diff --git a/.devcontainer/docker-compose.override-example.yml b/.devcontainer/docker-compose.override-example.yml index 67f4f3d0c479..a0f46a4324e6 100644 --- a/.devcontainer/docker-compose.override-example.yml +++ b/.devcontainer/docker-compose.override-example.yml @@ -5,4 +5,4 @@ services: volumes: # Uncomment the following lines to enable # # Mount TRTLLM data volume: - # - /home/scratch.trt_llm_data/:/home/scratch.trt_llm_data/:ro + # - /home/scratch.trt_llm_data_ci/:/home/scratch.trt_llm_data_ci/:ro diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 91062b138af6..f366ecede383 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -219,6 +219,12 @@ docs/source/performance/perf-benchmarking.md @NVIDIA/trtllm-bench-reviewers ## Any changes to versions, additions, or removals of third-party libraries /3rdparty/** @NVIDIA/trt-llm-oss-compliance +### Vendored Third-Party Code (triton-kernels) +## This is a temporary vendored copy of triton-kernels from the Triton project (MIT License). +## Do not accept contributions to this directory - it should only be updated via scripts/vendor_triton_kernels.py +## This can be removed if and when triton-kernels is published as a separate wheel. +/triton_kernels/** @NVIDIA/trt-llm-oss-compliance + ### Docker & Installation Scripts ## These scripts install and pin dependency versions /docker/common/** @NVIDIA/trt-llm-setup-infra-devs @NVIDIA/trt-llm-infra-devs @NVIDIA/trt-llm-oss-compliance @@ -233,4 +239,4 @@ docs/source/performance/perf-benchmarking.md @NVIDIA/trtllm-bench-reviewers # The rule below requires that any PR to release/**/* branches must be approved by at least one member # of the NVIDIA/trt-llm-release-branch-approval team, regardless of who else approves the PR. # Without approval from a member of this team, PRs cannot be merged to release branches. -# * @NVIDIA/trt-llm-release-branch-approval +* @NVIDIA/trt-llm-release-branch-approval diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b9dd903c6cef..b07fb415949c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1369,6 +1369,9 @@ common-files: &common_files | triton_backend/tools/whisper/client.py | )$ +# Global exclude pattern for vendored third-party code +exclude: '^triton_kernels/' + default_install_hook_types: [pre-commit, commit-msg] repos: - repo: https://github.com/pycqa/isort diff --git a/3rdparty/CMakeLists.txt b/3rdparty/CMakeLists.txt index 93565ae099b0..fd627e5a1043 100644 --- a/3rdparty/CMakeLists.txt +++ b/3rdparty/CMakeLists.txt @@ -108,11 +108,16 @@ FetchContent_Declare( SOURCE_SUBDIR dont-add-this-project-with-add-subdirectory) +set(_patch_file "${CMAKE_CURRENT_SOURCE_DIR}/patches/xgrammar_constexpr.patch") FetchContent_Declare( xgrammar GIT_REPOSITORY https://github.com/mlc-ai/xgrammar - GIT_TAG v0.1.25 # e4e816f5f0fe39f5b1601a17a4552307fa3b70ff + GIT_TAG v0.1.32 # 62e13551b9b63251114894c5ee638564b160dd48 GIT_SHALLOW TRUE # NOTE: TensorRT-LLM only uses the headers SOURCE_SUBDIR - dont-add-this-project-with-add-subdirectory) + dont-add-this-project-with-add-subdirectory + PATCH_COMMAND + bash -c "patch -p1 --forward --batch --dry-run -i '${_patch_file}' && \ + patch -p1 --forward --batch -i '${_patch_file}' || \ + echo 'Patch already applied, skipping.'") diff --git a/3rdparty/patches/xgrammar_constexpr.patch b/3rdparty/patches/xgrammar_constexpr.patch new file mode 100644 index 000000000000..f4e5be99a216 --- /dev/null +++ b/3rdparty/patches/xgrammar_constexpr.patch @@ -0,0 +1,19 @@ +--- a/cpp/grammar_functor.cc ++++ b/cpp/grammar_functor.cc +@@ -1750,11 +1750,11 @@ + void Apply(Grammar* grammar); + static std::optional HashSequence(const Grammar& grammar, int32_t sequence_id); + +- static const int16_t kNotEndStateFlag = -0x100; +- static const int16_t kEndStateFlag = -0x200; +- static const int16_t kSelfRecursionFlag = -0x300; +- static const int16_t kSimpleCycleFlag = -0x400; +- static const int16_t kUnKnownFlag = -0x500; ++ static constexpr int16_t kNotEndStateFlag = -0x100; ++ static constexpr int16_t kEndStateFlag = -0x200; ++ static constexpr int16_t kSelfRecursionFlag = -0x300; ++ static constexpr int16_t kSimpleCycleFlag = -0x400; ++ static constexpr int16_t kUnKnownFlag = -0x500; + + private: + Grammar* grammar_; diff --git a/ATTRIBUTIONS-Python.md b/ATTRIBUTIONS-Python.md index 3cff1f73986f..7f45848e75ea 100644 --- a/ATTRIBUTIONS-Python.md +++ b/ATTRIBUTIONS-Python.md @@ -5261,7 +5261,7 @@ For more information, please refer to - `Tracker`: https://github.com/tox-dev/py-filelock/issues -## flashinfer-python (0.3.1.post1) +## flashinfer-python (0.6.1) ### Licenses License: `Apache-2.0` @@ -33478,7 +33478,6 @@ limitations under the License. - `Homepage`: https://github.com/NVIDIA/Model-Optimizer -<<<<<<< HEAD ## nvidia-modelopt-core (0.33.1) ### Licenses @@ -33506,10 +33505,7 @@ limitations under the License. - `Homepage`: https://github.com/NVIDIA/Model-Optimizer -## nvidia-nccl-cu12 (2.27.3) -======= ## nvidia-nccl-cu13 (2.27.7) ->>>>>>> fd7624b32 (modify ATTRIBUTIONS-Python.md) ### Licenses License: `BSD-3-Clause` @@ -62379,7 +62375,7 @@ Copyright 2018- The Hugging Face team. All rights reserved. - `Homepage`: https://github.com/huggingface/transformers -## triton (3.5.0) +## triton (3.5.1) ### Licenses License: `MIT License` @@ -62417,6 +62413,40 @@ License: `MIT License` - `Homepage`: https://github.com/triton-lang/triton/ +## triton-kernels (3.5.1) + +### Licenses +License: `MIT License` + + - `LICENSE` (from triton repository root): +``` +Copyright 2018-2020 Philippe Tillet +Copyright 2020-2022 OpenAI + +Permission is hereby granted, free of charge, to any person obtaining +a copy of this software and associated documentation files +(the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, +publish, distribute, sublicense, and/or sell copies of the Software, +and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +``` + +### URLs + - `Source`: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels + + ## tritonclient (2.63.0) ### Licenses diff --git a/README.md b/README.md index 9e78da9a476d..a02688977d2f 100644 --- a/README.md +++ b/README.md @@ -8,9 +8,9 @@ state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.< [![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://nvidia.github.io/TensorRT-LLM/) [![python](https://img.shields.io/badge/python-3.12-green)](https://www.python.org/downloads/release/python-3123/) [![python](https://img.shields.io/badge/python-3.10-green)](https://www.python.org/downloads/release/python-31012/) -[![cuda](https://img.shields.io/badge/cuda-13.0.0-green)](https://developer.nvidia.com/cuda-downloads) -[![torch](https://img.shields.io/badge/torch-2.9.0-green)](https://pytorch.org) -[![version](https://img.shields.io/badge/release-1.2.0rc8-green)](https://github.com/NVIDIA/TensorRT-LLM/blob/main/tensorrt_llm/version.py) +[![cuda](https://img.shields.io/badge/cuda-13.1.0-green)](https://developer.nvidia.com/cuda-downloads) +[![torch](https://img.shields.io/badge/torch-2.9.1-green)](https://pytorch.org) +[![version](https://img.shields.io/badge/release-1.2.1-green)](https://github.com/NVIDIA/TensorRT-LLM/blob/main/tensorrt_llm/version.py) [![license](https://img.shields.io/badge/license-Apache%202-blue)](https://github.com/NVIDIA/TensorRT-LLM/blob/main/LICENSE) [Architecture](https://nvidia.github.io/TensorRT-LLM/developer-guide/overview.html)   |   [Performance](https://nvidia.github.io/TensorRT-LLM/developer-guide/perf-overview.html)   |   [Examples](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html)   |   [Documentation](https://nvidia.github.io/TensorRT-LLM/)   |   [Roadmap](https://github.com/NVIDIA/TensorRT-LLM/issues?q=is%3Aissue%20state%3Aopen%20label%3Aroadmap) diff --git a/constraints.txt b/constraints.txt index 9cea8d00a98b..17a66eff2535 100644 --- a/constraints.txt +++ b/constraints.txt @@ -1,5 +1,14 @@ -# These vulnerabilities were inherited from the base image (pytorch:25.10-py3) and should be removed when the base image +# These vulnerabilities were inherited from the base image (pytorch:25.12-py3) and should be removed when the base image # is updated. -# WAR against https://github.com/advisories/GHSA-gm62-xv2j-4w53 -# WAR against https://github.com/advisories/GHSA-2xpw-w6gg-jr37 -urllib3>=2.6.0 +# WAR against https://github.com/advisories/GHSA-38jv-5279-wg99 +urllib3>=2.6.3 +# WAR against https://github.com/advisories/GHSA-8rrh-rw8j-w5fx +wheel>=0.46.2 +# WAR against https://github.com/advisories/GHSA-7gcm-g887-7qv7 +protobuf>=6.33.5 +# WAR against https://github.com/advisories/GHSA-6mq8-rvhq-8wgg +aiohttp>=3.13.3 +# WAR against https://github.com/advisories/GHSA-qjxf-f2mg-c6mc +tornado>=6.5.5 +# WAR against https://github.com/advisories/GHSA-3936-cmfr-pm3m +black>=26.3.1 diff --git a/cpp/conan.lock b/cpp/conan.lock new file mode 100644 index 000000000000..ee470090c6cd --- /dev/null +++ b/cpp/conan.lock @@ -0,0 +1,9 @@ +{ + "version": "0.5", + "requires": [ + "libnuma/system#65d9e0e45ccc1e477b97d678fa7d56bb%1769137587.8781652" + ], + "build_requires": [], + "python_requires": [], + "config_requires": [] +} diff --git a/cpp/include/tensorrt_llm/batch_manager/kvCacheManager.h b/cpp/include/tensorrt_llm/batch_manager/kvCacheManager.h index 62fc4fcb301e..94717307b644 100644 --- a/cpp/include/tensorrt_llm/batch_manager/kvCacheManager.h +++ b/cpp/include/tensorrt_llm/batch_manager/kvCacheManager.h @@ -288,6 +288,9 @@ class KVCacheBlock void removeNextBlock(BlockKey const& blockKey); + void freeDescendantsRecursively(); + void freeBlockAndAllDescendants(); + //! \brief Find block matching blockKey. If allowPartial is true, the returned block may match only a prefix of //! blockKey. //! @return tuple of [partialMatch, numMatched, block], partialMatch is true if not all the tokens of the block were diff --git a/cpp/kernels/fmha_v2/src/fused_multihead_attention.cpp b/cpp/kernels/fmha_v2/src/fused_multihead_attention.cpp index 0343066f7661..5a32f0a5116d 100644 --- a/cpp/kernels/fmha_v2/src/fused_multihead_attention.cpp +++ b/cpp/kernels/fmha_v2/src/fused_multihead_attention.cpp @@ -342,6 +342,8 @@ static inline void set_params(bert::Fused_multihead_attention_params_v2& params, // Attention sinks. params.attention_sinks = reinterpret_cast(attention_sinks_d); + assert((attention_sinks_d == nullptr || launch_params.flash_attention) + && "attention sinks are only supported with flash attention"); #if defined(STORE_P) params.p_ptr = p_d; diff --git a/cpp/kernels/xqa/gen_cubins.py b/cpp/kernels/xqa/gen_cubins.py index a345861fb70a..9230cf94cfd9 100755 --- a/cpp/kernels/xqa/gen_cubins.py +++ b/cpp/kernels/xqa/gen_cubins.py @@ -89,7 +89,8 @@ #include "tensorrt_llm/common/config.h" -TRTLLM_NAMESPACE_BEGIN +namespace tensorrt_llm +{ namespace kernels { // clang-format off @@ -98,7 +99,7 @@ cpp_file_suffex_text = R""" // clang-format on } // namespace kernels -TRTLLM_NAMESPACE_END +} """ cubin_meta_info_struct_prefix_text = R""" @@ -438,8 +439,9 @@ def generate_header_file_contents( CompileMacroOption('HEAD_ELEMS', 'd', [128]), CompileMacroOption('BEAM_WIDTH', 'beam', [1]), CompileMacroOption('CACHE_ELEM_ENUM', 'kvt', [0, 1, 2]), - CompileMacroOption('TOKENS_PER_PAGE', 'pagedKV', - [0, 64, 128]), # 0 denotes contiguous kv cache. + CompileMacroOption( + 'TOKENS_PER_PAGE', 'pagedKV', + [0, 32, 64, 128]), # 0 denotes contiguous kv cache. CompileMacroOption('HEAD_GRP_SIZE', 'nqpkv', [0]), CompileMacroOption('M_TILESIZE', 'm', [16, 32]), ]] diff --git a/cpp/kernels/xqa/mha.cu b/cpp/kernels/xqa/mha.cu index 5881a93fc4fe..e215cb172dbd 100644 --- a/cpp/kernels/xqa/mha.cu +++ b/cpp/kernels/xqa/mha.cu @@ -465,13 +465,10 @@ using WarpAcc = WarpAccT; #if SPEC_DEC #define MMAS_N_PER_MASK 2 -__device__ inline void applyMaskFromInput(Warp const& warp, WarpAcc& acc, MaskType const* mask, uint32_t rowOffset, - uint32_t nbValidCols, uint32_t qSeqLen, uint32_t actualQSeqLen, uint32_t headGrpSize #if SLIDING_WINDOW && !IS_SPEC_DEC_TREE - , - int32_t tok0WinBeg, uint32_t seqIter, uint32_t const cacheSeqLen, uint32_t const warpTileTokenBeg -#endif -) +__device__ inline void applyMaskFromInputSlidingAndSpecDec(Warp const& warp, WarpAcc& acc, MaskType const* mask, + uint32_t rowOffset, uint32_t nbValidCols, uint32_t qSeqLen, uint32_t actualQSeqLen, uint32_t headGrpSize, + int32_t tok0WinBeg, uint32_t seqIter, uint32_t const cacheSeqLen, uint32_t const warpTileTokenBeg) { uint32_t const idxInQuad = laneId() % 4; uint32_t const idxQuad = laneId() / 4; @@ -479,7 +476,6 @@ __device__ inline void applyMaskFromInput(Warp const& warp, WarpAcc& acc, MaskTy uint32_t const nbPackedMasksPerRow = divUp(qSeqLen, 32u) * 2u; uint16_t const* uint16Mask = reinterpret_cast(mask); constexpr uint64_t fullMask = ~uint64_t{0}; -#if SLIDING_WINDOW && !IS_SPEC_DEC_TREE Range const tileRange = {warpTileTokenBeg, warpTileTokenBeg + warpTile.x}; Range const maxMaskOutRange = {0, mha::max(0, tok0WinBeg) + (nbValidRows / MMAS_N_PER_MASK - 1)}; bool const ctaNeedBegMask = tileRange.beg < maxMaskOutRange.end; @@ -487,11 +483,6 @@ __device__ inline void applyMaskFromInput(Warp const& warp, WarpAcc& acc, MaskTy int32_t const tok0NbMaskOut = int32_t(tok0WinBeg) - int32_t(warpTileTokenBeg); uint32_t const nbSeqItersWithoutSpecDecMask = (cacheSeqLen - actualQSeqLen) / ctaTile.x; bool const ctaNeedSpecDecMask = (seqIter >= nbSeqItersWithoutSpecDecMask); -#else - constexpr bool ctaNeedBegMask = false; - bool const ctaNeedSpecDecMask = true; - int32_t const tok0NbMaskOut = -2147483648; -#endif bool const needMask = ctaNeedBegMask || ctaNeedSpecDecMask; if (!needMask) @@ -559,6 +550,61 @@ __device__ inline void applyMaskFromInput(Warp const& warp, WarpAcc& acc, MaskTy } #endif +__device__ inline void applyMaskFromInput(Warp const& warp, WarpAcc& acc, MaskType const* mask, uint32_t rowOffset, + uint32_t nbValidCols, uint32_t qSeqLen, uint32_t actualQSeqLen, uint32_t headGrpSize) +{ + uint32_t const idxInQuad = laneId() % 4; + uint32_t const idxQuad = laneId() / 4; + // Packed mask is aligned with 32 bits (2 uint16_t). + uint32_t const nbPackedMasksPerRow = divUp(qSeqLen, 32u) * 2u; + uint16_t const* uint16Mask = reinterpret_cast(mask); +#pragma unroll + for (uint32_t m = 0; m < acc.rows; m++) + { +#pragma unroll + for (uint32_t i = 0; i < InstAcc::rows; i++) + { + uint32_t const tokenRow = min((rowOffset + instM * m + idxQuad + i * 8) / headGrpSize, actualQSeqLen - 1); +#pragma unroll + for (uint32_t mask_n = 0; mask_n < acc.cols / MMAS_N_PER_MASK; mask_n++) + { + uint32_t const firstCol = instN * mask_n * MMAS_N_PER_MASK + InstAcc::cols * idxInQuad; + uint32_t const lastCol = firstCol + instN * (MMAS_N_PER_MASK - 1) + InstAcc::cols - 1; + uint32_t const maskPos0 = firstCol + actualQSeqLen < nbValidCols + ? 0u + : min(firstCol + actualQSeqLen - nbValidCols, actualQSeqLen - 1); + uint32_t const maskPos1 = lastCol + actualQSeqLen < nbValidCols + ? 0u + : min(lastCol + actualQSeqLen - nbValidCols, actualQSeqLen - 1); + uint32_t packedMask = 0u; + uint32_t const maskPosStart = (maskPos0 / 16) * 16; + reinterpret_cast(&packedMask)[0] + = uint16Mask[tokenRow * nbPackedMasksPerRow + (maskPos0 / 16)]; + reinterpret_cast(&packedMask)[1] + = uint16Mask[tokenRow * nbPackedMasksPerRow + (maskPos1 / 16)]; +#pragma unroll + for (uint32_t nj = 0; nj < MMAS_N_PER_MASK; nj++) + { +#pragma unroll + for (uint32_t j = 0; j < InstAcc::cols; j++) + { + uint32_t const n = (mask_n * MMAS_N_PER_MASK + nj); + uint32_t const col = instN * n + InstAcc::cols * idxInQuad + j; + // bool const maskFlag = col + qSeqLen < nbValidCols ? true : mask[tokenRow * qSeqLen + (col + + // qSeqLen - nbValidCols)]; + bool const maskFlag = col + actualQSeqLen < nbValidCols + ? true + : packedMask & (1u << ((col + actualQSeqLen - nbValidCols) - maskPosStart)); + acc(m, n)(i, j) = maskFlag && col < nbValidCols ? acc(m, n)(i, j) : safeInitRowMax; + } + } + } + } + } +} + +#endif + __device__ inline QuadRegRowMax warpTileOnlineSoftmax(Warp const& warp, QuadRegRowMax const& rowMaxHint, WarpAcc& acc) { QuadRegRowMax rowMax = rowMaxHint; @@ -1655,7 +1701,7 @@ CUBIN_EXPORT __global__ uint32_t const tok0SeqLen = cacheSeqLen - actualQSeqLen + 1 + idxHeadTokenInGrp; // ctaTokOffset; int32_t const tok0WinBeg = int32_t(tok0SeqLen) - int32_t(slidingWinSize); uint32_t const nbTotalSkipTokens = mha::max(0, tok0WinBeg); - + bool const rtIsReallySliding = (cacheSeqLen + actualQSeqLen > slidingWinSize); #elif SLIDING_WINDOW bool const rtIsReallySliding = (cacheSeqLen > slidingWinSize); assert(!SPEC_DEC || !rtIsReallySliding); @@ -1673,7 +1719,8 @@ CUBIN_EXPORT __global__ uint32_t const nbSeqIters = useKVCache ? divUp(cacheSeqLen, ctaTile.x) : 0; #if SLIDING_WINDOW && SPEC_DEC && !IS_SPEC_DEC_TREE - uint32_t const nbSeqItersWithoutMask = nbSkipLeadingTiles; + uint32_t const nbSeqItersWithoutMask + = rtIsReallySliding ? nbSkipLeadingTiles : (cacheSeqLen - actualQSeqLen) / ctaTile.x; #elif SPEC_DEC uint32_t const nbSeqItersWithoutMask = (cacheSeqLen - actualQSeqLen) / ctaTile.x; #endif @@ -1960,12 +2007,18 @@ CUBIN_EXPORT __global__ if (seqIter >= nbSeqItersWithoutMask) { uint32_t const nbValidCols = (warpTileTokenBeg < cacheSeqLen ? cacheSeqLen - warpTileTokenBeg : 0U); - applyMaskFromInput(warp, acc, mask, idxHeadTokenInGrp, nbValidCols, qSeqLen, actualQSeqLen, headGrpSize #if SLIDING_WINDOW && !IS_SPEC_DEC_TREE - , - tok0WinBeg, seqIter, cacheSeqLen, warpTileTokenBeg + if (rtIsReallySliding) + { + applyMaskFromInputSlidingAndSpecDec(warp, acc, mask, idxHeadTokenInGrp, nbValidCols, qSeqLen, + actualQSeqLen, headGrpSize, tok0WinBeg, seqIter, cacheSeqLen, warpTileTokenBeg); + } + else #endif - ); + { + applyMaskFromInput( + warp, acc, mask, idxHeadTokenInGrp, nbValidCols, qSeqLen, actualQSeqLen, headGrpSize); + } } #else bool const isFirstIter = (seqIter == nbSkipLeadingTiles); diff --git a/cpp/kernels/xqa/mha_sm90.cu b/cpp/kernels/xqa/mha_sm90.cu index dc21872ac696..7f7164c9a24e 100644 --- a/cpp/kernels/xqa/mha_sm90.cu +++ b/cpp/kernels/xqa/mha_sm90.cu @@ -1138,6 +1138,10 @@ CUBIN_EXPORT __global__ auto& xBar = smem.xBar[idxXBuf]; auto& vBar = smem.vBar[idxVBuf]; auto const& vBuf = smem.vBuf(idxVBuf); +#if !SWAP_AB + CtaBarrierPair& vtBar = smem.vtBar[idxVBuf]; + auto& vtBuf = smem.vtBuf(idxVBuf); +#endif xBar.produced.arrive_and_wait(); #if SKIP_SOFTMAX_ATTN bool shouldSkipSoftmaxAttn = smem.skipSoftmaxVotesGemm0ToGemm1[idxXBuf]; // guarded by xBar @@ -1153,8 +1157,6 @@ CUBIN_EXPORT __global__ { arrive_tx_and_wait(vBar.produced, exactDiv(sizeof(SharedMem::VBuffer), gemm1NbThrds)); #if !SWAP_AB - CtaBarrierPair& vtBar = smem.vtBar[idxVBuf]; - auto& vtBuf = smem.vtBuf(idxVBuf); vtBar.consumed.arrive_and_wait(); transposeVTile(warpRank, laneId(), vtBuf, vBuf); vBar.consumed.arrive(); diff --git a/cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp b/cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp index c0bf858cc9ec..b0c4bd2fd1ce 100644 --- a/cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp +++ b/cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -89,6 +89,33 @@ std::vector getAllSequenceBlocks(BlockPtr lastBlock) return sequenceBlocks; } +// Compute maximum number of tokens that have been computed by prefill and generation. +// Accounts for chunked prefill to avoid storing state that hasn't been written to KV cache yet. +// We call LlmRequest::getContextRemainingLength to see how many tokens are still waiting to be computed in prefill. +// If this value is > 0 prefill is not finished yet, and number of computed tokens must be capped at the current context +// position. If it is == 0, we are in generation mode, and number of computed tokens equals number of unique tokens +// stored in request. +SizeType32 getMaterializedUniqueTokenCountForReuse( + VecUniqueTokens const& uniqueTokens, tensorrt_llm::batch_manager::LlmRequest const& llmRequest) +{ + auto const totalUniqueTokenCount = static_cast(uniqueTokens.size()); + if (llmRequest.getContextRemainingLength() > 0) + { + return std::min(totalUniqueTokenCount, llmRequest.getContextCurrentPosition()); + } + return totalUniqueTokenCount; +} + +// Compute number of tokens that can be stored for reuse. The last computed token is never stored in KV cache, hence +// cannot be stored for reuse. Number of tokens that can be stored for reuse is thus the greater of 0 or +// getMaterializedUniqueTokenCountForReuse() - 1. +SizeType32 getUsableUniqueTokenCountForReuse( + VecUniqueTokens const& uniqueTokens, tensorrt_llm::batch_manager::LlmRequest const& llmRequest) +{ + auto const materializedUniqueTokenCount = getMaterializedUniqueTokenCountForReuse(uniqueTokens, llmRequest); + return materializedUniqueTokenCount > 0 ? materializedUniqueTokenCount - 1 : 0; +} + } // namespace namespace tensorrt_llm::batch_manager::kv_cache_manager @@ -477,6 +504,32 @@ void KVCacheBlock::removeNextBlock(BlockKey const& blockKey) mNextBlocks.erase(blockKey); } +void KVCacheBlock::freeDescendantsRecursively() +{ + bool hasChildren = !mNextBlocks.empty(); + if (hasChildren) + { + for (auto it = mNextBlocks.begin(); it != mNextBlocks.end();) + { + it->second->freeDescendantsRecursively(); + TLLM_LOG_DEBUG("KVCacheBlock::freeDescendantsRecursively - Freeing block %d", it->second->getBlockId()); + it = mNextBlocks.erase(it); + } + } + mPrevBlock = nullptr; +} + +void KVCacheBlock::freeBlockAndAllDescendants() +{ + // free from previous block + if (mPrevBlock != nullptr) + { + mPrevBlock->removeNextBlock(mBlockKey); + mPrevBlock = nullptr; + } + freeDescendantsRecursively(); +} + bool KVCacheBlock::isFull() const { return mIsFull; @@ -796,8 +849,9 @@ void BlockManager::storeContextBlocks(GenerationRequest& sequence, LlmRequest co auto cacheBlockIds = sequence.getCacheBlockIds(windowSize); auto const& uniqueTokens = llmRequest.getUniqueTokens(beamIdx); + auto const usableUniqueTokenCount = getUsableUniqueTokenCountForReuse(uniqueTokens, llmRequest); auto blockedUniqueTokens - = chopVectorIntoBlocks(uniqueTokens, uniqueTokens.size() - 1, getTokensPerBlock(), false); + = chopVectorIntoBlocks(uniqueTokens, usableUniqueTokenCount, getTokensPerBlock(), false); auto blockKeys = buildBlockKeys(blockedUniqueTokens, llmRequest); (void) mWindowBlockManagers.at(windowSize).storeBlocks(std::move(blockKeys), cacheBlockIds[beamIdx]); } @@ -956,19 +1010,14 @@ void WindowBlockManager::freeLeafBlock(BlockPtr const& block) void WindowBlockManager::freeChildren(BlockPtr const& block) { - // Free all descendants of block - for (auto const& p : block->getNextBlocks()) - { - auto childBlock = p.second; - freeChildren(childBlock); - } - - // Free block + // Tell event manager we are freeing block if (mEventManager && blockInRadixTree(block)) { mEventManager->enqueueRemovedEvent(block, mWindowSize); } - freeLeafBlock(block); + + // Free block and all it's descendants from radix tree + block->freeBlockAndAllDescendants(); } BlockPtr WindowBlockManager::getFreeBlock(GenerationRequest& sequence, executor::RetentionPriority priority, @@ -1567,60 +1616,80 @@ std::pair> WindowBlockManager::sto auto searchRoot = mCachedBlocksRoot; bool needMatch = true; - auto numBlocks = blockKeys.size(); + // There is no guarantee that these vectors will be the same length. + // Only iterate as long as we have valid blockKey and blockId. + auto numBlocks = std::min(blockKeys.size(), blockIds.size()); std::vector storedBlocks; std::vector pinnedBlockIds; for (std::size_t blockCnt = 0; blockCnt < numBlocks; ++blockCnt) { - auto const bid = blockIds[blockCnt]; - TLLM_LOG_DEBUG("%s::storeBlocks - Searching match for block %d", mLogPrefix.c_str(), bid); - auto& block = mAllBlocksById[bid]; - auto const& blockKey = blockKeys[blockCnt]; - - auto [partialMatch, numMatched, matchedBlock] - = needMatch ? searchRoot->findMatchingBlock(blockKey, false, false) : std::make_tuple(false, 0, nullptr); - if (matchedBlock != nullptr) - { - // Found match - TLLM_LOG_DEBUG( - "%s::storeBlocks - Found matching block %d, traverse", mLogPrefix.c_str(), matchedBlock->getBlockId()); - searchRoot = matchedBlock; - // TODO possible optimization: if bid != matchedBlock->getBlockId(), - // block can be freed and inserted at mFreePrimaryBlocks.begin() - } - else - { - // No match - TLLM_LOG_DEBUG("%s::storeBlocks - No match, inserting block %d into search structure", mLogPrefix.c_str(), - block->getBlockId()); - TLLM_CHECK_WITH_INFO(block->getBlockId() == bid, - "Block id mismatch " + std::to_string(block->getBlockId()) + " != " + std::to_string(bid)); - needMatch = false; // no matching needed for following blocks - block->setBlockKey(blockKey, static_cast(blockKey.uniqueTokens.size()) == mTokensPerBlock); - block->setPrevBlock(searchRoot); - block->setPrevBlockInSeq(searchRoot); - searchRoot->addNextBlock(blockKey, block); - - // Sanity check. The list of stored blocks should be connected. - TLLM_CHECK(storedBlocks.empty() || block->getPrevBlock() == storedBlocks.back()); - - storedBlocks.push_back(block); - TLLM_CHECK(block->getPrevBlockInSeq() == nullptr - || block->getPrevBlockInSeq()->getHash() == searchRoot->getHash()); - auto oldHash = block->getHash(); - auto newHash = BlockKeyHasher()(blockKey, searchRoot->getHash()); - if (oldHash != newHash) + try + { + // Protect against blockIds being shorter than blockKeys. + auto const bid = blockIds.at(blockCnt); + TLLM_LOG_DEBUG("%s::storeBlocks - Searching match for block %d", mLogPrefix.c_str(), bid); + // We set blockId to an invalid value to indicate that a block has been released early for a limited + // attention layer. Make sure we don't store an invalid block because of this. + auto& block = mAllBlocksById.at(bid); + // Protect against blockKeys being shorter than blockIds. + auto const& blockKey = blockKeys.at(blockCnt); + + // If either of the above error conditions occur, std::vector::at will throw an exception, which is caught + // further down. This will prevent an invalid block from being stored for reuse. The catch clause exits loop + // early, preventing blocks following an invalid block from being reused. + + auto [partialMatch, numMatched, matchedBlock] = needMatch + ? searchRoot->findMatchingBlock(blockKey, false, false) + : std::make_tuple(false, 0, nullptr); + if (matchedBlock != nullptr) { - TLLM_LOG_DEBUG("#%d block hash %zx -> %zx", block->getBlockId(), oldHash, newHash); - block->setHash(newHash); + // Found match + TLLM_LOG_DEBUG("%s::storeBlocks - Found matching block %d, traverse", mLogPrefix.c_str(), + matchedBlock->getBlockId()); + searchRoot = matchedBlock; + // TODO possible optimization: if bid != matchedBlock->getBlockId(), + // block can be freed and inserted at mFreePrimaryBlocks.begin() + } + else + { + // No match + TLLM_LOG_DEBUG("%s::storeBlocks - No match, inserting block %d into search structure", + mLogPrefix.c_str(), block->getBlockId()); + TLLM_CHECK_WITH_INFO(block->getBlockId() == bid, + "Block id mismatch " + std::to_string(block->getBlockId()) + " != " + std::to_string(bid)); + needMatch = false; // no matching needed for following blocks + block->setBlockKey(blockKey, static_cast(blockKey.uniqueTokens.size()) == mTokensPerBlock); + block->setPrevBlock(searchRoot); + block->setPrevBlockInSeq(searchRoot); + searchRoot->addNextBlock(blockKey, block); + + // Sanity check. The list of stored blocks should be connected. + TLLM_CHECK(storedBlocks.empty() || block->getPrevBlock() == storedBlocks.back()); + + storedBlocks.push_back(block); + TLLM_CHECK(block->getPrevBlockInSeq() == nullptr + || block->getPrevBlockInSeq()->getHash() == searchRoot->getHash()); + auto oldHash = block->getHash(); + auto newHash = BlockKeyHasher()(blockKey, searchRoot->getHash()); + if (oldHash != newHash) + { + TLLM_LOG_DEBUG("#%d block hash %zx -> %zx", block->getBlockId(), oldHash, newHash); + block->setHash(newHash); + } + searchRoot = block; + numBlocksStoredForReuse++; + } + if (pinBlocks) + { + searchRoot->incRefCount(); + pinnedBlockIds.push_back(searchRoot->getBlockId()); } - searchRoot = block; - numBlocksStoredForReuse++; } - if (pinBlocks) + catch (std::out_of_range const& ex) { - searchRoot->incRefCount(); - pinnedBlockIds.push_back(searchRoot->getBlockId()); + TLLM_LOG_WARNING("Out of range access, terminating storeBlocks early."); + // Prevent blocks following an invalid block from being reused. + break; } } if (mEventManager) @@ -1882,11 +1951,9 @@ std::vector WindowBlockManager::storeBlocksForReuse( auto const& uniqueTokens = llmRequest->getUniqueTokens(beamIdx); auto const& cacheBlockIds = sequence.getCacheBlockIds(mWindowSize); - // TODO: get the caller to mark tokens as filled / not filled, so that the kv-cache manager doesn't - // have to guess. Only (length - 1) tokens of the sequence have their kv-state recorded in kv-cache. We assume - // the last token's state is not filled yet. - auto const usableSize = static_cast(uniqueTokens.size()) - 1; - auto blockedUniqueTokens = chopVectorIntoBlocks(uniqueTokens, usableSize, mTokensPerBlock, true); + auto const usableUniqueTokenCount = getUsableUniqueTokenCountForReuse(uniqueTokens, *llmRequest); + auto blockedUniqueTokens + = chopVectorIntoBlocks(uniqueTokens, usableUniqueTokenCount, mTokensPerBlock, true); auto blockKeys = buildBlockKeys(blockedUniqueTokens, *llmRequest); auto [numStored, pinnedBlockIds] = storeBlocks(std::move(blockKeys), cacheBlockIds[beamIdx], pinBlocks); @@ -1919,11 +1986,9 @@ std::optional WindowBlockManager::releaseBlocks( sequence.getRequestId()); } auto const& uniqueTokens = llmRequest->getUniqueTokens(/*beamIdx=*/0); - // Only (length - 1) tokens of the sequence have their kv-state - // recorded in kv-cache. We assume the last token's state is not filled yet. - auto const usableSize = static_cast(uniqueTokens.size()) - 1; - auto blockedUniqueTokens - = chopVectorIntoBlocks(uniqueTokens, usableSize, mTokensPerBlock, /*allowPartial=*/true); + auto const usableUniqueTokenCount = getUsableUniqueTokenCountForReuse(uniqueTokens, *llmRequest); + auto blockedUniqueTokens = chopVectorIntoBlocks( + uniqueTokens, usableUniqueTokenCount, mTokensPerBlock, /*allowPartial=*/true); auto blockKeys = buildBlockKeys(blockedUniqueTokens, *llmRequest); std::vector cacheBlockIds(allocatedBlocks.size()); diff --git a/cpp/tensorrt_llm/batch_manager/mlaCacheFormatter.cpp b/cpp/tensorrt_llm/batch_manager/mlaCacheFormatter.cpp index b2a60a3eda02..0a3f1d378d53 100644 --- a/cpp/tensorrt_llm/batch_manager/mlaCacheFormatter.cpp +++ b/cpp/tensorrt_llm/batch_manager/mlaCacheFormatter.cpp @@ -249,13 +249,25 @@ void MLACacheFormatter::format(tensorrt_llm::batch_manager::TransferSession& ses { TLLM_CHECK(preAllocSendBuffer->getDataType() == inputKvCacheBlocks.at(0)->getDataType()); } + // Connections are ordered CP-major (all connections for CP=0, then all for CP=1, etc.) + // from targetIRanks(). Within each CP domain, connections are ordered by (TP, PP). + // The bufferSizeForTarget is indexed as: cpDomainIdx * pPDomainSize + ppDomainIdx. + // So we need to compute cacheIdx based on the CP-major connection ordering. + auto connectionsPerCPDomain = connections.size() / cPDomainSize; + TLLM_CHECK_WITH_INFO(connectionsPerCPDomain > 0, "connectionsPerCPDomain must be > 0"); + auto sendBufferFun = [&](int deviceId, size_t processIdx) { NVTX3_SCOPED_RANGE(sendBufferFun); TLLM_CUDA_CHECK(cudaSetDevice(deviceId)); auto startTime = LlmRequest::getSteadyClockNow(); - auto cacheIdx = processIdx % (pPDomainSize * cPDomainSize); + // Compute cacheIdx based on CP-major connection ordering: + // - cpDomainIdx = which CP domain this connection belongs to. + // - ppDomainIdx = which PP domain within the CP domain (for PP > 1). + auto cpDomainIdx = processIdx / connectionsPerCPDomain; + auto ppDomainIdx = (processIdx % connectionsPerCPDomain) % pPDomainSize; + auto cacheIdx = cpDomainIdx * pPDomainSize + ppDomainIdx; if (cacheIdx < bufferCoverTargetNum) { size_t size = outputSplitCaches.at(cacheIdx)->getSizeInBytes(); diff --git a/cpp/tensorrt_llm/common/attentionOp.cpp b/cpp/tensorrt_llm/common/attentionOp.cpp index 32a9332a0191..845ad8084c40 100644 --- a/cpp/tensorrt_llm/common/attentionOp.cpp +++ b/cpp/tensorrt_llm/common/attentionOp.cpp @@ -1271,14 +1271,6 @@ int AttentionOp::mlaGeneration( mXqaDispatcher->run(xqaParams, kv_cache_buffer, kv_scale_cache_buffer); return 0; } - else if (mIsSpecDecodingEnabled && mUseSpecDecoding) - { - TLLM_CHECK_WITH_INFO(false, "No available XQA kernels are found for speculative decoding mode."); - } - else if (mFuseFp4Quant) - { - TLLM_CHECK_WITH_INFO(false, "No available kernels are found for FP4 output."); - } } // Use FMHA otherwise. diff --git a/cpp/tensorrt_llm/common/customAllReduceUtils.h b/cpp/tensorrt_llm/common/customAllReduceUtils.h index d718cbd188e9..f69eb0cb2565 100644 --- a/cpp/tensorrt_llm/common/customAllReduceUtils.h +++ b/cpp/tensorrt_llm/common/customAllReduceUtils.h @@ -115,7 +115,7 @@ inline AllReduceStrategyType selectStrategyLookUpTable( || num_token_index >= AllReduceBestStrategyTable.at(sm_version).at(tp_index).at(fusion_op_index).at(hidden_size_index).size()) { - return AllReduceStrategyType::NCCL_SYMMETRIC; + return AllReduceStrategyType::NCCL; } return static_cast( @@ -126,20 +126,20 @@ inline AllReduceStrategyType selectStrategyLookUpTable( inline AllReduceBestStrategyTableType AllReduceBestStrategyTableSM90 = {{ // TP=2 {// Fusion=NONE - {4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 0, 0}, - {4, 4, 5, 4, 4, 5, 5, 5, 4, 5, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 5, 4, 5, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 5, 5, 5, 5, 4, 4, 5, 0, 0, 0}, {4, 5, 4, 5, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, - {4, 4, 5, 5, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, - {4, 4, 4, 4, 5, 5, 5, 5, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 5, 5, 4, 5, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_FP8 - {4, 4, 4, 4, 4, 5, 5, 5, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 4, 4, 0, 0}, - {4, 4, 4, 4, 5, 5, 5, 5, 5, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 5, 5, 5, 5, 4, 5, 4, 4, 4, 4, 0, 0}, - {4, 4, 4, 5, 5, 4, 4, 4, 5, 4, 4, 4, 0, 0, 0}, {4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, - {4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_NVFP4 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, @@ -147,20 +147,20 @@ inline AllReduceBestStrategyTableType AllReduceBestStrategyTableSM90 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, { // TP=4 {// Fusion=NONE - {4, 4, 4, 4, 5, 4, 4, 5, 4, 5, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 4, 0, 0, 0, 0}, - {4, 4, 4, 4, 5, 4, 5, 5, 5, 4, 4, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 5, 5, 4, 5, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 5, 4, 5, 5, 0, 0, 0, 0}, {4, 4, 4, 5, 5, 5, 5, 4, 5, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 5, 4, 5, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_FP8 - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 5, 5, 4, 5, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 5, 4, 5, 5, 4, 4, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_NVFP4 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, @@ -168,20 +168,20 @@ inline AllReduceBestStrategyTableType AllReduceBestStrategyTableSM90 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, { // TP=8 {// Fusion=NONE - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 0, 5, 5, 0, 0, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 0, 5, 5, 0, 0, 0, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_FP8 - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 0, 5, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 0, 0, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 5, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 5, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 5, 0, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_NVFP4 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, @@ -191,67 +191,67 @@ inline AllReduceBestStrategyTableType AllReduceBestStrategyTableSM90 inline AllReduceBestStrategyTableType AllReduceBestStrategyTableSM100 = {{ // TP=2 {// Fusion=NONE - {4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0}, {4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, - {4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM - {4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_FP8 - {4, 4, 4, 4, 5, 4, 5, 5, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4, 4, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_NVFP4 - {4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, - {4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0}}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}}}, { // TP=4 {// Fusion=NONE - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_FP8 {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_NVFP4 {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0}}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0}}}, { // TP=8 {// Fusion=NONE {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_FP8 {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0, 0}}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 0, 0}}, {// Fusion=RESIDUAL_RMS_NORM_QUANT_NVFP4 {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0}, - {4, 4, 4, 4, 4, 4, 4, 4, 5, 0, 0, 0, 0, 0, 0}}}}; + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 0, 0}, {4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 0}, + {4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 0, 0}}}}; inline const std::unordered_map AllReduceBestStrategyTable = { {90, AllReduceBestStrategyTableSM90}, diff --git a/cpp/tensorrt_llm/common/ncclUtils.cpp b/cpp/tensorrt_llm/common/ncclUtils.cpp index 21b976d73d99..70e2a5dcb082 100644 --- a/cpp/tensorrt_llm/common/ncclUtils.cpp +++ b/cpp/tensorrt_llm/common/ncclUtils.cpp @@ -383,6 +383,21 @@ NCCLWindowBuffer NCCLWindowAllocator::requestBuffer(ncclComm_t comm, size_t size return bestFit->buffer; } + // No available buffer found, avoid registration during CUDA graph capture + auto stream = at::cuda::getCurrentCUDAStream(); + cudaStreamCaptureStatus capture_status = cudaStreamCaptureStatusNone; + auto capture_err = cudaStreamIsCapturing(stream, &capture_status); + if (capture_err != cudaSuccess) + { + TLLM_LOG_DEBUG("[NCCLUtil] cudaStreamIsCapturing failed: %s", cudaGetErrorString(capture_err)); + } + if (capture_err == cudaSuccess && capture_status != cudaStreamCaptureStatusNone) + { + TLLM_LOG_DEBUG("[NCCLUtil] Skipping NCCL window allocation during capture for comm %p (requested: %zu)", + static_cast(comm), size); + return NCCLWindowBuffer(); + } + // No available buffer found, allocate a new one TLLM_LOG_TRACE( "[NCCLUtil] Allocating new NCCL window buffer for comm %p, size=%zu", static_cast(comm), size); diff --git a/cpp/tensorrt_llm/common/ncclUtils.h b/cpp/tensorrt_llm/common/ncclUtils.h index c809394a482c..506dcc555788 100644 --- a/cpp/tensorrt_llm/common/ncclUtils.h +++ b/cpp/tensorrt_llm/common/ncclUtils.h @@ -21,6 +21,8 @@ #include "tensorrt_llm/common/logger.h" #if ENABLE_MULTI_DEVICE +#include +#include #include #include #endif @@ -32,7 +34,6 @@ #include #include #include -#include #include #include #include @@ -377,15 +378,23 @@ inline std::pair createNCCLWindowTensor( // Request buffer from allocator auto& allocator = NCCLWindowAllocator::getInstance(); - auto buffer = allocator.requestBuffer(comm, buffer_size); + NCCLWindowBuffer buffer; + + try + { + buffer = allocator.requestBuffer(comm, buffer_size); + } + catch (std::exception const& e) + { + TLLM_LOG_DEBUG("[createNCCLWindowTensor] requestBuffer failed; returning invalid buffer: %s", e.what()); + return std::make_pair(torch::Tensor(), NCCLWindowBuffer()); + } // Defensive validation: ensure buffer is valid before proceeding if (!buffer.isValid()) { - std::ostringstream oss; - oss << "Failed to allocate NCCL window buffer: invalid buffer returned from requestBuffer " - << "(comm=" << static_cast(comm) << ", buffer_size=" << buffer_size << ")"; - throw std::runtime_error(oss.str()); + TLLM_LOG_DEBUG("[createNCCLWindowTensor] invalid buffer returned from requestBuffer; returning invalid buffer"); + return std::make_pair(torch::Tensor(), NCCLWindowBuffer()); } // Create custom deleter that releases the buffer diff --git a/cpp/tensorrt_llm/kernels/communicationKernels/allReduceFusionKernels.cu b/cpp/tensorrt_llm/kernels/communicationKernels/allReduceFusionKernels.cu index 75bbddb56645..22d03117663e 100644 --- a/cpp/tensorrt_llm/kernels/communicationKernels/allReduceFusionKernels.cu +++ b/cpp/tensorrt_llm/kernels/communicationKernels/allReduceFusionKernels.cu @@ -137,17 +137,8 @@ public: // corresponding CTA has not been launched. for (int flag_idx = blockIdx.x; flag_idx < kBarrierFlagCount; flag_idx += gridDim.x) { -#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)) - asm volatile( - "st.global.relaxed.sys.b32 [%1], %0;" ::"r"(m_flag_value), "l"(m_target_flag + flag_idx * NRanks)); -#else st_flag(m_target_flag + flag_idx * NRanks, m_flag_value); -#endif } -#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)) - // Single release fence - asm volatile("fence.release.sys;"); -#endif while (ld_flag(m_current_flag) == prev_flag(m_flag_value)) { diff --git a/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fmhaRunner.cpp b/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fmhaRunner.cpp index ceb75638142b..fee6aa81fe37 100644 --- a/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fmhaRunner.cpp +++ b/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fmhaRunner.cpp @@ -389,9 +389,10 @@ void FusedMHARunnerV2::setupLaunchParams(MHARunnerParams runnerParams) } // Hopper: fallback to original fmha_v2 when head_size <= 64 and seq_len <= 256 // Only supports packed_qkv input + padding/causal mask. + // Does not support attention sinks. else if (isSm90 && !separateQKvInput && paddingOrCausalMask && (mFixedParams.headSize == 32 || mFixedParams.headSize == 64) && runnerParams.qSeqLen <= 256 - && !common::getEnvForceDeterministicAttention()) + && !common::getEnvForceDeterministicAttention() && runnerParams.attentionSinksPtr == nullptr) { mLaunchParams.flash_attention = false; // get max sequence length for non-flash-attention. diff --git a/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_v2.cpp b/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_v2.cpp index bef395a4e37a..648e32729072 100644 --- a/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_v2.cpp +++ b/cpp/tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_v2.cpp @@ -565,6 +565,11 @@ FusedMultiHeadAttentionXMMAKernelV2 const* getXMMAKernelsV2(Data_type inputType, { sm = kSM_120; } + // SM103 uses SM100 FMHA v2 kernels + if (sm == kSM_103) + { + sm = kSM_100; + } return FusedMHAKernelFactoryV2::Get().getXMMAKernels( sMhaKernelMetaInfosV2, sMhaKernelMetaInfosV2Size, inputType, outputType, sm); } diff --git a/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu b/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu index 2447273752f0..e7f3597e58bd 100644 --- a/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu +++ b/cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu @@ -2060,11 +2060,12 @@ void doGatedActivation(ActivationOutputType* output, GemmOutputType const* gemm_ template -__global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, float const* fp8_quant, - ScaleBiasType const* bias_ptr, bool bias_is_broadcast, int64_t const* expert_first_token_offset, - int num_experts_per_node, int64_t inter_size, float const* fc2_act_global_scale, bool use_per_expert_act_scale, +__global__ __launch_bounds__(ACTIVATION_THREADS_PER_BLOCK) void doActivationKernel(T* output, + GemmOutputType const* gemm_result, float const* fp8_quant, ScaleBiasType const* bias_ptr, bool bias_is_broadcast, + int64_t const* expert_first_token_offset, int num_experts_per_node, int64_t inter_size, + float const* fc2_act_global_scale, bool use_per_expert_act_scale, TmaWarpSpecializedGroupedGemmInput::ElementSF* fc2_act_sf_flat, ActivationParams activation_params, - GemmOutputType const* prequant_scale, int64_t const num_valid_tokens) + GemmOutputType const* prequant_scale) { #ifdef ENABLE_FP4 constexpr bool IsNVFP4 = std::is_same_v @@ -2076,6 +2077,8 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, constexpr bool IsMXFP8 = cute::dependent_false; #endif + // Prequant scale is for non-blockscaling low-precision kernels. Such as WINT4_A16. + constexpr bool MaybePrequantScale = !IsNVFP4 && !IsMXFP8 && !std::is_same_v; constexpr bool IsGated = ActFn::IS_GLU; size_t gated_size_mul = IsGated ? 2 : 1; size_t gated_off = IsGated ? inter_size : 0; @@ -2093,8 +2096,14 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, : TmaWarpSpecializedGroupedGemmInput::MinKDimAlignmentMXFPX; int64_t const padded_inter_size = ceilDiv(inter_size, min_k_dim_alignment) * min_k_dim_alignment; - // 2D grid: blockIdx.x for tokens in groups of kProcessRows, blockIdx.y for columns - int64_t const row_offset = blockIdx.x * kProcessRows; + int64_t const num_valid_tokens = expert_first_token_offset[num_experts_per_node]; + + // 2D grid: blockIdx.x for tokens, blockIdx.y for columns + // 2D block: blockDim.x threads each handle kProcessRows tokens, blockDim.y threads handle columns + // Each CTA processes kProcessRows * blockDim.x tokens total + // Grid stride loop variables + int64_t const rows_per_cta = kProcessRows * blockDim.x; + int64_t const grid_stride = static_cast(gridDim.x) * rows_per_cta; int64_t const col_offset = blockIdx.y * blockDim.y + threadIdx.y; assert(inter_size % ACTIVATION_ELEM_PER_THREAD == 0); @@ -2112,15 +2121,22 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, [[maybe_unused]] bool const do_k_padding = (IsNVFP4 || IsMXFP8) && col_offset >= k_padding_start && col_offset < k_padding_end; - auto main_loop = [&](auto has_prequant_scale, auto has_bias) - { - #if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)) - cudaGridDependencySynchronize(); + cudaGridDependencySynchronize(); #endif - constexpr bool has_prequant_scale_v = decltype(has_prequant_scale)::value; - constexpr bool has_bias_v = decltype(has_bias)::value; + // Grid stride loop for activation processing + for (int64_t row_offset = blockIdx.x * rows_per_cta; row_offset < num_valid_tokens; row_offset += grid_stride) + { + bool const per_expert_aux_input = bias_ptr || IsNVFP4 || IsMXFP8 || use_per_expert_act_scale + || activation_params.swiglu_alpha || activation_params.swiglu_beta || activation_params.swiglu_limit; + int32_t expert = 0; + if (per_expert_aux_input) + { + // Binary search once for the first token in this CTA chunk + // subsequent tokens use linear scan since tokens are sorted by expert + expert = findTotalEltsLessThanTarget(expert_first_token_offset, num_experts_per_node, row_offset + 1) - 1; + } // Process kProcessRows consecutive tokens, promoting per-expert data reuse #pragma unroll for (int i = 0; i < kProcessRows; ++i) @@ -2134,22 +2150,21 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, size_t gemm_result_offset = token * inter_size * gated_size_mul; size_t output_offset = token * inter_size; - int64_t expert = 0; float gate_alpha = 1.0f; float gate_beta = 0.0f; float gate_limit = std::numeric_limits::infinity(); - if (bias_ptr || IsNVFP4 || IsMXFP8 || use_per_expert_act_scale || activation_params.swiglu_alpha - || activation_params.swiglu_beta || activation_params.swiglu_limit) + if (per_expert_aux_input) { - // TODO this is almost certainly faster as a linear scan - expert = findTotalEltsLessThanTarget(expert_first_token_offset, num_experts_per_node, token + 1) - 1; - + // Use linear scan for subsequent tokens + while (i > 0 && expert + 1 < num_experts_per_node && expert_first_token_offset[expert + 1] <= token) + { + ++expert; + } gate_alpha = activation_params.swiglu_alpha ? activation_params.swiglu_alpha[expert] : 1.0f; gate_beta = activation_params.swiglu_beta ? activation_params.swiglu_beta[expert] : 0.0f; gate_limit = activation_params.swiglu_limit ? activation_params.swiglu_limit[expert] : std::numeric_limits::infinity(); } - size_t act_scale_idx = use_per_expert_act_scale ? expert : 0; float const quant_scale = fp8_quant ? fp8_quant[act_scale_idx] : 1.f; @@ -2187,7 +2202,7 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, // Use loadVec to force LDG.128 vectorized loads auto fc1_value = arrayConvert(loadVec(&gemm_result_vec[elem_index + gated_off_vec])); - if constexpr (has_bias_v) + if (bias_ptr) { fc1_value = fc1_value + arrayConvert(loadVec(&bias_ptr_vec[elem_index + gated_off_vec])); @@ -2199,7 +2214,7 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, { auto linear_value = arrayConvert(loadVec(&gemm_result_vec[elem_index])); - if constexpr (has_bias_v) + if (bias_ptr) { linear_value = linear_value + arrayConvert(loadVec(&bias_ptr_vec[elem_index])); @@ -2215,7 +2230,7 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, auto post_act_val = gate_act * quant_scale; // Apply prequant scale (shape [experts_per_rank, intermediate_size]) if provided - if constexpr (has_prequant_scale_v) + if (MaybePrequantScale && prequant_scale) { post_act_val = post_act_val * arrayConvert(loadVec(&prequant_scale_vec[elem_index])); @@ -2232,8 +2247,8 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, sizeof(res) == sizeof(*output_vec), "Quantized value must be the same size as the output"); output_vec[elem_index] = res; - // Pad zeros in the extra SFs along the K dimension, we do this to ensure there are no nan values in the - // padded SF atom. Only process padding for valid tokens in this block's row range. + // Pad zeros in the extra SFs along the K dimension, we do this to ensure there are no nan values in + // the padded SF atom. Only process padding for valid tokens in this block's row range. if (do_k_padding) { writeSF(num_tokens_before_expert, expert, /*source_row*/ -1, token, col_offset, @@ -2246,49 +2261,61 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, // Use storeVec to force STG.128 vectorized store storeVec(&output_vec[elem_index], arrayConvert(post_act_val)); } - } + } // end of inner unrolled loop + } // end of grid stride loop for activation #if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)) - cudaTriggerProgrammaticLaunchCompletion(); + cudaTriggerProgrammaticLaunchCompletion(); #endif - // Pad zeros in the extra SFs along the N dimension, we do this to ensure there are no nan values in the padded - // SF atom. This is handled by dedicated blocks beyond num_valid_tokens range. - if constexpr (IsNVFP4 || IsMXFP8) - { - constexpr int64_t min_num_tokens_alignment = IsNVFP4 - ? TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentNVFP4 - : TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentMXFPX; - static_assert((min_num_tokens_alignment & (min_num_tokens_alignment - 1)) == 0, - "Min num tokens alignment must be a power of two"); + // Pad zeros in the extra SFs along the N dimension, we do this to ensure there are no nan values in the padded + // SF atom. + if constexpr (IsNVFP4 || IsMXFP8) + { + constexpr int64_t min_num_tokens_alignment = IsNVFP4 + ? TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentNVFP4 + : TmaWarpSpecializedGroupedGemmInput::MinNDimAlignmentMXFPX; + int64_t const num_padding_tokens = (IsNVFP4 || IsMXFP8) ? min_num_tokens_alignment * num_experts_per_node : 0; - int64_t const padded_num_elems_in_col = padded_inter_size / VecSize; - int64_t const sf_col_offset = blockIdx.y * blockDim.x + threadIdx.x; + static_assert((min_num_tokens_alignment & (min_num_tokens_alignment - 1)) == 0, + "Min num tokens alignment must be a power of two"); - if (sf_col_offset >= padded_num_elems_in_col) - return; + int64_t const padded_num_elems_in_col = padded_inter_size / VecSize; - // Only blocks that handle padding tokens participate - int64_t const num_token_blocks = (num_valid_tokens + kProcessRows - 1) / kProcessRows; - if (blockIdx.x < num_token_blocks) - return; + // Early exit if this thread is out of bounds for SF columns + if (col_offset >= padded_num_elems_in_col) + return; - // This block handles N-dimension padding - int64_t const padding_block_idx = blockIdx.x - num_token_blocks; + // Grid stride loop for N-dimension SF padding + for (int64_t row_offset = blockIdx.x * rows_per_cta; row_offset < num_padding_tokens; row_offset += grid_stride) + { + int64_t num_tokens_before_expert[kProcessRows]; + int64_t num_tokens_after_expert[kProcessRows]; #pragma unroll for (int i = 0; i < kProcessRows; ++i) { - int64_t const padding_token = padding_block_idx * kProcessRows + i; - int64_t const num_padding_tokens = min_num_tokens_alignment * num_experts_per_node; + int64_t padding_token = row_offset + i; + padding_token = std::min(padding_token, num_padding_tokens); + int64_t expert = padding_token / min_num_tokens_alignment; + num_tokens_before_expert[i] = expert_first_token_offset[expert]; + num_tokens_after_expert[i] = expert_first_token_offset[expert + 1]; + } +#pragma unroll + for (int i = 0; i < kProcessRows; ++i) + { + int64_t const padding_token = row_offset + i; if (padding_token >= num_padding_tokens) - return; + break; int64_t expert = padding_token / min_num_tokens_alignment; - int64_t num_tokens_before_expert = expert_first_token_offset[expert]; - int64_t num_tokens_after_expert = expert_first_token_offset[expert + 1]; - int64_t tokens_to_expert = num_tokens_after_expert - num_tokens_before_expert; + int64_t tokens_to_expert = num_tokens_after_expert[i] - num_tokens_before_expert[i]; + + // Skip inactive experts: 0 tokens means 0 padding needed + if (tokens_to_expert == 0) + continue; + int64_t padding_to_expert = TmaWarpSpecializedGroupedGemmInput::alignToSfDim(tokens_to_expert, min_num_tokens_alignment) - tokens_to_expert; @@ -2298,31 +2325,12 @@ __global__ void doActivationKernel(T* output, GemmOutputType const* gemm_result, // The SF buffer is padded to a multiple of MinNDimAlignment for each expert // This means we can safely write to offset num_tokens_after_expert + padded_token, since the // next expert will leave space for the padding - writeSF(num_tokens_before_expert, expert, /*source_row*/ -1, - num_tokens_after_expert + expert_pad_idx, sf_col_offset, padded_inter_size, fc2_act_sf_flat, + writeSF(num_tokens_before_expert[i], expert, /*source_row*/ -1, + num_tokens_after_expert[i] + expert_pad_idx, col_offset, padded_inter_size, fc2_act_sf_flat, /* input_sf */ nullptr); // Pass nullptr input_sf so we write 0 } } - } - }; // end of lambda main_loop - - // Instantiate four code paths for the different combinations of prequant_scale and bias_ptr - // This ensures relevant codes will be executed in tight loop - if (prequant_scale && bias_ptr) - { - main_loop(/* has_prequant_scale */ std::true_type{}, /* has_bias */ std::true_type{}); - } - else if (!prequant_scale && !bias_ptr) - { - main_loop(/* has_prequant_scale */ std::false_type{}, /* has_bias */ std::false_type{}); - } - else if (!prequant_scale && bias_ptr) - { - main_loop(/* has_prequant_scale */ std::false_type{}, /* has_bias */ std::true_type{}); - } - else // prequant_scale && !bias_ptr - { - main_loop(/* has_prequant_scale */ std::true_type{}, /* has_bias */ std::false_type{}); + } // end of grid stride loop for SF padding } } @@ -2361,7 +2369,7 @@ void doActivation(T* output, GemmOutputType const* gemm_result, float const* fp8 = [&](auto block_scaling_type) -> void (*)(T*, GemmOutputType const*, float const*, ScaleBiasType const*, bool, int64_t const*, int, int64_t, float const*, bool, TmaWarpSpecializedGroupedGemmInput::ElementSF*, - ActivationParams, GemmOutputType const*, int64_t) + ActivationParams, GemmOutputType const*) { switch (activation_type.activation_type) { @@ -2399,12 +2407,15 @@ void doActivation(T* output, GemmOutputType const* gemm_result, float const* fp8 default: TLLM_CHECK_WITH_INFO(false, "Invalid activation type"); return nullptr; } }; - auto NVFP4 = tensorrt_llm::common::ConstExprWrapper{}; - auto MXFPX = tensorrt_llm::common::ConstExprWrapper{}; - auto NONE = tensorrt_llm::common::ConstExprWrapper{}; + [[maybe_unused]] auto NVFP4 + = tensorrt_llm::common::ConstExprWrapper{}; + [[maybe_unused]] auto MXFPX + = tensorrt_llm::common::ConstExprWrapper{}; + [[maybe_unused]] auto NONE + = tensorrt_llm::common::ConstExprWrapper{}; #ifdef ENABLE_FP4 if constexpr (std::is_same_v) { @@ -2427,12 +2438,16 @@ void doActivation(T* output, GemmOutputType const* gemm_result, float const* fp8 } }(); + static int32_t const sm_count = tensorrt_llm::common::getMultiProcessorCount(); + int32_t const max_blocks_per_sm + = tensorrt_llm::common::getMaxActiveBlocksPerSM(fn, ACTIVATION_THREADS_PER_BLOCK, 0); // X dimension for tokens in groups of num_rows_per_cta_v // Y dimension for columns - int64_t const num_token_blocks = (expanded_num_tokens + num_rows_per_cta_v - 1) / num_rows_per_cta_v; - int64_t const num_padding_blocks = (num_padding_tokens + num_rows_per_cta_v - 1) / num_rows_per_cta_v; - // Add extra blocks in X dimension for token-wise padding in FP4/MXFP8 modes - int32_t const grid_x = static_cast(num_token_blocks + num_padding_blocks); + // Each CTA processes num_rows_per_cta_v * threads_x tokens + int64_t const max_tokens = std::max(expanded_num_tokens, num_padding_tokens); + // Divide by rows_per_cta to get the number of token blocks needed + int64_t const max_token_blocks = (max_tokens + num_rows_per_cta_v - 1) / num_rows_per_cta_v; + int32_t const grid_x = std::min(sm_count * max_blocks_per_sm, static_cast(max_token_blocks)); int32_t const grid_y = static_cast( (num_elems_in_col + ACTIVATION_THREADS_PER_BLOCK - 1) / ACTIVATION_THREADS_PER_BLOCK); int32_t const threads = ACTIVATION_THREADS_PER_BLOCK; @@ -2449,7 +2464,7 @@ void doActivation(T* output, GemmOutputType const* gemm_result, float const* fp8 config.attrs = attrs; cudaLaunchKernelEx(&config, fn, output, gemm_result, fp8_quant, bias, bias_is_broadcast, expert_first_token_offset, num_experts_per_node, inter_size, quant_params.fp4.fc2.act_global_scale, - use_per_expert_act_scale, fc2_act_sf_flat, activation_type, prequant_scale, expanded_num_tokens); + use_per_expert_act_scale, fc2_act_sf_flat, activation_type, prequant_scale); }; // end lambda doActivationKernelLauncher // 256 threads per block * 256 blocks / 1 rows per block can be handled by 1-2 waves depending on SM arch diff --git