensors layout (channel). Non-quantizable +modules (layernorm, embed, router, lm_head, …) are passed through and recorded +in ``modules_to_not_convert`` / ``ignore`` so downstream loaders skip them. +""" + +import argparse +import gc +import json +import os +import shutil +import threading +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +import safetensors +import safetensors.torch +import torch +import torch.nn.functional as F +from tqdm import tqdm + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + +FP8_INFO = torch.finfo(torch.float8_e4m3fn) +FP8_MAX, FP8_MIN = FP8_INFO.max, FP8_INFO.min + + +def ceildiv(a: int, b: int) -> int: + return -(-a // b) + + +def block_fp8(weight: torch.Tensor, block_size: list[int]) -> tuple[torch.Tensor, torch.Tensor]: + block_n, block_k = block_size[0], block_size[1] + + shape_0, shape_1 = weight.shape + + n_tiles = ceildiv(shape_0, block_n) + k_tiles = ceildiv(shape_1, block_k) + + q_weight = F.pad( + weight, + (0, k_tiles * block_k - shape_1, 0, n_tiles * block_n - shape_0), + mode="constant", + value=0.0, + ) + + qweight = q_weight.reshape(n_tiles, block_n, k_tiles, block_k) + block_max = torch.max(torch.abs(qweight), dim=1, keepdim=True)[0] + block_max = torch.max(block_max, dim=3, keepdim=True)[0] + + scale = block_max.to(torch.float32) / FP8_MAX + qweight = ( + (qweight / scale) + .clamp(min=FP8_MIN, max=FP8_MAX) + .reshape((n_tiles * block_n, k_tiles * block_k)) + .to(torch.float8_e4m3fn) + ) + qweight = qweight[:shape_0, :shape_1].clone().detach() + scale = scale.reshape(n_tiles, k_tiles) + + return qweight, scale + + +def channel_fp8(weight: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + channel_max = torch.max(weight.abs(), dim=-1, keepdim=True)[0] + scale = channel_max.clamp(min=1e-12).to(torch.float32) / FP8_MAX + qweight = (weight / scale).clamp(min=FP8_MIN, max=FP8_MAX) + qweight = qweight.to(torch.float8_e4m3fn) + return qweight, scale + + +def tensor_fp8(weight: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + scale = weight.abs().max().clamp(min=1e-12).to(torch.float32) / FP8_MAX + qweight = (weight / scale).clamp(min=FP8_MIN, max=FP8_MAX) + qweight = qweight.to(torch.float8_e4m3fn) + scale = scale.view(1) + return qweight, scale + + +def quant_fp8( + weight: torch.Tensor, + strategy: str, + block_size: list[int] | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + if strategy == "tensor": + return tensor_fp8(weight) + elif strategy == "channel": + return channel_fp8(weight) + else: + return block_fp8(weight, block_size) + + +class ConversionResult: + def __init__(self) -> None: + self.lock = threading.Lock() + self.weight_map: dict[str, str] = {} + self.param_count: int = 0 + self.modules_to_not_convert: list[str] = [] + + def add_result( + self, + filename: str, + q_weights: dict[str, torch.Tensor], + module_names: list[str], + ) -> None: + with self.lock: + for k, v in q_weights.items(): + self.weight_map[k] = filename + self.param_count += len(v) + self.modules_to_not_convert.extend(module_names) + + +def _process_file( + input_path: str, + output_path: str, + filename: str, + strategy: str, + block_size: list[int] | None, + result_collector: ConversionResult, +) -> None: + if not filename.endswith(".safetensors"): + return + + logger.info(f"Processing {filename}, memory usage: {torch.cuda.memory_allocated()}") + weights: dict[str, torch.Tensor] = {} + q_weights: dict[str, torch.Tensor] = {} + + with safetensors.safe_open(os.path.join(input_path, filename), framework="pt", device="cuda") as f: + for k in f.keys(): + weights[k] = f.get_tensor(k) + + modules_to_not_convert: list[str] = [] + for key in weights.keys(): + if ( + "weight" in key + and "layernorm" not in key + and "embed" not in key + and "router" not in key + and "mlp.gate." not in key + and "norm" not in key + and "lm_head" not in key + and "eh_proj" not in key + and "weights_proj" not in key + and "conv1d" not in key + and "A_log" not in key + and "dt_bias" not in key + and "in_proj_a" not in key + and "in_proj_b" not in key + ): + qw, s = quant_fp8(weights[key], strategy, block_size) + q_weights[key] = qw + if block_size: + scale_name = key.replace(".weight", ".weight_scale_inv") + else: + scale_name = key.replace(".weight", ".weight_scale") + q_weights[scale_name] = s + else: + modules_to_not_convert.append(key.replace(".weight", "")) + q_weights[key] = weights[key] + + safetensors.torch.save_file(q_weights, os.path.join(output_path, filename), metadata={"format": "pt"}) + + result_collector.add_result(filename, q_weights, modules_to_not_convert) + + +def convert_fp8( + input_path: str, + output_path: str, + strategy: str, + block_size: list[int] | None = None, + max_workers: int = 4, + scale_fmt: str | None = None, +) -> None: + input_path = os.path.abspath(input_path) + os.makedirs(output_path, exist_ok=True) + + for filename in os.listdir(input_path): + if not filename.endswith(".safetensors") and not os.path.isdir(os.path.join(input_path, filename)): + shutil.copyfile(os.path.join(input_path, filename), os.path.join(output_path, filename)) + + safetensors_files = [f for f in os.listdir(input_path) if f.endswith(".safetensors")] + + result_collector = ConversionResult() + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + futures = [] + for filename in safetensors_files: + future = executor.submit( + _process_file, input_path, output_path, filename, strategy, block_size, result_collector + ) + futures.append(future) + + for future in tqdm(futures, desc="Processing files"): + future.result() + + if strategy == "block" or strategy == "tensor": + quantization_config: dict = { + "activation_scheme": "dynamic", + "fmt": "e4m3", + "quant_method": "fp8", + } + if block_size: + quantization_config["weight_block_size"] = block_size + if scale_fmt is not None: + quantization_config["scale_fmt"] = scale_fmt + if len(result_collector.modules_to_not_convert) > 0: + quantization_config["modules_to_not_convert"] = list(set(result_collector.modules_to_not_convert)) + else: + quant_group = { + "group_0": { + "input_activations": { + "actorder": None, + "block_structure": None, + "dynamic": True, + "group_size": None, + "num_bits": 8, + "observer": None, + "observer_kwargs": {}, + "strategy": "token", + "symmetric": True, + "type": "float", + }, + "output_activations": None, + "targets": ["Linear"], + "weights": { + "actorder": None, + "block_structure": None, + "dynamic": False, + "group_size": None, + "num_bits": 8, + "observer": "minmax", + "observer_kwargs": {}, + "strategy": strategy, + "symmetric": True, + "type": "float", + }, + }, + } + quantization_config = { + "config_groups": quant_group, + "format": "float-quantized", + "ignore": list(set(result_collector.modules_to_not_convert)), + "quant_method": "compressed-tensors", + "quantization_status": "compressed", + } + + config_path = Path(input_path) / "config.json" + if config_path.exists(): + with open(config_path) as f: + cfg = json.load(f) + cfg["quantization_config"] = quantization_config + with open(Path(output_path) / "config.json", "w") as f: + json.dump(cfg, f, indent=2) + + index_dict = {"weight_map": result_collector.weight_map, "metadata": {"total_size": result_collector.param_count}} + with open(Path(output_path) / "model.safetensors.index.json", "w") as f: + json.dump(index_dict, f, indent=2) + + gc.collect() + torch.cuda.empty_cache() + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--model-dir", type=str, help="Path to the directory of the HF safetensors model.") + parser.add_argument("--save-dir", type=str, help="Path to the directory to save the converted model.") + parser.add_argument("--strategy", type=str, default="block", choices=["block", "channel", "tensor"]) + parser.add_argument("--block-size", type=int, nargs="*", default=None, help="eg. --block-size 128 128") + parser.add_argument("--max-workers", type=int, default=1, help="Number of worker threads for parallel processing") + parser.add_argument("--scale-fmt", type=str, default=None, choices=["ue8m0"]) + return parser.parse_args() + + +def main() -> None: + args = _parse_args() + + if not os.path.exists(args.save_dir): + logger.info(f"Creating directory {args.save_dir}") + os.makedirs(args.save_dir) + elif not os.path.isdir(args.save_dir): + raise ValueError("The save_dir should be a directory.") + + convert_fp8(args.model_dir, args.save_dir, args.strategy, args.block_size, args.max_workers, args.scale_fmt) + + +if __name__ == "__main__": + main() diff --git a/scripts/tools/convert_hf_to_int4.py b/scripts/tools/convert_hf_to_int4.py new file mode 100644 index 000000000..294e91c3e --- /dev/null +++ b/scripts/tools/convert_hf_to_int4.py @@ -0,0 +1,361 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Convert HF safetensors checkpoints to W4A16 (compressed-tensors int4). + +Each ``.weight`` not matched by ``--ignore-rules`` is fake-quantized via the +``fake_int4_quant_cuda`` kernel, packed into int32, and written as a +``weight_packed`` / ``weight_scale`` / ``weight_shape`` triplet (plus +``weight_zero_point`` for asymmetric quant). Shards are processed in parallel. +The output ``config.json`` gets a ``quantization_config`` block in compressed- +tensors ``pack-quantized`` format. +""" + +import argparse +import gc +import json +import math +import os +import re +import shutil +import sys +import threading +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +import safetensors +import safetensors.torch +import torch +from tqdm import tqdm + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + +# Add the compiled CUDA kernel directory to sys.path so fake_int4_quant_cuda can be found +_repo_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +_kernel_dir = os.path.join(_repo_root, "relax", "backends", "megatron", "kernels", "int4_qat") +if _kernel_dir not in sys.path: + sys.path.insert(0, _kernel_dir) + +try: + import fake_int4_quant_cuda +except ImportError: + fake_int4_quant_cuda = None + + +def pack_to_int32( + value: torch.Tensor, + num_bits: int, + packed_dim: int = 1, + sym: bool = False, +) -> torch.Tensor: + if num_bits > 8: + raise ValueError("Packing is only supported for less than 8 bits") + + if num_bits < 1: + raise ValueError(f"num_bits must be at least 1, got {num_bits}") + + # Convert to unsigned range for packing, matching quantization offset + if sym: + offset = 1 << (num_bits - 1) + value = (value + offset).to(torch.uint8) + device = value.device + + pack_factor = 32 // num_bits + + if packed_dim == 0: + value = value.transpose(0, 1) + + rows, cols = value.shape + padded_cols = math.ceil(cols / pack_factor) * pack_factor + pad_len = padded_cols - cols + + if pad_len > 0: + value = torch.nn.functional.pad(value, (0, pad_len)) + + num_groups = padded_cols // pack_factor + + # Use int32 here + reshaped = value.view(rows, num_groups, pack_factor).to(torch.int32) + bit_shifts = torch.arange(pack_factor, device=device, dtype=torch.int32) * num_bits + packed = (reshaped << bit_shifts).sum(dim=2, dtype=torch.int32) + + if packed_dim == 0: + packed = packed.transpose(0, 1) + + return packed + + +def round_to_quantized_type_dtype( + tensor: torch.Tensor, + dtype: torch.dtype, + cast_to_original_dtype: bool = False, +) -> torch.Tensor: + original_dtype = tensor.dtype + iinfo = torch.iinfo(dtype) + rounded = torch.round(torch.clamp(tensor, iinfo.min, iinfo.max)).to(dtype) + if cast_to_original_dtype: + return rounded.to(original_dtype) + return rounded + + +@torch.no_grad() +def quantize( + x: torch.Tensor, + scale: torch.Tensor, + zero_point: torch.Tensor | None, + dtype: torch.dtype = torch.int8, +) -> torch.Tensor: + group_size = x.shape[-1] // scale.shape[-1] + output_dtype = dtype + output = torch.zeros_like(x).to(output_dtype) + + reshaped_dims = ( + math.ceil(x.shape[-1] / group_size), + group_size, + ) + x = x.unflatten(-1, reshaped_dims) + + scaled = x / scale.unsqueeze(-1) + + if zero_point is not None: + zero_point = zero_point.unsqueeze(-1) + scaled += zero_point.to(x.dtype) + + # clamp and round + output = round_to_quantized_type_dtype(tensor=scaled, dtype=dtype) + + output = output.flatten(start_dim=-2) + output = output.to(output_dtype) + + return output + + +def pack_layer( + weight: torch.Tensor, + group_size: int, + sym: bool = True, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]: + w, scale, zp = fake_int4_quant_cuda.fake_int4_quant_cuda(weight, (1, group_size), sym) + w = w.view(weight.shape[0], 1, weight.shape[1] // group_size, group_size) + scale = scale.view(weight.shape[0], 1, weight.shape[1] // group_size, 1) + zp = zp.view(weight.shape[0], 1, weight.shape[1] // group_size, 1) + if sym: + w = w * scale + else: + w = (w - zp) * scale + w = w.view(weight.shape) + scale = scale.view(weight.shape[0], -1).contiguous() + if not sym: + zp = zp.view(weight.shape[0], -1) + zeros = zp.t().contiguous().to(torch.float32) + zeros = zeros.to(dtype=torch.int32, device=w.device) + zeros = zeros.reshape(-1, zeros.shape[1] // 8, 8) + new_order_map = torch.tensor([0, 4, 1, 5, 2, 6, 3, 7], device=zeros.device) * 4 + zeros = zeros << new_order_map + packed_zp = torch.sum(zeros, dim=-1).to(torch.int32) + else: + zp = None + packed_zp = None + + quantized_weight = quantize( + x=w, + scale=scale, + zero_point=zp, + dtype=torch.int8 if sym else torch.uint8, + ) + packed_weight = pack_to_int32(quantized_weight, 4, sym=sym) + return packed_weight, scale, packed_zp + + +class ConversionResult: + def __init__(self) -> None: + self.lock = threading.Lock() + self.weight_map: dict[str, str] = {} + self.param_count: int = 0 + + def add_result(self, filename: str, q_weights: dict[str, torch.Tensor]) -> None: + with self.lock: + for k, v in q_weights.items(): + self.weight_map[k] = filename + self.param_count += len(v) + + +def _process_file( + input_path: str, + output_path: str, + filename: str, + group_size: int, + is_symmetric: bool, + ignore_rules: list[str], + result_collector: ConversionResult, +) -> None: + logger.info(f"Processing {filename}, memory usage: {torch.cuda.memory_allocated()}") + weights: dict[str, torch.Tensor] = {} + q_weights: dict[str, torch.Tensor] = {} + + with safetensors.safe_open(os.path.join(input_path, filename), framework="pt", device="cuda") as f: + for k in f.keys(): + weights[k] = f.get_tensor(k) + + for name, weight in list(weights.items()): + # Release the dict's reference immediately; the local `weight` keeps + # the tensor alive only for this iteration, preventing the entire + # shard from accumulating in memory alongside the quantized outputs. + del weights[name] + is_ignored = any( + (r.startswith("re:") and re.match(r[3:], name)) or r == name or name.startswith(r) for r in ignore_rules + ) + + if is_ignored or not name.endswith(".weight") or weight.dim() < 2: + logger.debug(f"Ignoring {name}, memory usage: {torch.cuda.memory_allocated()}") + q_weights[name] = weight + continue + + logger.debug(f"Packing {name}, memory usage: {torch.cuda.memory_allocated()}") + qw, s, zp = pack_layer(weight, group_size, is_symmetric) + qweight_name = name.replace(".weight", ".weight_packed") + scale_name = name.replace(".weight", ".weight_scale") + weight_shape = torch.tensor(weight.shape, dtype=torch.int32, device="cuda") + weight_shape_name = name.replace(".weight", ".weight_shape") + if zp is not None: + zp_name = name.replace(".weight", ".weight_zero_point") + q_weights[zp_name] = zp + q_weights[qweight_name] = qw + q_weights[scale_name] = s + q_weights[weight_shape_name] = weight_shape + + safetensors.torch.save_file(q_weights, os.path.join(output_path, filename), metadata={"format": "pt"}) + + result_collector.add_result(filename, q_weights) + + +def convert_int4( + input_path: str, + output_path: str, + group_size: int, + is_symmetric: bool, + ignore_rules: list[str], + max_workers: int, +) -> str: + input_path = os.path.abspath(input_path) + os.makedirs(output_path, exist_ok=True) + for filename in os.listdir(input_path): + if not filename.endswith(".safetensors") and not os.path.isdir(os.path.join(input_path, filename)): + shutil.copyfile(os.path.join(input_path, filename), os.path.join(output_path, filename)) + + safetensors_files = [f for f in os.listdir(input_path) if f.endswith(".safetensors")] + + result_collector = ConversionResult() + # debug in single thread + # for filename in safetensors_files: + # _process_file(input_path, output_path, filename, group_size, is_symmetric, ignore_rules, result_collector) + + # multi thread + with ThreadPoolExecutor(max_workers=max_workers) as executor: + futures = [] + for filename in safetensors_files: + future = executor.submit( + _process_file, + input_path, + output_path, + filename, + group_size, + is_symmetric, + ignore_rules, + result_collector, + ) + futures.append(future) + + for future in tqdm(futures, desc="Processing files"): + future.result() + + quant_group = { + "group_0": { + "input_activations": None, + "output_activations": None, + "targets": ["Linear"], + "weights": { + "actorder": None, + "block_structure": None, + "dynamic": False, + "group_size": group_size, + "num_bits": 4, + "observer": "minmax", + "observer_kwargs": {}, + "strategy": "group", + "symmetric": is_symmetric, + "type": "int", + }, + }, + } + quantization_config = { + "config_groups": quant_group, + "format": "pack-quantized", + "ignore": ignore_rules, + "kv_cache_scheme": None, + "quant_method": "compressed-tensors", + "quantization_status": "compressed", + } + + config_path = Path(input_path) / "config.json" + if config_path.exists(): + with open(config_path) as f: + cfg = json.load(f) + cfg["quantization_config"] = quantization_config + with open(Path(output_path) / "config.json", "w") as f: + json.dump(cfg, f, indent=2) + + index_dict = {"weight_map": result_collector.weight_map, "metadata": {"total_size": result_collector.param_count}} + with open(Path(output_path) / "model.safetensors.index.json", "w") as f: + json.dump(index_dict, f, indent=2) + + gc.collect() + torch.cuda.empty_cache() + + return output_path + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--model-dir", type=str, required=True, help="local BF16 path") + parser.add_argument("--save-dir", type=str, required=True) + parser.add_argument("--group-size", type=int, default=32, help="Group Size") + parser.add_argument("--is-symmetric", action="store_true", help="Whether to use symmetric quantization") + parser.add_argument( + "--ignore-rules", + nargs="+", + default=[ + "re:.*lm_head.*", + "re:.*norm.*", + "re:.*embed.*", + "re:.*self_attn.*", + "re:.*shared_experts.*", + "re:.*mlp\\.(gate|up|gate_up|down)_proj.*", + "re:.*mlp\\.gate\\.*", + ], + help="Ignore Rules", + ) + parser.add_argument("--max-workers", type=int, default=1, help="Number of worker threads for parallel processing") + + return parser.parse_args() + + +def main() -> None: + args = _parse_args() + + if not os.path.exists(args.save_dir): + logger.info(f"Creating directory {args.save_dir}") + os.makedirs(args.save_dir) + elif not os.path.isdir(args.save_dir): + raise ValueError("The save_dir should be a directory.") + + convert_int4( + args.model_dir, args.save_dir, args.group_size, args.is_symmetric, args.ignore_rules, args.max_workers + ) + logger.info(f"Conversion complete, output saved to {args.save_dir}") + + +if __name__ == "__main__": + main() diff --git a/scripts/tools/convert_moe_int4_to_bf16.py b/scripts/tools/convert_moe_int4_to_bf16.py new file mode 100644 index 000000000..20b7f8083 --- /dev/null +++ b/scripts/tools/convert_moe_int4_to_bf16.py @@ -0,0 +1,281 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +"""Convert compressed-tensors W4A16 quantized HF checkpoints to BF16. + +Adapted from slime/tools/convert_k2_thinking_int4_to_bf16.py. + +Default output is a sibling directory ``_bf16``. The output ``config.json`` has +``quantization_config`` removed; the original block is written to a sidecar +``quantization_config.json`` so QAT paths can read it back when needed. +""" + +import argparse +import json +import os +import shutil +from collections import defaultdict +from pathlib import Path + +import torch +from compressed_tensors.compressors import unpack_from_int32 +from safetensors.torch import safe_open, save_file +from tqdm import tqdm + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + + +def _quant_config(cfg: dict) -> dict: + """Return the quantization_config block, falling back to text_config (VLM + layout).""" + qc = cfg.get("quantization_config") + if qc: + return qc + return cfg.get("text_config", {}).get("quantization_config") or {} + + +def read_group_size(model_dir: str, config_path: str | None = None) -> int: + cfg_path = config_path or os.path.join(model_dir, "config.json") + with open(cfg_path) as f: + cfg = json.load(f) + return int( + _quant_config(cfg).get("config_groups", {}).get("group_0", {}).get("weights", {}).get("group_size", 128) + ) + + +def _dequantize_tensor( + weight_packed: torch.Tensor, + weight_scale: torch.Tensor, + weight_shape: torch.Tensor, + group_size: int, +) -> torch.Tensor: + if isinstance(weight_shape, torch.Tensor): + shape = tuple(int(v) for v in weight_shape.view(-1).tolist()) + else: + shape = tuple(weight_shape) + + weight = unpack_from_int32(weight_packed, 4, shape) + + if group_size > 0: + scale = weight_scale.to(torch.float32) + if scale.dim() == 1: + scale = scale.unsqueeze(1) + scales = torch.repeat_interleave(scale, repeats=group_size, dim=1) + else: + scales = weight_scale.to(torch.float32) + + if scales.shape != weight.shape: + if scales.numel() == weight.numel(): + scales = scales.reshape_as(weight) + else: + raise ValueError(f"scale shape {scales.shape} incompatible with weight shape {weight.shape}") + + return (weight.to(torch.float32) * scales).to(torch.bfloat16).contiguous() + + +def _is_quantized_weight_key(key: str) -> bool: + if ".mlp.experts." not in key or ".shared_experts." in key: + return False + suffixes = ("weight_packed", "weight_scale", "weight_shape") + for proj in ("gate_proj", "up_proj", "down_proj"): + for suffix in suffixes: + if key.endswith(f".{proj}.{suffix}"): + return True + return False + + +def _convert_file(input_path: str, output_path: str, group_size: int, skip_existing: bool) -> None: + if skip_existing and os.path.exists(output_path): + return + + # Memory ceiling: this loads ALL tensors of a single safetensors shard into + # GPU memory at once (one shard at a time). K2.6 shards are ~5GB packed → + # ~10GB BF16 dequantized, so a 40GB+ GPU is comfortable. If shards grow + # past that, stream key-by-key and keep only expert triplets resident. + tensors: dict[str, torch.Tensor] = {} + expert_buffers: dict[str, dict[str, dict[str, torch.Tensor]]] = defaultdict(lambda: defaultdict(dict)) + + device = "cuda" if torch.cuda.is_available() else "cpu" + with safe_open(input_path, framework="pt", device=device) as reader: + for key in reader.keys(): + tensor = reader.get_tensor(key) + if not _is_quantized_weight_key(key): + tensors[key] = tensor + continue + parts = key.split(".") + try: + expert_idx = parts.index("experts") + except ValueError: + tensors[key] = tensor + continue + prefix = ".".join(parts[: expert_idx + 2]) + project = parts[-2] + suffix = parts[-1] + expert_buffers[prefix][project][suffix] = tensor + + for prefix, components in expert_buffers.items(): + for proj_name in ("gate_proj", "up_proj", "down_proj"): + proj_data = components.get(proj_name, {}) + required = {"weight_packed", "weight_scale", "weight_shape"} + if not required.issubset(proj_data.keys()): + for suffix, value in proj_data.items(): + tensors[f"{prefix}.{proj_name}.{suffix}"] = value + continue + bf16_weight = _dequantize_tensor( + proj_data["weight_packed"].to(torch.int32), + proj_data["weight_scale"].to(torch.float32), + proj_data["weight_shape"], + group_size, + ) + tensors[f"{prefix}.{proj_name}.weight"] = bf16_weight + + cpu_tensors = {k: v.cpu() for k, v in tensors.items()} + os.makedirs(os.path.dirname(output_path), exist_ok=True) + save_file(cpu_tensors, output_path) + + +def _derive_extra_ignore_namespaces(src: str) -> list[str]: + """Return top-level module names whose subtree has plain ``.weight`` keys + but zero ``.weight_packed`` triplets in the source checkpoint. + + These namespaces are definitively not quantized; surfacing them in the + sidecar ignore list lets downstream quantizers reject them without model- + specific name knowledge. + """ + namespaces: dict[str, dict[str, bool]] = defaultdict(lambda: {"plain": False, "packed": False}) + for fname in os.listdir(src): + if not fname.endswith(".safetensors"): + continue + with safe_open(os.path.join(src, fname), framework="pt") as reader: + for key in reader.keys(): + top = key.split(".", 1)[0] + if key.endswith(".weight_packed"): + namespaces[top]["packed"] = True + elif key.endswith(".weight"): + namespaces[top]["plain"] = True + return sorted(top for top, info in namespaces.items() if info["plain"] and not info["packed"]) + + +def _copy_aux_files(src: str, dst: str, *, strip_quantization_config: bool) -> dict | None: + src_path = Path(src) + dst_path = Path(dst) + stripped: dict | None = None + + for fname in os.listdir(src_path): + # Safetensors shards are produced by _convert_file; the index is rewritten + # after the cast loop. Everything else (config.json, *.py, tokenizer*, + # chat_template.jinja, tiktoken.model, README/LICENSE, …) is copied + # verbatim so trust_remote_code modules find their sidecars. + if fname.endswith(".safetensors") or fname == "model.safetensors.index.json": + continue + full = src_path / fname + if not full.is_file(): + continue + target = dst_path / fname + if fname == "config.json" and strip_quantization_config: + with open(full) as f: + cfg = json.load(f) + stripped = cfg.pop("quantization_config", None) + text_cfg = cfg.get("text_config") + if stripped is None and isinstance(text_cfg, dict): + stripped = text_cfg.pop("quantization_config", None) + with open(target, "w") as f: + json.dump(cfg, f, indent=2) + else: + shutil.copy2(full, target) + return stripped + + +def cast( + src: str, + dst: str, + *, + group_size: int | None = None, + files: list[str] | None = None, + config_path: str | None = None, + overwrite: bool = False, + strip_quantization_config: bool = True, +) -> None: + """Cast a compressed-tensors W4A16 HF checkpoint at ``src`` into BF16 at + ``dst``.""" + src = os.path.abspath(src) + dst = os.path.abspath(dst) + if not os.path.isdir(src): + raise FileNotFoundError(f"model directory not found: {src}") + + os.makedirs(dst, exist_ok=True) + if group_size is None: + group_size = read_group_size(src, config_path) + logger.info(f"int4 → bf16 cast: src={src} dst={dst} group_size={group_size}") + + if files: + targets = [os.path.join(src, name) for name in files] + else: + targets = sorted(os.path.join(src, name) for name in os.listdir(src) if name.endswith(".safetensors")) + + if not targets: + logger.warning("no safetensors found in source directory") + return + + for path in tqdm(targets, desc="int4 → bf16", unit="file"): + if not os.path.isfile(path): + continue + rel = os.path.relpath(path, src) + _convert_file(path, os.path.join(dst, rel), group_size, skip_existing=not overwrite) + + stripped = _copy_aux_files(src, dst, strip_quantization_config=strip_quantization_config) + if stripped is not None: + # Augment the sidecar's ignore list with top-level namespaces that have + # no weight_packed triplets in source — these are guaranteed-not-quantized + # (for K2.x VLMs that is vision_tower / mm_projector, which the published + # config.ignore does not list). Lets the generic quantizer stay model- + # agnostic and decide skip purely from the config. + extra_ignore = _derive_extra_ignore_namespaces(src) + if extra_ignore: + existing = list(stripped.get("ignore", [])) + stripped["ignore"] = existing + [ns for ns in extra_ignore if ns not in existing] + logger.info(f"sidecar quantization_config.ignore extended with {extra_ignore}") + with open(os.path.join(dst, "quantization_config.json"), "w") as f: + json.dump(stripped, f, indent=2) + + weight_map: dict[str, str] = {} + for fname in sorted(os.listdir(dst)): + if not fname.endswith(".safetensors"): + continue + with safe_open(os.path.join(dst, fname), framework="pt") as reader: + for key in reader.keys(): + weight_map[key] = fname + with open(os.path.join(dst, "model.safetensors.index.json"), "w") as f: + json.dump({"metadata": {}, "weight_map": weight_map}, f, indent=2) + + logger.info(f"int4 → bf16 cast complete: {dst}") + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert compressed-tensors W4A16 MoE experts to BF16.") + parser.add_argument("--model-dir", required=True) + parser.add_argument("--output-dir", default=None, help="Default: _bf16") + parser.add_argument("--files", nargs="+", default=None) + parser.add_argument("--config-path", default=None) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--keep-quantization-config", action="store_true") + return parser.parse_args() + + +def main() -> None: + args = _parse_args() + output_dir = args.output_dir or f"{os.path.abspath(args.model_dir)}_bf16" + cast( + args.model_dir, + output_dir, + files=args.files, + config_path=args.config_path, + overwrite=args.overwrite, + strip_quantization_config=not args.keep_quantization_config, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh b/scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh new file mode 100755 index 000000000..b145fe80e --- /dev/null +++ b/scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh @@ -0,0 +1,179 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3-30B-A3B FP8 QAT 8xGPU colocate training script. +# +# FP8 QAT 说明： +# - 训练侧：Megatron-LM 原生 FP8 训练（--fp8-format e4m3 --fp8-recipe blockwise） +# - Rollout 侧：sglang 使用真实 FP8 权重推理 +# - 每个 step 结束，训练权重经 blockwise FP8 量化后通过 NCCL 同步到 rollout engine +# - 前提：--hf-checkpoint 需指向已量化好的 FP8 HF checkpoint +# +# Usage: +# bash scripts/training/text/run-qwen3-30B-A3B-8xgpu-fp8.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +echo "SCRIPT_DIR: $SCRIPT_DIR" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen3-30B-A3B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" +NUM_ROLLOUT="${NUM_ROLLOUT:=1000}" + +CKPT_ARGS=( + --hf-checkpoint ${MODEL_DIR}/Qwen3-30B-A3B-FP8 + --ref-load ${MODEL_DIR}/Qwen3-30B-A3B-FP8 + --megatron-to-hf-mode bridge + + --load ${EXP_DIR}/Qwen3-30B-A3B + --save ${EXP_DIR}/Qwen3-30B-A3B + --save-interval 100 +) + +PROMPT_SET=${DATA_DIR}/dapo-math-17k/dapo-math-17k.jsonl + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type dapo + --reward-key score + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 16 + --n-samples-per-prompt 8 + --rollout-max-response-len 8192 + --rollout-temperature 1 + + --global-batch-size 128 + --balance-data + --use-fault-tolerance + --train-iters 200 +) + +EVAL_ARGS=( + --skip-eval-before-train + --log-passrate + --eval-interval 20 + --eval-prompt-data aime ${DATA_DIR}/aime-2024/aime-2024.jsonl + --n-samples-per-eval-prompt 8 + --eval-max-response-len 16384 + --eval-top-p 0.7 +) + +PERF_ARGS=( + --tensor-model-parallel-size 4 + --sequence-parallel + --pipeline-model-parallel-size 1 + --context-parallel-size 1 + --expert-model-parallel-size 8 + --expert-tensor-parallel-size 1 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --use-dynamic-batch-size + --max-tokens-per-gpu 20480 + + # MoE dispatcher + --moe-flex-dispatcher-backend deepep + --moe-token-dispatcher-type flex + --moe-router-dtype fp32 + + # FP8 训练：使用 TransformerEngine blockwise e4m3 方案 + # NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1 需同步设置在 env_vars 中 + --transformer-impl transformer_engine + --bf16 + --fp8-format e4m3 + --fp8-recipe blockwise +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 1 + --sglang-mem-fraction-static 0.6 + --sglang-cuda-graph-bs 1 2 4 8 $(seq 16 8 256) +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen3-30B-A3B-fp8-r3${now} +) + +MISC_ARGS=( + --attention-dropout 0.0 + --hidden-dropout 0.0 + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + --attention-backend flash +) + +_EXTRA_ENV="{ + \"NVTE_FP8_BLOCK_SCALING_FP32_SCALES\": \"1\" +}" + +export RUNTIME_ENV_JSON=$(echo "${RUNTIME_ENV_JSON}" | jq --argjson extra "${_EXTRA_ENV}" '.env_vars += $extra') + +mkdir -p log +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 8], "rollout": [1, 8]}' \ + --max-staleness 0 \ + --num-data-storage-units 1 \ + --use-health-check \ + --colocate \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen3-30B-A3B-fp8-GRPO-gpu8-${now}.log diff --git a/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh b/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh new file mode 100755 index 000000000..830165775 --- /dev/null +++ b/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh @@ -0,0 +1,183 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3-30B-A3B INT4 fake-QAT 8xGPU colocate training script (bridge mode). +# +# INT4 fake-QAT 说明： +# - 训练侧：MoE expert 权重经 fake-quant STE 模拟 INT4 量化误差（symmetric, group_size=128） +# - 前向：对每个 expert weight 做 per-group symmetric INT4 fake-quant（round+clamp+dequant） +# - 反向：STE 直通，梯度等价于 BF16 训练，Master weight 保持 BF16 高精度更新 +# - 由 Megatron patch 在 TEGroupedLinear._get_weight_tensors() 中注入 +# - 仅覆盖 MoE expert 层（TEGroupedLinear），attention/dense 层不受影响 +# - Rollout 侧：SGLang 使用真实 INT4（compressed-tensors W4A16 asymmetric, group_size=128）推理 +# - 每个 step 结束，BF16 训练权重经 pack_layer() 量化打包为 AWQ INT4 格式后 +# 通过 NCCL 同步到 rollout engine（见 quantizer_compressed_tensors.py） +# - 前提： +# - --hf-checkpoint 需指向 W4A16 INT4 HF checkpoint（config.json 中须含 +# quantization_config: {quant_method: compressed-tensors, ...}） +# - relax/backends/megatron/kernels/int4_qat/ 下的 fake_int4_quant_cuda +# 须已编译安装（cd relax/backends/megatron/kernels/int4_qat && pip install -e .） +# +# Usage: +# bash scripts/training/text/run-qwen3-30B-A3B-8xgpu-int4-bridge.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +echo "SCRIPT_DIR: $SCRIPT_DIR" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen3-30B-A3B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" +EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +NUM_ROLLOUT="${NUM_ROLLOUT:=1000}" + +CKPT_ARGS=( + --hf-checkpoint ${EXP_DIR}/Qwen3-30B-A3B-int4 + # Megatron BF16 checkpoint + --ref-load ${EXP_DIR}/Qwen3-30B-A3B + --megatron-to-hf-mode bridge + --load ${EXP_DIR}/Qwen3-30B-A3B + --save ${EXP_DIR}/Qwen3-30B-A3B + --save-interval 100 +) + +PROMPT_SET=${EXP_DIR}/dapo-math-17k/dapo-math-17k.jsonl + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type dapo + --reward-key score + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 16 + --n-samples-per-prompt 8 + --rollout-max-response-len 8192 + --rollout-temperature 1 + + --balance-data + --use-fault-tolerance + --train-iters 200 +) + +EVAL_ARGS=( + --skip-eval-before-train + --log-passrate + --eval-interval 2000 + --eval-prompt-data aime ${EXP_DIR}/dapo-math-17k/dapo-100.jsonl + --n-samples-per-eval-prompt 8 + --eval-max-response-len 16384 + --eval-top-p 0.7 +) + +PERF_ARGS=( + --tensor-model-parallel-size 4 + --sequence-parallel + --pipeline-model-parallel-size 1 + --context-parallel-size 1 + --expert-model-parallel-size 8 + --expert-tensor-parallel-size 1 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --use-dynamic-batch-size + --max-tokens-per-gpu 20480 + + # MoE dispatcher + --moe-flex-dispatcher-backend deepep + --moe-token-dispatcher-type flex + --moe-router-dtype fp32 + + # INT4 fake-QAT 训练侧保持 BF16（假量化 STE 由 OPEN_TRAINING_INT4_FAKE_QAT_FLAG 控制） + --transformer-impl transformer_engine + --bf16 +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 1 + --sglang-mem-fraction-static 0.6 + --sglang-cuda-graph-bs 1 2 4 8 $(seq 16 8 128) +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen3-30B-A3B-int4-r3${now} +) + +MISC_ARGS=( + --attention-dropout 0.0 + --hidden-dropout 0.0 + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + --attention-backend flash +) + +_EXTRA_ENV="{ + \"OPEN_TRAINING_INT4_FAKE_QAT_FLAG\": \"1\", + \"OPEN_TRAINING_INT4_GROUP_SIZE\": \"128\" +}" + +export RUNTIME_ENV_JSON=$(echo "${RUNTIME_ENV_JSON}" | jq --argjson extra "${_EXTRA_ENV}" '.env_vars += $extra') + +mkdir -p log +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://127.0.0.1:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 8], "rollout": [1, 8]}' \ + --max-staleness 0 \ + --num-data-storage-units 1 \ + --use-health-check \ + --colocate \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen3-30B-A3B-int4-GRPO-gpu8-${now}.log From 21758b17c387619c78a59b8fa73bbf8b0677cb26 Mon Sep 17 00:00:00 2001 From: root Date: Thu, 28 May 2026 21:36:21 +0800 Subject: [PATCH 062/268] fix(megatron): skip IPC route for hybrid weight sync MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Avoid CUDA IPC across nodes in hybrid weight sync - In hybrid mode actor and rollout sit on separate placement groups, so rollout `engine_gpu_offsets` are local to the rollout pg and start at 0. - The previous numeric `gpu_offset < total_actor_gpus` check mis-classified cross-node engines as colocated and routed weights through CUDA IPC handles, which are not valid across nodes (`cudaErrorMapBufferObjectFailed` in `_rebuild_cuda_tensor`). - Short-circuit `colocate_engine_nums = 0` when `args.hybrid` so all engines go through the distributed (NCCL broadcast) path. (cherry picked from commit 387d76d488ed7752b659fc9df6a5a3cac226baa6) --- .../update_weight_from_tensor.py | 22 ++++++++++++++----- 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/relax/backends/megatron/weight_update/update_weight_from_tensor.py b/relax/backends/megatron/weight_update/update_weight_from_tensor.py index 7e4d5dd76..0c13d619f 100644 --- a/relax/backends/megatron/weight_update/update_weight_from_tensor.py +++ b/relax/backends/megatron/weight_update/update_weight_from_tensor.py @@ -87,12 +87,22 @@ def connect_rollout_engines( offset += c # Compute colocated engine count: engines whose GPUs fall within actor GPU range. - total_actor_gpus = self.args.actor_num_nodes * self.args.actor_num_gpus_per_node - colocate_engine_nums = 0 - for gpu_offset, gpu_count in zip(engine_gpu_offsets, engine_gpu_counts, strict=True): - if gpu_offset + gpu_count > total_actor_gpus: - break - colocate_engine_nums += 1 + # Hybrid mode gives actor and rollout separate placement groups (see + # controller.py: actor_rollout_pgs is None when hybrid), so rollout + # engine_gpu_offsets are local to rollout's pg and start at 0. The + # numeric `gpu_offset < total_actor_gpus` check below would then + # mis-classify cross-node engines as colocated and route weights via + # CUDA IPC handles, which are not valid across nodes + # (cudaErrorMapBufferObjectFailed in _rebuild_cuda_tensor). + if self.args.hybrid: + colocate_engine_nums = 0 + else: + total_actor_gpus = self.args.actor_num_nodes * self.args.actor_num_gpus_per_node + colocate_engine_nums = 0 + for gpu_offset, gpu_count in zip(engine_gpu_offsets, engine_gpu_counts, strict=True): + if gpu_offset + gpu_count > total_actor_gpus: + break + colocate_engine_nums += 1 self.use_distribute = len(rollout_engines) > colocate_engine_nums From ec92a90316d8397475aa9530ca5bc1ff44df7129 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Thu, 28 May 2026 22:22:46 +0800 Subject: [PATCH 063/268] docs: add Kimi K2.6 to model tables and Skills section MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 📝 Documentation ## Add Kimi K2.6 to supported models tables - Add Kimi K2.6 row (256B-A16B MoE, Vision+Language, INT4 QAT) to README.md - Add Kimi K2.6 row to README_zh.md - Add Kimi K2.6 to Vision modality column in docs/en/guide/introduction.md - Add Kimi K2.6 to Vision modality column in docs/zh/guide/introduction.md - Fix untranslated "**vision**" header in zh introduction table → "**视觉**" --- # ⭐ Feature ## Add AI Coding Skills section to README - Add "🛠️ AI Coding Skills" section before Citation in README.md and README_zh.md - Lists all 11 skills (code-review, debug-hang, dev, doc-writer, git-commit, model-integration, perf-doctor, redaccel-to-relax, ssh-ray-cluster, verl-to-relax, creating-skills) with one-line descriptions (cherry picked from commit bc96e1c0cea711eccb0411807d58b57699413935) --- README.md | 22 ++++++++++++++++++++++ README_zh.md | 34 ++++++++++++++++++++++++++++------ docs/en/guide/introduction.md | 4 ++-- docs/zh/guide/introduction.md | 4 ++-- 4 files changed, 54 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 56bcdb00c..a4ae0506b 100644 --- a/README.md +++ b/README.md @@ -109,6 +109,9 @@ Relax is designed for **omni-modal RL training** — text, vision, and audio in | **Qwen3-VL** | 4B, 30B-A3B | Vision + Language | Visual QA, image understanding, multimodal reasoning | Megatron | | **Qwen3.5** | 30B-A3B | Vision + Language | Visual QA, image understanding, multimodal reasoning | Megatron | | **Qwen3-Omni** | 30B-A3B | Text + Vision + Audio | Audio-visual QA, omni-modal understanding | Megatron | +| **Qwen3.6** | 35B-A3B (MoE) | Vision + Language | Visual QA, image understanding, multimodal reasoning | Megatron | +| **GLM5** | 744B-A40B (MoE) | Text | Math reasoning, code, multi-turn dialogue | Megatron | +| **Kimi K2.6** | ~1T-A32B (MoE) | Vision + Language | Visual QA, multimodal reasoning; INT4 QAT training | Megatron | > 📖 New architectures are integrated via [Megatron Bridge](relax/backends/megatron/mbridge/) for automatic HF ↔ Megatron weight conversion. @@ -256,6 +259,25 @@ We welcome contributions of all kinds! Please read our [Contributing Guide](docs ______________________________________________________________________ +## 🛠️ AI Coding Skills + +Relax ships a set of [Claude Code](https://claude.ai/code) slash-command skills under `skills/` to accelerate development and operations. Invoke them in Claude Code with `/skill-name`. + +| Skill | Description | +| :------------------- | :---------------------------------------------------------------------------------------------- | +| `/code-review` | Expert review of git changes — SOLID violations, security risks, ML/distributed training issues | +| `/debug-hang` | Automatically diagnose Ray distributed training hangs — collects call stacks and actor states | +| `/dev` | Develop and debug Relax code; submit and monitor jobs on a remote Ray cluster | +| `/doc-writer` | Write and maintain bilingual (English + Chinese) VitePress documentation | +| `/git-commit` | Create Conventional Commits with rich markdown body and auto-run pre-commit hooks | +| `/model-integration` | Step-by-step guide for integrating new model architectures into the training pipeline | +| `/perf-doctor` | Audit training launch scripts for performance and GPU memory misconfiguration | +| `/ssh-ray-cluster` | SSH into a remote Ray cluster head node to inspect status, logs, and debug jobs | +| `/verl-to-relax` | Migrate RL recipes from verl to Relax (rewards, tool envs, launch scripts) | +| `/creating-skills` | Guide for authoring new Claude Code skills following Anthropic best practices | + +______________________________________________________________________ + ## 📝 Citation If you find Relax useful in your research, please cite: diff --git a/README_zh.md b/README_zh.md index 3f79817cd..3c3754f98 100644 --- a/README_zh.md +++ b/README_zh.md @@ -103,12 +103,15 @@ ______________________________________________________________________ Relax 专为**全模态强化学习训练**设计 —— 文本、视觉、音频统一框架。通过 `--multimodal-keys` 参数灵活配置多模态数据，框架内置了完整的图像、视频、音频处理管线（`relax/utils/multimodal/`），支持图像 token 数量控制、视频帧率采样、音频采样率等精细调节。 -| 模型系列 | 规模 | 模态 | 典型任务 | 后端 | -| :------------- | :---------------- | :----------------- | :------------------------------------- | :------- | -| **Qwen3** | 4B, 30B-A3B (MoE) | 文本 | 数学推理、代码生成、多轮对话、工具调用 | Megatron | -| **Qwen3-VL** | 4B, 30B-A3B | 视觉 + 语言 | 视觉问答、图像理解、多模态推理 | Megatron | -| **Qwen3.5** | 30B-A3B | 视觉 + 语言 | 视觉问答、图像理解、多模态推理 | Megatron | -| **Qwen3-Omni** | 30B-A3B | 文本 + 视觉 + 音频 | 图文音频联合问答、全模态理解 | Megatron | +| 模型系列 | 规模 | 模态 | 典型任务 | 后端 | +| :------------- | :---------------- | :----------------- | :--------------------------------------- | :------- | +| **Qwen3** | 4B, 30B-A3B (MoE) | 文本 | 数学推理、代码生成、多轮对话、工具调用 | Megatron | +| **Qwen3-VL** | 4B, 30B-A3B | 视觉 + 语言 | 视觉问答、图像理解、多模态推理 | Megatron | +| **Qwen3.5** | 30B-A3B | 视觉 + 语言 | 视觉问答、图像理解、多模态推理 | Megatron | +| **Qwen3-Omni** | 30B-A3B | 文本 + 视觉 + 音频 | 图文音频联合问答、全模态理解 | Megatron | +| **Qwen3.6** | 35B-A3B (MoE) | 视觉 + 语言 | 视觉问答、图像理解、多模态推理 | Megatron | +| **GLM5** | 744B-A40B (MoE) | 文本 | 数学推理、代码生成、多轮对话 | Megatron | +| **Kimi K2.6** | ~1T-A32B (MoE) | 视觉 + 语言 | 视觉问答、多模态推理；支持 INT4 QAT 训练 | Megatron | > 📖 新模型架构通过 [Megatron Bridge](relax/backends/megatron/mbridge/) 接入，自动完成 HF ↔ Megatron 权重转换。 @@ -256,6 +259,25 @@ ______________________________________________________________________ ______________________________________________________________________ +## 🛠️ AI 编程技能（Skills） + +Relax 在 `skills/` 目录下内置了一套 [Claude Code](https://claude.ai/code) 斜杠命令技能，用于加速开发和运维工作。在 Claude Code 中以 `/技能名` 方式调用。 + +| 技能 | 描述 | +| :------------------- | :------------------------------------------------------------- | +| `/code-review` | 专业代码审查 —— 检测 SOLID 违规、安全风险、ML/分布式训练问题 | +| `/debug-hang` | 自动排查 Ray 分布式训练 hang 问题，收集调用栈与 Actor 状态 | +| `/dev` | 开发调试 Relax 代码；向远程 Ray 集群提交并监控训练任务 | +| `/doc-writer` | 编写和维护中英双语 VitePress 文档 | +| `/git-commit` | 生成 Conventional Commits 格式提交，自动运行 pre-commit 钩子 | +| `/model-integration` | 新模型架构接入训练管线的分步指南 | +| `/perf-doctor` | 审查训练启动脚本中的性能与显存配置问题 | +| `/ssh-ray-cluster` | SSH 连接远程 Ray 集群 Head 节点，检查状态、日志和调试任务 | +| `/verl-to-relax` | 将 RL 配方从 verl 迁移到 Relax（奖励函数、工具环境、启动脚本） | +| `/creating-skills` | 按 Anthropic 最佳实践编写新 Claude Code 技能的指南 | + +______________________________________________________________________ + ## 📝 引用 如果 Relax 对您的研究有帮助，请引用： diff --git a/docs/en/guide/introduction.md b/docs/en/guide/introduction.md index c9b5f0207..b0b6438ed 100644 --- a/docs/en/guide/introduction.md +++ b/docs/en/guide/introduction.md @@ -14,8 +14,8 @@ Relax natively supports multimodal RL training across text, images, videos, and | Modality | Capabilities | Representative Models | |----------|--------------|----------------------| -| **Text** | Math reasoning, code generation, multi-turn dialogue, tool use | Qwen3 | -| **Vision** | Visual QA, image understanding, multimodal reasoning | Qwen3-VL, Qwen3.5 | +| **Text** | Math reasoning, code generation, multi-turn dialogue, tool use | Qwen3, GLM5 | +| **Vision** | Visual QA, image understanding, multimodal reasoning | Qwen3-VL, Qwen3.5, Qwen3.6, Kimi K2.6 | | **Omni** | Joint image, text, and audio understanding | Qwen3-Omni | Multimodal data is flexibly configured via the `--multimodal-keys` parameter. The framework includes complete image, video, and audio processing pipelines (`relax/utils/multimodal/`), supporting fine-grained control over image token counts, video frame sampling, audio sample rates, and more. diff --git a/docs/zh/guide/introduction.md b/docs/zh/guide/introduction.md index 458ffc6ec..ca24007b0 100644 --- a/docs/zh/guide/introduction.md +++ b/docs/zh/guide/introduction.md @@ -14,8 +14,8 @@ Relax 原生支持文本、图像、视频、音频的全模态强化学习训 | 模态 | 能力 | 代表模型 | |------|------|----------| -| **文本** | 数学推理、代码生成、多轮对话、工具调用 | Qwen3 | -| **vision** | 视觉问答、图像理解、多模态推理 | Qwen3-VL, Qwen3.5 | +| **文本** | 数学推理、代码生成、多轮对话、工具调用 | Qwen3, GLM5 | +| **视觉** | 视觉问答、图像理解、多模态推理 | Qwen3-VL, Qwen3.5, Qwen3.6, Kimi K2.6 | | **Omni** | 图文音频联合理解 | Qwen3-Omni | 多模态数据通过 `--multimodal-keys` 参数灵活配置，框架内置了完整的图像、视频、音频处理管线（`relax/utils/multimodal/`），支持图像 token 数量控制、视频帧率采样、音频采样率配置等精细调节。 From 4bf681f6010c5d34bde01251a906b0607241eff7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Fri, 29 May 2026 16:10:00 +0800 Subject: [PATCH 064/268] fix(sglang): backport mamba pool sizing #24244 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Backport SGLang PR #24244 to docker patch Cherry-pick of upstream sgl-project/sglang#24244 ("size mamba mappings from req pool, not mamba pool"), manually ported onto the `update-transformers-v5` branch used by our docker image. Upstream patch context did not apply cleanly because that branch dropped the `mamba_layer_ids` kwarg from `_init_mamba_pool`; the semantic fix is identical. - Rename `_init_mamba_pool(size=...)` to `_init_mamba_pool(mamba_size=...)` to remove the parameter-name ambiguity that caused the bug - Size `req_index_to_mamba_index_mapping` and the ping-pong track buffer from `self.req_to_token.shape[0]` (req pool size) instead of from the mamba pool size — indices into these tensors are `req_pool_idx`, not mamba slot ids - Update both call sites in `HybridReqToTokenPool.__init__` and `HybridMambaDecodeReqToTokenPool.__init__` Fixes `torch.AcceleratorError: CUDA error: an illegal memory access was encountered` at `HybridReqToTokenPool.alloc -> req_index_to_mamba_index_mapping[select_index] = ...