a/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQARunner.cpp b/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQARunner.cpp index 165ffc284845..d9dc79662f61 100644 --- a/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQARunner.cpp +++ b/cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderXQARunner.cpp @@ -84,26 +84,8 @@ DecoderXQAImpl* DecoderXQARunner::getImplFromXQAParams(XQAParams const& xqaParam } else { - if (xqaParams.multi_query_tokens) - { - // Some multi_query kernels are not ported to JIT yet. - auto const grpSize = xqaParams.num_q_heads / xqaParams.num_kv_heads; - // Hopper XQA supports spec dec with JIT, but only for E4M3 kv cache data type. Only allow 64%grpSize==0 for - // now. - bool const supportedByHopperXqa - = (smVersion == 90 && xqaParams.kv_cache_data_type == XQADataType::DATA_TYPE_E4M3 && grpSize <= 64); - bool const supportedBySm120Mla = (smVersion == 120 && xqaParams.isMLA() - && xqaParams.kv_cache_data_type == XQADataType::DATA_TYPE_E4M3); - bool const supportedByAmpereXqa = (!xqaParams.isMLA() && (64 % grpSize == 0)); - - return (supportedByHopperXqa || supportedBySm120Mla || supportedByAmpereXqa) ? mJITImpl.get() - : mPrecompiledImpl.get(); - } - else - { - // regular decoding kernels uses JIT by default - return mJITImpl.get(); - } + // uses JIT by default + return mJITImpl.get(); } } diff --git a/cpp/tensorrt_llm/kernels/logitsBitmask.cu b/cpp/tensorrt_llm/kernels/logitsBitmask.cu index ac66967e0f85..0bb1ddd6be3f 100644 --- a/cpp/tensorrt_llm/kernels/logitsBitmask.cu +++ b/cpp/tensorrt_llm/kernels/logitsBitmask.cu @@ -64,8 +64,8 @@ __device__ PackedT packedNegativeInfinity() } // namespace template -__global__ void __launch_bounds__(kThreadsPerBlock) logitsBitmaskKernel( - T** __restrict__ logits, uint32_t const** __restrict__ bitmask, int32_t vocabSizePadded, int32_t bitmaskSize) +__global__ void __launch_bounds__(kThreadsPerBlock) + logitsBitmaskKernel(T** __restrict__ logits, uint32_t const** __restrict__ bitmask, int32_t vocabSizePadded) { int constexpr kAlignment = sizeof(PackedT) / sizeof(T); uint32_t constexpr kPackedMask = (1 << kAlignment) - 1; @@ -123,29 +123,28 @@ void logitsBitmaskDispatchToBitsPerThread( static int const smCount = tensorrt_llm::common::getMultiProcessorCount(); int32_t const numBlocksPerRow = ceilDiv(2048 / kThreadsPerBlock * smCount, batchSize); int32_t const numBitsPerThread = ceilDiv(vocabSizePadded, kThreadsPerBlock * numBlocksPerRow); - int32_t bitmaskSize = ceilDiv(vocabSizePadded, kBitsPerMaskElement); dim3 const block(kThreadsPerBlock); if (numBitsPerThread <= 4 && kAlignment <= 4) { dim3 const grid(ceilDiv(vocabSizePadded, kThreadsPerBlock * 4), batchSize); - logitsBitmaskKernel<<>>(logits, bitmask, vocabSizePadded, bitmaskSize); + logitsBitmaskKernel<<>>(logits, bitmask, vocabSizePadded); } else if (numBitsPerThread <= 8 && kAlignment <= 8) { dim3 const grid(ceilDiv(vocabSizePadded, kThreadsPerBlock * 8), batchSize); - logitsBitmaskKernel<<>>(logits, bitmask, vocabSizePadded, bitmaskSize); + logitsBitmaskKernel<<>>(logits, bitmask, vocabSizePadded); } else if (numBitsPerThread <= 16 && kAlignment <= 16) { dim3 const grid(ceilDiv(vocabSizePadded, kThreadsPerBlock * 16), batchSize); - logitsBitmaskKernel<<>>(logits, bitmask, vocabSizePadded, bitmaskSize); + logitsBitmaskKernel<<>>(logits, bitmask, vocabSizePadded); } else { dim3 const grid(ceilDiv(vocabSizePadded, kThreadsPerBlock * 32), batchSize); - logitsBitmaskKernel<<>>(logits, bitmask, vocabSizePadded, bitmaskSize); + logitsBitmaskKernel<<>>(logits, bitmask, vocabSizePadded); } } @@ -185,7 +184,7 @@ template void invokeLogitsBitmask<__nv_bfloat16>( template __global__ void __launch_bounds__(kThreadsPerBlock) contiguousLogitsBitmaskKernel(T* __restrict__ logits, uint32_t const* __restrict__ bitmask, int32_t const* __restrict__ tokenMask, int32_t const* __restrict__ d2t, - int32_t vocabSizePadded, int32_t bitmaskSize) + int32_t vocabSizePadded, int32_t bitmaskStride) { int constexpr kAlignment = sizeof(PackedT) / sizeof(T); uint32_t constexpr kPackedMask = (1 << kAlignment) - 1; @@ -199,7 +198,7 @@ __global__ void __launch_bounds__(kThreadsPerBlock) contiguousLogitsBitmaskKerne int const blockOffset = blockIdx.x * kThreadsPerBlock * kBitsPerThread; T* logitsGmemPtr = logits + batchIdx * vocabSizePadded + blockOffset; - uint32_t const* bitmaskGmemPtr = bitmask + batchIdx * bitmaskSize + blockOffset / kBitsPerMaskElement; + uint32_t const* bitmaskGmemPtr = bitmask + batchIdx * bitmaskStride + blockOffset / kBitsPerMaskElement; int const bitmaskInnerIdx = threadIdx.x % (kBitsPerMaskElement / kAlignment); T logitsReg[kAlignment]; @@ -224,7 +223,7 @@ __global__ void __launch_bounds__(kThreadsPerBlock) contiguousLogitsBitmaskKerne for (int i = 0; i < kAlignment; i++) { int const d2tOffset = blockOffset + offset + i + d2t[blockOffset + offset + i]; - bitmaskVal |= ((~bitmask[batchIdx * bitmaskSize + d2tOffset / kBitsPerMaskElement] + bitmaskVal |= ((~bitmask[batchIdx * bitmaskStride + d2tOffset / kBitsPerMaskElement] >> (d2tOffset % kBitsPerMaskElement)) & 1) << i; @@ -257,7 +256,7 @@ __global__ void __launch_bounds__(kThreadsPerBlock) contiguousLogitsBitmaskKerne template void contiguousLogitsBitmaskDispatchToBitsPerThread(T* logits, uint32_t const* bitmask, int32_t const* tokenMask, - int32_t const* d2t, int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskSize, cudaStream_t stream) + int32_t const* d2t, int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskStride, cudaStream_t stream) { int constexpr kAlignment = sizeof(PackedT) / sizeof(T); static int const smCount = tensorrt_llm::common::getMultiProcessorCount(); @@ -270,63 +269,69 @@ void contiguousLogitsBitmaskDispatchToBitsPerThread(T* logits, uint32_t const* b { dim3 const grid(ceilDiv(vocabSizePadded, kThreadsPerBlock * 4), batchSize); contiguousLogitsBitmaskKernel - <<>>(logits, bitmask, tokenMask, d2t, vocabSizePadded, bitmaskSize); + <<>>(logits, bitmask, tokenMask, d2t, vocabSizePadded, bitmaskStride); } else if (numBitsPerThread <= 8 && kAlignment <= 8) { dim3 const grid(ceilDiv(vocabSizePadded, kThreadsPerBlock * 8), batchSize); contiguousLogitsBitmaskKernel - <<>>(logits, bitmask, tokenMask, d2t, vocabSizePadded, bitmaskSize); + <<>>(logits, bitmask, tokenMask, d2t, vocabSizePadded, bitmaskStride); } else if (numBitsPerThread <= 16 && kAlignment <= 16) { dim3 const grid(ceilDiv(vocabSizePadded, kThreadsPerBlock * 16), batchSize); contiguousLogitsBitmaskKernel - <<>>(logits, bitmask, tokenMask, d2t, vocabSizePadded, bitmaskSize); + <<>>(logits, bitmask, tokenMask, d2t, vocabSizePadded, bitmaskStride); } else { dim3 const grid(ceilDiv(vocabSizePadded, kThreadsPerBlock * 32), batchSize); contiguousLogitsBitmaskKernel - <<>>(logits, bitmask, tokenMask, d2t, vocabSizePadded, bitmaskSize); + <<>>(logits, bitmask, tokenMask, d2t, vocabSizePadded, bitmaskStride); } } template void invokeContiguousLogitsBitmask(T* logits, uint32_t const* bitmask, int32_t const* tokenMask, int32_t const* d2t, - int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskSize, cudaStream_t stream) + int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskStride, cudaStream_t stream) { + // bitmaskStride may not equal to ceilDiv(vocabSizePadded, kBitsPerMaskElement) when: + // (1) d2t is present, then logits are "pruned" (e.g., EAGLE3), while bitmask is complete and should be accessed + // according to d2t. + // (2) logits are sharded along the vocabulary dimension, while the bitmask is complete and should be accessed + // according to the sharding. + // Dispatch to PackedT if (vocabSizePadded % (sizeof(float4) / sizeof(T)) == 0) { contiguousLogitsBitmaskDispatchToBitsPerThread( - logits, bitmask, tokenMask, d2t, batchSize, vocabSizePadded, bitmaskSize, stream); + logits, bitmask, tokenMask, d2t, batchSize, vocabSizePadded, bitmaskStride, stream); } else if (vocabSizePadded % (sizeof(float2) / sizeof(T)) == 0) { contiguousLogitsBitmaskDispatchToBitsPerThread( - logits, bitmask, tokenMask, d2t, batchSize, vocabSizePadded, bitmaskSize, stream); + logits, bitmask, tokenMask, d2t, batchSize, vocabSizePadded, bitmaskStride, stream); } else if (vocabSizePadded % (sizeof(float) / sizeof(T)) == 0) { contiguousLogitsBitmaskDispatchToBitsPerThread( - logits, bitmask, tokenMask, d2t, batchSize, vocabSizePadded, bitmaskSize, stream); + logits, bitmask, tokenMask, d2t, batchSize, vocabSizePadded, bitmaskStride, stream); } else { contiguousLogitsBitmaskDispatchToBitsPerThread( - logits, bitmask, tokenMask, d2t, batchSize, vocabSizePadded, bitmaskSize, stream); + logits, bitmask, tokenMask, d2t, batchSize, vocabSizePadded, bitmaskStride, stream); } } template void invokeContiguousLogitsBitmask(float* logits, uint32_t const* bitmask, int32_t const* tokenMask, - int32_t const* d2t, int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskSize, cudaStream_t stream); + int32_t const* d2t, int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskStride, cudaStream_t stream); template void invokeContiguousLogitsBitmask(half* logits, uint32_t const* bitmask, int32_t const* tokenMask, - int32_t const* d2t, int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskSize, cudaStream_t stream); + int32_t const* d2t, int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskStride, cudaStream_t stream); #ifdef ENABLE_BF16 template void invokeContiguousLogitsBitmask<__nv_bfloat16>(__nv_bfloat16* logits, uint32_t const* bitmask, - int32_t const* tokenMask, int32_t const* d2t, int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskSize, + int32_t const* tokenMask, int32_t const* d2t, int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskStride, cudaStream_t stream); #endif diff --git a/cpp/tensorrt_llm/kernels/logitsBitmask.h b/cpp/tensorrt_llm/kernels/logitsBitmask.h index e2e6cb28cd5d..e3fb6c08fb51 100644 --- a/cpp/tensorrt_llm/kernels/logitsBitmask.h +++ b/cpp/tensorrt_llm/kernels/logitsBitmask.h @@ -32,7 +32,7 @@ void invokeLogitsBitmask( template void invokeContiguousLogitsBitmask(T* logits, uint32_t const* bitmask, int32_t const* tokenMask, int32_t const* d2t, - int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskSize, cudaStream_t stream); + int32_t batchSize, int32_t vocabSizePadded, int32_t bitmaskStride, cudaStream_t stream); } // namespace kernels diff --git a/cpp/tensorrt_llm/kernels/quantization.cuh b/cpp/tensorrt_llm/kernels/quantization.cuh index 6589cc67d5ee..89b96b288b46 100644 --- a/cpp/tensorrt_llm/kernels/quantization.cuh +++ b/cpp/tensorrt_llm/kernels/quantization.cuh @@ -778,6 +778,7 @@ quantize_with_block_size( // Get the global scaling factor, which will be applied to the SF. // Note SFScale is the same as next GEMM's alpha, which is (448.f / (Alpha_A / 6.f)). + // This value is prepared by model, no need to be protected by ACKBULK float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[0]; // Is it swizzled layout? diff --git a/cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_kernel.cuh b/cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_kernel.cuh index 7f60e787bf24..6c1d72c35353 100644 --- a/cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_kernel.cuh +++ b/cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_kernel.cuh @@ -410,6 +410,8 @@ __global__ __launch_bounds__(384, 1) void tinygemm_kernel(__nv_bfloat16* output, __syncthreads(); + cudaTriggerProgrammaticLaunchCompletion(); + if (warp_id == 0) { @@ -440,10 +442,8 @@ __global__ __launch_bounds__(384, 1) void tinygemm_kernel(__nv_bfloat16* output, if (PROFILE && blockIdx.y == 0 && threadIdx.x == 0) profile[blockIdx.x].complete = gclock64(); - - if (threadIdx.x == 0 && blockIdx.x == 0 && blockIdx.y == 0) - cudaTriggerProgrammaticLaunchCompletion(); } + __syncthreads(); } #endif // end if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900) } diff --git a/cpp/tensorrt_llm/nanobind/CMakeLists.txt b/cpp/tensorrt_llm/nanobind/CMakeLists.txt index 9c64b3705d43..bfe88cc914ab 100755 --- a/cpp/tensorrt_llm/nanobind/CMakeLists.txt +++ b/cpp/tensorrt_llm/nanobind/CMakeLists.txt @@ -20,6 +20,7 @@ set(SRCS runtime/bindings.cpp runtime/hostfunc.cpp runtime/moeBindings.cpp + testing/kvCacheManagerTestUtilBinding.cpp testing/modelSpecBinding.cpp userbuffers/bindings.cpp thop/bindings.cpp diff --git a/cpp/tensorrt_llm/nanobind/bindings.cpp b/cpp/tensorrt_llm/nanobind/bindings.cpp index d9c2687c7f2a..41466244bb5f 100644 --- a/cpp/tensorrt_llm/nanobind/bindings.cpp +++ b/cpp/tensorrt_llm/nanobind/bindings.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -43,6 +43,7 @@ #include "tensorrt_llm/nanobind/executor/bindings.h" #include "tensorrt_llm/nanobind/process_group/bindings.h" #include "tensorrt_llm/nanobind/runtime/bindings.h" +#include "tensorrt_llm/nanobind/testing/kvCacheManagerTestUtilBinding.h" #include "tensorrt_llm/nanobind/testing/modelSpecBinding.h" #include "tensorrt_llm/nanobind/thop/bindings.h" #include "tensorrt_llm/nanobind/userbuffers/bindings.h" @@ -494,6 +495,7 @@ NB_MODULE(TRTLLM_NB_MODULE, m) tpb::Buffers::initBindings(mInternalBatchManager); tensorrt_llm::nanobind::runtime::initBindings(mInternalRuntime); tensorrt_llm::nanobind::testing::initBindings(mInternalTesting); + tensorrt_llm::nanobind::testing::initKvCacheTestUtilBindings(mInternalTesting); tpb::initBindings(mInternalBatchManager); tb::kv_cache_manager::KVCacheManagerConnectorBindings::initBindings(mInternalBatchManager); diff --git a/cpp/tensorrt_llm/nanobind/runtime/bindings.cpp b/cpp/tensorrt_llm/nanobind/runtime/bindings.cpp index 7a698b4eb68f..df8120b3ef82 100644 --- a/cpp/tensorrt_llm/nanobind/runtime/bindings.cpp +++ b/cpp/tensorrt_llm/nanobind/runtime/bindings.cpp @@ -347,9 +347,9 @@ void initBindings(nb::module_& m) nb::call_guard()); nb::class_(m, "McastGPUBuffer") - .def(nb::init(), nb::arg("buf_size"), - nb::arg("group_size"), nb::arg("group_rank"), nb::arg("split_color"), nb::arg("device_idx"), - nb::arg("mn_nvlink"), nb::call_guard()) + .def(nb::init(), nb::arg("buf_size"), + nb::arg("group_size"), nb::arg("group_rank"), nb::arg("device_idx"), nb::arg("mn_nvlink"), + nb::arg("mpi_comm_fortran_handle"), nb::call_guard()) .def("get_uc_buffer", &tensorrt_llm::runtime::McastGPUBuffer::getUCBuffer, nb::call_guard()) .def("get_mc_buffer", &tensorrt_llm::runtime::McastGPUBuffer::getMCBuffer, diff --git a/cpp/tensorrt_llm/nanobind/testing/kvCacheManagerTestUtilBinding.cpp b/cpp/tensorrt_llm/nanobind/testing/kvCacheManagerTestUtilBinding.cpp new file mode 100644 index 000000000000..3fb254a2391a --- /dev/null +++ b/cpp/tensorrt_llm/nanobind/testing/kvCacheManagerTestUtilBinding.cpp @@ -0,0 +1,37 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kvCacheManagerTestUtilBinding.h" +#include "tensorrt_llm/nanobind/common/customCasters.h" +#include "tensorrt_llm/testing/kvCacheManagerTestUtil.h" + +#include + +namespace nb = nanobind; + +namespace tensorrt_llm::nanobind::testing +{ + +void initKvCacheTestUtilBindings(nb::module_& m) +{ + m.def("simulate_prefill_completion_only_use_for_testing", + &tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion, nb::arg("llm_request"), + nb::call_guard(), + "NEVER USE IN PRODUCTION. Simulates prefill completion on an LlmRequest for test purposes."); +} + +} // namespace tensorrt_llm::nanobind::testing diff --git a/cpp/tensorrt_llm/nanobind/testing/kvCacheManagerTestUtilBinding.h b/cpp/tensorrt_llm/nanobind/testing/kvCacheManagerTestUtilBinding.h new file mode 100644 index 000000000000..db71de17016b --- /dev/null +++ b/cpp/tensorrt_llm/nanobind/testing/kvCacheManagerTestUtilBinding.h @@ -0,0 +1,29 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +namespace nb = nanobind; + +namespace tensorrt_llm::nanobind::testing +{ + +void initKvCacheTestUtilBindings(nb::module_& m); + +} // namespace tensorrt_llm::nanobind::testing diff --git a/cpp/tensorrt_llm/pybind/runtime/bindings.cpp b/cpp/tensorrt_llm/pybind/runtime/bindings.cpp index ee4303d31b82..8f9d0bc0258c 100644 --- a/cpp/tensorrt_llm/pybind/runtime/bindings.cpp +++ b/cpp/tensorrt_llm/pybind/runtime/bindings.cpp @@ -443,9 +443,9 @@ void initBindings(pybind11::module_& m) py::call_guard()); py::class_(m, "McastGPUBuffer") - .def(py::init(), py::arg("buf_size"), - py::arg("group_size"), py::arg("group_rank"), py::arg("split_color"), py::arg("device_idx"), - py::arg("mn_nvlink"), py::call_guard()) + .def(py::init(), py::arg("buf_size"), + py::arg("group_size"), py::arg("group_rank"), py::arg("device_idx"), py::arg("mn_nvlink"), + py::arg("mpi_comm_fortran_handle"), py::call_guard()) .def("get_uc_buffer", &tensorrt_llm::runtime::McastGPUBuffer::getUCBuffer, py::call_guard()) .def("get_mc_buffer", &tensorrt_llm::runtime::McastGPUBuffer::getMCBuffer, diff --git a/cpp/tensorrt_llm/runtime/ipcNvlsMemory.cu b/cpp/tensorrt_llm/runtime/ipcNvlsMemory.cu index 031ac92168a2..345930ab37fc 100644 --- a/cpp/tensorrt_llm/runtime/ipcNvlsMemory.cu +++ b/cpp/tensorrt_llm/runtime/ipcNvlsMemory.cu @@ -434,7 +434,7 @@ bool ipcNvlsSupported() TLLM_CUDA_CHECK(cudaDriverGetVersion(&cuda_driver_version)); if (cuda_driver_version < 12010) { - TLLM_LOG_ERROR("CUDA Driver version < 12010"); + TLLM_LOG_DEBUG("CUDA Driver version < 12010"); return false; } @@ -448,7 +448,7 @@ bool ipcNvlsSupported() CUCHECK(cuDeviceGetAttribute(&multicast_supported, CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED, current_dev)); if (!multicast_supported) { - TLLM_LOG_ERROR("CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED not supported on GPU%d.", cuda_dev); + TLLM_LOG_DEBUG("CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED not supported on GPU%d.", cuda_dev); return false; } } diff --git a/cpp/tensorrt_llm/runtime/mcastDeviceMemory.cpp b/cpp/tensorrt_llm/runtime/mcastDeviceMemory.cpp index 9be590c7fceb..cb1303fc84bc 100644 --- a/cpp/tensorrt_llm/runtime/mcastDeviceMemory.cpp +++ b/cpp/tensorrt_llm/runtime/mcastDeviceMemory.cpp @@ -38,7 +38,7 @@ inline size_t roundUp(size_t val, size_t gran) } // namespace McastDeviceMemory::McastDeviceMemory( - size_t bufSize, uint32_t groupSize, uint32_t groupRank, uint32_t splitColor, int deviceIdx, bool mnNvlink) + size_t bufSize, uint32_t groupSize, uint32_t groupRank, int deviceIdx, bool mnNvlink, int64_t mpiCommFortranHandle) : mIsMNNvlink(mnNvlink) , mDeviceIdx(deviceIdx) , mGroupSize(groupSize) @@ -48,7 +48,11 @@ McastDeviceMemory::McastDeviceMemory( , mAllocationSize(0) , mMcPtr(0) , mMcHandle(0) - , mGroupComm(tensorrt_llm::mpi::MpiComm::session().split(splitColor, mGroupRank)) +#if ENABLE_MULTI_DEVICE + , mGroupComm(MPI_Comm_f2c(mpiCommFortranHandle), false) +#else + , mGroupComm(nullptr, false) +#endif { TLLM_CUDA_CHECK(cudaSetDevice(mDeviceIdx)); @@ -66,9 +70,9 @@ McastDeviceMemory::McastDeviceMemory( int const world_rank{tensorrt_llm::mpi::MpiComm::session().getRank()}; TLLM_LOG_DEBUG( - "[McastDeviceMemory] World Rank: %u, Group Rank: %u, Group size: %u, GroupSplitColor: %u, isMultiNode: %d, " + "[McastDeviceMemory] World Rank: %u, Group Rank: %u, Group size: %u, isMultiNode: %d, " "device_idx: %d, Signal pad offset: %zu", - world_rank, mGroupRank, mGroupSize, splitColor, mIsMNNvlink, mDeviceIdx, mSignalPadOffset); + world_rank, mGroupRank, mGroupSize, mIsMNNvlink, mDeviceIdx, mSignalPadOffset); if (mIsMNNvlink) { diff --git a/cpp/tensorrt_llm/runtime/mcastDeviceMemory.h b/cpp/tensorrt_llm/runtime/mcastDeviceMemory.h index d9428b4126c7..537bf4978827 100644 --- a/cpp/tensorrt_llm/runtime/mcastDeviceMemory.h +++ b/cpp/tensorrt_llm/runtime/mcastDeviceMemory.h @@ -43,8 +43,8 @@ class McastDeviceMemory McastDeviceMemory(McastDeviceMemory const&) = delete; McastDeviceMemory& operator=(McastDeviceMemory const&) = delete; - McastDeviceMemory( - size_t bufSize, uint32_t groupSize, uint32_t groupRank, uint32_t splitColor, int deviceIdx, bool mnNvlink); + McastDeviceMemory(size_t bufSize, uint32_t groupSize, uint32_t groupRank, int deviceIdx, bool mnNvlink, + int64_t mpiCommFortranHandle); // We don't register the pointer in these two functions since we don't expect any python-level code would call // to obtain the raw pointers. diff --git a/cpp/tensorrt_llm/runtime/mcastGPUBuffer.h b/cpp/tensorrt_llm/runtime/mcastGPUBuffer.h index 4c011a790ba5..160ab63f0577 100644 --- a/cpp/tensorrt_llm/runtime/mcastGPUBuffer.h +++ b/cpp/tensorrt_llm/runtime/mcastGPUBuffer.h @@ -34,12 +34,12 @@ class McastGPUBuffer //! \param bufSize The total size of the buffer in bytes. //! \param groupSize The number of ranks in the communication group. //! \param groupRank The rank of the local process within the group. - //! \param splitColor The color of the split for topology split. - //! \param device The CUDA device for buffer allocation. + //! \param deviceIdx The CUDA device for buffer allocation. //! \param mnNvlink Flag indicating if multi-node NVLink is used. - McastGPUBuffer( - size_t bufSize, uint32_t groupSize, uint32_t groupRank, uint32_t splitColor, uint32_t deviceIdx, bool mnNvlink) - : mMcastDeviceMemory(bufSize, groupSize, groupRank, splitColor, deviceIdx, mnNvlink) + //! \param mpiCommFortranHandle The Fortran handle for the MPI communicator (from Python mpi4py). + McastGPUBuffer(size_t bufSize, uint32_t groupSize, uint32_t groupRank, uint32_t deviceIdx, bool mnNvlink, + int64_t mpiCommFortranHandle) + : mMcastDeviceMemory(bufSize, groupSize, groupRank, deviceIdx, mnNvlink, mpiCommFortranHandle) , mBufSize(bufSize) , mLocalDevice(at::Device(at::DeviceType::CUDA, deviceIdx)) { diff --git a/cpp/tensorrt_llm/runtime/utils/numpyUtils.cpp b/cpp/tensorrt_llm/runtime/utils/numpyUtils.cpp index 4c37f480bd41..6f95704455d3 100644 --- a/cpp/tensorrt_llm/runtime/utils/numpyUtils.cpp +++ b/cpp/tensorrt_llm/runtime/utils/numpyUtils.cpp @@ -105,6 +105,7 @@ void parseNpyIntro(FILE*& f_ptr, uint32_t& header_len, uint32_t& start_data) int parseNpyHeader(FILE*& f_ptr, uint32_t header_len, nvinfer1::DataType& type, std::vector& shapeVec) { char* header_c = (char*) malloc(header_len * sizeof(char)); + TLLM_CHECK_WITH_INFO(header_c != nullptr, "Failed to allocate memory for npy header"); size_t n_elems = fread((void*) header_c, sizeof(char), header_len, f_ptr); if (n_elems != header_len) { diff --git a/cpp/tensorrt_llm/testing/kvCacheManagerTestUtil.h b/cpp/tensorrt_llm/testing/kvCacheManagerTestUtil.h new file mode 100644 index 000000000000..d371e0c82520 --- /dev/null +++ b/cpp/tensorrt_llm/testing/kvCacheManagerTestUtil.h @@ -0,0 +1,43 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "tensorrt_llm/batch_manager/llmRequest.h" + +namespace tensorrt_llm::testing +{ + +/// @brief Test utilities for KV cache manager unit tests. NEVER use in production code. +class KvCacheManagerTestUtil +{ +public: + /// @brief Simulate completion of the prefill stage on an LlmRequest. + /// + /// NEVER CALL FROM PRODUCTION CODE. This is solely for use in unit tests. + /// + /// Most BlockManager/KVCacheManager functions (storeContextBlocks, releaseBlocks, + /// removeSequence, releaseSequence) require prefill to be complete before they are + /// called. This method updates llmRequest state as if prefill has just finished, + /// allowing unit tests to invoke those functions correctly. + static void simulatePrefillCompletion(batch_manager::LlmRequest& llmRequest) + { + llmRequest.setContextCurrentPosition(llmRequest.getPromptLen()); + } +}; + +} // namespace tensorrt_llm::testing diff --git a/cpp/tensorrt_llm/thop/allreduceOp.cpp b/cpp/tensorrt_llm/thop/allreduceOp.cpp index c75324251865..e9812b52c03a 100644 --- a/cpp/tensorrt_llm/thop/allreduceOp.cpp +++ b/cpp/tensorrt_llm/thop/allreduceOp.cpp @@ -42,6 +42,7 @@ #include #include #include +#include #include #include #include @@ -281,7 +282,7 @@ class AllreduceOp std::vector run(torch::Tensor const& input, torch::optional const& residual, torch::optional const& norm_weight, torch::optional const& scale, torch::optional const& bias, bool trigger_completion_at_end, - torch::optional workspace) noexcept + torch::optional workspace) { size_t size = input.numel(); size_t seq_len = input.size(0); @@ -295,7 +296,6 @@ class AllreduceOp auto const rank = getRank(); TLLM_LOG_DEBUG( "AllReduceOp runtime strategy for rank %d: " + tensorrt_llm::kernels::toString(runtime_strategy), rank); - // Dispatch to different allreduce implementations switch (runtime_strategy) { @@ -508,8 +508,9 @@ class AllreduceOp minRegistrationThreshold = static_cast(std::atoi(envThreshold)) * input.element_size(); } - // Search for existing buffer auto& allocator = NCCLWindowAllocator::getInstance(); + + // Search for existing buffer auto windowBuffer0 = allocator.searchBuffer(comm, input.data_ptr()); torch::Tensor inputTensor = input; @@ -532,11 +533,22 @@ class AllreduceOp // Large buffer: create window buffer and copy input (can swap inputTensor reference) auto [symmetricInput, symmetricBuffer0] = createNCCLWindowTensor(comm, input.sizes(), input.scalar_type()); - TLLM_CUDA_CHECK(cudaMemcpyAsync( - symmetricBuffer0.ptr, input.data_ptr(), bufferSizeBytes, cudaMemcpyDeviceToDevice, stream)); - windowBuffer0 = symmetricBuffer0; - inputTensor = symmetricInput; // Swap to window-backed tensor - inputPtr = windowBuffer0.ptr; + if (!symmetricBuffer0.isValid()) + { + TLLM_LOG_DEBUG( + "[runNCCLAllReduceSymmetric] No valid symmetric buffer available; " + "falling back to non-symmetric ncclAllReduce (input buffer)"); + // inputTensor and inputPtr remain pointing to original input + } + else + { + TLLM_CUDA_CHECK(cudaMemcpyAsync( + symmetricBuffer0.ptr, input.data_ptr(), bufferSizeBytes, cudaMemcpyDeviceToDevice, stream)); + + windowBuffer0 = symmetricBuffer0; + inputTensor = symmetricInput; // Swap to window-backed tensor + inputPtr = windowBuffer0.ptr; + } } } else @@ -547,8 +559,14 @@ class AllreduceOp // Use window-backed output buffer auto [normOut, windowBuffer1] = createNCCLWindowTensor(comm, input.sizes(), input.scalar_type()); - torch::Tensor outputTensor = normOut; - void* outputPtr = windowBuffer1.ptr; + torch::Tensor outputTensor = windowBuffer1.isValid() ? normOut : torch::empty_like(inputTensor); + void* outputPtr = windowBuffer1.isValid() ? windowBuffer1.ptr : outputTensor.data_ptr(); + if (!windowBuffer1.isValid()) + { + TLLM_LOG_DEBUG( + "[runNCCLAllReduceSymmetric] No valid symmetric buffer available; " + "using plain CUDA tensor for output"); + } // Perform allreduce NCCLCHECK_THROW(ncclAllReduce(inputPtr, outputPtr, size, (*getDtypeMap())[mType], ncclSum, comm, stream)); @@ -564,7 +582,7 @@ class AllreduceOp std::vector runLowPrecisionAllReduce(torch::Tensor const& input, torch::optional const& residual, torch::optional const& norm_weight, - torch::optional const& scale, torch::optional const& bias) noexcept + torch::optional const& scale, torch::optional const& bias) { #ifdef ENABLE_FP8 auto stream = at::cuda::getCurrentCUDAStream(input.get_device()); @@ -632,8 +650,7 @@ class AllreduceOp std::vector runFusionAllReduce(torch::Tensor const& input, torch::optional const& residual, torch::optional const& norm_weight, torch::optional const& scale, torch::optional const& bias, - bool trigger_completion_at_end, torch::optional workspace, - AllReduceStrategyType strategy) noexcept + bool trigger_completion_at_end, torch::optional workspace, AllReduceStrategyType strategy) { // Should handle only Lamport implementation auto stream = at::cuda::getCurrentCUDAStream(input.get_device()); @@ -1206,7 +1223,7 @@ class AllreduceOp if (ifFallbackToNCCL(seq_len, message_size_bytes, max_workspace_size)) { - return AllReduceStrategyType::NCCL_SYMMETRIC; + return AllReduceStrategyType::NCCL; } // This rule based heuristic only chooses between NCCL_SYMMETRIC and MIN_LATENCY strategies. @@ -1232,7 +1249,8 @@ class AllreduceOp bool ifFallbackToNCCL(size_t seq_len, size_t message_size_bytes, size_t max_workspace_size) { - // If messageSize is greater than maxWorkspaceSize or topology is unsuitable, use NCCL_SYMMETRIC fallback. + // If messageSize is greater than maxWorkspaceSize or topology is unsuitable, use NCCL fallback. + // TODO: Use NCCL_SYMMETRIC once the memory allocation issue is resolved. if (message_size_bytes > max_workspace_size || !mIsP2PSupported || !mIsNVLINKSupported) { return true; diff --git a/cpp/tensorrt_llm/thop/logitsBitmaskOp.cpp b/cpp/tensorrt_llm/thop/logitsBitmaskOp.cpp index 2f6eddd5ca0a..6c3e83fd85e1 100644 --- a/cpp/tensorrt_llm/thop/logitsBitmaskOp.cpp +++ b/cpp/tensorrt_llm/thop/logitsBitmaskOp.cpp @@ -23,6 +23,11 @@ TRTLLM_NAMESPACE_BEGIN namespace torch_ext { +namespace +{ +int32_t constexpr kBitsPerMaskElement = 32; +} // namespace + void logitsBitmask(torch::Tensor const& logits, torch::Tensor const& bitmask, at::optional const& tokenMask = at::nullopt, at::optional const& d2t = at::nullopt) { @@ -34,12 +39,15 @@ void logitsBitmask(torch::Tensor const& logits, torch::Tensor const& bitmask, TORCH_CHECK(bitmask.size(0) == batchSize, "bitmask must have the same batch size as logits."); int32_t vocabSizePadded = logits.size(1); - int32_t bitmaskSize = bitmask.size(1); + if (!d2t.has_value()) + { + TORCH_CHECK(bitmask.size(1) == tensorrt_llm::common::ceilDiv(vocabSizePadded, kBitsPerMaskElement), + "bitmask.size(1) must be equal to ceilDiv(vocab_size, 32)."); + } TORCH_CHECK(logits.is_cuda(), "logits must be a CUDA tensor."); TORCH_CHECK(logits.is_contiguous(), "logits must be contiguous."); TORCH_CHECK(logits.dim() == 2, "logits must be a 2D tensor."); TORCH_CHECK(bitmask.is_cuda(), "bitmask must be a CUDA tensor."); - TORCH_CHECK(bitmask.is_contiguous(), "bitmask must be contiguous."); TORCH_CHECK(bitmask.dim() == 2, "bitmask must be a 2D tensor."); TORCH_CHECK(bitmask.scalar_type() == torch::kUInt32 || bitmask.scalar_type() == torch::kInt32, "bitmask must have element type uint32 or int32."); @@ -52,7 +60,7 @@ void logitsBitmask(torch::Tensor const& logits, torch::Tensor const& bitmask, TORCH_CHECK(tokenMask->dim() == 1, "tokenMask must be a 1D tensor."); TORCH_CHECK(tokenMask->size(0) == batchSize, "tokenMask must have the same batch size as logits."); TORCH_CHECK(tokenMask->scalar_type() == torch::kInt32, "tokenMask must have element type int32."); - tokenMaskPtr = reinterpret_cast(tokenMask->data_ptr()); + tokenMaskPtr = static_cast(tokenMask->data_ptr()); } int32_t const* d2tPtr = nullptr; @@ -63,7 +71,7 @@ void logitsBitmask(torch::Tensor const& logits, torch::Tensor const& bitmask, TORCH_CHECK(d2t->dim() == 1, "d2t must be a 1D tensor."); TORCH_CHECK(d2t->size(0) == vocabSizePadded, "d2t must have the same vocab size as logits."); TORCH_CHECK(d2t->scalar_type() == torch::kInt32, "d2t must have element type int32."); - d2tPtr = reinterpret_cast(d2t->data_ptr()); + d2tPtr = static_cast(d2t->data_ptr()); } auto stream = at::cuda::getCurrentCUDAStream(logits.get_device()).stream(); @@ -72,23 +80,23 @@ void logitsBitmask(torch::Tensor const& logits, torch::Tensor const& bitmask, { case torch::kFloat32: { - tensorrt_llm::kernels::invokeContiguousLogitsBitmask(reinterpret_cast(logits.data_ptr()), - reinterpret_cast(bitmask.data_ptr()), tokenMaskPtr, d2tPtr, batchSize, vocabSizePadded, - bitmaskSize, stream); + tensorrt_llm::kernels::invokeContiguousLogitsBitmask(static_cast(logits.data_ptr()), + static_cast(bitmask.data_ptr()), tokenMaskPtr, d2tPtr, batchSize, vocabSizePadded, + bitmask.stride(0), stream); break; } case torch::kFloat16: { - tensorrt_llm::kernels::invokeContiguousLogitsBitmask<__half>(reinterpret_cast<__half*>(logits.data_ptr()), - reinterpret_cast(bitmask.data_ptr()), tokenMaskPtr, d2tPtr, batchSize, vocabSizePadded, - bitmaskSize, stream); + tensorrt_llm::kernels::invokeContiguousLogitsBitmask<__half>(static_cast<__half*>(logits.data_ptr()), + static_cast(bitmask.data_ptr()), tokenMaskPtr, d2tPtr, batchSize, vocabSizePadded, + bitmask.stride(0), stream); break; } case torch::kBFloat16: { tensorrt_llm::kernels::invokeContiguousLogitsBitmask<__nv_bfloat16>( - reinterpret_cast<__nv_bfloat16*>(logits.data_ptr()), reinterpret_cast(bitmask.data_ptr()), - tokenMaskPtr, d2tPtr, batchSize, vocabSizePadded, bitmaskSize, stream); + static_cast<__nv_bfloat16*>(logits.data_ptr()), static_cast(bitmask.data_ptr()), + tokenMaskPtr, d2tPtr, batchSize, vocabSizePadded, bitmask.stride(0), stream); break; } default: TORCH_CHECK(false, "logits dtype must be float, half or bfloat16."); break; diff --git a/cpp/tests/unit_tests/batch_manager/capacitySchedulerTest.cpp b/cpp/tests/unit_tests/batch_manager/capacitySchedulerTest.cpp index ba611e8720ad..875299adc833 100644 --- a/cpp/tests/unit_tests/batch_manager/capacitySchedulerTest.cpp +++ b/cpp/tests/unit_tests/batch_manager/capacitySchedulerTest.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -28,6 +28,7 @@ #include "tensorrt_llm/executor/executor.h" #include "tensorrt_llm/executor/requestUtils.h" #include "tensorrt_llm/executor/types.h" +#include "tensorrt_llm/testing/kvCacheManagerTestUtil.h" #include @@ -401,6 +402,7 @@ int runTest(CapacityScheduler& capacityScheduler, if (llmReq->getContextRemainingLength() == 0) { + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmReq); kvCacheManager->storeContextBlocks(*llmReq); if (crossKvCacheManager) { diff --git a/cpp/tests/unit_tests/batch_manager/kvCacheManagerTest.cpp b/cpp/tests/unit_tests/batch_manager/kvCacheManagerTest.cpp index 0803140f1ca8..2eaa90a25fa0 100644 --- a/cpp/tests/unit_tests/batch_manager/kvCacheManagerTest.cpp +++ b/cpp/tests/unit_tests/batch_manager/kvCacheManagerTest.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -31,6 +31,7 @@ #include "tensorrt_llm/runtime/bufferManager.h" #include "tensorrt_llm/runtime/iBuffer.h" #include "tensorrt_llm/runtime/samplingConfig.h" +#include "tensorrt_llm/testing/kvCacheManagerTestUtil.h" #include "gtest/gtest.h" #include @@ -342,6 +343,7 @@ void runPartialCopyTest() EXPECT_EQ(cudaDeviceSynchronize(), cudaSuccess); EXPECT_TRUE(blockManager.verifyQueueIntegrity(maxAttentionWindow)); } + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0, llmRequest0); blockManager.releaseSequence(seq0.getRequestId()); @@ -359,6 +361,7 @@ void runPartialCopyTest() auto cacheBlockIds1 = seq1.getCacheBlockIds(maxAttentionWindow).at(beamIdx); EXPECT_THAT(cacheBlockIds1, ::testing::ElementsAreArray({0, 1, 6})); // store blocks 0, 1 ([0,1,2,3,4,5,6,7], [8,9,10,11,12,13,14,15]) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.storeContextBlocks(seq1, *llmRequest1); EXPECT_EQ(cudaDeviceSynchronize(), cudaSuccess); @@ -419,7 +422,9 @@ void runPartialCopyTest() EXPECT_TRUE(block2->isPrimary()); EXPECT_EQ(cudaDeviceSynchronize(), cudaSuccess); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1, llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); blockManager.releaseBlocks(seq2, llmRequest2); blockManager.releaseSequence(seq1.getRequestId()); blockManager.releaseSequence(seq2.getRequestId()); @@ -654,6 +659,7 @@ TEST_F(KVCacheManagerTest, FindBlocksInReuseTreeByBlockKeysTest) auto cacheBlockIds = kvCacheManager.getSequence(requestId).getCacheBlockIds(maxAttentionWindow).at(beamIdx); EXPECT_THAT(cacheBlockIds, ::testing::ElementsAreArray({0, 1, 2})); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(requestId, llmRequest0); inputTokens->pop_back(); @@ -768,6 +774,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // blocks 0, 1, 2 are stored for reuse (blocks contain [0, 1, 2, 3], [4, 5, 6, 7], [8, 9]) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0, llmRequest0); blockManager.releaseSequence(seq0.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -793,6 +800,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // block 3 matches block 2 and will be freed (blocks contain [8, 9]) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1, llmRequest1); blockManager.releaseSequence(seq1.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -834,11 +842,13 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks - 1); // block 2 is stored for reuse (block contains [8]). nb! Last token of last block is never stored + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0_dup, llmRequest0); blockManager.releaseSequence(seq0_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // block 4 is stored for reuse (block contains [8, 9]). nb! Last token of last block is never stored + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1_dup, llmRequest1); blockManager.releaseSequence(seq1_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -892,9 +902,11 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // block 5 is not stored since it is last block and has only one token + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); blockManager.releaseBlocks(seq2, llmRequest2); blockManager.releaseSequence(seq2.getRequestId()); // block 4 is stored for reuse (block contains [8, 9]). nb! Last token of last block not stored + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest3); blockManager.releaseBlocks(seq3, llmRequest3); blockManager.releaseSequence(seq3.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -928,6 +940,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseTest) // llmRequest4Short tokens [0, 1, 2, 3, 4, 5, 6, 7, 8] // blocks 0 and 1 ([0, 1, 2, 3], [4, 5, 6, 7]) are already stored, // block 4 is freed + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest4Short); blockManager.releaseBlocks(seq4, llmRequest4Short); blockManager.releaseSequence(seq4.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -954,6 +967,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest4); blockManager.releaseBlocks(seq4_dup, llmRequest4); blockManager.releaseSequence(seq4_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -980,6 +994,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), blocksInPrimaryPool); EXPECT_EQ(blockManager.getNumFreeBlocks(), 0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest5); blockManager.releaseBlocks(seq5, llmRequest5); blockManager.releaseSequence(seq5.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1006,6 +1021,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 1); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - 1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest6); blockManager.releaseBlocks(seq6, llmRequest6); blockManager.releaseSequence(seq6.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1080,6 +1096,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithExtraIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // blocks 0, 1, 2 are stored for reuse (block 2 contains [(2, 0), (3, 0)]) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0, llmRequest0); blockManager.releaseSequence(seq0.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1109,6 +1126,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithExtraIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // block 3 matches block 2 and will be freed + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1, llmRequest1); blockManager.releaseSequence(seq1.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1156,11 +1174,13 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithExtraIdTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks + 1); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks - 1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0_dup, llmRequest0); blockManager.releaseSequence(seq0_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // blocks 2 is stored for reuse (block contains [(2, 0), (3, 0), (4, 0)]) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1_dup, llmRequest1); blockManager.releaseSequence(seq1_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1218,7 +1238,9 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithExtraIdTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks * 2); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks * 2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); blockManager.releaseBlocks(seq2, llmRequest2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest3); blockManager.releaseBlocks(seq3, llmRequest3); blockManager.releaseSequence(seq2.getRequestId()); blockManager.releaseSequence(seq3.getRequestId()); @@ -1303,6 +1325,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithMultimodalHashTest) // Block 0: [100, 101, 102, 103] ← Contains multimodal (102, 103) // Block 1: [104, 105, 0, 1] ← Contains multimodal (104, 105) // Block 2: [2, 3, 4] ← No multimodal + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0, llmRequest0); blockManager.releaseSequence(seq0.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1331,6 +1354,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithMultimodalHashTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // block 3 matches block 2 and will be freed + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1, llmRequest1); blockManager.releaseSequence(seq1.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1402,7 +1426,9 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithMultimodalHashTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks * 2); // clean up + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); blockManager.releaseBlocks(seq2, llmRequest2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest3); blockManager.releaseBlocks(seq3, llmRequest3); blockManager.releaseSequence(seq2.getRequestId()); blockManager.releaseSequence(seq3.getRequestId()); @@ -1474,6 +1500,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithLoraTaskIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // store blocks 0, 1, 2 for reuse ([0,1,2,3], [4,5,6,7], [8,9]) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0, llmRequest0); blockManager.releaseSequence(seq0.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1501,6 +1528,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithLoraTaskIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // store block 3 for reuse ([8,9]) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1, llmRequest1); blockManager.releaseSequence(seq1.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1547,11 +1575,13 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithLoraTaskIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks - 1); // store block 4 for reuse ([8]) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0_dup, llmRequest0); blockManager.releaseSequence(seq0_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // blocks 2 is stored for reuse (block contains [8, 9]). nb! Last token of last block is not stored + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1_dup, llmRequest1); blockManager.releaseSequence(seq1_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1581,6 +1611,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithLoraTaskIdTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // store blocks 5, 6, 7 for reuse ([0,1,2,3], [4,5,6,7], [8]) with loraTaskId 1 + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); blockManager.releaseBlocks(seq2, llmRequest2); blockManager.releaseSequence(seq2.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1609,6 +1640,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithLoraTaskIdTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // store block 7 for reuse ([8,9]) with loraTaskId 1 + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest3); blockManager.releaseBlocks(seq3, llmRequest3); blockManager.releaseSequence(seq3.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1639,6 +1671,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithLoraTaskIdTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // blocks 8 is stored with [4] and loraTaskId 0 + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest4); blockManager.releaseBlocks(seq4, llmRequest4); blockManager.releaseSequence(seq4.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1664,6 +1697,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithLoraTaskIdTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // blocks 9, 10, 11 are stored without loraTaskId + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest5); blockManager.releaseBlocks(seq5, llmRequest5); blockManager.releaseSequence(seq5.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1738,6 +1772,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithExtraIdAndLoraTaskIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // blocks 0, 1, 2 are stored for reuse (block 2 contains [(2, 0), (3, 0)] with loraTaskId 1) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0, llmRequest0); blockManager.releaseSequence(seq0.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1768,6 +1803,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithExtraIdAndLoraTaskIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // blocks 3, 4, 5 are stored for reuse (block 5 contains [(2, 0), (3, 0)] with loraTaskId 2) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1, llmRequest1); blockManager.releaseSequence(seq1.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1815,10 +1851,12 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithExtraIdAndLoraTaskIdTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks * 2); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks * 2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0_dup, llmRequest0); blockManager.releaseSequence(seq0_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1_dup, llmRequest1); blockManager.releaseSequence(seq1_dup.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -1901,8 +1939,11 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithExtraIdAndLoraTaskIdTest) EXPECT_EQ(blockManager.getNumAllocatedBlocks(), numBlocks * 3); EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks * 3); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); blockManager.releaseBlocks(seq2, llmRequest2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest3); blockManager.releaseBlocks(seq3, llmRequest3); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest4); blockManager.releaseBlocks(seq4, llmRequest4); blockManager.releaseSequence(seq2.getRequestId()); blockManager.releaseSequence(seq3.getRequestId()); @@ -1985,6 +2026,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithCacheSaltIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // Release blocks to make them available for reuse + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0, llmRequest0); blockManager.releaseSequence(seq0.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -2019,6 +2061,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithCacheSaltIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // Release blocks + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1, llmRequest1); blockManager.releaseSequence(seq1.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -2052,6 +2095,7 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithCacheSaltIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks); // Release blocks + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); blockManager.releaseBlocks(seq2, llmRequest2); blockManager.releaseSequence(seq2.getRequestId()); EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -2114,7 +2158,9 @@ TEST_F(KVCacheManagerTest, BlockManagerReuseWithCacheSaltIdTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), blocksInPrimaryPool - numBlocks * 2); // Clean up + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest3); blockManager.releaseBlocks(seq3, llmRequest3); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest4); blockManager.releaseBlocks(seq4, llmRequest4); blockManager.releaseSequence(seq3.getRequestId()); blockManager.releaseSequence(seq4.getRequestId()); @@ -2166,6 +2212,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerPerRequestStatsTest) EXPECT_EQ(llmRequest0->getAllocNewBlocksPerRequest(), numBlocks); EXPECT_EQ(llmRequest0->getMissedBlocksPerRequest(), numBlocks); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); EXPECT_NO_THROW((void) kvCacheManager.removeSequence(requestId, llmRequest0)); requestId = 1; @@ -2237,7 +2284,9 @@ TEST_F(KVCacheManagerTest, BlockManagerBlockPriorityTest) blockManager.addSequence(seq1, llmRequest1->getNumTokens(0), numContextBlocks1, *llmRequest1, maxAttentionWindow); // Release both sequences + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); blockManager.releaseBlocks(seq0, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); blockManager.releaseBlocks(seq1, llmRequest1); blockManager.releaseSequence(seq0.getRequestId()); blockManager.releaseSequence(seq1.getRequestId()); @@ -2252,6 +2301,7 @@ TEST_F(KVCacheManagerTest, BlockManagerBlockPriorityTest) auto numContextBlocks2 = tc::ceilDiv(inputLength2, blockManager.getTokensPerBlock()); blockManager.holdSequence(seq2.getRequestId()); blockManager.addSequence(seq2, llmRequest2->getNumTokens(0), numContextBlocks2, *llmRequest2, maxAttentionWindow); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); blockManager.releaseBlocks(seq2, llmRequest2); blockManager.releaseSequence(seq2.getRequestId()); @@ -2266,6 +2316,7 @@ TEST_F(KVCacheManagerTest, BlockManagerBlockPriorityTest) EXPECT_EQ(llmRequest3->getContextCurrentPosition(), 4); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest3); blockManager.releaseBlocks(seq3, llmRequest3); blockManager.releaseSequence(seq3.getRequestId()); EXPECT_EQ(blockManager.getNumFreeBlocks(), 4); @@ -2363,8 +2414,10 @@ TEST_F(KVCacheManagerTest, KVCacheManagerDecodeBlockPriorityTest) // remove both sequences, blocks get stored // leaf block 3 (priority 90), context blocks 2, 1, 0 (priority 5) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(0, llmRequest0); // leaf block 7 (priority 5), context blocks 6, 5, 4 (priority 90) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); (void) kvCacheManager.removeSequence(1, llmRequest1); // all blocks are available again. @@ -2380,6 +2433,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerDecodeBlockPriorityTest) auto llmRequest2 = std::make_shared(2, maxNewTokens, inputTokens2, samplingConfig, isStreaming); kvCacheManager.addSequence(2, inputLength2, beamWidth, llmRequest2); // leaf block 2 (priority 35), context blocks 3, 7 (priority 35) + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); (void) kvCacheManager.removeSequence(2, llmRequest2); // Uses 3 blocks 0, 1, 2 which contain [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11] @@ -2431,6 +2485,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerTimedEvictionTest) llmRequest0->setKvCacheRetentionConfig( KvCacheRetentionConfig({KvCacheRetentionConfig::TokenRangeRetentionConfig(0, std::nullopt, 80, 10ms)}, 80)); kvCacheManager.addSequence(0, inputLength0, beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(0, llmRequest0); auto inputTokens1 = std::make_shared(VecTokens{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}); @@ -2439,6 +2494,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerTimedEvictionTest) llmRequest1->setKvCacheRetentionConfig( KvCacheRetentionConfig({KvCacheRetentionConfig::TokenRangeRetentionConfig(0, std::nullopt, 50)}, 80)); kvCacheManager.addSequence(1, inputLength1, beamWidth, llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); (void) kvCacheManager.removeSequence(1, llmRequest1); std::this_thread::sleep_for(std::chrono::milliseconds(50)); @@ -2451,6 +2507,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerTimedEvictionTest) auto const inputLength2 = static_cast(inputTokens2->size()); auto llmRequest2 = std::make_shared(2, maxNewTokens, inputTokens2, samplingConfig, isStreaming); kvCacheManager.addSequence(2, inputLength2, beamWidth, llmRequest2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); (void) kvCacheManager.removeSequence(2, llmRequest2); // Check that the [12, 13, 14, 15] block is still in the cache @@ -2503,6 +2560,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerDecodeTimedEvictionTest) KvCacheRetentionConfig({KvCacheRetentionConfig::TokenRangeRetentionConfig(0, std::nullopt, 50)}, 50)); // Set all blocks to priority 50. kvCacheManager.addSequence(0, inputLength0, beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); kvCacheManager.storeContextBlocks(*llmRequest0); // Occupy a new block, block 3, adding 3 tokens to block 3. // [1, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [0, 0, 0] @@ -2511,6 +2569,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerDecodeTimedEvictionTest) kvCacheManager.addToken(0); llmRequest0->addNewToken(0, 0); } + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(0, llmRequest0); } { @@ -2522,6 +2581,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerDecodeTimedEvictionTest) llmRequest1->setKvCacheRetentionConfig(KvCacheRetentionConfig( {}, KvCacheRetentionConfig::kMaxRetentionPriority, 20ms)); // Set decode blocks to max priority for 20ms. kvCacheManager.addSequence(1, inputLength1, beamWidth, llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); kvCacheManager.storeContextBlocks(*llmRequest1); // Occupy a new block, block 3, adding 3 tokens to block 3. // [1, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [0, 0, 0] @@ -2530,6 +2590,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerDecodeTimedEvictionTest) kvCacheManager.addToken(1); llmRequest1->addNewToken(0, 0); } + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); (void) kvCacheManager.removeSequence(1, llmRequest1); } @@ -2541,6 +2602,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerDecodeTimedEvictionTest) auto const inputLength2 = static_cast(inputTokens2->size()); auto llmRequest2 = std::make_shared(2, maxNewTokens, inputTokens2, samplingConfig, isStreaming); kvCacheManager.addSequence(2, inputLength2, beamWidth, llmRequest2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); (void) kvCacheManager.removeSequence(2, llmRequest2); // 12 tokens, reusing block 4, 5. Block 6 is overwritten so no reuse. @@ -2588,6 +2650,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerSecondaryBlockPrimaryChildTest) // 12 tokens, get block 0, 1, 2 // [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11] kvCacheManager.addSequence(0, inputLength0, beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(0, llmRequest0); // store blocks 0, 1, 2 for reuse ([0,1,2,3], [4,5,6,7], [8,9,10]) @@ -2600,6 +2663,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerSecondaryBlockPrimaryChildTest) // Block 4 is now in primary (replacing 2) // Block 5 is now in primary (replacing 1) kvCacheManager.addSequence(1, inputLength1, beamWidth, llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); (void) kvCacheManager.removeSequence(1, llmRequest1); // store blocks 3, 4, 5 for reuse ([1,1,2,3], [4,5,6,7], [8,9,10]) @@ -2635,6 +2699,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerSecondaryBlockPrimaryChildTest) // If we do this, then the context current position at the bottom of this // unit test will be 9 because then the block content [4,5,6,7] can be // found and reused. + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); (void) kvCacheManager.removeSequence(2, llmRequest2); // 10 tokens, reusing the block 0 only because when we want to acquire @@ -2678,12 +2743,14 @@ TEST_F(KVCacheManagerTest, KVCacheManagerLeafBlockTest) auto const inputLength0 = static_cast(inputTokens0->size()); auto llmRequest0 = std::make_shared(0, maxNewTokens, inputTokens0, samplingConfig, isStreaming); kvCacheManager.addSequence(0, inputLength0, beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); kvCacheManager.storeContextBlocks(*llmRequest0); llmRequest0->addNewToken(0, 0); kvCacheManager.addToken(0); // The second block allocated should be first in line for eviction. + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(0, llmRequest0); auto inputTokens1 = std::make_shared(VecTokens{1, 1, 2, 3}); @@ -2702,7 +2769,9 @@ TEST_F(KVCacheManagerTest, KVCacheManagerLeafBlockTest) auto llmRequest2 = std::make_shared(2, maxNewTokens, inputTokens2, samplingConfig, isStreaming); kvCacheManager.addSequence(2, inputLength2, beamWidth, llmRequest2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); (void) kvCacheManager.removeSequence(1, llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); (void) kvCacheManager.removeSequence(2, llmRequest2); auto inputTokens3 = std::make_shared(VecTokens{2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); @@ -2761,6 +2830,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerLeafBlockWithDependentTest) auto llmRequest0 = std::make_shared(requestId0, maxNewTokens, inputTokens0, samplingConfig, isStreaming); kvCacheManager.addSequence(requestId0, inputLength0, beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); kvCacheManager.storeContextBlocks(*llmRequest0); GenerationRequest const& seq0 = kvCacheManager.getSequence(requestId0); @@ -2788,6 +2858,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerLeafBlockWithDependentTest) auto llmRequest1 = std::make_shared(requestId1, maxNewTokens, inputTokens1, samplingConfig, isStreaming); kvCacheManager.addSequence(requestId1, inputLength1, beamWidth, llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); kvCacheManager.storeContextBlocks(*llmRequest1); GenerationRequest const& seq1 = kvCacheManager.getSequence(requestId1); @@ -2795,6 +2866,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerLeafBlockWithDependentTest) EXPECT_EQ(blockManager.getNumFreeBlocks(), 0); // Free first sequence + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(requestId0, llmRequest0); // Verify that 3 primary blocks are free. @@ -2835,6 +2907,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerLeafBlockWithDependentTest) EXPECT_FALSE(block0->isPrimary()); // Cleanup + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); (void) kvCacheManager.removeSequence(requestId1, llmRequest1); } @@ -3363,6 +3436,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerMaxAttentionWindowSmallerThanBlockSizeT EXPECT_EQ(numBlocks, 3); EXPECT_THAT(seq0.getCacheBlockIds(onlyWindowSize).at(beamIdx), ::testing::ElementsAreArray({0, 1, 2})); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest); EXPECT_NO_THROW((void) kvCacheManager.removeSequence(requestId, llmRequest)); // no blocks stored because reuse is disabled EXPECT_EQ(blockManager.getNumAllocatedBlocks(), 0); @@ -3411,6 +3485,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStream) auto llmRequest0 = std::make_shared(0, 0, inputTokens0, samplingConfig, true); llmRequest0->setLoraTaskId(42); kvCacheManager.addSequence(0, inputTokens0->size(), beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); kvCacheManager.storeContextBlocks(*llmRequest0); events = getEvents(kvCacheManager); @@ -3423,6 +3498,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStream) EXPECT_EQ(std::get(events.front().data).blocks[0].cacheLevel, 0); kvCacheManager.addToken(0); llmRequest0->addNewToken(0, 0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(0, llmRequest0); auto newEvents = getEvents(kvCacheManager); @@ -3440,7 +3516,9 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStream) auto llmRequest1 = std::make_shared(1, 0, inputTokens1, samplingConfig, true); llmRequest1->setLoraTaskId(42); kvCacheManager.addSequence(1, inputTokens1->size(), beamWidth, llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); kvCacheManager.storeContextBlocks(*llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); (void) kvCacheManager.removeSequence(1, llmRequest1); events = getEvents(kvCacheManager); @@ -3471,7 +3549,9 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStream) EXPECT_THAT(std::get(events.front().data).blockHashes, ::testing::ElementsAreArray({firstSwapped})); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest2); (void) kvCacheManager.removeSequence(2, llmRequest2); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest3); (void) kvCacheManager.removeSequence(3, llmRequest3); events = getEvents(kvCacheManager); @@ -3526,6 +3606,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStream) EXPECT_EQ(offloadedBlocks, 2); EXPECT_EQ(removedBlocks, 1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest4); kvCacheManager.storeContextBlocks(*llmRequest4); events = getEvents(kvCacheManager); @@ -3601,6 +3682,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerMaxAttentionWindowWithReuseTest) auto numAllocatedPrimaryBlocks = blockManager.getNumAllocatedBlocks() - blocksInSecondaryPool; EXPECT_THAT(seq0.getCacheBlockIds(onlyWindowSize).at(beamIdx), ::testing::ElementsAreArray({0, 1, 2, 3, 4})); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest); EXPECT_NO_THROW(static_cast(kvCacheManager.removeSequence(requestId, llmRequest))); numAllocatedPrimaryBlocks = blockManager.getNumAllocatedBlocks() - blocksInSecondaryPool; EXPECT_EQ(numAllocatedPrimaryBlocks, 0); @@ -3627,6 +3709,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerMaxAttentionWindowWithReuseTest) kvCacheManager.addToken(requestId); numTokens = llmRequest->getNumTokens(beamIdx); EXPECT_THAT(seq1.getCacheBlockIds(onlyWindowSize).at(beamIdx), ::testing::ElementsAreArray({0, 5, 6})); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest); EXPECT_NO_THROW(static_cast(kvCacheManager.removeSequence(requestId, llmRequest))); /////////////////////////////////////////////////////////////////////////// @@ -3641,6 +3724,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerMaxAttentionWindowWithReuseTest) GenerationRequest const& seq2 = kvCacheManager.getSequence(requestId); EXPECT_EQ(llmRequest->getContextCurrentPosition(), 9); EXPECT_THAT(seq2.getCacheBlockIds(onlyWindowSize).at(beamIdx), ::testing::ElementsAreArray({0, 1, 7})); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest); EXPECT_NO_THROW(static_cast(kvCacheManager.removeSequence(requestId, llmRequest))); /////////////////////////////////////////////////////////////////////////// @@ -3663,6 +3747,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerMaxAttentionWindowWithReuseTest) llmRequest->addNewToken(1016, beamIdx); kvCacheManager.addToken(requestId); EXPECT_THAT(seq3.getCacheBlockIds(onlyWindowSize).at(beamIdx), ::testing::ElementsAreArray({0, 1, 2, 8, 9})); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest); EXPECT_NO_THROW(static_cast(kvCacheManager.removeSequence(requestId, llmRequest))); } @@ -3741,7 +3826,9 @@ TEST_F(KVCacheManagerTest, KVCacheManagerSWAInvalidateReuseTest) EXPECT_FALSE(blockManager.isSequenceValidForStoreForReuse(seq0.getRequestId(), onlyWindowSize)); EXPECT_TRUE(blockManager.isSequenceValidForStoreForReuse(seq1.getRequestId(), onlyWindowSize)); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); EXPECT_NO_THROW(static_cast(kvCacheManager.removeSequence(seq0.getRequestId(), llmRequest0))); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); EXPECT_NO_THROW(static_cast(kvCacheManager.removeSequence(seq1.getRequestId(), llmRequest1))); } @@ -3832,6 +3919,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerVariableWindowAttentionWithReuseTest) assertBlocks(seq0, {0, 1, 2}, {0, 1, 2}); auto numAllocatedPrimaryBlocks = blockManager.getNumAllocatedBlocks() - blocksInSecondaryPoolPerWindow * numWindows; + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest); EXPECT_NO_THROW(static_cast(kvCacheManager.removeSequence(requestId, llmRequest))); numAllocatedPrimaryBlocks = blockManager.getNumAllocatedBlocks() - blocksInSecondaryPoolPerWindow * numWindows; EXPECT_EQ(numAllocatedPrimaryBlocks, 0); @@ -3858,6 +3946,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerVariableWindowAttentionWithReuseTest) llmRequest->addNewToken(1009, beamIdx); kvCacheManager.addToken(requestId); assertBlocks(seq1, {0, 3, 4}, {0, 3, 4}); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest); EXPECT_NO_THROW(static_cast(kvCacheManager.removeSequence(requestId, llmRequest))); } @@ -3895,6 +3984,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStreamOverflow) auto llmRequest0 = std::make_shared(0, 0, inputTokens0, samplingConfig, true); llmRequest0->setLoraTaskId(42); kvCacheManager.addSequence(0, inputTokens0->size(), beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); kvCacheManager.storeContextBlocks(*llmRequest0); auto events = getEvents(kvCacheManager); @@ -3910,6 +4000,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStreamOverflow) events = getEvents(kvCacheManager); EXPECT_EQ(events.size(), 0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); (void) kvCacheManager.removeSequence(0, llmRequest0); events = getEvents(kvCacheManager); @@ -3955,6 +4046,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStreamPriority) llmRequest0->setKvCacheRetentionConfig(tle::KvCacheRetentionConfig( std::vector{tle::KvCacheRetentionConfig::TokenRangeRetentionConfig(0, std::nullopt, 50)}, 35)); kvCacheManager.addSequence(0, inputTokens0->size(), beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); kvCacheManager.storeContextBlocks(*llmRequest0); (void) kvCacheManager.removeSequence(0, llmRequest0); auto events = getEvents(kvCacheManager); @@ -3971,7 +4063,9 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStreamPriority) auto inputTokens1 = std::make_shared(VecTokens{0, 1, 2, 3, 4, 5, 6, 7}); auto llmRequest1 = std::make_shared(1, 0, inputTokens1, samplingConfig, true); kvCacheManager.addSequence(1, inputTokens1->size(), beamWidth, llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); kvCacheManager.storeContextBlocks(*llmRequest1); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest1); (void) kvCacheManager.removeSequence(1, llmRequest1); events = getEvents(kvCacheManager); @@ -4068,6 +4162,7 @@ TEST_F(KVCacheManagerTest, PinAndUnpinBlocksById) ASSERT_TRUE(lastBlockIdOpt.has_value()); auto const& allBlockIds = kvCacheManager.getCacheBlockIds(requestId, maxAttentionWindow)[0]; std::vector pinnedBlockIds(allBlockIds.begin(), allBlockIds.end()); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest); (void) kvCacheManager.removeSequence(requestId, llmRequest); auto const freeAfterRemovePinned = kvCacheManager.getNumFreeBlocks(); EXPECT_LT(freeAfterRemovePinned, totalBlocks); @@ -4121,6 +4216,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStreamBlocking) auto inputTokens0 = std::make_shared(VecTokens{0, 1, 2, 3, 4, 5, 6, 7}); auto llmRequest0 = std::make_shared(0, 0, inputTokens0, samplingConfig, true); kvCacheManager.addSequence(0, inputTokens0->size(), beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); kvCacheManager.storeContextBlocks(*llmRequest0); kvCacheManager.flushIterationEvents(); @@ -4176,6 +4272,7 @@ TEST_F(KVCacheManagerTest, KVCacheManagerEventStreamWindowSize) auto inputTokens0 = std::make_shared(VecTokens{0, 1, 2, 3, 4, 5, 6, 7}); auto llmRequest0 = std::make_shared(0, 0, inputTokens0, samplingConfig, true); kvCacheManager.addSequence(0, inputTokens0->size(), beamWidth, llmRequest0); + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*llmRequest0); kvCacheManager.storeContextBlocks(*llmRequest0); events = getEvents(kvCacheManager); @@ -4963,6 +5060,7 @@ TEST_P(FillKvCacheAndCompleteRequestsTest, FillKvCacheWithRequestsAndCompleteOne llmRequest.addNewToken(0, 0); kvCacheManager->addToken(llmRequest.mRequestId); } + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(llmRequest); (void) kvCacheManager->removeSequence(llmRequest.mRequestId, llmRequest); } auto const [expectedNumFreeBlocks, _] = params.kvCacheManagerInstantiationParameters.blocksPerWindow.at( @@ -5348,6 +5446,93 @@ TEST_P(NeededBlocksOneStepTest, NeededBlocksOneStepTestCorrectlyEstimated) } } +TEST_F(KVCacheManagerTest, KVCacheManagerStoreContextBlocksUsesMaterializedContextExtent) +{ + auto constexpr numLayers = 12; + auto constexpr numHeads = 6; + auto constexpr sizePerHead = 16; + auto constexpr tokensPerBlock = 4; + auto constexpr maxBlocksPerSeq = 4; + auto constexpr maxNumSequences = 8; + auto constexpr blocksInPrimaryPool = 8; + auto constexpr blocksInSecondaryPool = 0; + auto constexpr onboardBlocks = true; + auto const stream = std::make_shared(); + + auto constexpr beamWidth = 1; + auto constexpr kMaterializedContextLength = 5; + auto constexpr kReusableContextLength = 4; + SizeType32 constexpr maxNewTokens = 8; + tr::SamplingConfig const samplingConfig{beamWidth}; + bool constexpr isStreaming{false}; + + auto const maxAttentionWindow = tokensPerBlock * maxBlocksPerSeq; + auto const blocksPerWindow = BlocksPerWindow{{maxAttentionWindow, {blocksInPrimaryPool, blocksInSecondaryPool}}}; + + KVCacheManager kvCacheManager(numLayers, numHeads, sizePerHead, tokensPerBlock, blocksPerWindow, maxNumSequences, + beamWidth, std::vector{maxAttentionWindow}, std::nullopt, nvinfer1::DataType::kHALF, + false, stream, maxAttentionWindow, true, onboardBlocks); + kvCacheManager.allocatePools(false); + + auto inputTokens = std::make_shared(VecTokens{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + auto const inputLength = static_cast(inputTokens->size()); + + auto llmRequest0 = std::make_shared(0, maxNewTokens, inputTokens, samplingConfig, isStreaming); + kvCacheManager.addSequence(0, inputLength, beamWidth, llmRequest0); + llmRequest0->setContextCurrentPosition(kMaterializedContextLength); + + kvCacheManager.storeContextBlocks(*llmRequest0); + (void) kvCacheManager.removeSequence(0, std::nullopt); + + auto llmRequest1 = std::make_shared(1, maxNewTokens, inputTokens, samplingConfig, isStreaming); + kvCacheManager.addSequence(1, inputLength, beamWidth, llmRequest1); + + EXPECT_EQ(llmRequest1->getContextCurrentPosition(), kReusableContextLength); +} + +TEST_F(KVCacheManagerTest, KVCacheManagerReleaseBlocksUsesMaterializedContextExtent) +{ + auto constexpr numLayers = 12; + auto constexpr numHeads = 6; + auto constexpr sizePerHead = 16; + auto constexpr tokensPerBlock = 4; + auto constexpr maxBlocksPerSeq = 4; + auto constexpr maxNumSequences = 8; + auto constexpr blocksInPrimaryPool = 8; + auto constexpr blocksInSecondaryPool = 0; + auto constexpr onboardBlocks = true; + auto const stream = std::make_shared(); + + auto constexpr beamWidth = 1; + auto constexpr kMaterializedContextLength = 5; + auto constexpr kReusableContextLength = 4; + SizeType32 constexpr maxNewTokens = 8; + tr::SamplingConfig const samplingConfig{beamWidth}; + bool constexpr isStreaming{false}; + + auto const maxAttentionWindow = tokensPerBlock * maxBlocksPerSeq; + auto const blocksPerWindow = BlocksPerWindow{{maxAttentionWindow, {blocksInPrimaryPool, blocksInSecondaryPool}}}; + + KVCacheManager kvCacheManager(numLayers, numHeads, sizePerHead, tokensPerBlock, blocksPerWindow, maxNumSequences, + beamWidth, std::vector{maxAttentionWindow}, std::nullopt, nvinfer1::DataType::kHALF, + false, stream, maxAttentionWindow, true, onboardBlocks); + kvCacheManager.allocatePools(false); + + auto inputTokens = std::make_shared(VecTokens{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + auto const inputLength = static_cast(inputTokens->size()); + + auto llmRequest0 = std::make_shared(0, maxNewTokens, inputTokens, samplingConfig, isStreaming); + kvCacheManager.addSequence(0, inputLength, beamWidth, llmRequest0); + llmRequest0->setContextCurrentPosition(kMaterializedContextLength); + + (void) kvCacheManager.removeSequence(0, llmRequest0); + + auto llmRequest1 = std::make_shared(1, maxNewTokens, inputTokens, samplingConfig, isStreaming); + kvCacheManager.addSequence(1, inputLength, beamWidth, llmRequest1); + + EXPECT_EQ(llmRequest1->getContextCurrentPosition(), kReusableContextLength); +} + INSTANTIATE_TEST_SUITE_P(NeededBlocksOneStepTestCorrectlyEstimated, NeededBlocksOneStepTest, ::testing::Values( GetNeededBlocksOneStepOneRequestParameters{ diff --git a/cpp/tests/unit_tests/multi_gpu/cacheTransceiverTest.cpp b/cpp/tests/unit_tests/multi_gpu/cacheTransceiverTest.cpp index 5e7528b8dd4e..e1d31f0ee834 100644 --- a/cpp/tests/unit_tests/multi_gpu/cacheTransceiverTest.cpp +++ b/cpp/tests/unit_tests/multi_gpu/cacheTransceiverTest.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -41,6 +41,7 @@ #include "tensorrt_llm/executor/executor.h" #include "tensorrt_llm/runtime/common.h" #include "tensorrt_llm/runtime/utils/mpiUtils.h" +#include "tensorrt_llm/testing/kvCacheManagerTestUtil.h" #include #include #include @@ -406,6 +407,7 @@ TEST_F(SymmetricalCacheTest, SimpleTest) mFutures.clear(); for (auto& request : requests) { + tensorrt_llm::testing::KvCacheManagerTestUtil::simulatePrefillCompletion(*request); mManager->removeSequence(request->mRequestId, request); } requests.clear(); @@ -552,9 +554,10 @@ class AsymmetricalCacheTest : public ::testing::TestWithParammLlmRequest); mManager->removeSequence(request->mLlmRequest->mRequestId, request->mLlmRequest); } requests.clear(); @@ -1814,6 +1818,63 @@ INSTANTIATE_TEST_CASE_P(AsymmetricCaseTest1WithCPForMLA, AsymmetricalCacheTest, /*generationDP*/ testing::Values(false), /*isWindow*/ testing::Values(false), testing::Values(false), testing::Values(0), testing::Values(128))); +// Tests high context PP with TP and CP variations on gen side with uneven layer distribution. +INSTANTIATE_TEST_CASE_P(AsymmetricCaseTest0WithCPForMLAUnevenLayer, AsymmetricalCacheTest, + testing::Combine(/*contextTp*/ testing::Values(1), + /*contextPp*/ testing::Values(1, 2, 4), + /*contextCp*/ testing::Values(1), + /*genTp*/ testing::Values(1, 2), + /*genPp*/ testing::Values(1), + /*genCp*/ testing::Values(2), + /*numLayers*/ testing::Values(5, 6, 7), + /*numHeads*/ testing::Values(1), + /*sizePerHead*/ testing::Values(4), + /*tokensPerBlock*/ testing::Values(8), + /*dataType*/ testing::Values(nvinfer1::DataType::kFLOAT, nvinfer1::DataType::kINT8), + /*kvFactor*/ testing::Values(1), + /*isMLA*/ testing::Values(true), + /*contextDP*/ testing::Values(false), + /*generationDP*/ testing::Values(false), + /*isWindow*/ testing::Values(false), testing::Values(false), testing::Values(0), testing::Values(128))); + +// Tests high context PP with PP and CP on gen side with uneven layer distribution. +INSTANTIATE_TEST_CASE_P(AsymmetricCaseTest1WithCPForMLAUnevenLayer, AsymmetricalCacheTest, + testing::Combine(/*contextTp*/ testing::Values(1), + /*contextPp*/ testing::Values(1, 2, 4), + /*contextCp*/ testing::Values(1), + /*genTp*/ testing::Values(1), + /*genPp*/ testing::Values(2), + /*genCp*/ testing::Values(2), + /*numLayers*/ testing::Values(5, 6, 7), + /*numHeads*/ testing::Values(1), + /*sizePerHead*/ testing::Values(4), + /*tokensPerBlock*/ testing::Values(8), + /*dataType*/ testing::Values(nvinfer1::DataType::kFLOAT, nvinfer1::DataType::kINT8), + /*kvFactor*/ testing::Values(1), + /*isMLA*/ testing::Values(true), + /*contextDP*/ testing::Values(false), + /*generationDP*/ testing::Values(false), + /*isWindow*/ testing::Values(false), testing::Values(false), testing::Values(0), testing::Values(128))); + +// Tests high context PP with pure CP on gen side with uneven layer distribution. +INSTANTIATE_TEST_CASE_P(AsymmetricCaseTest2WithCPForMLAUnevenLayer, AsymmetricalCacheTest, + testing::Combine(/*contextTp*/ testing::Values(1), + /*contextPp*/ testing::Values(1, 2, 4), + /*contextCp*/ testing::Values(1), + /*genTp*/ testing::Values(1), + /*genPp*/ testing::Values(1), + /*genCp*/ testing::Values(4), + /*numLayers*/ testing::Values(5, 6, 7), + /*numHeads*/ testing::Values(1), + /*sizePerHead*/ testing::Values(4), + /*tokensPerBlock*/ testing::Values(8), + /*dataType*/ testing::Values(nvinfer1::DataType::kFLOAT, nvinfer1::DataType::kINT8), + /*kvFactor*/ testing::Values(1), + /*isMLA*/ testing::Values(true), + /*contextDP*/ testing::Values(false), + /*generationDP*/ testing::Values(false), + /*isWindow*/ testing::Values(false), testing::Values(false), testing::Values(0), testing::Values(128))); + INSTANTIATE_TEST_CASE_P(AsymmetricCaseTestWithDPForMLA1, AsymmetricalCacheTestWithDP, testing::Combine(testing::Values(1, 2), testing::Values(1, 2), testing::Values(1), testing::Values(1, 2), testing::Values(1, 2), testing::Values(1), testing::Values(4), testing::Values(1), testing::Values(4), diff --git a/docker/Dockerfile.multi b/docker/Dockerfile.multi index a6bf164d1ad0..c2a2b8513389 100644 --- a/docker/Dockerfile.multi +++ b/docker/Dockerfile.multi @@ -75,6 +75,10 @@ RUN GITHUB_MIRROR=${GITHUB_MIRROR} \ COPY constraints.txt /tmp/constraints.txt RUN pip3 install --no-cache-dir -r /tmp/constraints.txt && rm /tmp/constraints.txt +# Remove nbconvert to avoid https://github.com/advisories/GHSA-xm59-rqc7-hhvf in the base NGC PyTorch image. +# Remove pillow to avoid https://github.com/advisories/GHSA-cfh3-3jmp-rvhc in the base NGC PyTorch image. +RUN pip3 uninstall -y nbconvert pillow || true + # Install UCX, NIXL, etcd # TODO: Combine these into the main install.sh script RUN GITHUB_MIRROR=${GITHUB_MIRROR} bash ./install_ucx.sh && \ @@ -82,7 +86,13 @@ RUN GITHUB_MIRROR=${GITHUB_MIRROR} bash ./install_ucx.sh && \ bash ./install_etcd.sh && \ rm install_ucx.sh && \ rm install_nixl.sh && \ - rm install_etcd.sh + rm install_etcd.sh && \ + rm -rf /root/.cache/pip && \ + rm -rf /root/.cache/uv/archive-v0 && \ + # WAR against https://github.com/advisories/GHSA-58pv-8j8x-9vj2 + rm -rf /usr/local/lib/python3.12/dist-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info && \ + # WAR against https://github.com/advisories/GHSA-8rrh-rw8j-w5fx + rm -rf /usr/local/lib/python3.12/dist-packages/setuptools/_vendor/wheel-0.45.1.dist-info # Generate OSS attribution file for devel image ARG TRT_LLM_VER @@ -175,7 +185,12 @@ RUN chmod -R a+w examples && \ benchmarks/cpp/gptManagerBenchmark.cpp \ benchmarks/cpp/disaggServerBenchmark.cpp \ benchmarks/cpp/CMakeLists.txt && \ - rm -rf /root/.cache/pip + rm -rf /root/.cache/pip && \ + rm -rf /root/.cache/uv/archive-v0 && \ + # WAR against https://github.com/advisories/GHSA-58pv-8j8x-9vj2 + rm -rf /usr/local/lib/python3.12/dist-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info && \ + # WAR against https://github.com/advisories/GHSA-8rrh-rw8j-w5fx + rm -rf /usr/local/lib/python3.12/dist-packages/setuptools/_vendor/wheel-0.45.1.dist-info ARG GIT_COMMIT ARG TRT_LLM_VER diff --git a/docker/common/install_etcd.sh b/docker/common/install_etcd.sh index a598daacc85d..663650dff438 100644 --- a/docker/common/install_etcd.sh +++ b/docker/common/install_etcd.sh @@ -2,7 +2,7 @@ set -ex -ETCD_VER=v3.5.21 +ETCD_VER=v3.6.9 # choose either URL DOWNLOAD_URL=https://storage.googleapis.com/etcd diff --git a/docs/source/blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.md b/docs/source/blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.md index fef8dcc93a21..1ed93734a41f 100644 --- a/docs/source/blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.md +++ b/docs/source/blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.md @@ -124,7 +124,7 @@ In the Dynamo workflow, requests are initially processed by pre- and post-proces Dynamo also includes built-in support for Kubernetes deployment, monitoring, and metrics collection. The development team is actively working on enabling dynamic instance scaling, further enhancing its suitability for production environments. -For more information on how to use Dynamo with TensorRT LLM, please refer to [this documentation](https://docs.nvidia.com/dynamo/latest/backends/trtllm/README.html). +For more information on how to use Dynamo with TensorRT LLM, please refer to [this documentation](https://docs.dynamo.nvidia.com/dynamo/backends/tensor-rt-llm). ### Triton Inference Server diff --git a/docs/source/deployment-guide/configuring-cpu-affinity.md b/docs/source/deployment-guide/configuring-cpu-affinity.md new file mode 100644 index 000000000000..01ca2c0bb6f3 --- /dev/null +++ b/docs/source/deployment-guide/configuring-cpu-affinity.md @@ -0,0 +1,208 @@ +# CPU Affinity configuration in TensorRT LLM + +## NUMA-aware affinity in TensorRT LLM + +TensorRT LLM is frequently deployed on +[NUMA](https://en.wikipedia.org/wiki/Non-uniform_memory_access) systems. In +order to ensure consistent and optimal performance on these systems, it is +critical to set the CPU affinity of the workers/tasks launched as part of a +particular TensorRT LLM instance so as to minimize latency and maximize bandwidth of +CPU↔GPU and CPU↔DRAM communication. + +Because TensorRT LLM does the work of allocating GPU/CUDA devices to ranks, it +is logically the ideal place for the CPU affinity to be determined and set. For +this reason, TensorRT LLM provides a mechanism to automatically set CPU +affinity according to NUMA topology. In some situations/deployments, the user +may wish to configure CPU affinity manually (i.e. using +[numactl](https://github.com/numactl/numactl), [wrappers around the +same](https://github.com/NVIDIA/mlperf-common/blob/main/client/bindpcie), or +mpirun). For this reason, this feature is only activated if it is explicitly +enabled or if CPU affinity is not already constrained by the user or +environment. It is controlled by the TLLM_NUMA_AWARE_WORKER_AFFINITY +environment variable as follows: + + +| TLLM_NUMA_AWARE_WORKER_AFFINITY | Behavior | +|---------------------------------|------------------------------------------------------------------------------------------------------------------------------| +| | Affinity is auto-configured if it is unconstrained, and cleared if it is constrained by the user and/or environment | +| 1 | Affinity is unconditionally auto-configured. | +| 0 or any other value | Affinity remains as configured by the user and/or environment | + + +## Other environmental considerations + +Whether or not the user chooses to manually configure CPU affinity or have +TensorRT LLM configure it automatically, the environment can also constrain the +CPU affinity in a way that subverts the user's intent. Both OpenMPI and Slurm +may configure CPU affinity, so the following additional configuration is +recommended to avoid this. + +### OpenMPI + +By default, OpenMPI chooses a rank-wise CPU affinity that is not sensitized to +the NUMA-topology of the system. Because it does not know which GPU a +particular rank will be communicating with (this is determined by TensorRT LLM at +runtime), it cannot set the CPU affinity accordingly. For this reason, it is +recommended that OpenMPI's default binding policy be disabled as follows: + +```bash +export OMPI_MCA_hwloc_base_binding_policy=none +export OMPI_MCA_rmaps_base_inherit=1 +``` + +The first environment variable ensures that OpenMPI will not attempt to bind or +set the affinity of the ranks that are created at launch. + +The second ensures that OpenMPI's binding policy will propagate to MPI workers +that are spawned by `mpi4py`'s `MPIPoolExecutor` class within TensorRT LLM +(when using mpirun). + +### Slurm + +If Slurm is configured to use a affinity or cgroup task plugin, then Slurm may +also configure CPU affinity by default in a way that is not sensitized to NUMA +topology. To prevent this, Slurm jobs should be launched accordingly: + +#### srun + +The srun parameters should include `--cpu-bind=none` and exclude `--exclusive`: + +```bash +srun --cpu-bind=none ... +``` + +#### sbatch + +The sbatch script should set `SLURM_CPU_BIND` environment variable to "none": + +```bash +export SLURM_CPU_BIND=none +``` + +Note: if this environment variable is set, it is not necessary to supply the +`--cpu-bind=none` to each job step (srun invocation) + +## CPU affinity configuration examples + +### Using NUMA-aware autoconfiguration + +To explicitly enable the NUMA-aware autoconfiguration feature in TensorRT LLM, +simply set `TLLM_NUMA_AWARE_WORKER_AFFINITY` in the launch script (prior to +`trtllm-bench` or `trtllm-serve`) as follows: + +```bash +export TLLM_NUMA_AWARE_WORKER_AFFINITY=1 +``` + +Because autoconfiguration happens within TensorRT LLM itself, it will override +any CPU affinity or binding that has been previously set by OpenMPI or Slurm. + +### NUMA-aware CPU affinity using [bindpcie](https://github.com/NVIDIA/mlperf-common/blob/main/client/bindpcie) + +The bindpcie script is designed to set a per-rank CPU affinity that is ideal +for NUMA topology. While setting `TLLM_NUMA_AWARE_WORKER_AFFINITY=1` usually +achieves the same result in terms of the CPU affinity that is set, this +approach has the distinct advantage that the optimal CPU affinity gets set +_upon launching_ TensorRT LLM, guaranteeing that each worker/rank executes on +the optimal NUMA node from inception. The NUMA-aware CPU affinity +autoconfiguration mechanism in TensorRT LLM, on the other hand, is triggered by +each worker/rank upon its own PID _after_ it has already launched. If the +worker/rank executes on a NUMA node other than the optimal NUMA node at some +point between the launch of the process and the NUMA-aware autoconfiguration, +it is possible that some CPU memory may have been allocated/touched on what +will become a remote NUMA node after the point of autoconfiguration, +potentially negatively impacting performance. In practice, this effect has been +observed to have minimal performance impact, but some degradation of +performance due to remote NUMA node access is still theoretically possible. + +The `bindpcie` script can only be applied to deployments that make use of +`trtllm-llmapi-launch` within an sbatch script. One example of how to apply +bindpcie to `trtllm-serve` in an sbatch script is as follows: + +```bash +# Prevent TensorRT LLM from autoconfiguring or clearing CPU affinity +export TLLM_NUMA_AWARE_WORKER_AFFINITY=0 + +# Prevent OpenMPI from overriding affinity set by bindpcie +export OMPI_MCA_hwloc_base_binding_policy=none + +# Ensure that MPI binding policy propagates to any MPI workers dynamically +# spawned by MPIPoolExecutor +export OMPI_MCA_rmaps_base_inherit=1 + +# Prevent Slurm from assigning a default CPU affinity +export SLURM_CPU_BIND=none + +srun -l \ + --container-image=${CONTAINER_IMAGE} \ + --container-mounts=${MOUNT_DIR}:${MOUNT_DEST} \ + --container-workdir=${WORKDIR} \ + --export=ALL,PYTHONPATH=${SOURCE_ROOT} \ + --mpi=pmix \ + bash -c " + set -ex + $PROLOGUE + export PATH=$PATH:~/.local/bin + + bindpcie trtllm-llmapi-launch \ + trtllm-serve $LOCAL_MODEL \ + ${ADDITIONAL_OPTIONS} +``` + +> [!NOTE] +> This is not a complete or exhaustive example of an sbatch script to launch +> trtllm-serve and is only intended to highlight the application of bindpcie +> within an existing sbatch script. + +### Using [numactl](https://github.com/numactl/numactl) + +```bash +# Prevent TensorRT LLM from autoconfiguring or clearing CPU affinity +export TLLM_NUMA_AWARE_WORKER_AFFINITY=0 + +# Prevent OpenMPI from overriding affinity set by numactl +export OMPI_MCA_hwloc_base_binding_policy=none + +# Ensure that MPI binding policy propagates to any MPI workers dynamically +# spawned by MPIPoolExecutor +export OMPI_MCA_rmaps_base_inherit=1 + +# Use numactl to specify CPU and memory binding for all ranks (not per-rank) +numactl --physcpubind=0,1,16,17 --membind=0 mpirun --report-bindings --oversubscribe --allow-run-as-root \ + +``` + +### Using mpirun + +If a manually-specified per-rank CPU affinity is desired when running on a +single node with mpirun, this can be achieved most easily using an OpenMPI +rankfile. The following is an example of how a rankfile can be used to +arbitrarily map each of 4 MPI ranks to a distinct set of 4 cores: + +```bash +# Prevent TensorRT LLM from autoconfiguring or clearing CPU affinity +export TLLM_NUMA_AWARE_WORKER_AFFINITY=0 + +# Not strictly needed here, since we are overriding with explicit bindings from +# a rankfile +# export OMPI_MCA_hwloc_base_binding_policy=none + +# Ensure that MPI binding policy propagates to any MPI workers dynamically +# spawned by MPIPoolExecutor +export OMPI_MCA_rmaps_base_inherit=1 + +# Create a rankfile to enumerate a set of 4 cores to which each rank is bound +cat > ./rankfile < +``` + +See the official [OpenMPI Documentation](https://www.open-mpi.org/doc/) for +more details on mapping and binding of MPI ranks. diff --git a/docs/source/developer-guide/perf-overview.md b/docs/source/developer-guide/perf-overview.md index 8602ff18969e..ba0df2ed16f1 100644 --- a/docs/source/developer-guide/perf-overview.md +++ b/docs/source/developer-guide/perf-overview.md @@ -9,25 +9,24 @@ It should *not* be considered as the peak performance that can be delivered by T Not all configurations were tested for all GPUs. -We attempted to keep commands as simple as possible to ease reproducibility and left many options at their default settings. -Tuning batch sizes, parallelism configurations, and other options may lead to improved performance depending on your situation. +Commands are kept as simple as possible to ease reproducibility, with many options left at their default settings. +Tune batch sizes, parallelism configurations, and other options to improve performance for your specific situation. -For DeepSeek R1 performance, please check out our [performance guide](../blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.md) +For DeepSeek R1 performance, see the [performance guide](../blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.md). -For more information on benchmarking with `trtllm-bench` see this NVIDIA [blog post](https://developer.nvidia.com/blog/llm-inference-benchmarking-performance-tuning-with-tensorrt-llm/). +For more information on benchmarking with `trtllm-bench`, see this NVIDIA [blog post](https://developer.nvidia.com/blog/llm-inference-benchmarking-performance-tuning-with-tensorrt-llm/). -## Throughput Measurements - -The below table shows performance data where a local inference client is fed requests at an high rate / no delay between messages, -and shows the throughput scenario under maximum load. The reported metric is `Output Throughput per GPU (tokens/sec/GPU)`. +For NUMA systems, consult the ["CPU Affinity configuration in TensorRT LLM"](../deployment-guide/configuring-cpu-affinity.md) guide to achieve best performance. These options were enabled for relevant tests. -The performance numbers below were collected using the steps described in this document. +## Throughput Measurements -Testing was performed on models with weights quantized using [ModelOpt](https://nvidia.github.io/Model-Optimizer/) and published by NVIDIA on the [Model Optimizer HuggingFace Collection](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4). +The tables below show performance data where a local inference client sends requests at a high rate with no delay between requests, +representing the throughput scenario under maximum load. The reported metric is `Output Throughput per GPU (tokens/sec/GPU)`. -RTX 6000 Pro Blackwell Server Edition data is now included in the perf overview. RTX 6000 systems can benefit from enabling pipeline parallelism (PP) in LLM workloads, so we included several new benchmarks for this GPU at various TP x PP combinations. That data is presented in a separate table for each network. +The performance numbers below were collected using the steps described in [Reproducing Benchmarked Results](#reproducing-benchmarked-results). +All tested models use weights quantized with [ModelOpt](https://nvidia.github.io/Model-Optimizer/) and published by NVIDIA on the [Model Optimizer HuggingFace Collection](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4). ### Hardware The following GPU variants were used for testing: @@ -37,6 +36,10 @@ The following GPU variants were used for testing: - GB200 192GB (GB200 NVL72) - RTX 6000 Pro Blackwell Server Edition +Note: As of `release/1.2`, support for B300 and GB300 is in beta, and performance data should not be considered finalized. +- B300 288GB (DGX B300) (beta) +- GB300 288 GB (GB300 NVL72) (beta) + Other hardware variants may have different TDP, memory bandwidth, core count, or other features leading to performance differences on these workloads. ### FP4 Models @@ -46,7 +49,6 @@ nvidia/DeepSeek-R1-0528-NVFP4-v2 nvidia/Qwen3-235B-A22B-FP4 nvidia/Qwen3-30B-A3B-FP4 nvidia/Llama-3.3-70B-Instruct-FP4 -nvidia/Llama-4-Maverick-17B-128E-Instruct-NVFP4 ``` ### FP8 Models @@ -55,27 +57,29 @@ nvidia/Llama-4-Maverick-17B-128E-Instruct-NVFP4 deepseek-ai/DeepSeek-R1-0528 nvidia/Qwen3-235B-A22B-FP8 nvidia/Llama-3.3-70B-Instruct-FP8 -nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8 ``` # Performance Summary - All Networks -## Units - -All performance values are measured in `output tokens per second per GPU`, where `output tokens` includes the first and all subsequent generated tokens (input tokens are not included). +All performance values are measured in `output tokens per second per GPU`, where `output tokens` refers to all generated tokens (excluding input/prompt tokens). Data in these tables is taken from the `Per GPU Output Throughput (tps/gpu)` metric reported by `trtllm-bench`. -The calculations for metrics reported by trtllm-bench can be found in the dataclasses [reporting.py](../../../tensorrt_llm/bench/dataclasses/reporting.py#L570) and [statistics.py](../../../tensorrt_llm/bench/dataclasses/statistics.py#L188) +The metric calculations are defined in [reporting.py](../../../tensorrt_llm/bench/dataclasses/reporting.py#L570) and [statistics.py](../../../tensorrt_llm/bench/dataclasses/statistics.py#L188). + +RTX 6000 systems can benefit from enabling pipeline parallelism (PP) in LLM workloads. Results for various TP x PP combinations on this GPU are presented in a separate table for each network. +## Units + +All performance values are measured in **output tokens per second per GPU**. ## Table of Contents - [Deepseek R1 0528](#deepseek-r1-0528) + - [Deepseek R1 0528 - RTX 6000 Pro Blackwell Server Edition](#deepseek-r1-0528-rtx-configurations) - [GPT-OSS 120B](#gpt-oss-120b) - [GPT-OSS 20B](#gpt-oss-20b) - [LLaMA v3.3 70B](#llama-v33-70b) - [LLaMA v3.3 70B - RTX 6000 Pro Blackwell Server Edition](#llama-v33-70b-rtx-configurations) -- [LLaMA v4 Maverick](#llama-v4-maverick) - [Qwen3 235B A22B](#qwen3-235b-a22b) - [Qwen3 235B A22B - RTX 6000 Pro Blackwell Server Edition](#qwen3-235b-a22b-rtx-configurations) - [Qwen3 30B A3B](#qwen3-30b-a3b) @@ -87,15 +91,15 @@ The calculations for metrics reported by trtllm-bench can be found in the datacl # Deepseek R1 0528 -| Sequence Length (ISL/OSL) | B200
DEP4 (FP4) | GB200
DEP4 (FP4) | H200
DEP8 (FP8) | -|---|---|---|---| -| 1000/1000 | 6,463 | 6,939 | 1,627 | -| 1024/1024 | 6,430 | 6,924 | 1,620 | -| 1024/8192 | 3,862 | 4,379 | 1,218 | -| 1024/32768 | 1,451 | 1,465 | 438 | -| 8192/1024 | 1,168 | 1,192 | | +| Sequence Length (ISL/OSL) | B200
DEP4 (FP4) | B300* (beta)
DEP4 (FP4) | GB200
DEP4 (FP4) | GB300* (beta)
DEP4 (FP4) | H200
DEP8 (FP8) | +|---|---|---|---|---|---| +| 1024/1024 | 5,757 | 6,046 | 4,989 | 4,981 | 1,724 | +| 1024/8192 | 4,228 | 6,235 | 4,474 | 6,842 | 1,335 | +| 1024/32768 | 1,472 | 2,177 | 1,582 | 2,904 | | +| 8192/1024 | 1,176 | 1,489 | 1,276 | 1,599 | | +| 32768/1024 | | | | 335 | | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- @@ -103,16 +107,15 @@ unit: `output tokens per second per GPU` # GPT-OSS 120B -| Sequence Length (ISL/OSL) | B200
DEP2 (FP4) | GB200
TP1 (FP4) | H200
TP1 (FP8) | H100
DEP4 (FP8) | -|---|---|---|---|---| -| 1000/1000 | 25,943 | 27,198 | 6,868 | 4,685 | -| 1024/1024 | 25,870 | 26,609 | 6,798 | 4,715 | -| 1024/8192 | 17,289 | 14,800 | 3,543 | | -| 1024/32768 | 6,279 | 5,556 | | 1,177 | -| 8192/1024 | 6,111 | 6,835 | 1,828 | 1,169 | -| 32768/1024 | 1,392 | 1,645 | 519 | 333 | +| Sequence Length (ISL/OSL) | B200
DEP2 (FP4) | B300* (beta)
TP1 (FP4) | GB200
TP1 (FP4) | GB300* (beta)
TP1 (FP4) | H200
DEP2 (FP8) | +|---|---|---|---|---|---| +| 1024/1024 | 29,061 | 34,737 | 29,056 | 38,702 | 6,391 | +| 1024/8192 | 16,434 | 18,338 | 17,079 | 19,866 | 4,260 | +| 1024/32768 | 7,020 | 7,284 | 6,313 | 7,958 | | +| 8192/1024 | 6,938 | 8,361 | 7,874 | 9,407 | 1,881 | +| 32768/1024 | 1,623 | 2,052 | 1,929 | 2,307 | 500 | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- @@ -120,16 +123,15 @@ unit: `output tokens per second per GPU` # GPT-OSS 20B -| Sequence Length (ISL/OSL) | B200
TP1 (FP4) | GB200
TP1 (FP4) | H200
TP1 (FP8) | H100
TP1 (FP8) | -|---|---|---|---|---| -| 1000/1000 | 53,812 | 55,823 | 13,858 | 11,557 | -| 1024/1024 | 53,491 | 56,528 | 13,890 | 11,403 | -| 1024/8192 | 34,702 | 38,100 | 12,743 | 8,617 | -| 1024/32768 | 14,589 | 16,463 | | | -| 8192/1024 | 11,904 | 12,941 | 4,015 | 3,366 | -| 32768/1024 | 2,645 | 2,905 | 915 | 785 | +| Sequence Length (ISL/OSL) | B200
TP1 (FP4) | B300* (beta)
TP1 (FP4) | GB200
TP1 (FP4) | GB300* (beta)
TP1 (FP4) | H200
TP1 (FP8) | +|---|---|---|---|---|---| +| 1024/1024 | 59,084 | 60,925 | 51,870 | 53,604 | 14,719 | +| 1024/8192 | 37,824 | 42,986 | 35,280 | 44,998 | 12,441 | +| 1024/32768 | 15,799 | 17,461 | 17,414 | 19,147 | 4,240 | +| 8192/1024 | 13,439 | 14,955 | 15,466 | 16,880 | 4,196 | +| 32768/1024 | 2,970 | 3,408 | 3,331 | 3,833 | 965 | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- @@ -137,49 +139,31 @@ unit: `output tokens per second per GPU` # LLaMA v3.3 70B -| Sequence Length (ISL/OSL) | B200
TP1 (FP4) | GB200
TP1 (FP4) | H200
TP2 (FP8) | H100
TP2 (FP8) | -|---|---|---|---|---| -| 1000/1000 | 6,920 | 7,769 | 2,587 | 2,209 | -| 1024/1024 | 6,842 | 7,751 | 2,582 | | -| 1024/8192 | 3,242 | 3,805 | 2,009 | | -| 8192/1024 | 1,362 | 1,491 | 537 | 398 | -| 32768/1024 | 274 | 302 | 120 | | +| Sequence Length (ISL/OSL) | B200
TP1 (FP4) | B300* (beta)
TP1 (FP4) | GB200
TP1 (FP4) | GB300* (beta)
TP1 (FP4) | H200
TP2 (FP8) | +|---|---|---|---|---|---| +| 1024/1024 | 6,943 | 8,196 | 7,576 | 8,910 | 2,637 | +| 1024/8192 | 3,270 | 3,926 | 3,856 | 4,739 | 1,995 | +| 8192/1024 | 1,347 | 1,597 | 1,593 | 1,900 | 544 | +| 32768/1024 | 279 | 351 | 315 | 418 | 120 | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- - + # LLaMA v3.3 70B - RTX 6000 Pro Blackwell Server Edition *Shows Tensor Parallel (TP) and Pipeline Parallel (PP) configurations* -| Sequence Length (ISL/OSL) | **1 GPUs**
TP1,PP1 (FP4) | **2 GPUs**
TP1,PP2 (FP4) | -|---|---|---| -| 1000/1000 | 1,724 | 1,901 | -| 1024/1024 | 1,708 | 1,887 | -| 8192/1024 | 296 | 327 | -| 32768/1024 | | 67 | - -unit: `output tokens per second per GPU` - ---- - - - -# LLaMA v4 Maverick - -| Sequence Length (ISL/OSL) | B200
DEP4 (FP4) | GB200
DEP4 (FP4) | H200
DEP8 (FP8) | -|---|---|---|---| -| 1000/1000 | 11,337 | 11,828 | 4,146 | -| 1024/1024 | 11,227 | 11,905 | 4,180 | -| 1024/8192 | 5,174 | 5,508 | 1,157 | -| 1024/32768 | 2,204 | 2,300 | 679 | -| 8192/1024 | 3,279 | 3,444 | 1,276 | -| 32768/1024 | 859 | 963 | | +| Sequence Length (ISL/OSL) | **1 GPUs**
TP1,PP1 (FP4) | **2 GPUs**
TP1,PP2 (FP4) | **4 GPUs**
TP1,PP4 (FP4) | **8 GPUs**
TP1,PP8 (FP4) | +|---|---|---|---|---| +| 1024/1024 | 1,724 | 1,881 | 1,798 | 1,545 | +| 1024/8192 | | | 675 | 630 | +| 8192/1024 | 306 | 329 | 323 | 307 | +| 32768/1024 | | 66 | 66 | 64 | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- @@ -187,33 +171,32 @@ unit: `output tokens per second per GPU` # Qwen3 235B A22B -| Sequence Length (ISL/OSL) | B200
DEP4 (FP4) | GB200
DEP4 (FP4) | H200
DEP4 (FP8) | H100
DEP8 (FP8) | -|---|---|---|---|---| -| 1000/1000 | 5,764 | 6,172 | 3,288 | 1,932 | -| 1024/1024 | 5,756 | 5,862 | 3,268 | 1,935 | -| 1024/8192 | 3,389 | 3,423 | 1,417 | 873 | -| 1024/32768 | 1,255 | | | | -| 8192/1024 | 1,410 | 1,464 | 627 | | -| 32768/1024 | 319 | 333 | 134 | | +| Sequence Length (ISL/OSL) | B200
DEP4 (FP4) | B300* (beta)
DEP4 (FP4) | GB200
DEP4 (FP4) | GB300* (beta)
DEP4 (FP4) | H200
DEP4 (FP8) | +|---|---|---|---|---|---| +| 1024/1024 | 6,423 | 8,143 | 6,777 | 8,430 | 3,494 | +| 1024/8192 | 3,881 | 4,938 | 3,955 | 5,110 | 1,677 | +| 1024/32768 | 1,216 | 1,922 | 1,240 | 1,841 | | +| 8192/1024 | 1,518 | 1,730 | 1,575 | 1,791 | 679 | +| 32768/1024 | 326 | 383 | 342 | 398 | 143 | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- - + # Qwen3 235B A22B - RTX 6000 Pro Blackwell Server Edition *Shows Tensor Parallel (TP) and Pipeline Parallel (PP) configurations* -| Sequence Length (ISL/OSL) | **4 GPUs**
DEP2,PP2 (FP4) | **8 GPUs**
DEP8,PP1 (FP4) | +| Sequence Length (ISL/OSL) | **4 GPUs**
TP1,PP4 (FP4) | **8 GPUs**
TP1,PP8 (FP4) | |---|---|---| -| 1000/1000 | 1,731 | 969 | -| 1024/1024 | 1,732 | 963 | -| 1024/8192 | 644 | 711 | -| 32768/1024 | 70 | | +| 1024/1024 | 1,529 | 1,185 | +| 1024/8192 | 495 | 495 | +| 8192/1024 | 343 | 337 | +| 32768/1024 | 81 | 83 | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- @@ -221,50 +204,43 @@ unit: `output tokens per second per GPU` # Qwen3 30B A3B -| Sequence Length (ISL/OSL) | B200
TP1 (FP4) | GB200
TP1 (FP4) | -|---|---|---| -| 1000/1000 | 26,971 | 22,856 | -| 1024/1024 | 26,611 | 22,201 | -| 1024/8192 | 13,497 | 14,272 | -| 1024/32768 | 4,494 | 4,925 | -| 8192/1024 | 5,735 | 6,201 | -| 32768/1024 | 1,265 | 1,380 | +| Sequence Length (ISL/OSL) | B200
TP1 (FP4) | B300* (beta)
TP1 (FP4) | GB200
TP1 (FP4) | GB300* (beta)
TP1 (FP4) | +|---|---|---|---|---| +| 1024/1024 | 26,431 | 30,714 | 19,682 | 22,536 | +| 1024/8192 | 13,940 | 15,182 | 14,916 | 16,920 | +| 1024/32768 | 4,570 | 5,209 | | | +| 8192/1024 | 5,945 | 6,374 | 6,450 | 6,947 | +| 32768/1024 | 1,284 | 1,452 | 1,398 | 1,575 | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- - + # Qwen3 30B A3B - RTX 6000 Pro Blackwell Server Edition *Shows Tensor Parallel (TP) and Pipeline Parallel (PP) configurations* -| Sequence Length (ISL/OSL) | **2 GPUs**
DEP2,PP1 (FP4) | **4 GPUs**
DEP2,PP2 (FP4) | **8 GPUs**
DEP8,PP1 (FP4) | **1 GPUs**
TP1,PP1 (FP4) | +| Sequence Length (ISL/OSL) | **1 GPUs**
TP1,PP1 (FP4) | **2 GPUs**
TP1,PP2 (FP4) | **4 GPUs**
TP1,PP4 (FP4) | **8 GPUs**
TP1,PP8 (FP4) | |---|---|---|---|---| -| 1000/1000 | 8,409 | 7,059 | 3,985 | 9,938 | -| 1024/1024 | | 7,019 | | 9,755 | -| 1024/8192 | 3,577 | | 2,406 | 3,621 | -| 8192/1024 | | 1,416 | | 1,914 | -| 32768/1024 | | | 180 | 374 | +| 1024/1024 | 9,908 | 8,187 | 6,931 | 3,065 | +| 1024/8192 | 3,628 | 3,281 | 3,024 | 1,968 | +| 1024/32768 | | | | 918 | +| 8192/1024 | 1,925 | 1,805 | 1,656 | 1,193 | +| 32768/1024 | 372 | 356 | 349 | 318 | -unit: `output tokens per second per GPU` +*Unit: output tokens per second per GPU* --- - - ## Reproducing Benchmarked Results -```{note} -Only the models shown in the table above are supported by this workflow. -``` - -The following tables are references for commands that are used as part of the benchmarking process. For a more detailed description of this benchmarking workflow, see the [benchmarking suite documentation](./perf-benchmarking.md). +The following tables provide reference commands used in the benchmarking process. For a more detailed description, see the [benchmarking suite documentation](./perf-benchmarking.md). ### Command Overview -Testing was performed using the PyTorch backend - this workflow does not require an engine to be built. +This workflow uses the PyTorch backend and does not require building an engine. | Stage | Description | Command | | :- | - | - | @@ -288,23 +264,20 @@ Testing was performed using the PyTorch backend - this workflow does not require ### Preparing a Dataset -In order to prepare a dataset, you can use the provided [script](source:benchmarks/cpp/prepare_dataset.py). -To generate a synthetic dataset, run the following command: +Use the provided [script](source:benchmarks/cpp/prepare_dataset.py) to generate a synthetic dataset. +Run the following command: ```shell python benchmarks/cpp/prepare_dataset.py --tokenizer=$model_name --stdout token-norm-dist --num-requests=$num_requests --input-mean=$isl --output-mean=$osl --input-stdev=0 --output-stdev=0 > $dataset_file ``` -The command will generate a text file located at the path specified `$dataset_file` where all requests are of the same -input/output sequence length combinations. The script works by using the tokenizer to retrieve the vocabulary size and -randomly sample token IDs from it to create entirely random sequences. In the command above, all requests will be uniform +This generates a text file at the path specified by `$dataset_file` where all requests share the same +input/output sequence length combination. The script uses the tokenizer to retrieve the vocabulary size and +randomly samples token IDs to create entirely random sequences. All requests are uniform because the standard deviations for both input and output sequences are set to 0. -For each input and output sequence length combination, the table below details the `$num_requests` that were used. For -shorter input and output lengths, a larger number of messages were used to guarantee that the system hit a steady state -because requests enter and exit the system at a much faster rate. For longer input/output sequence lengths, requests -remain in the system longer and therefore require less requests to achieve steady state. +The table below lists the `$num_requests` used for each input/output sequence length combination. Shorter sequences cycle through faster and need more requests to reach steady state; longer sequences need fewer. | Input Length | Output Length | Number of Requests | @@ -317,13 +290,11 @@ remain in the system longer and therefore require less requests to achieve stead ### Running the Benchmark -To run the benchmark with the generated data set, simply use the `trtllm-bench throughput` subcommand. The benchmarker will -run an offline maximum throughput scenario such that all requests are queued in rapid succession. You simply need to provide -a model name (HuggingFace reference or path to a local model), a [generated dataset](#preparing-a-dataset), and a file containing any desired extra options to the LLM APIs (details in [tensorrt_llm/llmapi/llm_args.py:LlmArgs](source:tensorrt_llm/llmapi/llm_args.py)). +Run the benchmark using the `trtllm-bench throughput` subcommand. This runs an offline maximum throughput scenario where all requests are queued in rapid succession. Provide a model name (HuggingFace reference or path to a local model), a [generated dataset](#preparing-a-dataset), and a YAML config file with additional LLM options (see [tensorrt_llm/llmapi/llm_args.py:LlmArgs](source:tensorrt_llm/llmapi/llm_args.py)). For dense / non-MoE models: ```shell -trtllm-bench --tp $tp_size --pp $pp_size --model $model_name throughput --dataset $dataset_file --backend pytorch --config $llm_options +trtllm-bench --tp $tp_size --pp $pp_size --model $model_name throughput --dataset $dataset_file --backend pytorch --config $llm_options --concurrency -1 ``` Llama 3.3 @@ -337,7 +308,7 @@ cuda_graph_config: For MoE models: ```shell -trtllm-bench --tp $tp_size --pp $pp_size --ep $ep_size --model $model_name throughput --dataset $dataset_file --backend pytorch --config $llm_options +trtllm-bench --tp $tp_size --pp $pp_size --ep $ep_size --model $model_name throughput --dataset $dataset_file --backend pytorch --config $llm_options --concurrency -1 ``` GPT-OSS: @@ -352,7 +323,7 @@ kv_cache_config: dtype: fp8 # Hopper: use auto moe_config: - backend: CUTLASS + backend: TRTLLM # Hopper: use TRITON ``` @@ -374,6 +345,20 @@ kv_cache_config: dtype: fp8 ``` +Kimi K2: + +`llm_options.yml` +```yaml +enable_attention_dp: true +cuda_graph_config: + enable_padding: true + batch_sizes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 384] +moe_config: + backend: CUTLASS +kv_cache_config: + dtype: auto +``` + Qwen3 MoE, Llama4 Maverick: `llm_options.yml` @@ -384,9 +369,7 @@ cuda_graph_config: batch_sizes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 384, 512, 1024, 2048, 4096, 8192] ``` -In many cases, we also use a higher KV cache percentage by setting `--kv_cache_free_gpu_mem_fraction 0.95` in the benchmark command. This allows us to obtain better performance than the default setting of `0.90`. We fall back to `0.90` or lower if out-of-memory errors are encountered. - -The results will be printed to the terminal upon benchmark completion. For example, +Results are printed to the terminal upon benchmark completion. For example: ```shell =========================================================== @@ -434,6 +417,5 @@ Average time-per-output-token [TPOT] (ms): 182.9639 [Latency] AVERAGE: 75903.4456 ``` -> [!WARNING] In some cases, the benchmarker may not print anything at all. This behavior usually -means that the benchmark has hit an out of memory issue. Try reducing the KV cache percentage -using the `--kv_cache_free_gpu_mem_fraction` option to lower the percentage of used memory. +> [!WARNING] If the benchmarker does not print any output, this typically indicates an out-of-memory issue. Reduce the KV cache percentage +using the `--kv_cache_free_gpu_mem_fraction` option to lower memory usage. diff --git a/docs/source/features/disagg-serving.md b/docs/source/features/disagg-serving.md index 5811a52109fd..ac8367acc127 100644 --- a/docs/source/features/disagg-serving.md +++ b/docs/source/features/disagg-serving.md @@ -107,7 +107,7 @@ In the Dynamo workflow, requests are initially processed by pre- and post-proces Dynamo also includes built-in support for Kubernetes deployment, monitoring, and metrics collection. The development team is actively working on enabling dynamic instance scaling, further enhancing its suitability for production environments. -For more information on how to use Dynamo with TensorRT-LLM, please refer to [this documentation](https://docs.nvidia.com/dynamo/latest/backends/trtllm/README.html). +For more information on how to use Dynamo with TensorRT-LLM, please refer to [this documentation](https://docs.dynamo.nvidia.com/dynamo/backends/tensor-rt-llm). ### trtllm-serve diff --git a/docs/source/features/sampling.md b/docs/source/features/sampling.md index 5d6db8626b9a..1db9455305f6 100644 --- a/docs/source/features/sampling.md +++ b/docs/source/features/sampling.md @@ -1,19 +1,50 @@ # Sampling -The PyTorch backend supports most of the sampling features that are supported on the C++ backend, such as temperature, top-k and top-p sampling, beam search, stop words, bad words, penalty, context and generation logits, log probability and logits processors + +The PyTorch backend supports a wide variety of features, listed below: + +| Forward Pass | Sampling Strategies | Sampling Features | +|--------------------|----------------------------------|--------------------------------| +| No drafting | Greedy | Guided Decoding | +| Draft target model | TopP | Plugging Logits Post-Processor | +| Eagle 3 | TopK | Temperature | +| Ngram | TopK + TopP | MinP | +| | Beam Search | Embedding / Logits Bias | +| | Best of / n (composable) | Stop criteria | +| | Rejection sampling (composable) | Return Logits | +| | | Return LogProbs | +| | | TopK LogProbs | ## General usage -To use the feature: +There are two sampling backends available. -1. Enable the `enable_trtllm_sampler` option in the `LLM` class -2. Pass a [`SamplingParams`](source:tensorrt_llm/sampling_params.py#L125) object with the desired options to the `generate()` function +* Torch Sampler +* TRTLLM Sampler -The following example prepares two identical prompts which will give different results due to the sampling parameters chosen: +Torch Sampler currently supports a superset of features of TRTLLM Sampler, and is intended as the long-term solution. One can specify which sampler to use explicitly with: ```python -from tensorrt_llm import LLM, SamplingParams +from tensorrt_llm import LLM + +# Chooses TorchSampler explicitly llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8', - enable_trtllm_sampler=True) + sampler_type="TorchSampler") + +# Chooses TRTLLMSampler explicitly +llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8', + sampler_type="TRTLLMSampler") +``` + +By default, the sampling backend is chosen to be `auto`. This will use: + +* TRTLLM Sampler when using Beam Search. +* Torch Sampler otherwise. + +Here is an example to run a model with basic usage of sampling parameters. This example prepares two identical prompts which will give different results due to the sampling parameters chosen: + +```python +from tensorrt_llm import LLM, SamplingParams +llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8') sampling_params = SamplingParams( temperature=1.0, top_k=8, @@ -23,7 +54,24 @@ llm.generate(["Hello, my name is", "Hello, my name is"], sampling_params) ``` -Note: The `enable_trtllm_sampler` option is not currently supported when using speculative decoders, such as MTP or Eagle-3, so there is a smaller subset of sampling options available. +It is also possible to specify different sampling parameters on a per-prompt basis: + +```python +from tensorrt_llm import LLM, SamplingParams +llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8') +sampling_params_0 = SamplingParams( + temperature=1.0, + top_k=8, + top_p=0.5, + ) +sampling_params_1 = SamplingParams( + top_k=4, + ) +llm.generate(["Hello, my name is", + "Hello, my name is"], + [sampling_params_0, + sampling_params_1]) +``` ### LLM API sampling behavior when using Torch Sampler @@ -79,8 +127,6 @@ To enable beam search, you must: 1. Enable the `use_beam_search` option in the `SamplingParams` object 2. Set the `max_beam_width` parameter in the `LLM` class to match the `best_of` parameter in `SamplingParams` -3. Disable overlap scheduling using the `disable_overlap_scheduler` parameter of the `LLM` class -4. Disable the usage of CUDA Graphs by passing `None` to the `cuda_graph_config` parameter of the `LLM` class Parameter Configuration: - `best_of`: Controls the number of beams processed during generation (beam width) @@ -93,10 +139,8 @@ The following example demonstrates beam search with a beam width of 4, returning ```python from tensorrt_llm import LLM, SamplingParams llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8', - enable_trtllm_sampler=True, max_beam_width=4, # must equal SamplingParams.best_of - disable_overlap_scheduler=True, - cuda_graph_config=None) + ) sampling_params = SamplingParams( best_of=4, # must equal LLM.max_beam_width use_beam_search=True, diff --git a/docs/source/index.rst b/docs/source/index.rst index 49c5e1546cc1..93603e3e1a38 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -14,6 +14,7 @@ Welcome to TensorRT LLM's Documentation! overview.md quick-start-guide.md installation/index.rst + supported-hardware.md .. toctree:: @@ -25,6 +26,7 @@ Welcome to TensorRT LLM's Documentation! examples/trtllm_serve_examples examples/dynamo_k8s_example.rst deployment-guide/index.rst + deployment-guide/configuring-cpu-affinity.md .. toctree:: :maxdepth: 2 diff --git a/docs/source/installation/linux.md b/docs/source/installation/linux.md index a9704f9cadcd..59fa15a9749c 100644 --- a/docs/source/installation/linux.md +++ b/docs/source/installation/linux.md @@ -9,15 +9,15 @@ Before the pre-built Python wheel can be installed via `pip`, a few prerequisites must be put into place: - Install CUDA Toolkit 13.0 following the [CUDA Installation Guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/) + Install CUDA Toolkit 13.1 following the [CUDA Installation Guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/) and make sure `CUDA_HOME` environment variable is properly set. - The `cuda-compat-13-0` package may be required depending on your system's NVIDIA GPU + The `cuda-compat-13-1` package may be required depending on your system's NVIDIA GPU driver version. For additional information, refer to the [CUDA Forward Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/forward-compatibility.html). ```bash # By default, PyTorch CUDA 12.8 package is installed. Install PyTorch CUDA 13.0 package to align with the CUDA version used for building TensorRT LLM wheels. - pip3 install torch==2.9.0 torchvision --index-url https://download.pytorch.org/whl/cu130 + pip3 install torch==2.9.1 torchvision --index-url https://download.pytorch.org/whl/cu130 sudo apt-get -y install libopenmpi-dev @@ -37,8 +37,11 @@ Once all prerequisites are in place, TensorRT LLM can be installed as follows: ```bash - pip3 install --upgrade pip setuptools && pip3 install tensorrt_llm + pip3 install --ignore-installed pip setuptools wheel && pip3 install tensorrt_llm ``` + + > **Note:** The TensorRT LLM wheel on PyPI is built with PyTorch 2.9.1. This version may be incompatible with the NVIDIA NGC PyTorch 25.12 container, which uses a more recent PyTorch build from the main branch. If you are using this container or a similar environment, please install the pre-built wheel located at `/app/tensorrt_llm` inside the TensorRT LLM NGC Release container instead. + **This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.** 2. Sanity check the installation by running the following in Python (tested on Python 3.12): @@ -72,5 +75,5 @@ There are some known limitations when you pip install pre-built TensorRT LLM whe ```bash CURRENT_TORCH_VERSION=$(python3 -c "import torch; print(torch.