` when running hybrid attention + linear-state models under SGLang where `max_mamba_cache_size < max_running_requests` (easy to hit with `--sglang-mem-fraction-static 0.7` on tight GPU memory). Note: regenerating the patch via `git diff` also moved `base_processor.py` from the end of the file to its alphabetical position under `multimodal/processors/`, and added `@@` function context labels. No semantic change; `git apply` is order-agnostic. (cherry picked from commit 435400361a382a7a82f322e8c50b0517d205690e) --- .codex/agents | 1 + .codex/commands | 1 + .codex/skills | 1 + docker/Dockerfile | 22 +- docker/patch/latest/sglang.patch | 106 +++++++--- .../patch/megatron/20260506-85bced0ae.patch | 192 +++++++++++++++++- relax/backends/megatron/actor.py | 4 +- relax/backends/megatron/checkpoint.py | 11 +- relax/backends/megatron/loss.py | 19 +- .../hf_weight_iterator_bridge.py | 147 +++++++++++++- relax/utils/arguments.py | 9 +- requirements.txt | 1 - scripts/entrypoint/local.sh | 2 +- .../text/run-qwen3-30B-A3B-int4-8xgpu.sh | 4 +- scripts/training/text/run-qwen35-9B-8xgpu.sh | 2 + skills/perf-doctor/SKILL.md | 7 +- skills/perf-doctor/references/baselines.md | 72 +++++++ skills/perf-doctor/references/rules.md | 15 ++ 18 files changed, 557 insertions(+), 59 deletions(-) create mode 120000 .codex/agents create mode 120000 .codex/commands create mode 120000 .codex/skills create mode 100644 skills/perf-doctor/references/baselines.md diff --git a/.codex/agents b/.codex/agents new file mode 120000 index 000000000..c43818efc --- /dev/null +++ b/.codex/agents @@ -0,0 +1 @@ +../.opencode/agents \ No newline at end of file diff --git a/.codex/commands b/.codex/commands new file mode 120000 index 000000000..8f0a3d838 --- /dev/null +++ b/.codex/commands @@ -0,0 +1 @@ +../.opencode/commands \ No newline at end of file diff --git a/.codex/skills b/.codex/skills new file mode 120000 index 000000000..42c5394a1 --- /dev/null +++ b/.codex/skills @@ -0,0 +1 @@ +../skills \ No newline at end of file diff --git a/docker/Dockerfile b/docker/Dockerfile index c4ed2623d..a02d196ee 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -75,6 +75,12 @@ ARG ENABLE_SGLANG_PATCH=1 WORKDIR /root +# Build the INT4 fake-quant CUDA extension required by INT4 QAT rollout weight sync. +COPY relax/backends/megatron/kernels/int4_qat /tmp/int4_qat +RUN cd /tmp/int4_qat && \ + pip install . --no-build-isolation --no-cache-dir && \ + rm -rf /tmp/int4_qat + ARG MEGATRON_BRIDGE_COMMIT=2faedbf6fe3c422835a44b2b360cadcb2a116a54 ENV MEGATRON_BRIDGE_COMMIT=${MEGATRON_BRIDGE_COMMIT} \ PYTHONPATH=/root/Megatron-LM/ @@ -90,7 +96,7 @@ RUN rm -rf /root/Megatron-LM && git clone https://github.com/NVIDIA-NeMo/Megatro COPY requirements.txt /tmp/requirements.txt RUN pip install -r /tmp/requirements.txt --no-cache-dir && \ - pip install --no-cache-dir tensordict==0.10.0 pyvers==0.1.0 'nvidia-modelopt[hf]==0.44.0' --no-deps && \ + pip install --no-cache-dir "compressed_tensors>=0.13.0" tensordict==0.10.0 pyvers==0.1.0 'nvidia-modelopt[hf]==0.44.0' --no-deps && \ pip install "transferqueue @ git+https://github.com/redai-infra/TransferQueue.git" --no-deps COPY docker/patch/${PATCH_VERSION}/megatron.patch /root/Megatron-LM/ @@ -115,17 +121,3 @@ RUN if [ "$ENABLE_SGLANG_PATCH" = "1" ]; then \ fi && \ rm sglang.patch; \ fi - -# Build the INT4 fake-quant CUDA extension required by INT4 QAT rollout weight sync. -COPY relax/backends/megatron/kernels/int4_qat /tmp/int4_qat -RUN cd /tmp/int4_qat && \ - pip install . --no-build-isolation && \ - rm -rf /tmp/int4_qat - -# FROM relax as relax-release - -# WORKDIR /root/Relax - -# COPY . . - -# RUN pip install -e . --no-deps diff --git a/docker/patch/latest/sglang.patch b/docker/patch/latest/sglang.patch index 0ec42aed7..bc07e6372 100644 --- a/docker/patch/latest/sglang.patch +++ b/docker/patch/latest/sglang.patch @@ -97,7 +97,7 @@ index bc21f8882..87329e5a0 100644 dp_size=self.dp_size, pp_size=self.pp_size, diff --git a/python/sglang/srt/disaggregation/decode.py b/python/sglang/srt/disaggregation/decode.py -index 958cff1cd..845d32120 100644 +index 958cff1cd..102545ba8 100644 --- a/python/sglang/srt/disaggregation/decode.py +++ b/python/sglang/srt/disaggregation/decode.py @@ -19,7 +19,8 @@ Life cycle of a request in the decode server @@ -110,6 +110,15 @@ index 958cff1cd..845d32120 100644 import logging from collections import deque from dataclasses import dataclass +@@ -190,7 +191,7 @@ class HybridMambaDecodeReqToTokenPool(HybridReqToTokenPool): + mamba_size if mamba_size is not None else size + ) + pre_alloc_size + self._init_mamba_pool( +- size=effective_mamba_size, ++ mamba_size=effective_mamba_size, + mamba_spec_state_size=size + pre_alloc_size, + cache_params=cache_params, + device=device, @@ -342,6 +343,16 @@ class DecodePreallocQueue: self.is_mla_backend, ) @@ -2203,10 +2212,56 @@ index e614b7743..1de8b7dfb 100644 self._inc_hit_count(new_node, chunked) total_prefix_length += prefix_len diff --git a/python/sglang/srt/mem_cache/memory_pool.py b/python/sglang/srt/mem_cache/memory_pool.py -index 16b1410c3..ca897fe10 100644 +index 16b1410c3..3604f963d 100644 --- a/python/sglang/srt/mem_cache/memory_pool.py +++ b/python/sglang/srt/mem_cache/memory_pool.py -@@ -1804,9 +1804,12 @@ class NSATokenToKVPool(MLATokenToKVPool): +@@ -470,7 +470,7 @@ class HybridReqToTokenPool(ReqToTokenPool): + self.enable_mamba_extra_buffer = enable_mamba_extra_buffer + self.enable_memory_saver = enable_memory_saver + self._init_mamba_pool( +- size=mamba_size, ++ mamba_size=mamba_size, + mamba_spec_state_size=mamba_spec_state_size, + cache_params=cache_params, + device=device, +@@ -480,7 +480,7 @@ class HybridReqToTokenPool(ReqToTokenPool): + + def _init_mamba_pool( + self, +- size: int, ++ mamba_size: int, + mamba_spec_state_size: int, + cache_params: BaseLinearStateParams, + device: str, +@@ -488,7 +488,7 @@ class HybridReqToTokenPool(ReqToTokenPool): + speculative_num_draft_tokens: int = None, + ): + self.mamba_pool = MambaPool( +- size=size, ++ size=mamba_size, + spec_state_size=mamba_spec_state_size, + cache_params=cache_params, + device=device, +@@ -498,13 +498,16 @@ class HybridReqToTokenPool(ReqToTokenPool): + self.mamba_map = {layer_id: i for i, layer_id in enumerate(cache_params.layers)} + + self.device = device ++ # Indexed by req_pool_idx, so size from the req pool buffer ++ # (self.req_to_token.shape[0]), not from the mamba state pool size. ++ req_pool_size = self.req_to_token.shape[0] + self.req_index_to_mamba_index_mapping: torch.Tensor = torch.zeros( +- size, dtype=torch.int32, device=self.device ++ req_pool_size, dtype=torch.int32, device=self.device + ) + if enable_mamba_extra_buffer: + self.req_index_to_mamba_ping_pong_track_buffer_mapping: torch.Tensor = ( + torch.zeros( +- (size, self.mamba_ping_pong_track_buffer_size), ++ (req_pool_size, self.mamba_ping_pong_track_buffer_size), + dtype=torch.int32, + device=self.device, + ) +@@ -1804,9 +1807,12 @@ class NSATokenToKVPool(MLATokenToKVPool): else: assert self.page_size == 64 with ( @@ -2222,7 +2277,7 @@ index 16b1410c3..ca897fe10 100644 ): self.index_k_with_scale_buffer = [ torch.zeros( -@@ -1828,6 +1831,11 @@ class NSATokenToKVPool(MLATokenToKVPool): +@@ -1828,6 +1834,11 @@ class NSATokenToKVPool(MLATokenToKVPool): ) for _ in range(layer_num) ] @@ -2234,7 +2289,7 @@ index 16b1410c3..ca897fe10 100644 self._finalize_allocation_log(size) def get_index_k_with_scale_buffer(self, layer_id: int) -> torch.Tensor: -@@ -1902,6 +1910,50 @@ class NSATokenToKVPool(MLATokenToKVPool): +@@ -1902,6 +1913,50 @@ class NSATokenToKVPool(MLATokenToKVPool): self.index_k_with_scale_buffer[i][0].nbytes for i in range(self.layer_num) ] return data_ptrs, data_lens, item_lens @@ -2991,6 +3046,27 @@ index 38a4767b0..644d7ed6c 100644 hidden_states, residual = layer( positions, hidden_states, +diff --git a/python/sglang/srt/multimodal/processors/base_processor.py b/python/sglang/srt/multimodal/processors/base_processor.py +index 4cc4cfb50..2a641bb00 100644 +--- a/python/sglang/srt/multimodal/processors/base_processor.py ++++ b/python/sglang/srt/multimodal/processors/base_processor.py +@@ -332,6 +332,7 @@ class BaseMultimodalProcessor(ABC): + else: + kwargs["audios"] = audios + ++ gpu_id = self.server_args.base_gpu_id + processor = self._processor + if ( + hasattr(processor, "image_processor") +@@ -343,7 +344,7 @@ class BaseMultimodalProcessor(ABC): + elif _is_xpu: + kwargs["device"] = "xpu" + elif not _is_npu: +- kwargs["device"] = "cuda" ++ kwargs["device"] = f"cuda:{gpu_id}" if gpu_id is not None else "cuda:0" + elif processor.__class__.__name__ not in { + "Qwen2_5_VLProcessor", + "Qwen3VLProcessor", diff --git a/python/sglang/srt/multimodal/processors/glm4v.py b/python/sglang/srt/multimodal/processors/glm4v.py index 33cce6fe2..0970c4550 100644 --- a/python/sglang/srt/multimodal/processors/glm4v.py @@ -3396,23 +3472,3 @@ index 3be16446e..a0ef921da 100644 assert expect_name == actual_name, f"{expect_name=} {actual_name=}" assert ( expect_should_compare == actual_should_compare -diff --git a/python/sglang/srt/multimodal/processors/base_processor.py b/python/sglang/srt/multimodal/processors/base_processor.py ---- a/python/sglang/srt/multimodal/processors/base_processor.py -+++ b/python/sglang/srt/multimodal/processors/base_processor.py -@@ -332,6 +332,7 @@ - else: - kwargs["audios"] = audios - -+ gpu_id = self.server_args.base_gpu_id - processor = self._processor - if ( - hasattr(processor, "image_processor") -@@ -343,7 +344,7 @@ - elif _is_xpu: - kwargs["device"] = "xpu" - elif not _is_npu: -- kwargs["device"] = "cuda" -+ kwargs["device"] = f"cuda:{gpu_id}" if gpu_id is not None else "cuda:0" - elif processor.__class__.__name__ not in { - "Qwen2_5_VLProcessor", - "Qwen3VLProcessor", diff --git a/docker/patch/megatron/20260506-85bced0ae.patch b/docker/patch/megatron/20260506-85bced0ae.patch index 7cb2aebff..780f26f4d 100644 --- a/docker/patch/megatron/20260506-85bced0ae.patch +++ b/docker/patch/megatron/20260506-85bced0ae.patch @@ -805,8 +805,198 @@ index 8df4df1e5..fa86be44d 100644 + qkv, gate, beta, alpha, batch, seq_len + ) nvtx_range_pop(suffix="prepare_qkv_for_gated_delta_rule") - + # Calculate g and beta +@@ -800,7 +801,7 @@ def get_parameter_local_cp( + slices = [slice(None)] * param.dim() + dim_size = param.size(dim=dim) + slices[dim] = slice(cp_rank * dim_size // cp_size, (cp_rank + 1) * dim_size // cp_size) +- param = param[slices] ++ param = param[tuple(slices)] + return param + + +diff --git a/megatron/core/optimizer/cpu_offloading/hybrid_optimizer.py b/megatron/core/optimizer/cpu_offloading/hybrid_optimizer.py +index c87ccd5ff..fd2d9d8f7 100644 +--- a/megatron/core/optimizer/cpu_offloading/hybrid_optimizer.py ++++ b/megatron/core/optimizer/cpu_offloading/hybrid_optimizer.py +@@ -1,6 +1,6 @@ + # Copyright (c) 2025, NVIDIA CORPORATION and Alibaba PAI. All rights reserved. + from collections import defaultdict +-from typing import Dict ++from typing import Dict, Tuple + + import torch + +@@ -249,14 +249,82 @@ class HybridDeviceOptimizer(torch.optim.Optimizer): + return cpu_optimizers + + def _get_sub_optimizer_param_groups(self, offload_fraction: float): +- params = [] ++ # Batched/chunked implementation: the original per-parameter ++ # `.cpu().pin_memory()` + `.clone().float()` is O(num_params) Python ++ # calls and pins one buffer at a time. For MoE models that means tens ++ # of thousands of tiny pinned allocations and individual H2D copies, ++ # which dominates init time. Instead, we group params by their final ++ # (dtype, device), allocate one contiguous pinned/cuda buffer per ++ # bucket, and do a single batched D2H copy + dtype cast per bucket. ++ # The per-param "effective" tensor is a view into that buffer, which ++ # preserves storage-pinned semantics and remains a unique hashable ++ # Tensor object usable as a dict key, matching the original API. ++ ++ # Phase 1: decide which params to offload using the original greedy ++ # iteration order so behavior matches exactly. ++ gpu_params_total_numel = 0 ++ all_params = [] + for group in self.param_groups: +- params.extend(group["params"]) +- params_total_numel = sum([param.numel() for param in params]) +- gpu_params_total_numel = sum([param.numel() for param in params if param.is_cuda]) +- cpu_params_total_numel = params_total_numel - gpu_params_total_numel ++ for param in group["params"]: ++ all_params.append(param) ++ if param.is_cuda: ++ gpu_params_total_numel += param.numel() + offload_threshold = gpu_params_total_numel * offload_fraction ++ ++ offload_ids = set() + offload_params_numel = 0 ++ for param in all_params: ++ if offload_params_numel < offload_threshold and param.is_cuda: ++ offload_ids.add(id(param)) ++ offload_params_numel += param.numel() ++ ++ fp32 = self.param_update_in_fp32 ++ ++ def _target_of(param): ++ """Return (target_dtype, target_device_key, needs_copy) per orig param. ++ ++ target_device_key: ++ - "cpu_pinned" → offloaded copy on pinned host memory ++ - cuda device → GPU-resident fp32 master copy ++ - None → reuse orig param (no copy needed) ++ """ ++ is_off = id(param) in offload_ids ++ cast_needed = fp32 and param.dtype != torch.float32 ++ if is_off and cast_needed: ++ return torch.float32, "cpu_pinned", True ++ if is_off: ++ return param.dtype, "cpu_pinned", True ++ if cast_needed: ++ return torch.float32, param.device, True ++ return param.dtype, None, False ++ ++ # Phase 2: bucket by (target_dtype, target_device) and batch-allocate. ++ buckets: Dict[Tuple[torch.dtype, object], list] = defaultdict(list) ++ targets: Dict[int, Tuple[torch.dtype, object, bool]] = {} ++ for param in all_params: ++ td, tdev, needs = _target_of(param) ++ targets[id(param)] = (td, tdev, needs) ++ if needs: ++ buckets[(td, tdev)].append(param) ++ ++ effective_of: Dict[int, torch.Tensor] = {} ++ for (td, tdev), bucket_params in buckets.items(): ++ total = sum(p.numel() for p in bucket_params) ++ if tdev == "cpu_pinned": ++ buf = torch.empty(total, dtype=td, pin_memory=True, device="cpu") ++ else: ++ buf = torch.empty(total, dtype=td, device=tdev) ++ offset = 0 ++ for p in bucket_params: ++ n = p.numel() ++ view = buf.narrow(0, offset, n).view(p.shape) ++ # Single copy_ does D2H (if needed) AND dtype cast in one shot. ++ view.copy_(p.detach()) ++ effective_of[id(p)] = view ++ offset += n ++ ++ # Phase 3: assemble per-group outputs and bookkeeping dicts, preserving ++ # the original parameter order within each group. + cpu_param_groups = [] + gpu_param_groups = [] + gpu_params_map_cpu_copy = {} +@@ -267,25 +335,24 @@ class HybridDeviceOptimizer(torch.optim.Optimizer): + cpu_group = group.copy() + gpu_group["params"] = [] + cpu_group["params"] = [] +- for param in group["params"]: +- orig_param = param +- cpu_copy = False +- if offload_params_numel < offload_threshold and param.is_cuda: +- param = param.detach().clone().cpu().pin_memory() +- offload_params_numel += param.numel() +- cpu_copy = True +- if self.param_update_in_fp32 and param.dtype != torch.float32: +- param = param.detach().clone().float() +- param_to_fp32_param[orig_param] = param +- +- if cpu_copy: +- gpu_params_map_cpu_copy[orig_param] = param +- cpu_copys_map_gpu_param[param] = orig_param +- +- if param.is_cuda: +- gpu_group["params"].append(param) ++ for orig_param in group["params"]: ++ td, tdev, needs = targets[id(orig_param)] ++ effective = effective_of.get(id(orig_param), orig_param) ++ ++ # Match original semantics: when both offloaded and cast, ++ # gpu_params_map_cpu_copy / cpu_copys_map_gpu_param point at ++ # the (single) fp32 CPU copy, not at an intermediate bf16 copy. ++ is_off = id(orig_param) in offload_ids ++ if is_off: ++ gpu_params_map_cpu_copy[orig_param] = effective ++ cpu_copys_map_gpu_param[effective] = orig_param ++ if fp32 and orig_param.dtype != torch.float32: ++ param_to_fp32_param[orig_param] = effective ++ ++ if effective.is_cuda: ++ gpu_group["params"].append(effective) + else: +- cpu_group["params"].append(param) ++ cpu_group["params"].append(effective) + if len(gpu_group["params"]) != 0: + gpu_param_groups.append(gpu_group) + if len(cpu_group["params"]) != 0: +@@ -377,8 +444,35 @@ class HybridDeviceOptimizer(torch.optim.Optimizer): + """ + Update the fp32 parameters by the new parameters. + """ +- for param, fp32_param in self.param_to_fp32_param.items(): +- fp32_param.data.copy_(param) ++ # Batched: collapse N independent (fp32_param.data <- param) copies ++ # into `torch._foreach_copy_` dispatches. Replaces an O(num_params) ++ # Python loop (each issuing its own ATen kernel) with one fused ++ # multi-tensor op per (src_device, dst_device) bucket. Critical for ++ # MoE models with tens of thousands of expert weights, which would ++ # otherwise serialize tens of thousands of single-tensor copies. ++ if not self.param_to_fp32_param: ++ return ++ # Bucket by (src_device, dst_device); _foreach_copy_ requires the ++ # tensors in a single call to share a device. ++ buckets: Dict[Tuple[torch.device, torch.device], Tuple[list, list]] = defaultdict( ++ lambda: ([], []) ++ ) ++ for src_param, fp32_param in self.param_to_fp32_param.items(): ++ key = (src_param.device, fp32_param.device) ++ buckets[key][0].append(src_param) ++ buckets[key][1].append(fp32_param.data) ++ for (src_dev, dst_dev), (srcs, dsts) in buckets.items(): ++ if src_dev == dst_dev: ++ torch._foreach_copy_(dsts, srcs) ++ else: ++ # Cross-device (e.g. GPU bf16 -> CPU pinned fp32). _foreach_copy_ ++ # may not handle this everywhere, so issue a non-blocking copy ++ # per tensor and let the H2D/D2H engine batch internally; then ++ # synchronize once at the end to preserve completion semantics. ++ for dst, src in zip(dsts, srcs): ++ dst.copy_(src, non_blocking=True) ++ if src_dev.type == "cuda": ++ torch.cuda.synchronize(src_dev) + + def _register_load_state_dict_hooks(self): + def pre_load_state_dict_hook(self, state_dict): --- a/megatron/bridge/models/kimi_vl/modeling_kimi_k25_vl.py +++ b/megatron/bridge/models/kimi_vl/modeling_kimi_k25_vl.py @@ -16,6 +16,8 @@ diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index 9af591ede..d113ca5a9 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -1131,7 +1131,7 @@ def update_weights(self) -> None: ): print_memory("before update_weights") self.weight_updater.update_weights() - print_memory("after update_weights") + print_memory("after update_weights", clear_before_print=True) if self.args.ci_test and len(rollout_engines) > 0: engine = random.choice(rollout_engines) @@ -1301,7 +1301,7 @@ def recv_weight_fully_async(self, rollout_id) -> None: print_memory("before update_weights") run(self.checkpoint_engine_client.init_process_groups_for_actor_fwd_ref(rollout_id)) run(self.checkpoint_engine_client.recv_weight_fully_async()) - print_memory("after update_weights") + print_memory("after update_weights", clear_before_print=True) def load_other_checkpoint(self, model_tag: str, path: str) -> None: old_args = self.args.load, self.args.no_load_optim, self.args.no_load_rng, self.args.finetune diff --git a/relax/backends/megatron/checkpoint.py b/relax/backends/megatron/checkpoint.py index 8f727a18b..549a7fd23 100644 --- a/relax/backends/megatron/checkpoint.py +++ b/relax/backends/megatron/checkpoint.py @@ -345,7 +345,16 @@ def _load_checkpoint_hf(ddp_model, optimizer, args, load_path: str): assert args.megatron_to_hf_mode == "bridge", "Only bridge mode is supported for loading HF checkpoint" from megatron.bridge import AutoBridge - source_path = load_path or args.hf_checkpoint + # Prefer ref_load (if it's an HF dir) over hf_checkpoint on fallback. INT4 QAT + # runs set --hf-checkpoint to a compressed-tensors packed dir that the bridge + # cannot read; --ref-load points at the BF16 HF dir that it can. Mirrors the + # `args.load = args.ref_load or args.hf_checkpoint` remap in arguments.py. + if load_path is not None: + source_path = load_path + elif args.ref_load and _is_hf_checkpoint(args.ref_load): + source_path = args.ref_load + else: + source_path = args.hf_checkpoint logger.info( f"Load checkpoint from HuggingFace model into Megatron (requested_path={load_path}, source_path={source_path})" ) diff --git a/relax/backends/megatron/loss.py b/relax/backends/megatron/loss.py index 08fa3d56d..e93176bba 100644 --- a/relax/backends/megatron/loss.py +++ b/relax/backends/megatron/loss.py @@ -622,14 +622,20 @@ def vanilla_tis_function( rollout_log_probs = torch.cat(rollout_log_probs, dim=0) old_log_probs = torch.cat(train_log_probs, dim=0) - tis = torch.exp(old_log_probs - rollout_log_probs) - tis_abs = (torch.exp(old_log_probs - rollout_log_probs) - 1).abs() + log_ratio = old_log_probs - rollout_log_probs + tis = torch.exp(log_ratio) + tis_abs = (tis - 1).abs() tis_weights = torch.clamp(tis, min=args.tis_clip_low, max=args.tis_clip) tis_clipfrac = (tis_weights != tis).float() + # K3 KL ≈ E[exp(log_ratio) - log_ratio - 1]; direct KL = E[log π_rollout - log π_train]. + mismatch_k3_kl = tis - log_ratio - 1 + mismatch_kl = -log_ratio metrics = { "tis": tis.clone().detach(), "tis_clipfrac": tis_clipfrac.clone().detach(), "tis_abs": tis_abs.clone().detach(), + "mismatch_kl": mismatch_kl.clone().detach(), + "mismatch_k3_kl": mismatch_k3_kl.clone().detach(), } pg_loss = pg_loss * tis_weights return pg_loss, loss_masks, metrics @@ -647,16 +653,21 @@ def icepop_function( rollout_log_probs = torch.cat(rollout_log_probs, dim=0) old_log_probs = torch.cat(train_log_probs, dim=0) - ice_ratio = torch.exp(old_log_probs - rollout_log_probs) - ice_abs = (torch.exp(old_log_probs - rollout_log_probs) - 1).abs() + log_ratio = old_log_probs - rollout_log_probs + ice_ratio = torch.exp(log_ratio) + ice_abs = (ice_ratio - 1).abs() ice_weight = torch.where( (ice_ratio >= args.tis_clip_low) & (ice_ratio <= args.tis_clip), ice_ratio, torch.zeros_like(ice_ratio) ) ice_clipfrac = (ice_weight != ice_ratio).float() + mismatch_k3_kl = ice_ratio - log_ratio - 1 + mismatch_kl = -log_ratio metrics = { "tis": ice_ratio.clone().detach(), "tis_clipfrac": ice_clipfrac.clone().detach(), "tis_abs": ice_abs.clone().detach(), + "mismatch_kl": mismatch_kl.clone().detach(), + "mismatch_k3_kl": mismatch_k3_kl.clone().detach(), } pg_loss = pg_loss * ice_weight return pg_loss, loss_masks, metrics diff --git a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py index 9e99fbfc4..646e3c2a7 100644 --- a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py +++ b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py @@ -9,9 +9,13 @@ from megatron.core import mpu from relax.utils import device as device_utils +from relax.utils import megatron_bridge_utils from relax.utils.logging_utils import get_logger from relax.utils.types import ParamInfo +from ..misc_utils import strip_param_name_prefix +from ..weight_conversion import postprocess_hf_param +from ..weight_conversion.processors import quantize_params from .bridge_converter import BridgeConverter from .common import all_gather_param, named_params_and_buffers from .hf_weight_iterator_base import HfWeightIteratorBase @@ -29,20 +33,90 @@ def __init__(self, *args, **kwargs): self._bridge_converter = BridgeConverter( args=self.args, model=self.model, quantization_config=self.quantization_config ) - buckets_result = _build_param_info_buckets(self.args, self.model) - self._expert_buckets, self._non_expert_buckets, self._vanilla_key_map = buckets_result self._quantize_experts_before_broadcast = ( self.quantization_config is not None and self.quantization_config.get("quant_method") == "compressed-tensors" and mpu.get_expert_tensor_parallel_world_size() == 1 ) + # The bucketed PP/EP/TP broadcast path (``_iter_hf_params``) is only + # required for the INT4 quantize-before-broadcast optimization. The + # BF16 path hung in colocate runs (Qwen3.6-35B-A3B with PP=4 EP=2); + # for that case we keep the upstream ``megatron-bridge`` path which is + # the proven implementation used before commit ec24de0a. + if self._quantize_experts_before_broadcast: + buckets_result = _build_param_info_buckets(self.args, self.model) + self._expert_buckets, self._non_expert_buckets, self._vanilla_key_map = buckets_result + self._bridge = None + else: + from megatron.bridge import AutoBridge + + self._bridge = AutoBridge.from_hf_pretrained(self.args.hf_checkpoint, trust_remote_code=True) + self._expert_buckets = self._non_expert_buckets = self._vanilla_key_map = None def get_hf_weight_chunks(self, megatron_local_weights): + if self._quantize_experts_before_broadcast: + iterator = self._iter_hf_params(megatron_local_weights) + else: + iterator = self._iter_hf_params_via_upstream_bridge(megatron_local_weights) yield from _chunk_with_mla_pairing( - self._iter_hf_params(megatron_local_weights), + iterator, chunk_size=self.args.update_weight_buffer_size, ) + def _iter_hf_params_via_upstream_bridge(self, megatron_local_weights): + """BF16 path: delegate PP broadcast / TP gather to ``megatron-bridge``. + + Yields one ``(hf_name, tensor)`` at a time; the caller bundles them + into chunks via ``_chunk_with_mla_pairing``. + """ + renamed_megatron_local_weights = {strip_param_name_prefix(k): v for k, v in megatron_local_weights.items()} + with megatron_bridge_utils.patch_megatron_model(self.model): + conversion_tasks = self._bridge.get_conversion_tasks(self.model) + conversion_tasks = _process_conversion_tasks(conversion_tasks, renamed_megatron_local_weights) + + named_weights = self._bridge.export_hf_weights(self.model, cpu=False, conversion_tasks=conversion_tasks) + + hf_to_megatron_mapping = None + for item in named_weights: + # Compatibility shim: old megatron-bridge yields 3-tuples + # ``(hf_param_name, weight, megatron_param_name)`` while the + # official bridge yields 2-tuples ``(hf_param_name, weight)``. + if len(item) == 3: + hf_param_name, weight, megatron_param_name = item + elif len(item) == 2: + hf_param_name, weight = item + if hf_to_megatron_mapping is None: + hf_to_megatron_mapping = _build_hf_to_megatron_mapping(conversion_tasks) + # With PP > 1 ``export_hf_weights`` yields params from ALL + # PP ranks but the mapping only covers this rank's tasks. + # Fall back to ``hf_param_name`` for remote PP rank params + # — safe because downstream regexes don't match HF names. + megatron_param_name = hf_to_megatron_mapping.get(hf_param_name, hf_param_name) + else: + raise ValueError( + f"Unexpected named_weights tuple length {len(item)} from " + f"megatron-bridge.export_hf_weights(); expected 2 (new) or 3 (old). " + f"Item: {item!r}" + ) + + processed_weight = postprocess_hf_param( + args=self.args, + megatron_param_name=megatron_param_name, + hf_param_name=hf_param_name, + param=weight, + ) + + converted_named_params = [(hf_param_name, processed_weight)] + + quantized_batch = quantize_params( + args=self.args, + megatron_name=megatron_param_name, + converted_named_params=converted_named_params, + quantization_config=self.quantization_config, + ) + + yield from quantized_batch + def _iter_hf_params(self, megatron_local_weights): """Load params from CPU backuper dict, broadcast across PP/EP, TP- gather, bridge-convert, and quantize. @@ -597,3 +671,70 @@ def _chunk_with_mla_pairing(named_params, chunk_size): if bucket: yield bucket + + +def _build_hf_to_megatron_mapping(conversion_tasks): + """Reconstruct ``hf_name -> megatron_name`` from a list of conversion + tasks. + + Needed because the official ``megatron-bridge.export_hf_weights`` yields + 2-tuples ``(hf_name, weight)`` and drops the megatron name. We rebuild it + from ``task.mapping.hf_param`` — pure metadata, no collective ops. + """ + hf_to_megatron_mapping = {} + + for task in conversion_tasks: + megatron_param_name = task.param_name + hf_param = task.mapping.hf_param + + if isinstance(hf_param, str): + hf_to_megatron_mapping[hf_param] = megatron_param_name + elif isinstance(hf_param, dict): + for hf_name in hf_param.values(): + hf_to_megatron_mapping[hf_name] = megatron_param_name + else: + raise TypeError( + f"Unexpected mapping.hf_param type {type(hf_param).__name__} " + f"for megatron param '{megatron_param_name}': {hf_param!r}" + ) + + return hf_to_megatron_mapping + + +def _process_conversion_tasks(vanilla_conversion_tasks, new_weight_dict): + """Splice the freshly-trained weights into each conversion task. + + ``build_conversion_tasks`` may return ``None`` entries for global params + with no mapping; filter them so downstream consumers never see ``None``. + """ + + def _handle_one(task): + if task is None: + return None + if task.param_weight is None: + return task + + weight_dict_key = f"vp_stages.{task.vp_stage}.{task.param_name}" + assert weight_dict_key in new_weight_dict, ( + f"{weight_dict_key=} not in new_weight_dict ({task.vp_stage=}, {task.param_name=}, {list(new_weight_dict)=})" + ) + + new_param_weight = new_weight_dict[weight_dict_key] + new_param_weight = new_param_weight.cuda() + return dataclasses.replace(task, param_weight=new_param_weight) + + valid_tasks = [t for t in vanilla_conversion_tasks if t is not None] + return _MapWithLen(_handle_one, valid_tasks) + + +class _MapWithLen: + def __init__(self, fn, xs): + self.fn = fn + self.xs = xs + + def __len__(self): + return len(self.xs) + + def __iter__(self): + for x in self.xs: + yield self.fn(x) diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index 79f04a083..b4e6dc894 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -1559,9 +1559,12 @@ def add_metrics_service_arguments(parser): collection.""" parser.add_argument( "--use-metrics-service", - action="store_true", - default=False, - help="Enable metrics service for centralized metrics collection and reporting", + action=argparse.BooleanOptionalAction, + default=True, + help=( + "Enable metrics service for centralized metrics collection and reporting. " + "Default: True. Use --no-use-metrics-service to disable." + ), ) parser.add_argument( "--timeline-dump-dir", diff --git a/requirements.txt b/requirements.txt index 5d289572b..8c6abe491 100644 --- a/requirements.txt +++ b/requirements.txt @@ -31,7 +31,6 @@ loguru av==17.0.0 transformers==5.3.0 huggingface_hub==1.7.2 -compressed_tensors>=0.13.0 blessed==1.38.0 gpustat dool diff --git a/scripts/entrypoint/local.sh b/scripts/entrypoint/local.sh index 592dcb28a..90745c481 100644 --- a/scripts/entrypoint/local.sh +++ b/scripts/entrypoint/local.sh @@ -64,7 +64,7 @@ export PYTHONPATH=${RELAX}:$MEGATRON:$RELAX:${PYTHONPATH:-} export MODEL_CONFIG_DIR="${_LOCAL_SH_DIR}/../models" # ── NVLink detection ──────────────────────────────────────────────────────── -NVLINK_COUNT=$(nvidia-smi topo -m 2>/dev/null | grep -o 'NV[0-9][0-9]*' | wc -l) +NVLINK_COUNT=$(nvidia-smi topo -m 2>/dev/null | grep -o 'NV[0-9][0-9]*' | wc -l || true) if [ "$NVLINK_COUNT" -gt 0 ]; then export HAS_NVLINK=1 else diff --git a/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh b/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh index 830165775..9410e3fe8 100755 --- a/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh +++ b/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh @@ -45,8 +45,8 @@ CKPT_ARGS=( # Megatron BF16 checkpoint --ref-load ${EXP_DIR}/Qwen3-30B-A3B --megatron-to-hf-mode bridge - --load ${EXP_DIR}/Qwen3-30B-A3B - --save ${EXP_DIR}/Qwen3-30B-A3B + --load ${EXP_DIR}/Qwen3-30B-A3B_dist + --save ${EXP_DIR}/Qwen3-30B-A3B_dist --save-interval 100 ) diff --git a/scripts/training/text/run-qwen35-9B-8xgpu.sh b/scripts/training/text/run-qwen35-9B-8xgpu.sh index 7112073ad..ed2da5ad2 100644 --- a/scripts/training/text/run-qwen35-9B-8xgpu.sh +++ b/scripts/training/text/run-qwen35-9B-8xgpu.sh @@ -99,6 +99,8 @@ GRPO_ARGS=( --eps-clip 0.2 --eps-clip-high 0.28 --use-tis + # icepop: drop tokens with ratio outside [tis-clip-low, tis-clip] instead of clamping (vanilla TIS). + --custom-tis-function-path relax.backends.megatron.loss.icepop_function ) OPTIMIZER_ARGS=( diff --git a/skills/perf-doctor/SKILL.md b/skills/perf-doctor/SKILL.md index 2310bf15a..3092d7543 100644 --- a/skills/perf-doctor/SKILL.md +++ b/skills/perf-doctor/SKILL.md @@ -24,7 +24,8 @@ argument-hint: - flag 解析：TP/PP/CP/EP/ETP、`--colocate` vs `--fully-async`、`--rollout-max-response-len`、`--max-tokens-per-gpu`、`--resource`、`--num-iters-per-train-update`、`--max-staleness`、`--num-data-storage-units` - **默认 GPU 假设：H20 96GB**，除非用户在 prompt 里给出别的（A100 80G / H100 80G 等） 3. **加载规则** — 读 `references/rules.md`，逐条判断 applies / borderline / not-applicable -4. **输出报告** — 严格按下方 [输出模板](#输出模板) 渲染 +4. **对照 baseline** — 读 `references/baselines.md`，若用户脚本与某条 baseline 同模型 + 同 GPU 数量级，把 baseline 的并行 / batch / mem 配置作为合理区间锚点；偏离 ≥ 2 档时把 baseline 数值写进对应 finding 的 `Cost` 一栏佐证 +5. **输出报告** — 严格按下方 [输出模板](#输出模板) 渲染 ## 触发判断原则 @@ -78,3 +79,7 @@ argument-hint: ## Rule catalog 完整规则在 `references/rules.md`。每条规则字段：`Category` / `Severity` / `Trigger` / `Why` / `Fix` / `Skip when`。新规则直接往该文件追加即可，无需改 SKILL.md。 + +## Baselines + +经过验证的参考配置在 `references/baselines.md`，作为合理区间锚点用。新 baseline 按文件末尾模板追加即可。 diff --git a/skills/perf-doctor/references/baselines.md b/skills/perf-doctor/references/baselines.md new file mode 100644 index 000000000..d209fb2f5 --- /dev/null +++ b/skills/perf-doctor/references/baselines.md @@ -0,0 +1,72 @@ +# perf-doctor known-good baselines + +经过验证、能正常跑通且性能合理的参考配置。perf-doctor 在诊断时如果用户脚本与某条 baseline **同模型 + 同 GPU 数量级**，应把 baseline 的并行维 / batch / mem 类配置作为 "合理区间锚点"： + +- 用户配置与 baseline 差异 ≤ 1 档（如 TP 2→4、CP 4→2）：视为正常调优，不报 +- 差异 ≥ 2 档或方向相反（如 baseline EP=16 用户给 EP=1）：作为佐证写进对应 R-PXX finding 的 `Cost` 一栏 + +baseline 不是硬规则，是 cross-reference；脚本注释明确说在做对照实验则忽略。 + +--- + +## Qwen3.5-35B-A3B · 64×H800 80GB · sync (colocate) + +| 维度 | 值 | +|---|---| +| GPUs | 64（8 节点 × 8 卡 H800-80G）| +| Mode | `--colocate`（sync）| +| Model | Qwen3.5-35B-A3B（MoE，总参 35B / 激活 ~3B / 128 experts）| +| TP | 2 | +| PP | 2 | +| CP | 4 | +| EP | 16 | +| ETP | 1 | +| DP | 4（= 64 / (TP·PP·CP)）| +| GBS | 256 | +| max-resp-len | 40960（40K context）| +| max-tokens-per-gpu | 见下注 | +| 关键 flags | `--use-dynamic-batch-size` · `--balance-data` · `--moe-token-dispatcher-type flex` · `--moe-flex-dispatcher-backend deepep` · `--attention-backend flash` · `--sglang-load-format dummy` | + +**Why this is balanced:** + +- **TP=2**：MoE A3B 激活参数小，TP 大了 all-reduce 占比反而上升；2 够装单层 +- **PP=2**：35B 在 TP2·EP16 下单层可装，但 40K context 的 activation 需要纵向再切一刀 +- **CP=4**：40K context 必须切 sequence 维（CP=1 时 attention 显存 O(seq²) 爆），10K/CP rank 是 H800 的舒适区 +- **EP=16**：128 experts / 16 = 每 EP rank 8 expert，配合 DeepEP dispatcher 通信成本最低 +- **DP=4 + balance-data**：变长 + 大 DP 必开 balance，否则 straggler 拖整批 +- **sync 模式**：colocate 复用 64 卡，避免 fully-async 在 H800 上 actor/rollout 拆分难匹配 + +**Memory budget（粗算）：** + +- Weight: 35B × 2B (bf16) / (TP·EP) = 70G / 32 ≈ **2.2G/卡**（expert）+ dense 部分 / TP·PP ≈ 数 G +- Optimizer (Adam fp32): 35B × 12B / (TP·EP·DP) ≈ **3.3G/卡**（不开 cpu offload） +- Activation: bsz/DP × seq/CP × hidden × layers/PP ≈ **20~40G/卡**（dynamic batch + selective recompute） +- KV cache (SGLang colocate): mem-fraction-static 0.75 → **~50G/卡** 共享 +- **结论：** sync 时 weight+optim+act ≤ 50G，剩 30G 给 SGLang 切换够用；不需要 `--optimizer-cpu-offload` + +**何时偏离这条 baseline 是合理的：** + +- 64×H20 96G（非 H800）→ 显存更宽，PP 可降到 1；通信带宽不同，CP 可能降到 2 +- 改 fully-async → 需要拆 actor/rollout 资源池，参数会重新算 +- context 降到 8K 以下 → CP 可以从 4 降到 1 + +--- + +## 模板：新增 baseline 时按此填 + +```markdown +## · × · + +| 维度 | 值 | +|---|---| +| GPUs | N (nodes × g/n) | +| Mode | colocate / fully-async / hybrid | +| Model | 名称 + dense/MoE + 关键尺寸 | +| TP / PP / CP / EP / ETP / DP | … | +| GBS / max-resp-len | … | +| 关键 flags | 列必备 perf flag | + +**Why this is balanced:** 每维度一句话说明 +**Memory budget:** 粗算，避免迷信 +**何时偏离合理:** 列 1–3 个常见变体场景 +``` diff --git a/skills/perf-doctor/references/rules.md b/skills/perf-doctor/references/rules.md index 26e453f39..6c9bb2ad0 100644 --- a/skills/perf-doctor/references/rules.md +++ b/skills/perf-doctor/references/rules.md @@ -144,3 +144,18 @@ - **Why:** sync 模式 batch size 稳定，CUDA graph 能省每步 launch overhead 5-15% - **Fix:** 参考 async 脚本：`--sglang-cuda-graph-bs 1 2 4 8 $(seq 16 8 256)` - **Skip when:** rollout batch 高度动态、或 SGLang 版本 graph 有 bug + +### R-P13 — `--balance-data` 使用与模式约束 +- **Category:** performance (DP 负载均衡) +- **Severity:** critical（纯 fully-async 误开）/ warn（sync/hybrid 漏开） +- **Trigger:** 满足任一： + - **(critical)** 出现 `--fully-async` 且**没有** `--hybrid`，但 ARGS 里还有 `--balance-data` —— Relax 启动校验会直接 `ValueError` 退出（见 `relax/utils/arguments.py:2369`） + - **(warn)** `--colocate` 或 `--hybrid` 模式 + 数据是变长（开了 `--use-dynamic-batch-size` 或 `--rollout-max-response-len >= 4096`）+ DP（= world_size / TP / PP / CP）> 1，但**没**带 `--balance-data` +- **Why:** + - `--balance-data` 用 SeqlenBalancedSampler（Karmarkar-Karp）按 token 数把 sample 均摊到各 DP rank，消掉 straggler，变长序列大 DP 场景下省 10~30% step 时间 + - **纯 fully-async 模式下 actor 通过 StreamDataLoader 消费 rollout 流，和静态 balance 语义不兼容**，框架直接 raise（错误信息：`--balance-data is not supported in pure fully-async mode`）；想用 balance 必须切到 `--hybrid` + - 同 prompt 的不同 response 可能被分到不同 step，对纯算法精度影响通常可忽略，但对依赖"同 prompt 同 step"的算法（如某些 group-norm advantage）需要确认 +- **Fix:** + - 纯 fully-async：从 ARGS 里**删掉** `--balance-data`，或同时加 `--hybrid` 切到混合模式 + - sync / hybrid 变长场景：加 `--balance-data` +- **Skip when:** 数据定长（多选题 / 固定长度 eval）；DP=1；算法依赖 "同 prompt response 必须在同一 train step"；脚本注释里明确说明在做 baseline 对照实验 From 62a92e82e0c4679a2a18442f025517be4d88fbe1 Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Fri, 29 May 2026 17:41:10 +0800 Subject: [PATCH 065/268] fix: ci tests --- .../megatron/weight_update/test_broadcast_quantized.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/backends/megatron/weight_update/test_broadcast_quantized.py b/tests/backends/megatron/weight_update/test_broadcast_quantized.py index 3a0a68836..ebeff7f0a 100644 --- a/tests/backends/megatron/weight_update/test_broadcast_quantized.py +++ b/tests/backends/megatron/weight_update/test_broadcast_quantized.py @@ -13,6 +13,7 @@ import sys from unittest.mock import MagicMock, patch +import pytest import torch from relax.utils.types import ParamInfo @@ -39,6 +40,8 @@ _saved[_mod] = sys.modules[_mod] sys.modules[_mod] = MagicMock() +pytest.importorskip("triton") + from relax.backends.megatron.weight_update.hf_weight_iterator_bridge import ( # noqa: E402 _broadcast_quantized_bucket, _broadcast_quantized_phase, From 555ea808d157875670965162d1b628e1833245f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= <595403043@qq.com> Date: Fri, 29 May 2026 23:50:33 +0800 Subject: [PATCH 066/268] docs: add projects built upon Relax --- README.md | 8 ++++++++ README_zh.md | 8 ++++++++ 2 files changed, 16 insertions(+) diff --git a/README.md b/README.md index a4ae0506b..79bc01d64 100644 --- a/README.md +++ b/README.md @@ -253,6 +253,14 @@ ______________________________________________________________________ ______________________________________________________________________ +## 🧩 Projects Built upon Relax + +| Project | Description | +| :------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [HyperEyes](https://github.com/DeepExperience/HyperEyes) | A parallel multimodal search agent that uses Relax for efficient RL training, combining visual grounding and retrieval to search across multiple entities concurrently. | + +______________________________________________________________________ + ## 🤝 Contributing We welcome contributions of all kinds! Please read our [Contributing Guide](docs/en/guide/how-to-contribute.md) to get started. diff --git a/README_zh.md b/README_zh.md index 3c3754f98..e1b1221fc 100644 --- a/README_zh.md +++ b/README_zh.md @@ -253,6 +253,14 @@ ______________________________________________________________________ ______________________________________________________________________ +## 🧩 基于 Relax 构建的项目 + +| 项目 | 描述 | +| :------------------------------------------------------- | :------------------------------------------------------------------------------------------------ | +| [HyperEyes](https://github.com/DeepExperience/HyperEyes) | 一个并行多模态搜索智能体，使用 Relax 进行高效的 RL 训练，结合视觉定位与检索能力并发搜索多个实体。 | + +______________________________________________________________________ + ## 🤝 参与贡献 欢迎各种形式的贡献！请阅读 [贡献指南](docs/zh/guide/how-to-contribute.md) 了解详情。 From 45c167718a0d7d5d8b4e84d9c072126c127ed703 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=A8=E7=9D=BF?= Date: Mon, 1 Jun 2026 13:33:02 +0800 Subject: [PATCH 067/268] fix(gitleaks): tighten secret allowlists MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🔒 Security ## Narrow gitleaks scanning exceptions - Run gitleaks over the full working tree from pre-commit - Replace broad docs/tests allowlists with generated-artifact-only exclusions - Keep exact placeholder IP allowlists for 10.0.0.1 and 192.168.1.100 - Tighten QS_ and LTA token rules to reduce identifier false positives --- # 📝 Documentation ## Replace non-allowlisted private endpoints - Update rollout and checkpoint docs to use documentation IP ranges where values are not explicitly allowlisted --- # ✅ Tests ## Align Ray address fixtures - Update rollout manager test fixtures for the remaining non-allowlisted example endpoint --- .gitleaks.toml | 26 +++++++++++--------- .pre-commit-config.yaml | 2 ++ docs/draft/distributed_checkpoint_service.md | 2 +- docs/draft/elastic_rollout.md | 8 +++--- docs/en/guide/distributed-checkpoint.md | 2 +- docs/en/guide/elastic-rollout.md | 10 ++++---- docs/zh/guide/distributed-checkpoint.md | 2 +- docs/zh/guide/elastic-rollout.md | 10 ++++---- skills/git-commit/SKILL.md | 25 ++++++++++++++++--- tests/distributed/ray/test_utils.py | 4 +-- 10 files changed, 58 insertions(+), 33 deletions(-) diff --git a/.gitleaks.toml b/.gitleaks.toml index 0b2298890..828be99d8 100644 --- a/.gitleaks.toml +++ b/.gitleaks.toml @@ -24,7 +24,7 @@ tags = ["internal", "path"] [[rules]] id = "platform-env-qs" description = "Detects platform injected environment variables with QS_ prefix" -regex = '''(?i)QS_[A-Z_]+''' +regex = '''\bQS_[A-Z][A-Z0-9_]*\b''' tags = ["internal", "env"] [rules.allowlist] regexTarget = "match" @@ -51,7 +51,7 @@ tags = ["internal", "token"] [[rules]] id = "token-lta" description = "Detects LTA token with LTA prefix" -regex = '''(?i)LTA[a-zA-Z0-9-]{16,}''' +regex = '''\bLTA[a-zA-Z0-9-]{16,}\b''' tags = ["internal", "token"] [rules.allowlist] regexTarget = "match" @@ -69,8 +69,8 @@ tags = ["internal", "ip", "private"] [rules.allowlist] regexTarget = "match" regexes = [ - # 10.0.0.1 is a documentation/example placeholder IP, not a real host - '''10\.0\.0\.1''', + # 10.0.0.1 is a conventional placeholder IP in examples and tests. + '''^10\.0\.0\.1$''', ] paths = ['''scripts/ci/benchmark\.sh$'''] @@ -87,15 +87,19 @@ id = "private-ip-192-168-range" description = "Detects hardcoded private IP addresses in 192.168.0.0/16 range (RFC 1918)" regex = '''\b192\.168\.\d{1,3}\.\d{1,3}\b''' tags = ["internal", "ip", "private"] + [rules.allowlist] + regexTarget = "match" + regexes = [ + # 192.168.1.100 is used as a conventional example endpoint in docs. + '''^192\.168\.1\.100$''', + ] -# Allowlist: Ignore common false positives -[[allowlists]] -description = "Ignore test files and documentation" +# Allowlist: Ignore scanner metadata and generated docs artifacts +[allowlist] +description = "Ignore scanner metadata and generated docs artifacts" paths = [ '''\.gitleaks\.toml$''', + '''docs/Dockerfile''', '''\.git/.*''', - '''test.*/.*''', - '''.*_test\.py$''', - '''docs?/.*''', - '''scripts/ci/benchmark\.sh$''', + '''docs/\.vitepress/(dist|cache)/.*''', ] diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7c814f1b6..698c303ed 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -76,3 +76,5 @@ repos: rev: v8.24.2 hooks: - id: gitleaks + entry: gitleaks dir --redact --verbose . + pass_filenames: false diff --git a/docs/draft/distributed_checkpoint_service.md b/docs/draft/distributed_checkpoint_service.md index 9dd590dee..29dff7372 100644 --- a/docs/draft/distributed_checkpoint_service.md +++ b/docs/draft/distributed_checkpoint_service.md @@ -577,7 +577,7 @@ manager = TopologyManager( # Register nodes manager.register(RoleInfo(role_name="actor", rank=0, ip="10.0.0.1", port=20000)) -manager.register(RoleInfo(role_name="rollout", rank=0, ip="10.0.0.2", port=20001)) +manager.register(RoleInfo(role_name="rollout", rank=0, ip="192.0.2.2", port=20001)) # Get peer peer = manager.get_peer("actor", 0, "rollout") diff --git a/docs/draft/elastic_rollout.md b/docs/draft/elastic_rollout.md index 0d1b61546..dd68aeb65 100644 --- a/docs/draft/elastic_rollout.md +++ b/docs/draft/elastic_rollout.md @@ -173,7 +173,7 @@ Content-Type: application/json // external 模式参数 "external_engine_addrs": [ // 外部引擎地址列表 "192.168.1.100:8000", - "192.168.1.101:8000" + "198.51.100.101:8000" ], // 通用参数 @@ -256,7 +256,7 @@ GET /rollout/engines "engines": [ { "engine_id": "engine_0", - "url": "http://192.168.1.10:8000", + "url": "http://198.51.100.10:8000", "status": "ACTIVE", "weight_version": "42", "is_healthy": true, @@ -264,7 +264,7 @@ GET /rollout/engines } ], "router_info": { - "ip": "192.168.1.1", + "ip": "198.51.100.1", "port": 30000, "policy": "cache_aware" } @@ -709,7 +709,7 @@ Content-Type: application/json }, { "engine_id": "engine_2", - "url": "http://192.168.1.101:8000", + "url": "http://198.51.100.101:8000", "engine_group_index": 1, "source": "external", "is_scaled_out": true, diff --git a/docs/en/guide/distributed-checkpoint.md b/docs/en/guide/distributed-checkpoint.md index fa20e419b..15c11f674 100644 --- a/docs/en/guide/distributed-checkpoint.md +++ b/docs/en/guide/distributed-checkpoint.md @@ -562,7 +562,7 @@ manager = TopologyManager( # Register nodes manager.register(RoleInfo(role_name="actor", rank=0, ip="10.0.0.1", port=20000)) -manager.register(RoleInfo(role_name="rollout", rank=0, ip="10.0.0.2", port=20001)) +manager.register(RoleInfo(role_name="rollout", rank=0, ip="192.0.2.2", port=20001)) # Get peer peer = manager.get_peer("actor", 0, "rollout") diff --git a/docs/en/guide/elastic-rollout.md b/docs/en/guide/elastic-rollout.md index 033f361e7..c57d01226 100644 --- a/docs/en/guide/elastic-rollout.md +++ b/docs/en/guide/elastic-rollout.md @@ -146,7 +146,7 @@ curl -X POST http:///rollout/scale_out \ -d '{ "engine_urls": [ "http://192.168.1.100:30000", - "http://192.168.1.101:30000" + "http://198.51.100.101:30000" ] }' ``` @@ -408,13 +408,13 @@ curl http:///rollout/engines "engines": [ { "engine_id": "engine_0", - "url": "http://192.168.1.10:30000", + "url": "http://198.51.100.10:30000", "status": "ACTIVE", "is_healthy": true }, { "engine_id": "engine_1", - "url": "http://192.168.1.11:30000", + "url": "http://198.51.100.11:30000", "status": "ACTIVE", "is_healthy": true } @@ -494,8 +494,8 @@ curl -X POST http://localhost:8000/rollout/scale_out \ -H "Content-Type: application/json" \ -d '{ "engine_urls": [ - "http://10.0.1.50:30000", - "http://10.0.1.51:30000" + "http://192.0.2.50:30000", + "http://192.0.2.51:30000" ] }' ``` diff --git a/docs/zh/guide/distributed-checkpoint.md b/docs/zh/guide/distributed-checkpoint.md index 0c001d9c7..c55916b39 100644 --- a/docs/zh/guide/distributed-checkpoint.md +++ b/docs/zh/guide/distributed-checkpoint.md @@ -562,7 +562,7 @@ manager = TopologyManager( # 注册节点 manager.register(RoleInfo(role_name="actor", rank=0, ip="10.0.0.1", port=20000)) -manager.register(RoleInfo(role_name="rollout", rank=0, ip="10.0.0.2", port=20001)) +manager.register(RoleInfo(role_name="rollout", rank=0, ip="192.0.2.2", port=20001)) # 获取对等体 peer = manager.get_peer("actor", 0, "rollout") diff --git a/docs/zh/guide/elastic-rollout.md b/docs/zh/guide/elastic-rollout.md index 574c0dc11..359f89722 100644 --- a/docs/zh/guide/elastic-rollout.md +++ b/docs/zh/guide/elastic-rollout.md @@ -146,7 +146,7 @@ curl -X POST http:///rollout/scale_out \ -d '{ "engine_urls": [ "http://192.168.1.100:30000", - "http://192.168.1.101:30000" + "http://198.51.100.101:30000" ] }' ``` @@ -387,13 +387,13 @@ curl http:///rollout/engines "engines": [ { "engine_id": "engine_0", - "url": "http://192.168.1.10:30000", + "url": "http://198.51.100.10:30000", "status": "ACTIVE", "is_healthy": true }, { "engine_id": "engine_1", - "url": "http://192.168.1.11:30000", + "url": "http://198.51.100.11:30000", "status": "ACTIVE", "is_healthy": true } @@ -473,8 +473,8 @@ curl -X POST http://localhost:8000/rollout/scale_out \ -H "Content-Type: application/json" \ -d '{ "engine_urls": [ - "http://10.0.1.50:30000", - "http://10.0.1.51:30000" + "http://192.0.2.50:30000", + "http://192.0.2.51:30000" ] }' ``` diff --git a/skills/git-commit/SKILL.md b/skills/git-commit/SKILL.md index c940282f9..41fd502c8 100644 --- a/skills/git-commit/SKILL.md +++ b/skills/git-commit/SKILL.md @@ -18,20 +18,37 @@ Creates git commits following Conventional Commits format with rich markdown bod ## Quick start ```bash -# 1. Stage changes +# 0. Inspect current state +git status -sb +git diff --cached --stat +git diff --stat + +# 1. Stage only the intended changes git add +git diff --cached --stat # 2. Run pre-commit checks (MUST do before committing) pre-commit run --all-files --show-diff-on-failure # If it fails or auto-fixes files: re-stage with git add, then re-run until clean -# 3. Re-stage (pre-commit may have modified files) +# 3. Re-stage intended files (pre-commit may have modified files), then verify index git add +git diff --cached --stat +git diff --stat # 4. Create commit with detailed markdown body git commit -F /tmp/commitmsg.txt ``` +## Staging discipline + +- Treat the index as the source of truth for the commit. Use `git diff --cached --stat` and `git diff --cached --name-status` before writing the message. +- Do not infer commit contents from memory, `git diff` alone, or previous discussion. If `git diff --stat` is empty but `git status -sb` shows `M `, the changes are staged. +- Leave unrelated untracked files and unrelated unstaged edits alone unless the user explicitly asks to include them. +- Do not use broad `git add .` when unrelated untracked files exist. Stage explicit paths. +- After pre-commit, re-check both staged and unstaged diffs. If hooks changed files, stage only the intended paths and rerun pre-commit until clean. +- If `git commit` fails with `.git/index.lock: Operation not permitted` in a sandboxed environment, do not restage or modify files. Rerun only `git commit -F /tmp/commitmsg.txt` with the required sandbox escalation, then remove the temp file after success. + ## Commit message structure ### 1. Subject line (first line) @@ -122,15 +139,17 @@ rm /tmp/commitmsg.txt ``` > **Note:** HEREDOC with `git commit -m` can fail with emoji/unicode. -> Always prefer `printf ... > /tmp/commitmsg.txt && git commit -F /tmp/commitmsg.txt && rm /tmp/commitmsg.txt`. +> Prefer `printf ... > /tmp/commitmsg.txt`, `git commit -F /tmp/commitmsg.txt`, and `rm /tmp/commitmsg.txt` as separate commands so failures are easy to recover from. ## Important rules - **ALWAYS** run `pre-commit run --all-files --show-diff-on-failure` before `git commit`, then `git add` again to stage any auto-fixed changes +- **ALWAYS** verify staged contents with `git diff --cached --stat` before committing - **ALWAYS** include scope in parentheses (kebab-case) - **ALWAYS** use present tense imperative verb for the subject - **ALWAYS** include a markdown body with heading(s) for non-trivial commits - **ALWAYS** prefer `git commit -F ` for commits with markdown body +- **NEVER** stage unrelated untracked files or unstaged edits while creating a commit - **NEVER** end subject with a period - **NEVER** exceed 50 chars in the subject line - **NEVER** use generic messages ("update code", "fix bug", "changes") diff --git a/tests/distributed/ray/test_utils.py b/tests/distributed/ray/test_utils.py index 8cbf55c97..8d4bcfc29 100644 --- a/tests/distributed/ray/test_utils.py +++ b/tests/distributed/ray/test_utils.py @@ -109,13 +109,13 @@ def test_localhost(self): class TestCollectExistingEngineAddrs: def test_all_live_engines(self, patch_ray_get): e1 = make_mock_engine(url="http://10.0.0.1:30000") - e2 = make_mock_engine(url="http://10.0.0.2:30000") + e2 = make_mock_engine(url="http://192.0.2.2:30000") group = make_engine_group(engines=[e1, e2]) srv = make_rollout_server(engine_groups=[group]) manager = create_test_manager(servers={"default": srv}) addrs = manager._collect_existing_engine_addrs(srv) - assert addrs == {"10.0.0.1:30000", "10.0.0.2:30000"} + assert addrs == {"10.0.0.1:30000", "192.0.2.2:30000"} def test_dead_engines_excluded(self, patch_ray_get): e1 = make_mock_engine(url="http://10.0.0.1:30000") From 09fa804c562b7701ff177da750cd0ae3a6062c80 Mon Sep 17 00:00:00 2001 From: yangrui6 Date: Mon, 1 Jun 2026 15:47:20 +0800 Subject: [PATCH 068/268] fix(scripts,ci): clean up dir vars and narrow gitleaks scope MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # 🐛 Bug Fix ## Extract `MODEL_DIR` / `DATA_DIR` / `EXP_DIR` in training scripts - Fix `EXP_DIR="${MODEL_DIR:=...}"` variable-name typo in `run-qwen3-30B-A3B-int4-8xgpu.sh` and `run-qwen3-4B-8xgpu-hybrid-async.sh` - Split into the canonical three-way `EXP_DIR` / `MODEL_DIR` / `DATA_DIR` form already used by `run-qwen3-30B-A3B-fp8-8xgpu.sh` - Route `--hf-checkpoint` and `--ref-load` via `${MODEL_DIR}`, prompt / eval data via `${DATA_DIR}`, keep `--save` / `--load` on `${EXP_DIR}` ## Allow `vision_dp_when_cp` to pass through model provider - Extend the Megatron-Bridge override allowlist in `get_model_provider_func` so the CLI flag is no longer silently dropped --- # 🔧 CI/CD ## Restrict gitleaks pre-commit hook to tracked content - Switch the entry to the upstream-recommended `gitleaks git --pre-commit --staged` form so the hook scans only staged changes; the previous `gitleaks dir .