__version__)") echo "torch==$CURRENT_TORCH_VERSION" > /tmp/torch-constraint.txt - pip3 install --upgrade pip setuptools && pip3 install tensorrt_llm -c /tmp/torch-constraint.txt + pip3 install --ignore-installed pip setuptools wheel && pip3 install tensorrt_llm -c /tmp/torch-constraint.txt ``` diff --git a/docs/source/legacy/reference/support-matrix.md b/docs/source/legacy/reference/support-matrix.md index c05c689ab42e..8cf072d0af00 100644 --- a/docs/source/legacy/reference/support-matrix.md +++ b/docs/source/legacy/reference/support-matrix.md @@ -154,9 +154,9 @@ The following table shows the supported software for TensorRT-LLM. * - - Software Compatibility * - Container - - [25.10](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) + - [25.12](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) * - TensorRT - - [10.13](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html) + - [10.14](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html) * - Precision - - Blackwell (SM100/SM103/SM120) - FP32, FP16, BF16, FP8, FP4, INT8, INT4 diff --git a/docs/source/models/supported-models.md b/docs/source/models/supported-models.md index 75f91dfef646..d794ee7cddfe 100644 --- a/docs/source/models/supported-models.md +++ b/docs/source/models/supported-models.md @@ -14,6 +14,7 @@ The following is a table of supported models for the PyTorch backend: | `GptOssForCausalLM` | GPT-OSS | `openai/gpt-oss-120b` | | `LlamaForCausalLM` | Llama 3.1, Llama 3, Llama 2, LLaMA | `meta-llama/Meta-Llama-3.1-70B` | | `Llama4ForConditionalGeneration` | Llama 4 | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | +| `MiniMaxM2ForCausalLM` | MiniMax M2/M2.1 | `MiniMaxAI/MiniMax-M2` | | `MistralForCausalLM` | Mistral | `mistralai/Mistral-7B-v0.1` | | `MixtralForCausalLM` | Mixtral | `mistralai/Mixtral-8x7B-v0.1` | | `MllamaForConditionalGeneration` | Llama 3.2 | `meta-llama/Llama-3.2-11B-Vision` | diff --git a/docs/source/overview.md b/docs/source/overview.md index e7aeed43a237..0eee6b6f9fd7 100644 --- a/docs/source/overview.md +++ b/docs/source/overview.md @@ -51,13 +51,6 @@ TensorRT LLM strives to support the most popular models on **Day 0**. - **[Guided Decoding](./features/guided-decoding.md)**: Advanced sampling with stop words, bad words, and custom constraints - **[Disaggregated Serving (Beta)](./features/disagg-serving.md)**: Separate context and generation phases across different GPUs for optimal resource utilization -### 🔧 **Latest GPU Architecture Support** - -TensorRT LLM supports the full spectrum of NVIDIA GPU architectures: -- **NVIDIA Blackwell**: B200, GB200, B300, GB300, and RTX Pro 6000 SE with FP4 optimization -- **NVIDIA Ada Lovelace**: L40/L40S, RTX 40 series with FP8 acceleration -- **NVIDIA Ampere**: A100, RTX 30 series for production workloads - ## What Can You Do With TensorRT LLM? Whether you're building the next generation of AI applications, optimizing existing LLM deployments, or exploring the frontiers of large language model technology, TensorRT LLM provides the tools, performance, and flexibility you need to succeed in the era of generative AI.To get started, refer to the {ref}`quick-start-guide`. diff --git a/docs/source/release-notes.md b/docs/source/release-notes.md index 76a4988f0809..b689787813a5 100644 --- a/docs/source/release-notes.md +++ b/docs/source/release-notes.md @@ -4,6 +4,98 @@ All published functionality in the Release Notes has been fully tested and verified with known limitations documented. To share feedback about this release, access our [NVIDIA Developer Forum](https://forums.developer.nvidia.com/). +## TensorRT-LLM Release 1.2.1 + +### Highlights + +- **Fixed Issue** + - Fixed an issue that caused KV cache corruption (#12770) + +- **Infrastructure Changes** + - Upgraded xgrammar and flashinfer (#12811) + +## TensorRT-LLM Release 1.2 + +### Highlights + +- **Model Support** + - Added beta support for K-EXAONE, Nemotron Nano V3, Qwen3-Next and Qwen3-VL. + - Improved GPT-OSS, Nemotron, EXAONE, GLM, Starcoder2, Qwen3, KimiK2, DeepSeek v3.2 and Mistral Large 3 support and validation. + - Expanded Blackwell/Hopper/Ampere enablement including B300/GB200/GB300 and SM120/SM121/SM103 paths. + - Broadened low-precision and MoE capabilities (FP8/NVFP4/MXFP4/INT4-AWQ), including routing and kernels. + +- **Features** + - **Speculative Decoding:** + - Enabled MTP>1 support for DeepSeek v3.2 + - **Disaggregated Serving:** + - Added service discovery mechanism for dynamic scaling + - Added support for cancelling requests + - Added NIXL-LibFabric support + - Added support for Mooncake transfer engine as a cache transceiver backend + - **Sampling:** + - Implemented batched sampling using FlashInfer sampling + - Added support for returning logprobs incrementally with streaming mode in PyTorch backend + - Added Beam Search support to TorchSampler + - **Performance:** + - Improved TorchSampler performance + - Enabled PDL by default and added PDL support for indexer TopK and additional kernels. + - Improved trtllm-gen kernels + - Enabled early exit with overlap scheduler + - Added NUMA-aware CPU affinity automatic configuration + - **Expert Parallelism:** + - Enabled EPLB for trtllm-gen and cutlass backend + - Enabled CuteDSL MoE with large EP + - Added CUDA graph support for DeepEP + - Multiple performance improvements + - **Hardware:** + - DGX Spark Support (Beta) + - **Others:** + - Helix parallelism support + - New Ray orchestrator type + +- **Documentation** + - **Deployment Guides:** + - Added comprehensive deployment guides for KimiK2, Qwen3 and Qwen3-Next. + - Added new guide on CPU Affinity configuration. + - Updated GPT-OSS guide. + - **Developer Guides:** + - Added developer guide about KV Cache Transmission. + - New section on MoE Expert Load Balance Analysis (Perfect Router) in Performance Analysis guide. + - New section on API Change Principles in LLM API Change guide. + - **Feature Documentation:** + - Created new guides for Additional Outputs, Helix Parallelism, KV Cache Connector, Ray Orchestrator, Sparse Attention and Torch Compile & Piecewise CUDA Graph. + - Also updated the Feature Combination Matrix and Paged Attention, IFB, and Request Scheduling guide. + - **Tech Blogs:** Published blogs on: + - "[Scaling Expert Parallelism in TensorRT LLM (Part 3: Pushing the Performance Boundary)](./blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md)" + - "[Optimizing DeepSeek-V3.2 on NVIDIA Blackwell GPUs](./blogs/tech_blog/blog15_Optimizing_DeepSeek_V32_on_NVIDIA_Blackwell_GPUs.md)". + - **Examples:** + - Added new section on disaggregated serving service discovery method. + - Added examples for K-EXAONE, Nemotron Nano V2 VL and Nemotron Nano V3. + - Added RocketKV usage documentation. + +- **Infrastructure Changes** + + - The base Docker image for TensorRT-LLM is updated to `nvcr.io/nvidia/pytorch:25.12-py3`. + - The base Docker image for TensorRT-LLM Backend is updated to `nvcr.io/nvidia/tritonserver:25.12-py3`. + - The dependent public PyTorch version is updated to 2.9.1. + - The dependent transformers version is updated to 4.57.3. + - The dependent triton version is updated to 3.5.1. + - The dependent NIXL version is updated to 0.8.0. + +- **API Changes** + - **Breaking Changes:** + - FlashInfer sampling now used by default with PyTorch backend. + - Changes to sampling strategy in some previously undefined cases. + - **OpenAI API:** + - Enabled n > 1 with PyTorch backend + - Added support for GET/DELETE v1/responses + +- **Fixed multiple Issues** + +- **Known Issues** + - **DGX Spark:** DGX Spark support is in beta. Only single-node configurations and the models listed above have been validated in this release. + - **Disaggregated Serving:** A hang may occur in disaggregated serving with context pipeline parallelism and generation tensor parallelism configurations. + ## TensorRT-LLM Release 1.1 ### Key Features and Enhancements diff --git a/docs/source/supported-hardware.md b/docs/source/supported-hardware.md new file mode 100644 index 000000000000..a8c96e99eeb2 --- /dev/null +++ b/docs/source/supported-hardware.md @@ -0,0 +1,7 @@ +# Supported Hardware + +TensorRT LLM supports the full spectrum of NVIDIA GPU architectures: +- **NVIDIA Blackwell**: B200, GB200, B300, GB300, DGX Spark +- **NVIDIA Hopper**: H100, H200, GH200 +- **NVIDIA Ada Lovelace**: L20, L40/L40S +- **NVIDIA Ampere**: A100 diff --git a/examples/constraints.txt b/examples/constraints.txt index 02571fa1b68c..7be86f4c4d17 100644 --- a/examples/constraints.txt +++ b/examples/constraints.txt @@ -1,3 +1,3 @@ -tensorrt_llm==1.2.0rc8 +tensorrt_llm==1.2.1 evaluate~=0.4.1 rouge_score~=0.1.2 diff --git a/examples/disaggregated/slurm/benchmark/run_benchmark.sh b/examples/disaggregated/slurm/benchmark/run_benchmark.sh index fdf71b6ac11c..7b0bee3a9e3b 100644 --- a/examples/disaggregated/slurm/benchmark/run_benchmark.sh +++ b/examples/disaggregated/slurm/benchmark/run_benchmark.sh @@ -6,9 +6,9 @@ set -u trap 'echo "Error occurred at line $LINENO"; exit 1' ERR # Add parameter validation -if [ "$#" -lt 9 ]; then - echo "Error: Missing required arguments" - echo "Usage: $0 model_name dataset_file multi_round concurrency_list streaming log_path hostname port" +if [ "$#" -lt 10 ]; then + echo "Error: Missing required arguments, got $# arguments, args: $@" + echo "Usage: $0 model_name dataset_file multi_round num_gen_servers concurrency_list streaming log_path hostname port ucx_warmup_requests" exit 1 fi @@ -21,6 +21,7 @@ streaming=$6 log_path=$7 hostname=$8 port=$9 +ucx_warmup_requests=${10} # check process id is not 0 if [[ ${SLURM_PROCID} != "0" ]]; then @@ -28,6 +29,23 @@ if [[ ${SLURM_PROCID} != "0" ]]; then exit 0 fi +# warmup requests for ucx connections +if [ "${ucx_warmup_requests}" -gt 0 ]; then + echo "warming up ucx connections with small requests... ${ucx_warmup_requests}" + python -m tensorrt_llm.serve.scripts.benchmark_serving \ + --model ${model_name} \ + --dataset-name random \ + --random-ids \ + --random-input-len 100 \ + --random-output-len 10 \ + --num-prompts ${ucx_warmup_requests} \ + --host ${hostname} \ + --port ${port} \ + --ignore-eos \ + --non-streaming + echo "UCX warmup done" +fi + echo "Hostname: ${hostname}, Port: ${port}" echo "Starting benchmark..." for concurrency in ${concurrency_list}; do diff --git a/examples/disaggregated/slurm/benchmark/run_benchmark_nv_sa.sh b/examples/disaggregated/slurm/benchmark/run_benchmark_nv_sa.sh index ef3ea39f6ba7..98e089f8a87c 100644 --- a/examples/disaggregated/slurm/benchmark/run_benchmark_nv_sa.sh +++ b/examples/disaggregated/slurm/benchmark/run_benchmark_nv_sa.sh @@ -42,6 +42,7 @@ streaming=$8 log_path=$9 hostname=${10} port=${11} +ucx_warmup_requests=${12} # check process id is not 0 if [[ ${SLURM_PROCID} != "0" ]]; then @@ -59,6 +60,23 @@ fi echo "Cloning benchmark repository..." git clone "${BENCH_SERVING_REPO}" "${BENCH_SERVING_DIR}" +# warmup requests for ucx connections +if [ "${ucx_warmup_requests}" -gt 0 ]; then + echo "warming up ucx connections with small requests... ${ucx_warmup_requests}" + python -m tensorrt_llm.serve.scripts.benchmark_serving \ + --model ${model_name} \ + --dataset-name random \ + --random-ids \ + --random-input-len 100 \ + --random-output-len 10 \ + --num-prompts ${ucx_warmup_requests} \ + --host ${hostname} \ + --port ${port} \ + --ignore-eos \ + --non-streaming + echo "UCX warmup done" +fi + # Run benchmarks echo "Starting benchmark..." for concurrency in ${concurrency_list}; do diff --git a/examples/disaggregated/slurm/benchmark/submit.py b/examples/disaggregated/slurm/benchmark/submit.py index a12e675134bd..5128a6aed8d0 100644 --- a/examples/disaggregated/slurm/benchmark/submit.py +++ b/examples/disaggregated/slurm/benchmark/submit.py @@ -198,7 +198,8 @@ def submit_job(config, log_dir, dry_run): gen_pp_size = worker_config['gen'].get('pipeline_parallel_size', 1) gen_world_size = gen_tp_size * gen_cp_size * gen_pp_size gen_nodes = calculate_nodes(gen_world_size, gen_num, gpus_per_node) - + ucx_warmup_requests = 2 * ctx_world_size * \ + gen_world_size if benchmark_config['mode'] == "e2e" else 0 total_nodes = ctx_nodes + gen_nodes total_tasks = total_nodes * gpus_per_node @@ -343,14 +344,14 @@ def submit_job(config, log_dir, dry_run): if benchmark_config['use_nv_sa_benchmark']: benchmark_cmd = [ f"bash {env_config['work_dir']}/run_benchmark_nv_sa.sh", - f"'{env_config['model_path']}' {isl} {osl} {benchmark_config['benchmark_ratio']} {benchmark_config['multi_round']} {gen_num} '{benchmark_config['concurrency_list']}' {benchmark_config['streaming']} '{log_dir}' {disagg_server_hostname} {disagg_server_port}", + f"'{env_config['model_path']}' {isl} {osl} {benchmark_config['benchmark_ratio']} {benchmark_config['multi_round']} {gen_num} '{benchmark_config['concurrency_list']}' {benchmark_config['streaming']} '{log_dir}' {disagg_server_hostname} {disagg_server_port} {ucx_warmup_requests}", f"&> {log_dir}/6_bench.log" ] client_cmds.append(" ".join(client_slurm_prefix + benchmark_cmd)) else: benchmark_cmd = [ f"bash {env_config['work_dir']}/run_benchmark.sh", - f"'{env_config['model_path']}' '{benchmark_config['dataset_file']}' {benchmark_config['multi_round']} {gen_num} '{benchmark_config['concurrency_list']}' {benchmark_config['streaming']} '{log_dir}' {disagg_server_hostname} {disagg_server_port}", + f"'{env_config['model_path']}' '{benchmark_config['dataset_file']}' {benchmark_config['multi_round']} {gen_num} '{benchmark_config['concurrency_list']}' {benchmark_config['streaming']} '{log_dir}' {disagg_server_hostname} {disagg_server_port} {ucx_warmup_requests}", f"&> {log_dir}/6_bench.log" ] client_cmds.append(" ".join(client_slurm_prefix + benchmark_cmd)) diff --git a/examples/llm-api/llm_sparse_attention.py b/examples/llm-api/llm_sparse_attention.py index 3ebe4dcb61a6..b1407e9561d7 100644 --- a/examples/llm-api/llm_sparse_attention.py +++ b/examples/llm-api/llm_sparse_attention.py @@ -37,7 +37,7 @@ def parse_arguments(): '--model_path', type=str, default= - "/home/scratch.trt_llm_data/llm-models/llama-3.1-model/Llama-3.1-8B-Instruct" + "/home/scratch.trt_llm_data_ci/llm-models/llama-3.1-model/Llama-3.1-8B-Instruct" ) parser.add_argument( '--input_file', diff --git a/examples/models/contrib/bloom/README.md b/examples/models/contrib/bloom/README.md index e0ab0ad6553c..4542d6147f6a 100644 --- a/examples/models/contrib/bloom/README.md +++ b/examples/models/contrib/bloom/README.md @@ -189,7 +189,7 @@ Note we use `--bin_model_dir` instead of `--model_dir` since SmoothQuant model n ``` # Quantize HF Bloom 3B into FP8 and export trtllm checkpoint -python ../../../quantization/quantize.py --model_dir /home/scratch.trt_llm_data/llm-models/bloom-3b \ +python ../../../quantization/quantize.py --model_dir /home/scratch.trt_llm_data_ci/llm-models/bloom-3b \ --dtype float16 \ --qformat fp8 \ --kv_cache_dtype fp8 \ @@ -230,7 +230,7 @@ mpirun -n 8 --allow-run-as-root \ --engine_dir ./bloom/176B/trt_engines/fp16/8-gpu/ python ../../../summarize.py --test_trt_llm \ - --hf_model_dir /home/scratch.trt_llm_data/llm-models/bloom-3b \ + --hf_model_dir /home/scratch.trt_llm_data_ci/llm-models/bloom-3b \ --data_type fp16 \ --engine_dir /tmp/bloom/3b/trt_engines/fp8/1-gpu/ ``` diff --git a/examples/models/core/bert/utils.py b/examples/models/core/bert/utils.py index 82bf5382aacf..b3714e523e87 100644 --- a/examples/models/core/bert/utils.py +++ b/examples/models/core/bert/utils.py @@ -18,7 +18,7 @@ # NOTE: This routine is copied from from tests/unittests/utils/llm_data.py def llm_models_root(check=False) -> Optional[Path]: - root = Path("/home/scratch.trt_llm_data/llm-models/") + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") if "LLM_MODELS_ROOT" in os.environ: root = Path(os.environ.get("LLM_MODELS_ROOT")) @@ -28,7 +28,7 @@ def llm_models_root(check=False) -> Optional[Path]: if check: assert root.exists(), \ - "You shall set LLM_MODELS_ROOT env or be able to access /home/scratch.trt_llm_data to run this test" + "You shall set LLM_MODELS_ROOT env or be able to access /home/scratch.trt_llm_data_ci to run this test" return root if root.exists() else None diff --git a/examples/models/core/gpt_oss/README.md b/examples/models/core/gpt_oss/README.md index 85cb21f6ebe5..22761c10e991 100644 --- a/examples/models/core/gpt_oss/README.md +++ b/examples/models/core/gpt_oss/README.md @@ -107,33 +107,10 @@ Once again, the function call works successfully, this time using a different fu ## Using OpenAI Triton Kernels for MoE -OpenAI ships a set of Triton kernels optimized for its MoE models. TensorRT-LLM can leverage these kernels; enable them with the steps below: -1. **Build and install Triton** (tested with the commit below): +OpenAI ships a set of Triton kernels optimized for its MoE models. -```bash -git clone https://github.com/triton-lang/triton.git -cd triton -# Specific commit verified with TensorRT-LLM -git checkout f3067cd3bd0c29065fa4ecdb724b6f29cbabea5f -pip install -r python/requirements.txt # build-time dependencies -pip install wheel build -python3 setup.py bdist_wheel -pip install ./dist/*.whl -``` - -2. **Expose the Triton kernels to TensorRT-LLM** - The kernels are not packaged in the wheel, so set the environment variable `TRITON_ROOT` to your Triton clone: - -```bash -export TRITON_ROOT=/local/user/triton -# TensorRT-LLM expects the kernels at: -# $TRITON_ROOT/python/triton_kernels -``` - -3. **Select Triton as the MoE backend** - -• **trtllm-serve** (or other similar commands) — add this snippet to the YAML file passed via `--config`: +To use the Triton MoE backend with **trtllm-serve** (or other similar commands), add this snippet to the YAML file passed via `--config`: ```yaml moe_config: diff --git a/examples/models/core/multimodal/README.md b/examples/models/core/multimodal/README.md index 96ba6102a8e7..c82c81f8c8be 100644 --- a/examples/models/core/multimodal/README.md +++ b/examples/models/core/multimodal/README.md @@ -901,7 +901,7 @@ Note that for instruct Vision model, please set the `max_encoder_input_len` as ` ## NeVA -[NeVA](https://docs.nvidia.com/nemo-framework/user-guide/latest/vlms/neva.html) is a groundbreaking addition to the NeMo Multimodal ecosystem. This model seamlessly integrates large language-centric models with a vision encoder, that can be deployed in TensorRT-LLM. +[NeVA](https://docs.nvidia.com/nemo-framework/user-guide/24.12/nemotoolkit/multimodal/mllm/neva.html) is a groundbreaking addition to the NeMo Multimodal ecosystem. This model seamlessly integrates large language-centric models with a vision encoder, that can be deployed in TensorRT-LLM. 1. Generate TRT-LLM engine for NVGPT following example in `examples/models/core/gpt/README.md`. To adhere to the NVGPT conventions of the conversion script, some layer keys have to be remapped using `--nemo_rename_key`. diff --git a/examples/scaffolding/contrib/mcp/weather/uv.lock b/examples/scaffolding/contrib/mcp/weather/uv.lock new file mode 100644 index 000000000000..a6c1670c970b --- /dev/null +++ b/examples/scaffolding/contrib/mcp/weather/uv.lock @@ -0,0 +1,8 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" + +[[package]] +name = "mcp" +version = "0.1.0" +source = { virtual = "." } diff --git a/examples/serve/aiperf_client_for_multimodal.sh b/examples/serve/aiperf_client_for_multimodal.sh index 14fbcfb614a6..cec9c0163d42 100644 --- a/examples/serve/aiperf_client_for_multimodal.sh +++ b/examples/serve/aiperf_client_for_multimodal.sh @@ -2,7 +2,7 @@ aiperf profile \ -m Qwen2.5-VL-3B-Instruct \ - --tokenizer Qwen/Qwen2.5-VL-3B-Instruct \ + --tokenizer ${AIPERF_TOKENIZER_PATH:-Qwen/Qwen2.5-VL-3B-Instruct} \ --endpoint-type chat \ --random-seed 123 \ --image-width-mean 64 \ diff --git a/jenkins/Build.groovy b/jenkins/Build.groovy index 35b95d4cac9e..f36071acb8db 100644 --- a/jenkins/Build.groovy +++ b/jenkins/Build.groovy @@ -454,7 +454,7 @@ def runLLMBuild(pipeline, buildFlags, tarName, is_linux_x86_64) def llmPath = sh (script: "realpath ${LLM_ROOT}",returnStdout: true).trim() // TODO: Remove after the cmake version is upgraded to 3.31.8 // Get triton tag from docker/dockerfile.multi - def tritonShortTag = "r25.10" + def tritonShortTag = "r25.12" sh "cd ${LLM_ROOT}/triton_backend/inflight_batcher_llm && mkdir build && cd build && cmake .. -DTRTLLM_DIR=${llmPath} -DTRITON_COMMON_REPO_TAG=${tritonShortTag} -DTRITON_CORE_REPO_TAG=${tritonShortTag} -DTRITON_THIRD_PARTY_REPO_TAG=${tritonShortTag} -DTRITON_BACKEND_REPO_TAG=${tritonShortTag} -DUSE_CXX11_ABI=ON && make -j${buildJobs} install" // Step 3: packaging wheels into tarfile diff --git a/jenkins/GenerateLock.groovy b/jenkins/GenerateLock.groovy deleted file mode 100644 index b38970102941..000000000000 --- a/jenkins/GenerateLock.groovy +++ /dev/null @@ -1,134 +0,0 @@ -@Library(['trtllm-jenkins-shared-lib@main']) _ - -def createKubernetesPodConfig() -{ - def targetCloud = "kubernetes-cpu" - def selectors = """ - nvidia.com/node_type: builder - kubernetes.io/os: linux""" - def image = "urm.nvidia.com/docker/ubuntu:22.04" - def podConfig = [ - cloud: targetCloud, - namespace: "sw-tensorrt", - yaml: """ - apiVersion: v1 - kind: Pod - spec: - qosClass: Guaranteed - nodeSelector: ${selectors} - containers: - - name: alpine - image: ${image} - command: ['cat'] - tty: true - resources: - requests: - cpu: '8' - memory: 32Gi - ephemeral-storage: 200Gi - limits: - cpu: '8' - memory: 32Gi - ephemeral-storage: 200Gi - imagePullPolicy: Always - qosClass: Guaranteed - """.stripIndent(), - ] - - return podConfig -} - -def generate() -{ - sh "pwd && ls -alh" - - container("alpine") { - def LLM_REPO = "https://github.com/NVIDIA/TensorRT-LLM.git" - if (params.repoUrlKey == "tensorrt_llm_internal") { - withCredentials([string(credentialsId: 'default-llm-repo', variable: 'DEFAULT_LLM_REPO')]) { - LLM_REPO = DEFAULT_LLM_REPO - } - } - if (params.repoUrlKey == "custom_repo") { - if (params.customRepoUrl == "") { - throw new Exception("Invalid custom repo url provided") - } - LLM_REPO = params.customRepoUrl - } - sh "apt update" - sh "apt install -y python3-dev git curl git-lfs" - sh "git config --global --add safe.directory ${env.WORKSPACE}" - sh "git config --global user.email \"90828364+tensorrt-cicd@users.noreply.github.com\"" - sh "git config --global user.name \"TensorRT LLM\"" - trtllm_utils.checkoutSource(LLM_REPO, params.branchName, env.WORKSPACE, false, true) - sh "python3 --version" - sh "curl -sSL https://install.python-poetry.org | POETRY_VERSION=1.8.5 python3 -" - sh "cd ${env.WORKSPACE}" - sh "/root/.local/bin/poetry -h" - sh "export PATH=\"/root/.local/bin:\$PATH\" && python3 scripts/generate_lock_file.py" - def count = sh(script: "git status --porcelain security_scanning/ | wc -l", returnStdout: true).trim() - echo "Changed/untracked file count: ${count}" - if (count == "0") { - echo "No update that needs to be checked in" - } else { - sh "git status" - sh "git add \$(find . -type f \\( -name 'poetry.lock' -o -name 'pyproject.toml' -o -name 'metadata.json' \\))" - sh "git commit -s -m \"[None][infra] Check in most recent lock file from nightly pipeline\"" - withCredentials([ - string(credentialsId: 'svc_tensorrt_gitlab_api_token_no_username_as_string', variable: 'GITLAB_API_TOKEN'), - usernamePassword( - credentialsId: 'github-cred-trtllm-ci', - usernameVariable: 'NOT_IN_USE', - passwordVariable: 'GITHUB_API_TOKEN' - ) - ]) { - def authedUrl - if (params.repoUrlKey == "tensorrt_llm_internal") { - authedUrl = LLM_REPO.replaceFirst('https://', "https://svc_tensorrt:${GITLAB_API_TOKEN}@") - } else { - authedUrl = LLM_REPO.replaceFirst('https://', "https://svc_tensorrt:${GITHUB_API_TOKEN}@") - } - sh "git remote set-url origin ${authedUrl}" - sh "git fetch origin ${params.branchName}" - sh "git status" - sh "git rebase origin/${params.branchName}" - sh "git push origin HEAD:${params.branchName}" - } - } - } -} - - -pipeline { - agent { - kubernetes createKubernetesPodConfig() - } - parameters { - string(name: 'branchName', defaultValue: 'main', description: 'the branch to generate the lock files') - choice(name: 'repoUrlKey', choices: ['tensorrt_llm_github','tensorrt_llm_internal', 'custom_repo'], description: "The repo url to process, choose \"custom_repo\" if you want to set your own repo") - string(name: 'customRepoUrl', defaultValue: '', description: 'Your custom repo to get processed, need to select \"custom_repo\" for repoUrlKey, otherwise it will be ignored') - } - options { - skipDefaultCheckout() - // to better analyze the time for each step/test - timestamps() - } - - triggers { - parameterizedCron(''' - H 2 * * * %branchName=main;repoUrlKey=tensorrt_llm_github - ''') - } - - stages { - stage("Generating Poetry Locks"){ - agent { - kubernetes createKubernetesPodConfig() - } - steps - { - generate() - } - } - } // stages -} // pipeline diff --git a/jenkins/L0_Test.groovy b/jenkins/L0_Test.groovy index 901ed9e932dd..d231ce53e600 100644 --- a/jenkins/L0_Test.groovy +++ b/jenkins/L0_Test.groovy @@ -681,6 +681,13 @@ def runLLMTestlistWithAgent(pipeline, platform, testList, config=VANILLA_CONFIG, dockerArgs += " --device=/dev/gdrdrv:/dev/gdrdrv" } } + if (fileExists('/home/scratch.trt_llm_data_ci')) { + dockerArgs += " -v /home/scratch.trt_llm_data_ci:/scratch.trt_llm_data:ro " + } else if (fileExists('/home/scratch.trt_llm_data')) { + dockerArgs += " -v /home/scratch.trt_llm_data:/scratch.trt_llm_data:ro " + } else { + echo "Existing TRT-LLM data scratch mount points cannot be set up in this cluster, ignore..." + } } dockerArgs = "${dockerArgs} " + @@ -689,7 +696,6 @@ def runLLMTestlistWithAgent(pipeline, platform, testList, config=VANILLA_CONFIG, "--entrypoint=\"\" " + "--security-opt seccomp=unconfined " + "-u root:root " + - "-v /home/scratch.trt_llm_data:/scratch.trt_llm_data:ro " + "-v /tmp/ccache:${CCACHE_DIR}:rw " + "-v /tmp/pipcache/http-v2:/root/.cache/pip/http-v2:rw " + "--cap-add=SYSLOG" @@ -871,7 +877,7 @@ def getMountListForSlurmTest(SlurmCluster cluster, boolean useSbatch = false) // data/cache mounts if (cluster.containerRuntime.toString() == "DOCKER") { mounts += [ - "/home/scratch.trt_llm_data:/scratch.trt_llm_data:ro", + "/home/scratch.trt_llm_data_ci:/scratch.trt_llm_data:ro", ] } else if (cluster.containerRuntime.toString() == "ENROOT") { if (!cluster.scratchPath) { @@ -1834,7 +1840,10 @@ def createKubernetesPodConfig(image, type, arch = "amd64", gpuCount = 1, perfMod server: 10.117.145.14 path: /vol/scratch1/scratch.michaeln_blossom """ - if (type.contains("6000d") || type.contains("gh200")) { + + // Austin FlexCache looks slow and unstable recently. Remove gh200 temporarily. + // That means gh200 nodes will use the default Blossom data scratch. + if (type.contains("6000d")) { // rtx-pro-6000d and gh200 nodes are located in Austin DC, we use the FlexCache to speed up the data access. llmModelVolume = """ - name: scratch-trt-llm-data @@ -1988,7 +1997,7 @@ def launchTestListCheck(pipeline) def llmPath = sh (script: "realpath .", returnStdout: true).trim() def llmSrc = "${llmPath}/TensorRT-LLM/src" trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install -r ${llmSrc}/requirements-dev.txt") - sh "NVIDIA_TRITON_SERVER_VERSION=25.10 LLM_ROOT=${llmSrc} LLM_BACKEND_ROOT=${llmSrc}/triton_backend python3 ${llmSrc}/scripts/check_test_list.py --l0 --qa --waive" + sh "NVIDIA_TRITON_SERVER_VERSION=25.12 LLM_ROOT=${llmSrc} LLM_BACKEND_ROOT=${llmSrc}/triton_backend python3 ${llmSrc}/scripts/check_test_list.py --l0 --qa --waive" } catch (InterruptedException e) { throw e } catch (Exception e) { @@ -3270,12 +3279,12 @@ def launchTestJobs(pipeline, testFilter) "DGX_H100-4_GPUs-PyTorch-Others-1": ["dgx-h100-x4-oci", "l0_dgx_h100", 1, 1, 4], "DGX_H100-4_GPUs-PyTorch-Ray-1": ["dgx-h100-x4-oci", "l0_dgx_h100", 1, 1, 4], "DGX_H100-4_GPUs-AutoDeploy-1": ["dgx-h100-x4-oci", "l0_dgx_h100", 1, 1, 4], - "DGX_B200-4_GPUs-PyTorch-1": ["b200-x4-lbd", "l0_dgx_b200", 1, 1, 4, 1, true], - "DGX_B200-4_GPUs-PyTorch-Ray-1": ["b200-x4-lbd", "l0_dgx_b200", 1, 1, 4, 1, true], - "DGX_B200-4_GPUs-AutoDeploy-1": ["b200-x4-lbd", "l0_dgx_b200", 1, 1, 4, 1, true], - "DGX_B200-8_GPUs-PyTorch-1": ["b200-x8-lbd", "l0_dgx_b200", 1, 1, 8, 1, true], - "DGX_B200-4_GPUs-PyTorch-Post-Merge-1": ["b200-x4-lbd", "l0_dgx_b200", 1, 2, 4, 1, true], - "DGX_B200-4_GPUs-PyTorch-Post-Merge-2": ["b200-x4-lbd", "l0_dgx_b200", 2, 2, 4, 1, true], + "DGX_B200-4_GPUs-PyTorch-1": ["b200-x4", "l0_dgx_b200", 1, 1, 4, 1, true], + "DGX_B200-4_GPUs-PyTorch-Ray-1": ["b200-x4", "l0_dgx_b200", 1, 1, 4, 1, true], + "DGX_B200-4_GPUs-AutoDeploy-1": ["b200-x4", "l0_dgx_b200", 1, 1, 4, 1, true], + "DGX_B200-8_GPUs-PyTorch-1": ["b200-x8", "l0_dgx_b200", 1, 1, 8, 1, true], + "DGX_B200-4_GPUs-PyTorch-Post-Merge-1": ["b200-x4", "l0_dgx_b200", 1, 2, 4, 1, true], + "DGX_B200-4_GPUs-PyTorch-Post-Merge-2": ["b200-x4", "l0_dgx_b200", 2, 2, 4, 1, true], "B300-PyTorch-1": ["b300-single", "l0_b300", 1, 1], "DGX_B300-4_GPUs-PyTorch-1": ["b300-x4", "l0_dgx_b300", 1, 1, 4], "DGX_B300-4_GPUs-PyTorch-Post-Merge-1": ["b300-x4", "l0_dgx_b300", 1, 2, 4], @@ -3432,7 +3441,9 @@ def launchTestJobs(pipeline, testFilter) ] aarch64SanityCheckConfigs = [ - /* //Disable PY312-UB2404 temporarily since lack of official PyTorch for CUDA 13.1. + // Workaround PyTorch 2.9.1 vs. 2.10.0a0 incompatibility issue. Once resolved, change back to: + // 1. DLFW_IMAGE -> UBUNTU_24_04_IMAGE + // 2. Extra PyTorch CUDA install: false -> true "PY312-UB2404": [ LLM_DOCKER_IMAGE, "GH200", @@ -3441,7 +3452,7 @@ def launchTestJobs(pipeline, testFilter) "", DLFW_IMAGE, false, // Extra PyTorch CUDA 13.0 install - ],*/ + ], "PY312-DLFW": [ LLM_DOCKER_IMAGE, "GH200", @@ -3547,9 +3558,9 @@ def launchTestJobs(pipeline, testFilter) // Use internal mirror instead of https://download.pytorch.org/whl/cu130 for better network stability. // PyTorch CUDA 13.0 package and torchvision package can be installed as expected. if (k8s_arch == "amd64") { - trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install torch==2.9.1+cu130 torchvision==0.24.1+cu130 --extra-index-url https://urm.nvidia.com/artifactory/api/pypi/pytorch-cu128-remote/simple") + trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install torch==2.9.1+cu130 torchvision==0.24.1+cu130 --extra-index-url https://urm.nvidia.com/artifactory/api/pypi/pytorch-cu128-remote/simple --extra-index-url https://download.pytorch.org/whl/cu130") } else { - trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install torch==2.9.1+cu130 torchvision==0.24.1 --extra-index-url https://urm.nvidia.com/artifactory/api/pypi/pytorch-cu128-remote/simple") + trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install torch==2.9.1+cu130 torchvision==0.24.1 --extra-index-url https://urm.nvidia.com/artifactory/api/pypi/pytorch-cu128-remote/simple --extra-index-url https://download.pytorch.org/whl/cu130") } } diff --git a/jenkins/TensorRT_LLM_PLC.groovy b/jenkins/TensorRT_LLM_PLC.groovy new file mode 100644 index 000000000000..37624e8a7d50 --- /dev/null +++ b/jenkins/TensorRT_LLM_PLC.groovy @@ -0,0 +1,266 @@ +@Library(['trtllm-jenkins-shared-lib@main']) _ + +def createKubernetesPodConfig() +{ + def targetCloud = "kubernetes-cpu" + def selectors = """ + nvidia.com/node_type: builder + kubernetes.io/os: linux""" + def image = "urm.nvidia.com/docker/ubuntu:22.04" + def podConfig = [ + cloud: targetCloud, + namespace: "sw-tensorrt", + yaml: """ + apiVersion: v1 + kind: Pod + spec: + nodeSelector: ${selectors} + containers: + - name: alpine + image: ${image} + command: ['cat'] + tty: true + resources: + requests: + cpu: '8' + memory: 32Gi + ephemeral-storage: 200Gi + limits: + cpu: '8' + memory: 32Gi + ephemeral-storage: 200Gi + imagePullPolicy: Always + - name: docker + image: urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:202505221445_docker_dind_withbash + tty: true + resources: + requests: + cpu: 16 + memory: 72Gi + ephemeral-storage: 200Gi + limits: + cpu: 16 + memory: 256Gi + ephemeral-storage: 200Gi + imagePullPolicy: Always + securityContext: + privileged: true + capabilities: + add: + - SYS_ADMIN + qosClass: Guaranteed + """.stripIndent(), + ] + return podConfig +} + +def getLLMRepo () { + def LLM_REPO = "https://github.com/NVIDIA/TensorRT-LLM.git" + if (params.repoUrlKey == "tensorrt_llm_internal") { + withCredentials([string(credentialsId: 'default-llm-repo', variable: 'DEFAULT_LLM_REPO')]) { + LLM_REPO = DEFAULT_LLM_REPO + } + } + if (params.repoUrlKey == "custom_repo") { + if (params.customRepoUrl == "") { + throw new Exception("Invalid custom repo url provided") + } + LLM_REPO = params.customRepoUrl + } + return LLM_REPO +} + +def checkoutSource () +{ + def LLM_REPO = getLLMRepo() + sh "git config --global --add safe.directory ${env.WORKSPACE}" + trtllm_utils.checkoutSource(LLM_REPO, params.branchName, env.WORKSPACE, false, true) +} + +def getPulseToken() { + def token + //Configure credential 'starfleet-client-id' under Jenkins Credential Manager + withCredentials([usernamePassword( + credentialsId: "NSPECT_CLIENT-prod", + usernameVariable: 'SF_CLIENT_ID', + passwordVariable: 'SF_CLIENT_SECRET' + )]) { + def AuthHeader = sh(script: "set +x && echo -n $SF_CLIENT_ID:$SF_CLIENT_SECRET | base64 -w0", returnStdout: true).trim() + token= sh(script: "curl -s --request POST --header \"Authorization: Basic ${AuthHeader}\" --header \"Content-Type: application/x-www-form-urlencoded\" \"https://4ubglassowmtsi7ogqwarmut7msn1q5ynts62fwnr1i.ssa.nvidia.com/token?grant_type=client_credentials&scope=verify:nspectid%20sourcecode:blackduck%20update:report\" | jq \".access_token\" | tr -d '\"'", returnStdout: true).trim() + } + return token +} + +def generateLockFiles(llmRepo, branchName) +{ + container("alpine") { + sh "apt update" + sh "apt install -y python3-dev git curl git-lfs" + sh "python3 --version" + sh "curl -sSL https://install.python-poetry.org | python3 -" + sh "/root/.local/bin/poetry -h" + sh "git config --global --add safe.directory ${env.WORKSPACE}" + sh "git config --global user.email \"90828364+tensorrt-cicd@users.noreply.github.com\"" + sh "git config --global user.name \"TensorRT LLM\"" + sh "export PATH=\"/root/.local/bin:\$PATH\" && python3 scripts/generate_lock_file.py" + def count = sh(script: "git status --porcelain security_scanning/ | wc -l", returnStdout: true).trim() + echo "Changed/untracked file count: ${count}" + if (count == "0") { + echo "No update that needs to be checked in" + } else { + sh "git status" + sh "git add -u security_scanning/" + sh "git add \$(find . -type f \\( -name 'poetry.lock' -o -name 'pyproject.toml' -o -name 'metadata.json' \\))" + sh "git commit -s -m \"[None][infra] Check in most recent lock file from nightly pipeline\"" + withCredentials([ + string(credentialsId: 'svc_tensorrt_gitlab_api_token_no_username_as_string', variable: 'GITLAB_API_TOKEN'), + usernamePassword( + credentialsId: 'github-cred-trtllm-ci', + usernameVariable: 'NOT_IN_USE', + passwordVariable: 'GITHUB_API_TOKEN' + ) + ]) { + def authedUrl + if (params.repoUrlKey == "tensorrt_llm_internal") { + authedUrl = llmRepo.replaceFirst('https://', "https://svc_tensorrt:${GITLAB_API_TOKEN}@") + } else { + authedUrl = llmRepo.replaceFirst('https://', "https://svc_tensorrt:${GITHUB_API_TOKEN}@") + } + sh "git remote set-url origin ${authedUrl}" + sh "git fetch origin ${branchName}" + sh "git status" + sh "git rebase origin/${branchName}" + sh "git push origin HEAD:${branchName}" + } + } + } +} + +def sonar_scan() +{ + container("alpine") { + sh "mkdir -p $JENKINS_HOME" + def scannerHome = tool 'sonarScanner' + sh "apt update" + sh "apt install -y git git-lfs openjdk-17-jdk" + checkoutSource() + sh "cd ${env.WORKSPACE}" + withSonarQubeEnv() { + sh "${scannerHome}/bin/sonar-scanner -Dsonar.projectKey=GPUSW_TensorRT-LLM-Team_TensorRT-LLM_tensorrt-llm -Dsonar.sources=. -Dsonar.branch.name=${params.branchName}" + } + } +} + +def pulseScan(llmRepo, branchName) { + container("docker") { + sh "apk add jq curl" + def token = getPulseToken() + withCredentials([ + usernamePassword( + credentialsId: "svc_tensorrt_gitlab_read_api_token", + usernameVariable: 'USERNAME', + passwordVariable: 'PASSWORD' + ), + string(credentialsId: 'default-git-url', variable: 'DEFAULT_GIT_URL') + ]) { + trtllm_utils.llmExecStepWithRetry(this, script: "docker login ${DEFAULT_GIT_URL}:5005 -u ${USERNAME} -p ${PASSWORD}") + docker.withRegistry("https://${DEFAULT_GIT_URL}:5005") { + docker.image("pstooling/pulse-group/pulse-open-source-scanner/pulse-oss-cli:stable") + .inside("--user 0 --privileged -v /var/run/docker.sock:/var/run/docker.sock") { + withEnv([ + "PULSE_NSPECT_ID=NSPECT-95LK-6FZF", + "PULSE_BEARER_TOKEN=${token}", + "PULSE_REPO_URL=${llmRepo}", + "PULSE_SCAN_PROJECT=TRT-LLM", + "PULSE_SCAN_PROJECT_VERSION=${branchName}", + "PULSE_SCAN_VULNERABILITY_REPORT=nspect_scan_report.json" + ]) { + sh 'pulse scan --no-fail .' + } + } + } + } + sh "ls" + sh "cat nspect_scan_report.json" + withCredentials([string(credentialsId: 'trtllm_plc_slack_webhook', variable: 'PLC_SLACK_WEBHOOK')]) { + def jobPath = env.JOB_NAME.replaceAll("/", "%2F") + def pipelineUrl = "${env.JENKINS_URL}blue/organizations/jenkins/${jobPath}/detail/${jobPath}/${env.BUILD_NUMBER}/pipeline" + sh """ + export TRTLLM_PLC_WEBHOOK=${PLC_SLACK_WEBHOOK} + python3 -m venv venv + source venv/bin/activate + pip install requests + python ./jenkins/scripts/submit_vulnerability_report.py --build-url ${pipelineUrl} + """ + } + } +} + +pipeline { + agent { + kubernetes createKubernetesPodConfig() + } + parameters { + string(name: 'branchName', defaultValue: 'main', description: 'the branch to generate the lock files') + choice(name: 'repoUrlKey', choices: ['tensorrt_llm_github','tensorrt_llm_internal', 'custom_repo'], description: "The repo url to process, choose \"custom_repo\" if you want to set your own repo") + string(name: 'customRepoUrl', defaultValue: '', description: 'Your custom repo to get processed, need to select \"custom_repo\" for repoUrlKey, otherwise it will be ignored') + } + options { + skipDefaultCheckout() + timestamps() + timeout(time: 60, unit: 'MINUTES') + } + environment { + LLM_REPO = getLLMRepo() + BRANCH_NAME = "${params.branchName}" + } + + triggers { + parameterizedCron(''' + H 2 * * * %branchName=main;repoUrlKey=tensorrt_llm_github + H 3 * * * %branchName=release/1.2;repoUrlKey=tensorrt_llm_github + ''') + } + + stages { + stage('TRT-LLM PLC Jobs') { + parallel { + stage("Source Code OSS Scanning"){ + agent { + kubernetes createKubernetesPodConfig() + } + stages { + stage("Prepare Environment"){ + steps + { + checkoutSource() + sh "cd ${env.WORKSPACE}" + } + } + stage("Generate Lock Files"){ + steps + { + generateLockFiles(env.LLM_REPO, env.BRANCH_NAME) + } + } + stage("Run Pulse Scanning"){ + steps + { + pulseScan(env.LLM_REPO, env.BRANCH_NAME) + } + } + } + } + stage("SonarQube Code Analysis"){ + agent { + kubernetes createKubernetesPodConfig() + } + steps + { + sonar_scan() + } + } + } + } + } // stages +} // pipeline diff --git a/jenkins/current_image_tags.properties b/jenkins/current_image_tags.properties index 24e44e26fabe..418133db4f49 100644 --- a/jenkins/current_image_tags.properties +++ b/jenkins/current_image_tags.properties @@ -13,7 +13,7 @@ # images are adopted from PostMerge pipelines, the abbreviated commit hash is used instead. IMAGE_NAME=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm -LLM_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.12-py3-x86_64-ubuntu24.04-trt10.14.1.48-skip-tritondevel-202601011103-9818 -LLM_SBSA_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.12-py3-aarch64-ubuntu24.04-trt10.14.1.48-skip-tritondevel-202601011103-9818 -LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-rocky8-x86_64-rocky8-py310-trt10.14.1.48-skip-tritondevel-202601011103-9818 -LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-rocky8-x86_64-rocky8-py312-trt10.14.1.48-skip-tritondevel-202601011103-9818 +LLM_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.12-py3-x86_64-ubuntu24.04-trt10.14.1.48-skip-tritondevel-202604101938-12939 +LLM_SBSA_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:pytorch-25.12-py3-sbsa-ubuntu24.04-trt10.14.1.48-skip-tritondevel-202604101938-12939 +LLM_ROCKYLINUX8_PY310_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-rocky8-x86_64-rocky8-py310-trt10.14.1.48-skip-tritondevel-202604101938-12939 +LLM_ROCKYLINUX8_PY312_DOCKER_IMAGE=urm.nvidia.com/sw-tensorrt-docker/tensorrt-llm:cuda-13.1.0-devel-rocky8-x86_64-rocky8-py312-trt10.14.1.48-skip-tritondevel-202604101938-12939 diff --git a/jenkins/scripts/submit_vulnerability_report.py b/jenkins/scripts/submit_vulnerability_report.py new file mode 100644 index 000000000000..fb294d98f9f9 --- /dev/null +++ b/jenkins/scripts/submit_vulnerability_report.py @@ -0,0 +1,60 @@ +import argparse +import json +import os +from pathlib import Path + +import requests + +# slack workflow setting can be edited at https://slack.com/shortcuts/Ft0AAER075L3/a314cf6f4a81543cc37b75702c5d10f1 +SLACK_WEBHOOK_URL = os.environ.get("TRTLLM_PLC_WEBHOOK") +# this json file will be generated from pulse in pipeline scanning +INPUT_FILE = "./nspect_scan_report.json" + +parser = argparse.ArgumentParser() +parser.add_argument("--build-url", required=True, help="Jenkins build URL") + +args = parser.parse_args() + +# Throw error if not set +if not SLACK_WEBHOOK_URL: + raise EnvironmentError("Error: Environment variable 'TRTLLM_PLC_WEBHOOK' is not set!") + +# Read file +raw_input = Path(INPUT_FILE).read_text() +vulnerabilities = json.loads(raw_input) + + +def safe(value, default="N/A"): + return value if value else default + + +# Build attachment text +message_lines = ["* TensorRT LLM Source Code Vulnerability Scan Report*\n"] + +severity_rank = {"Critical": 4, "High": 3, "Medium": 2, "Low": 1} + +for v in vulnerabilities: + sev = v.get("Severity", "Low") + if severity_rank.get(sev, 0) <= 2: + continue + shortTermVersion = safe(v.get("Upgrade-Guidance", {}).get("Short-Term")) + longTermVersion = safe(v.get("Upgrade-Guidance", {}).get("Long-Term")) + lines = [ + f"🔴 *{safe(v.get('Severity'))}* — *{safe(v.get('Package Name'))}* `{safe(v.get('Package Version'))}`", + f"• *CVE:* {safe(v.get('Related Vuln'))} | *BDSA:* {safe(v.get('CVE ID'))}", + f"• *Score:* {safe(v.get('Score'))}", + f"• *Status:* {safe(v.get('Status'))}", + f"• *Published:* {safe(v.get('Vulnerability Published Date'))}", + f"• *Upgrade:* `{shortTermVersion}` → `{longTermVersion}`", + "─" * 40, # separator line + ] + message_lines.extend(lines) + +message_text = "\n".join(message_lines) + +payload = {"report": message_text, "pipelineUrl": args.build_url} + +print(payload) +# Send to Slack +resp = requests.post(SLACK_WEBHOOK_URL, json=payload, timeout=60) +resp.raise_for_status() diff --git a/pyproject.toml b/pyproject.toml index 031d41850dc6..8d32a0d203fe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,13 +10,15 @@ build-backend = "setuptools.build_meta" #################################################################################################### [tool.isort] line_length = 80 +known_first_party = ["tensorrt_llm"] +known_third_party = ["triton_kernels"] [tool.yapf] based_on_style = "pep8" column_limit = 80 [tool.codespell] -skip = ".git,3rdparty,tests/integration/test_input_files**,**.jsonl,**.json" +skip = ".git,3rdparty,triton_kernels,tests/integration/test_input_files**,**.jsonl,**.json" exclude-file = "examples/models/core/whisper/tokenizer.py" ignore-words-list = "rouge,inout,atleast,strat,nd,subtile,thrid,improbe,NotIn,te,iteract,anythin,tru,Tracin,vEw,dOut" @@ -42,6 +44,7 @@ fix = true # orders of magnitude faster, so we should move to deprecate `yapf`. exclude = [ "**3rdparty/**", + "triton_kernels/**", ".devcontainer/make_env.py", ".github/scripts/label_community_user.py", ".github/scripts/pr_checklist_check.py", @@ -1461,6 +1464,7 @@ convention = "google" [tool.ruff.lint.isort] known-first-party = ["tensorrt_llm"] +known-third-party = ["triton_kernels"] split-on-trailing-comma = false @@ -1493,7 +1497,7 @@ disallow_untyped_calls = false disallow_incomplete_defs = false disallow_untyped_defs = false warn_return_any = false -exclude = [] +exclude = ["triton_kernels"] [[tool.mypy.overrides]] diff --git a/requirements-dev.txt b/requirements-dev.txt index 4ff771f9554a..a972122b05aa 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -36,5 +36,5 @@ opentelemetry-api>=1.26.0 opentelemetry-exporter-otlp>=1.26.0 opentelemetry-semantic-conventions-ai>=0.4.1 fuzzywuzzy==0.18.0 -aiperf==0.3.0 +aiperf==0.4.0 nanobind>=2.9.0 diff --git a/requirements.txt b/requirements.txt index 2e789cbc7f61..65d622cd91b3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,14 +28,13 @@ nvidia-modelopt[torch]~=0.37.0 # torch 2.9.1+cu130 depends on nvidia-nccl-cu13==2.27.7 nvidia-nccl-cu13>=2.27.7,<=2.28.9 nvidia-cuda-nvrtc -transformers==4.57.1 +transformers==4.57.3 prometheus_client prometheus_fastapi_instrumentator pydantic>=2.9.1 pydantic-settings[yaml] omegaconf pillow -wheel<=0.45.1 optimum # evaluate needs datasets>=2.0.0 which triggers datasets>3.1.0 which is not stable: https://github.com/huggingface/datasets/issues/7467 datasets==3.1.0 @@ -53,9 +52,9 @@ ordered-set peft patchelf einops -flashinfer-python>=0.3.0,<0.4.0 +flashinfer-python==0.6.4 opencv-python-headless -xgrammar==0.1.25 +xgrammar==0.1.32 llguidance==0.7.29 jsonschema backoff @@ -63,10 +62,9 @@ nvtx matplotlib # FIXME: this is added to make nvtx happy meson ninja -etcd3 @ git+https://github.com/kragniz/python-etcd3.git@e58a899579ba416449c4e225b61f039457c8072a blake3 soundfile -triton==3.5.1 +triton==3.5.1 # NOTE: if you update this, you must also run scripts/vendor_triton_kernels.py to vendor the new version of triton_kernels tiktoken blobfile openai-harmony==0.0.4 @@ -74,7 +72,8 @@ nvidia-cutlass-dsl==4.3.4; python_version >= "3.10" plotly numexpr<2.14.0 # WAR for attempted use of nonexistent numpy.typing partial_json_parser -apache-tvm-ffi==0.1.4 # used for reduce nvidia-cutlass-dsl host overhead +apache-tvm-ffi==0.1.6 # used for reduce nvidia-cutlass-dsl host overhead torch-c-dlpack-ext==0.1.3 # used for reduce nvidia-cutlass-dsl host overhead, optional package for improved torch tensor calling perf mistral-common==1.8.6 -torchao>=0.14.1 +torchao>=0.14.1,<0.16.0 +etcd-sdk-python==0.0.7 diff --git a/scripts/vendor_triton_kernels.py b/scripts/vendor_triton_kernels.py new file mode 100755 index 000000000000..e50096ed4dc5 --- /dev/null +++ b/scripts/vendor_triton_kernels.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python3 +"""Script to vendor triton-kernels into TensorRT-LLM. + +This script: +1. Clones the Triton repo at a specific tag to a temp directory +2. Copies the triton_kernels module to the repo root as a top-level package +3. Adds attribution headers to all Python files +4. Copies the LICENSE file from Triton +5. Creates a VERSION file to track the vendored version +6. Creates a README.md with clear copyright attribution + +To update to a new version: + python scripts/vendor_triton_kernels.py --tag v3.6.0 +""" + +import argparse +import shutil +import subprocess +import tempfile +from pathlib import Path + +REPO_ROOT = Path(__file__).parent.parent.resolve() +TRITON_REPO_URL = "https://github.com/triton-lang/triton.git" +TRITON_KERNELS_MODULE_PATH = "python/triton_kernels/triton_kernels" +DEST_PATH = REPO_ROOT / "triton_kernels" + +VENDORED_NOTICE = "# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY." +ATTRIBUTION_HEADER = f"""\ +{VENDORED_NOTICE} +# Source: https://github.com/triton-lang/triton/tree/{{tag}}/{{original_file}} +# Triton is licensed under the MIT License. +""" + + +def clone_triton(tag: str, dest_dir: str) -> tuple[Path, Path]: + """Clone the Triton repo at the specified tag. Returns (module_path, repo_root).""" + print(f"Cloning Triton repo at tag {tag}...") + + subprocess.run( + ["git", "clone", "--depth", "1", "--branch", tag, TRITON_REPO_URL, dest_dir], + check=True, + capture_output=True, + text=True, + ) + + repo_root = Path(dest_dir) + triton_kernels_module_path = repo_root / TRITON_KERNELS_MODULE_PATH + if not triton_kernels_module_path.exists(): + raise RuntimeError(f"triton_kernels module not found at {triton_kernels_module_path}") + + return triton_kernels_module_path, repo_root + + +def add_attribution_header(file_path: Path, tag: str, original_rel_path: str) -> None: + content = file_path.read_text() + + # Handle shebang and encoding declarations + lines = content.split("\n") + insert_pos = 0 + preserved_lines = [] + + for i, line in enumerate(lines): + if line.startswith("#!") or line.startswith("# -*-") or line.startswith("# coding"): + preserved_lines.append(line) + insert_pos = i + 1 + else: + break + + header = ATTRIBUTION_HEADER.format(tag=tag, original_file=original_rel_path) + + new_content = "\n".join(preserved_lines) + if preserved_lines: + new_content += "\n" + new_content += header + + # Add blank line between header and content if file has content + remaining_content = "\n".join(lines[insert_pos:]) + if remaining_content.strip(): + new_content += "\n" + new_content += remaining_content + + file_path.write_text(new_content) + + +def copy_triton_kernels(src_path: Path, dest_path: Path, tag: str) -> list[str]: + """Copy triton_kernels module to destination and add attribution headers.""" + print(f"Copying triton_kernels to {dest_path}...") + + if dest_path.exists(): + print(f" Removing existing {dest_path}") + shutil.rmtree(dest_path) + + shutil.copytree(src_path, dest_path) + + # Add attribution headers to all existing Python files + python_files = [] + for py_file in dest_path.rglob("*.py"): + rel_path = py_file.relative_to(dest_path) + original_rel_path = f"{TRITON_KERNELS_MODULE_PATH}/{rel_path}" + python_files.append(str(rel_path)) + add_attribution_header(py_file, tag, original_rel_path) + + # Create __init__.py files in subdirs that don't have them. + # Triton's upstream code relies on implicit namespace packages (PEP 420), but + # setuptools' find_packages() requires __init__.py to discover subpackages. + for subdir in dest_path.rglob("*"): + if subdir.is_dir(): + init_file = subdir / "__init__.py" + if not init_file.exists(): + print(f" Creating {init_file.relative_to(dest_path)}") + init_file.write_text(f"{VENDORED_NOTICE}\n") + + print(f" Copied triton_kernels module to {dest_path}") + return python_files + + +def copy_license(triton_repo_root: Path, dest_path: Path) -> None: + """Copy the Triton LICENSE file.""" + print("Copying LICENSE file...") + + license_src = triton_repo_root / "LICENSE" + license_dest = dest_path / "LICENSE" + + shutil.copy2(license_src, license_dest) + print(f" Copied LICENSE to {license_dest}") + + +def create_version_file(dest_path: Path, tag: str) -> None: + """Create a VERSION file to track which version was vendored.""" + print("Creating VERSION file...") + + version_file = dest_path / "VERSION" + version_content = f"""{tag} +# This file tracks the version of triton-kernels that was vendored. +# To update, run: python scripts/vendor_triton_kernels.py --tag +""" + + version_file.write_text(version_content) + print(f" Created {version_file}") + + +def create_readme(dest_path: Path, tag: str) -> None: + """Create a README.md with clear copyright attribution.""" + print("Creating README.md...") + + readme_file = dest_path / "README.md" + readme_content = f"""# Vendored triton_kernels + +This directory contains code vendored from the [Triton](https://github.com/triton-lang/triton) project. + +| | | +|---|---| +| **Copyright** | The Triton Authors | +| **License** | MIT (see [LICENSE](LICENSE) file in this directory) | +| **Source** | https://github.com/triton-lang/triton/tree/{tag}/python/triton_kernels/triton_kernels | +| **Version** | `{tag}` | + +## Attribution + +This code is the work of the Triton authors and is included here under the MIT License. +Each Python file includes an attribution header indicating its origin. + +## Do Not Edit + +This code is vendored verbatim and should not be modified directly. +To update to a newer version, run: + +```bash +python scripts/vendor_triton_kernels.py --tag +``` +""" + + readme_file.write_text(readme_content) + print(f" Created {readme_file}") + + +def main(): + parser = argparse.ArgumentParser(description="Vendor triton-kernels into TensorRT-LLM") + parser.add_argument( + "--tag", + required=True, + help="Triton git tag to vendor from. See the list of tags at https://github.com/triton-lang/triton/tags", + ) + args = parser.parse_args() + + print(f"Vendoring triton-kernels from Triton {args.tag}") + print(f"Destination: {DEST_PATH}") + + with tempfile.TemporaryDirectory() as tmp_dir: + triton_kernels_src, triton_repo_root = clone_triton(args.tag, tmp_dir) + copy_triton_kernels(triton_kernels_src, DEST_PATH, args.tag) + copy_license(triton_repo_root, DEST_PATH) + create_version_file(DEST_PATH, args.tag) + create_readme(DEST_PATH, args.tag) + + print("SUCCESS: triton-kernels has been vendored.") + + +if __name__ == "__main__": + main() diff --git a/security_scanning/cpp/kernels/fmha_v2/poetry.lock b/security_scanning/cpp/kernels/fmha_v2/poetry.lock index c5e0ba403a74..0321db5bb6ce 100644 --- a/security_scanning/cpp/kernels/fmha_v2/poetry.lock +++ b/security_scanning/cpp/kernels/fmha_v2/poetry.lock @@ -1,14 +1,15 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. 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false python-versions = ">=3.9" +groups = ["main"] +markers = "python_version == \"3.10\"" files = [ {file = "typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"}, {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"}, ] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.10,<3.13" -content-hash = "86e8a5637db9664835177096b43d6a39adce5b79d65070b2f6331e1061211931" +content-hash = "c1a33478981dc903de66264835980cb7f88c528a0099da710b14a2753b4a0ca1" diff --git a/security_scanning/cpp/kernels/fmha_v2/pyproject.toml b/security_scanning/cpp/kernels/fmha_v2/pyproject.toml index 96a4ab8d3ad4..b1fccbd2a133 100644 --- a/security_scanning/cpp/kernels/fmha_v2/pyproject.toml +++ b/security_scanning/cpp/kernels/fmha_v2/pyproject.toml @@ -1,17 +1,18 @@ -[tool.poetry] +[project] name = "unknown-package" version = "0.1.0" description = "" -authors = ["TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"] -readme = "README.md" - -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -pytest = "7.2.2" -pytest-cpp = "2.3.0" -pytest-xdist = "3.2.1" +authors = [ + {name = "TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"} +] +requires-python = ">=3.10,<3.13" +dependencies = [ + "pytest (==7.2.2)", + "pytest-cpp (==2.3.0)", + "pytest-xdist (==3.2.1)" +] [build-system] -requires = ["poetry-core"] +requires = ["poetry-core>=2.0.0,<3.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/security_scanning/docs/poetry.lock b/security_scanning/docs/poetry.lock index dfbd9be33e19..d7f3998cde1b 100644 --- a/security_scanning/docs/poetry.lock +++ b/security_scanning/docs/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. [[package]] name = "accessible-pygments" @@ -6,6 +6,7 @@ version = "0.0.5" description = "A collection of accessible pygments styles" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7"}, {file = "accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872"}, @@ -24,6 +25,7 @@ version = "1.0.0" description = "A light, configurable Sphinx theme" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b"}, {file = "alabaster-1.0.0.tar.gz", hash = "sha256:c00dca57bca26fa62a6d7d0a9fcce65f3e026e9bfe33e9c538fd3fbb2144fd9e"}, @@ -35,6 +37,7 @@ version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, @@ -46,6 +49,7 @@ version = "2.2.0" description = "Seamlessly integrate pydantic models in your Sphinx documentation." optional = false python-versions = "<4.0.0,>=3.8.1" +groups = ["main"] files = [ {file = "autodoc_pydantic-2.2.0-py3-none-any.whl", hash = "sha256:8c6a36fbf6ed2700ea9c6d21ea76ad541b621fbdf16b5a80ee04673548af4d95"}, ] @@ -61,21 +65,22 @@ erdantic = ["erdantic (<2.0)"] linting = ["ruff (>=0.4.0,<0.5.0)"] security = ["pip-audit (>=2.7.2,<3.0.0)"] test = ["coverage (>=7,<8)", "defusedxml (>=0.7.1)", "pytest (>=8.0.0,<9.0.0)", "pytest-sugar (>=1.0.0,<2.0.0)"] -type-checking = ["mypy (>=1.9,<2.0)", "types-docutils (>=0.20,<0.21)", "typing-extensions (>=4.11,<5.0)"] +type-checking = ["mypy (>=1.9,<2.0)", "types-docutils (>=0.20,<0.21)", "typing-extensions (>=4.11,<5.0) ; python_version <= \"3.9\""] [[package]] name = "babel" -version = "2.17.0" +version = "2.18.0" description = "Internationalization utilities" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ - {file = "babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"}, - {file = "babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d"}, + {file = "babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35"}, + {file = "babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d"}, ] [package.extras] -dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"] +dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""] [[package]] name = "beautifulsoup4" @@ -83,6 +88,7 @@ version = "4.14.3" description = "Screen-scraping library" optional = false python-versions = ">=3.7.0" +groups = ["main"] files = [ {file = "beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb"}, {file = "beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86"}, @@ -105,6 +111,7 @@ version = "4.36.0" description = "Sphinx Doxygen renderer" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "breathe-4.36.0-py3-none-any.whl", hash = "sha256:af85436f1f09e842bd1fd95617281211c635f8768d245ff830c59b979888d1d5"}, {file = "breathe-4.36.0.tar.gz", hash = "sha256:14860b73118ac140b7a3f55446890c777d1b67149cb024279fe3710dad7f535c"}, @@ -120,146 +127,165 @@ test = ["pytest (>=8.0)"] [[package]] name = "certifi" -version = "2026.1.4" +version = "2026.2.25" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ - {file = "certifi-2026.1.4-py3-none-any.whl", hash = "sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c"}, - {file = "certifi-2026.1.4.tar.gz", hash = "sha256:ac726dd470482006e014ad384921ed6438c457018f4b3d204aea4281258b2120"}, + {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"}, + {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"}, ] [[package]] name = "charset-normalizer" -version = "3.4.4" +version = "3.4.7" description = "The Real First Universal Charset Detector. 