` form scanned the full working tree and false-positived on untracked `log/` files --- .pre-commit-config.yaml | 2 +- relax/backends/megatron/model_provider.py | 1 + .../training/text/run-qwen3-30B-A3B-int4-8xgpu.sh | 12 +++++++----- .../training/text/run-qwen3-4B-8xgpu-hybrid-async.sh | 12 +++++++----- 4 files changed, 16 insertions(+), 11 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 698c303ed..1751c8265 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -76,5 +76,5 @@ repos: rev: v8.24.2 hooks: - id: gitleaks - entry: gitleaks dir --redact --verbose . + entry: gitleaks git --pre-commit --redact --staged --verbose pass_filenames: false diff --git a/relax/backends/megatron/model_provider.py b/relax/backends/megatron/model_provider.py index 1ed759c11..9fb9d92f9 100644 --- a/relax/backends/megatron/model_provider.py +++ b/relax/backends/megatron/model_provider.py @@ -273,6 +273,7 @@ def wrapped_model_provider( "freeze_vision_projection", # https://github.com/redai-infra/Megatron-Bridge/commit/960bb5f18800d3e1fb9815e95daa185ab06c09ea "vision_dp_when_tp", + "vision_dp_when_cp", "calculate_per_token_loss", # Allow CLI to override layer count / MoE frequency for layer-reduced training "num_layers", diff --git a/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh b/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh index 9410e3fe8..c6b528603 100755 --- a/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh +++ b/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh @@ -37,20 +37,22 @@ fi source "${MODEL_CONFIG_DIR}/qwen3-30B-A3B.sh" PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" -EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" NUM_ROLLOUT="${NUM_ROLLOUT:=1000}" CKPT_ARGS=( - --hf-checkpoint ${EXP_DIR}/Qwen3-30B-A3B-int4 + --hf-checkpoint ${MODEL_DIR}/Qwen3-30B-A3B-int4 # Megatron BF16 checkpoint - --ref-load ${EXP_DIR}/Qwen3-30B-A3B + --ref-load ${MODEL_DIR}/Qwen3-30B-A3B --megatron-to-hf-mode bridge --load ${EXP_DIR}/Qwen3-30B-A3B_dist --save ${EXP_DIR}/Qwen3-30B-A3B_dist --save-interval 100 ) -PROMPT_SET=${EXP_DIR}/dapo-math-17k/dapo-math-17k.jsonl +PROMPT_SET=${DATA_DIR}/dapo-math-17k/dapo-math-17k.jsonl ROLLOUT_ARGS=( --prompt-data ${PROMPT_SET} @@ -75,7 +77,7 @@ EVAL_ARGS=( --skip-eval-before-train --log-passrate --eval-interval 2000 - --eval-prompt-data aime ${EXP_DIR}/dapo-math-17k/dapo-100.jsonl + --eval-prompt-data aime ${DATA_DIR}/dapo-math-17k/dapo-100.jsonl --n-samples-per-eval-prompt 8 --eval-max-response-len 16384 --eval-top-p 0.7 diff --git a/scripts/training/text/run-qwen3-4B-8xgpu-hybrid-async.sh b/scripts/training/text/run-qwen3-4B-8xgpu-hybrid-async.sh index d69d2b944..62e22f124 100644 --- a/scripts/training/text/run-qwen3-4B-8xgpu-hybrid-async.sh +++ b/scripts/training/text/run-qwen3-4B-8xgpu-hybrid-async.sh @@ -21,21 +21,23 @@ fi source "${MODEL_CONFIG_DIR}/qwen3-4B.sh" PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" -EXP_DIR="${MODEL_DIR:=${SCRIPT_DIR}/../../../../exps}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" NUM_ROLLOUT="${NUM_ROLLOUT:=200}" CKPT_ARGS=( - --hf-checkpoint ${EXP_DIR}/Qwen3-4B/ - --ref-load ${EXP_DIR}/Qwen3-4B/ + --hf-checkpoint ${MODEL_DIR}/Qwen3-4B/ + --ref-load ${MODEL_DIR}/Qwen3-4B/ --megatron-to-hf-mode bridge # --load ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save-interval 100 ) -PROMPT_SET=${EXP_DIR}/dapo-math-17k/dapo-math-17k.jsonl +PROMPT_SET=${DATA_DIR}/dapo-math-17k/dapo-math-17k.jsonl ROLLOUT_ARGS=( --prompt-data ${PROMPT_SET} @@ -60,7 +62,7 @@ EVAL_ARGS=( --skip-eval-before-train --log-passrate --eval-interval 20 - --eval-prompt-data aime ${EXP_DIR}/aime-2024/aime-2024.jsonl + --eval-prompt-data aime ${DATA_DIR}/aime-2024/aime-2024.jsonl --n-samples-per-eval-prompt 8 --eval-max-response-len 16384 --eval-top-p 0.7 From 52f6d6ad0aec9173e79c0e617f4db0c3528c7644 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Mon, 1 Jun 2026 18:57:36 +0800 Subject: [PATCH 069/268] feat(perf): replace FLOPS calculation with verl-style FlopsCounter and add MFU metrics Replace the old bottom-up per-component FLOPS calculator (flops_utils.py) with verl's 6N formula-based FlopsCounter, supporting per-model-type estimators for dense, MoE, MLA+MoE, and vision-language architectures. Add MFU (Model FLOPS Utilization) metrics via GPU peak FLOPS auto-detection. Supported model families: Qwen2/3/3.5/3.6, LLaMA, Mistral, DeepSeek-V3, GLM4/GLM4V/GLM46V (dense & MoE & MLA), MiniCPM-V/O, SEED-OSS, MIMO, Qwen3-VL, Qwen3-VL-MoE, Qwen3-Omni-MoE. Co-Authored-By: Claude Opus 4.7 --- relax/backends/megatron/actor.py | 62 ++- relax/backends/megatron/data.py | 9 +- relax/utils/training/flops_counter.py | 559 +++++++++++++++++++++ relax/utils/training/flops_utils.py | 127 ----- relax/utils/training/train_metric_utils.py | 46 +- relax/utils/utils.py | 70 +++ tests/utils/test_flops_counter.py | 322 ++++++++++++ 7 files changed, 1051 insertions(+), 144 deletions(-) create mode 100644 relax/utils/training/flops_counter.py delete mode 100644 relax/utils/training/flops_utils.py create mode 100644 tests/utils/test_flops_counter.py diff --git a/relax/backends/megatron/actor.py b/relax/backends/megatron/actor.py index d113ca5a9..6cd90a31b 100644 --- a/relax/backends/megatron/actor.py +++ b/relax/backends/megatron/actor.py @@ -40,7 +40,14 @@ from relax.utils.training import train_dump_utils from relax.utils.training.routing_replay import RoutingReplay from relax.utils.types import RolloutBatch -from relax.utils.utils import get_debug_data, get_serve_url, merge_dict_list, process_args +from relax.utils.utils import ( + _extract_audio_seqlens, + _extract_images_seqlens, + get_debug_data, + get_serve_url, + merge_dict_list, + process_args, +) from ...utils.profile_utils import TrainProfiler from ...utils.training.tensor_backper import TensorBackuper @@ -111,6 +118,10 @@ def _init( self.tokenizer = AutoTokenizer.from_pretrained(self.args.hf_checkpoint, trust_remote_code=True) dist.barrier(group=get_gloo_group()) + from relax.utils.training.flops_counter import FlopsCounter + + self.flops_counter = FlopsCounter(self.hf_config) + self.train_parallel_config = { "dp_size": mpu.get_data_parallel_world_size(with_context_parallel=False), } @@ -463,6 +474,7 @@ def train(self, rollout_id: int) -> None: data_fields += ["rollout_routed_experts"] if self.args.use_rollout_routing_replay else [] if self.args.multimodal_keys is not None: data_fields.append("multimodal_train_inputs") + if self.args.use_opd and self.args.opd_type == "sglang": data_fields.append("teacher_log_probs") if self.args.opd_log_prob_top_k > 0: @@ -623,7 +635,21 @@ def train_actor(self, rollout_id: int, rollout_data: RolloutBatch) -> None: ) all_total_lengths = sum(all_total_lengths, []) # flatten Timer().seq_lens = all_total_lengths - log_perf_data(rollout_id, self.args) + mm_inputs = rollout_data.get("multimodal_train_inputs") + if mm_inputs is not None: + images_seqlens = _extract_images_seqlens(mm_inputs) + all_images_seqlens = [None] * mpu.get_data_parallel_world_size(with_context_parallel=False) + dist.all_gather_object( + all_images_seqlens, images_seqlens, group=mpu.get_data_parallel_group(with_context_parallel=False) + ) + Timer().images_seqlens = sum(all_images_seqlens, []) + audio_seqlens = _extract_audio_seqlens(mm_inputs) + all_audio_seqlens = [None] * mpu.get_data_parallel_world_size(with_context_parallel=False) + dist.all_gather_object( + all_audio_seqlens, audio_seqlens, group=mpu.get_data_parallel_group(with_context_parallel=False) + ) + Timer().audio_seqlens = sum(all_audio_seqlens, []) + log_perf_data(rollout_id, self.args, flops_counter=self.flops_counter) is_train_done = (rollout_id + 1) == self.args.num_rollout if self.args.save is not None and ( self.args.rotate_ckpt @@ -896,7 +922,21 @@ def train_hybrid(self, rollout_id) -> None: ) all_total_lengths = sum(all_total_lengths, []) # flatten Timer().seq_lens = all_total_lengths - log_perf_data(rollout_id, self.args) + mm_inputs = rollout_data.get("multimodal_train_inputs") + if mm_inputs is not None: + images_seqlens = _extract_images_seqlens(mm_inputs) + all_images_seqlens = [None] * mpu.get_data_parallel_world_size(with_context_parallel=False) + dist.all_gather_object( + all_images_seqlens, images_seqlens, group=mpu.get_data_parallel_group(with_context_parallel=False) + ) + Timer().images_seqlens = sum(all_images_seqlens, []) + audio_seqlens = _extract_audio_seqlens(mm_inputs) + all_audio_seqlens = [None] * mpu.get_data_parallel_world_size(with_context_parallel=False) + dist.all_gather_object( + all_audio_seqlens, audio_seqlens, group=mpu.get_data_parallel_group(with_context_parallel=False) + ) + Timer().audio_seqlens = sum(all_audio_seqlens, []) + log_perf_data(rollout_id, self.args, flops_counter=self.flops_counter) is_train_done = (rollout_id + 1) == self.args.num_rollout if self.args.save is not None and ( @@ -1036,7 +1076,21 @@ def train_async(self, rollout_id) -> None: ) all_total_lengths = sum(all_total_lengths, []) # flatten Timer().seq_lens = all_total_lengths - log_perf_data(rollout_id, self.args) + mm_inputs = rollout_data.get("multimodal_train_inputs") + if mm_inputs is not None: + images_seqlens = _extract_images_seqlens(mm_inputs) + all_images_seqlens = [None] * mpu.get_data_parallel_world_size(with_context_parallel=False) + dist.all_gather_object( + all_images_seqlens, images_seqlens, group=mpu.get_data_parallel_group(with_context_parallel=False) + ) + Timer().images_seqlens = sum(all_images_seqlens, []) + audio_seqlens = _extract_audio_seqlens(mm_inputs) + all_audio_seqlens = [None] * mpu.get_data_parallel_world_size(with_context_parallel=False) + dist.all_gather_object( + all_audio_seqlens, audio_seqlens, group=mpu.get_data_parallel_group(with_context_parallel=False) + ) + Timer().audio_seqlens = sum(all_audio_seqlens, []) + log_perf_data(rollout_id, self.args, flops_counter=self.flops_counter) tracking_utils.flush_metrics(self.args, compute_rollout_step(self.args, rollout_id)) @timer diff --git a/relax/backends/megatron/data.py b/relax/backends/megatron/data.py index cc4c1c7f5..28eb69a42 100644 --- a/relax/backends/megatron/data.py +++ b/relax/backends/megatron/data.py @@ -21,7 +21,7 @@ from relax.utils.metrics.metric_utils import compute_pass_rate, compute_rollout_step from relax.utils.timer import Timer from relax.utils.training import train_metric_utils -from relax.utils.training.flops_utils import calculate_fwd_flops +from relax.utils.training.flops_counter import FlopsCounter from relax.utils.types import RolloutBatch from .cp_utils import get_sum_of_sample_mean, maybe_padded_total_lengths, slice_with_cp @@ -875,7 +875,7 @@ def log_perf_data_fwd(args, rollout_id): tracking_utils.log(args, log_dict, step_key="actor_fwd/step") -def log_perf_data(rollout_id: int, args: Namespace) -> None: +def log_perf_data(rollout_id: int, args: Namespace, flops_counter: FlopsCounter | None = None) -> None: train_metric_utils.log_perf_data_raw( rollout_id=rollout_id, args=args, @@ -884,9 +884,8 @@ def log_perf_data(rollout_id: int, args: Namespace) -> None: and mpu.is_pipeline_last_stage() and mpu.get_data_parallel_rank(with_context_parallel=True) == 0 ), - compute_total_fwd_flops=lambda seq_lens: ( - calculate_fwd_flops(seqlens=seq_lens, args=args) / dist.get_world_size() / 1e12 - ), + flops_counter=flops_counter, + world_size=dist.get_world_size(), ) diff --git a/relax/utils/training/flops_counter.py b/relax/utils/training/flops_counter.py new file mode 100644 index 000000000..68fb2fa51 --- /dev/null +++ b/relax/utils/training/flops_counter.py @@ -0,0 +1,559 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# FLOPS estimation adapted from verl (volcengine/verl) under Apache License 2.0. + +import inspect + +from relax.utils.logging_utils import get_logger + + +logger = get_logger(__name__) + +# BF16 theoretical peak FLOPS per device (raw FLOPS). +_DEVICE_FLOPS = { + "CPU": 448e9, + "GB200": 2.5e15, + "B200": 2.25e15, + "MI300X": 1336e12, + "H100": 989e12, + "H800": 989e12, + "H200": 989e12, + "A100": 312e12, + "A800": 312e12, + "L40S": 362.05e12, + "L40": 181.05e12, + "A40": 149.7e12, + "L20": 119.5e12, + "H20": 148e12, + "910B": 354e12, + "Ascend910": 354e12, + "RTX 3070 Ti": 21.75e12, +} + + +def _unit_convert(value: float, target_unit: str) -> float: + units = ["B", "K", "M", "G", "T", "P"] + if value <= 0 or value == float("inf"): + return value + ptr = 0 + while ptr < len(units) and units[ptr] != target_unit: + value /= 1000 + ptr += 1 + return value + + +def get_device_peak_flops(unit: str = "T", device_name: str | None = None) -> float: + """Get theoretical BF16 peak FLOPS for the current GPU. + + Returns ``float('inf')`` for unknown GPUs (MFU silently skipped). + """ + if device_name is None: + from relax.utils.device import get_device_properties + + device_name = get_device_properties().name + + flops = float("inf") + for key, value in sorted(_DEVICE_FLOPS.items(), key=lambda x: len(x[0]), reverse=True): + if key in device_name: + flops = value + break + + if flops == float("inf"): + logger.warning("Unknown GPU '%s' — MFU will not be reported.", device_name) + + return _unit_convert(flops, unit) + + +# --------------------------------------------------------------------------- +# Per-model-type FLOPS estimators (6N formula, fwd+bwd inclusive) +# +# Each function returns achieved TFLOPS = total_flops / delta_time / 1e12. +# The "6" factor = 2 (matmul multiply+add) × 3 (1x fwd + 2x bwd). +# Attention uses "6" with causal mask (/2 for QK^T and /2 for A*V, cancels +# one factor of 2), giving 6 * S^2 * D * H per layer for causal models, +# or 12 for full-attention (ViT). +# --------------------------------------------------------------------------- + + +def _estimate_qwen2_flops(config, tokens_sum, batch_seqlens, delta_time): + """Dense transformer: Qwen2, Qwen3, LLaMA, Mistral, etc.""" + hidden_size = config.hidden_size + vocab_size = config.vocab_size + num_hidden_layers = config.num_hidden_layers + num_key_value_heads = config.num_key_value_heads + num_attention_heads = config.num_attention_heads + intermediate_size = config.intermediate_size + + head_dim = getattr(config, "head_dim", hidden_size // num_attention_heads) + q_size = num_attention_heads * head_dim + k_size = num_key_value_heads * head_dim + v_size = num_key_value_heads * head_dim + + mlp_N = hidden_size * intermediate_size * 3 + attn_linear_N = hidden_size * (q_size + k_size + v_size + num_attention_heads * head_dim) + emd_and_lm_head_N = vocab_size * hidden_size * 2 + dense_N = (mlp_N + attn_linear_N) * num_hidden_layers + emd_and_lm_head_N + dense_N_flops = 6 * dense_N * tokens_sum + + seqlen_square_sum = sum(s * s for s in batch_seqlens) + attn_qkv_flops = 6 * seqlen_square_sum * head_dim * num_attention_heads * num_hidden_layers + + return (dense_N_flops + attn_qkv_flops) / delta_time / 1e12 + + +def _estimate_qwen2_moe_flops(config, tokens_sum, batch_seqlens, delta_time): + """MoE transformer: Qwen2-MoE, Qwen3-MoE.""" + hidden_size = config.hidden_size + vocab_size = config.vocab_size + num_hidden_layers = config.num_hidden_layers + num_key_value_heads = config.num_key_value_heads + num_attention_heads = config.num_attention_heads + moe_intermediate_size = config.moe_intermediate_size + moe_topk = config.num_experts_per_tok + num_experts = config.num_experts + + head_dim = getattr(config, "head_dim", hidden_size // num_attention_heads) + q_size = num_attention_heads * head_dim + k_size = num_key_value_heads * head_dim + v_size = num_key_value_heads * head_dim + + moe_mlp_N = hidden_size * moe_topk * moe_intermediate_size * 3 + hidden_size * num_experts + attn_linear_N = hidden_size * (q_size + k_size + v_size + num_attention_heads * head_dim) + emd_and_lm_head_N = vocab_size * hidden_size * 2 + dense_N = (moe_mlp_N + attn_linear_N) * num_hidden_layers + emd_and_lm_head_N + dense_N_flops = 6 * dense_N * tokens_sum + + seqlen_square_sum = sum(s * s for s in batch_seqlens) + attn_qkv_flops = 6 * seqlen_square_sum * head_dim * num_attention_heads * num_hidden_layers + + return (dense_N_flops + attn_qkv_flops) / delta_time / 1e12 + + +def _estimate_qwen3_vl_flops(config, tokens_sum, batch_seqlens, delta_time, **kargs): + """Qwen3-VL (dense text + ViT).""" + hidden_size = config.text_config.hidden_size + vocab_size = config.text_config.vocab_size + num_hidden_layers = config.text_config.num_hidden_layers + num_key_value_heads = config.text_config.num_key_value_heads + num_attention_heads = config.text_config.num_attention_heads + intermediate_size = config.text_config.intermediate_size + + head_dim = hidden_size // num_attention_heads + q_size = num_attention_heads * head_dim + k_size = num_key_value_heads * head_dim + v_size = num_key_value_heads * head_dim + + mlp_N = hidden_size * intermediate_size * 3 + attn_linear_N = hidden_size * (q_size + k_size + v_size + num_attention_heads * head_dim) + emd_and_lm_head_N = vocab_size * hidden_size * 2 + dense_N = (mlp_N + attn_linear_N) * num_hidden_layers + emd_and_lm_head_N + dense_N_flops = 6 * dense_N * tokens_sum + + seqlen_square_sum = sum(s * s for s in batch_seqlens) + attn_qkv_flops = 6 * seqlen_square_sum * head_dim * num_attention_heads * num_hidden_layers + + images_seqlens = kargs.get("images_seqlens", None) + vit_flops = _estimate_qwen3_vit_flop(images_seqlens, config.vision_config) if images_seqlens is not None else 0 + + return (dense_N_flops + attn_qkv_flops + vit_flops) / delta_time / 1e12 + + +def _estimate_qwen3_vl_moe_flops(config, tokens_sum, batch_seqlens, delta_time, **kargs): + """Qwen3-VL-MoE (MoE text + ViT).""" + hidden_size = config.text_config.hidden_size + vocab_size = config.text_config.vocab_size + num_hidden_layers = config.text_config.num_hidden_layers + num_key_value_heads = config.text_config.num_key_value_heads + num_attention_heads = config.text_config.num_attention_heads + moe_intermediate_size = config.text_config.moe_intermediate_size + moe_num_expert = config.text_config.num_experts + moe_topk = config.text_config.num_experts_per_tok + + head_dim = getattr( + config.text_config, "head_dim", config.text_config.hidden_size // config.text_config.num_attention_heads + ) + q_size = num_attention_heads * head_dim + k_size = num_key_value_heads * head_dim + v_size = num_key_value_heads * head_dim + + moe_gate_N = hidden_size * moe_num_expert + moe_expertmlp_N = hidden_size * moe_intermediate_size * moe_topk * 3 + attn_linear_N = hidden_size * (q_size + k_size + v_size + num_attention_heads * head_dim) + emd_and_lm_head_N = vocab_size * hidden_size * 2 + moe_N = (moe_gate_N + moe_expertmlp_N + attn_linear_N) * num_hidden_layers + emd_and_lm_head_N + dense_N_flops = 6 * moe_N * tokens_sum + + seqlen_square_sum = sum(s * s for s in batch_seqlens) + attn_qkv_flops = 6 * seqlen_square_sum * head_dim * num_attention_heads * num_hidden_layers + + images_seqlens = kargs.get("images_seqlens", None) + vit_flops = _estimate_qwen3_vit_flop(images_seqlens, config.vision_config) if images_seqlens is not None else 0 + + return (dense_N_flops + attn_qkv_flops + vit_flops) / delta_time / 1e12 + + +def _estimate_qwen3_omni_moe_flops(config, tokens_sum, batch_seqlens, delta_time, **kargs): + """Qwen3-Omni-MoE (MoE text + ViT + Audio encoder).""" + thinker = config.thinker_config if hasattr(config, "thinker_config") else config + text_config = thinker.text_config if hasattr(thinker, "text_config") else thinker + + hidden_size = text_config.hidden_size + vocab_size = text_config.vocab_size + num_hidden_layers = text_config.num_hidden_layers + num_key_value_heads = text_config.num_key_value_heads + num_attention_heads = text_config.num_attention_heads + moe_intermediate_size = text_config.moe_intermediate_size + moe_num_expert = text_config.num_experts + moe_topk = text_config.num_experts_per_tok + + head_dim = getattr(text_config, "head_dim", hidden_size // num_attention_heads) + q_size = num_attention_heads * head_dim + k_size = num_key_value_heads * head_dim + v_size = num_key_value_heads * head_dim + + moe_gate_N = hidden_size * moe_num_expert + moe_expertmlp_N = hidden_size * moe_intermediate_size * moe_topk * 3 + attn_linear_N = hidden_size * (q_size + k_size + v_size + num_attention_heads * head_dim) + emd_and_lm_head_N = vocab_size * hidden_size * 2 + moe_N = (moe_gate_N + moe_expertmlp_N + attn_linear_N) * num_hidden_layers + emd_and_lm_head_N + dense_N_flops = 6 * moe_N * tokens_sum + + seqlen_square_sum = sum(s * s for s in batch_seqlens) + attn_qkv_flops = 6 * seqlen_square_sum * head_dim * num_attention_heads * num_hidden_layers + + images_seqlens = kargs.get("images_seqlens", None) + vision_config = getattr(thinker, "vision_config", None) + vit_flops = _estimate_qwen3_vit_flop(images_seqlens, vision_config) if images_seqlens else 0 + + audio_seqlens = kargs.get("audio_seqlens", None) + audio_config = getattr(thinker, "audio_config", None) + audio_flops = _estimate_qwen3_audio_flop(audio_seqlens, audio_config) if audio_seqlens else 0 + + return (dense_N_flops + attn_qkv_flops + vit_flops + audio_flops) / delta_time / 1e12 + + +def _get_audio_encoder_seqlens(feature_lengths, n_window=100): + """Convert raw mel feature lengths to audio encoder transformer seq + lengths. + + Mirrors ``_get_feat_extract_output_lengths`` in the Qwen3-Omni codebase. + """ + result = [] + for fl in feature_lengths: + leave = fl % n_window + feat = (leave - 1) // 2 + 1 + out = ((feat - 1) // 2 + 1 - 1) // 2 + 1 + (fl // n_window) * 13 + result.append(out) + return result + + +def _estimate_qwen3_audio_flop(audio_seqlens, config): + """Whisper-style audio encoder FLOPS (Qwen3-Omni). + + ``audio_seqlens`` are raw mel feature lengths (pre-conv). They are converted + to transformer sequence lengths internally. + + Architecture: 2x Conv1d stem -> N transformer layers (windowed attention) -> + downsampling conv -> output projection. + """ + if config is None or not audio_seqlens: + return 0 + + d_model = config.d_model + encoder_layers = getattr(config, "encoder_layers", config.num_hidden_layers) + num_heads = config.encoder_attention_heads + ffn_dim = config.encoder_ffn_dim + num_mel_bins = config.num_mel_bins + output_dim = getattr(config, "output_dim", d_model) + n_window = getattr(config, "n_window", 100) + head_dim = d_model // num_heads + + # Convert raw mel lengths to transformer seq lengths + encoder_seqlens = _get_audio_encoder_seqlens(audio_seqlens, n_window) + tokens_sum = sum(encoder_seqlens) + + # Conv stem: conv1 (mel->d_model, k=3) + conv2 (d_model->d_model, k=3, stride=2) + conv_N = num_mel_bins * d_model * 3 + d_model * d_model * 3 + + # Transformer layers: self-attention (windowed) + FFN (GELU, not GLU -> 2 linear layers) + attn_linear_N = d_model * (4 * d_model) + ffn_N = d_model * ffn_dim * 2 + transformer_N = (attn_linear_N + ffn_N) * encoder_layers + + # Output projection: d_model -> output_dim + output_proj_N = d_model * output_dim + + dense_N = conv_N + transformer_N + output_proj_N + dense_N_flops = 6 * dense_N * tokens_sum + + # Windowed full attention (no causal mask -> coefficient 12) + # Each token attends to at most n_window tokens + effective_seqlen_sq_sum = sum(s * min(s, n_window) for s in encoder_seqlens) + attn_qkv_flops = 12 * effective_seqlen_sq_sum * head_dim * num_heads * encoder_layers + + return dense_N_flops + attn_qkv_flops + + +def _estimate_qwen3_vit_flop(images_seqlens, config): + if config is None: + return 0 + tokens_sum = sum(images_seqlens) + + num_heads = config.num_heads + depth = config.depth + dim = config.hidden_size + mlp_hidden_dim = config.intermediate_size + out_hidden_size = config.out_hidden_size + spatial_merge_size = config.spatial_merge_size + head_dim = dim // num_heads + + patch_embed_N = dim * config.in_channels * config.temporal_patch_size * config.patch_size * config.patch_size + mlp_N = dim * mlp_hidden_dim * 2 # no GLU in ViT + attn_linear_N = dim * (4 * dim) + merger_N = (out_hidden_size + (dim * (spatial_merge_size**2))) * (dim * (spatial_merge_size**2)) + deepstack_visual_indexes = getattr(config, "deepstack_visual_indexes", None) + deepstack_merger_N = merger_N * len(deepstack_visual_indexes) if deepstack_visual_indexes is not None else 0 + dense_N = patch_embed_N + (mlp_N + attn_linear_N) * depth + deepstack_merger_N + merger_N + dense_N_flops = 6 * dense_N * tokens_sum + + # ViT uses full attention (no causal mask) -> coefficient 12 instead of 6 + seqlen_square_sum = sum(s * s for s in images_seqlens) + attn_qkv_flops = 12 * seqlen_square_sum * head_dim * num_heads * depth + + return dense_N_flops + attn_qkv_flops + + +def _count_qwen3_5_layer_types(config): + layer_types = getattr(config, "layer_types", None) + if layer_types: + num_full_attn_layers = sum(layer_type == "full_attention" for layer_type in layer_types) + num_linear_attn_layers = sum(layer_type == "linear_attention" for layer_type in layer_types) + return num_full_attn_layers, num_linear_attn_layers + + full_attention_interval = getattr(config, "full_attention_interval", 4) + num_full_attn_layers = sum( + not bool((layer_idx + 1) % full_attention_interval) for layer_idx in range(config.num_hidden_layers) + ) + return num_full_attn_layers, config.num_hidden_layers - num_full_attn_layers + + +def _compute_qwen3_5_hybrid_attn_params(config): + hidden_size = config.hidden_size + num_attention_heads = config.num_attention_heads + num_key_value_heads = config.num_key_value_heads + head_dim = getattr(config, "head_dim", hidden_size // num_attention_heads) + + q_size = num_attention_heads * head_dim + k_size = num_key_value_heads * head_dim + v_size = num_key_value_heads * head_dim + + num_full_attn_layers, num_linear_attn_layers = _count_qwen3_5_layer_types(config) + + full_attn_linear_N = hidden_size * (2 * q_size + k_size + v_size + q_size) + + linear_k_size = config.linear_num_key_heads * config.linear_key_head_dim + linear_v_size = config.linear_num_value_heads * config.linear_value_head_dim + linear_attn_linear_N = hidden_size * (2 * linear_k_size + 3 * linear_v_size + 2 * config.linear_num_value_heads) + conv_N = config.linear_conv_kernel_dim * (2 * linear_k_size + linear_v_size) + + attn_linear_N = full_attn_linear_N * num_full_attn_layers + attn_linear_N += (linear_attn_linear_N + conv_N) * num_linear_attn_layers + + return attn_linear_N, num_full_attn_layers, num_linear_attn_layers, head_dim, num_attention_heads + + +def _compute_qwen3_5_gdn_recurrence_flops(config, tokens_sum, num_linear_attn_layers): + return ( + 15 + * config.linear_key_head_dim + * config.linear_value_head_dim + * config.linear_num_value_heads + * tokens_sum + * num_linear_attn_layers + ) + + +def _estimate_qwen3_5_flops(config, tokens_sum, batch_seqlens, delta_time, **kargs): + """Qwen3.5 hybrid attention (full attention + GatedDeltaNet linear + attention).""" + text_config = config.text_config if hasattr(config, "text_config") else config + hidden_size = text_config.hidden_size + vocab_size = text_config.vocab_size + num_hidden_layers = text_config.num_hidden_layers + + attn_linear_N, num_full_attn_layers, num_linear_attn_layers, head_dim, num_attention_heads = ( + _compute_qwen3_5_hybrid_attn_params(text_config) + ) + + if hasattr(text_config, "num_experts"): + moe_gate_N = hidden_size * text_config.num_experts + moe_expertmlp_N = hidden_size * text_config.moe_intermediate_size * text_config.num_experts_per_tok * 3 + moe_sharedexpertmlp_N = hidden_size * text_config.shared_expert_intermediate_size * 3 + moe_sharedexpert_gate_N = hidden_size + mlp_N = (moe_gate_N + moe_expertmlp_N + moe_sharedexpertmlp_N + moe_sharedexpert_gate_N) * num_hidden_layers + else: + mlp_N = hidden_size * text_config.intermediate_size * 3 * num_hidden_layers + + emd_and_lm_head_N = vocab_size * hidden_size * 2 + dense_N_flops = 6 * (mlp_N + attn_linear_N + emd_and_lm_head_N) * tokens_sum + + seqlen_square_sum = sum(s * s for s in batch_seqlens) + attn_qkv_flops = 6 * seqlen_square_sum * head_dim * num_attention_heads * num_full_attn_layers + + gdn_recurrence_flops = _compute_qwen3_5_gdn_recurrence_flops(text_config, tokens_sum, num_linear_attn_layers) + + images_seqlens = kargs.get("images_seqlens", None) + if images_seqlens is not None and hasattr(config, "vision_config"): + vit_flops = _estimate_qwen3_vit_flop(images_seqlens, config.vision_config) + else: + vit_flops = 0 + + flops_all_token = dense_N_flops + attn_qkv_flops + gdn_recurrence_flops + vit_flops + return flops_all_token / delta_time / 1e12 + + +def _estimate_deepseek_v3_flops(config, tokens_sum, batch_seqlens, delta_time): + """DeepSeek-V3 (MLA attention + MoE).""" + hidden_size = config.hidden_size + vocab_size = config.vocab_size + moe_intermediate_size = config.moe_intermediate_size + num_hidden_layers = config.num_hidden_layers + first_k_dense_replace = config.first_k_dense_replace + num_query_heads = config.num_attention_heads + moe_num_expert = config.n_routed_experts + moe_topk = config.num_experts_per_tok + share_expert_num = config.n_shared_experts + + moe_gate_N = hidden_size * moe_num_expert + moe_expertmlp_N = hidden_size * moe_intermediate_size * (moe_topk + share_expert_num) * 3 + + # MLA attention linear params + attn_linear_N = 0 + q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim + if config.q_lora_rank is None: + attn_linear_N += hidden_size * num_query_heads * q_head_dim + else: + attn_linear_N += hidden_size * config.q_lora_rank + attn_linear_N += num_query_heads * q_head_dim * config.q_lora_rank + + attn_linear_N += hidden_size * (config.kv_lora_rank + config.qk_rope_head_dim) + attn_linear_N += num_query_heads * (q_head_dim - config.qk_rope_head_dim + config.v_head_dim) * config.kv_lora_rank + attn_linear_N += num_query_heads * config.v_head_dim * hidden_size + + emd_and_lm_head_N = vocab_size * hidden_size * 2 + moe_N = ( + (moe_gate_N + moe_expertmlp_N + attn_linear_N) * (num_hidden_layers - first_k_dense_replace) + + (hidden_size * config.intermediate_size * 3 + attn_linear_N) * first_k_dense_replace + + emd_and_lm_head_N + ) + dense_N_flops = 6 * moe_N * tokens_sum + + seqlen_square_sum = sum(s * s * num_hidden_layers for s in batch_seqlens) + # MLA causal: 3 * 2 * seq^2 * dim / 2 = 3 * seq^2 * dim per component + attn_qkv_flops = 3 * seqlen_square_sum * (q_head_dim + config.v_head_dim) * num_query_heads + + return (dense_N_flops + attn_qkv_flops) / delta_time / 1e12 + + +def _estimate_fallback_flops(config, tokens_sum, batch_seqlens, delta_time): + """Fallback for unknown model types using standard dense transformer + formula.""" + text_config = config.text_config if hasattr(config, "text_config") else config + required = ("hidden_size", "vocab_size", "num_hidden_layers", "num_attention_heads", "intermediate_size") + if not all(hasattr(text_config, attr) for attr in required): + return 0 + + hidden_size = text_config.hidden_size + vocab_size = text_config.vocab_size + num_hidden_layers = text_config.num_hidden_layers + num_attention_heads = text_config.num_attention_heads + intermediate_size = text_config.intermediate_size + num_key_value_heads = getattr(text_config, "num_key_value_heads", num_attention_heads) + head_dim = getattr(text_config, "head_dim", hidden_size // num_attention_heads) + + q_size = num_attention_heads * head_dim + k_size = num_key_value_heads * head_dim + v_size = num_key_value_heads * head_dim + + mlp_N = hidden_size * intermediate_size * 3 + attn_linear_N = hidden_size * (q_size + k_size + v_size + num_attention_heads * head_dim) + emd_and_lm_head_N = vocab_size * hidden_size * 2 + dense_N = (mlp_N + attn_linear_N) * num_hidden_layers + emd_and_lm_head_N + dense_N_flops = 6 * dense_N * tokens_sum + + seqlen_square_sum = sum(s * s for s in batch_seqlens) + attn_qkv_flops = 6 * seqlen_square_sum * head_dim * num_attention_heads * num_hidden_layers + + return (dense_N_flops + attn_qkv_flops) / delta_time / 1e12 + + +_ESTIMATE_FUNC = { + # Dense transformers (SwiGLU + GQA) + "qwen2": _estimate_qwen2_flops, + "qwen3": _estimate_qwen2_flops, + "qwen3_5": _estimate_qwen3_5_flops, + "llama": _estimate_qwen2_flops, + "mistral": _estimate_qwen2_flops, + "glm4": _estimate_qwen2_flops, + "glm4v": _estimate_qwen2_flops, + "glm46v": _estimate_qwen2_flops, + "glm": _estimate_qwen2_flops, + "minicpmv": _estimate_qwen2_flops, + "minicpmo": _estimate_qwen2_flops, + "seed_oss": _estimate_qwen2_flops, + "mimo": _estimate_qwen2_flops, + # MoE transformers (SwiGLU + GQA + router gate + topk experts) + "qwen2_moe": _estimate_qwen2_moe_flops, + "qwen3_moe": _estimate_qwen2_moe_flops, + "qwen3_5_moe": _estimate_qwen3_5_flops, + "qwen3_next": _estimate_qwen2_moe_flops, + "qwen3_omni_moe": _estimate_qwen3_omni_moe_flops, + "glm4_moe": _estimate_qwen2_moe_flops, + "glm4v_moe": _estimate_qwen2_moe_flops, + # MLA + MoE (DeepSeek-V3 style: MLA attention + shared experts + dense-replace) + "deepseek_v3": _estimate_deepseek_v3_flops, + "glm4_moe_lite": _estimate_deepseek_v3_flops, + "glm_moe_dsa": _estimate_deepseek_v3_flops, + # Vision-language + "qwen2_vl": _estimate_qwen2_flops, + "qwen2_5_vl": _estimate_qwen2_flops, + "qwen3_vl": _estimate_qwen3_vl_flops, + "qwen3_vl_moe": _estimate_qwen3_vl_moe_flops, +} + + +class FlopsCounter: + """Estimate training FLOPS and MFU based on HuggingFace model config. + + Example:: + + counter = FlopsCounter(hf_config) + estimated_tflops, peak_tflops = counter.estimate(batch_seqlens, delta_time) + mfu = estimated_tflops / peak_tflops / world_size + """ + + def __init__(self, config): + if hasattr(config, "model_type") and config.model_type not in _ESTIMATE_FUNC: + logger.warning( + "Unsupported model_type '%s' for FLOPS estimation — falling back to dense transformer formula. " + "Supported: %s.", + config.model_type, + list(_ESTIMATE_FUNC.keys()), + ) + self.config = config + + def estimate(self, batch_seqlens, delta_time, **kwargs): + """Return (estimated_tflops, peak_device_tflops). + + ``estimated_tflops`` includes fwd+bwd (6N formula). + """ + tokens_sum = sum(batch_seqlens) + model_type = getattr(self.config, "model_type", None) + func = _ESTIMATE_FUNC.get(model_type, _estimate_fallback_flops) + sig = inspect.signature(func) + if any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()): + estimated_tflops = func(self.config, tokens_sum, batch_seqlens, delta_time, **kwargs) + else: + estimated_tflops = func(self.config, tokens_sum, batch_seqlens, delta_time) + peak_tflops = get_device_peak_flops(unit="T") + return estimated_tflops, peak_tflops diff --git a/relax/utils/training/flops_utils.py b/relax/utils/training/flops_utils.py deleted file mode 100644 index 75afccc05..000000000 --- a/relax/utils/training/flops_utils.py +++ /dev/null @@ -1,127 +0,0 @@ -def calculate_embedding_flops(seqlen, hidden_size): - return 2 * seqlen * hidden_size - - -def calculate_lm_head_flops(seqlen, hidden_size, vocab_size): - return 2 * seqlen * hidden_size * vocab_size - - -def calculate_qkv_projection_flops(args, seqlen, hidden_size, num_attention_heads, num_query_groups): - if args.q_lora_rank is None: - q_flops = 2 * seqlen * hidden_size * num_attention_heads * args.kv_channels - else: - q_flops = ( - 2 - * seqlen - * args.q_lora_rank - * (args.hidden_size + args.num_attention_heads * (args.qk_head_dim + args.qk_pos_emb_head_dim)) - ) - if args.kv_lora_rank is None: - kv_flops = 2 * 2 * seqlen * hidden_size * num_query_groups * args.kv_channels - else: - kv_flops = ( - 2 - * seqlen - * ( - args.kv_lora_rank - * (args.hidden_size + args.num_attention_heads * (args.qk_head_dim + args.v_head_dim)) - + args.hidden_size * args.qk_pos_emb_head_dim - ) - ) - - return q_flops + kv_flops - - -def calculate_attention_flops(args, seqlen, num_attention_heads): - # QK^T with causal - if args.qk_pos_emb_head_dim: - flops = 2 * num_attention_heads * seqlen * seqlen * (args.qk_head_dim + args.qk_pos_emb_head_dim) / 2 - else: - flops = 2 * num_attention_heads * seqlen * seqlen * args.kv_channels / 2 - # A*V - if args.v_head_dim: - flops += num_attention_heads * seqlen * seqlen * args.v_head_dim - else: - flops += num_attention_heads * seqlen * seqlen * args.kv_channels - return flops - - -def calculate_output_flops(seqlen, hidden_size): - return 2 * seqlen * hidden_size * hidden_size - - -def calculate_mlp_flops(seqlen, hidden_size, ffn_hidden_size): - return 2 * seqlen * hidden_size * ffn_hidden_size * 3 - - -def calculate_layer_flops(args, seqlen, hidden_size, num_attention_heads, num_query_groups, ffn_hidden_size): - return ( - calculate_qkv_projection_flops(args, seqlen, hidden_size, num_attention_heads, num_query_groups) - + calculate_attention_flops(args, seqlen, num_attention_heads) - + calculate_output_flops(seqlen, hidden_size) - + calculate_mlp_flops(seqlen, hidden_size, ffn_hidden_size) - ) - - -def calculate_fwd_flops( - seqlens, - args, -): - hidden_size = args.hidden_size - num_attention_heads = args.num_attention_heads - num_query_groups = args.num_query_groups - vocab_size = args.vocab_size - - total_flops = 0 - - dense_ffn = args.ffn_hidden_size - if args.num_experts is None: - num_dense_layers = args.num_layers - num_moe_layers = 0 - else: - shared_expert_ffn = getattr(args, "moe_shared_expert_intermediate_size", None) - if shared_expert_ffn is None: - shared_expert_ffn = 0 - - moe_ffn = args.moe_ffn_hidden_size * args.moe_router_topk + shared_expert_ffn - if hasattr(args, "moe_layer_freq"): - if isinstance(args.moe_layer_freq, list): - num_dense_layers = sum(1 for freq in args.moe_layer_freq if freq == 0) - num_moe_layers = sum(1 for freq in args.moe_layer_freq if freq > 0) - else: - num_dense_layers = sum(1 for i in range(args.num_layers) if i % args.moe_layer_freq != 0) - num_moe_layers = sum(1 for i in range(args.num_layers) if i % args.moe_layer_freq == 0) - else: - num_dense_layers = 0 - num_moe_layers = args.num_layers - - for seqlen in seqlens: - if num_dense_layers > 0: - total_flops += ( - calculate_layer_flops( - args, - seqlen, - hidden_size, - num_attention_heads, - num_query_groups, - dense_ffn, - ) - * num_dense_layers - ) - - if num_moe_layers > 0: - total_flops += ( - calculate_layer_flops( - args, - seqlen, - hidden_size, - num_attention_heads, - num_query_groups, - moe_ffn, - ) - * num_moe_layers - ) - - total_flops += calculate_lm_head_flops(seqlen, hidden_size, vocab_size) - - return total_flops diff --git a/relax/utils/training/train_metric_utils.py b/relax/utils/training/train_metric_utils.py index 8a44cd333..4c180b40f 100644 --- a/relax/utils/training/train_metric_utils.py +++ b/relax/utils/training/train_metric_utils.py @@ -1,8 +1,10 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. +from __future__ import annotations + from argparse import Namespace -from collections.abc import Callable from copy import deepcopy +from typing import TYPE_CHECKING from relax.utils import tracking_utils from relax.utils.logging_utils import get_logger @@ -10,11 +12,19 @@ from relax.utils.timer import Timer +if TYPE_CHECKING: + from relax.utils.training.flops_counter import FlopsCounter + + logger = get_logger(__name__) def log_perf_data_raw( - rollout_id: int, args: Namespace, is_primary_rank: bool, compute_total_fwd_flops: Callable + rollout_id: int, + args: Namespace, + is_primary_rank: bool, + flops_counter: FlopsCounter | None = None, + world_size: int = 1, ) -> None: timer_instance = Timer() log_dict_raw = deepcopy(timer_instance.log_dict()) @@ -25,18 +35,38 @@ def log_perf_data_raw( log_dict = {f"perf/{key}_time": val for key, val in log_dict_raw.items()} - if ("perf/actor_train_time" in log_dict) and (compute_total_fwd_flops is not None): - total_fwd_flops = compute_total_fwd_flops(seq_lens=timer_instance.seq_lens) + if ("perf/actor_train_time" in log_dict) and (flops_counter is not None): + seq_lens = timer_instance.seq_lens + images_seqlens = getattr(timer_instance, "images_seqlens", None) or None + audio_seqlens = getattr(timer_instance, "audio_seqlens", None) or None + estimated_tflops, peak_tflops = flops_counter.estimate( + batch_seqlens=seq_lens, delta_time=1.0, images_seqlens=images_seqlens, audio_seqlens=audio_seqlens + ) + # estimated_tflops is total fwd+bwd TFLOPS at delta_time=1 => raw TFLOPS count + # Normalize to per-GPU + per_gpu_tflops = estimated_tflops / world_size if "perf/log_probs_time" in log_dict: - log_dict["perf/log_probs_tflops"] = total_fwd_flops / log_dict["perf/log_probs_time"] + # Forward only = fwd+bwd / 3 + log_dict["perf/log_probs_tflops"] = per_gpu_tflops / 3 / log_dict["perf/log_probs_time"] if "perf/ref_log_probs_time" in log_dict: - log_dict["perf/ref_log_probs_tflops"] = total_fwd_flops / log_dict["perf/ref_log_probs_time"] + log_dict["perf/ref_log_probs_tflops"] = per_gpu_tflops / 3 / log_dict["perf/ref_log_probs_time"] if log_dict["perf/actor_train_time"] > 0: - log_dict["perf/actor_train_tflops"] = 3 * total_fwd_flops / log_dict["perf/actor_train_time"] - log_dict["perf/actor_train_tok_per_s"] = sum(timer_instance.seq_lens) / log_dict["perf/actor_train_time"] + # Training includes fwd+bwd, use full 6N flops + log_dict["perf/actor_train_tflops"] = per_gpu_tflops / log_dict["perf/actor_train_time"] + log_dict["perf/actor_train_tok_per_s"] = sum(seq_lens) / log_dict["perf/actor_train_time"] + + # MFU = achieved_per_gpu_tflops / device_peak_tflops + log_dict["perf/device_peak_tflops"] = peak_tflops + if peak_tflops not in (float("inf"), 0): + if "perf/actor_train_tflops" in log_dict: + log_dict["perf/mfu/actor_train"] = log_dict["perf/actor_train_tflops"] / peak_tflops + if "perf/log_probs_tflops" in log_dict: + log_dict["perf/mfu/actor_infer"] = log_dict["perf/log_probs_tflops"] / peak_tflops + if "perf/ref_log_probs_tflops" in log_dict: + log_dict["perf/mfu/ref_infer"] = log_dict["perf/ref_log_probs_tflops"] / peak_tflops if "perf/train_wait_time" in log_dict and "perf/train_time" in log_dict: total_time = log_dict["perf/train_wait_time"] + log_dict["perf/train_time"] diff --git a/relax/utils/utils.py b/relax/utils/utils.py index 6f84a3bf3..3b16b4744 100644 --- a/relax/utils/utils.py +++ b/relax/utils/utils.py @@ -20,6 +20,76 @@ CURRENT_ROLLOUT_BATCH = [] +def _extract_images_seqlens(multimodal_train_inputs) -> list[int]: + """Extract per-image ViT token counts from multimodal_train_inputs. + + Accepts either: + - ``list[dict | None]``: per-sample dicts (pre-batch format) + - ``dict``: concatenated tensors (post-``prepare_batch`` format) + + For each image, the ViT input sequence length = H * W (repeated T times + along the temporal axis). + """ + if isinstance(multimodal_train_inputs, dict): + grid_thw = multimodal_train_inputs.get("image_grid_thw") + if grid_thw is None: + return [] + if isinstance(grid_thw, torch.Tensor): + seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]) + return seqlens.tolist() + return [int(h * w) for t, h, w in grid_thw for _ in range(int(t))] + + images_seqlens: list[int] = [] + for mm_input in multimodal_train_inputs: + if mm_input is None: + continue + grid_thw = mm_input.get("image_grid_thw") + if grid_thw is None: + continue + if isinstance(grid_thw, torch.Tensor): + seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]) + images_seqlens.extend(seqlens.tolist()) + elif isinstance(grid_thw, (list, np.ndarray)): + for t, h, w in grid_thw: + images_seqlens.extend([int(h * w)] * int(t)) + return images_seqlens + + +def _extract_audio_seqlens(multimodal_train_inputs) -> list[int]: + """Extract per-audio raw mel feature lengths from multimodal_train_inputs. + + Accepts either: + - ``list[dict | None]``: per-sample dicts (pre-batch format) + - ``dict``: concatenated tensors (post-``prepare_batch`` format) + + Returns the effective mel frame count for each audio clip, derived from + ``feature_attention_mask.sum(-1)``. + """ + if isinstance(multimodal_train_inputs, dict): + feat_mask = multimodal_train_inputs.get("feature_attention_mask") + if feat_mask is None: + return [] + if isinstance(feat_mask, torch.Tensor): + return feat_mask.sum(-1).tolist() + return torch.tensor(feat_mask).sum(-1).tolist() + + audio_seqlens: list[int] = [] + for mm_input in multimodal_train_inputs: + if mm_input is None: + continue + feat_mask = mm_input.get("feature_attention_mask") + if feat_mask is None: + continue + if isinstance(feat_mask, torch.Tensor): + lengths = feat_mask.sum(-1) + audio_seqlens.extend(lengths.tolist()) + elif isinstance(feat_mask, (list, np.ndarray)): + feat_mask_t = torch.tensor(feat_mask) + lengths = feat_mask_t.sum(-1) + audio_seqlens.extend(lengths.tolist()) + return audio_seqlens + + def convert_samples_to_train_data(args: Any, samples: list[Sample] | list[list[Sample]]): """Convert inference generated samples to training data.""" raw_rewards, rewards = post_process_rewards(args, samples) diff --git a/tests/utils/test_flops_counter.py b/tests/utils/test_flops_counter.py new file mode 100644 index 000000000..6aaccba3f --- /dev/null +++ b/tests/utils/test_flops_counter.py @@ -0,0 +1,322 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +import math + +import pytest + +from relax.utils.training.flops_counter import _DEVICE_FLOPS, FlopsCounter, get_device_peak_flops + + +# --------------------------------------------------------------------------- +# Helper: lightweight config object (mirrors verl test pattern) +# --------------------------------------------------------------------------- +class Config: + def __init__(self, config_dict): + for key, value in config_dict.items(): + if isinstance(value, dict): + value = Config(value) + setattr(self, key, value) + + +# --------------------------------------------------------------------------- +# GPU peak FLOPS tests +# --------------------------------------------------------------------------- +@pytest.mark.parametrize( + "device_name, expected_raw_flops", + [ + ("NVIDIA H100 80GB HBM3", 989e12), + ("NVIDIA H800", 989e12), + ("NVIDIA A100-SXM4-80GB", 312e12), + ("NVIDIA A800-SXM4-80GB", 312e12), + ("NVIDIA L40S", 362.05e12), + ("NVIDIA H20", 148e12), + ("Ascend910B3", 354e12), + ], +) +def test_get_device_peak_flops_known_gpus(device_name, expected_raw_flops): + result = get_device_peak_flops(unit="T", device_name=device_name) + expected_tflops = expected_raw_flops / 1e12 + assert math.isclose(result, expected_tflops, rel_tol=1e-6), ( + f"Expected {expected_tflops} TFLOPS for {device_name}, got {result}" + ) + + +def test_get_device_peak_flops_unknown_gpu(): + result = get_device_peak_flops(unit="T", device_name="SomeUnknownGPU-XYZ") + assert result == float("inf") + + +def test_get_device_peak_flops_unit_conversion(): + tflops = get_device_peak_flops(unit="T", device_name="NVIDIA H100") + gflops = get_device_peak_flops(unit="G", device_name="NVIDIA H100") + assert math.isclose(tflops * 1000, gflops, rel_tol=1e-6) + + +def test_device_flops_table_has_common_gpus(): + common_keys = ["H100", "H800", "A100", "A800", "H20"] + for key in common_keys: + assert key in _DEVICE_FLOPS, f"Missing common GPU {key} in _DEVICE_FLOPS" + + +# --------------------------------------------------------------------------- +# FlopsCounter model FLOPS tests (expected values from verl test suite) +# --------------------------------------------------------------------------- +FLOPS_TEST_CONFIGS = { + "qwen3_dense": { + "config": { + "model_type": "qwen3", + "vocab_size": 151936, + "hidden_size": 4096, + "intermediate_size": 12288, + "num_hidden_layers": 36, + "num_attention_heads": 32, + "num_key_value_heads": 8, + "head_dim": 128, + }, + "batch_seqlens_list": [[512, 1024, 2048], [4096, 4096, 4096]], + "expected_tflops_list": [180997438046208 / 1e12, 648394032807936 / 1e12], + }, + "qwen3_moe": { + "config": { + "model_type": "qwen3_moe", + "hidden_size": 2048, + "vocab_size": 151936, + "num_hidden_layers": 48, + "num_key_value_heads": 4, + "num_attention_heads": 32, + "head_dim": 128, + "moe_intermediate_size": 768, + "num_experts_per_tok": 8, + "num_experts": 128, + }, + "batch_seqlens_list": [[512, 1024, 2048], [4096, 4096, 4096]], + "expected_tflops_list": [78593069678592 / 1e12, 306570470621184 / 1e12], + }, + "deepseek_v3": { + "config": { + "model_type": "deepseek_v3", + "hidden_size": 7168, + "vocab_size": 129280, + "moe_intermediate_size": 2048, + "num_hidden_layers": 61, + "first_k_dense_replace": 3, + "num_attention_heads": 128, + "n_routed_experts": 256, + "num_experts_per_tok": 8, + "n_shared_experts": 1, + "kv_lora_rank": 512, + "qk_rope_head_dim": 64, + "v_head_dim": 128, + "intermediate_size": 18432, + "qk_nope_head_dim": 128, + "q_lora_rank": 1536, + }, + "batch_seqlens_list": [[512, 1024, 2048], [4096, 4096, 4096]], + "expected_tflops_list": [848766538088448 / 1e12, 3145850406567936 / 1e12], + }, +} + + +FLOPS_TEST_VL_CONFIGS = { + "qwen3_5": { + "config": { + "model_type": "qwen3_5", + "text_config": { + "vocab_size": 248320, + "hidden_size": 4096, + "intermediate_size": 12288, + "num_hidden_layers": 32, + "num_attention_heads": 16, + "num_key_value_heads": 4, + "head_dim": 256, + "linear_conv_kernel_dim": 4, + "linear_key_head_dim": 128, + "linear_value_head_dim": 128, + "linear_num_key_heads": 16, + "linear_num_value_heads": 32, + "layer_types": ["linear_attention" if bool((i + 1) % 4) else "full_attention" for i in range(32)], + }, + "vision_config": { + "num_heads": 16, + "depth": 27, + "hidden_size": 1152, + "intermediate_size": 4304, + "out_hidden_size": 4096, + "spatial_merge_size": 2, + "temporal_patch_size": 2, + "in_channels": 3, + "patch_size": 16, + }, + }, + "batch_seqlens_list": [[512, 1024, 2048], [4096, 4096, 4096]], + "images_seqlens_list": [[512, 1024, 2048], [4096, 4096, 4096]], + "expected_tflops_list": [206090394402816 / 1e12, 724521757704192 / 1e12], + }, + "qwen3_5_moe": { + "config": { + "model_type": "qwen3_5_moe", + "text_config": { + "vocab_size": 248320, + "hidden_size": 2048, + "num_hidden_layers": 40, + "num_attention_heads": 16, + "num_key_value_heads": 2, + "head_dim": 256, + "linear_conv_kernel_dim": 4, + "linear_key_head_dim": 128, + "linear_value_head_dim": 128, + "linear_num_key_heads": 16, + "linear_num_value_heads": 32, + "moe_intermediate_size": 512, + "shared_expert_intermediate_size": 512, + "num_experts_per_tok": 8, + "num_experts": 256, + "layer_types": ["linear_attention" if bool((i + 1) % 4) else "full_attention" for i in range(40)], + }, + "vision_config": { + "num_heads": 16, + "depth": 27, + "hidden_size": 1152, + "intermediate_size": 4304, + "out_hidden_size": 2048, + "spatial_merge_size": 2, + "temporal_patch_size": 2, + "in_channels": 3, + "patch_size": 16, + }, + }, + "batch_seqlens_list": [[512, 1024, 2048], [4096, 4096, 4096]], + "images_seqlens_list": [[512, 1024, 2048], [4096, 4096, 4096]], + "expected_tflops_list": [88082762170368 / 1e12, 321470349705216 / 1e12], + }, +} + +FLOPS_TEST_OMNI_CONFIGS = { + "qwen3_omni_moe": { + "config": { + "model_type": "qwen3_omni_moe", + "thinker_config": { + "text_config": { + "hidden_size": 2048, + "vocab_size": 3584, + "num_hidden_layers": 28, + "num_key_value_heads": 4, + "num_attention_heads": 28, + "head_dim": 128, + "moe_intermediate_size": 768, + "num_experts_per_tok": 8, + "num_experts": 128, + }, + "vision_config": { + "num_heads": 16, + "depth": 27, + "hidden_size": 1152, + "intermediate_size": 4304, + "out_hidden_size": 3584, + "spatial_merge_size": 2, + "temporal_patch_size": 2, + "in_channels": 3, + "patch_size": 16, + "deepstack_visual_indexes": [8, 16, 24], + }, + "audio_config": { + "d_model": 1280, + "num_hidden_layers": 32, + "encoder_attention_heads": 20, + "encoder_ffn_dim": 5120, + "num_mel_bins": 128, + "output_dim": 3584, + "n_window": 100, + }, + }, + }, + "batch_seqlens": [512, 1024, 2048], + "images_seqlens": [512, 1024], + "audio_seqlens": [500, 1000], + "expected_tflops": 43060677083136 / 1e12, + }, +} + + +@pytest.mark.parametrize("config_name", list(FLOPS_TEST_CONFIGS.keys())) +def test_flops_counter_model_estimation(config_name): + test_data = FLOPS_TEST_CONFIGS[config_name] + config = Config(test_data["config"]) + counter = FlopsCounter(config) + + for batch_seqlens, expected_tflops in zip( + test_data["batch_seqlens_list"], test_data["expected_tflops_list"], strict=True + ): + # delta_time=1 so returned value = raw TFLOPS + estimated_tflops, _ = counter.estimate(batch_seqlens, delta_time=1.0) + assert math.isclose(estimated_tflops, expected_tflops, rel_tol=1e-6), ( + f"{config_name}: expected {expected_tflops:.2f} TFLOPS, got {estimated_tflops:.2f}" + ) + + +@pytest.mark.parametrize("config_name", list(FLOPS_TEST_VL_CONFIGS.keys())) +def test_flops_counter_vl_estimation(config_name): + test_data = FLOPS_TEST_VL_CONFIGS[config_name] + config = Config(test_data["config"]) + counter = FlopsCounter(config) + + for batch_seqlens, images_seqlens, expected_tflops in zip( + test_data["batch_seqlens_list"], + test_data["images_seqlens_list"], + test_data["expected_tflops_list"], + strict=True, + ): + estimated_tflops, _ = counter.estimate(batch_seqlens, delta_time=1.0, images_seqlens=images_seqlens) + assert math.isclose(estimated_tflops, expected_tflops, rel_tol=1e-6), ( + f"{config_name}: expected {expected_tflops:.2f} TFLOPS, got {estimated_tflops:.2f}" + ) + + +@pytest.mark.parametrize("config_name", list(FLOPS_TEST_OMNI_CONFIGS.keys())) +def test_flops_counter_omni_estimation(config_name): + test_data = FLOPS_TEST_OMNI_CONFIGS[config_name] + config = Config(test_data["config"]) + counter = FlopsCounter(config) + + estimated_tflops, _ = counter.estimate( + test_data["batch_seqlens"], + delta_time=1.0, + images_seqlens=test_data["images_seqlens"], + audio_seqlens=test_data["audio_seqlens"], + ) + assert math.isclose(estimated_tflops, test_data["expected_tflops"], rel_tol=1e-6), ( + f"{config_name}: expected {test_data['expected_tflops']:.2f} TFLOPS, got {estimated_tflops:.2f}" + ) + + +def test_mfu_calculation(): + peak_tflops = get_device_peak_flops(unit="T", device_name="NVIDIA H100 80GB HBM3") + achieved_tflops = 200.0 + mfu = achieved_tflops / peak_tflops + assert 0 < mfu < 1 + assert math.isclose(mfu, 200.0 / 989.0, rel_tol=1e-6) + + +def test_unknown_model_type_fallback(): + config = Config( + { + "model_type": "unknown_model_xyz", + "hidden_size": 4096, + "vocab_size": 151936, + "intermediate_size": 12288, + "num_hidden_layers": 36, + "num_attention_heads": 32, + "num_key_value_heads": 8, + "head_dim": 128, + } + ) + counter = FlopsCounter(config) + estimated, _ = counter.estimate([1024, 2048], delta_time=1.0) + assert estimated > 0 + + +def test_unknown_model_type_missing_fields_returns_zero(): + config = Config({"model_type": "unknown_model_xyz"}) + counter = FlopsCounter(config) + estimated, _ = counter.estimate([1024, 2048], delta_time=1.0) + assert estimated == 0 From d9441d89ad6073a83d56cc4125bd9c497fef42c3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=AE=81=E6=9C=AC=E5=93=B2?= Date: Mon, 1 Jun 2026 21:37:34 +0800 Subject: [PATCH 070/268] feat(weight-update,scripts): add Qwen3.5-397B support and unify bridge converter path MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # ⭐ Feature ## Add Qwen3.5-397B-A17B model config and training scripts - Add model config `scripts/models/qwen35-397B-A17B.sh` (60 layers, 512 experts, MoE) - Add 128xGPU text training script with DeepEP flex dispatcher - Add 128xGPU multimodal training script for open-r1mm dataset - Add `--warm-hf-checkpoint-page-cache` CLI flag to optionally pre-read HF checkpoints --- # ♻️ Refactor ## Unify bridge converter expert/non-expert weight sync paths - Remove dual BF16/INT4 code paths in `HfWeightIteratorBridge`; always use bucket-based broadcast - Remove upstream `megatron-bridge` fallback (`_iter_hf_params_via_upstream_bridge`) - Rename `_broadcast_quantized_*` to `_broadcast_converted_*` (no longer quantization-specific) - Add `broadcast_and_apply_configs()` for PP-rank config exchange in `BridgeConverter` - Add prefix-aware fallback module lookup for tasks missing `megatron_module` - Fix EP `src_rank` dedup: keep lowest rank when multiple EP ranks own the same expert param - Add error logging with param shape/mapping details on `megatron_to_hf` failure --- # 🔩 Chore ## Update scripts and tooling - Enable `--warm-hf-checkpoint-page-cache` across all existing bridge-mode training scripts - Scale up Qwen3.5-35B-A3B multimodal script (CP=4, EP=16, 128xGPU resources) - Fix `xargs` in `ray-job.sh` with `--no-run-if-empty` to avoid error on empty input - Extend `.gitleaks.toml` to exclude logs, caches, and build artifacts - Simplify `ssh-ray-cluster` SKILL.md to a concise 3-step debug loop - Rename test file to `test_broadcast_converted.py` matching function renames --- .gitleaks.toml | 12 + relax/backends/megatron/checkpoint.py | 3 +- .../weight_update/bridge_converter.py | 76 ++++- .../hf_weight_iterator_bridge.py | 247 +++----------- relax/utils/arguments.py | 6 + scripts/entrypoint/ray-job.sh | 2 +- scripts/models/qwen35-397B-A17B.sh | 59 ++++ .../genrm/run-qwen3-4B-8xgpu-genrm.sh | 1 + scripts/training/hpc/run-qwen3-4B-pr-8xgpu.sh | 1 + .../multimodal/run-qwen3-vl-30B-A3B-8xgpu.sh | 1 + .../multimodal/run-qwen3-vl-4B-2xgpu.sh | 1 + .../multimodal/run-qwen3-vl-4B-8xgpu.sh | 1 + .../multimodal/run-qwen3-vl-4B-geo3k-8xgpu.sh | 1 + .../multimodal/run-qwen35-35B-A3B-8xgpu.sh | 7 +- .../run-qwen35-397B-A17B-128xgpu.sh | 190 +++++++++++ .../run-qwen35-9B-8xgpu-openr1mm-async.sh | 1 + ...n-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh | 1 + .../multimodal/run-qwen35-9B-8xgpu-video.sh | 1 + .../run-qwen36-35B-A3B-8xgpu-image.sh | 1 + .../training/text/run-qwen3-30B-A3B-8xgpu.sh | 1 + .../text/run-qwen3-30B-A3B-fp8-8xgpu.sh | 1 + .../text/run-qwen3-30B-A3B-int4-8xgpu.sh | 1 + .../training/text/run-qwen3-4B-4xgpu-async.sh | 1 + .../training/text/run-qwen3-4B-8xgpu-async.sh | 1 + .../text/run-qwen3-4B-8xgpu-hybrid-async.sh | 1 + scripts/training/text/run-qwen3-4B-8xgpu.sh | 1 + .../training/text/run-qwen3-4B-fp16-8xgpu.sh | 1 + .../text/run-qwen35-397B-A17B-128xgpu.sh | 196 +++++++++++ .../text/run-qwen35-9B-8xgpu-async.sh | 1 + scripts/training/text/run-qwen35-9B-8xgpu.sh | 1 + .../text/run-qwen36-35B-A3B-8xgpu-vpp.sh | 1 + .../training/text/run-qwen36-35B-A3B-8xgpu.sh | 1 + skills/ssh-ray-cluster/SKILL.md | 316 +++--------------- ...antized.py => test_broadcast_converted.py} | 32 +- 34 files changed, 663 insertions(+), 505 deletions(-) create mode 100644 scripts/models/qwen35-397B-A17B.sh create mode 100644 scripts/training/multimodal/run-qwen35-397B-A17B-128xgpu.sh create mode 100644 scripts/training/text/run-qwen35-397B-A17B-128xgpu.sh rename tests/backends/megatron/weight_update/{test_broadcast_quantized.py => test_broadcast_converted.py} (95%) diff --git a/.gitleaks.toml b/.gitleaks.toml index 828be99d8..306f5d212 100644 --- a/.gitleaks.toml +++ b/.gitleaks.toml @@ -102,4 +102,16 @@ paths = [ '''docs/Dockerfile''', '''\.git/.*''', '''docs/\.vitepress/(dist|cache)/.*''', + '''^log/.*''', + '''^logs/.*''', + '''\.log$''', + '''\.ruff_cache/.*''', + '''__pycache__/.*''', + '''\.pytest_cache/.*''', + '''\.mypy_cache/.*''', + '''build/.*''', + '''\.egg-info/.*''', + '''dist/.*''', + '''\.ninja_log$''', + '''\.ninja$''', ] diff --git a/relax/backends/megatron/checkpoint.py b/relax/backends/megatron/checkpoint.py index 549a7fd23..dee7ccf74 100644 --- a/relax/backends/megatron/checkpoint.py +++ b/relax/backends/megatron/checkpoint.py @@ -359,7 +359,8 @@ def _load_checkpoint_hf(ddp_model, optimizer, args, load_path: str): f"Load checkpoint from HuggingFace model into Megatron (requested_path={load_path}, source_path={source_path})" ) - _warm_hf_checkpoint_page_cache(source_path) + if getattr(args, "warm_hf_checkpoint_page_cache", False): + _warm_hf_checkpoint_page_cache(source_path) with megatron_bridge_utils.patch_megatron_model(ddp_model): bridge = AutoBridge.from_hf_pretrained(source_path, trust_remote_code=True) diff --git a/relax/backends/megatron/weight_update/bridge_converter.py b/relax/backends/megatron/weight_update/bridge_converter.py index fbf69aa0a..608eb0ce8 100644 --- a/relax/backends/megatron/weight_update/bridge_converter.py +++ b/relax/backends/megatron/weight_update/bridge_converter.py @@ -1,11 +1,15 @@ # Copyright (c) 2026 Relax Authors. All Rights Reserved. +import dataclasses import re from argparse import Namespace from collections.abc import Sequence +from types import SimpleNamespace from typing import Any import torch +import torch.distributed as dist +from megatron.core import mpu from relax.backends.megatron.misc_utils import strip_param_name_prefix from relax.backends.megatron.weight_conversion.processors import quantize_params, remove_padding @@ -41,6 +45,7 @@ def __init__( self._bridge_task_map: dict[str, Any] | None = None self._bridge_mapping_registry: Any = None self._bridge_expert_transposes_down: bool = True + self._configs_broadcast_done: bool = False # ------------------------------------------------------------------ # Lazy initialisation @@ -107,6 +112,24 @@ def init_tasks(self) -> None: inner_tp._detected_type = inner_tp._detect_parallelism_type(task.megatron_module) inner_tp._mapping = inner_tp._get_or_create_mapping(inner_tp._detected_type) + self._config_map: dict[str, Any] = {} + for task in self._bridge_task_map.values(): + if task.megatron_module is not None: + prefix = task.global_param_name.split(".")[0] + if prefix not in self._config_map: + self._config_map[prefix] = task.megatron_module.config + + # Patch local tasks that have megatron_module=None (Phase 2 tasks + # from named_params_and_buffers that AutoBridge didn't produce). + for name, task in list(self._bridge_task_map.items()): + if task.megatron_module is None: + prefix = name.split(".")[0] + config = self._config_map.get(prefix) + if config is not None: + self._bridge_task_map[name] = dataclasses.replace( + task, megatron_module=SimpleNamespace(config=config) + ) + self._bridge_expert_transposes_down = False for task in self._bridge_task_map.values(): cls = type(task.mapping) @@ -116,6 +139,40 @@ def init_tasks(self) -> None: logger.info("Bridge task map initialized with %d local tasks", len(self._bridge_task_map)) + def broadcast_and_apply_configs(self) -> None: + """Broadcast ``_config_map`` across PP ranks and patch remaining tasks. + + Must be called by all PP ranks after :meth:`init_tasks`. After this + call every task in ``_bridge_task_map`` has a non-None + ``megatron_module`` with the correct ``.config`` for QKV split. + + Safe to call multiple times; the broadcast only runs once. + """ + if self._configs_broadcast_done: + return + self._configs_broadcast_done = True + pp_size = mpu.get_pipeline_model_parallel_world_size() + if pp_size > 1: + all_config_maps: list[dict[str, Any] | None] = [None] * pp_size + dist.all_gather_object( + obj=self._config_map, + object_list=all_config_maps, + group=mpu.get_pipeline_model_parallel_group(), + ) + for remote_map in all_config_maps: + for prefix, cfg in remote_map.items(): + if prefix not in self._config_map: + self._config_map[prefix] = cfg + + for name, task in list(self._bridge_task_map.items()): + if task.megatron_module is None: + prefix = name.split(".")[0] + config = self._config_map.get(prefix) + if config is not None: + self._bridge_task_map[name] = dataclasses.replace( + task, megatron_module=SimpleNamespace(config=config) + ) + # ------------------------------------------------------------------ # Mapping collection # ------------------------------------------------------------------ @@ -174,11 +231,16 @@ def convert(self, name: str, param: torch.Tensor) -> list[tuple[str, torch.Tenso f"Bridge mapping registry has no entry for '{global_name}'. " f"Available task map keys: {list(self._bridge_task_map.keys())[:10]}..." ) + prefix = global_name.split(".")[0] + config = self._config_map.get(prefix) + if config is None: + config = next(iter(self._config_map.values()), None) + donor = SimpleNamespace(config=config) if config is not None else None task = WeightConversionTask( param_name=global_name, global_param_name=global_name, mapping=mapping, - megatron_module=None, + megatron_module=donor, param_weight=None, ) if isinstance(mapping, AutoMapping) and mapping._mapping is None: @@ -213,7 +275,17 @@ def convert(self, name: str, param: torch.Tensor) -> list[tuple[str, torch.Tenso patched_classes.add(cls) param = remove_padding(name, param, self._args.vocab_size) - converted_dict = mapping.megatron_to_hf(param, task.megatron_module) + try: + converted_dict = mapping.megatron_to_hf(param, task.megatron_module) + except Exception: + logger.error( + "megatron_to_hf failed: name=%s mapping=%s param.shape=%s module=%s", + global_name, + type(mapping).__name__, + tuple(param.shape), + type(task.megatron_module).__name__ if task.megatron_module else "None", + ) + raise finally: for m, (pp, tp, etp, ep) in zip(all_mappings, saved_groups): m.pp_group = pp diff --git a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py index 646e3c2a7..8f62ce131 100644 --- a/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py +++ b/relax/backends/megatron/weight_update/hf_weight_iterator_bridge.py @@ -9,13 +9,9 @@ from megatron.core import mpu from relax.utils import device as device_utils -from relax.utils import megatron_bridge_utils from relax.utils.logging_utils import get_logger from relax.utils.types import ParamInfo -from ..misc_utils import strip_param_name_prefix -from ..weight_conversion import postprocess_hf_param -from ..weight_conversion.processors import quantize_params from .bridge_converter import BridgeConverter from .common import all_gather_param, named_params_and_buffers from .hf_weight_iterator_base import HfWeightIteratorBase @@ -33,101 +29,23 @@ def __init__(self, *args, **kwargs): self._bridge_converter = BridgeConverter( args=self.args, model=self.model, quantization_config=self.quantization_config ) - self._quantize_experts_before_broadcast = ( - self.quantization_config is not None - and self.quantization_config.get("quant_method") == "compressed-tensors" - and mpu.get_expert_tensor_parallel_world_size() == 1 - ) - # The bucketed PP/EP/TP broadcast path (``_iter_hf_params``) is only - # required for the INT4 quantize-before-broadcast optimization. The - # BF16 path hung in colocate runs (Qwen3.6-35B-A3B with PP=4 EP=2); - # for that case we keep the upstream ``megatron-bridge`` path which is - # the proven implementation used before commit ec24de0a. - if self._quantize_experts_before_broadcast: - buckets_result = _build_param_info_buckets(self.args, self.model) - self._expert_buckets, self._non_expert_buckets, self._vanilla_key_map = buckets_result - self._bridge = None - else: - from megatron.bridge import AutoBridge - - self._bridge = AutoBridge.from_hf_pretrained(self.args.hf_checkpoint, trust_remote_code=True) - self._expert_buckets = self._non_expert_buckets = self._vanilla_key_map = None + buckets_result = _build_param_info_buckets(self.args, self.model) + self._expert_buckets, self._non_expert_buckets, self._vanilla_key_map = buckets_result def get_hf_weight_chunks(self, megatron_local_weights): - if self._quantize_experts_before_broadcast: - iterator = self._iter_hf_params(megatron_local_weights) - else: - iterator = self._iter_hf_params_via_upstream_bridge(megatron_local_weights) yield from _chunk_with_mla_pairing( - iterator, + self._iter_hf_params(megatron_local_weights), chunk_size=self.args.update_weight_buffer_size, ) - def _iter_hf_params_via_upstream_bridge(self, megatron_local_weights): - """BF16 path: delegate PP broadcast / TP gather to ``megatron-bridge``. - - Yields one ``(hf_name, tensor)`` at a time; the caller bundles them - into chunks via ``_chunk_with_mla_pairing``. - """ - renamed_megatron_local_weights = {strip_param_name_prefix(k): v for k, v in megatron_local_weights.items()} - with megatron_bridge_utils.patch_megatron_model(self.model): - conversion_tasks = self._bridge.get_conversion_tasks(self.model) - conversion_tasks = _process_conversion_tasks(conversion_tasks, renamed_megatron_local_weights) - - named_weights = self._bridge.export_hf_weights(self.model, cpu=False, conversion_tasks=conversion_tasks) - - hf_to_megatron_mapping = None - for item in named_weights: - # Compatibility shim: old megatron-bridge yields 3-tuples - # ``(hf_param_name, weight, megatron_param_name)`` while the - # official bridge yields 2-tuples ``(hf_param_name, weight)``. - if len(item) == 3: - hf_param_name, weight, megatron_param_name = item - elif len(item) == 2: - hf_param_name, weight = item - if hf_to_megatron_mapping is None: - hf_to_megatron_mapping = _build_hf_to_megatron_mapping(conversion_tasks) - # With PP > 1 ``export_hf_weights`` yields params from ALL - # PP ranks but the mapping only covers this rank's tasks. - # Fall back to ``hf_param_name`` for remote PP rank params - # — safe because downstream regexes don't match HF names. - megatron_param_name = hf_to_megatron_mapping.get(hf_param_name, hf_param_name) - else: - raise ValueError( - f"Unexpected named_weights tuple length {len(item)} from " - f"megatron-bridge.export_hf_weights(); expected 2 (new) or 3 (old). " - f"Item: {item!r}" - ) - - processed_weight = postprocess_hf_param( - args=self.args, - megatron_param_name=megatron_param_name, - hf_param_name=hf_param_name, - param=weight, - ) - - converted_named_params = [(hf_param_name, processed_weight)] - - quantized_batch = quantize_params( - args=self.args, - megatron_name=megatron_param_name, - converted_named_params=converted_named_params, - quantization_config=self.quantization_config, - ) - - yield from quantized_batch - def _iter_hf_params(self, megatron_local_weights): - """Load params from CPU backuper dict, broadcast across PP/EP, TP- - gather, bridge-convert, and quantize. + """Yield individual (name, tensor) pairs for all params. - Expert weights (ETP=1, INT4 quantized) use an optimized path: - load → local bridge convert + quantize → PP+EP broadcast (INT4). - Each rank only converts its own params, and broadcasts transmit - INT4 (~4× smaller than BF16). + Expert weights: load → TP gather + convert (src_rank only) → + PP+EP broadcast via _broadcast_converted_bucket. - Non-expert weights use the original path: PP/EP broadcast (BF16) → - TP all-gather → bridge convert → quantize. + Non-expert weights: PP/EP broadcast (BF16) → TP all-gather → + bridge convert. """ param_count = 0 t_bcast_total = 0.0 @@ -138,47 +56,29 @@ def _iter_hf_params(self, megatron_local_weights): rank = dist.get_rank() # Eagerly init bridge converter so all ranks are ready before broadcast. self._bridge_converter.init_tasks() + self._bridge_converter.broadcast_and_apply_configs() # --- Expert weights: quantize-before-broadcast path --- - if self._quantize_experts_before_broadcast: - for bucket_infos in self._expert_buckets: - t_c0 = time.monotonic() - params = _load_to_gpu(bucket_infos, megatron_local_weights, self._vanilla_key_map, device, rank) - all_converted = [] - for info, param in zip(bucket_infos, params, strict=True): - if rank == info.src_rank: - all_converted.append(self._bridge_converter.convert(info.name, param)) - else: - all_converted.append(None) - del params - t_convert_total += time.monotonic() - t_c0 - - t_b0 = time.monotonic() - results = _broadcast_quantized_bucket(bucket_infos, all_converted, device) - t_bcast_total += time.monotonic() - t_b0 - param_count += len(results) - yield from results - del all_converted, results - else: - for bucket_infos in self._expert_buckets: - t_b0 = time.monotonic() - params = _load_and_broadcast(bucket_infos, megatron_local_weights, self._vanilla_key_map, device, rank) - t_b1 = time.monotonic() - t_bcast_total += t_b1 - t_b0 - - for info, param in zip(bucket_infos, params, strict=True): - t_g0 = time.monotonic() - gathered = all_gather_param(self.args, info.name, param) - t_g1 = time.monotonic() - t_gather_total += t_g1 - t_g0 - - converted = self._bridge_converter.convert(info.name, gathered) - t_convert_total += time.monotonic() - t_g1 - param_count += len(converted) - yield from converted - del gathered, converted - - del params + for bucket_infos in self._expert_buckets: + t_c0 = time.monotonic() + params = _load_to_gpu(bucket_infos, megatron_local_weights, self._vanilla_key_map, device, rank) + all_converted = [] + for info, param in zip(bucket_infos, params, strict=True): + gathered = all_gather_param(self.args, info.name, param) + if rank == info.src_rank: + all_converted.append(self._bridge_converter.convert(info.name, gathered)) + else: + all_converted.append(None) + del gathered + del params + t_convert_total += time.monotonic() - t_c0 + + t_b0 = time.monotonic() + results = _broadcast_converted_bucket(bucket_infos, all_converted, device) + t_bcast_total += time.monotonic() - t_b0 + param_count += len(results) + yield from results + del all_converted, results # --- Non-expert weights: original path --- for bucket_infos in self._non_expert_buckets: @@ -195,6 +95,7 @@ def _iter_hf_params(self, megatron_local_weights): converted = self._bridge_converter.convert(info.name, gathered) t_convert_total += time.monotonic() - t_g1 + param_count += len(converted) yield from converted del gathered, converted @@ -269,6 +170,8 @@ def _build_param_info_buckets(args, model): local_infos[name] = info # Exchange across EP so every rank has all expert indices. + # Only expert params need src_rank update — non-expert params are + # replicated across EP and already have the correct PP-local src_rank. if ep_size > 1: ep_infos_list: list[None | tuple[int, dict]] = [None] * ep_size dist.all_gather_object( @@ -280,6 +183,8 @@ def _build_param_info_buckets(args, model): for name, info in infos.items(): if name not in local_infos: local_infos[name] = dataclasses.replace(info, src_rank=src_rank) + elif ".experts." in name and info.src_rank < local_infos[name].src_rank: + local_infos[name] = dataclasses.replace(local_infos[name], src_rank=info.src_rank) # Sort deterministically and split expert / non-expert. all_infos = sorted(local_infos.values(), key=lambda info: info.name) @@ -321,7 +226,8 @@ def _load_to_gpu(bucket_infos, megatron_local_weights, vanilla_key_map, device, for info in bucket_infos: if rank == info.src_rank: vanilla_key = vanilla_key_map[info.name] - gpu_tensor = megatron_local_weights[vanilla_key].to(device=device, non_blocking=True) + cpu_tensor = megatron_local_weights[vanilla_key] + gpu_tensor = cpu_tensor.to(device=device, non_blocking=True) param = torch.nn.Parameter(gpu_tensor, requires_grad=False) else: param = torch.nn.Parameter(torch.empty(info.shape, dtype=info.dtype, device=device), requires_grad=False) @@ -377,8 +283,8 @@ def _load_and_broadcast(bucket_infos, megatron_local_weights, vanilla_key_map, d return params -def _broadcast_quantized_bucket(bucket_infos, all_converted, device): - """Broadcast quantized expert tensors across PP and EP groups. +def _broadcast_converted_bucket(bucket_infos, all_converted, device): + """Broadcast converted expert tensors across PP and EP groups. ``all_converted[i]`` is ``bridge_converter.convert()`` output for ``bucket_infos[i]`` on the owning rank, or ``None`` on non-owners. @@ -389,7 +295,7 @@ def _broadcast_quantized_bucket(bucket_infos, all_converted, device): pp_size = mpu.get_pipeline_model_parallel_world_size() if pp_size > 1: - all_converted = _broadcast_quantized_phase( + all_converted = _broadcast_converted_phase( bucket_infos, all_converted, device, @@ -399,7 +305,7 @@ def _broadcast_quantized_bucket(bucket_infos, all_converted, device): ep_size = mpu.get_expert_model_parallel_world_size() if ep_size > 1: - all_converted = _broadcast_quantized_phase( + all_converted = _broadcast_converted_phase( bucket_infos, all_converted, device, @@ -522,14 +428,14 @@ def _decode_metadata(meta_tensor, slot_size): return slots -def _broadcast_quantized_phase(bucket_infos, all_converted, device, rank, group): - """Single-group broadcast of quantized tensors using only NCCL. +def _broadcast_converted_phase(bucket_infos, all_converted, device, rank, group): + """Single-group broadcast of converted tensors using only NCCL. 1. Each rank encodes its owned tensors' metadata into an int64 tensor. 2. Two allreduce calls exchange metadata: one for sizes (MAX), one for the content (SUM). Empty slots are encoded as zeros so the SUM correctly merges non-overlapping contributions. - 3. Quantized data tensors are broadcast from their owners. + 3. Data tensors are broadcast from their owners. """ group_ranks = dist.get_process_group_ranks(group) group_ranks_set = set(group_ranks) @@ -671,70 +577,3 @@ def _chunk_with_mla_pairing(named_params, chunk_size): if bucket: yield bucket - - -def _build_hf_to_megatron_mapping(conversion_tasks): - """Reconstruct ``hf_name -> megatron_name`` from a list of conversion - tasks. - - Needed because the official ``megatron-bridge.export_hf_weights`` yields - 2-tuples ``(hf_name, weight)`` and drops the megatron name. We rebuild it - from ``task.mapping.hf_param`` — pure metadata, no collective ops. - """ - hf_to_megatron_mapping = {} - - for task in conversion_tasks: - megatron_param_name = task.param_name - hf_param = task.mapping.hf_param - - if isinstance(hf_param, str): - hf_to_megatron_mapping[hf_param] = megatron_param_name - elif isinstance(hf_param, dict): - for hf_name in hf_param.values(): - hf_to_megatron_mapping[hf_name] = megatron_param_name - else: - raise TypeError( - f"Unexpected mapping.hf_param type {type(hf_param).__name__} " - f"for megatron param '{megatron_param_name}': {hf_param!r}" - ) - - return hf_to_megatron_mapping - - -def _process_conversion_tasks(vanilla_conversion_tasks, new_weight_dict): - """Splice the freshly-trained weights into each conversion task. - - ``build_conversion_tasks`` may return ``None`` entries for global params - with no mapping; filter them so downstream consumers never see ``None``. - """ - - def _handle_one(task): - if task is None: - return None - if task.param_weight is None: - return task - - weight_dict_key = f"vp_stages.{task.vp_stage}.