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[package.extras] -test = ["pytest (>=6.0.0)", "setuptools (>=65)"] +test = ["pytest (>=6.0.0)", "setuptools (>=77)"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.10,<3.13" -content-hash = "b82072ec62a375be38c6e988944f61d59b6be86e14f52c6ab8296bfa966e2c4d" +content-hash = "fef81843f0886daa4a857820e7c67a3767075a8811beec2851a071f39a0a4aa6" diff --git a/security_scanning/docs/pyproject.toml b/security_scanning/docs/pyproject.toml index 8312d49c6a72..ba8e27bf9579 100644 --- a/security_scanning/docs/pyproject.toml +++ b/security_scanning/docs/pyproject.toml @@ -1,23 +1,23 @@ -[tool.poetry] +[project] name = "docs" version = "0.1.0" description = "" -authors = ["TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"] -readme = "README.md" - -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -sphinx = ">=7.0" -sphinx-argparse = "^0.5.2" -sphinx-click = "^6.2.0" -nvidia-sphinx-theme = "^0.0.9.post1" -myst-parser = "^4.0.1" -breathe = "^4.36.0" 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by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. [[package]] name = "annotated-types" @@ -6,6 +6,7 @@ version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, @@ -13,13 +14,14 @@ files = [ [[package]] name = "anyio" -version = "4.12.1" +version = "4.13.0" description = "High-level concurrency and networking framework on top of asyncio or Trio" optional = false -python-versions = ">=3.9" +python-versions = ">=3.10" +groups = ["main"] files = [ - {file = "anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c"}, - {file = "anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703"}, 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\"win32\") and extra == \"cusolver\""} +nvidia-cusparse = {version = "==12.6.3.3.*", optional = true, markers = "(sys_platform == \"linux\" or sys_platform == \"win32\") and extra == \"cusparse\""} +nvidia-nvjitlink = {version = "==13.0.88.*", optional = true, markers = "(sys_platform == \"linux\" or sys_platform == \"win32\") and extra == \"nvjitlink\""} +nvidia-nvtx = {version = "==13.0.85.*", optional = true, markers = "(sys_platform == \"linux\" or sys_platform == \"win32\") and extra == \"nvtx\""} + +[package.extras] +all = ["nvidia-cublas (==13.1.0.3.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-cccl (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-crt (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-culibos (==13.0.85.*) ; sys_platform == \"linux\"", "nvidia-cuda-cupti (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-cuxxfilt (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-nvcc (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-nvrtc (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-opencl (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-profiler-api (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-runtime (==13.0.96.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cuda-sanitizer-api (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cufft (==12.0.0.61.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cufile (==1.15.1.6.*) ; sys_platform == \"linux\"", "nvidia-curand (==10.4.0.35.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cusolver (==12.0.4.66.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-cusparse (==12.6.3.3.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-npp (==13.0.1.2.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvfatbin (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvjitlink (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvjpeg (==13.0.1.86.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvml-dev (==13.0.87.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvptxcompiler (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvtx (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvvm (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cccl = ["nvidia-cuda-cccl (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +crt = ["nvidia-cuda-crt (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cublas = ["nvidia-cublas (==13.1.0.3.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cudart = ["nvidia-cuda-runtime (==13.0.96.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cufft = ["nvidia-cufft (==12.0.0.61.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cufile = ["nvidia-cufile (==1.15.1.6.*) ; sys_platform == \"linux\""] +culibos = ["nvidia-cuda-culibos (==13.0.85.*) ; sys_platform == \"linux\""] +cupti = ["nvidia-cuda-cupti (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +curand = ["nvidia-curand (==10.4.0.35.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cusolver = ["nvidia-cusolver (==12.0.4.66.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cusparse = ["nvidia-cusparse (==12.6.3.3.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cuxxfilt = ["nvidia-cuda-cuxxfilt (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +npp = ["nvidia-npp (==13.0.1.2.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvcc = ["nvidia-cuda-nvcc (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvfatbin = ["nvidia-nvfatbin (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvjitlink = ["nvidia-nvjitlink (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvjpeg = ["nvidia-nvjpeg (==13.0.1.86.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvml = ["nvidia-nvml-dev (==13.0.87.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvptxcompiler = ["nvidia-nvptxcompiler (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvrtc = ["nvidia-cuda-nvrtc (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvtx = ["nvidia-nvtx (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvvm = ["nvidia-nvvm (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +opencl = ["nvidia-cuda-opencl (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +profiler = ["nvidia-cuda-profiler-api (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +sanitizer = ["nvidia-cuda-sanitizer-api (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] + [[package]] name = "dacite" version = "1.9.2" description = "Simple creation of data classes from dictionaries." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "dacite-1.9.2-py3-none-any.whl", hash = "sha256:053f7c3f5128ca2e9aceb66892b1a3c8936d02c686e707bee96e19deef4bc4a0"}, {file = "dacite-1.9.2.tar.gz", hash = "sha256:6ccc3b299727c7aa17582f0021f6ae14d5de47c7227932c47fec4cdfefd26f09"}, @@ -515,6 +663,7 @@ version = "1.1.0" description = "Python library for extract property from data." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "DataProperty-1.1.0-py3-none-any.whl", hash = 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+groups = ["main"] files = [ - {file = "dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049"}, - {file = "dill-0.4.0.tar.gz", hash = "sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0"}, + {file = "dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d"}, + {file = "dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa"}, ] [package.extras] @@ -603,6 +755,7 @@ version = "0.4.6" description = "HuggingFace community-driven open-source library of evaluation" optional = false python-versions = ">=3.8.0" +groups = ["main"] files = [ {file = "evaluate-0.4.6-py3-none-any.whl", hash = "sha256:bca85bc294f338377b7ac2f861e21c308b11b2a285f510d7d5394d5df437db29"}, {file = "evaluate-0.4.6.tar.gz", hash = "sha256:e07036ca12b3c24331f83ab787f21cc2dbf3631813a1631e63e40897c69a3f21"}, @@ -611,7 +764,7 @@ files = [ [package.dependencies] datasets = ">=2.0.0" dill = "*" -fsspec = {version = ">=2021.05.0", extras = ["http"]} +fsspec = {version = ">=2021.5.0", extras = ["http"]} huggingface-hub = ">=0.7.0" multiprocess = "*" numpy = ">=1.17" @@ -638,6 +791,8 @@ version = "1.3.1" description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" +groups = ["main"] +markers = "python_version == \"3.10\"" files = [ {file = "exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598"}, {file = "exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219"}, @@ -655,34 +810,37 @@ version = "2.2.1" description = "Get the currently executing AST node of a frame, and other information" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "executing-2.2.1-py2.py3-none-any.whl", hash = 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sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-npp (==13.0.1.2.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvfatbin (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvjitlink (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvjpeg (==13.0.1.86.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvml-dev (==13.0.87.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvptxcompiler (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvtx (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\"", "nvidia-nvvm (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cccl = ["nvidia-cuda-cccl (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +crt = ["nvidia-cuda-crt (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cublas = ["nvidia-cublas (==13.1.0.3.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cudart = ["nvidia-cuda-runtime (==13.0.96.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cufft = ["nvidia-cufft (==12.0.0.61.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cufile = ["nvidia-cufile (==1.15.1.6.*) ; sys_platform == \"linux\""] +culibos = ["nvidia-cuda-culibos (==13.0.85.*) ; sys_platform == \"linux\""] +cupti = ["nvidia-cuda-cupti (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +curand = ["nvidia-curand (==10.4.0.35.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cusolver = ["nvidia-cusolver (==12.0.4.66.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cusparse = ["nvidia-cusparse (==12.6.3.3.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +cuxxfilt = ["nvidia-cuda-cuxxfilt (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +npp = ["nvidia-npp (==13.0.1.2.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvcc = ["nvidia-cuda-nvcc (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvfatbin = ["nvidia-nvfatbin (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvjitlink = ["nvidia-nvjitlink (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvjpeg = ["nvidia-nvjpeg (==13.0.1.86.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvml = ["nvidia-nvml-dev (==13.0.87.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvptxcompiler = ["nvidia-nvptxcompiler (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvrtc = ["nvidia-cuda-nvrtc (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvtx = ["nvidia-nvtx (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +nvvm = ["nvidia-nvvm (==13.0.88.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +opencl = ["nvidia-cuda-opencl (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +profiler = ["nvidia-cuda-profiler-api (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] +sanitizer = ["nvidia-cuda-sanitizer-api (==13.0.85.*) ; sys_platform == \"linux\" or sys_platform == \"win32\""] + [[package]] name = "dataproperty" version = "1.1.0" description = "Python library for extract property from data." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "DataProperty-1.1.0-py3-none-any.whl", hash = "sha256:c61fcb2e2deca35e6d1eb1f251a7f22f0dcde63e80e61f0cc18c19f42abfd25b"}, {file = "dataproperty-1.1.0.tar.gz", hash = "sha256:b038437a4097d1a1c497695c3586ea34bea67fdd35372b9a50f30bf044d77d04"}, @@ -442,22 +585,23 @@ test = ["pytest (>=6.0.1)", "pytest-md-report (>=0.6.2)", "tcolorpy (>=0.1.2)"] [[package]] name = "datasets" -version = "4.5.0" +version = "4.8.4" description = "HuggingFace community-driven open-source library of datasets" optional = false -python-versions = ">=3.9.0" +python-versions = ">=3.10.0" +groups = ["main"] files = [ - {file = "datasets-4.5.0-py3-none-any.whl", hash = "sha256:b5d7e08096ffa407dd69e58b1c0271c9b2506140839b8d99af07375ad31b6726"}, - {file = "datasets-4.5.0.tar.gz", hash = "sha256:00c698ce1c2452e646cc5fad47fef39d3fe78dd650a8a6eb205bb45eb63cd500"}, + {file = "datasets-4.8.4-py3-none-any.whl", hash = "sha256:cdc8bee4698e549d78bf1fed6aea2eebc760b22b084f07e6fc020c6577a6ce6d"}, + {file = "datasets-4.8.4.tar.gz", hash = "sha256:a1429ed853275ce7943a01c6d2e25475b4501eb758934362106a280470df3a52"}, ] [package.dependencies] -dill = ">=0.3.0,<0.4.1" +dill = ">=0.3.0,<0.4.2" filelock = "*" -fsspec = {version = ">=2023.1.0,<=2025.10.0", extras = ["http"]} +fsspec = {version = ">=2023.1.0,<=2026.2.0", extras = ["http"]} httpx = "<1.0.0" huggingface-hub = ">=0.25.0,<2.0" -multiprocess = "<0.70.19" +multiprocess = "<0.70.20" numpy = ">=1.17" packaging = "*" pandas = "*" @@ -470,7 +614,7 @@ xxhash = "*" [package.extras] audio = ["torch (>=2.8.0)", "torchcodec (>=0.6.0)"] benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"] -dev = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "lz4", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4)", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.8.0)", "torchcodec (>=0.7.0)", "torchdata", "transformers", "transformers (>=4.42.0)", "zstandard"] +dev = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "sqlalchemy", "tensorflow (>=2.16.0) ; python_version >= \"3.10\" and sys_platform != \"win32\" and python_version < \"3.14\"", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0) ; python_version < \"3.10\" and sys_platform != \"win32\"", "tiktoken", "torch", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers", "transformers (>=4.42.0)", "zstandard"] docs = ["tensorflow (>=2.6.0)", "torch", "transformers"] jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"] nibabel = ["ipyniivue (==2.4.2)", "nibabel (>=5.3.2)"] @@ -478,20 +622,21 @@ pdfs = ["pdfplumber (>=0.11.4)"] quality = ["ruff (>=0.3.0)"] tensorflow = ["tensorflow (>=2.6.0)"] tensorflow-gpu = ["tensorflow (>=2.6.0)"] -tests = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "lz4", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4)", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.8.0)", "torchcodec (>=0.7.0)", "torchdata", "transformers (>=4.42.0)", "zstandard"] -tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "h5py", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "lz4", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4)", "polars[timezone] 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\"3.10\" and sys_platform != \"win32\"", "tiktoken", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers (>=4.42.0)", "zstandard"] +tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark ; python_version < \"3.14\"", "lz4 ; python_version < \"3.14\"", "moto[server]", "nibabel (>=5.3.1)", "numba (>=0.56.4) ; python_version < \"3.14\"", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pylance", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "sqlalchemy", "tiktoken", "torch (>=2.8.0)", "torchcodec (>=0.7.0) ; python_version < \"3.14\"", "torchdata", "transformers (>=4.42.0)", "zstandard"] torch = ["torch"] vision = ["Pillow (>=9.4.0)"] [[package]] name = "dill" -version = "0.4.0" +version = "0.4.1" description = "serialize all of Python" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" +groups = ["main"] files = [ - {file = "dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049"}, - {file = "dill-0.4.0.tar.gz", hash = "sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0"}, + {file = "dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d"}, + {file = "dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa"}, ] [package.extras] @@ -504,6 +649,7 @@ version = "0.4.6" description = "HuggingFace community-driven open-source library of evaluation" optional = false python-versions = ">=3.8.0" +groups = ["main"] files = [ {file = "evaluate-0.4.6-py3-none-any.whl", hash = "sha256:bca85bc294f338377b7ac2f861e21c308b11b2a285f510d7d5394d5df437db29"}, {file = "evaluate-0.4.6.tar.gz", hash = "sha256:e07036ca12b3c24331f83ab787f21cc2dbf3631813a1631e63e40897c69a3f21"}, @@ -512,7 +658,7 @@ files = [ [package.dependencies] datasets = ">=2.0.0" dill = "*" -fsspec = {version = ">=2021.05.0", extras = ["http"]} +fsspec = {version = ">=2021.5.0", extras = ["http"]} huggingface-hub = ">=0.7.0" multiprocess = "*" numpy = ">=1.17" @@ -539,6 +685,8 @@ version = "1.3.1" description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" +groups = ["main"] +markers = "python_version == \"3.10\"" files = [ {file = "exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598"}, {file = "exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219"}, @@ -552,13 +700,14 @@ test = ["pytest (>=6)"] [[package]] name = "filelock" -version = "3.20.3" +version = "3.25.2" description = "A platform independent file lock." optional = false python-versions = ">=3.10" +groups = ["main"] files = [ - {file = "filelock-3.20.3-py3-none-any.whl", hash = "sha256:4b0dda527ee31078689fc205ec4f1c1bf7d56cf88b6dc9426c4f230e46c2dce1"}, - {file = "filelock-3.20.3.tar.gz", hash = "sha256:18c57ee915c7ec61cff0ecf7f0f869936c7c30191bb0cf406f1341778d0834e1"}, + {file = "filelock-3.25.2-py3-none-any.whl", hash = "sha256:ca8afb0da15f229774c9ad1b455ed96e85a81373065fb10446672f64444ddf70"}, + {file = "filelock-3.25.2.tar.gz", hash = "sha256:b64ece2b38f4ca29dd3e810287aa8c48182bbecd1ae6e9ae126c9b35f1382694"}, ] [[package]] @@ -567,6 +716,7 @@ version = "1.8.0" description = "A list-like structure which implements collections.abc.MutableSequence" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b37f6d31b3dcea7deb5e9696e529a6aa4a898adc33db82da12e4c60a7c4d2011"}, {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ef2b7b394f208233e471abc541cc6991f907ffd47dc72584acee3147899d6565"}, @@ -702,13 +852,14 @@ files = [ [[package]] name = "fsspec" -version = "2025.10.0" +version = "2026.2.0" description = "File-system specification" optional = false -python-versions = ">=3.9" +python-versions = ">=3.10" +groups = ["main"] files = [ - {file = "fsspec-2025.10.0-py3-none-any.whl", hash = "sha256:7c7712353ae7d875407f97715f0e1ffcc21e33d5b24556cb1e090ae9409ec61d"}, - {file = "fsspec-2025.10.0.tar.gz", hash = "sha256:b6789427626f068f9a83ca4e8a3cc050850b6c0f71f99ddb4f542b8266a26a59"}, + {file = "fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437"}, + {file = "fsspec-2026.2.0.tar.gz", hash = "sha256:6544e34b16869f5aacd5b90bdf1a71acb37792ea3ddf6125ee69a22a53fb8bff"}, ] [package.dependencies] @@ -722,9 +873,9 @@ dask = ["dask", "distributed"] dev = ["pre-commit", "ruff (>=0.5)"] doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", 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"9a089e6e7897e91b2e789baa9f30183194b6d8cabf21bef9193f374fb75ad137" +content-hash = "9b21a24bd320b638cb4343da8c7beeda0064fec50232199eb2a2796b011deac6" diff --git a/security_scanning/examples/llm-eval/lm-eval-harness/pyproject.toml b/security_scanning/examples/llm-eval/lm-eval-harness/pyproject.toml index 3b6a921df224..3c6eca5cb5ee 100644 --- a/security_scanning/examples/llm-eval/lm-eval-harness/pyproject.toml +++ b/security_scanning/examples/llm-eval/lm-eval-harness/pyproject.toml @@ -1,15 +1,16 @@ -[tool.poetry] +[project] name = "unknown-package" version = "0.1.0" description = "" -authors = ["TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"] -readme = "README.md" - -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -lm-eval = {version = "0.4.7", extras = ["api"]} +authors = [ + {name = "TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"} +] +requires-python = ">=3.10,<3.13" +dependencies = [ + "lm-eval[api] (==0.4.7)" +] [build-system] -requires = ["poetry-core"] +requires = ["poetry-core>=2.0.0,<3.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/security_scanning/examples/longbench/poetry.lock b/security_scanning/examples/longbench/poetry.lock index 5f736027c2ff..5b5a2d27a0c4 100644 --- a/security_scanning/examples/longbench/poetry.lock +++ b/security_scanning/examples/longbench/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. [[package]] name = "fuzzywuzzy" @@ -6,6 +6,7 @@ version = "0.18.0" description = "Fuzzy string matching in python" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "fuzzywuzzy-0.18.0-py2.py3-none-any.whl", hash = "sha256:928244b28db720d1e0ee7587acf660ea49d7e4c632569cad4f1cd7e68a5f0993"}, {file = "fuzzywuzzy-0.18.0.tar.gz", hash = "sha256:45016e92264780e58972dca1b3d939ac864b78437422beecebb3095f8efd00e8"}, @@ -20,6 +21,7 @@ version = "0.42.1" description = "Chinese Words Segmentation Utilities" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "jieba-0.42.1.tar.gz", hash = "sha256:055ca12f62674fafed09427f176506079bc135638a14e23e25be909131928db2"}, ] @@ -30,6 +32,7 @@ version = "1.0.1" description = "Full Python ROUGE Score Implementation (not a wrapper)" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "rouge-1.0.1-py3-none-any.whl", hash = "sha256:28d118536e8c774dc47d1d15ec266479b4dd0914c4672ce117d4002789bdc644"}, {file = "rouge-1.0.1.tar.gz", hash = "sha256:12b48346ca47d6bcf3c45061f315452b9ccec0620ee895ec85b7efc3d54aae34"}, @@ -44,12 +47,13 @@ version = "1.17.0" description = "Python 2 and 3 compatibility utilities" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" +groups = ["main"] files = [ {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"}, {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"}, ] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.10,<3.13" -content-hash = "e9c4183aae1a1f26f6db7e20b8e17adc6cb42f08e4686fe044616df4244a1063" +content-hash = "7dff1a515b2d49d6deec3a6c47a544d60e9427ad72db58c6b479db68dcebed4a" diff --git a/security_scanning/examples/longbench/pyproject.toml b/security_scanning/examples/longbench/pyproject.toml index 7c36288a8333..6af1eb3d6d68 100644 --- a/security_scanning/examples/longbench/pyproject.toml +++ b/security_scanning/examples/longbench/pyproject.toml @@ -1,17 +1,18 @@ -[tool.poetry] +[project] name = "longbench-examples" version = "0.1.0" description = "" -authors = ["TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"] -readme = "README.md" - -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -jieba = "^0.42.1" -fuzzywuzzy = "^0.18.0" -rouge = "^1.0.1" +authors = [ + {name = "TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"} +] +requires-python = ">=3.10,<3.13" +dependencies = [ + "jieba (>=0.42.1,<0.43.0)", + "fuzzywuzzy (>=0.18.0,<0.19.0)", + "rouge (>=1.0.1,<2.0.0)" +] [build-system] -requires = ["poetry-core"] +requires = ["poetry-core>=2.0.0,<3.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/security_scanning/examples/lookahead/poetry.lock b/security_scanning/examples/lookahead/poetry.lock index 81a211d91cf5..7cd33de141e2 100644 --- a/security_scanning/examples/lookahead/poetry.lock +++ b/security_scanning/examples/lookahead/poetry.lock @@ -1,14 +1,15 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. 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Easy to code. Based on Python type hints." +optional = false +python-versions = ">=3.10" +groups = ["main"] +files = [ + {file = "typer-0.24.1-py3-none-any.whl", hash = "sha256:112c1f0ce578bfb4cab9ffdabc68f031416ebcc216536611ba21f04e9aa84c9e"}, + {file = "typer-0.24.1.tar.gz", hash = "sha256:e39b4732d65fbdcde189ae76cf7cd48aeae72919dea1fdfc16593be016256b45"}, +] + +[package.dependencies] +annotated-doc = ">=0.0.2" +click = ">=8.2.1" +rich = ">=12.3.0" +shellingham = ">=1.3.0" [[package]] name = "typing-extensions" @@ -1045,6 +1179,7 @@ version = "4.15.0" description = "Backported and Experimental Type Hints for Python 3.9+" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"}, {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"}, @@ -1056,16 +1191,17 @@ version = "2.6.3" description = "HTTP library with thread-safe connection pooling, file post, and more." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"}, {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"}, ] [package.extras] -brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"] +brotli = ["brotli (>=1.2.0) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=1.2.0.0) ; platform_python_implementation != \"CPython\""] h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] -zstd = ["backports-zstd (>=1.0.0)"] +zstd = ["backports-zstd (>=1.0.0) ; python_version < \"3.14\""] [[package]] name = "zipp" @@ -1073,13 +1209,14 @@ version = "3.23.0" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"}, {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"}, ] [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] @@ -1087,6 +1224,6 @@ test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_it type = ["pytest-mypy"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.10,<3.13" -content-hash = "135f99d6c9e67efd1aa903bbb0b78bf5a92601011f11c3a97cded911f6b6bd93" +content-hash = "1a0408843545599d14703172c47d5c1b8e409aa0bfe322c979c3f42be556e3be" diff --git a/security_scanning/examples/models/contrib/mmdit/pyproject.toml b/security_scanning/examples/models/contrib/mmdit/pyproject.toml index f5732ec14d49..186d23496387 100644 --- a/security_scanning/examples/models/contrib/mmdit/pyproject.toml +++ b/security_scanning/examples/models/contrib/mmdit/pyproject.toml @@ -1,16 +1,17 @@ -[tool.poetry] +[project] name = "unknown-package" version = "0.1.0" description = "" -authors = ["TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"] -readme = "README.md" - -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -transformers = ">=4.56.0" -diffusers = ">=0.32.2" +authors = [ + {name = "TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"} +] +requires-python = ">=3.10,<3.13" +dependencies = [ + "transformers (>=4.56.0)", + "diffusers (>=0.32.2)" +] [build-system] -requires = ["poetry-core"] +requires = ["poetry-core>=2.0.0,<3.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/security_scanning/examples/models/contrib/mpt/poetry.lock b/security_scanning/examples/models/contrib/mpt/poetry.lock index 6656d47b81f5..4c2d02fbb45c 100644 --- a/security_scanning/examples/models/contrib/mpt/poetry.lock +++ b/security_scanning/examples/models/contrib/mpt/poetry.lock @@ -1,14 +1,15 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. 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[[package]] name = "antlr4-python3-runtime" @@ -6,6 +6,7 @@ version = "4.7.2" description = "ANTLR 4.7.2 runtime for Python 3.6.3" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "antlr4-python3-runtime-4.7.2.tar.gz", hash = "sha256:168cdcec8fb9152e84a87ca6fd261b3d54c8f6358f42ab3b813b14a7193bb50b"}, ] @@ -16,6 +17,7 @@ version = "1.9.1" description = "Convert latex to sympy with ANTLR and support Matrix, Linear Algebra and CAS functions." optional = false python-versions = "*" +groups = ["main"] files = [ {file = "latex2sympy2-1.9.1-py3-none-any.whl", hash = "sha256:44f24d263d235164a91173167a30d449f4360e3f0a59239ce6b843c50a41c601"}, ] @@ -30,6 +32,7 @@ version = "1.3.0" description = "Python library for arbitrary-precision floating-point arithmetic" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"}, @@ -38,7 +41,7 @@ files = [ [package.extras] develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] docs = ["sphinx"] -gmpy = ["gmpy2 (>=2.1.0a4)"] +gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""] tests = ["pytest (>=4.6)"] [[package]] @@ -47,6 +50,7 @@ version = "1.14.0" description = "Computer algebra system (CAS) in Python" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5"}, {file = "sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517"}, @@ -64,11 +68,12 @@ version = "1.1" description = "Convert number words eg. three hundred and forty two to numbers (342)." optional = false python-versions = "*" +groups = ["main"] files = [ {file = "word2number-1.1.zip", hash = "sha256:70e27a5d387f67b04c71fbb7621c05930b19bfd26efd6851e6e0f9969dcde7d0"}, ] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.10,<3.13" -content-hash = "7388346b17dfce54931b1bc95f4ec053ff53b62e6f9e872523553e7a54751b10" +content-hash = "be37982ce1a794e4b30e0edf46062a9f00456a47820130b5b588fdb57035c125" diff --git a/security_scanning/examples/scaffolding/contrib/Dynasor/pyproject.toml b/security_scanning/examples/scaffolding/contrib/Dynasor/pyproject.toml index dac999e262ec..68f907481beb 100644 --- a/security_scanning/examples/scaffolding/contrib/Dynasor/pyproject.toml +++ b/security_scanning/examples/scaffolding/contrib/Dynasor/pyproject.toml @@ -1,16 +1,17 @@ -[tool.poetry] +[project] name = "unknown-package" version = "0.1.0" description = "" -authors = ["TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"] -readme = "README.md" - -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -latex2sympy2 = "^1.9.1" -word2number = "^1.1" +authors = [ + {name = "TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"} +] +requires-python = ">=3.10,<3.13" +dependencies = [ + "latex2sympy2 (>=1.9.1,<2.0.0)", + "word2number (>=1.1,<2.0)" +] [build-system] -requires = ["poetry-core"] +requires = ["poetry-core>=2.0.0,<3.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/security_scanning/examples/serve/poetry.lock b/security_scanning/examples/serve/poetry.lock index 9e5d64e68b32..b2866448d584 100644 --- a/security_scanning/examples/serve/poetry.lock +++ b/security_scanning/examples/serve/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. 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b/security_scanning/tests/integration/defs/perf/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.4 and should not be changed by hand. 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">=3.10,<3.13" -content-hash = "11259cec29634d239bae24095d70d265571dc33638c2fba166ae583601e1dd2b" +content-hash = "b2de2e9f38fdf21154596290cfc48f0dc987ac02a9ad333453e4e7d24d353af9" diff --git a/security_scanning/triton_backend/pyproject.toml b/security_scanning/triton_backend/pyproject.toml index 65b22102d8a5..5af383cb47bc 100644 --- a/security_scanning/triton_backend/pyproject.toml +++ b/security_scanning/triton_backend/pyproject.toml @@ -1,20 +1,20 @@ -[tool.poetry] +[project] name = "triton-backend" version = "0.1.0" description = "" -authors = ["TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"] -readme = "README.md" - -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -regex = "^2026.1.15" -fire = "^0.7.1" -transformers = "4.57.1" -pandas = "^2.3.3" -tabulate = "^0.9.0" -torchao = ">=0.14.1" +authors = [ + {name = "TensorRT LLM [90828364+tensorrt-cicd@users.noreply.github.com]"} +] +requires-python = ">=3.10,<3.13" +dependencies = [ + "regex (>=2026.4.4,<2027.0.0)", + "fire (>=0.7.1,<0.8.0)", + "transformers (==4.57.3)", + "tabulate (>=0.10.0,<0.11.0)", + "torchao (>=0.14.1)" +] [build-system] -requires = ["poetry-core"] +requires = ["poetry-core>=2.0.0,<3.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/setup.py b/setup.py index ae2be9d9bc02..71a1accdc279 100644 --- a/setup.py +++ b/setup.py @@ -305,6 +305,12 @@ def extract_from_precompiled(precompiled_location: str, package_data: List[str], with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() +packages = find_packages() +# Add vendored triton_kernels as an explicit top-level package. +# This is vendored from the Triton project and kept at repo root so its +# internal absolute imports (e.g., "from triton_kernels.foo import bar") work. +packages += find_packages(include=["triton_kernels", "triton_kernels.*"]) + # https://setuptools.pypa.io/en/latest/references/keywords.html setup( name='tensorrt_llm', @@ -318,7 +324,7 @@ def extract_from_precompiled(precompiled_location: str, package_data: List[str], author="NVIDIA Corporation", url="https://github.com/NVIDIA/TensorRT-LLM", download_url="https://github.com/NVIDIA/TensorRT-LLM/tags", - packages=find_packages(), + packages=packages, # TODO Add windows support for python bindings. classifiers=[ "Development Status :: 4 - Beta", @@ -331,6 +337,7 @@ def extract_from_precompiled(precompiled_location: str, package_data: List[str], keywords="nvidia tensorrt deeplearning inference", package_data={ 'tensorrt_llm': package_data, + 'triton_kernels': ['LICENSE', 'VERSION', 'README.md'], }, license_files=get_license(), entry_points={ diff --git a/tensorrt_llm/__init__.py b/tensorrt_llm/__init__.py index cea56431b77e..7f4a25dd8386 100644 --- a/tensorrt_llm/__init__.py +++ b/tensorrt_llm/__init__.py @@ -61,6 +61,43 @@ def _preload_python_lib(): _preload_python_lib() import sys +from pathlib import Path + + +def _setup_vendored_triton_kernels(): + """Ensure our vendored triton_kernels takes precedence over any existing installation. + + Some environments bundle triton_kernels, which can conflict with our vendored version. This function: + 1. Clears any pre-loaded triton_kernels from sys.modules + 2. Temporarily adds our package root to sys.path + 3. Imports triton_kernels (caching our version in sys.modules) + 4. Removes the package root from sys.path + """ + + # Clear any pre-loaded triton_kernels from cache + for mod in list(sys.modules.keys()): + if mod == "triton_kernels" or mod.startswith("triton_kernels."): + del sys.modules[mod] + + # Temporarily add our package root to sys.path + root = Path(__file__).parent.parent + + vendored = root / "triton_kernels" + if not vendored.exists(): + raise RuntimeError( + f"Vendored triton_kernels module not found at {vendored}") + + should_add_to_path = str(root) not in sys.path + if should_add_to_path: + sys.path.insert(0, str(root)) + + import triton_kernels # noqa: F401 + + if should_add_to_path: + sys.path.remove(str(root)) + + +_setup_vendored_triton_kernels() # Need to import torch before tensorrt_llm library, otherwise some shared binary files # cannot be found for the public PyTorch, raising errors like: diff --git a/tensorrt_llm/_torch/attention_backend/flashinfer.py b/tensorrt_llm/_torch/attention_backend/flashinfer.py index a6f52ee4a7dd..4766b49a6cd0 100644 --- a/tensorrt_llm/_torch/attention_backend/flashinfer.py +++ b/tensorrt_llm/_torch/attention_backend/flashinfer.py @@ -425,6 +425,8 @@ def prefill_plan(): paged_kv_indices_buffer=self._paged_kv_indices, paged_kv_last_page_len_buffer=self._paged_kv_last_page_len, use_tensor_cores=use_tensor_cores, + backend="fa2" + if torch.cuda.get_device_capability(0) == (9, 0) else "auto", ) def decode_plan(): diff --git a/tensorrt_llm/_torch/attention_backend/trtllm.py b/tensorrt_llm/_torch/attention_backend/trtllm.py index c54776d23320..4b82be030ca9 100644 --- a/tensorrt_llm/_torch/attention_backend/trtllm.py +++ b/tensorrt_llm/_torch/attention_backend/trtllm.py @@ -1824,6 +1824,15 @@ def is_chunked_prefill_for_mla_context( and metadata.num_ctx_cached_tokens > 0 and metadata.runtime_features.chunked_prefill) + def is_chunked_prefill_mla_context_for_warmup( + self, + metadata: TrtllmAttentionMetadata, + ) -> bool: + """Chunked prefill MLA context check for warmup; does not check num_ctx_cached_tokens.""" + return (self.is_mla_enable and metadata.kv_cache_manager is not None + and metadata.enable_context_mla_with_cached_kv + and metadata.runtime_features.chunked_prefill) + def load_paged_kv_cache_for_mla( self, metadata: TrtllmAttentionMetadata, diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.py index c8c2f8351563..ac530fd7eaa1 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.py @@ -26,156 +26,6 @@ ) -# TODO: remove this when flashinfer version is updated to >0.5 -def fast_decode_plan( - wrapper: flashinfer.BatchDecodeWithPagedKVCacheWrapper, - indptr: torch.Tensor, - indices: torch.Tensor, - last_page_len: torch.Tensor, - num_qo_heads: int, - num_kv_heads: int, - head_dim: int, - page_size: int, - pos_encoding_mode: str = "NONE", - window_left: int = -1, - logits_soft_cap: Optional[float] = None, - q_data_type: Optional[Union[str, torch.dtype]] = None, - kv_data_type: Optional[Union[str, torch.dtype]] = None, - data_type: Optional[Union[str, torch.dtype]] = None, - sm_scale: Optional[float] = None, - rope_scale: Optional[float] = None, - rope_theta: Optional[float] = None, - non_blocking: bool = True, - fixed_split_size: Optional[int] = None, - disable_split_kv: bool = False, - global_override_indptr_cpu: Optional[torch.Tensor] = None, -) -> None: - """ - Copied from flashinfer.decode.fast_decode_plan in flashinfer version >0.5. - Does not exist in flashinfer version 0.3.1, hence copied here. - """ - batch_size = len(last_page_len) - if logits_soft_cap is None: - logits_soft_cap = 0.0 - - # Handle data types consistently - if data_type is not None: - if q_data_type is None: - q_data_type = data_type - if kv_data_type is None: - kv_data_type = data_type - elif q_data_type is None: - q_data_type = "float16" - - if kv_data_type is None: - kv_data_type = q_data_type - - if wrapper.use_tensor_cores: - qo_indptr_host = torch.arange(batch_size + 1, dtype=torch.int32, device="cpu") - # Here we set fixed_split_size to -1 to avoid the assertion error in flashinfer's plan function - if fixed_split_size is None: - fixed_split_size = -1 - - if wrapper.is_cuda_graph_enabled: - if batch_size != wrapper._fixed_batch_size: - raise ValueError( - "The batch size should be fixed in cudagraph mode, the runtime batch size {} " - " mismatches the batch size set during initialization {}".format( - batch_size, wrapper._fixed_batch_size - ) - ) - if len(indices) > len(wrapper._paged_kv_indices_buf): - raise ValueError( - "The size of indices should be less than or equal to the allocated buffer" - ) - else: - wrapper._paged_kv_indptr_buf = indptr - wrapper._paged_kv_indices_buf = indices - wrapper._paged_kv_last_page_len_buf = last_page_len - if wrapper.use_tensor_cores: - wrapper._qo_indptr_buf = qo_indptr_host.to(wrapper.device, non_blocking=non_blocking) - - # Create empty tensors for dtype info if needed - empty_q_data = torch.empty( - 0, - dtype=(getattr(torch, q_data_type) if isinstance(q_data_type, str) else q_data_type), - device=wrapper.device, - ) - - empty_kv_cache = torch.empty( - 0, - dtype=(getattr(torch, kv_data_type) if isinstance(kv_data_type, str) else kv_data_type), - device=wrapper.device, - ) - - indptr_host = ( - global_override_indptr_cpu if global_override_indptr_cpu is not None else indptr.cpu() - ) - - with torch.cuda.device(wrapper.device): - if wrapper.use_tensor_cores: - # ALSO convert last_page_len to CPU - if page_size == 1: - # When page size is 1, last_page_len is always 1. - # Directly construct the host tensor rather than executing a device-to-host copy. - last_page_len_host = torch.ones((batch_size,), dtype=torch.int32, device="cpu") - else: - last_page_len_host = last_page_len.cpu() - - kv_lens_arr_host = flashinfer.get_seq_lens(indptr_host, last_page_len_host, page_size) - - try: - # Make sure we pass exactly 15 arguments for tensor core version - wrapper._plan_info = wrapper._cached_module.plan( - wrapper._float_workspace_buffer, - wrapper._int_workspace_buffer, - wrapper._pin_memory_int_workspace_buffer, - qo_indptr_host, - indptr_host, - kv_lens_arr_host, - batch_size, # total_num_rows - batch_size, - num_qo_heads, - num_kv_heads, - page_size, - wrapper.is_cuda_graph_enabled, - head_dim, - head_dim, - False, # causal - ) - except Exception as e: - raise RuntimeError(f"Error in standard plan: {e}") from e - else: - try: - # Make sure we pass exactly 15 arguments for standard version - wrapper._plan_info = wrapper._cached_module.plan( - wrapper._float_workspace_buffer, - wrapper._int_workspace_buffer, - wrapper._pin_memory_int_workspace_buffer, - indptr_host, - batch_size, - num_qo_heads, - num_kv_heads, - page_size, - wrapper.is_cuda_graph_enabled, - window_left, - logits_soft_cap, - head_dim, - head_dim, - empty_q_data, - empty_kv_cache, - ) - except Exception as e: - raise RuntimeError(f"Error in standard plan: {e}") from e - - wrapper._pos_encoding_mode = pos_encoding_mode - wrapper._window_left = window_left - wrapper._logits_soft_cap = logits_soft_cap - wrapper._sm_scale = sm_scale - wrapper._rope_scale = rope_scale - wrapper._rope_theta = rope_theta - - @dataclass class PlanParams: """Parameters that affect the flashinfer execution plan.""" @@ -233,12 +83,14 @@ def _init_decode_wrapper( paged_kv_indices_buffer=indices, paged_kv_last_page_len_buffer=last_page_len, use_tensor_cores=True, + backend="fa2" if torch.cuda.get_device_capability(0) == (9, 0) else "auto", ) else: return flashinfer.BatchDecodeWithPagedKVCacheWrapper( self.workspace_buffer, "NHD", use_tensor_cores=True, + backend="fa2" if torch.cuda.get_device_capability(0) == (9, 0) else "auto", ) def init_workspace(self, workspace_buffer: torch.Tensor): @@ -268,7 +120,7 @@ def plan_generate_only( for plan_params in self.cached_cuda_graph_decode_wrappers: if plan_params.num_seq == num_seq: wrapper = self.cached_cuda_graph_decode_wrappers[plan_params] - fast_decode_plan( + flashinfer.decode.fast_decode_plan( wrapper, cu_num_pages, cache_loc, diff --git a/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py b/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py index d47d44378d9f..c0a55d6c2354 100644 --- a/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py +++ b/tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py @@ -4,36 +4,15 @@ import torch import torch.nn.functional as F - -IS_TRITON_KERNELS_AVAILABLE = True -TRITON_KERNELS_UNAVAILABLE_REASON = "" - -try: - from triton_kernels.matmul_ogs import ( - FlexCtx, - FnSpecs, - FusedActivation, - PrecisionConfig, - matmul_ogs, - ) - from triton_kernels.numerics import InFlexData - from triton_kernels.routing import RoutingData, routing - from triton_kernels.swiglu import swiglu_fn - from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor - from triton_kernels.tensor_details import layout - from triton_kernels.tensor_details.layout import StridedLayout - - from tensorrt_llm._torch.modules.fused_moe.fused_moe_triton import TritonEPRouter - -except Exception as _e: - IS_TRITON_KERNELS_AVAILABLE = False - TRITON_KERNELS_UNAVAILABLE_REASON = f"{type(_e).__name__}: {_e}" - - FlexCtx = FnSpecs = FusedActivation = PrecisionConfig = matmul_ogs = None - InFlexData = RoutingData = routing = swiglu_fn = None - FP4 = convert_layout = wrap_torch_tensor = None - layout = StridedLayout = None - TritonEPRouter = None +from triton_kernels.matmul_ogs import FlexCtx, FnSpecs, FusedActivation, PrecisionConfig, matmul_ogs +from triton_kernels.numerics import InFlexData +from triton_kernels.routing import RoutingData, routing +from triton_kernels.swiglu import swiglu_fn +from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor +from triton_kernels.tensor_details import layout +from triton_kernels.tensor_details.layout import StridedLayout + +from tensorrt_llm._torch.modules.fused_moe.fused_moe_triton import TritonEPRouter # copied from transformers.integrations.mxfp4::swizzle_mxfp4 with minor modification diff --git a/tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.py b/tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.py index b60a9f875c64..4113732111ab 100644 --- a/tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.py +++ b/tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.py @@ -9,7 +9,6 @@ register_ad_pattern, ) -from ...custom_ops.fused_moe.mxfp4_moe import IS_TRITON_KERNELS_AVAILABLE from ...utils.module import get_submodule_of_param from ...utils.node_utils import is_op from ..interface import BaseTransform, TransformInfo, TransformRegistry @@ -220,11 +219,7 @@ def _apply( shared_config, ) -> Tuple[GraphModule, TransformInfo]: qcfg = factory.get_quant_config() - if ( - not qcfg - or qcfg.get("quant_method", "") != self.algo_name - or not IS_TRITON_KERNELS_AVAILABLE - ): + if not qcfg or qcfg.get("quant_method", "") != self.algo_name: return gm, TransformInfo( skipped=True, num_matches=0, is_clean=True, has_valid_shapes=True ) diff --git a/tensorrt_llm/_torch/auto_deploy/utils/_config.py b/tensorrt_llm/_torch/auto_deploy/utils/_config.py index dab09e9f97fb..3c7c11a8a009 100644 --- a/tensorrt_llm/_torch/auto_deploy/utils/_config.py +++ b/tensorrt_llm/_torch/auto_deploy/utils/_config.py @@ -41,7 +41,8 @@ def __init__(self, *args, **kwargs): "specify the `yaml_default` field in your pydantic model instead." ) - def _read_files(self, files: PathType | None) -> dict[str, Any]: + def _read_files(self, files: PathType | None, **kwargs: Any) -> dict[str, Any]: + """Read and deep-merge YAML files. Accepts deep_merge kwarg for parent API compatibility.""" if files is None: return {} if isinstance(files, (str, os.PathLike)): diff --git a/tensorrt_llm/_torch/autotuner.py b/tensorrt_llm/_torch/autotuner.py index c78b1a0319a4..89fb89a05436 100644 --- a/tensorrt_llm/_torch/autotuner.py +++ b/tensorrt_llm/_torch/autotuner.py @@ -505,9 +505,8 @@ def load_cache(self, file_path: Union[str, Path], rank: int) -> None: fcntl.flock(f, fcntl.LOCK_SH) current_cache_contents = json.load(f) self._deserialize_metadata(current_cache_contents["metadata"]) - assert f"rank_{rank}" in current_cache_contents, f"Rank {rank} cache not found in {file_path}" self.cache = self._deserialize_cache_data( - current_cache_contents[f'rank_{rank}']) + current_cache_contents.get(f'rank_{rank}', {})) logger.info( f"[AutoTuner] Successfully loaded cache from {file_path} using JSON format" ) @@ -1105,7 +1104,10 @@ def pure_profile(stream: torch.cuda.Stream, repeat: int): disable_short_profile = os.environ.get( "TLLM_AUTOTUNER_DISABLE_SHORT_PROFILE", "0") == "1" - if fewer_repeat_avg_time > short_profile_threshold_ms and not disable_short_profile: + + # Disable this feature for merged tuning strategy to avoid potential hang due to asymmetric tuning. + if fewer_repeat_avg_time > short_profile_threshold_ms and not disable_short_profile \ + and tuning_config.distributed_tuning_strategy != DistributedTuningStrategy.MERGE: # directly use the few repeat estimated time to avoid redundant profiling avg_time = fewer_repeat_avg_time else: diff --git a/tensorrt_llm/_torch/compilation/patterns/ar_residual_norm.py b/tensorrt_llm/_torch/compilation/patterns/ar_residual_norm.py index 55e79f72a119..3d2b2e96245a 100644 --- a/tensorrt_llm/_torch/compilation/patterns/ar_residual_norm.py +++ b/tensorrt_llm/_torch/compilation/patterns/ar_residual_norm.py @@ -713,6 +713,65 @@ def target_finalize_pattern( search_fn_pattern=trtllm_allreduce_default, ) + def insert_copy_for_graph_output(custom_pass: PatternMatcherPass): + trtllm_allreduce_default = CallFunction( + torch.ops.trtllm.allreduce.default, KeywordArg("input"), + KeywordArg("residual"), KeywordArg("gamma"), KeywordArg("scale"), + None, None, mapping.tp_group, int(AllReduceStrategy.UB), + KeywordArg("fusion_op"), KeywordArg("eps"), Ignored()) + + def empty_copy_for_graph_output_pattern( + input: torch.Tensor, + residual: torch.Tensor, + gamma: torch.Tensor, + scale: Optional[torch.Tensor], + fusion_op: int, + eps: float, + ): + return + + def target_copy_for_graph_output_pattern( + input: torch.Tensor, + residual: torch.Tensor, + gamma: torch.Tensor, + scale: Optional[torch.Tensor], + fusion_op: int, + eps: float, + ): + allreduce_output = torch.ops.trtllm.allreduce( + input, residual, gamma, scale, None, None, mapping.tp_group, + int(AllReduceStrategy.UB), fusion_op, eps, False) + non_ub_tensor = torch.empty_like(allreduce_output[0]) + non_ub_tensor.copy_(allreduce_output[0]) + allreduce_output[0] = non_ub_tensor + return allreduce_output + + def extra_check(match: Match) -> bool: + ar_node = match.ctx.pattern_to_node[trtllm_allreduce_default] + assert isinstance(ar_node, torch.fx.graph.Node) + for user_node in ar_node.users: + if not isinstance(user_node, torch.fx.graph.Node): + continue + if user_node.op == "call_function" and user_node.target == getitem and user_node.args[ + 1] == 0: + # Check whether the getitem is connected to output + for getitem_user in user_node.users: + if not isinstance(getitem_user, torch.fx.graph.Node): + continue + if getitem_user.op == "output": + return True + return False + + register_replacement( + empty_copy_for_graph_output_pattern, + target_copy_for_graph_output_pattern, + [], + fwd_only, + custom_pass, + search_fn_pattern=trtllm_allreduce_default, + extra_check=extra_check, + ) + custom_passes.append(PatternMatcherPass()) register_convert_supported_ar_to_ub(custom_passes[-1]) @@ -722,6 +781,9 @@ def target_finalize_pattern( custom_passes.append(PatternMatcherPass()) register_ub_finalize_patterns(custom_passes[-1]) + custom_passes.append(PatternMatcherPass()) + insert_copy_for_graph_output(custom_passes[-1]) + def register_ar_fusions(custom_passes: List[PatternMatcherPass], mapping: Mapping, enable_ub: bool): diff --git a/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py b/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py index c176fe70b315..a4290ecb580e 100644 --- a/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py +++ b/tensorrt_llm/_torch/custom_ops/torch_custom_ops.py @@ -1450,17 +1450,17 @@ def _( def deep_gemm_gen_tuning_buckets(x: int): buckets = tuple(range(8, 128, 8)) + # Clamp x to be between 4096 and 8192. if x >= 128: + x = min(x, 8192) + x = max(x, 4096) buckets += tuple(range(128, x, 128)) return buckets class fp8SwapABGemmRunner(TunableRunner): - tuning_config = TuningConfig( - dynamic_tensor_specs=(DynamicTensorSpec( - 0, 0, deep_gemm_gen_tuning_buckets), ), - tune_max_num_tokens=4096, - ) + tuning_config = TuningConfig(dynamic_tensor_specs=(DynamicTensorSpec( + 0, 0, deep_gemm_gen_tuning_buckets), ), ) def __init__(self, output_dtype: torch.dtype, disable_ue8m0_cast: bool): self.output_dtype = output_dtype @@ -1477,9 +1477,7 @@ def get_valid_tactics( inputs: List[torch.Tensor], profile: OptimizationProfile, ) -> List[int]: - # Encode swap_ab as False (0) and True (1). Currently enabled when GEMM m <= 128. - input, _, _ = inputs - return [0, 1] if input.shape[0] <= 128 else [0] + return [0] def forward( self, @@ -1494,8 +1492,7 @@ def forward( dtype=self.output_dtype, ) - forward_func = deep_gemm.fp8_gemm_ntt if tactic == 1 else deep_gemm.fp8_gemm_nt - forward_func( + deep_gemm.fp8_gemm_nt( (a, a_sf), (weight, weight_scale), output, @@ -1511,14 +1508,13 @@ def fp8_swap_ab_gemm( weight_scale: torch.Tensor, output_dtype: torch.dtype = torch.bfloat16, disable_ue8m0_cast: bool = False, - tune_max_num_tokens: int = 4096, ) -> torch.Tensor: tuner = AutoTuner.get() fp8_swap_ab_gemm_runner = fp8SwapABGemmRunner( output_dtype, disable_ue8m0_cast, ) - fp8SwapABGemmRunner.tuning_config.tune_max_num_tokens = tune_max_num_tokens + _, best_tactic = tuner.choose_one( "trtllm::fp8_swap_ab_gemm", [fp8_swap_ab_gemm_runner], @@ -1538,7 +1534,6 @@ def _( weight_scale: torch.Tensor, output_dtype: torch.dtype = torch.bfloat16, disable_ue8m0_cast: bool = False, - tune_max_num_tokens: int = 4096, ) -> torch.Tensor: return input.new_empty((input.size(0), weight.size(0)), dtype=output_dtype) @@ -1695,8 +1690,7 @@ def get_valid_tactics( **kwargs, ) -> List[int]: valid_strategies = [ - # TODO: NCCL_SYMMETRIC will cause hang during tuning process - # AllReduceStrategy.NCCL_SYMMETRIC.value, + AllReduceStrategy.NCCL_SYMMETRIC.value, AllReduceStrategy.NCCL.value, ] # Fallback in allreduceOp is set to NCCL_SYMMETRIC as default @@ -1724,8 +1718,7 @@ def forward( ) -> torch.Tensor: input, residual, norm_weight, scale, bias, workspace = inputs if tactic == -1: - # TODO: Use NCCL instead of NCCL_SYMMETRIC to avoid hanging during tuning process - tactic = AllReduceStrategy.NCCL.value + tactic = AllReduceStrategy.NCCL_SYMMETRIC.value return torch.ops.trtllm.allreduce( input, diff --git a/tensorrt_llm/_torch/distributed/communicator.py b/tensorrt_llm/_torch/distributed/communicator.py index bf97cd19e695..7eeb748212a6 100644 --- a/tensorrt_llm/_torch/distributed/communicator.py +++ b/tensorrt_llm/_torch/distributed/communicator.py @@ -465,8 +465,6 @@ def recv_object(self, src, tag=0): def tp_comm(self): if self._tp_comm is None: mapping = self.mapping - if mapping.has_cp_helix(): - mapping = mapping.repurpose_helix_cp_to_tp() new_group = mpi_comm().group.Incl(mapping.tp_group) self._tp_comm = mpi_comm().Create_group(new_group) return self._tp_comm @@ -475,8 +473,6 @@ def tp_comm(self): def pp_comm(self): if self._pp_comm is None: mapping = self.mapping - if mapping.has_cp_helix(): - mapping = mapping.repurpose_helix_cp_to_tp() new_group = mpi_comm().group.Incl(mapping.pp_group) self._pp_comm = mpi_comm().Create_group(new_group) return self._pp_comm diff --git a/tensorrt_llm/_torch/distributed/ops.py b/tensorrt_llm/_torch/distributed/ops.py index 713d728566b8..84468dc612f0 100644 --- a/tensorrt_llm/_torch/distributed/ops.py +++ b/tensorrt_llm/_torch/distributed/ops.py @@ -65,14 +65,12 @@ def get_or_scale_allreduce_mnnvl_workspace( """ NUM_LAMPORT_BUFFERS = 3 - if not hasattr(_thread_local, - f'allreduce_mnnvl_workspaces_{mapping.pp_rank}'): - setattr(_thread_local, f'allreduce_mnnvl_workspaces_{mapping.pp_rank}', - {}) + + # Use MNNVLAllReduce class to share across threads + allreduce_mnnvl_workspaces = MNNVLAllReduce.allreduce_mnnvl_workspaces + # A safe method to get the element size of the dtype elem_size = torch.tensor([], dtype=dtype).element_size() - allreduce_mnnvl_workspaces = getattr( - _thread_local, f'allreduce_mnnvl_workspaces_{mapping.pp_rank}') force_mn = os.environ.get("TRTLLM_FORCE_MNNVL_AR", "0") == "1" use_fabric_handle = force_mn or mapping.is_multi_node() @@ -101,19 +99,19 @@ def get_or_scale_allreduce_mnnvl_workspace( # Increase the buffer size in 8 MiB granularity to avoid frequently scaling the buffer buffer_size_bytes = math.ceil(req_buffer_size_bytes / (8 * 1024 * 1024)) * (8 * 1024 * 1024) - if mapping.tp_rank == 0: - logger.debug( - f"[MNNVL] Requested {req_buffer_size_bytes} bytes, is larger than the current workspace size. Scaling workspace for pp_rank {mapping.pp_rank}, tp_size {mapping.tp_size} from {allreduce_mnnvl_workspaces[mapping]['buffer_size_bytes']} to {buffer_size_bytes} bytes" - ) + logger.debug( + f"[MNNVL] Requested {req_buffer_size_bytes} bytes, is larger than the current workspace size. Scaling workspace for pp_rank {mapping.pp_rank}, tp_size {mapping.tp_size} from {allreduce_mnnvl_workspaces[mapping]['buffer_size_bytes']} to {buffer_size_bytes} bytes" + ) # Each workspace contains NUM_LAMPORT_BUFFERS buffers. workspace_size_bytes = NUM_LAMPORT_BUFFERS * buffer_size_bytes + # Pass the pre-split MPI communicator's Fortran handle to avoid redundant splitting in C++ mcast_buf_handle = McastGPUBuffer( workspace_size_bytes, mapping.tp_size, mapping.tp_rank, - mapping.pp_rank * mapping.cp_size + mapping.cp_rank, mapping.local_rank, use_fabric_handle, # whether to use fabric handle or POSIX FD ipc + comm.py2f(), # Fortran handle for the MPI communicator ) # We use per FP32 element in the buffer for lamport sync @@ -517,6 +515,7 @@ class MNNVLAllReduce(nn.Module): This class handles the MNNVL-specific allreduce operations, which can be more efficient for certain operations when using NVLink for multi-node communication. """ + allreduce_mnnvl_workspaces: Dict[Mapping, Dict] = {} def __init__(self, mapping: Mapping, dtype: torch.dtype): super().__init__() @@ -530,7 +529,7 @@ def __init__(self, mapping: Mapping, dtype: torch.dtype): ) # Initialize the workspace - _ = get_or_scale_allreduce_mnnvl_workspace(self.mapping, self.dtype) + get_or_scale_allreduce_mnnvl_workspace(self.mapping, self.dtype) @staticmethod def get_supported_dtypes(): diff --git a/tensorrt_llm/_torch/memory_buffer_utils.py b/tensorrt_llm/_torch/memory_buffer_utils.py index 8d75cd354a6d..3be29e30a745 100644 --- a/tensorrt_llm/_torch/memory_buffer_utils.py +++ b/tensorrt_llm/_torch/memory_buffer_utils.py @@ -79,16 +79,20 @@ def get_buffer(self, tensor_shape: list[int], dtype: torch.dtype, best_fit_block = block smallest_sufficient_size = block.buffer.numel() - if reserve_buffer and best_fit_block is not None: + if best_fit_block is not None: + if reserve_buffer: + best_fit_block.is_reserved = True # A suitable buffer was found, so reuse it. - best_fit_block.is_reserved = True return self._view_as(best_fit_block.buffer, tensor_shape, dtype) for block in list(candidate_blocks): if not block.is_reserved: # Need to call del BufferBlock.buffer, otherwise memory isn't # released and OOM may happen. + buffer_size = block.buffer.numel() del block.buffer + if buffer_size >= 1024 * 1024 * 1024: + torch.cuda.empty_cache() candidate_blocks.remove(block) # No suitable buffer was found, so allocate a new one. diff --git a/tensorrt_llm/_torch/models/checkpoints/hf/qwen3_moe_weight_mapper.py b/tensorrt_llm/_torch/models/checkpoints/hf/qwen3_moe_weight_mapper.py index 1c12b2bd5f43..2ad24e129be7 100644 --- a/tensorrt_llm/_torch/models/checkpoints/hf/qwen3_moe_weight_mapper.py +++ b/tensorrt_llm/_torch/models/checkpoints/hf/qwen3_moe_weight_mapper.py @@ -16,9 +16,6 @@ def init_model_and_config(self, model: Union[nn.Module, DecoderModelForCausalLM], config: ModelConfig): super().init_model_and_config(model, config) - self._num_kv_heads = model.config.num_key_value_heads if hasattr( - model.config, 'num_key_value_heads' - ) and model.config.num_key_value_heads is not None else model.config.num_attention_heads def should_skip_module(self, module_name: str) -> bool: if module_name.startswith("draft_model"): @@ -49,3 +46,11 @@ def _duplicate_kv_weights(self, module: nn.Module, new_name: str, return processed_weights return weights + + @property + def _num_kv_heads(self) -> int: + num_kv_heads = self._model.config.num_key_value_heads if hasattr( + self._model.config, 'num_key_value_heads' + ) and self._model.config.num_key_value_heads is not None else self._model.config.num_attention_heads + + return num_kv_heads diff --git a/tensorrt_llm/_torch/models/checkpoints/hf/qwen3vl_moe_weight_mapper.py b/tensorrt_llm/_torch/models/checkpoints/hf/qwen3vl_moe_weight_mapper.py index cb72762c5de5..12bddd4da85c 100644 --- a/tensorrt_llm/_torch/models/checkpoints/hf/qwen3vl_moe_weight_mapper.py +++ b/tensorrt_llm/_torch/models/checkpoints/hf/qwen3vl_moe_weight_mapper.py @@ -1,4 +1,8 @@ from torch import nn +from transformers.models.qwen3_vl_moe.configuration_qwen3_vl_moe import ( + Qwen3VLMoeTextConfig, + Qwen3VLMoeVisionConfig, +) from tensorrt_llm._torch.models.checkpoints.hf.qwen3_moe_weight_mapper import Qwen3MoeHfWeightMapper from tensorrt_llm._torch.models.modeling_utils import register_mapper @@ -22,3 +26,20 @@ def handle_special_instance_module( module.load_weights( weights=[updated_module_weights], allow_partial_loading=allow_partial_loading ) + + @property + def _num_kv_heads(self) -> int: + config = self._model.config + if isinstance(config, Qwen3VLMoeTextConfig): + num_kv_heads = getattr(config, "num_key_value_heads", None) + if num_kv_heads is None: + num_kv_heads = config.num_attention_heads + elif isinstance(config, Qwen3VLMoeVisionConfig): + num_kv_heads = config.num_heads + else: + raise TypeError( + "Expected `Qwen3VLMoeTextConfig` or `Qwen3VLMoeVisionConfig`, " + f"got {type(config).__name__}" + ) + + return num_kv_heads diff --git a/tensorrt_llm/_torch/models/modeling_llama.py b/tensorrt_llm/_torch/models/modeling_llama.py index e4a57c3fc78b..831cb42d95ea 100644 --- a/tensorrt_llm/_torch/models/modeling_llama.py +++ b/tensorrt_llm/_torch/models/modeling_llama.py @@ -778,21 +778,32 @@ def forward( ) # Fully Connected if self.PRE_MLP_FUSION: + has_lora = bool(kwargs.get('lora_params')) + if self.is_nvfp4 or self.is_fp8_quant: - scale = self.mlp.gate_up_proj.input_scale + # WAR: Skip FP8/NVFP4 quantization when LoRA is active + # since LoRA grouped_gemm does not support FP8 yet + # see: cpp/tensorrt_llm/thop/loraOp.cpp::lora_grouped_gemm + if has_lora: + scale = None # To prevent quantization + fusion_op = AllReduceFusionOp.RESIDUAL_RMS_NORM # Use non-quantizing fusion + else: + scale = self.mlp.gate_up_proj.input_scale + fusion_op = self.pre_mlp_fusion_op else: scale = None + fusion_op = self.pre_mlp_fusion_op all_reduce_output = self.all_reduce( hidden_states, all_reduce_params=AllReduceParams( - fusion_op=self.pre_mlp_fusion_op, + fusion_op=fusion_op, residual=residual, norm_weight=self.post_attention_layernorm.weight, scale=scale, eps=self.post_attention_layernorm.variance_epsilon, )) - if self.is_nvfp4: + if fusion_op == AllReduceFusionOp.RESIDUAL_RMS_NORM_QUANT_NVFP4: act_fp4, act_sf, residual = all_reduce_output hidden_states = Fp4QuantizedTensor(act_fp4, act_sf) else: @@ -841,24 +852,30 @@ def forward( # The next layernorm exists but it could be the last decoder layer. # Adjust the scale and fusion pattern. + has_lora = bool(kwargs.get('lora_params')) + + # WAR: Skip FP8/NVFP4 quantization when LoRA is active + # since LoRA grouped_gemm does not support FP8 yet if not (self.next_attn is not None and (self.is_nvfp4 or self.is_fp8_quant)) \ - or not hasattr(self.next_attn.qkv_proj, 'input_scale'): + or not hasattr(self.next_attn.qkv_proj, 'input_scale') \ + or has_lora: scale = None - self.post_mlp_fusion_op = AllReduceFusionOp.RESIDUAL_RMS_NORM + post_fusion_op = AllReduceFusionOp.RESIDUAL_RMS_NORM else: scale = self.next_attn.qkv_proj.input_scale + post_fusion_op = self.post_mlp_fusion_op all_reduce_output = self.all_reduce( hidden_states, all_reduce_params=AllReduceParams( - fusion_op=self.post_mlp_fusion_op, + fusion_op=post_fusion_op, residual=residual, norm_weight=self.next_layer_layernorm.weight, scale=scale, eps=self.next_layer_layernorm.variance_epsilon, )) - if self.post_mlp_fusion_op == AllReduceFusionOp.RESIDUAL_RMS_NORM_QUANT_NVFP4: + if post_fusion_op == AllReduceFusionOp.RESIDUAL_RMS_NORM_QUANT_NVFP4: act_fp4, act_sf, residual = all_reduce_output hidden_states = Fp4QuantizedTensor(act_fp4, act_sf) else: diff --git a/tensorrt_llm/_torch/models/modeling_llama_min_latency.py b/tensorrt_llm/_torch/models/modeling_llama_min_latency.py index 9af2d99f0b83..ae3d1601fbe5 100644 --- a/tensorrt_llm/_torch/models/modeling_llama_min_latency.py +++ b/tensorrt_llm/_torch/models/modeling_llama_min_latency.py @@ -88,9 +88,10 @@ def __init__( self.enable_trtllm_gen = enable_trtllm_gen self.position_ids = None - def load_weights(self, weights: List[Dict]): + def load_weights(self, weights: List[Dict], allow_partial_loading: bool): - super().load_weights(weights) + super().load_weights(weights, + allow_partial_loading=allow_partial_loading) # After loading weights, calculate the combined scale (input_scale * weight_scale) for special kernels and # trtllm-gen kernels. @@ -384,11 +385,6 @@ def __init__( and self.floor_scale == 8192.0 \ and self.attn_scale == 0.1 - qkv_shard_indices_mapping = { - "q": (0, self.q_size), - "k": (self.q_size, self.kv_size), - "v": (self.q_size + self.kv_size, self.kv_size), - } # When min-latency QKV gemm is enabled, override qkv_proj. self.qkv_proj = Llama4MinLatencyLinear( self.hidden_size, @@ -405,7 +401,6 @@ def __init__( enable_fused_gemm_attn_scaling=self. enable_fused_gemm_attn_scaling, enable_trtllm_gen=True, - fused_weight_shard_indices_mapping=qkv_shard_indices_mapping, ) def _forward_nope( diff --git a/tensorrt_llm/_torch/models/modeling_qwen3vl.py b/tensorrt_llm/_torch/models/modeling_qwen3vl.py index 4bdcfc767844..e08a11e5749a 100644 --- a/tensorrt_llm/_torch/models/modeling_qwen3vl.py +++ b/tensorrt_llm/_torch/models/modeling_qwen3vl.py @@ -739,9 +739,11 @@ def forward( # Getting positional embedding rotary_pos_emb = self.rot_pos_emb(grid_thw) + pos_embeds = self.fast_pos_embed_interpolate(grid_thw) # From this point, pure GPU operation hidden_states = self.patch_embed(pixel_values) + hidden_states = hidden_states + pos_embeds seq_len, _ = hidden_states.size() hidden_states = hidden_states.reshape(seq_len, -1) @@ -1139,6 +1141,7 @@ def _get_requests_with_mm_data(self, multimodal_params): "video": "<|vision_start|><|video_pad|><|vision_end|>", }, placeholder_placement=MultimodalPlaceholderPlacement.BEFORE_TEXT, + placeholders_separator="", ), ) class Qwen3VLModel(Qwen3VLModelBase): diff --git a/tensorrt_llm/_torch/models/modeling_qwen3vl_moe.py b/tensorrt_llm/_torch/models/modeling_qwen3vl_moe.py index a7a0050383c3..66da3c153e2a 100644 --- a/tensorrt_llm/_torch/models/modeling_qwen3vl_moe.py +++ b/tensorrt_llm/_torch/models/modeling_qwen3vl_moe.py @@ -9,6 +9,7 @@ MultimodalPlaceholderMetadata, MultimodalPlaceholderPlacement, register_input_processor, + support_multimodal_disaggregated, ) from .checkpoints.base_weight_mapper import BaseWeightMapper from .checkpoints.hf.qwen3vl_moe_weight_mapper import Qwen3VLMoeHfWeightMapper @@ -21,6 +22,14 @@ from .modeling_utils import ModelConfig, register_auto_model, register_vision_encoder +# NOTE: this is technically not strictly necessary, since the underlying mechanism for registering +# support is tacked onto the input processor class (`Qwen3VLInputProcessorBase`). Given that +# the `Qwen3VLModel` (defined via the import of `modeling_qwen3vl.py` in this file) has that +# decorator applied to it, and uses the same input processor class, we get it "for free" here. +# However, we keep it here to explicitly signify intent that this is supported. This also shields +# it from e.g. the input processor classes becoming specialized between `Qwen3VLModel` and the +# below MoE class. +@support_multimodal_disaggregated @register_vision_encoder(Qwen3VisionModelBase, vlm_base_model=Qwen3VisionModel) @register_auto_model("Qwen3VLMoeForConditionalGeneration") @register_input_processor( @@ -32,6 +41,7 @@ "video": "<|vision_start|><|video_pad|><|vision_end|>", }, placeholder_placement=MultimodalPlaceholderPlacement.BEFORE_TEXT, + placeholders_separator="", ), ) class Qwen3MoeVLModel(Qwen3VLModelBase): diff --git a/tensorrt_llm/_torch/models/modeling_speculative.py b/tensorrt_llm/_torch/models/modeling_speculative.py index 33940091e9dc..e57432512553 100755 --- a/tensorrt_llm/_torch/models/modeling_speculative.py +++ b/tensorrt_llm/_torch/models/modeling_speculative.py @@ -469,9 +469,26 @@ def forward( ) def load_weights(self, weights: Dict, weight_mapper: BaseWeightMapper): + # Remap weight names: some Eagle3 checkpoints use "layers.X.*" naming convention + # while the model expects "midlayer.*" naming. Handle both formats. + import re + remapped_weights = {} + # Access num_layers from the inner draft model (self.model is Eagle3DraftModel) + num_layers = self.model.num_layers + for k, v in weights.items(): + new_k = k + # For single-layer models: "layers.0.*" -> "midlayer.*" + # For multi-layer models: "layers.X.*" -> "midlayer.X.*" + if num_layers == 1: + # Single layer: layers.0.foo -> midlayer.foo + new_k = re.sub(r'^layers\.0\.', 'midlayer.', new_k) + else: + # Multi-layer: layers.X.foo -> midlayer.X.foo + new_k = re.sub(r'^layers\.(\d+)\.', r'midlayer.\1.', new_k) + remapped_weights[new_k] = v new_weights = {} - for k, v in weights.items(): + for k, v in remapped_weights.items(): if 'lm_head' not in k: new_k = "model." + k else: diff --git a/tensorrt_llm/_torch/modules/attention.py b/tensorrt_llm/_torch/modules/attention.py index 69ae313713ad..9b5a4f9a085a 100644 --- a/tensorrt_llm/_torch/modules/attention.py +++ b/tensorrt_llm/_torch/modules/attention.py @@ -1,3 +1,4 @@ +import functools import math import weakref from typing import List, Optional, Union, cast @@ -436,6 +437,7 @@ def _attn_impl( output: Optional[torch.Tensor] = None, output_sf: Optional[torch.Tensor] = None, attention_sinks: Optional[torch.Tensor] = None, + has_lora: bool = False, ): num_tokens = attn_metadata.num_tokens @@ -449,7 +451,8 @@ def _attn_impl( out_scale_sf = None # Don't set out_scale if o_proj has pre_quant_scale - this prevents FP8/FP4 output # and keeps attention output in BF16 for better precision when applying pre_quant_scale - if self._use_quantize_output(): + # Also don't set out_scale if LoRA is active - LoRA grouped_gemm doesn't support FP8 + if self._use_quantize_output() and not has_lora: out_scale = self.o_proj.inv_input_scale out_scale_sf = self.o_proj.input_scale @@ -502,6 +505,7 @@ def forward_impl( attention_mask_data: Optional[torch.Tensor], mrope_config: Optional[dict], attention_sinks: Optional[torch.Tensor] = None, + has_lora: bool = False, ): mrope_rotary_cos_sin = None mrope_position_deltas = None @@ -547,7 +551,8 @@ def forward_impl( mrope_position_deltas, attention_window_size, attention_mask_data, - attention_sinks=attention_sinks) + attention_sinks=attention_sinks, + has_lora=has_lora) if output_sf is not None: output = Fp4QuantizedTensor(output, output_sf) @@ -622,7 +627,8 @@ def forward( attention_window_size, attention_mask_data, mrope_config=mrope_config, - attention_sinks=attention_sinks) + attention_sinks=attention_sinks, + has_lora=bool(lora_params)) if self.attn_output_gate: gate = torch.sigmoid(gate) @@ -1750,6 +1756,54 @@ def forward_context_with_chunked_prefill( return attn_output + @staticmethod + @functools.cache + def cached_warmup_forward_context_with_chunked_prefill( + num_heads_tp, qk_nope_head_dim, qk_rope_head_dim, kv_lora_rank, + v_head_dim, dtype, device): + """Warmup torch.compile for cat operations with different tensor layouts. + + Tensors are marked with torch._dynamo.maybe_mark_dynamic(..., 0) on the + num_tokens dimension, so for num_tokens != 1 a single warmup run is + enough and the compiled kernel generalizes across varying num_tokens at + runtime. num_tokens=1 still triggers recompile (torch.compile specializes + for it), so it is warmed up separately. Do not use torch.compile with + dynamic=True here because it completely ignores tensor layout/stride + information, resulting in significantly degraded performance. + """ + + def warmup(num_tokens): + chunked_k_nope = k_nope = torch.empty( + num_tokens, + num_heads_tp * (qk_nope_head_dim + v_head_dim), + dtype=dtype, + device=device)[:, :num_heads_tp * qk_nope_head_dim].view( + num_tokens, num_heads_tp, qk_nope_head_dim) + chunked_k_pe = torch.empty(num_tokens, + 1, + qk_rope_head_dim, + dtype=dtype, + device=device).expand( + -1, num_heads_tp, -1) + k_pe = torch.empty(num_tokens, + 1, + kv_lora_rank + qk_rope_head_dim, + dtype=dtype, + device=device)[:, :, -qk_rope_head_dim:].expand( + -1, num_heads_tp, -1) + torch._dynamo.maybe_mark_dynamic(chunked_k_nope, 0) + torch._dynamo.maybe_mark_dynamic(chunked_k_pe, 0) + torch._dynamo.maybe_mark_dynamic(k_pe, 0) + maybe_compiled_cat((chunked_k_nope, chunked_k_pe), dim=-1) + maybe_compiled_cat((k_nope, k_pe), dim=-1) + + # With dim 0 (num_tokens) marked dynamic, one warmup suffices for all + # num_tokens != 1 at runtime. + warmup(2) + + # num_tokens=1 still triggers recompile; warm it separately. + warmup(1) + def forward_context( self, q: torch.Tensor, @@ -1763,6 +1817,12 @@ def forward_context( if isinstance(self.mha, TrtllmAttention): assert isinstance(attn_metadata, TrtllmAttentionMetadata) trtllm_attention = cast(TrtllmAttention, self.mha) + if trtllm_attention.is_chunked_prefill_mla_context_for_warmup( + attn_metadata): + self.cached_warmup_forward_context_with_chunked_prefill( + self.num_heads_tp, self.qk_nope_head_dim, + self.qk_rope_head_dim, self.kv_lora_rank, self.v_head_dim, + q.dtype, q.device) if trtllm_attention.is_chunked_prefill_for_mla_context( attn_metadata): return self.forward_context_with_chunked_prefill( diff --git a/tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py b/tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py index d0fbd6485655..153eff2539d4 100644 --- a/tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py +++ b/tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py @@ -312,7 +312,7 @@ def determine_communication_method( 2. Validates if current AllToAll strategy can be used for given workload 3. Falls back to AllGather if current strategy cannot be used (logs info message) - After calling this method, use _is_using_alltoall() to check which method is active. + After calling this method, use enable_alltoall to check which method is active. Args: all_rank_num_tokens: Token counts per rank @@ -347,23 +347,6 @@ def determine_communication_method( # Switch to AllGather (always works) self.comm = AllGatherReduceScatter(mapping=self.mapping) - def _is_using_alltoall(self) -> bool: - """ - Check if current communication strategy uses alltoall - - Returns: - True: Strategy uses alltoall (NVLINK, DeepEP, etc.) - False: Strategy uses allgather (AllGatherReduceScatter or None) - - Note: Can be called anytime. If comm is None, returns False (no alltoall). - Typically called after determine_communication_method() to get accurate result. - """ - if self.comm is None: - return False # No strategy means no alltoall - - # AllGather uses allgather, all others use alltoall - return not isinstance(self.comm, AllGatherReduceScatter) - def _create_comm_strategy_auto(self) -> Communication: """ Auto-create the best communication strategy based on hardware and configuration @@ -792,11 +775,7 @@ def _forward_multiple_chunks( Same as original implementation - chunking logic is backend-agnostic - Note: use_all_to_all is determined internally via _is_using_alltoall() - """ - # Determine if using alltoall - use_all_to_all = self._is_using_alltoall() # ========== Chunk preparation ========== if self.use_dp: # When using DP: need all ranks' token counts for reducescatter @@ -810,7 +789,7 @@ def _forward_multiple_chunks( chunk_size_list = all_rank_chunk_size_list[self.rank] # For alltoall, replace 0 with 1 (avoid empty tensor) - if use_all_to_all: + if self.enable_alltoall: all_rank_num_tokens_list = [ [1 if val == 0 else val for val in val_list] for val_list in all_rank_num_tokens_list @@ -824,7 +803,7 @@ def _forward_multiple_chunks( router_logits_list = router_logits.split(chunk_size_list) # Determine if we need multiple streams for overlapped execution - use_multi_stream = not use_all_to_all and self.aux_stream is not None + use_multi_stream = not self.enable_alltoall and self.aux_stream is not None # ========== Setup auxiliary stream ========== if use_multi_stream: @@ -1064,7 +1043,8 @@ def _get_backend_kwargs( # Only the non-alltoall case is considered for profiling in the warmup phase. # Therefore, to get the correct tactics during the actual inference, the inputs to the tuner # should be the same as when not using alltoall. - if self._is_using_alltoall(): + kwargs["enable_alltoall"] = self.enable_alltoall + if self.enable_alltoall: if all_rank_num_tokens is not None: kwargs["tuner_num_tokens"] = sum(all_rank_num_tokens) else: @@ -1072,9 +1052,6 @@ def _get_backend_kwargs( x.shape[0] * self.mapping.tp_size if x is not None else None ) kwargs["tuner_top_k"] = self.routing_method.top_k - else: - kwargs["tuner_num_tokens"] = None - kwargs["tuner_top_k"] = None # Get moe_output for NVLinkOneSided backend kwargs["moe_output"] = self._get_nvlink_onesided_moe_output( diff --git a/tensorrt_llm/_torch/modules/fused_moe/create_moe.py b/tensorrt_llm/_torch/modules/fused_moe/create_moe.py index 530d903ad354..a25c639c53be 100644 --- a/tensorrt_llm/_torch/modules/fused_moe/create_moe.py +++ b/tensorrt_llm/_torch/modules/fused_moe/create_moe.py @@ -195,6 +195,7 @@ def create_moe_backend( swiglu_beta=swiglu_beta, swiglu_limit=swiglu_limit, init_load_balancer=init_load_balancer, + without_comm=without_comm, activation_type=activation_type, ) elif moe_cls == WideEPMoE: @@ -255,6 +256,7 @@ def create_moe_backend( weight_loading_mode=weight_loading_mode, apply_router_weight_on_input=apply_router_weight_on_input, layer_idx=layer_idx, + without_comm=without_comm, ) elif moe_cls == TritonFusedMoE: assert not apply_router_weight_on_input, "apply_router_weight_on_input is not supported in TritonFusedMoE." diff --git a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py index 41eacfe65c39..b0a54e4e0d52 100755 --- a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py +++ b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py @@ -403,6 +403,7 @@ def run_moe( tuner_num_tokens: Optional[int] = None, tuner_top_k: Optional[int] = None, moe_output: Optional[torch.Tensor] = None, + enable_alltoall: Optional[bool] = None, ) -> torch.Tensor: """ Run MoE computation with Cutlass backend. @@ -421,6 +422,7 @@ def run_moe( tuner_num_tokens: Number of tokens for profiling tuner (optional) tuner_top_k: Top-k value for profiling tuner (optional) moe_output: Pre-allocated output buffer (optional) + enable_alltoall: Whether alltoall communication is enabled (optional). If None, defaults to self.enable_alltoall. Returns: final_hidden_states: Output tensor from MoE computation @@ -433,6 +435,9 @@ def run_moe( elif self.has_w4a16_mxfp4: weight_dtype = torch.uint8 + if enable_alltoall is None: + enable_alltoall = self.enable_alltoall + result = torch.ops.trtllm.fused_moe( x, token_selected_experts, @@ -454,7 +459,7 @@ def run_moe( ep_rank=self.ep_rank, cluster_size=self.cluster_size, cluster_rank=self.cluster_rank, - enable_alltoall=self.enable_alltoall, + enable_alltoall=enable_alltoall, use_deepseek_fp8_block_scale=self.has_deepseek_fp8_block_scales, use_w4_group_scaling=self.has_w4afp8 or self.has_w4a16_mxfp4, use_int8_woq_per_channel=self.has_int8_woq_per_channel, diff --git a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py index 22d95411bc12..c256b1313c94 100755 --- a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py +++ b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py @@ -1,32 +1,19 @@ from __future__ import annotations import os -import sys from typing import Dict, List, NamedTuple, Optional import torch import torch.nn as nn import triton import triton.language as tl - -IS_TRITON_KERNELS_AVAILABLE = False -# We expect to find triton_kernels under $TRITON_ROOT/python/triton_kernels -# Triton upstream commit f3067cd3bd0c29065fa4ecdb724b6f29cbabea5f has been verified. -triton_root = os.getenv('TRITON_ROOT') -if triton_root: - triton_root = os.path.abspath( - os.path.join(triton_root, 'python', 'triton_kernels')) - if os.path.exists(triton_root) and triton_root not in sys.path: - sys.path.insert(0, triton_root) - assert triton.__version__ >= "3.4.0", "Triton kernels are detected but the Triton wheel is too old" - import triton_kernels.swiglu - from triton_kernels.matmul_ogs import (FlexCtx, FnSpecs, FusedActivation, - PrecisionConfig, matmul_ogs) - from triton_kernels.numerics import InFlexData - from triton_kernels.numerics_details.mxfp import downcast_to_mxfp_torch - from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor - from triton_kernels.tensor_details import layout - IS_TRITON_KERNELS_AVAILABLE = True +import triton_kernels.swiglu +from triton_kernels.matmul_ogs import (FlexCtx, FnSpecs, FusedActivation, + PrecisionConfig, matmul_ogs) +from triton_kernels.numerics import InFlexData +from triton_kernels.numerics_details.mxfp import downcast_to_mxfp_torch +from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor +from triton_kernels.tensor_details import layout from ...model_config import ModelConfig from ..linear import TensorParallelMode, load_weight_shard @@ -214,11 +201,16 @@ def create_weights(self, module: torch.nn.Module): module.intermediate_size_per_partition, module.hidden_size, ) + # Bias shapes use the output dimension (last dim) of the transposed weight shapes + w3_w1_bias_shape = (w3_w1_weight_shape[0], w3_w1_weight_shape[2]) + w2_bias_shape = (w2_weight_shape[0], w2_weight_shape[2]) super().create_weights(module, weight_dtype, w3_w1_weight_shape, w2_weight_shape, - bias_dtype=torch.float32) + bias_dtype=torch.float32, + w3_w1_bias_shape=w3_w1_bias_shape, + w2_bias_shape=w2_bias_shape) self.setup_quant_scales(module) def setup_quant_scales(self, module: torch.nn.Module): @@ -404,12 +396,17 @@ def create_weights(self, module: torch.nn.Module): module.intermediate_size_per_partition, module.hidden_size, ) + # Bias shapes use the output dimension (last dim) of the transposed weight shapes + w3_w1_bias_shape = (w3_w1_weight_shape[0], w3_w1_weight_shape[2]) + w2_bias_shape = (w2_weight_shape[0], w2_weight_shape[2]) FusedMoEMethodBase.create_weights(self, module, weight_dtype, w3_w1_weight_shape, w2_weight_shape, - bias_dtype=torch.float32) + bias_dtype=torch.float32, + w3_w1_bias_shape=w3_w1_bias_shape, + w2_bias_shape=w2_bias_shape) fc31_dequant = nn.Parameter(torch.empty( module.expert_size_per_partition, dtype=torch.float32), @@ -1295,8 +1292,6 @@ def __init__( weight_loading_mode=weight_loading_mode, layer_idx=layer_idx, ) - if not IS_TRITON_KERNELS_AVAILABLE: - raise ImportError("Triton kernels are not available.") if torch.cuda.get_device_capability()[0] != 9 and self.ep_size > 1: raise NotImplementedError( "TritonFusedMoE is only supported on Hopper with EP size > 1.") diff --git a/tensorrt_llm/_torch/modules/linear.py b/tensorrt_llm/_torch/modules/linear.py index 210fd9dd6ad0..90ffe7abea11 100644 --- a/tensorrt_llm/_torch/modules/linear.py +++ b/tensorrt_llm/_torch/modules/linear.py @@ -2089,6 +2089,7 @@ def __init__( disable_deep_gemm: bool = False, fused_weight_shard_indices_mapping: Optional[dict] = None, nvfp4_allowed_backends: Optional[List[str]] = None, + enable_gemm_allreduce_fusion: bool = True, ): """ Args: @@ -2167,11 +2168,14 @@ def __init__( device_supported = get_sm_version() >= 100 nvls_supported = ipc_nvls_supported() + enable_gemm_allreduce_fusion_env = (os.environ.get( + "TRTLLM_GEMM_ALLREDUCE_FUSION_ENABLED", "0") == "1") self.use_fused_gemm_allreduce = all([ self.reduce_output, mpi_enabled, dtype_supported, in_features_aligned, out_features_aligned, tp_valid, quant_valid, - device_supported, nvls_supported + device_supported, nvls_supported, enable_gemm_allreduce_fusion, + enable_gemm_allreduce_fusion_env ]) self.enable_cuda_core = False diff --git a/tensorrt_llm/_torch/modules/triton_linear.py b/tensorrt_llm/_torch/modules/triton_linear.py index dfc3d584e64a..f22f35bd0c8e 100644 --- a/tensorrt_llm/_torch/modules/triton_linear.py +++ b/tensorrt_llm/_torch/modules/triton_linear.py @@ -4,20 +4,15 @@ import torch from torch.nn.parameter import Parameter +from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig, matmul_ogs +from triton_kernels.numerics import InFlexData from tensorrt_llm._torch.peft.lora.layer import LoraLayer from tensorrt_llm.mapping import Mapping from ...models.modeling_utils import QuantConfig -# Reuse the common Triton import setup -from .fused_moe.fused_moe_triton import (IS_TRITON_KERNELS_AVAILABLE, - maybe_update_stride, +from .fused_moe.fused_moe_triton import (maybe_update_stride, swizzle_weight_and_scale) - -if IS_TRITON_KERNELS_AVAILABLE: - from triton_kernels.matmul_ogs import (FlexCtx, PrecisionConfig, matmul_ogs) - from triton_kernels.numerics import InFlexData - from .linear import (Linear, LinearMethodBase, TensorParallelMode, WeightsLoadingConfig, copy_weight, load_weight_shard, load_weights_fused_gate_up_helper, @@ -383,9 +378,6 @@ def __init__( use_custom_cublas_mm: bool = False, lora: Optional[LoraLayer] = None, ): - if not IS_TRITON_KERNELS_AVAILABLE: - raise ImportError("Triton kernels are not available. " - "Please install the required dependencies.") assert not use_custom_cublas_mm, "TritonLinear does not support custom cublas mm." super().__init__( diff --git a/tensorrt_llm/_torch/pyexecutor/_util.py b/tensorrt_llm/_torch/pyexecutor/_util.py index dae057b61ea4..66016855ff68 100644 --- a/tensorrt_llm/_torch/pyexecutor/_util.py +++ b/tensorrt_llm/_torch/pyexecutor/_util.py @@ -686,7 +686,7 @@ def _create_kv_cache_manager( execution_stream=execution_stream, ) else: - # NOTE: this is a workaround for VSWA to switch to calculate_max_num_blocks_from_cpp in KVCahceManager + # NOTE: this is a workaround for VSWA to switch to calculate_max_num_blocks_for_vswa in KVCahceManager is_vswa = kv_cache_config.max_attention_window is not None and len( set(kv_cache_config.max_attention_window)) > 1 binding_model_config = model_engine.model.model_config.get_bindings_model_config( diff --git a/tensorrt_llm/_torch/pyexecutor/executor_request_queue.py b/tensorrt_llm/_torch/pyexecutor/executor_request_queue.py index cb421865200f..080a615e75e4 100644 --- a/tensorrt_llm/_torch/pyexecutor/executor_request_queue.py +++ b/tensorrt_llm/_torch/pyexecutor/executor_request_queue.py @@ -1,6 +1,7 @@ import dataclasses import datetime import heapq +import os import queue import threading import time @@ -611,12 +612,20 @@ def _broadcast_new_requests( with nvtx_range("recv_requests_from_prev_pp"): payloads = self.dist.recv_object(self.dist.prev_pp_rank, tag) + # isend new requests may cause deadlock, when CUDA_LAUNCH_BLOCKING=1 or PP microbatches can't overlap, + # the deadlock will happen deterministicly: + # 1. rank1 will wait on nccl.send(rank2), without invoking mpi.wait(isend-handle) + # 2. rank2 will wait on mpi.recv(rank1) but never receive the new requests. + # 3. rank1 will hang on nccl.send because rank2 will never reach nccl.recv(rank1). + pp_send_func = self.dist.isend_object if os.environ.get( + "TRTLLM_PP_REQ_SEND_ASYNC", "0") == "1" else self.dist.send_object + if not self.dist.is_last_pp_rank: if self.send_requests_handler is not None: with nvtx_range("wait_prev_send_requests_handler"): self.send_requests_handler.wait() with nvtx_range("send_requests_to_next_pp"): - self.send_requests_handler = self.dist.isend_object( + self.send_requests_handler = pp_send_func( payloads, self.dist.next_pp_rank, tag) return payloads diff --git a/tensorrt_llm/_torch/pyexecutor/guided_decoder.py b/tensorrt_llm/_torch/pyexecutor/guided_decoder.py index 0d4095160447..610d4e1d1d8a 100644 --- a/tensorrt_llm/_torch/pyexecutor/guided_decoder.py +++ b/tensorrt_llm/_torch/pyexecutor/guided_decoder.py @@ -145,11 +145,13 @@ def __init__(self, guided_decoding_config: GuidedDecodingConfig, max_num_sequences: int, vocab_size_padded: int, - max_num_draft_tokens: int = 0): + max_num_draft_tokens: int = 0, + rank: int = 0): self.guided_decoding_backend = guided_decoding_config.backend self.max_num_sequences = max_num_sequences self.vocab_size_padded = vocab_size_padded self.max_num_draft_tokens = max_num_draft_tokens + self.rank = rank if self.guided_decoding_backend == GuidedDecodingConfig.GuidedDecodingBackend.XGRAMMAR: self.grammar_matcher_factory = XGrammarMatcherFactory( @@ -274,6 +276,7 @@ def _build(self, requests: GuidedRequests) -> List[Tuple[int, str]]: assert len(req.draft_tokens) == 0 self.num_advanced_draft_tokens[ slot] += self.num_advanced_tokens[slot] + except Exception as e: error_msg = f"Guided decoding error: {str(e)}" failed_requests.append((req.request_id, error_msg)) @@ -305,9 +308,26 @@ def _apply_bitmask(self, """ if num_bitmask_tokens is None: num_bitmask_tokens = requests.num_bitmask_tokens + + # In general, the logits passed to GuidedDecoder are complete in the vocabulary dimension. + # In some special cases (e.g., MTP), the logits are sharded in the vocabulary dimension. + vocab_size_padded = self.vocab_size_padded if d2t is None else d2t.size( + 0) + assert vocab_size_padded % logits.size(1) == 0 + tp_size = vocab_size_padded // logits.size(1) + assert self.bitmask_size % tp_size == 0 + tp_rank = self.rank % tp_size + bitmask_start = tp_rank * self.bitmask_size // tp_size + bitmask_end = bitmask_start + self.bitmask_size // tp_size + + if d2t is not None: + d2t_start = tp_rank * vocab_size_padded // tp_size + d2t_end = d2t_start + vocab_size_padded // tp_size + d2t = d2t[d2t_start:d2t_end] + torch.ops.trtllm.logits_bitmask( logits[:num_bitmask_tokens], - self.bitmask[:num_bitmask_tokens], + self.bitmask[:num_bitmask_tokens, bitmask_start:bitmask_end], token_mask=self.token_mask[:num_bitmask_tokens], d2t=d2t) @@ -387,10 +407,9 @@ def _rollback_draft_tokens(self, requests: GuidedRequests) -> None: for req in requests.valid_requests(): slot = req.seq_slot - if self.num_advanced_draft_tokens[slot] <= 0: - continue - self.grammar_matchers[slot].rollback( - self.num_advanced_draft_tokens[slot]) + if self.num_advanced_draft_tokens[slot] > 0: + self.grammar_matchers[slot].rollback( + self.num_advanced_draft_tokens[slot]) # Reset the drafting states. self.num_advanced_draft_tokens[slot] = 0 self.is_draft_terminated[slot] = False @@ -423,9 +442,13 @@ def __init__(self, guided_decoding_config: GuidedDecodingConfig, max_num_sequences: int, vocab_size_padded: int, - max_num_draft_tokens: int = 0): - super().__init__(guided_decoding_config, max_num_sequences, - vocab_size_padded, max_num_draft_tokens) + max_num_draft_tokens: int = 0, + rank: int = 0): + super().__init__(guided_decoding_config=guided_decoding_config, + max_num_sequences=max_num_sequences, + vocab_size_padded=vocab_size_padded, + max_num_draft_tokens=max_num_draft_tokens, + rank=rank) # self.requests should be accessed by normal host code; # self.requests_hostfunc should be accessed by hostfunc (CUDA callback). self.requests_hostfunc: Optional[GuidedRequests] = None diff --git a/tensorrt_llm/_torch/pyexecutor/model_engine.py b/tensorrt_llm/_torch/pyexecutor/model_engine.py index fdfb511e5e06..8663ffb53b5c 100644 --- a/tensorrt_llm/_torch/pyexecutor/model_engine.py +++ b/tensorrt_llm/_torch/pyexecutor/model_engine.py @@ -48,8 +48,7 @@ get_spec_metadata, update_spec_config_from_model_config) from ..speculative.drafting_loops import BaseDraftingLoopWrapper -from ..speculative.eagle3 import (Eagle3OneModelSpecMetadata, - Eagle3ResourceManager, Eagle3SpecMetadata) +from ..speculative.eagle3 import Eagle3ResourceManager, Eagle3SpecMetadata from ..speculative.mtp import SampleStateTensorsMTP from ..speculative.utils import SpecDecodingTensor from ..utils import (get_model_extra_attrs, @@ -2684,9 +2683,9 @@ def previous_seq_slots_device(): num_accepted_draft_tokens)] if isinstance(spec_metadata, Eagle3SpecMetadata): spec_metadata.request_accepted_path = request_accepted_path - if isinstance(spec_metadata, Eagle3OneModelSpecMetadata): - spec_metadata.populate_sampling_params_for_one_model( - scheduled_requests.all_requests()) + # No-op for non 1-model + spec_metadata.populate_sampling_params_for_one_model( + scheduled_requests.all_requests()) spec_metadata.prepare() inputs['spec_metadata'] = spec_metadata diff --git a/tensorrt_llm/_torch/pyexecutor/py_executor.py b/tensorrt_llm/_torch/pyexecutor/py_executor.py index 94fa5020ca7f..757f1cd6dc45 100644 --- a/tensorrt_llm/_torch/pyexecutor/py_executor.py +++ b/tensorrt_llm/_torch/pyexecutor/py_executor.py @@ -54,7 +54,7 @@ from .model_engine import ModelEngine from .resource_manager import ResourceManager from .sampler import (AsyncWorkerMixin, Sampler, SamplerEvent, SampleState, - SampleStateTensors) + SampleStateTensors, TRTLLMSampler) from .scheduler import (RequestScheduler, ScheduledRequests, SerializableSchedulerOutput) @@ -252,9 +252,19 @@ def __init__(self, self.send_schedule_handler = None self.pp_scheduler_max_retry_count = int( os.environ.get("TLLM_PP_SCHEDULER_MAX_RETRY_COUNT", 10)) + self.pp_multi_stream_sample = os.environ.get( + "TRTLLM_PP_MULTI_STREAM_SAMPLE", "1") == "1" self.sample_stream = torch.cuda.Stream() self.start_sample_event = torch.cuda.Event() self.finish_sample_event = torch.cuda.Event() + if (self.dist.pp_size > 1 and self.pp_multi_stream_sample + and isinstance(self.sampler, TRTLLMSampler)): + # TRTLLM sampler uses default stream for store and algorithms. + # To enable multi-stream sampling, we need to re-initialize + # the sampler store and algorithms on the sample stream. + with torch.cuda.stream(self.sample_stream): + self.sampler._initialize_store() + self.sampler._instantiate_algorithms() # Set of request IDs that are currently in flight across all micro batches. # The scheduler will avoid scheduling requests that are already in flight. @@ -1094,8 +1104,7 @@ def _executor_loop_pp(self): guided_decoder_failed_requests = self.guided_decoder.execute( batch_outputs['logits']) - if os.environ.get("TRTLLM_PP_MULTI_STREAM_SAMPLE", - "1") == "1": + if self.pp_multi_stream_sample: # Wait for the previous sample to finish. self.finish_sample_event.wait() # Copy the batch outputs as sampler inputs diff --git a/tensorrt_llm/_torch/pyexecutor/py_executor_creator.py b/tensorrt_llm/_torch/pyexecutor/py_executor_creator.py index 25f391d81353..704a319737c0 100644 --- a/tensorrt_llm/_torch/pyexecutor/py_executor_creator.py +++ b/tensorrt_llm/_torch/pyexecutor/py_executor_creator.py @@ -281,16 +281,12 @@ def create_py_executor( ) llm_args.disable_overlap_scheduler = True - if spec_config is not None and spec_config.spec_dec_mode.use_one_engine(): - if not spec_config.allow_advanced_sampling: - logger.warning( - f"Falling back to greedy decoding for {spec_config.decoding_type}. If you " - "want to use non-greedy sampling, please set allow_advanced_sampling=True." - ) - elif spec_config.spec_dec_mode.is_mtp_one_model(): - logger.warning( - "Advanced sampling is not supported for MTP yet - this will be added soon." - ) + if spec_config is not None and spec_config.spec_dec_mode.use_one_engine( + ) and not spec_config.allow_advanced_sampling: + logger.warning( + f"Falling back to greedy decoding for {spec_config.decoding_type}. If you " + "want to use non-greedy sampling, please set allow_advanced_sampling=True." + ) if mm_encoder_only: llm_args.mm_encoder_only = True @@ -505,7 +501,8 @@ def drafting_loop_wrapper(model): kwargs = { "guided_decoding_config": guided_decoding_config, "max_num_sequences": max_batch_size, - "vocab_size_padded": model_engine.model.vocab_size_padded + "vocab_size_padded": model_engine.model.vocab_size_padded, + "rank": mapping.rank, } if spec_config is not None: kwargs[ diff --git a/tensorrt_llm/_torch/pyexecutor/resource_manager.py b/tensorrt_llm/_torch/pyexecutor/resource_manager.py index fd7a6d717637..236da8889731 100644 --- a/tensorrt_llm/_torch/pyexecutor/resource_manager.py +++ b/tensorrt_llm/_torch/pyexecutor/resource_manager.py @@ -1,18 +1,21 @@ import copy import enum import math +import os from abc import ABC, abstractmethod from collections import OrderedDict, defaultdict, deque from typing import (TYPE_CHECKING, Dict, Iterable, List, Optional, Set, Tuple, Union) import torch +from mpi4py import MPI import tensorrt_llm import tensorrt_llm.bindings from tensorrt_llm._torch.distributed.communicator import Distributed, ReduceOp -from tensorrt_llm.llmapi.llm_args import (KvCacheConfig, PeftCacheConfig, - PybindMirror) +from tensorrt_llm._utils import (get_size_in_bytes, mpi_comm, mpi_disabled, + torch_comm) +from tensorrt_llm.llmapi.llm_args import KvCacheConfig, PeftCacheConfig from tensorrt_llm.lora_helper import LoraConfig from tensorrt_llm.lora_manager import LoraManager, LoraModelConfig from tensorrt_llm.runtime import ModelConfig as ModelConfigPython @@ -301,12 +304,44 @@ def append_to_kv_heads_per_layer(num_kv_heads_per_layer: List[int], ) assert isinstance( kv_cache_config, KvCacheConfig - ), "calculate_max_num_blocks_from_cpp only accepts KvCacheConfig" - blocks_per_window = self.calculate_max_num_blocks_from_cpp( + ), "calculate_max_num_blocks_for_vswa only accepts KvCacheConfig" + blocks_per_window = self.calculate_max_num_blocks_for_vswa( kv_cache_config=kv_cache_config, model_config=model_config, extra_cost_memory=0, ) + if mapping.world_size > 1: + # make sure all ranks use the same number of primary/secondary blocks + if mpi_disabled(): + for window_size, ( + primary_blocks, + secondary_blocks) in blocks_per_window.items(): + reduced_primary_blocks = torch_comm().allreduce( + primary_blocks, + op=torch.distributed.ReduceOp.MIN) + reduced_secondary_blocks = torch_comm().allreduce( + secondary_blocks, + op=torch.distributed.ReduceOp.MIN) + blocks_per_window[window_size] = ( + reduced_primary_blocks, + reduced_secondary_blocks) + else: + for window_size, ( + primary_blocks, + secondary_blocks) in blocks_per_window.items(): + reduced_primary_blocks = mpi_comm().allreduce( + primary_blocks, op=MPI.MIN) + reduced_secondary_blocks = mpi_comm().allreduce( + secondary_blocks, op=MPI.MIN) + blocks_per_window[window_size] = ( + reduced_primary_blocks, + reduced_secondary_blocks) + logger.info( + f"[MPI rank={mapping.rank}] Original blocks_per_window: {blocks_per_window}" + ) + logger.info( + f"[MPI rank={mapping.rank}] Reduced blocks_per_window: {blocks_per_window}" + ) else: # Standard case: use original Python implementation self.blocks_in_primary_pool, self.blocks_in_secondary_pool = self.calculate_max_num_blocks( @@ -985,8 +1020,8 @@ def _get_window_size_to_layers(self) -> dict[int, list[int]]: window_size_to_layers_map[window_size].append(local_layer_idx) return window_size_to_layers_map - @staticmethod def adjust_window_sizes_for_vswa( + self, window_size_to_layers: Dict[int, List[int]], max_attention_window_vec: List[int], kv_cache_config: KvCacheConfig, @@ -1003,8 +1038,7 @@ def adjust_window_sizes_for_vswa( def calculate_cache_size_per_token(layers: Set[int]) -> int: # Same as BaseKVCacheManager::calculateCacheSizePerTokenForSingleWindowSize - total_kv_heads = sum(model_config.num_kv_heads_per_layer[i] - for i in layers) + total_kv_heads = sum(self.num_kv_heads_per_layer[i] for i in layers) return total_kv_heads * kv_factor * model_config.head_size # Calculate the required memory bytes per sequence. @@ -1020,15 +1054,17 @@ def calculate_cache_size_per_token(layers: Set[int]) -> int: quant_vector_size=16, scaling_factor_dtype=DataType.FP8) required_mem_bytes_per_seq += window_size * cache_size_bytes_per_token - logger.debug( + logger.info( f'Required memory per sequence: {required_mem_bytes_per_seq} bytes') + logger.info(f"Memory bytes in pool: {pool_memory_bytes}") if required_mem_bytes_per_seq < pool_memory_bytes: # No need to adjust the window sizes. + logger.info("No need to adjust the window sizes, returning") return (copy.deepcopy(window_size_to_layers), max_attention_window_vec) - logger.debug( + logger.info( f'Adjusting the window sizes {list(window_size_to_layers)} to fit ' f'the memory {pool_memory_bytes} bytes.') adjusted_window_size_to_layers = {} @@ -1102,14 +1138,12 @@ def calculate_cache_size_per_token(layers: Set[int]) -> int: return (adjusted_window_size_to_layers, adjusted_max_attention_window_vec) - def calculate_max_num_blocks_from_cpp( + def calculate_max_num_blocks_for_vswa( self, kv_cache_config: KvCacheConfig, model_config: ModelConfigCpp, extra_cost_memory: int = 0) -> dict[int, tuple[int, int]]: """ - This function is a wrapper of KVCacheManagerCpp.calculate_max_num_blocks. - The final goal is to switch to the C++ implementation of calculate_max_num_blocks. Currently, this function is added to support *ONLY* VSWA. Args: @@ -1119,6 +1153,15 @@ def calculate_max_num_blocks_from_cpp( Returns: A dict of (max_attention_window, (blocks_in_primary_pool, blocks_in_secondary_pool)). + + Environment variable TRTLLM_WINDOW_SIZE_SHARES is used to adjust the memory + share of each window size. By default, we allocate equal proportion shares of + memory for all window sizes (see the else case). With TRTLLM_WINDOW_SIZE_SHARES, + we can override this behavior to adjust the memory share of each window size. + + For example, if we have window size of [512, 32768], then setting + TRTLLM_WINDOW_SIZE_SHARES=0.4,0.6 will be allocating 40% of the memory to + window size 512 and 60% of the memory to window size 32768. """ # VSWA on Torch backend has not supported the cross attention. @@ -1162,19 +1205,70 @@ def calculate_max_num_blocks_from_cpp( ) self.max_attention_window_vec = max_attention_window_vec - blocks_per_window = KVCacheManagerCpp.calculate_max_num_blocks( - config=PybindMirror.maybe_to_pybind(kv_cache_config), - # TODO: support cross attention - is_cross_attention=is_cross_attention, - dtype=self.dtype, - model_config=model_config, - world_config=world_config_cpp, - window_size_to_layers=window_size_to_layers, - allotted_primary_mem_bytes=self._primary_pool_memory_bytes, - allotted_secondary_mem_bytes=self._secondary_pool_memory_bytes, - extra_cost_memory=extra_cost_memory, - kv_factor=self.kv_factor, - ) + def calculate_cache_size_per_token(layers: Set[int]) -> int: + # Same as BaseKVCacheManager::calculateCacheSizePerTokenForSingleWindowSize + total_kv_heads = sum(self.num_kv_heads_per_layer[i] for i in layers) + return total_kv_heads * self.kv_factor * model_config.head_size + + logger.info( + f"Primary pool memory bytes: {self._primary_pool_memory_bytes}") + logger.info( + f"Secondary pool memory bytes: {self._secondary_pool_memory_bytes}") + + if os.getenv("TRTLLM_WINDOW_SIZE_SHARES") is not None: + logger.info("Environment variable TRTLLM_WINDOW_SIZE_SHARES is set") + window_size_shares = os.getenv("TRTLLM_WINDOW_SIZE_SHARES").split( + ",") + window_size_shares = [float(share) for share in window_size_shares] + assert len(window_size_shares) == len( + window_size_to_layers + ), "Number of shares in TRTLLM_WINDOW_SIZE_SHARES must match number of window sizes" + assert sum( + window_size_shares + ) == 1.0, "Sum of shares in TRTLLM_WINDOW_SIZE_SHARES must be 1.0" + else: + logger.info( + "Using default allocation of equal proportion of memory to each window size" + ) + window_size_shares = [ + 1.0 / len(window_size_to_layers) for _ in window_size_to_layers + ] + + logger.info(f"Derived window_size_shares: {window_size_shares}") + + blocks_per_window = {} + for window_idx, (window_size, layers) in enumerate( + sorted(window_size_to_layers.items())): + cache_size_per_token = calculate_cache_size_per_token(layers) + cache_size_bytes_per_token = get_size_in_bytes( + cache_size_per_token, self.dtype) + + primary_tokens = self._primary_pool_memory_bytes * window_size_shares[ + window_idx] / cache_size_bytes_per_token + secondary_tokens = self._secondary_pool_memory_bytes * window_size_shares[ + window_idx] / cache_size_bytes_per_token + + if kv_cache_config.max_tokens is not None: + if self.is_vswa: + logger.info( + f"kv_cache_config.max_tokens is not None ({kv_cache_config.max_tokens}) but we are operating on VSWA scheme. Ignoring the configuration." + ) + if not self.is_vswa: + logger.info( + f"kv_cache_config.max_tokens is {kv_cache_config.max_tokens}" + ) + if kv_cache_config.max_tokens < primary_tokens: + logger.info( + f"kv_cache_config.max_tokens {kv_cache_config.max_tokens} is less than primary_tokens {primary_tokens}. Reducing primary_tokens to {kv_cache_config.max_tokens}" + ) + primary_tokens = kv_cache_config.max_tokens + + primary_blocks = int(primary_tokens // self.tokens_per_block) + secondary_blocks = int(secondary_tokens // self.tokens_per_block) + logger.info( + f"Window size = {window_size}, primary_blocks: {primary_blocks}, secondary_blocks: {secondary_blocks}" + ) + blocks_per_window[window_size] = (primary_blocks, secondary_blocks) return blocks_per_window def _validate_and_adjust_attention_windows( diff --git a/tensorrt_llm/_torch/pyexecutor/sampler.py b/tensorrt_llm/_torch/pyexecutor/sampler.py index 3475963bb01c..1418910a3fdb 100644 --- a/tensorrt_llm/_torch/pyexecutor/sampler.py +++ b/tensorrt_llm/_torch/pyexecutor/sampler.py @@ -357,9 +357,21 @@ def _request_get_sampling_params(request: LlmRequest) -> UtilsSamplingParams: ) +def _request_sampling_params_cachable(params: UtilsSamplingParams) -> bool: + return not params.use_beam_search + + def _request_strategy(request: LlmRequest, *, vocab_size: int) -> Strategy: + # We try to cache the resolved strategy on the request object, as it's not cheap enough to + # resolve it on every iteration. + if hasattr(request, "py_sampling_strategy"): + return request.py_sampling_strategy + params = _request_get_sampling_params(request) - return resolve_sampling_strategy(params, vocab_size=vocab_size) + sampling_strategy = resolve_sampling_strategy(params, vocab_size=vocab_size) + if _request_sampling_params_cachable(params): + request.py_sampling_strategy = resolve_sampling_strategy(params, vocab_size=vocab_size) + return sampling_strategy def _group_requests_by_strategy_key( @@ -1971,8 +1983,8 @@ def _apply_embedding_bias( # Since read-caching is expected to help in typical cases, option (ii) is implemented here. # Track which logits require logit bias application - logits_bias_mask = torch.zeros((logits.size(0),), dtype=torch.bool, pin_memory=True) - + request_steps_list = request_steps.tolist() + logits_bias_masks = [False] * logits.size(0) _next_bias_index = 0 def provision_bias_index() -> int: @@ -1992,11 +2004,11 @@ def provision_bias_index() -> int: # Collect bias information req_bias = None - for i, (req, steps) in enumerate(zip(requests, request_steps)): - steps = int(steps.item()) + for i, (req, steps) in enumerate(zip(requests, request_steps_list)): req_bias = req._py_embedding_bias_1d if req_bias is not None: - logits_bias_mask[i : (i + steps)] = True + for j in range(i, i + steps): + logits_bias_masks[j] = True req_bias_index = bias_to_index[req_bias] bias_gather_indices.extend(repeat(req_bias_index, steps)) @@ -2007,7 +2019,9 @@ def provision_bias_index() -> int: bias_gather_indices_cuda = torch.tensor( bias_gather_indices, pin_memory=True, dtype=torch.int32 ).to(logits.device, non_blocking=True) - logits_bias_mask_cuda = logits_bias_mask.to(logits.device, non_blocking=True) + logits_bias_mask_cuda = torch.tensor( + logits_bias_masks, pin_memory=True, dtype=torch.bool + ).to(logits.device, non_blocking=True) biases_tensor = torch.empty((len(bias_to_index), *req_bias.shape), pin_memory=True) biases_tensor = torch.stack( tuple(bias_to_index.keys()), @@ -2166,7 +2180,7 @@ def _sample_batched_by_strategy( group_strategies_per_step = [ # convert from per-request to per-step strat - for strat, steps in zip(group_strategies, req_num_steps[group_req_indices]) + for strat, steps in zip(group_strategies, req_num_steps[group_req_indices].tolist()) for _ in range(steps) ] @@ -2651,11 +2665,12 @@ def _are_stop_words( words_device = words.to("cuda", non_blocking=True) draft_token_length = get_draft_token_length(request) + max_draft_token_length = self.max_tokens - 1 for beam_idx in range(self.max_beam_width): new_tokens = padded_tokens[request_idx, beam_idx] for step_idx in range(draft_token_length + 1): - size_per_step = new_tokens.size(0) - draft_token_length + step_idx + size_per_step = new_tokens.size(0) - max_draft_token_length + step_idx matches = [] for word, L in zip(words_device, lens): truncated_seq = new_tokens[size_per_step - L : size_per_step] diff --git a/tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py b/tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py index 786c953b0fdf..0e1ec5f566a0 100644 --- a/tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py +++ b/tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py @@ -612,20 +612,29 @@ def sample( generator: Optional[torch.Generator] = None, group_metadata: StrategyMetadata | None = None, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: - logits = self._prepare_logits_with_temperature( - logits, group_logit_indices, self._temperature - ) - new_tokens = flashinfer.sampling.sampling_from_logits( + new_tokens, _ = self._sample_with_probs( logits, - # NB: Leveraging 'indices' would require applying temperature+softmax before batching, - # because 'flashinfer.sampling.softmax' has no 'indices' argument; but that would - # compute unnecessarily softmax also for situations allowing - # flashinfer.sampling...._sampling_from_logits. - # indices=group_logit_indices, - deterministic=True, + group_logit_indices=group_logit_indices, + top_k=None, + top_p=None, + temperature=self._temperature, generator=generator, - check_nan=self._flashinfer_check_nans(logits), ) + # FIXME: https://nvbugs/5791242 + # logits = self._prepare_logits_with_temperature( + # logits, group_logit_indices, self._temperature + # ) + # new_tokens = flashinfer.sampling.sampling_from_logits( + # logits, + # # NB: Leveraging 'indices' would require applying temperature+softmax before batching, + # # because 'flashinfer.sampling.softmax' has no 'indices' argument; but that would + # # compute unnecessarily softmax also for situations allowing + # # flashinfer.sampling...._sampling_from_logits. + # # indices=group_logit_indices, + # deterministic=True, + # generator=generator, + # check_nan=self._flashinfer_check_nans(logits), + # ) return new_tokens, None class BeamSearchSampleOnly(BeamSearchMixin, StrategyImplSampleOnly): diff --git a/tensorrt_llm/_torch/speculative/eagle3.py b/tensorrt_llm/_torch/speculative/eagle3.py index 6fa7fba8583e..a0caa87139bc 100644 --- a/tensorrt_llm/_torch/speculative/eagle3.py +++ b/tensorrt_llm/_torch/speculative/eagle3.py @@ -601,14 +601,9 @@ def prepare_1st_drafter_inputs( hidden_states = draft_model.apply_eagle3_fc(hidden_states) # context - input_ctx_ids = input_ids[:attn_metadata.num_ctx_tokens] - input_ids_ctx = torch.empty_like(input_ctx_ids, - dtype=torch.int32, - device="cuda") - input_ids_ctx[:-1].copy_(input_ctx_ids[1:]) - input_ids_ctx[ - spec_metadata. - gather_ids[:num_contexts]] = accepted_tokens[:num_contexts, 0] + input_ids_ctx = self._prepare_context_input_ids( + input_ids, attn_metadata.num_ctx_tokens, spec_metadata.gather_ids, + accepted_tokens, num_contexts) # generation input_ids_gen = accepted_tokens[num_contexts:, :].flatten() diff --git a/tensorrt_llm/_torch/speculative/interface.py b/tensorrt_llm/_torch/speculative/interface.py index 8dffa020c2bd..0831dd68cf57 100644 --- a/tensorrt_llm/_torch/speculative/interface.py +++ b/tensorrt_llm/_torch/speculative/interface.py @@ -163,8 +163,7 @@ def attention_need_spec_dec_mode( is_trtllm_attention = issubclass(attention_backend, TrtllmAttention) # Always use the multi-token query mode for 1-model if the kernels are available. - xqa_supported = get_sm_version() < 120 - use_case_1 = self.use_one_engine() and xqa_supported + use_case_1 = self.use_one_engine() # For 2-model, we need to enable it when we process multiple tokens at once. This occurs with # the target model (verification) or on the first draft for CDL based speculation. use_case_2 = not self.use_one_engine() and ( @@ -416,6 +415,180 @@ def set_guided_decoder(self, self.guided_decoder = guided_decoder return True + def _prepare_attn_metadata_for_spec_dec(self, attn_metadata): + """ + Prepare attention metadata before speculative decoding draft token generation. + Saves current state for later restoration. + """ + attn_metadata.prepare_for_spec_dec("_seq_lens", "_seq_lens_cuda") + + def _restore_attn_metadata_from_spec_dec(self, attn_metadata): + """ + Restore attention metadata after speculative decoding draft token generation. + """ + attn_metadata.restore_from_spec_dec() + attn_metadata.on_update() + + def _apply_force_accepted_tokens(self, num_accepted_tokens, num_contexts): + """ + Apply forced number of accepted tokens if environment variable is set. + This is used for testing and debugging. + + Args: + num_accepted_tokens: Tensor of shape [batch_size] with current accepted counts + num_contexts: Number of context (prefill) requests + + Returns: + Modified num_accepted_tokens tensor + + Note: + For MTPWorker, self.max_draft_len equals num_nextn_predict_layers (mtp_num_modules). + For Eagle3OneModelWorker, self.max_draft_len equals spec_config.max_draft_len. + """ + if self.force_num_accepted_tokens != 0: + # total tokens per iteration = accepted draft tokens + 1 target token + force_total_tokens = min(self.force_num_accepted_tokens + 1, + self.max_draft_len + 1) + num_accepted_tokens[num_contexts:] = force_total_tokens + return num_accepted_tokens + + def _sample_and_accept_draft_tokens_base( + self, + logits: torch.Tensor, + draft_tokens: torch.Tensor, + num_contexts: int, + batch_size: int, + spec_metadata, + ): + """ + Base implementation for sampling and accepting draft tokens. + Uses strict acceptance (token equality with cumulative product). + + This is the common logic shared between Eagle3 and MTP (when relaxed + acceptance is disabled). + + Args: + logits: [num_tokens, vocab_size] - Target model logits + draft_tokens: [num_gens, max_draft_len] - Previously predicted draft tokens + num_contexts: Number of context requests + batch_size: Total number of requests + spec_metadata: Speculative decoding metadata + + Returns: + accepted_tokens: [batch_size, max_draft_len + 1] - Accepted tokens + num_accepted_tokens: [batch_size] - Number of accepted tokens per request + """ + num_gens = batch_size - num_contexts + + if logits.dim() == 1: + logits = logits.unsqueeze(0) + + # Allocate return buffers + accepted_tokens = torch.empty((batch_size, self.max_draft_len + 1), + dtype=torch.int, + device=logits.device) + num_accepted_tokens = torch.ones(batch_size, + dtype=torch.int, + device=logits.device) + + # Sample tokens using per-request sampling parameters + target_tokens = self._sample_tokens_for_batch(logits, spec_metadata, + num_contexts, batch_size) + + # Context requests: only accept the sampled token (no draft tokens yet) + accepted_tokens[:num_contexts, 0] = target_tokens[:num_contexts] + + # Generation requests: verify draft tokens against target tokens + gen_target_tokens = target_tokens[num_contexts:].reshape( + num_gens, self.max_draft_len + 1) + accepted_tokens[num_contexts:, :] = gen_target_tokens + + # Compare draft tokens with target tokens using cumulative product + # Counts consecutive matches from the start + num_accepted_tokens[num_contexts:] += torch.cumprod( + (draft_tokens == gen_target_tokens[:, :self.max_draft_len]).int(), + dim=-1).sum(1) + + # Apply force override if set + num_accepted_tokens = self._apply_force_accepted_tokens( + num_accepted_tokens, num_contexts) + + return accepted_tokens, num_accepted_tokens + + def _draft_sampler_greedy(self, logits: torch.Tensor, d2t=None): + """ + Simple greedy draft token sampling using argmax. + + Args: + logits: [num_tokens, vocab_size] - Draft model logits + d2t: Optional dictionary offset tensor for vocab mapping + + Returns: + draft_tokens: [num_tokens] - Sampled draft token ids (int32) + """ + draft_tokens = torch.argmax(logits, dim=-1) + + # Apply d2t (offsets between draft and target model dictionaries) + if d2t is not None: + draft_tokens = d2t[draft_tokens] + draft_tokens + + return draft_tokens.type(torch.int32) + + def _execute_guided_decoder_if_present(self, logits): + """Execute guided decoder on target model logits if available.""" + if self.guided_decoder is not None: + self.guided_decoder.execute(logits) + + def _prepare_next_new_tokens(self, accepted_tokens, next_draft_tokens, + batch_indices_cuda, batch_size, + num_accepted_tokens): + """ + Prepare next_new_tokens for overlap scheduler support. + + Args: + accepted_tokens: [batch_size, max_draft_len + 1] - Accepted tokens + next_draft_tokens: [batch_size, max_draft_len] - Predicted draft tokens + batch_indices_cuda: Batch indices tensor + batch_size: Number of requests + num_accepted_tokens: [batch_size] - Number of accepted tokens per request + + Returns: + next_new_tokens: [batch_size, max_draft_len + 1] - Input tokens for next iteration + """ + next_new_tokens = accepted_tokens[batch_indices_cuda[:batch_size], + num_accepted_tokens - 1].unsqueeze(1) + next_new_tokens = torch.concat([next_new_tokens, next_draft_tokens], + dim=1) + return next_new_tokens + + def _prepare_context_input_ids(self, input_ids, num_ctx_tokens, gather_ids, + accepted_tokens, num_contexts): + """ + Prepare context input IDs for draft model forward. + Shifts input IDs left by 1 and places the first accepted token at gather positions. + + Args: + input_ids: Original input IDs tensor + num_ctx_tokens: Number of context tokens + gather_ids: Indices for placing accepted tokens (last token positions) + accepted_tokens: [batch_size, max_draft_len + 1] - Accepted tokens + num_contexts: Number of context requests + + Returns: + input_ids_ctx: Prepared context input IDs + """ + if num_ctx_tokens > 0: + input_prompt_ids = input_ids[:num_ctx_tokens] + input_ids_ctx = torch.empty_like(input_prompt_ids, + dtype=torch.int32, + device="cuda") + input_ids_ctx[:-1].copy_(input_prompt_ids[1:]) + input_ids_ctx[ + gather_ids[:num_contexts]] = accepted_tokens[:num_contexts, 0] + return input_ids_ctx + else: + return torch.empty(0, dtype=torch.int32, device="cuda") + def _sample_tokens_for_batch( self, logits: torch.Tensor, diff --git a/tensorrt_llm/_torch/speculative/mtp.py b/tensorrt_llm/_torch/speculative/mtp.py index 1e67caf561ce..c9a09eedfb66 100644 --- a/tensorrt_llm/_torch/speculative/mtp.py +++ b/tensorrt_llm/_torch/speculative/mtp.py @@ -1015,13 +1015,9 @@ def prepare_drafter_inputs( # context if num_contexts > 0: hidden_states_ctx = hidden_states[:num_ctx_tokens, :] - input_prompt_ids = input_ids[:num_ctx_tokens] - input_ids_ctx = torch.empty_like(input_prompt_ids, - dtype=torch.int32, - device="cuda") - input_ids_ctx[:-1].copy_(input_prompt_ids[1:]) - input_ids_ctx[last_tokens_idx[:num_contexts]] = \ - accepted_tokens[:num_contexts, 0] + input_ids_ctx = self._prepare_context_input_ids( + input_ids, num_ctx_tokens, last_tokens_idx, accepted_tokens, + num_contexts) return_input_ids_list.append(input_ids_ctx) return_hidden_states_list.append(hidden_states_ctx) # generation @@ -1364,13 +1360,9 @@ def prepare_drafter_inputs( num_contexts = attn_metadata.num_contexts # context - input_prompt_ids = input_ids[:attn_metadata.num_ctx_tokens] - input_ids_ctx = torch.empty_like(input_prompt_ids, - dtype=torch.int32, - device="cuda") - input_ids_ctx[:-1].copy_(input_prompt_ids[1:]) - input_ids_ctx[ - last_tokens_idx[:num_contexts]] = accepted_tokens[:num_contexts, 0] + input_ids_ctx = self._prepare_context_input_ids( + input_ids, attn_metadata.num_ctx_tokens, last_tokens_idx, + accepted_tokens, num_contexts) # generation input_ids_gen = accepted_tokens[num_contexts:, :].flatten() diff --git a/tensorrt_llm/_torch/speculative/utils.py b/tensorrt_llm/_torch/speculative/utils.py index 6a22ad19bd4b..1eb036de99e7 100644 --- a/tensorrt_llm/_torch/speculative/utils.py +++ b/tensorrt_llm/_torch/speculative/utils.py @@ -31,6 +31,7 @@ def get_spec_metadata(spec_config, mtp_num_modules=spec_config.num_nextn_predict_layers, max_num_requests=max_num_requests, mtp_hidden_states_manager=spec_resource_manager, + allow_advanced_sampling=spec_config.allow_advanced_sampling, ) if spec_config.spec_dec_mode.is_mtp_eagle(): return Eagle3SpecMetadata( diff --git a/tensorrt_llm/commands/bench.py b/tensorrt_llm/commands/bench.py index ab4755082f6d..02f89e45fca8 100644 --- a/tensorrt_llm/commands/bench.py +++ b/tensorrt_llm/commands/bench.py @@ -11,12 +11,23 @@ from tensorrt_llm.logger import logger, severity_map +class NotRequiredForHelp(click.Option): + """A click.Option that is not enforced as required when --help is in args.""" + + def handle_parse_result(self, ctx, opts, args): + help_flags = ctx.help_option_names or ['--help'] + if any(arg in help_flags for arg in args): + self.required = False + return super().handle_parse_result(ctx, opts, args) + + @click.group(name="trtllm-bench", context_settings={'show_default': True}) @click.option( "--model", "-m", required=True, type=str, + cls=NotRequiredForHelp, help="The Huggingface name of the model to benchmark.", ) @click.option( @@ -54,6 +65,9 @@ def main( revision: Optional[str], ) -> None: logger.set_level(log_level) + if model is None: + return + ctx.obj = BenchmarkEnvironment(model=model, checkpoint_path=model_path, workspace=workspace, diff --git a/tensorrt_llm/executor/utils.py b/tensorrt_llm/executor/utils.py index e52ea481fb01..9801ee1cd230 100644 --- a/tensorrt_llm/executor/utils.py +++ b/tensorrt_llm/executor/utils.py @@ -55,9 +55,9 @@ def create_mpi_comm_session( logger_debug( f"Using RemoteMpiPoolSessionClient to bind to external MPI processes at {get_spawn_proxy_process_ipc_addr_env()}\n", "yellow") - get_spawn_proxy_process_ipc_hmac_key_env() + hmac_key = get_spawn_proxy_process_ipc_hmac_key_env() return RemoteMpiCommSessionClient( - addr=get_spawn_proxy_process_ipc_addr_env()) + addr=get_spawn_proxy_process_ipc_addr_env(), hmac_key=hmac_key) else: logger_debug( f"Using MpiCommSession to bind to external MPI processes\n", diff --git a/tensorrt_llm/llmapi/llm.py b/tensorrt_llm/llmapi/llm.py index 71d34af1a647..7bf0c030b65e 100644 --- a/tensorrt_llm/llmapi/llm.py +++ b/tensorrt_llm/llmapi/llm.py @@ -513,6 +513,23 @@ def generate_async( else: # Convert to shared tensor handle to reduce IPC overhead multimodal_params.to_handle("multimodal_data") + if (disaggregated_params is not None) and ( + "mrope_config" + in multimodal_params.multimodal_data): + # Propagate mRoPE handles during context-only P -> D so decode-only + # can rebuild mrope_config without raw multimodal inputs. + mrope_config = multimodal_params.multimodal_data[ + "mrope_config"] + mrope_position_ids = mrope_config.get( + "mrope_position_ids") + mrope_position_deltas = mrope_config.get( + "mrope_position_deltas") + if (mrope_position_ids is not None + and mrope_position_deltas is not None): + disaggregated_params.mrope_position_ids_handle = ( + mrope_position_ids) + disaggregated_params.mrope_position_deltas_handle = ( + mrope_position_deltas) else: raise TypeError( f"The inputs must be type str or list of int, but got {type(inputs)}" diff --git a/tensorrt_llm/llmapi/llm_args.py b/tensorrt_llm/llmapi/llm_args.py index 1caeff83a22c..e078c1b29181 100644 --- a/tensorrt_llm/llmapi/llm_args.py +++ b/tensorrt_llm/llmapi/llm_args.py @@ -1132,6 +1132,12 @@ def __init__(self, **kwargs): self.max_total_draft_tokens = kwargs[ 'num_nextn_predict_layers'] # Current MTP only support linear tree + if not self.mtp_eagle_one_model: + logger.warning( + "2-model style MTP is deprecated. The mtp_eagle_one_model flag will do nothing " + "in release 1.3. After that, the flag will be removed entirely." + ) + @classmethod def from_dict(cls, data: dict): out = cls(**data) diff --git a/tensorrt_llm/llmapi/mgmn_leader_node.py b/tensorrt_llm/llmapi/mgmn_leader_node.py index 85f8561ebe48..2b1d11b0ccf4 100644 --- a/tensorrt_llm/llmapi/mgmn_leader_node.py +++ b/tensorrt_llm/llmapi/mgmn_leader_node.py @@ -9,7 +9,9 @@ from tensorrt_llm._utils import global_mpi_rank, mpi_world_size from tensorrt_llm.executor.ipc import ZeroMqQueue -from tensorrt_llm.executor.utils import get_spawn_proxy_process_ipc_addr_env +from tensorrt_llm.executor.utils import ( + get_spawn_proxy_process_ipc_addr_env, + get_spawn_proxy_process_ipc_hmac_key_env) from tensorrt_llm.llmapi.mpi_session import RemoteMpiCommSessionServer from tensorrt_llm.llmapi.utils import logger_debug @@ -23,6 +25,7 @@ def launch_server_main(sub_comm=None): comm=sub_comm, n_workers=num_ranks, addr=get_spawn_proxy_process_ipc_addr_env(), + hmac_key=get_spawn_proxy_process_ipc_hmac_key_env(), is_comm=True) logger_debug( f"MPI Comm Server started at {get_spawn_proxy_process_ipc_addr_env()}") @@ -32,8 +35,9 @@ def launch_server_main(sub_comm=None): def stop_server_main(): - queue = ZeroMqQueue((get_spawn_proxy_process_ipc_addr_env(), None), - use_hmac_encryption=False, + hmac_key = get_spawn_proxy_process_ipc_hmac_key_env() + queue = ZeroMqQueue((get_spawn_proxy_process_ipc_addr_env(), hmac_key), + use_hmac_encryption=bool(hmac_key), is_server=False, socket_type=zmq.PAIR) diff --git a/tensorrt_llm/llmapi/rlhf_utils.py b/tensorrt_llm/llmapi/rlhf_utils.py index ce6eaa5b4ff8..52219f998526 100644 --- a/tensorrt_llm/llmapi/rlhf_utils.py +++ b/tensorrt_llm/llmapi/rlhf_utils.py @@ -1,9 +1,9 @@ import base64 -import pickle # nosec B403 from typing import Optional import torch +from tensorrt_llm import serialization from tensorrt_llm._ray_utils import control_action_decorator from tensorrt_llm._torch.modules.fused_moe.moe_load_balancer import MoeLoadBalancer from tensorrt_llm._torch.utils import get_device_uuid @@ -62,8 +62,35 @@ def update_weights(self, ipc_handles: Optional[dict] = None): serialized_handles = ipc_handles[device_uuid] if isinstance(serialized_handles, str): # Data is base64-encoded pickled bytes - deserialize it + # using restricted unpickler from tensorrt_llm.serialization logger.info("Deserializing base64-encoded weight handles") - all_handles = pickle.loads(base64.b64decode(serialized_handles)) # nosec B301 + decoded_data = base64.b64decode(serialized_handles) + # Allow basic builtins and all torch modules + approved_imports = { + "builtins": [ + "list", + "tuple", + "str", + "int", + "float", + "bool", + "bytes", + "dict", + "NoneType", + "type", + ], + } + all_handles = serialization.loads( + decoded_data, + approved_imports=approved_imports, + approved_module_patterns=[r"^torch.*"], + ) + + # Verify the result is a list as expected + if not isinstance(all_handles, list): + raise ValueError( + f"Deserialized data must be a list, got {type(all_handles).__name__} instead" + ) else: # Data is already in the correct format (backward compatibility) all_handles = serialized_handles diff --git a/tensorrt_llm/llmapi/utils.py b/tensorrt_llm/llmapi/utils.py index 88e22fc639c3..f79823d844aa 100644 --- a/tensorrt_llm/llmapi/utils.py +++ b/tensorrt_llm/llmapi/utils.py @@ -15,7 +15,7 @@ import traceback import warnings import weakref -from functools import cache, wraps +from functools import wraps from pathlib import Path from queue import Queue from typing import (Any, Callable, Iterable, List, Optional, Tuple, Type, @@ -355,7 +355,6 @@ def enable_llmapi_debug() -> bool: return _enable_llmapi_debug_ -@cache def enable_worker_single_process_for_tp1() -> bool: ''' Tell whether to make worker use single process for TP1. This is helpful for return-logits performance and debugging. ''' diff --git a/tensorrt_llm/serialization.py b/tensorrt_llm/serialization.py index 4045df36177b..ac303fec9f85 100644 --- a/tensorrt_llm/serialization.py +++ b/tensorrt_llm/serialization.py @@ -2,6 +2,7 @@ # pickle is not secure, but but this whole file is a wrapper to make it # possible to mitigate the primary risk of code injection via pickle. import pickle # nosec B403 +import re from functools import partial # This is an example class (white list) to showcase how to guard serialization with approved classes. @@ -126,19 +127,31 @@ def register_approved_class(obj): class Unpickler(pickle.Unpickler): - def __init__(self, *args, approved_imports={}, **kwargs): + def __init__(self, + *args, + approved_imports={}, + approved_module_patterns=None, + **kwargs): super().__init__(*args, **kwargs) self.approved_imports = approved_imports + self.approved_module_patterns = approved_module_patterns or [] # only import approved classes, this is the security boundary. def find_class(self, module, name): - if name not in self.approved_imports.get(module, []): - # If this is triggered when it shouldn't be, then the module - # and class should be added to the approved_imports. If the class - # is being used as part of a routine scenario, then it should be added - # to the appropriate base classes above. - raise ValueError(f"Import {module} | {name} is not allowed") - return super().find_class(module, name) + # Check exact match in approved_imports + if name in self.approved_imports.get(module, []): + return super().find_class(module, name) + + # Check regex pattern match in approved_module_patterns + for pattern in self.approved_module_patterns: + if re.match(pattern, module): + return super().find_class(module, name) + + # If this is triggered when it shouldn't be, then the module + # and class should be added to the approved_imports. If the class + # is being used as part of a routine scenario, then it should be added + # to the appropriate base classes above. + raise ValueError(f"Import {module} | {name} is not allowed") # these are taken from the pickle module to allow for this to be a drop in replacement @@ -156,13 +169,15 @@ def load(file, encoding="ASCII", errors="strict", buffers=None, - approved_imports={}): + approved_imports={}, + approved_module_patterns=None): return Unpickler(file, fix_imports=fix_imports, buffers=buffers, encoding=encoding, errors=errors, - approved_imports=approved_imports).load() + approved_imports=approved_imports, + approved_module_patterns=approved_module_patterns).load() def loads(s, @@ -172,7 +187,8 @@ def loads(s, encoding="ASCII", errors="strict", buffers=None, - approved_imports={}): + approved_imports={}, + approved_module_patterns=None): if isinstance(s, str): raise TypeError("Can't load pickle from unicode string") file = io.BytesIO(s) @@ -181,4 +197,5 @@ def loads(s, buffers=buffers, encoding=encoding, errors=errors, - approved_imports=approved_imports).load() + approved_imports=approved_imports, + approved_module_patterns=approved_module_patterns).load() diff --git a/tensorrt_llm/serve/cluster_storage.py b/tensorrt_llm/serve/cluster_storage.py index e6fe14eda440..d2ba0d911717 100644 --- a/tensorrt_llm/serve/cluster_storage.py +++ b/tensorrt_llm/serve/cluster_storage.py @@ -1,13 +1,16 @@ import abc import asyncio +import importlib.metadata as importlib_metadata +import importlib.util +import sys import time from dataclasses import dataclass from enum import IntEnum from functools import wraps from typing import Callable, Dict, List, Optional +from urllib.parse import urlparse import aiohttp -import etcd3 from fastapi import FastAPI from fastapi.responses import JSONResponse from pydantic import BaseModel @@ -15,6 +18,45 @@ from tensorrt_llm.logger import logger +def _find_module_file_in_distribution(dist, module_name: str): + module_path = module_name.replace(".", "/") + candidates = (f"{module_path}/__init__.py", f"{module_path}.py") + for dist_file in dist.files or []: + dist_file_str = str(dist_file) + if dist_file_str.endswith(candidates): + return dist.locate_file(dist_file) + return None + + +def load_module_from_distribution(dist_name: str, module_name: str): + dist = importlib_metadata.distribution(dist_name) + + module_file = _find_module_file_in_distribution(dist, module_name) + if not module_file: + raise ModuleNotFoundError( + f"{module_name} not found in distribution {dist_name}") + + load_name = module_name + spec = importlib.util.spec_from_file_location(load_name, module_file) + if spec is None or spec.loader is None: + raise ImportError( + f"Could not create a module spec for {module_name} in {dist_name}") + + module = importlib.util.module_from_spec(spec) + sys.modules[load_name] = module + try: + spec.loader.exec_module(module) + except Exception: + sys.modules.pop(load_name, None) + raise + return module + + +# pyectd and etcd-sdk-python both have package name "pyetcd", we need to find the correct one +# by distribution name +etcd3 = load_module_from_distribution("etcd-sdk-python", "pyetcd") + + class StorageItem(BaseModel): key: str value: Optional[str] = "" @@ -455,14 +497,25 @@ async def wrap(*args, **kwargs): class Etcd3ClusterStorage(ClusterStorage): + @staticmethod + def _connect(cluster_uri: str) -> etcd3.MultiEndpointEtcd3Client: + logger.info(f"Connecting to {cluster_uri}") + endpoints = [] + for url in cluster_uri.split(","): + parsed_url = urlparse(url) + endpoints.append( + etcd3.Endpoint(parsed_url.hostname, + parsed_url.port, + secure=False)) + return etcd3.MultiEndpointEtcd3Client(endpoints, timeout=10) + def __init__(self, cluster_uri: str, cluster_name: str, one_single_lease: bool = False, **kwargs): - cluster_uri = cluster_uri.replace("etcd://", "") - self._host, self._port = cluster_uri.rsplit(":", 1) - self._client = etcd3.client(self._host, self._port) + self._cluster_uri = cluster_uri + self._client = self._connect(self._cluster_uri) self._leases = {} self._instance_lease = None self._watch_handles = {} @@ -486,8 +539,7 @@ def client(self): return self._client def reconnect(self): - logger.info(f"Reconnecting to {self._host}:{self._port}") - self._client = etcd3.client(self._host, self._port) + self._client = self._connect(self._cluster_uri) async def start(self): # nothing to do diff --git a/tensorrt_llm/serve/harmony_adapter.py b/tensorrt_llm/serve/harmony_adapter.py index 1ba163581edc..f9e168a3942d 100644 --- a/tensorrt_llm/serve/harmony_adapter.py +++ b/tensorrt_llm/serve/harmony_adapter.py @@ -1521,10 +1521,10 @@ def _is_tool_call_allowed(self, tool_call: dict[str, Any], return True -_SERVE_HARMONY_ADAPTER: HarmonyAdapter = None +_SERVE_HARMONY_ADAPTER: HarmonyAdapter | None = None -def get_harmony_adapter(): +def get_harmony_adapter() -> HarmonyAdapter: global _SERVE_HARMONY_ADAPTER if _SERVE_HARMONY_ADAPTER is None: _SERVE_HARMONY_ADAPTER = HarmonyAdapter() @@ -1535,8 +1535,8 @@ def get_harmony_adapter(): def handle_streaming_response(tools: List[ChatCompletionToolsParam], tool_choice: str, result: GenerationResult, model: str, request_id: str, done: bool, - num_prompt_tokens: int) -> List[str]: - first_iteration = True + num_prompt_tokens: int, + first_iteration: bool) -> List[str]: output = result.outputs[0] # Convert tools to dictionary format for harmony adapter (standard pattern) diff --git a/tensorrt_llm/serve/metadata_server.py b/tensorrt_llm/serve/metadata_server.py index c4c046938d49..1453ca196e1e 100644 --- a/tensorrt_llm/serve/metadata_server.py +++ b/tensorrt_llm/serve/metadata_server.py @@ -3,12 +3,7 @@ from typing import Optional from tensorrt_llm.llmapi.disagg_utils import MetadataServerConfig -from tensorrt_llm.logger import logger - -try: - import etcd3 -except Exception as e: - logger.warning(f"etcd3 is not installed correctly: {e}") +from tensorrt_llm.serve.cluster_storage import etcd3 class RemoteDictionary(ABC): diff --git a/tensorrt_llm/serve/openai_server.py b/tensorrt_llm/serve/openai_server.py index afb97aa6f0c6..264f87bd0f71 100644 --- a/tensorrt_llm/serve/openai_server.py +++ b/tensorrt_llm/serve/openai_server.py @@ -870,11 +870,12 @@ async def create_harmony_response( return chat_response async def create_streaming_generator(promise: RequestOutput, postproc_params: PostprocParams): - if not self.postproc_worker_enabled: - post_processor, args = postproc_params.post_processor, postproc_params.postproc_args - async for res in promise: - pp_results = res.outputs[0]._postprocess_result if self.postproc_worker_enabled else post_processor(res, args) + if not self.postproc_worker_enabled: + post_processor, args = postproc_params.post_processor, postproc_params.postproc_args + pp_results = post_processor(res, args) + else: + pp_results = res.outputs[0]._postprocess_result for pp_res in pp_results: yield pp_res diff --git a/tensorrt_llm/serve/postprocess_handlers.py b/tensorrt_llm/serve/postprocess_handlers.py index 01ffb648e27d..a82a06b079e6 100644 --- a/tensorrt_llm/serve/postprocess_handlers.py +++ b/tensorrt_llm/serve/postprocess_handlers.py @@ -547,7 +547,9 @@ def chat_harmony_streaming_post_processor( request_id=args.request_id, done=rsp._done, num_prompt_tokens=args.num_prompt_tokens, + first_iteration=args.first_iteration, ) + args.first_iteration = False return response diff --git a/tensorrt_llm/tokenizer/tokenizer.py b/tensorrt_llm/tokenizer/tokenizer.py index 25c224cc24a3..7c373c462795 100644 --- a/tensorrt_llm/tokenizer/tokenizer.py +++ b/tensorrt_llm/tokenizer/tokenizer.py @@ -1,4 +1,5 @@ import os +import pickle # nosec B403 from pathlib import Path from typing import Any, Dict, List, Optional, Tuple, Union @@ -25,13 +26,50 @@ class TokenizerBase(PreTrainedTokenizerBase): ''' This is a protocol for the tokenizer. Users can implement their own tokenizer by inheriting this class. ''' +def _reconstruct_transformers_tokenizer(inner_bytes: bytes): + '''Reconstruct a TransformersTokenizer from cloudpickle-serialized bytes.''' + # nosec B301: inner_bytes is from cloudpickle.dumps() in __reduce__, not untrusted data + return TransformersTokenizer(pickle.loads(inner_bytes)) # nosec B301 + + class TransformersTokenizer(TokenizerBase): ''' A wrapper for the Transformers' tokenizer. This is the default tokenizer for LLM. ''' def __init__(self, tokenizer): self.tokenizer = tokenizer - self._all_special_tokens_set = set(self.tokenizer.all_special_tokens) + if hasattr(self.tokenizer, "all_special_tokens"): + self._all_special_tokens_set = set(self.tokenizer.all_special_tokens) + else: + self._all_special_tokens_set = set() + + def __reduce__(self): + # In multi-node scenarios, AutoTokenizer.from_pretrained with + # trust_remote_code=True may create dynamic Python modules cached at + # $HOME/.cache/huggingface/modules/transformers_modules/. + # These modules are node-local and won't exist on other nodes. + # Standard pickle serializes classes by reference, so deserializing + # a tokenizer that uses such a dynamic class fails on other nodes. + # + # To solve this, we use cloudpickle (if available) to serialize the + # inner tokenizer by value, embedding the class definition in the + # serialized bytes. This follows vLLM PR #6751. + # + # See: https://github.com/vllm-project/vllm/pull/6751 + try: + import cloudpickle + # Ensure dynamic transformers_modules are registered for by-value + # serialization, regardless of how the tokenizer was created. + maybe_register_transformers_modules_by_value() + inner_bytes = cloudpickle.dumps(self.tokenizer) + return (_reconstruct_transformers_tokenizer, (inner_bytes, )) + except ImportError: + # cloudpickle not installed; fall back to default pickling. + # This may fail on other nodes if the tokenizer uses dynamic + # modules from trust_remote_code. + logger.warning( + "cloudpickle is not installed. TransformersTokenizer will not be serializable across nodes in multi-node setups. Install cloudpickle to fix this: pip install cloudpickle") + return (TransformersTokenizer, (self.tokenizer, )) def __call__(self, text: str, *args, **kwargs) -> Any: return self.tokenizer(text, *args, **kwargs) @@ -340,6 +378,41 @@ def _llguidance_tokenizer_info(tokenizer): return tokenizer_info +def maybe_register_transformers_modules_by_value(): + '''Register transformers dynamic modules for by-value serialization. + + With trust_remote_code, AutoTokenizer.from_pretrained may create dynamic + modules cached at $HOME/.cache/huggingface/modules/transformers_modules/. + These modules are node-local and won't exist on other nodes in multi-node + setups. This function registers them with cloudpickle for by-value + serialization, so that objects from these modules can be pickled and + sent to other nodes without needing the module files there. + + References: + https://github.com/vllm-project/vllm/pull/6751 + https://github.com/cloudpipe/cloudpickle#overriding-pickles-serialization-mechanism-for-importable-constructs + ''' + try: + import transformers_modules + except ImportError: + # No dynamic modules were created, nothing to register. + return + + try: + import cloudpickle + cloudpickle.register_pickle_by_value(transformers_modules) + except ImportError: + logger.warning( + "cloudpickle is not installed. Objects from trust_remote_code " + "dynamic modules may not be serializable across nodes in " + "multi-node setups. Install cloudpickle to fix this: " + "pip install cloudpickle") + except Exception as e: + logger.warning( + f"Failed to register transformers_modules for by-value " + f"serialization: {e}") + + def load_hf_tokenizer(model_dir: str, trust_remote_code: bool = True, use_fast: bool = True, @@ -356,7 +429,7 @@ def load_hf_tokenizer(model_dir: str, ''' try: - return TransformersTokenizer.from_pretrained( + tokenizer = TransformersTokenizer.from_pretrained( model_dir, legacy=False, padding_side='left', @@ -365,6 +438,11 @@ def load_hf_tokenizer(model_dir: str, use_fast=use_fast, **kwargs) + if trust_remote_code: + maybe_register_transformers_modules_by_value() + + return tokenizer + except Exception as e: logger.warning( f"Failed to load hf tokenizer from {model_dir}, encounter error: {e}" diff --git a/tensorrt_llm/version.py b/tensorrt_llm/version.py index 434ba8f8e28b..68372ba7bcfe 100644 --- a/tensorrt_llm/version.py +++ b/tensorrt_llm/version.py @@ -12,4 +12,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "1.2.0rc8" +__version__ = "1.2.1" diff --git a/tests/integration/defs/accuracy/accuracy_core.py b/tests/integration/defs/accuracy/accuracy_core.py index e30c6e2c2cb4..d7b885ad742f 100644 --- a/tests/integration/defs/accuracy/accuracy_core.py +++ b/tests/integration/defs/accuracy/accuracy_core.py @@ -452,7 +452,7 @@ class LongBenchV2(AccuracyTask): EVALUATOR_KWARGS = dict( dataset_path=DATASET_DIR, length="medium", - max_len=120000, + max_input_length=120000, apply_chat_template=True, random_seed=0, ) diff --git a/tests/integration/defs/accuracy/references/gsm8k.yaml b/tests/integration/defs/accuracy/references/gsm8k.yaml index a4365a5ce9d6..eafaeebdafdf 100644 --- a/tests/integration/defs/accuracy/references/gsm8k.yaml +++ b/tests/integration/defs/accuracy/references/gsm8k.yaml @@ -234,10 +234,6 @@ microsoft/phi-4: accuracy: 90.64 mistralai/Codestral-22B-v0.1: - accuracy: 67.10 -GPT-OSS/BF16: - - accuracy: 90.3 - - kv_cache_quant_algo: FP8 - accuracy: 90.3 GPT-OSS/120B-MXFP4: - accuracy: 90.3 - spec_dec_algo: Eagle diff --git a/tests/integration/defs/accuracy/references/mmlu.yaml b/tests/integration/defs/accuracy/references/mmlu.yaml index 208a31f52b2e..d8d3949909bf 100644 --- a/tests/integration/defs/accuracy/references/mmlu.yaml +++ b/tests/integration/defs/accuracy/references/mmlu.yaml @@ -235,11 +235,11 @@ Qwen3/Qwen3-235B-A22B: accuracy: 86 - quant_algo: NVFP4 kv_cache_quant_algo: FP8 - accuracy: 86 + accuracy: 85.5 - spec_dec_algo: Eagle quant_algo: NVFP4 kv_cache_quant_algo: FP8 - accuracy: 86 + accuracy: 85.5 Qwen3/Qwen3-Next-80B-A3B-Thinking: - accuracy: 86 Qwen3/Qwen3-Next-80B-A3B-Instruct: diff --git a/tests/integration/defs/accuracy/test_disaggregated_serving.py b/tests/integration/defs/accuracy/test_disaggregated_serving.py index 4b9b2a71f9f2..4261572a4de1 100644 --- a/tests/integration/defs/accuracy/test_disaggregated_serving.py +++ b/tests/integration/defs/accuracy/test_disaggregated_serving.py @@ -48,8 +48,8 @@ def result(self): DuckLLM = namedtuple('DuckLLM', ['args', 'tokenizer', 'generate_async']) -DEFAULT_TEST_TIMEOUT = 1200 -DEFAULT_SERVER_WAITING_TIMEOUT = 1200 +DEFAULT_TEST_TIMEOUT = 2400 +DEFAULT_SERVER_WAITING_TIMEOUT = 2400 @functools.lru_cache(maxsize=1) @@ -207,9 +207,16 @@ def _apply_perf_flags(cfg: Optional[Dict[str, Any]]): env["TRTLLM_USE_UCX_KVCACHE"] = "1" if enable_perf: env["TRTLLM_KVCACHE_TIME_OUTPUT_PATH"] = kv_cache_perf_dir + + cache_transceiver_config_backend = ctx_server_config.get( + "cache_transceiver_config", {}).get("backend", "DEFAULT") + if cache_transceiver_config_backend == "NIXL": + env["UCX_MM_ERROR_HANDLING"] = "y" gpu_range = range(current_gpu_offset, current_gpu_offset + ctx_total_gpus) env["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, gpu_range)) + if not has_nvlink(): + env["UCX_TLS"] = "^cuda_ipc" current_gpu_offset += ctx_total_gpus ctx_server_args = ctx_args + [ @@ -230,6 +237,10 @@ def _apply_perf_flags(cfg: Optional[Dict[str, Any]]): env["TRTLLM_USE_UCX_KVCACHE"] = "1" if enable_perf: env["TRTLLM_KVCACHE_TIME_OUTPUT_PATH"] = kv_cache_perf_dir + cache_transceiver_config_backend = gen_server_config.get( + "cache_transceiver_config", {}).get("backend", "DEFAULT") + if cache_transceiver_config_backend == "NIXL": + env["UCX_MM_ERROR_HANDLING"] = "y" gpu_range = range(current_gpu_offset, current_gpu_offset + gen_total_gpus) env["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, gpu_range)) diff --git a/tests/integration/defs/accuracy/test_llm_api_autodeploy.py b/tests/integration/defs/accuracy/test_llm_api_autodeploy.py index f189417926f4..1cca6b8e5b81 100644 --- a/tests/integration/defs/accuracy/test_llm_api_autodeploy.py +++ b/tests/integration/defs/accuracy/test_llm_api_autodeploy.py @@ -44,7 +44,7 @@ def get_default_kwargs(self, enable_chunked_prefill=False): }, "compile_model": { "backend": - "torch-opt", + "torch-cudagraph", "cuda_graph_batch_sizes": [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], }, @@ -193,10 +193,10 @@ def test_bf16(self): # TODO: multi-stream MOE seems to increase the memory usage kwargs["max_batch_size"] = 32 kwargs["free_mem_ratio"] = 0.4 - sampling_params = self.get_default_sampling_params() with AutoDeployLLM(model=self.MODEL_PATH_BF16, tokenizer=self.MODEL_PATH_BF16, **kwargs) as llm: + sampling_params = self.get_default_sampling_params() task = MMLU(self.MODEL_NAME) task.evaluate(llm, sampling_params=sampling_params) task = GSM8K(self.MODEL_NAME) @@ -205,6 +205,7 @@ def test_bf16(self): @pytest.mark.skip_less_device_memory(32000) def test_fp8(self): kwargs = self.get_default_kwargs() + kwargs["max_batch_size"] = 64 with AutoDeployLLM(model=self.MODEL_PATH_FP8, tokenizer=self.MODEL_PATH_FP8, **kwargs) as llm: diff --git a/tests/integration/defs/accuracy/test_llm_api_pytorch.py b/tests/integration/defs/accuracy/test_llm_api_pytorch.py index fc4ba2952090..d6c4b4cee489 100644 --- a/tests/integration/defs/accuracy/test_llm_api_pytorch.py +++ b/tests/integration/defs/accuracy/test_llm_api_pytorch.py @@ -48,8 +48,6 @@ def patched_start_mpi_pool(self): from tensorrt_llm import LLM from tensorrt_llm._torch.model_config import MoeLoadBalancerConfig -from tensorrt_llm._torch.modules.fused_moe.fused_moe_triton import \ - IS_TRITON_KERNELS_AVAILABLE from tensorrt_llm.llmapi import (AutoDecodingConfig, CudaGraphConfig, DeepSeekSparseAttentionConfig, EagleDecodingConfig, KvCacheConfig, MoeConfig, @@ -1118,6 +1116,7 @@ def test_fp8_prequantized(self): task = MMLU(self.MODEL_NAME) task.evaluate(llm) + @skip_pre_hopper def test_fp8_vswa_reuse(self): # NOTE: Test with VSWA kv cache config. kv_cache_config = KvCacheConfig( @@ -1132,6 +1131,7 @@ def test_fp8_vswa_reuse(self): task = MMLU(self.MODEL_NAME) task.evaluate(llm) + @skip_pre_hopper @pytest.mark.parametrize("backend", ["xgrammar"]) def test_fp8_guided_decoding_vswa_reuse(self, backend: str, mocker): mocker.patch.dict(os.environ, {"TRTLLM_XGUIDANCE_LENIENT": "1"}) @@ -2973,6 +2973,7 @@ class TestKimiK2(LlmapiAccuracyTestHarness): @pytest.mark.skip_less_mpi_world_size(8) @skip_post_blackwell @skip_pre_hopper + @pytest.mark.skip_less_device_memory(180000) @pytest.mark.parametrize( "tp_size,pp_size,ep_size,fp8kv,attention_dp,cuda_graph,overlap_scheduler,max_batch_size", [(8, 1, 8, False, False, True, True, 16)], @@ -3614,7 +3615,7 @@ def test_nvfp4( task = GSM8K(self.MODEL_NAME) task.evaluate(llm) - @pytest.mark.parametrize("moe_backend", ["CUTLASS", "TRITON", "TRTLLM"]) + @pytest.mark.parametrize("moe_backend", ["CUTLASS", "TRTLLM"]) @pytest.mark.parametrize( "tp_size,pp_size,ep_size,attention_dp,cuda_graph,overlap_scheduler", [ (1, 1, 1, False, True, True), @@ -3645,11 +3646,6 @@ def test_w4a8_mxfp4(self, moe_backend, tp_size, pp_size, ep_size, patch_mpi_pool_session_for_env(mocker, {"ENABLE_CONFIGURABLE_MOE": env_value}) - if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("TRITON moe backend is not available.") - if get_sm_version() < 90: - pytest.skip("TRITON moe backend requires Hopper or newer.") if moe_backend in ["CUTLASS", "TRTLLM"] and get_sm_version() < 100: pytest.skip( "CUTLASS or TRTLLM moe backend requires Blackwell or newer.") @@ -3675,11 +3671,15 @@ def test_w4a8_mxfp4(self, moe_backend, tp_size, pp_size, ep_size, task = MMLU(self.MODEL_NAME) task.evaluate(llm) - @skip_pre_blackwell + @pytest.mark.parametrize("moe_backend", [ + pytest.param("TRITON", marks=skip_no_hopper), + pytest.param("TRTLLM", marks=skip_pre_blackwell) + ]) @pytest.mark.parametrize( - "tp_size,pp_size,ep_size,attention_dp,cuda_graph,overlap_scheduler,moe_backend", - [(1, 1, 1, False, True, True, "TRTLLM")], - ids=["latency-TRTLLM"]) + "tp_size,pp_size,ep_size,attention_dp,cuda_graph,overlap_scheduler", [ + (1, 1, 1, False, True, True), + ], + ids=["latency"]) def test_w4a16_mxfp4(self, tp_size, pp_size, ep_size, attention_dp, cuda_graph, overlap_scheduler, moe_backend): pytorch_config = dict( @@ -4107,11 +4107,12 @@ class TestGPTOSS(LlmapiAccuracyTestHarness): @pytest.mark.parametrize( "kv_cache_dtype", ["auto", pytest.param("fp8", marks=skip_pre_blackwell)]) - @pytest.mark.parametrize( - "moe_backend", - ["CUTLASS", - pytest.param("TRTLLM", marks=skip_pre_blackwell), "TRITON"], - ids=["cutlass", "trtllm", "triton"]) + @pytest.mark.parametrize("moe_backend", [ + "CUTLASS", + pytest.param("TRTLLM", marks=skip_pre_blackwell), + pytest.param("TRITON", marks=skip_no_hopper) + ], + ids=["cutlass", "trtllm", "triton"]) @pytest.mark.parametrize("cuda_graph,overlap_scheduler", [ (True, True), ]) @@ -4120,8 +4121,6 @@ def test_w4_1gpu(self, kv_cache_dtype, moe_backend, cuda_graph, mocker.patch.object(GSM8K, "MAX_OUTPUT_LEN", 8192) mocker.patch.dict(GSM8K.EVALUATE_KWARGS, {"scores_filter": "exact_match,flexible-extract"}) - if moe_backend == "TRITON" and not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") pytorch_config = dict( disable_overlap_scheduler=not overlap_scheduler, @@ -4158,11 +4157,12 @@ def test_dummy_load_format(self): @pytest.mark.parametrize( "kv_cache_dtype", ["auto", pytest.param("fp8", marks=skip_pre_blackwell)]) - @pytest.mark.parametrize( - "moe_backend", - ["CUTLASS", - pytest.param("TRTLLM", marks=skip_pre_blackwell), "TRITON"], - ids=["cutlass", "trtllm", "triton"]) + @pytest.mark.parametrize("moe_backend", [ + "CUTLASS", + pytest.param("TRTLLM", marks=skip_pre_blackwell), + pytest.param("TRITON", marks=skip_no_hopper) + ], + ids=["cutlass", "trtllm", "triton"]) @pytest.mark.parametrize( "tp_size,pp_size,ep_size,attention_dp,cuda_graph,overlap_scheduler", [ (4, 1, 1, False, True, True), @@ -4191,10 +4191,6 @@ def test_w4_4gpus(self, kv_cache_dtype, moe_backend, tp_size, pp_size, patch_mpi_pool_session_for_env(mocker, {"ENABLE_CONFIGURABLE_MOE": env_value}) - if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") - MAX_OUTPUT_LEN = 128179 MAX_INPUT_LEN = 32768 @@ -4252,11 +4248,12 @@ def test_w4_4gpus(self, kv_cache_dtype, moe_backend, tp_size, pp_size, extra_evaluator_kwargs=extra_evaluator_kwargs) @pytest.mark.skip_less_device(8) - @pytest.mark.parametrize( - "moe_backend", - ["CUTLASS", - pytest.param("TRTLLM", marks=skip_pre_blackwell), "TRITON"], - ids=["cutlass", "trtllm", "triton"]) + @pytest.mark.parametrize("moe_backend", [ + "CUTLASS", + pytest.param("TRTLLM", marks=skip_pre_blackwell), + pytest.param("TRITON", marks=skip_no_hopper) + ], + ids=["cutlass", "trtllm", "triton"]) @pytest.mark.parametrize( "tp_size,pp_size,ep_size,attention_dp,cuda_graph,overlap_scheduler", [ (8, 1, 1, False, True, True), @@ -4269,9 +4266,6 @@ def test_w4_8gpus(self, moe_backend, tp_size, pp_size, ep_size, mocker.patch.object(GSM8K, "MAX_OUTPUT_LEN", 8192) mocker.patch.dict(GSM8K.EVALUATE_KWARGS, {"scores_filter": "exact_match,flexible-extract"}) - if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") pytorch_config = dict( disable_overlap_scheduler=not overlap_scheduler, @@ -4293,6 +4287,7 @@ def test_w4_8gpus(self, moe_backend, tp_size, pp_size, ep_size, extra_evaluator_kwargs=self.extra_evaluator_kwargs) @pytest.mark.skip_less_device(4) + @skip_no_hopper @pytest.mark.parametrize( "kv_cache_dtype", ["auto", pytest.param("fp8", marks=skip_pre_blackwell)]) @@ -4307,14 +4302,8 @@ def test_w4a16(self, kv_cache_dtype, tp_size, pp_size, ep_size, mocker.patch.object(GSM8K, "MAX_OUTPUT_LEN", 8192) mocker.patch.dict(GSM8K.EVALUATE_KWARGS, {"scores_filter": "exact_match,flexible-extract"}) - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") monkeypatch.setenv("OVERRIDE_QUANT_ALGO", "W4A16_MXFP4") - pytorch_config = dict( - disable_overlap_scheduler=not overlap_scheduler, - cuda_graph_config=CudaGraphConfig() if cuda_graph else None) - kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.5, dtype=kv_cache_dtype) @@ -4323,11 +4312,12 @@ def test_w4a16(self, kv_cache_dtype, tp_size, pp_size, ep_size, pipeline_parallel_size=pp_size, moe_expert_parallel_size=ep_size, kv_cache_config=kv_cache_config, - **pytorch_config, + disable_overlap_scheduler=not overlap_scheduler, + cuda_graph_config=CudaGraphConfig() if cuda_graph else None, enable_attention_dp=attention_dp, moe_config=MoeConfig(backend="TRITON")) with llm: - model_name = "GPT-OSS/BF16" + model_name = "GPT-OSS/120B-MXFP4" task = GSM8K(model_name) task.evaluate(llm, extra_evaluator_kwargs=self.extra_evaluator_kwargs) @@ -4336,11 +4326,12 @@ def test_w4a16(self, kv_cache_dtype, tp_size, pp_size, ep_size, @pytest.mark.parametrize( "kv_cache_dtype", ["auto", pytest.param("fp8", marks=skip_pre_blackwell)]) - @pytest.mark.parametrize( - "moe_backend", - ["CUTLASS", - pytest.param("TRTLLM", marks=skip_pre_blackwell), "TRITON"], - ids=["cutlass", "trtllm", "triton"]) + @pytest.mark.parametrize("moe_backend", [ + "CUTLASS", + pytest.param("TRTLLM", marks=skip_pre_blackwell), + pytest.param("TRITON", marks=skip_no_hopper) + ], + ids=["cutlass", "trtllm", "triton"]) @pytest.mark.parametrize( "tp_size,pp_size,ep_size,attention_dp,cuda_graph,overlap_scheduler", [ (2, 1, 1, False, True, True), @@ -4351,10 +4342,6 @@ def test_w4a16(self, kv_cache_dtype, tp_size, pp_size, ep_size, def test_w4_2gpus(self, kv_cache_dtype, moe_backend, tp_size, pp_size, ep_size, attention_dp, cuda_graph, overlap_scheduler, mocker): - if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") - pytorch_config = dict( disable_overlap_scheduler=not overlap_scheduler, cuda_graph_config=CudaGraphConfig() if cuda_graph else None) @@ -4423,16 +4410,13 @@ def test_w4_2gpus_nvfp4(self, tp_size, pp_size, ep_size, attention_dp, @pytest.mark.parametrize( "kv_cache_dtype", ["auto", pytest.param("fp8", marks=skip_pre_blackwell)]) - @pytest.mark.parametrize( - "moe_backend", - ["CUTLASS", - pytest.param("TRTLLM", marks=skip_pre_blackwell), "TRITON"], - ids=["cutlass", "trtllm", "triton"]) + @pytest.mark.parametrize("moe_backend", [ + "CUTLASS", + pytest.param("TRTLLM", marks=skip_pre_blackwell), + pytest.param("TRITON", marks=skip_no_hopper) + ], + ids=["cutlass", "trtllm", "triton"]) def test_w4_chunked_prefill(self, kv_cache_dtype, moe_backend, mocker): - if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") - MAX_OUTPUT_LEN = 128179 MAX_INPUT_LEN = 32768 @@ -4492,17 +4476,14 @@ def test_w4_chunked_prefill(self, kv_cache_dtype, moe_backend, mocker): ids=["overlap_scheduler", "no_overlap_scheduler"]) @pytest.mark.parametrize("one_model", [True, False], ids=["one_model", "two_model"]) - @pytest.mark.parametrize( - "moe_backend", - ["CUTLASS", - pytest.param("TRTLLM", marks=skip_pre_blackwell), "TRITON"], - ids=["cutlass", "trtllm", "triton"]) + @pytest.mark.parametrize("moe_backend", [ + "CUTLASS", + pytest.param("TRTLLM", marks=skip_pre_blackwell), + pytest.param("TRITON", marks=skip_no_hopper) + ], + ids=["cutlass", "trtllm", "triton"]) def test_eagle3_4gpus(self, moe_backend, one_model, overlap_scheduler, mocker): - if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") - if get_sm_version() == 90: pytest.skip( "https://nvbugs/5636916: Remaining Hopper Eagle Accuracy Issue for only TP=4" @@ -4684,17 +4665,14 @@ def test_eagle3_guided_decoding_4gpus(self, one_model, mocker): ids=["overlap_scheduler", "no_overlap_scheduler"]) @pytest.mark.parametrize("one_model", [True, False], ids=["one_model", "two_model"]) - @pytest.mark.parametrize( - "moe_backend", - ["CUTLASS", - pytest.param("TRTLLM", marks=skip_pre_blackwell), "TRITON"], - ids=["cutlass", "trtllm", "triton"]) + @pytest.mark.parametrize("moe_backend", [ + "CUTLASS", + pytest.param("TRTLLM", marks=skip_pre_blackwell), + pytest.param("TRITON", marks=skip_no_hopper) + ], + ids=["cutlass", "trtllm", "triton"]) def test_eagle3_2gpus(self, moe_backend, one_model, overlap_scheduler, mocker): - if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") - MAX_OUTPUT_LEN = 128179 MAX_INPUT_LEN = 32768 @@ -4991,7 +4969,7 @@ def test_fp8_8gpus(self): pytest.skip(f"Model directory {model_dir} does not exist") # Configure model settings - kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.8, + kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.4, enable_block_reuse=True, enable_partial_reuse=False, dtype="fp8") @@ -5276,6 +5254,7 @@ class TestNemotronV3Nano(LlmapiAccuracyTestHarness): EXTRA_EVALUATOR_KWARGS = dict(chat_template_kwargs=dict( enable_thinking=False)) + @pytest.mark.skip_less_device_memory(80000) def test_auto_dtype(self): with LLM( f"{llm_models_root()}/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", @@ -5316,6 +5295,7 @@ class TestNemotronV3Super(LlmapiAccuracyTestHarness): EXTRA_EVALUATOR_KWARGS = dict(chat_template_kwargs=dict( enable_thinking=False)) + @skip_pre_hopper @pytest.mark.skip_less_device_memory(64000) @pytest.mark.skip_less_mpi_world_size(4) @pytest.mark.parametrize( diff --git a/tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py b/tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py index 399fa66a4f7b..c5026501090d 100644 --- a/tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py +++ b/tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py @@ -103,6 +103,7 @@ def test_auto_dtype(self): task.evaluate(llm, sampling_params=self.sampling_params) +@skip_pre_hopper class TestNVILA_8B(LlmapiAccuracyTestHarness): MODEL_NAME = "Efficient-Large-Model/NVILA-8B" MODEL_PATH = f"{llm_models_root()}/vila/NVILA-8B" @@ -159,6 +160,7 @@ def test_auto_dtype(self): task.evaluate(llm, sampling_params=self.sampling_params) +@skip_pre_hopper class TestNemotron_Nano_12B_V2_VL(LlmapiAccuracyTestHarness): MODEL_NAME = "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16" MODEL_PATH = f"{llm_models_root()}/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16" diff --git a/tests/integration/defs/agg_unit_mem_df.csv b/tests/integration/defs/agg_unit_mem_df.csv index d5e54357db5c..e14a32221190 100644 --- a/tests/integration/defs/agg_unit_mem_df.csv +++ b/tests/integration/defs/agg_unit_mem_df.csv @@ -114,3 +114,4 @@ unittest/_torch/thop/parallel,NVIDIA B200,16, unittest/_torch/attention,NVIDIA H100,4, unittest/_torch/misc,NVIDIA H100,4, unittest/_torch/thop/parallel,NVIDIA H100,16, +unittest/_torch/flashinfer/test_trtllm_flashinfer_symbol_collision.py,NVIDIA H100 PCIe,1, diff --git a/tests/integration/defs/conftest.py b/tests/integration/defs/conftest.py index c06b0d18bc20..411c43179d06 100644 --- a/tests/integration/defs/conftest.py +++ b/tests/integration/defs/conftest.py @@ -83,7 +83,7 @@ def wget(url, out): def llm_models_root() -> str: """Return LLM_MODELS_ROOT path if it is set in env, assert when it's set but not a valid path.""" - root = Path("/home/scratch.trt_llm_data/llm-models/") + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") if "LLM_MODELS_ROOT" in os.environ: root = Path(os.environ.get("LLM_MODELS_ROOT")) diff --git a/tests/integration/defs/disaggregated/test_auto_scaling.py b/tests/integration/defs/disaggregated/test_auto_scaling.py index 1dd37a17a19b..4821ee847638 100644 --- a/tests/integration/defs/disaggregated/test_auto_scaling.py +++ b/tests/integration/defs/disaggregated/test_auto_scaling.py @@ -2,7 +2,6 @@ import os import shutil import subprocess -import sys import tempfile import traceback import uuid @@ -147,14 +146,14 @@ def _run_worker(model_name, env = os.environ.copy() log_file = None log_path = None + stdout = None + stderr = None if save_log: log_path = os.path.join(work_dir, f"worker_{role}_{port}.log") log_file = open(log_path, "w+") stdout = log_file stderr = log_file - else: - stdout = sys.stdout - stderr = sys.stderr + if device != -1: env["CUDA_VISIBLE_DEVICES"] = str(device) print(f"Running {role} on port {port}") @@ -187,14 +186,13 @@ def run_disagg_server(disagg_cluster_config, work_dir, port=0, save_log=False): cmds = ["trtllm-serve", "disaggregated", "-c", disagg_server_config_path] log_file = None log_path = None + stdout = None + stderr = None if save_log: log_path = os.path.join(work_dir, "disagg_server.log") log_file = open(log_path, "w+") stdout = log_file stderr = log_file - else: - stdout = sys.stdout - stderr = sys.stderr p = subprocess.Popen(cmds, stdout=stdout, stderr=stderr) return ProcessWrapper(p, log_file=log_file, log_path=log_path, port=port) @@ -465,7 +463,7 @@ async def test_worker_restart(model_name, disagg_server_config, worker_config, worker_config, work_dir, port=0, - device=2) + device=0) await wait_for_disagg_server_status(disagg_port, 1, 1) await asyncio.sleep(CHECK_STATUS_INTERVAL) verify_cluster_info(True, 1, 1, port=disagg_port) @@ -487,7 +485,7 @@ async def test_worker_restart(model_name, disagg_server_config, worker_config, worker_config, work_dir, port=0, - device=3) + device=1) await wait_for_disagg_server_status(disagg_port, 1, 1) await asyncio.sleep(CHECK_STATUS_INTERVAL) verify_cluster_info(True, 1, 1, port=disagg_port) diff --git a/tests/integration/defs/disaggregated/test_disaggregated.py b/tests/integration/defs/disaggregated/test_disaggregated.py index 86ba1c451774..023681c0ff90 100644 --- a/tests/integration/defs/disaggregated/test_disaggregated.py +++ b/tests/integration/defs/disaggregated/test_disaggregated.py @@ -365,8 +365,13 @@ def run_disaggregated_test(example_dir, """Run disaggregated test with given configuration.""" cleanup_output_files() run_env = env.copy() - run_env["UCX_TLS"] = "^ib" + # on some CI nodes , we set UCX_TLS to "^ib" to avoid the issue that IB equipped but not available. + # we set UCX_MM_ERROR_HANDLING to "y" to avoid the issue that NIXL cannot use IB or TCP for notify on some CI nodes, + # setting it to "y" will enable NIXL to use system memory for notify. + + run_env["UCX_TLS"] = "^ib" + run_env["UCX_MM_ERROR_HANDLING"] = "y" num_ranks, config_file = get_test_config(test_desc, example_dir, os.path.dirname(__file__)) @@ -1190,6 +1195,7 @@ def test_disaggregated_deepseek_v3_lite_fp8_nixl(disaggregated_test_root, env = llm_venv._new_env.copy() env["TRTLLM_USE_NIXL_KVCACHE"] = "1" env["UCX_TLS"] = "^ib" + env["UCX_MM_ERROR_HANDLING"] = "y" run_disaggregated_test(disaggregated_example_root, "deepseek_v3_lite_fp8_nixl", env=env, @@ -1497,6 +1503,7 @@ def run_disaggregated_benchmark(example_dir, """Run disaggregated test with given configuration.""" run_env = env.copy() run_env["UCX_TLS"] = "^ib" + run_env["UCX_MM_ERROR_HANDLING"] = "y" workers_cmd = [ 'mpirun', '--allow-run-as-root', '--oversubscribe', '-n', str(num_ranks), 'trtllm-serve', 'disaggregated_mpi_worker', '-c', @@ -1677,6 +1684,7 @@ def run_disaggregated_aiperf(config_file, cleanup_output_files() run_env = env.copy() run_env["UCX_TLS"] = "^ib" + run_env["UCX_MM_ERROR_HANDLING"] = "y" workers_cmd = [ 'mpirun', '--allow-run-as-root', '--oversubscribe', '-n', diff --git a/tests/integration/defs/disaggregated/test_disaggregated_single_gpu.py b/tests/integration/defs/disaggregated/test_disaggregated_single_gpu.py index 4e146f3df0e0..1d10ead5f826 100644 --- a/tests/integration/defs/disaggregated/test_disaggregated_single_gpu.py +++ b/tests/integration/defs/disaggregated/test_disaggregated_single_gpu.py @@ -184,7 +184,11 @@ def verify_disaggregated(model, generation_overlap, enable_cuda_graph, prompt, port_name = mpi_publish_name() - with MPIPoolExecutor(max_workers=2, env={"UCX_TLS": "^ib"}) as executor: + with MPIPoolExecutor(max_workers=2, + env={ + "UCX_TLS": "^ib", + "UCX_MM_ERROR_HANDLING": "y" + }) as executor: futures = [] try: for worker_arg in worker_args: @@ -330,7 +334,11 @@ def test_disaggregated_llama_context_capacity(model, enable_cuda_graph, prompt = "European Union is a political and economic union of 27 countries. The European Union is headquartered in Brussels, Belgium. The first president of the European Union was Jean-Claude Juncker. The current president is Ursula von der Leyen. The European Union is a major economic and political entity." - with MPIPoolExecutor(max_workers=2, env={"UCX_TLS": "^ib"}) as executor: + with MPIPoolExecutor(max_workers=2, + env={ + "UCX_TLS": "^ib", + "UCX_MM_ERROR_HANDLING": "y" + }) as executor: futures = [] try: for worker_arg in worker_args: @@ -440,6 +448,7 @@ def test_disaggregated_spec_dec_batch_slot_limit(model, spec_dec_model_path, with MPIPoolExecutor(max_workers=2, env={ "UCX_TLS": "^ib", + "UCX_MM_ERROR_HANDLING": "y", "OMPI_MCA_rmaps_base_oversubscribe": "1" }, mpi_info=mpi_info) as executor: diff --git a/tests/integration/defs/examples/serve/test_serve.py b/tests/integration/defs/examples/serve/test_serve.py index d5bfb03f3093..c769be461039 100644 --- a/tests/integration/defs/examples/serve/test_serve.py +++ b/tests/integration/defs/examples/serve/test_serve.py @@ -7,13 +7,14 @@ import pytest import requests import yaml +from defs.common import get_free_port_in_ci from defs.conftest import llm_models_root, skip_no_hopper from defs.trt_test_alternative import popen, print_error, print_info from openai import OpenAI from requests.exceptions import RequestException -def check_server_ready(http_port="8000", timeout_timer=600, sleep_interval=5): +def check_server_ready(http_port, timeout_timer=600, sleep_interval=5): timer = 0 while timer <= timeout_timer: try: @@ -49,7 +50,7 @@ def wait_for_log(log_queue, expected_log, timeout=10): return False -def check_openai_chat_completion(http_port="8000", +def check_openai_chat_completion(http_port, model_name="TinyLlama-1.1B-Chat-v1.0"): """ Test the launched trtllm-serve server using OpenAI client. @@ -127,6 +128,7 @@ def test_config_file_loading(serve_test_root, config_flag): assert os.path.exists( model_path), f"model_path does not exist: {model_path}" + port = get_free_port_in_ci() cmd = [ "trtllm-serve", "serve", @@ -134,7 +136,7 @@ def test_config_file_loading(serve_test_root, config_flag): "--host", "0.0.0.0", "--port", - "8000", + str(port), "--backend", "pytorch", config_flag, @@ -143,11 +145,11 @@ def test_config_file_loading(serve_test_root, config_flag): print_info(f"Launching trtllm-serve with {config_flag}...") with popen(cmd): - check_server_ready() + check_server_ready(http_port=port) # Extract model name from the model path for consistency model_name = model_path.split('/')[-1] # "Qwen3-30B-A3B-FP8" # Test the server with OpenAI chat completion - check_openai_chat_completion(model_name=model_name) + check_openai_chat_completion(http_port=port, model_name=model_name) @skip_no_hopper @@ -170,7 +172,7 @@ def test_env_overrides_pdl(tmp_path): } })) - port = 8001 + port = get_free_port_in_ci() env = os.environ.copy() # Pre-load with 0 to verify override works env.update({ @@ -198,7 +200,7 @@ def test_env_overrides_pdl(tmp_path): [output_queue.put(line) for line in iter(proc.stdout.readline, '')], daemon=True).start() - check_server_ready(http_port=str(port), timeout_timer=300) + check_server_ready(http_port=port, timeout_timer=300) response = OpenAI(base_url=f"http://localhost:{port}/v1", api_key="tensorrt_llm").chat.completions.create( model="TinyLlama-1.1B-Chat-v1.0", diff --git a/tests/integration/defs/perf/disagg/execution/executor.py b/tests/integration/defs/perf/disagg/execution/executor.py index 8dec350f77e5..b5f2e62d69f1 100644 --- a/tests/integration/defs/perf/disagg/execution/executor.py +++ b/tests/integration/defs/perf/disagg/execution/executor.py @@ -219,6 +219,7 @@ def submit_test_job(test_config) -> tuple: # Write temporary config file with replaced environment variables logger.info(f"Creating temporary config: {temp_config_path}") + with open(temp_config_path, "w") as f: yaml.dump( test_config.config_data, @@ -727,12 +728,18 @@ def _check_perf_result( result["error"] = error_msg return result - # Parse metrics and save to CSV + # Parse metrics and save to CSV (LogParser reads file once, also extracts failed/total) log_parser = LogParser(benchmark_type, config, metrics_config, result_dir) parse_result = log_parser.parse(model_name, timestamps=timestamps, test_name=test_name) + failed_requests = parse_result.get("failed_requests", 0) + total_requests = parse_result.get("total_requests", 0) + if not parse_result["status"]: - result["error"] = "Failed to parse benchmark logs" + error_msg = "Failed to parse benchmark logs" + if failed_requests > 0: + error_msg += f" ({failed_requests}/{total_requests} requests failed)" + result["error"] = error_msg return result # Check if df is None @@ -752,6 +759,15 @@ def _check_perf_result( result["success"] = True result["status"] = "SUCCESS" + + # Override success if any requests failed (metrics still saved for analysis) + if failed_requests > 0: + error_msg = f"Benchmark had {failed_requests}/{total_requests} failed requests" + logger.error(error_msg) + result["success"] = False + result["status"] = "FAILED" + result["error"] = error_msg + return result @staticmethod diff --git a/tests/integration/defs/perf/disagg/poetry.lock b/tests/integration/defs/perf/disagg/poetry.lock index e525b1bc775d..6d12c6360309 100644 --- a/tests/integration/defs/perf/disagg/poetry.lock +++ b/tests/integration/defs/perf/disagg/poetry.lock @@ -15,15 +15,15 @@ files = [ [[package]] name = "exceptiongroup" -version = "1.3.0" +version = "1.3.1" description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" groups = ["main"] markers = "python_version == \"3.10\"" files = [ - {file = "exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10"}, - {file = "exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88"}, + {file = "exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598"}, + {file = "exceptiongroup-1.3.1.tar.gz", hash = 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errors="replace") as log_file: log_content = log_file.read() + # Extract failed/total request counts from log (for executor to mark failed cases) + # Use findall + last match to handle multi-concurrency logs correctly + failed_requests = 0 + total_requests = 0 + failed_matches = re.findall(r"Total failed requests:\s*(\d+)", log_content) + total_matches = re.findall(r"Total requests:\s*(\d+)", log_content) + if failed_matches: + failed_requests = int(failed_matches[-1]) + if total_matches: + total_requests = int(total_matches[-1]) + # Use metrics_config for extraction raw_results = self._extract_log( self.metrics_config.extractor_pattern, self.metrics_config.metric_names, log_content ) if len(raw_results) == 0: logger.warning("No metrics extracted from log file") - return {"status": False, "df": None} + return { + "status": False, + "df": None, + "failed_requests": failed_requests, + "total_requests": total_requests, + } # Convert to perf result format df = self._convert_to_perf_result_format(raw_results, model_name, timestamps, test_name) - return {"status": True, "df": df} + return { + "status": True, + "df": df, + "failed_requests": failed_requests, + "total_requests": total_requests, + } def _convert_to_perf_result_format( self, diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen13_tep4_bs1_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen13_tep4_bs1_eplb0_mtp0-Default.yaml index 2fdb13e41011..603f7b20a5ef 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen13_tep4_bs1_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen13_tep4_bs1_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -83,7 +83,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen5_tep4_bs4_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen5_tep4_bs4_eplb0_mtp0-Default.yaml index c894c1e19327..4c7e5b6ac2af 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen5_tep4_bs4_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen5_tep4_bs4_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '4' @@ -78,12 +78,12 @@ worker_config: num_postprocess_workers: 4 allreduce_strategy: MNNVL ctx: - max_batch_size: 4 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen6_tep8_bs1_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen6_tep8_bs1_eplb0_mtp3-Default.yaml index 8010f1aaaa26..f5b5cdbca60f 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen6_tep8_bs1_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen6_tep8_bs1_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -44,10 +44,10 @@ accuracy: enable_accuracy_test: false worker_config: gen: - tensor_parallel_size: 8 - moe_expert_parallel_size: 8 + tensor_parallel_size: 4 + moe_expert_parallel_size: 4 enable_attention_dp: false - pipeline_parallel_size: 4 + pipeline_parallel_size: 1 max_batch_size: 1 # mtp_size=3 ⇒ max_num_tokens = 1 * (3 + 1) = 4 max_num_tokens: 4 @@ -87,7 +87,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen7_tep8_bs1_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen7_tep8_bs1_eplb0_mtp0-Default.yaml index 66f080aa4ea3..d552e1ce3a9a 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen7_tep8_bs1_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen7_tep8_bs1_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -83,7 +83,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep4_bs2_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep4_bs2_eplb0_mtp0-Default.yaml index 9462ae86bfb0..68cbe5b5d1c0 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep4_bs2_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep4_bs2_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '2' @@ -78,12 +78,12 @@ worker_config: num_postprocess_workers: 4 allreduce_strategy: MNNVL ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep8_bs1_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep8_bs1_eplb0_mtp0-Default.yaml index 0087e871cade..33881dadab68 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep8_bs1_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep8_bs1_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -83,7 +83,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen11_tep4_bs2_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen11_tep4_bs2_eplb0_mtp0-Default.yaml index 826b01a6bbe8..b77c8c421c6e 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen11_tep4_bs2_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen11_tep4_bs2_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '2' @@ -78,12 +78,12 @@ worker_config: num_postprocess_workers: 4 allreduce_strategy: MNNVL ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen14_tep4_bs1_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen14_tep4_bs1_eplb0_mtp0-Default.yaml index ada7c29e9930..aa497577d76c 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen14_tep4_bs1_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen14_tep4_bs1_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -83,7 +83,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_dep16_bs1_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_dep16_bs1_eplb0_mtp3-Default.yaml index ba0b4d003ed0..2b632a31c64b 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_dep16_bs1_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_dep16_bs1_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -86,7 +86,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_dep8_bs4_eplb0_mtp2-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_dep8_bs4_eplb0_mtp2-Default.yaml index 81fbcf581e83..9c5b8196f242 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_dep8_bs4_eplb0_mtp2-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_dep8_bs4_eplb0_mtp2-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '4' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 2 ctx: - max_batch_size: 4 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp0-Default.yaml index 675e3e66f2d6..facbb7353139 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -83,7 +83,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp3-Default.yaml index cabf7bcf5ca9..aff1ada26896 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -87,7 +87,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs2_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs2_eplb0_mtp3-Default.yaml index 0aa4795d7cba..cb25ca5b4bdd 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs2_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs2_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '2' @@ -82,12 +82,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 3 ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen5_tep8_bs2_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen5_tep8_bs2_eplb0_mtp3-Default.yaml index 278d4afa785a..c2b4755efc73 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen5_tep8_bs2_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen5_tep8_bs2_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '2' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 3 ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen7_tep4_bs2_eplb0_mtp2-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen7_tep4_bs2_eplb0_mtp2-Default.yaml index 144a05c12079..3ca45ccbad41 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen7_tep4_bs2_eplb0_mtp2-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen7_tep4_bs2_eplb0_mtp2-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '2' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 2 ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen7_tep8_bs1_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen7_tep8_bs1_eplb0_mtp0-Default.yaml index 065c280cea8f..d0ebc8b4f6cf 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen7_tep8_bs1_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen7_tep8_bs1_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1' @@ -83,7 +83,7 @@ worker_config: max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen8_tep4_bs4_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen8_tep4_bs4_eplb0_mtp0-Default.yaml index 69f5763bb401..cec8989c2e2e 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen8_tep4_bs4_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx1_pp8_gen8_tep4_bs4_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '4' @@ -78,12 +78,12 @@ worker_config: num_postprocess_workers: 4 allreduce_strategy: MNNVL ctx: - max_batch_size: 4 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp4_gen7_tep8_bs2_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp4_gen7_tep8_bs2_eplb0_mtp3-Default.yaml index c59bbe4ac95a..cdf60ffb7fda 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp4_gen7_tep8_bs2_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp4_gen7_tep8_bs2_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '2' @@ -81,12 +81,12 @@ worker_config: num_nextn_predict_layers: 3 allreduce_strategy: MNNVL ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp8_gen1_dep16_bs8_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp8_gen1_dep16_bs8_eplb0_mtp0-Default.yaml index ff9a9e62cf8b..7fce79834453 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp8_gen1_dep16_bs8_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp8_gen1_dep16_bs8_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '8' diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp8_gen1_dep32_bs2_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp8_gen1_dep32_bs2_eplb0_mtp0-Default.yaml index 63b81d5c7adc..e657dff55658 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp8_gen1_dep32_bs2_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx2_pp8_gen1_dep32_bs2_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '2' @@ -77,12 +77,12 @@ worker_config: stream_interval: 20 num_postprocess_workers: 4 ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp4_gen1_dep8_bs16_eplb0_mtp1-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp4_gen1_dep8_bs16_eplb0_mtp1-Default.yaml index 6dee7193fcda..2499d48ae5e5 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp4_gen1_dep8_bs16_eplb0_mtp1-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp4_gen1_dep8_bs16_eplb0_mtp1-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '128' @@ -80,12 +80,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 1 ctx: - max_batch_size: 16 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep16_bs16_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep16_bs16_eplb0_mtp0-Default.yaml index 5578abce0b81..395f88c4d25e 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep16_bs16_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep16_bs16_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '16' @@ -77,12 +77,12 @@ worker_config: stream_interval: 20 num_postprocess_workers: 4 ctx: - max_batch_size: 16 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep16_bs8_eplb0_mtp2-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep16_bs8_eplb0_mtp2-Default.yaml index 1d5531383dd0..57495ff5205a 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep16_bs8_eplb0_mtp2-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep16_bs8_eplb0_mtp2-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '8' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 2 ctx: - max_batch_size: 8 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep32_bs2_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep32_bs2_eplb0_mtp3-Default.yaml index 12246924dadf..6359f8d75f0b 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep32_bs2_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep32_bs2_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '2' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 3 ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep32_bs4_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep32_bs4_eplb0_mtp0-Default.yaml index 21735da39881..07d33bc2e649 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep32_bs4_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx3_pp8_gen1_dep32_bs4_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '4' @@ -77,12 +77,12 @@ worker_config: stream_interval: 20 num_postprocess_workers: 4 ctx: - max_batch_size: 4 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 8 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep16_bs16_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep16_bs16_eplb0_mtp0-Default.yaml index 74477f81a26f..a4df67f23fd8 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep16_bs16_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep16_bs16_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '256' @@ -77,12 +77,12 @@ worker_config: stream_interval: 20 num_postprocess_workers: 4 ctx: - max_batch_size: 16 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep16_bs8_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep16_bs8_eplb0_mtp3-Default.yaml index bf927bc583b5..a1ab0b18a681 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep16_bs8_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep16_bs8_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '128' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 3 ctx: - max_batch_size: 8 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep32_bs2_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep32_bs2_eplb0_mtp3-Default.yaml index 693a9c995fc2..5f1605580be6 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep32_bs2_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep32_bs2_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '64' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 3 ctx: - max_batch_size: 2 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep32_bs4_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep32_bs4_eplb0_mtp0-Default.yaml index 83967207ee51..688ad820e4c2 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep32_bs4_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx5_pp4_gen1_dep32_bs4_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '128' @@ -77,12 +77,12 @@ worker_config: stream_interval: 20 num_postprocess_workers: 4 ctx: - max_batch_size: 4 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx7_pp4_gen1_dep16_bs16_eplb0_mtp1-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx7_pp4_gen1_dep16_bs16_eplb0_mtp1-Default.yaml index 90a2879c52ce..74e251b41f2d 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx7_pp4_gen1_dep16_bs16_eplb0_mtp1-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx7_pp4_gen1_dep16_bs16_eplb0_mtp1-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '256' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 1 ctx: - max_batch_size: 16 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx7_pp4_gen1_dep16_bs32_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx7_pp4_gen1_dep16_bs32_eplb0_mtp0-Default.yaml index 80da2262de4a..846d378e9ce9 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx7_pp4_gen1_dep16_bs32_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx7_pp4_gen1_dep16_bs32_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '512' @@ -77,12 +77,12 @@ worker_config: stream_interval: 20 num_postprocess_workers: 4 ctx: - max_batch_size: 32 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep16_bs32_eplb0_mtp1-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep16_bs32_eplb0_mtp1-Default.yaml index cb0ab7c034d4..1e060c2a7a60 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep16_bs32_eplb0_mtp1-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep16_bs32_eplb0_mtp1-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '512' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 1 ctx: - max_batch_size: 32 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs4_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs4_eplb0_mtp3-Default.yaml index 9e94dd864aa4..de8cb92e5308 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs4_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs4_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '128' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 3 ctx: - max_batch_size: 4 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs8_eplb0_mtp0-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs8_eplb0_mtp0-Default.yaml index 984fb5bb6d5a..44a3865b321a 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs8_eplb0_mtp0-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs8_eplb0_mtp0-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '256' @@ -77,12 +77,12 @@ worker_config: stream_interval: 20 num_postprocess_workers: 4 ctx: - max_batch_size: 8 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs8_eplb0_mtp3-Default.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs8_eplb0_mtp3-Default.yaml index 0b1402b3cc5f..f6fc331f104b 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs8_eplb0_mtp3-Default.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/perf/deepseek-r1-fp4_128k8k_ctx8_pp4_gen1_dep32_bs8_eplb0_mtp3-Default.yaml @@ -17,7 +17,7 @@ slurm: benchmark: mode: e2e use_nv_sa_benchmark: true - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '256' @@ -81,12 +81,12 @@ worker_config: decoding_type: MTP num_nextn_predict_layers: 3 ctx: - max_batch_size: 8 + max_batch_size: 1 max_num_tokens: 131104 max_seq_len: 131104 tensor_parallel_size: 1 moe_expert_parallel_size: 1 - enable_attention_dp: true + enable_attention_dp: false pipeline_parallel_size: 4 print_iter_log: true cuda_graph_config: null diff --git a/tests/integration/defs/perf/disagg/test_configs/disagg/stress/deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml b/tests/integration/defs/perf/disagg/test_configs/disagg/stress/deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml index 9d5ca29c1220..e70b08648358 100644 --- a/tests/integration/defs/perf/disagg/test_configs/disagg/stress/deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/disagg/stress/deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml @@ -24,7 +24,7 @@ slurm: extra_args: "--gres=gpu:4" numa_bind: true benchmark: - mode: gen_only + mode: e2e use_nv_sa_benchmark: false multi_round: 20 benchmark_ratio: 0.8 @@ -94,6 +94,7 @@ worker_config: max_tokens_in_buffer: 8320 backend: DEFAULT stream_interval: 20 + num_postprocess_workers: 4 ctx: enable_layerwise_nvtx_marker: true max_batch_size: 4 diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/accuracy/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/accuracy/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml index 914924bfd25c..eda39f856abe 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/accuracy/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/accuracy/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml @@ -25,7 +25,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '2048' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/accuracy/kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/accuracy/kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL.yaml index d68362abf040..4a4a92166448 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/accuracy/kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/accuracy/kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL.yaml @@ -25,7 +25,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 1.0 streaming: true concurrency_list: '16384' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-NIXL.yaml index 3c33b288e560..d59577d9b27d 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: 512 1024 diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-UCX.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-UCX.yaml index 0a6135f34a25..f97e3b733f40 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-UCX.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep16_bs64_eplb288_mtp3_ccb-UCX.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: 512 1024 diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml index 75accd863165..0e2fd7c705a9 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '512' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml index 2df13f962f1b..2d09fc2e293e 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx1_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '512' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-NIXL.yaml index 3c0b8d2e7a7c..82e0ed1b9a7b 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '2048' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-UCX.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-UCX.yaml index 2dd7fd80b2dd..01a97277f8c8 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-UCX.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/Qwen3-235B-A22B-FP4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp1_ccb-UCX.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '2048' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml index fedb8825b218..dca9e11674e0 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '1024' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-UCX.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-UCX.yaml index 57664549800e..da38c1188e02 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-UCX.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-UCX.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '1024' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml index 4d4f8cb7db2c..da6d8e673609 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '2048' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-UCX.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-UCX.yaml index fc1242294366..4bd9ebd02cc1 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-UCX.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-UCX.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '2048' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml index d2f81b865eda..68871fa34ee7 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml @@ -21,7 +21,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '12288' @@ -89,6 +89,7 @@ worker_config: max_tokens_in_buffer: 8320 backend: DEFAULT stream_interval: 20 + num_postprocess_workers: 4 ctx: enable_layerwise_nvtx_marker: true max_batch_size: 4 diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-DEFAULT.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-DEFAULT.yaml index a0d6836d5556..6fc3dbbb41f4 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-DEFAULT.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-DEFAULT.yaml @@ -23,7 +23,7 @@ hardware: benchmark: mode: e2e use_nv_sa_benchmark: false - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1024' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml index 60b022a21d05..89467c096fd4 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '1024' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-UCX.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-UCX.yaml index f4cfcda4e672..473e4f0eb421 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-UCX.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-UCX.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '1024' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml index dafc6a7df79d..22de7c4b86bf 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '512' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml index bb54d661a527..17820ad45608 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-UCX.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '512' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml index 58ef6a5bedbb..59b1db798a8b 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb288_mtp0_ccb-NIXL.yaml @@ -13,14 +13,14 @@ slurm: script_file: disaggr_torch.slurm partition: account: - job_time: 02:00:00 + job_time: 04:00:00 job_name: unified-benchmark extra_args: "--gres=gpu:4" numa_bind: true benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '1024' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml index 9da7bfd08b92..bc529a2010e5 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL.yaml @@ -13,14 +13,14 @@ slurm: script_file: disaggr_torch.slurm partition: account: - job_time: 02:00:00 + job_time: 04:00:00 job_name: unified-benchmark extra_args: "--gres=gpu:4" numa_bind: true benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '2048' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml index 21ab0d765e27..43973a8f3532 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_1k1k_ctx2_gen1_dep48_bs16_eplb288_mtp3_ccb-DEFAULT.yaml @@ -14,14 +14,14 @@ slurm: script_file: disaggr_torch.slurm partition: account: - job_time: 02:00:00 + job_time: 04:00:00 job_name: unified-benchmark extra_args: "--gres=gpu:4" numa_bind: true benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '12288' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-DEFAULT.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-DEFAULT.yaml index 5697528941f8..5b3981d76986 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-DEFAULT.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx2_gen1_dep32_bs128_eplb288_mtp3_ccb-DEFAULT.yaml @@ -13,7 +13,7 @@ slurm: script_file: disaggr_torch.slurm partition: account: - job_time: 02:00:00 + job_time: 04:00:00 job_name: disaggr-test extra_args: "--gres=gpu:4" numa_bind: true @@ -24,7 +24,7 @@ hardware: benchmark: mode: e2e use_nv_sa_benchmark: false - multi_round: 1 + multi_round: 8 benchmark_ratio: 0.8 streaming: true concurrency_list: '1024' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml index 721969229546..909d8cff112b 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx6_gen1_dep16_bs64_eplb288_mtp0_ccb-NIXL.yaml @@ -13,14 +13,14 @@ slurm: script_file: disaggr_torch.slurm partition: account: - job_time: 02:00:00 + job_time: 04:00:00 job_name: unified-benchmark extra_args: "--gres=gpu:4" numa_bind: true benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '1024' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml index 6bfa477ad76b..69d163c73c6a 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/deepseek-v32-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL.yaml @@ -13,14 +13,14 @@ slurm: script_file: disaggr_torch.slurm partition: account: - job_time: 02:00:00 + job_time: 04:00:00 job_name: unified-benchmark extra_args: "--gres=gpu:4" numa_bind: true benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 0.8 streaming: true concurrency_list: '512' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL.yaml index dbba4ca355b8..771dba0b9c15 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 1.0 streaming: true concurrency_list: '16384' diff --git a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/kimi-k2-thinking-fp4_8k1k_ctx8_gen1_dep32_bs256_eplb416_mtp0_ccb-NIXL.yaml b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/kimi-k2-thinking-fp4_8k1k_ctx8_gen1_dep32_bs256_eplb416_mtp0_ccb-NIXL.yaml index 6178b8e9298c..74beec805da5 100644 --- a/tests/integration/defs/perf/disagg/test_configs/wideep/perf/kimi-k2-thinking-fp4_8k1k_ctx8_gen1_dep32_bs256_eplb416_mtp0_ccb-NIXL.yaml +++ b/tests/integration/defs/perf/disagg/test_configs/wideep/perf/kimi-k2-thinking-fp4_8k1k_ctx8_gen1_dep32_bs256_eplb416_mtp0_ccb-NIXL.yaml @@ -19,7 +19,7 @@ slurm: benchmark: mode: gen_only use_nv_sa_benchmark: false - multi_round: 8 + multi_round: 1 benchmark_ratio: 1.0 streaming: true concurrency_list: '8192' diff --git a/tests/integration/defs/perf/disagg/test_disagg.py b/tests/integration/defs/perf/disagg/test_disagg.py index 36e786949d25..75de1aedb596 100644 --- a/tests/integration/defs/perf/disagg/test_disagg.py +++ b/tests/integration/defs/perf/disagg/test_disagg.py @@ -112,8 +112,8 @@ def test_benchmark(self, request, batch_manager, test_config: TestConfig): ) assert job_id, f"Failed to submit job for {test_config.test_id}\n{error_msg}" - # Wait for completion (timeout: 10 hours = 36000 seconds) - JobManager.wait_for_completion(job_id, 36000, test_config, check_early_failure=True) + # Wait for completion (timeout: 15 hours = 54000 seconds) + JobManager.wait_for_completion(job_id, 54000, test_config, check_early_failure=True) # End tracking test case test_tracker.end_test_case() @@ -123,7 +123,9 @@ def test_benchmark(self, request, batch_manager, test_config: TestConfig): # Check results and generate report result = JobManager.check_result(job_id, test_config, timestamps, full_test_name) - assert result["success"], f"Performance test failed: {job_id}" + assert result["success"], ( + f"Performance test failed: {job_id}\n{result.get('error', 'Unknown error')}" + ) except Exception as e: test_tracker.end_test_case() @@ -188,8 +190,8 @@ def test_accuracy(self, request, batch_manager, test_config: TestConfig): # Validate submission result assert job_id, f"Failed to get job_id for {test_config.test_id}" - # Wait for completion (timeout: 10 hours = 36000 seconds) - JobManager.wait_for_completion(job_id, 36000, test_config, check_early_failure=True) + # Wait for completion (timeout: 15 hours = 54000 seconds) + JobManager.wait_for_completion(job_id, 54000, test_config, check_early_failure=True) # End tracking test case test_tracker.end_test_case() @@ -272,8 +274,8 @@ def test_stress(self, request, batch_manager, test_config: TestConfig): ) assert job_id, f"Failed to submit job for {test_config.test_id}\n{error_msg}" - # Wait for completion (timeout: 10 hours = 36000 seconds) - JobManager.wait_for_completion(job_id, 36000, test_config, check_early_failure=True) + # Wait for completion (timeout: 15 hours = 54000 seconds) + JobManager.wait_for_completion(job_id, 54000, test_config, check_early_failure=True) # End tracking test case test_tracker.end_test_case() diff --git a/tests/integration/defs/perf/disagg/testlist/release_sanity.txt b/tests/integration/defs/perf/disagg/testlist/release_sanity.txt new file mode 100644 index 000000000000..51a9fe242f18 --- /dev/null +++ b/tests/integration/defs/perf/disagg/testlist/release_sanity.txt @@ -0,0 +1,33 @@ +# ============================================================ +# Release Sanity Test List +# Coverage: disagg + wideep, GB200 + GB300, key models, +# NIXL/UCX, 1k1k/8k1k/128k8k, mtp0/mtp3, accuracy +# ============================================================ + +# ---- disagg: deepseek-r1-fp4 GB200 ------------------------- +test_disagg.py::TestDisaggBenchmark::test_benchmark[disagg_perf_deepseek-r1-fp4_1k1k_ctx1_gen1_dep32_bs32_eplb0_mtp0_ccb-NIXL] +test_disagg.py::TestDisaggBenchmark::test_benchmark[disagg_perf_deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb0_mtp3_ccb-NIXL] +test_disagg.py::TestDisaggBenchmark::test_benchmark[disagg_perf_deepseek-r1-fp4_1k1k_ctx1_gen4_tep8_bs32_eplb0_mtp3_ccb-UCX] +test_disagg.py::TestDisaggBenchmark::test_benchmark[disagg_perf_deepseek-r1-fp4_128k8k_ctx1_pp8_gen1_tep8_bs1_eplb0_mtp3-Default] + +# ---- disagg: deepseek-r1-fp4 GB300 ------------------------- +test_disagg.py::TestDisaggBenchmark::test_benchmark[disagg_perf_deepseek-r1-fp4_1k1k_ctx1_gen4_tep8_bs32_eplb0_mtp0_ccb-NIXL] +test_disagg.py::TestDisaggBenchmark::test_benchmark[disagg_perf_deepseek-r1-fp4_1k1k_ctx1_gen4_tep8_bs32_eplb0_mtp0_ccb-UCX] +test_disagg.py::TestDisaggBenchmark::test_benchmark[disagg_perf_deepseek-r1-fp4_128k8k_ctx1_pp4_gen8_tep8_bs1_eplb0_mtp0-Default] + +# ---- disagg: Qwen3-235B-A22B-FP4 ---------- +test_disagg.py::TestDisaggBenchmark::test_benchmark[disagg_perf_Qwen3-235B-A22B-FP4_1k1k_ctx1_gen4_tep8_bs32_eplb0_mtp0_ccb-NIXL] + +# ---- wideep: deepseek-r1-fp4 -------------------------------- +test_disagg.py::TestDisaggBenchmark::test_benchmark[wideep_perf_deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL] +test_disagg.py::TestDisaggBenchmark::test_benchmark[wideep_perf_deepseek-r1-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL] + +# ---- wideep: deepseek-v32-fp4 -------------- +test_disagg.py::TestDisaggBenchmark::test_benchmark[wideep_perf_deepseek-v32-fp4_8k1k_ctx8_gen1_dep32_bs16_eplb288_mtp3_ccb-NIXL] + +# ---- wideep: kimi-k2-thinking-fp4 ---------- +test_disagg.py::TestDisaggBenchmark::test_benchmark[wideep_perf_kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL] + +# ---- accuracy ----------------------------------------------- +test_disagg.py::TestDisaggBenchmark::test_accuracy[wideep_accuracy_deepseek-r1-fp4_1k1k_ctx2_gen1_dep16_bs128_eplb288_mtp3_ccb-NIXL] +test_disagg.py::TestDisaggBenchmark::test_accuracy[wideep_accuracy_kimi-k2-thinking-fp4_1k1k_ctx3_gen1_dep32_bs1024_eplb384_mtp0_ccb-NIXL] diff --git a/tests/integration/defs/perf/disagg/utils/common.py b/tests/integration/defs/perf/disagg/utils/common.py index b4aecc116d6d..0e528befd3c8 100644 --- a/tests/integration/defs/perf/disagg/utils/common.py +++ b/tests/integration/defs/perf/disagg/utils/common.py @@ -2,42 +2,48 @@ import os -# GPU resource configuration -# Centralized configuration for all GPU-specific parameters +# GPU resource configuration - centralized config for all GPU-specific parameters GPU_RESOURCE_CONFIG = { - # OCI GB200 - "GB200": { - "slurm_extra_args": "--gres=gpu:4", # SLURM extra arguments (empty string if not required) + "GB200-OCI": { # OCI GB200 + "slurm_extra_args": "--gres=gpu:4", + "set_segment": True, + "lock_freq_graphics_mhz": 2062, + "lock_freq_memory_mhz": 3996, + }, + "GB200-LYRIS": { # Lyris GB200 + "slurm_extra_args": "", + "set_segment": True, + "lock_freq_graphics_mhz": None, + "lock_freq_memory_mhz": None, + }, + "GB300-LYRIS": { # Lyris GB300 + "slurm_extra_args": "", "set_segment": True, - "lock_freq_graphics_mhz": 2062, # GPU graphics clock lock frequency (MHz) - "lock_freq_memory_mhz": 3996, # GPU memory clock lock frequency (MHz) + "lock_freq_graphics_mhz": None, + "lock_freq_memory_mhz": None, }, - # OCI GB300 - "GB300": { - "slurm_extra_args": "", # GB300 does not require extra args + "GB300": { # GB300 on dlcluster + "slurm_extra_args": "", "set_segment": True, - "lock_freq_graphics_mhz": None, # TODO: Set GB300 lock frequency + "lock_freq_graphics_mhz": None, "lock_freq_memory_mhz": None, }, - # H100 "H100": { - "slurm_extra_args": "", # H100 does not require extra args + "slurm_extra_args": "", "set_segment": False, - "lock_freq_graphics_mhz": None, # TODO: Set H100 lock frequency + "lock_freq_graphics_mhz": None, "lock_freq_memory_mhz": None, }, - # B200 - "B200": { + "B200": { # B200 on computelab "slurm_extra_args": "--gres=gpu:4", "set_segment": False, - "lock_freq_graphics_mhz": None, # TODO: Set B200 lock frequency + "lock_freq_graphics_mhz": None, "lock_freq_memory_mhz": None, }, - # B300 - "B300": { + "B300": { # B300 on computelab "slurm_extra_args": "--gres=gpu:4", "set_segment": False, - "lock_freq_graphics_mhz": None, # TODO: Set B300 lock frequency + "lock_freq_graphics_mhz": None, "lock_freq_memory_mhz": None, }, } @@ -48,7 +54,7 @@ class EnvManager: @staticmethod def get_gpu_type() -> str: - return os.getenv("GPU_TYPE", "GB200") + return os.getenv("GPU_TYPE", "GB200-OCI") @staticmethod def get_slurm_partition() -> str: @@ -60,7 +66,18 @@ def get_slurm_account() -> str: @staticmethod def get_slurm_job_name() -> str: - return os.getenv("SLURM_JOB_NAME", "unified-benchmark") + """Get SLURM job name: {SLURM_ACCOUNT}-{base}. + + Example: myaccount-unified.benchmark + Customize base via SLURM_JOB_BASE_NAME env var (default: unified.benchmark) + """ + account = EnvManager.get_slurm_account() + base = os.getenv("SLURM_JOB_BASE_NAME", "unified.benchmark") + + # Only use account as prefix if it's set and not a placeholder + if account and not account.startswith("<"): + return f"{account}-{base}" + return base @staticmethod def get_slurm_set_segment() -> bool: diff --git a/tests/integration/defs/perf/disagg/utils/config_loader.py b/tests/integration/defs/perf/disagg/utils/config_loader.py index 567834d6a774..89a052eb471d 100644 --- a/tests/integration/defs/perf/disagg/utils/config_loader.py +++ b/tests/integration/defs/perf/disagg/utils/config_loader.py @@ -230,6 +230,9 @@ def scan_configs( if gpu_type is None: gpu_type = EnvManager.get_gpu_type() + # Normalize GPU type by extracting base name before any variant suffix + # e.g., GB200-LYRIS -> GB200, GB300-LYRIS -> GB300, GB200-OCI -> GB200 + gpu_type = gpu_type.split("-")[0] configs = [] if not self.base_dir.exists(): @@ -340,10 +343,18 @@ def _load_config_file(self, yaml_path: Path, test_type: str, test_category: str) metadata = config_data.get("metadata", {}) model_name = metadata.get("model_name", "unknown") supported_gpus = metadata.get("supported_gpus", ["GB200", "GB300", "H100", "B200", "B300"]) - # Override config with environment variables (in memory only, do not write back) config_data = self._apply_env_overrides(config_data, model_name) + # Apply concurrency override if DISAGG_CONCURRENCY_OVERRIDE is set + # This enables running a single test case with a specific concurrency level + # e.g., DISAGG_CONCURRENCY_OVERRIDE=2048 will override concurrency_list to "2048" + concurrency_override = os.getenv("DISAGG_CONCURRENCY_OVERRIDE", "") + if concurrency_override: + original = config_data.get("benchmark", {}).get("concurrency_list", "") + config_data["benchmark"]["concurrency_list"] = concurrency_override + logger.info(f"Concurrency overridden: '{original}' -> '{concurrency_override}'") + # Generate benchmark_type from sequence configuration benchmark_type = self._generate_benchmark_type(config_data) diff --git a/tests/integration/defs/perf/pytorch_model_config.py b/tests/integration/defs/perf/pytorch_model_config.py index baf05df2fac7..ccda28db2fd4 100644 --- a/tests/integration/defs/perf/pytorch_model_config.py +++ b/tests/integration/defs/perf/pytorch_model_config.py @@ -270,7 +270,7 @@ def get_model_yaml_config(model_label: str, 'max_batch_size': 720, }, 'moe_config': { - 'backend': 'CUTLASS' + 'backend': 'TRTLLM' }, 'stream_interval': 10, 'num_postprocess_workers': 4 diff --git a/tests/integration/defs/perf/test_perf.py b/tests/integration/defs/perf/test_perf.py index 44145728aeab..428d89d7ff5b 100644 --- a/tests/integration/defs/perf/test_perf.py +++ b/tests/integration/defs/perf/test_perf.py @@ -236,6 +236,7 @@ "llama_v3.3_nemotron_super_49b_fp8", "llama_v3.1_nemotron_ultra_253b", "llama_v3.1_nemotron_ultra_253b_fp8", + "kimi_k2_nvfp4", } diff --git a/tests/integration/defs/stress_test/stress_test.py b/tests/integration/defs/stress_test/stress_test.py index f81f0ab2bd25..901c81c252b4 100644 --- a/tests/integration/defs/stress_test/stress_test.py +++ b/tests/integration/defs/stress_test/stress_test.py @@ -32,6 +32,7 @@ import json import os import re +import socket import subprocess import tempfile import threading @@ -44,7 +45,7 @@ import pytest import requests import yaml -from defs.common import parse_gsm8k_output +from defs.common import get_free_port_in_ci, parse_gsm8k_output from defs.conftest import get_device_count, get_device_memory, llm_models_root from defs.trt_test_alternative import (Popen, cleanup_process_tree, print_info, print_warning) @@ -72,10 +73,18 @@ GRACEFUL_TERMINATION_TIMEOUT = 300 # seconds - set longer when stress large model +def _get_default_port() -> int: + """Get a default port using CI allocation if available, otherwise use 8000.""" + try: + return get_free_port_in_ci() + except Exception: + return 8000 + + @dataclass(frozen=True) class ServerConfig: """Dataclass to store server configuration for trtllm-serve""" - port: int = 8000 + port: int = field(default_factory=_get_default_port) host: str = "localhost" pp_size: int = 1 ep_size: Optional[int] = 1 @@ -167,8 +176,7 @@ class PerformanceParams: # Ensure indefinite runs specially for different concurrency values test_timeout: int = 3600 # 1 hours for tinyllama and llama-v3-8b-instruct-hf concurrency_list: List[int] = field( - default_factory=lambda: - [8, 16, 32, 64, 128, 256, 384, 512, 640, 768, 896, 1024]) + default_factory=lambda: [8, 16, 32, 64, 128, 256]) @property def request_count_list(self) -> List[int]: @@ -341,6 +349,26 @@ def check_server_health(server_url: str, return False, f"Unexpected error during health check: {str(e)}" +def is_port_available(port: int, + host: str = "localhost") -> Tuple[bool, Optional[str]]: + """ + Check if a port is available for binding. + + Args: + port: Port number to check + host: Host to bind to + + Returns: + Tuple of (is_available, error_message) + """ + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + try: + s.bind((host, port)) + return True, None + except OSError as e: + return False, f"Port {port} is already in use on {host}: {e}" + + @pytest.mark.parametrize( "test_mode", ["stress-test", "stress-stage-alone", "stress-test-with-accuracy"], @@ -350,25 +378,45 @@ def check_server_health(server_url: str, ["GUARANTEED_NO_EVICT", "MAX_UTILIZATION"], ids=lambda x: x) @pytest.mark.parametrize("stress_time_timeout", [(180, 300), (300, 450), - (600, 900), (3600, 5400)], + (600, 900), (3600, 10800)], ids=lambda x: f"stress_time_{x[0]}s_timeout_{x[1]}s") @pytest.mark.parametrize( "config", [ # Configuration for TinyLlama model + # memory_requirement is in MiB (12 GB = 12288 MiB) ModelConfig(model_dir="llama-models-v2/TinyLlama-1.1B-Chat-v1.0", tp_size=1, - memory_requirement=12), + memory_requirement=12288), # Configuration for Llama-v3 model + # memory_requirement is in MiB (12 GB = 12288 MiB) ModelConfig(model_dir="llama-models-v3/llama-v3-8b-instruct-hf", tp_size=1, - memory_requirement=12), + memory_requirement=12288), # Configuration for DeepSeek-V3 model - ModelConfig(model_dir="DeepSeek-V3", tp_size=8, memory_requirement=96), - # Configuration for DeepSeek-R1 model + # memory_requirement is in MiB (96 GB = 98304 MiB) + ModelConfig( + model_dir="DeepSeek-V3", tp_size=8, memory_requirement=98304), + # Configuration for DeepSeek-R1 model with FP8 checkpoints (8 GPU setup) + # memory_requirement is in MiB (96 GB = 98304 MiB) ModelConfig(model_dir="DeepSeek-R1/DeepSeek-R1", tp_size=8, - memory_requirement=96), + memory_requirement=98304), + # Configuration for DeepSeek-R1 model with FP8 checkpoints (4 GPU setup, requires GB300 288GB) + # memory_requirement is in MiB (256 GB = 262144 MiB) + ModelConfig(model_dir="DeepSeek-R1/DeepSeek-R1", + tp_size=4, + memory_requirement=262144), + # Configuration for DeepSeek-R1 model with NVFP4 checkpoints (8 GPU setup) + # memory_requirement is in MiB (96 GB = 98304 MiB) + ModelConfig(model_dir="DeepSeek-R1/DeepSeek-R1-0528-FP4", + tp_size=8, + memory_requirement=98304), + # Configuration for DeepSeek-R1 model with NVFP4 checkpoints (4 GPU setup) + # memory_requirement is in MiB (168 GB = 172032 MiB) + ModelConfig(model_dir="DeepSeek-R1/DeepSeek-R1-0528-FP4", + tp_size=4, + memory_requirement=172032), ], ids=lambda x: f"{os.path.basename(x.model_dir)}_tp{x.tp_size}") def test_run_stress_test(config, stress_time_timeout, backend, @@ -450,9 +498,12 @@ def stress_test(config, return # Skip if not enough GPU memory + # get_device_memory() returns per-GPU memory in MiB if get_device_memory() < config.memory_requirement: pytest.skip( - f"Not enough GPU memory. Required: {config.memory_requirement}GB") + f"Not enough GPU memory. Required: {config.memory_requirement} MiB ({config.memory_requirement // 1024} GB), " + f"Available: {get_device_memory()} MiB ({get_device_memory() // 1024} GB)" + ) # Skip if not enough GPUs for tensor parallelism if get_device_count() < config.tp_size: @@ -478,19 +529,20 @@ def stress_test(config, 36000 # 10 hours for DeepSeek-V3 or DeepSeek-R1, change this value if needed ) - # For DeepSeek-V3 specific server parameters + # For DeepSeek-V3 or DeepSeek-R1 specific server parameters if "DeepSeek-V3" in config.model_dir or "DeepSeek-R1" in config.model_dir: test_server_config = ServerConfig( port=test_server_config.port, host=test_server_config.host, pp_size=test_server_config.pp_size, - ep_size=8, # DeepSeek-V3 or DeepSeek-R1 specific ep_size + ep_size=config. + tp_size, # ep_size matches tp_size for DeepSeek models max_batch_size= 2048, # DeepSeek-V3 or DeepSeek-R1 specific max_batch_size max_num_tokens= - 2048, # DeepSeek-V3 or DeepSeek-R1 specific max_num_tokens + 8192, # DeepSeek-V3 or DeepSeek-R1 specific max_num_tokens kv_cache_free_gpu_memory_fraction= - 0.7, # DeepSeek-V3 or DeepSeek-R1 specific kv_cache fraction + 0.85, # DeepSeek-V3 or DeepSeek-R1 specific kv_cache fraction capacity_scheduler_policy=test_server_config. capacity_scheduler_policy, wait_interval=test_server_config.wait_interval, @@ -519,6 +571,10 @@ def stress_test(config, else: stress_config = None + # Check if port is available + is_available, port_error = is_port_available(test_server_config.port, + test_server_config.host) + # Check if server is already running is_healthy, _ = check_server_health(test_server_config.url, test_server_config.health_check_timeout) @@ -530,6 +586,9 @@ def stress_test(config, # Start server print_info("Starting trtllm-serve server...") print_info(f"Model path: {model_path}") + print_info( + f"Server port: {test_server_config.port} (allocated via CI port mechanism)" + ) # Verify that model path exists if not os.path.exists(model_path): @@ -548,11 +607,40 @@ def stress_test(config, extra_llm_options["enable_attention_dp"] = True + # Set MOE backend based on GPU architecture and checkpoint type + # B200/GB200 (Blackwell, SM100+) with FP8 checkpoints: use DEEPGEMM backend + # B200/GB200 (Blackwell, SM100+) with NVFP4 checkpoints: use CUTEDSL backend + # H100/H200 (Hopper, SM90) with FP8 checkpoints: use CUTLASS backend (default) + try: + import torch + if torch.cuda.is_available(): + device_capability = torch.cuda.get_device_capability(0) + is_blackwell = device_capability[0] >= 10 + is_nvfp4 = "FP4" in config.model_dir.upper() + + if is_blackwell: + if is_nvfp4: + moe_backend = "CUTEDSL" + else: + moe_backend = "DEEPGEMM" + + extra_llm_options["moe_config"] = { + "backend": moe_backend, + } + checkpoint_type = "NVFP4" if is_nvfp4 else "FP8" + print_info( + f"Detected GPU architecture is SM{device_capability[0]}{device_capability[1]} (Blackwell), " + f"using {moe_backend} MOE backend for DeepSeek-R1/DeepSeek-V3 with {checkpoint_type} checkpoints" + ) + except Exception as e: + print_warning(f"Failed to detect GPU architecture: {e}. " + "Using default MOE backend (CUTLASS).") + if config.backend == "pytorch": extra_llm_options.update({ "cuda_graph_config": { "enable_padding": True, - "batch_sizes": [1, 2, 4, 8, 16, 32, 64, 128, 256, 384], + "batch_sizes": [1, 2, 4, 8, 16, 32, 64, 128], }, "print_iter_log": True, }) @@ -759,6 +847,7 @@ def create_aiperf_command(model_name, model_path, request_count, concurrency, + server_url, input_len_mean=PerformanceParams.input_len_mean, input_len_std=PerformanceParams.input_len_std, output_len_mean=PerformanceParams.output_len_mean, @@ -772,6 +861,7 @@ def create_aiperf_command(model_name, model_path: Path to the model request_count: Number of requests to send concurrency: Number of concurrent requests + server_url: URL of the server (e.g., "localhost:8000") input_len_mean: Mean input length input_len_std: Standard deviation of input length output_len_mean: Mean output length @@ -790,6 +880,8 @@ def create_aiperf_command(model_name, model_path, "--endpoint-type", "completions", + "-u", + server_url, "--random-seed", "123", "--synthetic-input-tokens-mean", @@ -928,6 +1020,7 @@ def measure_capacity_stage(model_name, model_path=model_path, request_count=request_count, concurrency=concurrency, + server_url=f"{server_config.host}:{server_config.port}", input_len_mean=performance_params.input_len_mean, input_len_std=performance_params.input_len_std, output_len_mean=performance_params.output_len_mean, @@ -1007,6 +1100,18 @@ def stress_stage(model_name, request_count = int(stress_request_rate * stress_time) test_timeout = stress_config.stress_timeout + # Cap request count for large MoE models (DeepSeek-V3/R1) to prevent timeout + if "DeepSeek-V3" in model_path or "DeepSeek-R1" in model_path: + max_sustainable_rate = 3.0 # req/s - conservative estimate + max_request_count = int(max_sustainable_rate * + stress_config.stress_time) + if request_count > max_request_count: + print_info( + f"Capping request_count from {request_count} to {max_request_count} " + f"for DeepSeek V3/R1 model (sustainable rate: {max_sustainable_rate} req/s)" + ) + request_count = max_request_count + print_info( f"Running stress test with concurrency={stress_concurrency}, request_count={request_count}" ) @@ -1023,6 +1128,7 @@ def stress_stage(model_name, model_path=model_path, request_count=request_count, concurrency=stress_concurrency, + server_url=f"{server_config.host}:{server_config.port}", input_len_mean=PerformanceParams.input_len_mean, input_len_std=PerformanceParams.input_len_std, output_len_mean=PerformanceParams.output_len_mean, diff --git a/tests/integration/defs/test_e2e.py b/tests/integration/defs/test_e2e.py index 4a27fc997d07..3b71152a27a2 100644 --- a/tests/integration/defs/test_e2e.py +++ b/tests/integration/defs/test_e2e.py @@ -516,6 +516,7 @@ def prepare_dataset(self): str(self.num_requests), ] print(f"Running command: {' '.join(command)}") + subprocess.run(command, check=True, env=self.llm_venv._new_env) def build_engine(self): if self.skip_engine_build: @@ -629,11 +630,8 @@ def test_trtllm_bench_llmapi_launch(llm_root, llm_venv, model_name, ids=["llama3_1-8b"]) @pytest.mark.parametrize("model_subdir", ["llama-3.1-model/Meta-Llama-3.1-8B"], ids=["llama_v3_1"]) -@pytest.mark.parametrize("use_pytorch_backend", [False], ids=["trt_backend"]) -def test_trtllm_bench_mig_launch(llm_root, llm_venv, model_name, model_subdir, - use_pytorch_backend): +def test_trtllm_bench_mig_launch(llm_root, llm_venv, model_name, model_subdir): "run bench mark in MIG mode, check if the throughput is increasing by concurrency" - skip_engine_build = False results = {} concurrency_list = [1, 32, 64, 128] @@ -644,19 +642,17 @@ def test_trtllm_bench_mig_launch(llm_root, llm_venv, model_name, model_subdir, model_name=model_name, model_subdir=model_subdir, streaming=False, - use_pytorch_backend=use_pytorch_backend, + use_pytorch_backend=True, use_mpirun=False, tp_size=1, concurrency=concurrency, - num_requests=num_requests, - skip_engine_build=skip_engine_build) + num_requests=num_requests) output = runner() results[concurrency] = output print(f"\n=== Benchmark Results Comparison ===") print(f"Model: {model_name}") - print(f"Backend: {'PyTorch' if use_pytorch_backend else 'TensorRT'}") print( f"{'Concurrency':<15} {'Throughput':<15} {'Latency':<15} {'Num Requests':<15}" ) diff --git a/tests/integration/test_lists/qa/llm_function_core.txt b/tests/integration/test_lists/qa/llm_function_core.txt index 1928574cf389..0df47e9e503a 100644 --- a/tests/integration/test_lists/qa/llm_function_core.txt +++ b/tests/integration/test_lists/qa/llm_function_core.txt @@ -144,7 +144,7 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_cutl accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_trtllm-torch_compile=False] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_trtllm-torch_compile=True] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-CUTLASS] -accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-TRITON] +accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a16_mxfp4[latency-TRITON] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-TRTLLM] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-latency-TRTLLM] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-latency-CUTLASS] @@ -279,19 +279,11 @@ accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[xgrammar-mtp_nextn=2] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[llguidance-mtp_nextn=0] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[llguidance-mtp_nextn=2] -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:none-pp1tp2cp2] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:with_padding-pp1tp2cp2] -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:none-pp1tp2cp2] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:with_padding-pp1tp2cp2] -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:none-pp1tp1cp4] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:with_padding-pp1tp1cp4] -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:none-pp1tp1cp4] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:with_padding-pp1tp1cp4] -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:none-pp2tp1cp2] -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:without_padding-pp2tp1cp2] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:with_padding-pp2tp1cp2] -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:none-pp2tp1cp2] -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:without_padding-pp2tp1cp2] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:with_padding-pp2tp1cp2] accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_nixl_backend accuracy/test_disaggregated_serving.py::TestGemma3_1BInstruct::test_auto_dtype[False] diff --git a/tests/integration/test_lists/qa/llm_function_core_sanity.txt b/tests/integration/test_lists/qa/llm_function_core_sanity.txt index 8fae1d62933f..f8160c3cae55 100644 --- a/tests/integration/test_lists/qa/llm_function_core_sanity.txt +++ b/tests/integration/test_lists/qa/llm_function_core_sanity.txt @@ -165,7 +165,7 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_trtl accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_trtllm-torch_compile=True] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a16_mxfp4[latency-TRTLLM] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-CUTLASS] -accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-TRITON] +accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a16_mxfp4[latency-TRITON] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-TRTLLM] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-latency-CUTLASS] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-latency-TRTLLM] diff --git a/tests/integration/test_lists/qa/llm_function_nim.txt b/tests/integration/test_lists/qa/llm_function_nim.txt index 0b26975d263c..67e026ffdaf7 100644 --- a/tests/integration/test_lists/qa/llm_function_nim.txt +++ b/tests/integration/test_lists/qa/llm_function_nim.txt @@ -141,7 +141,7 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_trtl accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_w4a8_mxfp4[fp8-latency] accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_w4a8_mxfp4[mxfp8-latency] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-CUTLASS] -accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-TRITON] +accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a16_mxfp4[latency-TRITON] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-TRTLLM] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-latency-TRTLLM] accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-latency-CUTLASS] diff --git a/tests/integration/test_lists/qa/llm_function_stress.txt b/tests/integration/test_lists/qa/llm_function_stress.txt index a4f744b469d6..05fb1a19d163 100644 --- a/tests/integration/test_lists/qa/llm_function_stress.txt +++ b/tests/integration/test_lists/qa/llm_function_stress.txt @@ -1,6 +1,10 @@ -stress_test/stress_test.py::test_run_stress_test[DeepSeek-V3_tp8-stress_time_3600s_timeout_5400s-GUARANTEED_NO_EVICT-pytorch-stress-test-with-accuracy] -stress_test/stress_test.py::test_run_stress_test[DeepSeek-V3_tp8-stress_time_3600s_timeout_5400s-MAX_UTILIZATION-pytorch-stress-test-with-accuracy] -stress_test/stress_test.py::test_run_stress_test[DeepSeek-R1_tp8-stress_time_3600s_timeout_5400s-MAX_UTILIZATION-pytorch-stress-test-with-accuracy] +stress_test/stress_test.py::test_run_stress_test[DeepSeek-V3_tp8-stress_time_3600s_timeout_10800s-GUARANTEED_NO_EVICT-pytorch-stress-test-with-accuracy] +stress_test/stress_test.py::test_run_stress_test[DeepSeek-V3_tp8-stress_time_3600s_timeout_10800s-MAX_UTILIZATION-pytorch-stress-test-with-accuracy] +stress_test/stress_test.py::test_run_stress_test[DeepSeek-R1_tp8-stress_time_3600s_timeout_10800s-MAX_UTILIZATION-pytorch-stress-test-with-accuracy] +stress_test/stress_test.py::test_run_stress_test[DeepSeek-R1_tp4-stress_time_3600s_timeout_10800s-GUARANTEED_NO_EVICT-pytorch-stress-test-with-accuracy] +stress_test/stress_test.py::test_run_stress_test[DeepSeek-R1_tp4-stress_time_3600s_timeout_10800s-MAX_UTILIZATION-pytorch-stress-test-with-accuracy] +stress_test/stress_test.py::test_run_stress_test[DeepSeek-R1-0528-FP4_tp4-stress_time_3600s_timeout_10800s-GUARANTEED_NO_EVICT-pytorch-stress-test-with-accuracy] +stress_test/stress_test.py::test_run_stress_test[DeepSeek-R1-0528-FP4_tp4-stress_time_3600s_timeout_10800s-MAX_UTILIZATION-pytorch-stress-test-with-accuracy] disaggregated/test_disaggregated.py::test_disaggregated_stress_test[input8k-output1k-conc512-deepseek_r1_v2_fp4_stress] disaggregated/test_disaggregated.py::test_disaggregated_stress_test[input8k-output1k-conc512-gpt_oss_120b_stress] accuracy/test_llm_api_pytorch.py::TestDeepSeekR1LongBenchV2::test_fp8_8gpus diff --git a/tests/integration/test_lists/qa/llm_perf_core.yml b/tests/integration/test_lists/qa/llm_perf_core.yml index 76d67941a6bd..a89c181f702b 100644 --- a/tests/integration/test_lists/qa/llm_perf_core.yml +++ b/tests/integration/test_lists/qa/llm_perf_core.yml @@ -251,11 +251,11 @@ llm_perf_core: #Mistral-Small-3.1-24B-Instruct-2503 - perf/test_perf.py::test_perf[mistral_small_v3.1_24b-bench-pytorch-bfloat16-maxbs:4096-maxnt:20000-input_output_len:20000,2000-reqs:300-con:200-gpus:2] TIMEOUT(120) - perf/test_perf.py::test_perf[starcoder_15b-bench-float16-input_output_len:512,200-gpus:4] - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:512-input_output_len:128,128-ep:4-tp:4-gpus:4] - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-streaming-float4-maxbs:512-input_output_len:128,128-ep:4-tp:4-gpus:4] - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:1000,1000-reqs:2000-ep:4-tp:4-gpus:4] TIMEOUT(120) - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:1000,2000-reqs:3000-ep:4-tp:4-gpus:4] TIMEOUT(120) - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:1000-maxnt:5000-kv_frac:0.85-input_output_len:5000,500-reqs:20000-ep:4-tp:4-gpus:4] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:512-input_output_len:128,128-ep:4-tp:4-gpus:4] + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-streaming-float4-maxbs:512-input_output_len:128,128-ep:4-tp:4-gpus:4] + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:1000,1000-reqs:2000-ep:4-tp:4-gpus:4] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:1000,2000-reqs:3000-ep:4-tp:4-gpus:4] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:1000-maxnt:5000-kv_frac:0.85-input_output_len:5000,500-reqs:20000-ep:4-tp:4-gpus:4] TIMEOUT(120) #llama_v3.1_405b_instruct_fp4 - perf/test_perf.py::test_perf[llama_v3.1_405b_instruct_fp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:128,128-gpus:4] - perf/test_perf.py::test_perf[llama_v3.1_405b_instruct_fp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:1000,1000-gpus:4] @@ -285,10 +285,10 @@ llm_perf_core: tests: # for chunked prefill cases - perf/test_perf.py::test_perf[deepseek_v3_lite_nvfp4-bench-pytorch-float4-maxbs:512-maxnt:2048-kv_frac:0.85-input_output_len:3000,500-reqs:200] - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:512-maxnt:2048-kv_frac:0.85-input_output_len:5000,500-reqs:200-ep:4-tp:4-gpus:4] TIMEOUT(120) - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:256-maxnt:1024-kv_frac:0.85-input_output_len:2000,2000-reqs:200-ep:4-tp:4-gpus:4] TIMEOUT(120) - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:1000-maxnt:5000-kv_frac:0.85-input_output_len:5000,500-reqs:2000-ep:4-tp:4-gpus:4] TIMEOUT(120) - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:32-maxnt:32768-input_output_len:8192,1024-reqs:20-con:1-ep:1-tp:4-gpus:4] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:512-maxnt:2048-kv_frac:0.85-input_output_len:5000,500-reqs:200-ep:4-tp:4-gpus:4] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:256-maxnt:1024-kv_frac:0.85-input_output_len:2000,2000-reqs:200-ep:4-tp:4-gpus:4] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:1000-maxnt:5000-kv_frac:0.85-input_output_len:5000,500-reqs:2000-ep:4-tp:4-gpus:4] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:32-maxnt:32768-input_output_len:8192,1024-reqs:20-con:1-ep:1-tp:4-gpus:4] TIMEOUT(120) # 12: B200, B300 test cases - condition: @@ -304,6 +304,31 @@ llm_perf_core: - perf/test_perf.py::test_perf[kimi_k2_nvfp4-bench-pytorch-float4-maxbs:512-maxnt:2048-kv_frac:0.6-input_output_len:1000,1000-ep:8-tp:8-gpus:8] - perf/test_perf.py::test_perf[kimi_k2_nvfp4-bench-pytorch-float4-maxbs:512-maxnt:2048-kv_frac:0.6-input_output_len:5000,500-reqs:2000-ep:8-tp:8-gpus:8] TIMEOUT(120) + # llama_v3.1_405b_instruct_fp4 + - perf/test_perf.py::test_perf[llama_v3.1_405b_instruct_fp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:128,128-tp:8-gpus:8] + - perf/test_perf.py::test_perf[llama_v3.1_405b_instruct_fp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:512,32-tp:8-gpus:8] + - perf/test_perf.py::test_perf[llama_v3.1_405b_instruct_fp4-bench-pytorch-float4-maxbs:1024-maxnt:4096-kv_frac:0.85-input_output_len:500,2000-tp:8-gpus:8] + - perf/test_perf.py::test_perf[llama_v3.1_405b_instruct_fp4-bench-pytorch-float4-maxbs:1024-maxnt:4096-kv_frac:0.85-input_output_len:1000,1000-tp:8-gpus:8] + - perf/test_perf.py::test_perf[llama_v3.1_405b_instruct_fp4-bench-pytorch-float4-maxbs:4096-maxnt:20000-kv_frac:0.85-input_output_len:20000,2000-tp:8-gpus:8] TIMEOUT(120) + + # deepseek_r1_fp8 + - perf/test_perf.py::test_perf[deepseek_r1_fp8-bench-pytorch-float8-maxbs:512-input_output_len:128,128-ep:8-tp:8-gpus:8] + - perf/test_perf.py::test_perf[deepseek_r1_fp8-bench-pytorch-float8-maxbs:1-input_output_len:1000,2000-reqs:10-ep:4-tp:8-gpus:8] + - perf/test_perf.py::test_perf[deepseek_r1_fp8-bench-pytorch-float8-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:49152-con:3072-ep:8-tp:8-gpus:8] + + # deepseek_r1_0528_fp4 + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:1000,1000-reqs:20000-ep:8-tp:8-gpus:8] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-kv_frac:0.85-input_output_len:1000,2000-reqs:3000-ep:8-tp:8-gpus:8] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:1000-maxnt:5000-kv_frac:0.85-input_output_len:5000,500-reqs:20000-ep:4-tp:4-gpus:4] TIMEOUT(120) + + # gpt_oss_120b_fp4 + - perf/test_perf.py::test_perf[gpt_oss_120b_fp4-bench-pytorch-float4-maxbs:720-maxnt:16384-input_output_len:1024,1024-reqs:1280-con:256-ep:8-tp:8-gpus:8] + - perf/test_perf.py::test_perf[gpt_oss_120b_fp4-bench-pytorch-float4-maxbs:720-maxnt:16384-input_output_len:1024,1024-reqs:2560-con:512-ep:8-tp:8-gpus:8] + - perf/test_perf.py::test_perf[gpt_oss_120b_fp4-bench-pytorch-float4-maxbs:720-maxnt:16384-input_output_len:1024,1024-reqs:5120-con:1024-ep:8-tp:8-gpus:8] TIMEOUT(120) + - perf/test_perf.py::test_perf[gpt_oss_120b_fp4-bench-pytorch-float4-maxbs:720-maxnt:16384-input_output_len:1024,1024-reqs:20480-con:4096-ep:8-tp:8-gpus:8] TIMEOUT(180) + - perf/test_perf.py::test_perf[gpt_oss_120b_fp4-bench-pytorch-float4-maxbs:720-maxnt:16384-input_output_len:1024,1024-reqs:8-con:1-ep:8-tp:8-gpus:8] + - perf/test_perf.py::test_perf[gpt_oss_120b_fp4-bench-pytorch-float4-maxbs:720-maxnt:16384-input_output_len:1024,1024-reqs:100-con:32-ep:8-tp:8-gpus:8] + # 13: H100, H20, H200, B200, B300 test cases - condition: ranges: @@ -443,8 +468,8 @@ llm_perf_core: - perf/test_perf.py::test_perf[llama_v4_maverick_17b_128e_instruct_fp8-bench-pytorch-streaming-float8-input_output_len:2000,500-ep:8-tp:8-gpus:8] - perf/test_perf.py::test_perf[qwen3_235b_a22b_fp4-bench-pytorch-float4-input_output_len:1000,2000-con:8-ep:8-tp:8-gpus:8] - perf/test_perf.py::test_perf[qwen3_235b_a22b_fp4-bench-pytorch-float4-input_output_len:1000,2000-con:512-ep:8-tp:8-gpus:8] - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:1-input_output_len:1000,2000-reqs:10-ep:4-tp:8-gpus:8] TIMEOUT(120) - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:10000-con:3072-ep:8-tp:8-gpus:8] TIMEOUT(120) #max throughput test - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-streaming-float4-maxbs:512-maxnt:5220-input_output_len:4000,2000-reqs:512-ep:8-tp:8-gpus:8] + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:1-input_output_len:1000,2000-reqs:10-ep:4-tp:8-gpus:8] TIMEOUT(120) + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-float4-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:10000-con:3072-ep:8-tp:8-gpus:8] TIMEOUT(120) #max throughput test + - perf/test_perf.py::test_perf[deepseek_r1_0528_fp4-bench-pytorch-streaming-float4-maxbs:512-maxnt:5220-input_output_len:4000,2000-reqs:512-ep:8-tp:8-gpus:8] - perf/test_perf.py::test_perf[deepseek_v3_lite_nvfp4-bench-pytorch-float4-maxbs:1-input_output_len:1000,2000-reqs:10-ep:4-tp:8-gpus:8] - perf/test_perf.py::test_perf[deepseek_v3_lite_nvfp4-bench-pytorch-float4-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:10000-con:3072-ep:8-tp:8-gpus:8] TIMEOUT(120) diff --