{task.param_name}" - assert weight_dict_key in new_weight_dict, ( - f"{weight_dict_key=} not in new_weight_dict ({task.vp_stage=}, {task.param_name=}, {list(new_weight_dict)=})" - ) - - new_param_weight = new_weight_dict[weight_dict_key] - new_param_weight = new_param_weight.cuda() - return dataclasses.replace(task, param_weight=new_param_weight) - - valid_tasks = [t for t in vanilla_conversion_tasks if t is not None] - return _MapWithLen(_handle_one, valid_tasks) - - -class _MapWithLen: - def __init__(self, fn, xs): - self.fn = fn - self.xs = xs - - def __len__(self): - return len(self.xs) - - def __iter__(self): - for x in self.xs: - yield self.fn(x) diff --git a/relax/utils/arguments.py b/relax/utils/arguments.py index b4e6dc894..d337b41a8 100644 --- a/relax/utils/arguments.py +++ b/relax/utils/arguments.py @@ -251,6 +251,12 @@ def add_train_arguments(parser): default="raw", help="The method to convert megatron weights to hugging face weights for SGLang.", ) + parser.add_argument( + "--warm-hf-checkpoint-page-cache", + action="store_true", + default=False, + help="Pre-read HF checkpoint files into OS page cache before bridge loading to speed up NFS-backed mmap.", + ) parser.add_argument( "--custom-model-provider-path", type=str, diff --git a/scripts/entrypoint/ray-job.sh b/scripts/entrypoint/ray-job.sh index 1a31d9333..dc685a8e0 100755 --- a/scripts/entrypoint/ray-job.sh +++ b/scripts/entrypoint/ray-job.sh @@ -71,7 +71,7 @@ echo "=== Reserving sglang port ranges on all GPU nodes ===" python ${DIR}/../tools/run_on_each_ray_node.py --timeout 30 "sysctl -w net.ipv4.ip_local_reserved_ports=15000-16800,30000-30300" || echo "reserve_ports failed (non-fatal)" # kill old tasks -ray job list | grep RUNNING | grep -v job_id=None | grep -oP "submission_id='\\K[^']+" | xargs ray job stop || true +ray job list | grep RUNNING | grep -v job_id=None | grep -oP "submission_id='\\K[^']+" | xargs --no-run-if-empty ray job stop || true set -x diff --git a/scripts/models/qwen35-397B-A17B.sh b/scripts/models/qwen35-397B-A17B.sh new file mode 100644 index 000000000..6e916501f --- /dev/null +++ b/scripts/models/qwen35-397B-A17B.sh @@ -0,0 +1,59 @@ +# Copyright (c) 2026 Relax Authors. All Rights Reserved. + +NLAYERS=60 +FIRST_K_DENSE_REPLACE=0 + +arr=() +for ((i=0; i/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen35-397B-A17B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/openr1mm}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" +NUM_ROLLOUT="${NUM_ROLLOUT:=1000}" + +CKPT_ARGS=( + --hf-checkpoint ${MODEL_DIR}/Qwen3.5-397B-A17B/ + --ref-load ${MODEL_DIR}/Qwen3.5-397B-A17B/ + --megatron-to-hf-mode bridge + + --load ${EXP_DIR}/Qwen3.5-397B-A17B_mcore_128xgpu/ + --save ${EXP_DIR}/Qwen3.5-397B-A17B_mcore_128xgpu/ + --save-interval 100 + --max-actor-ckpt-to-keep 1 + --no-save-optim + --no-save-rng + --no-load-optim + --no-load-rng +) + +PROMPT_SET=${DATA_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet +SYSTEM_PROMPT="A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within and tags, respectively, i.e., reasoning process here answer here " + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type openr1mm + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 32 + --n-samples-per-prompt 8 + --rollout-max-response-len 4096 + --rollout-max-prompt-len 2048 + --rollout-temperature 1 + --global-batch-size 256 + --use-fault-tolerance + --balance-data + --rollout-health-check-timeout 120 + --system-prompt "${SYSTEM_PROMPT}" + --multimodal-keys '{"image":"image"}' + --use-streaming-dataset +) + +PERF_ARGS=( + --tensor-model-parallel-size 2 + --sequence-parallel + --pipeline-model-parallel-size 4 + --context-parallel-size 4 + --expert-model-parallel-size 32 + --expert-tensor-parallel-size 1 + --decoder-first-pipeline-num-layers 11 + --decoder-last-pipeline-num-layers 5 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + --use-distributed-optimizer + + --calculate-per-token-loss + --use-dynamic-batch-size + --vision-dp-when-cp + --vision-dp-when-tp + --max-tokens-per-gpu 16384 + --log-probs-max-tokens-per-gpu 32768 + + --moe-flex-dispatcher-backend deepep + --moe-token-dispatcher-type flex +) + +GRPO_ARGS=( + --advantage-estimator grpo + # --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 5e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + # NOTE(wuhuan): to avoid algorithm performance degradation + --no-rope-fusion + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 + --update-weight-buffer-size $(( 4 * 512 * 1024 * 1024 )) \ + +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 32 + --sglang-mem-fraction-static 0.7 + # dp attention + --sglang-enable-dp-attention + --sglang-dp-size 32 + --sglang-moe-dense-tp-size 1 + --sglang-enable-dp-lm-head + --sglang-ep-size 32 + --sglang-load-format dummy + + --sglang-cuda-graph-max-bs 8 + --sglang-server-concurrency 1024 + --sglang-watchdog-timeout 3600 + --sglang-enable-nan-detection +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen35-397B-A17B-128x-sync-${now} +) + +MISC_ARGS=( + --attention-dropout 0.0 + --hidden-dropout 0.0 + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # gated delta net does not support flash attention backend + --attention-backend flash +) +RUNTIME_ENV_JSON=$(python3 -c ' +import json, os +d = json.loads(os.environ["RUNTIME_ENV_JSON"]) +d.setdefault("env_vars", {}).update({ + "TORCH_DIST_INIT_BARRIER": "1", + "TORCH_NCCL_BLOCKING_WAIT": "0", + "TORCH_NCCL_ASYNC_ERROR_HANDLING": "1", + "TORCH_DISTRIBUTED_DEFAULT_TIMEOUT": "3600", + "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256", +}) +print(json.dumps(d)) +') +export RUNTIME_ENV_JSON + + +mkdir -p log +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://${HOST_IP}:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 128], "rollout": [1, 128]}' \ + --num-data-storage-units 16 \ + --colocate \ + --max-staleness 0 \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen35-397B-A17B-MM-GRPO-gpu128-sync-${now}.log diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh index bef691a1c..497f04769 100644 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-async.sh @@ -40,6 +40,7 @@ CKPT_ARGS=( --save-interval 100 --max-actor-ckpt-to-keep 1 --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache ) PROMPT_SET=${DATA_DIR}/multimodal-open-r1-8k-verified/data/train-00000-of-00001_converted_noextract.parquet diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh index 9ecca8ae6..19bab41d8 100644 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-openr1mm-hybrid-async.sh @@ -36,6 +36,7 @@ CKPT_ARGS=( --ref-load ${MODEL_DIR}/Qwen3.5-9B # --hf-checkpoint ${MODEL_DIR}/Qwen3-VL-4B-Instruct --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache # --ref-load ${MODEL_DIR}/Qwen3-VL-4B-Instruct # --load ${EXP_DIR}/Qwen3.5-9B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3.5-9B_mcore_8xgpu/ diff --git a/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh b/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh index 655fb7dbc..f22c65b24 100755 --- a/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh +++ b/scripts/training/multimodal/run-qwen35-9B-8xgpu-video.sh @@ -30,6 +30,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3.5-9B --ref-load ${MODEL_DIR}/Qwen3.5-9B --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache ) SYSTEM_PROMPT="'Please think about this question as if you were a human pondering deeply, carefully considering the video information before answering, engaging in an internal dialogue using expressions such as let me think, wait, hmm, oh I see, or let's break it down, including self-reflection or verification in the reasoning process, providing the detailed reasoning between the tags, and finally giving only the single option letter (e.g., A, B, C, D, etc.) as the final answer within the tags.'" diff --git a/scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu-image.sh b/scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu-image.sh index c43d01e5e..bc48a267a 100644 --- a/scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu-image.sh +++ b/scripts/training/multimodal/run-qwen36-35B-A3B-8xgpu-image.sh @@ -31,6 +31,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3.6-35B-A3B --ref-load ${MODEL_DIR}/Qwen3.6-35B-A3B --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/save/Qwen3.6-35B_mcore_8xgpu/ --save ${EXP_DIR}/save/Qwen3.6-35B_mcore_8xgpu/ diff --git a/scripts/training/text/run-qwen3-30B-A3B-8xgpu.sh b/scripts/training/text/run-qwen3-30B-A3B-8xgpu.sh index 68ad5a757..ade38037b 100644 --- a/scripts/training/text/run-qwen3-30B-A3B-8xgpu.sh +++ b/scripts/training/text/run-qwen3-30B-A3B-8xgpu.sh @@ -33,6 +33,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3-30B-A3B --ref-load ${MODEL_DIR}/Qwen3-30B-A3B --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/Qwen3-30B-A3B-mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-30B-A3B-mcore_8xgpu/ diff --git a/scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh b/scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh index b145fe80e..4c92829d2 100755 --- a/scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh +++ b/scripts/training/text/run-qwen3-30B-A3B-fp8-8xgpu.sh @@ -37,6 +37,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3-30B-A3B-FP8 --ref-load ${MODEL_DIR}/Qwen3-30B-A3B-FP8 --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/Qwen3-30B-A3B --save ${EXP_DIR}/Qwen3-30B-A3B diff --git a/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh b/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh index c6b528603..69019b964 100755 --- a/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh +++ b/scripts/training/text/run-qwen3-30B-A3B-int4-8xgpu.sh @@ -47,6 +47,7 @@ CKPT_ARGS=( # Megatron BF16 checkpoint --ref-load ${MODEL_DIR}/Qwen3-30B-A3B --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/Qwen3-30B-A3B_dist --save ${EXP_DIR}/Qwen3-30B-A3B_dist --save-interval 100 diff --git a/scripts/training/text/run-qwen3-4B-4xgpu-async.sh b/scripts/training/text/run-qwen3-4B-4xgpu-async.sh index 8314ad437..402503d1d 100644 --- a/scripts/training/text/run-qwen3-4B-4xgpu-async.sh +++ b/scripts/training/text/run-qwen3-4B-4xgpu-async.sh @@ -30,6 +30,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3-4B/ --ref-load ${MODEL_DIR}/Qwen3-4B/ --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache ) PROMPT_SET=${DATA_DIR}/dapo-math-17k/dapo-math-17k.jsonl diff --git a/scripts/training/text/run-qwen3-4B-8xgpu-async.sh b/scripts/training/text/run-qwen3-4B-8xgpu-async.sh index 39b4ae194..04dd8027d 100644 --- a/scripts/training/text/run-qwen3-4B-8xgpu-async.sh +++ b/scripts/training/text/run-qwen3-4B-8xgpu-async.sh @@ -32,6 +32,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3-4B/ --ref-load ${MODEL_DIR}/Qwen3-4B/ --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache # --load ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save-interval 100 diff --git a/scripts/training/text/run-qwen3-4B-8xgpu-hybrid-async.sh b/scripts/training/text/run-qwen3-4B-8xgpu-hybrid-async.sh index 62e22f124..111df44eb 100644 --- a/scripts/training/text/run-qwen3-4B-8xgpu-hybrid-async.sh +++ b/scripts/training/text/run-qwen3-4B-8xgpu-hybrid-async.sh @@ -32,6 +32,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3-4B/ --ref-load ${MODEL_DIR}/Qwen3-4B/ --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache # --load ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save-interval 100 diff --git a/scripts/training/text/run-qwen3-4B-8xgpu.sh b/scripts/training/text/run-qwen3-4B-8xgpu.sh index fbfe29fe5..f4db39f27 100644 --- a/scripts/training/text/run-qwen3-4B-8xgpu.sh +++ b/scripts/training/text/run-qwen3-4B-8xgpu.sh @@ -31,6 +31,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3-4B/ --ref-load ${MODEL_DIR}/Qwen3-4B/ --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save-interval 100 diff --git a/scripts/training/text/run-qwen3-4B-fp16-8xgpu.sh b/scripts/training/text/run-qwen3-4B-fp16-8xgpu.sh index 57c76587d..07a0155f9 100644 --- a/scripts/training/text/run-qwen3-4B-fp16-8xgpu.sh +++ b/scripts/training/text/run-qwen3-4B-fp16-8xgpu.sh @@ -32,6 +32,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3-4B/ --ref-load ${MODEL_DIR}/Qwen3-4B/ --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-4B_mcore_8xgpu/ --save-interval 100 diff --git a/scripts/training/text/run-qwen35-397B-A17B-128xgpu.sh b/scripts/training/text/run-qwen35-397B-A17B-128xgpu.sh new file mode 100644 index 000000000..ee2ddc791 --- /dev/null +++ b/scripts/training/text/run-qwen35-397B-A17B-128xgpu.sh @@ -0,0 +1,196 @@ +#!/bin/bash + +# Copyright (c) 2026 Relax Authors. All Rights Reserved. +# +# Qwen3.5-397B-A17B 128xGPU (16-node) fully sync training script for DAPO math dataset. +# +# Usage: +# bash scripts/training/text/run-qwen35-397B-A17B-128xgpu.sh + +set -ex +set -o pipefail + +now=$(date "+%Y-%m-%d-%H:%M:%S") +echo "当前时间: $now" + +SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +# Auto-source local environment when not launched via an external entrypoint +if [ -z "${RELAX_ENTRYPOINT_MODE:-}" ]; then + source "${SCRIPT_DIR}/../../entrypoint/local.sh" +fi +source "${MODEL_CONFIG_DIR}/qwen35-397B-A17B.sh" + +PROJECT_NAME="${PROJECT_NAME:=Relax/dev/dapo-math}" +EXP_DIR="${EXP_DIR:-${SCRIPT_DIR}/../../../../exps}" +MODEL_DIR="${MODEL_DIR:-${EXP_DIR}}" +DATA_DIR="${DATA_DIR:-${EXP_DIR}}" +NUM_ROLLOUT="${NUM_ROLLOUT:=1000}" + +CKPT_ARGS=( + --hf-checkpoint ${MODEL_DIR}/Qwen3.5-397B-A17B/ + --ref-load ${MODEL_DIR}/Qwen3.5-397B-A17B/ + --megatron-to-hf-mode bridge + + --load ${EXP_DIR}/Qwen3.5-397B-A17B_mcore_128xgpu/ + --save ${EXP_DIR}/Qwen3.5-397B-A17B_mcore_128xgpu/ + --save-interval 100 + --max-actor-ckpt-to-keep 1 + --no-save-optim + --no-save-rng + --no-load-optim + --no-load-rng +) + +PROMPT_SET=${DATA_DIR}/dapo-math-17k/dapo-math-17k.jsonl + +ROLLOUT_ARGS=( + --prompt-data ${PROMPT_SET} + --input-key prompt + --label-key label + --apply-chat-template + --rollout-shuffle + --rm-type dapo + --reward-key score + --num-rollout ${NUM_ROLLOUT} + --rollout-batch-size 16 + --n-samples-per-prompt 8 + --rollout-max-response-len 8192 + --rollout-temperature 1 + --global-batch-size 128 + --use-fault-tolerance + --balance-data + --rollout-health-check-timeout 120 +) + +EVAL_ARGS=( + --log-passrate + --skip-eval-before-train + --eval-interval 20 + --eval-prompt-data aime ${DATA_DIR}/aime-2024/aime-2024.jsonl + --n-samples-per-eval-prompt 8 + --eval-max-response-len 8192 + --eval-top-p 0.7 +) + +PERF_ARGS=( + --tensor-model-parallel-size 2 + --sequence-parallel + --pipeline-model-parallel-size 4 + --context-parallel-size 4 + --expert-model-parallel-size 32 + --expert-tensor-parallel-size 1 + --decoder-first-pipeline-num-layers 15 + --decoder-last-pipeline-num-layers 5 + + --recompute-granularity full + --recompute-method uniform + --recompute-num-layers 1 + + --calculate-per-token-loss + --use-dynamic-batch-size + # --vision-dp-when-tp + + --max-tokens-per-gpu 16384 + --log-probs-max-tokens-per-gpu 32768 + + --moe-flex-dispatcher-backend deepep + --moe-token-dispatcher-type flex +) + +GRPO_ARGS=( + --advantage-estimator grpo + --use-kl-loss + --kl-loss-coef 0.00 + --kl-loss-type low_var_kl + --entropy-coef 0.00 + --eps-clip 0.2 + --eps-clip-high 0.28 + --use-tis +) + +OPTIMIZER_ARGS=( + --optimizer adam + --lr 1e-6 + --lr-decay-style constant + --weight-decay 0.1 + --adam-beta1 0.9 + --adam-beta2 0.98 + + --optimizer-cpu-offload + --overlap-cpu-optimizer-d2h-h2d + --use-precision-aware-optimizer + + # NOTE(wuhuan): to avoid algorithm performance degradation + --no-rope-fusion + --moe-router-load-balancing-type "none" + --moe-aux-loss-coeff 0.0 + --update-weight-buffer-size $(( 4 * 512 * 1024 * 1024 )) \ + +) + +SGLANG_ARGS=( + --rollout-num-gpus-per-engine 32 + --sglang-mem-fraction-static 0.7 + # dp attention + --sglang-enable-dp-attention + --sglang-dp-size 32 + --sglang-moe-dense-tp-size 1 + --sglang-enable-dp-lm-head + --sglang-ep-size 32 + --sglang-load-format dummy + + --sglang-cuda-graph-max-bs 8 + --sglang-server-concurrency 1024 + --sglang-watchdog-timeout 3600 + --sglang-enable-nan-detection +) + +WANDB_ARGS=( + --use-clearml + --use-metrics-service + --tb-project-name ${PROJECT_NAME} + --tb-experiment-name qwen35-397B-A17B-128x-sync-${now} +) + +MISC_ARGS=( + --attention-dropout 0.0 + --hidden-dropout 0.0 + --accumulate-allreduce-grads-in-fp32 + --attention-softmax-in-fp32 + # gated delta net does not support flash attention backend + --attention-backend flash +) +RUNTIME_ENV_JSON=$(python3 -c ' +import json, os +d = json.loads(os.environ["RUNTIME_ENV_JSON"]) +d.setdefault("env_vars", {}).update({ + "TORCH_DIST_INIT_BARRIER": "1", + "TORCH_NCCL_BLOCKING_WAIT": "0", + "TORCH_NCCL_ASYNC_ERROR_HANDLING": "1", + "TORCH_DISTRIBUTED_DEFAULT_TIMEOUT": "3600", + "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256", +}) +print(json.dumps(d)) +') +export RUNTIME_ENV_JSON + + +mkdir -p log +ray job submit ${RAY_NO_WAIT:+--no-wait} --address="http://${HOST_IP}:8265" \ + ${WORKING_DIR:+--working-dir "${WORKING_DIR}"} \ + --runtime-env-json="${RUNTIME_ENV_JSON}" \ + -- python3 -m relax.entrypoints.train \ + --resource '{"actor": [1, 128], "rollout": [1, 128]}' \ + --num-data-storage-units 16 \ + --colocate \ + --max-staleness 0 \ + "${MODEL_ARGS[@]}" \ + "${CKPT_ARGS[@]}" \ + "${ROLLOUT_ARGS[@]}" \ + "${OPTIMIZER_ARGS[@]}" \ + "${GRPO_ARGS[@]}" \ + "${WANDB_ARGS[@]}" \ + "${PERF_ARGS[@]}" \ + "${EVAL_ARGS[@]}" \ + "${SGLANG_ARGS[@]}" \ + "${MISC_ARGS[@]}" 2>&1 | tee log/qwen35-397B-A17B-GRPO-gpu128-sync-${now}.log diff --git a/scripts/training/text/run-qwen35-9B-8xgpu-async.sh b/scripts/training/text/run-qwen35-9B-8xgpu-async.sh index 7bfb13404..0b02c1a6a 100755 --- a/scripts/training/text/run-qwen35-9B-8xgpu-async.sh +++ b/scripts/training/text/run-qwen35-9B-8xgpu-async.sh @@ -31,6 +31,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3.5-9B --ref-load ${MODEL_DIR}/Qwen3.5-9B --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/Qwen3-9B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-9B_mcore_8xgpu/ diff --git a/scripts/training/text/run-qwen35-9B-8xgpu.sh b/scripts/training/text/run-qwen35-9B-8xgpu.sh index ed2da5ad2..b64a79494 100644 --- a/scripts/training/text/run-qwen35-9B-8xgpu.sh +++ b/scripts/training/text/run-qwen35-9B-8xgpu.sh @@ -30,6 +30,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3.5-9B --ref-load ${MODEL_DIR}/Qwen3.5-9B --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/Qwen3-9B_mcore_8xgpu/ --save ${EXP_DIR}/Qwen3-9B_mcore_8xgpu/ diff --git a/scripts/training/text/run-qwen36-35B-A3B-8xgpu-vpp.sh b/scripts/training/text/run-qwen36-35B-A3B-8xgpu-vpp.sh index 1a5d705dc..4d46dbf2e 100755 --- a/scripts/training/text/run-qwen36-35B-A3B-8xgpu-vpp.sh +++ b/scripts/training/text/run-qwen36-35B-A3B-8xgpu-vpp.sh @@ -30,6 +30,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3.6-35B-A3B/ --ref-load ${MODEL_DIR}/Qwen3.6-35B-A3B/ --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/save/Qwen3.6-35B-A3B_mcore_8xgpu/ --save ${EXP_DIR}/save/Qwen3.6-35B-A3B_mcore_8xgpu/ diff --git a/scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh b/scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh index 8016c5032..583853eef 100755 --- a/scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh +++ b/scripts/training/text/run-qwen36-35B-A3B-8xgpu.sh @@ -30,6 +30,7 @@ CKPT_ARGS=( --hf-checkpoint ${MODEL_DIR}/Qwen3.6-35B-A3B/ --ref-load ${MODEL_DIR}/Qwen3.6-35B-A3B/ --megatron-to-hf-mode bridge + --warm-hf-checkpoint-page-cache --load ${EXP_DIR}/save/Qwen3.6-35B-A3B_mcore_8xgpu/ --save ${EXP_DIR}/save/Qwen3.6-35B-A3B_mcore_8xgpu/ diff --git a/skills/ssh-ray-cluster/SKILL.md b/skills/ssh-ray-cluster/SKILL.md index f12cfe0a2..6477fc2bb 100644 --- a/skills/ssh-ray-cluster/SKILL.md +++ b/skills/ssh-ray-cluster/SKILL.md @@ -1,315 +1,75 @@ --- name: ssh-ray-cluster -description: Connect to a remote Ray cluster head node via SSH (paramiko) to execute - commands, check cluster status, inspect logs, and debug training jobs. Use this - skill when the user asks to SSH into a remote machine, check Ray cluster status, - or run remote commands on the Ray head node. +description: 3-step debug loop for remote Ray cluster — submit task via SSH, check + logs locally, analyze errors and fix code, repeat until resolved. --- -# SSH to Ray Cluster +# SSH Debug Loop -This skill provides a standardized way to connect to a remote Ray cluster head node via SSH using `paramiko`, execute commands, and retrieve results. It is used for cluster inspection, log retrieval, and remote debugging. - -______________________________________________________________________ +Three-step cycle: **submit** -> **check logs** -> **analyze & fix** -> repeat. ## Prerequisites -The user must provide the following details (ask if missing — do not invent -values, and do not write them into this skill file): - -| Parameter | Purpose | -| --------------------- | ---------------------------------------- | -| `host` | Remote machine IP | -| `port` | SSH port | -| `username` | SSH username | -| `password` | SSH password | -| `RELAX_PROJECT_ROOT` | Absolute path to the Relax project root | - -Connection details and the project root are typically recorded in the -session's auto-memory (see `reference_ray_cluster_ssh.md`). Read them from -memory or ask the user — do not hard-code them in this skill or in scripts -checked into the repo. - -______________________________________________________________________ - -## HARD REQUIREMENT — always run project commands from the Relax project root - -A one-shot `paramiko.exec_command` starts the remote shell in the user's -home directory (typically `/root` or another non-project dir), **not** in -the Relax project root. Any command that touches a project-relative path -(`scripts/...`, `log/...`, `relax/...`, `tests/...`, `pyproject.toml`, -etc.) MUST be prefixed with `cd "$RELAX_PROJECT_ROOT"` (or the resolved -path) **inside the same command string** — splitting `cd` into a separate -`exec_command` call does NOT work, because each call opens a fresh shell -back at the home directory. - -`RELAX_PROJECT_ROOT` is a session-level value supplied by the user / read -from auto-memory (see Prerequisites). Do **not** hard-code its value in -this skill, in checked-in scripts, or in any reusable artifact — resolve -it at command-build time from memory or by asking the user. - -### Required pattern for any project-relative command - -```python -# RELAX_PROJECT_ROOT must be resolved from memory or user input first. -cmd = ( - f'cd {shlex.quote(RELAX_PROJECT_ROOT)} && ' - '' -) -ssh.exec_command(cmd, timeout=...) -``` - -Examples that REQUIRE the `cd` prefix: +Read SSH credentials and `RELAX_PROJECT_ROOT` from auto-memory (`reference_ray_cluster_ssh.md`). Ask the user if missing — never hard-code in this file. -- `bash scripts/entrypoint/ray-job.sh ...` -- `bash scripts/training/text/run---.sh` -- `python scripts/tools/run_on_each_ray_node.py ...` -- `bash scripts/tools/kill_for_ray.sh` -- `tail -n 100 log/-*.log` -- `pre-commit run --all-files` -- `pytest tests/test_foo.py` +## Step 1: Submit Task via SSH -Examples that do NOT need the `cd` (they take absolute paths or are -host-global tools that touch no project files): - -- `ray status`, `ray job list`, `ray job logs `, `ray job status ` -- `nvidia-smi ...` -- `ls /tmp/ray/session_latest/logs/` -- `ps -ef | grep ...` - -When in doubt: add the `cd`. It is harmless on host-global commands and -mandatory on project-relative ones. - -### Symptom that the `cd` was lost - -``` -bash: scripts/...: No such file or directory -python: can't open file '/scripts/...' -ls: cannot access 'log/': No such file or directory -``` - -Fix: add `cd "$RELAX_PROJECT_ROOT" && ` to the front of the command and -re-run. Do NOT retry blindly — a missing `cd` will keep failing the same -way. - -______________________________________________________________________ - -## Connection Pattern - -Use Python's `paramiko` library to establish SSH connections. Always use a **one-shot** pattern: connect, execute, close. Do not try to maintain persistent connections across tool calls. - -### Basic connection template +Use paramiko to SSH into the cluster, `cd` to the project root, and execute the user's command. ```python python3 -c " -import paramiko +import paramiko, shlex ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) -ssh.connect('', port=, username='', password='', timeout=10) - -stdin, stdout, stderr = ssh.exec_command('', timeout=30) -output = stdout.read().decode() -errors = stderr.read().decode() -print(output) -if errors: - print('STDERR:', errors) - -ssh.close() -" -``` - -### Multi-command template - -When you need to run multiple commands in sequence: - -```python -python3 -c " -import paramiko -ssh = paramiko.SSHClient() -ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) -ssh.connect('', port=, username='', password='', timeout=10) - -commands = [ - ('Description 1', 'command1'), - ('Description 2', 'command2'), -] - -for desc, cmd in commands: - print(f'=== {desc} ===') - stdin, stdout, stderr = ssh.exec_command(cmd, timeout=30) +ssh.connect(HOST, port=PORT, username=USER, password=PASS, timeout=10) +cmd = f'cd {shlex.quote(RELAX_PROJECT_ROOT)} && ' +try: + stdin, stdout, stderr = ssh.exec_command(cmd, timeout=60) print(stdout.read().decode()) err = stderr.read().decode() - if err: - print('STDERR:', err) - print() - -ssh.close() + if err: print('STDERR:', err) +except Exception: pass # long-running commands may timeout — that's OK +finally: ssh.close() " ``` -______________________________________________________________________ - -## Common Operations - -### 1. Check Ray cluster status +**Key rule**: All project-relative commands (`bash scripts/...`, `tail log/...`) MUST have `cd $RELAX_PROJECT_ROOT &&` in the **same** command string. Paramiko opens a fresh shell each call. +For backgrounded launches, verify separately: ```bash -ray status 2>&1 | head -30 +pgrep -af 'ray-job.sh' | head +ray job list 2>&1 | grep RUNNING | head ``` -Shows active/idle nodes, GPU/CPU usage, pending demands. +## Step 2: Check Logs Locally -### 2. List Ray jobs +The log file is on a shared filesystem mounted locally. Read it directly: ```bash -ray job list 2>&1 | head -50 -``` - -Shows all submitted jobs with their status (RUNNING, FAILED, SUCCEEDED). +# Find the latest log +ls -lt log/-*.log | head -5 -### 3. Get running job logs - -```bash -ray job logs 2>&1 | tail -100 +# Read the tail for errors +tail -200 log/.log ``` -### 4. Check GPU usage across nodes - -```bash -nvidia-smi --query-gpu=index,memory.used,memory.total,utilization.gpu --format=csv,noheader -``` - -### 5. Check specific worker node logs - -SGLang engine logs are typically found in Ray's log directory: - -```bash -ls -lt /tmp/ray/session_latest/logs/ | head -20 -``` - -### 6. Kill residual processes - -```bash -cd "$RELAX_PROJECT_ROOT" && bash scripts/tools/kill_for_ray.sh -``` - -### 7. Run command on all nodes - -```bash -cd "$RELAX_PROJECT_ROOT" && python scripts/tools/run_on_each_ray_node.py command "" -``` - -### 8. Launch / relaunch a training run - -Per the HARD REQUIREMENT above, the `cd` into the project root and the -launch must be in the **same** command string. - -```bash -cd "$RELAX_PROJECT_ROOT" && \ - nohup bash scripts/entrypoint/ray-job.sh > 2>&1 & -``` - -Verify CWD before launch by chaining `pwd && ls scripts/entrypoint/ray-job.sh` -in the same command — if `pwd` doesn't report the project root, the cd was -dropped and the launch will fail. - -______________________________________________________________________ - -## Working directory pitfall (`cd` over SSH) — supplementary patterns - -See the HARD REQUIREMENT section near the top for the rule. Two equivalent -patterns satisfy it; mixing them does not: - -1. **Single-line, single-shell** (preferred for paramiko `exec_command`): - chain `cd` with `&&` inside the *same* quoted command string, e.g. - `ssh ... 'cd "$RELAX_PROJECT_ROOT" && bash scripts/...'`. If you split - the `cd` into a separate `ssh` / `exec_command` invocation, the next call - starts back in the home directory. - -2. **Heredoc to remote bash** (useful for multi-step launches): - - ```bash - ssh ... bash < > 2>&1 & - echo "PID=\$!" - EOF - ``` - -Symptom that the cd was lost: `bash: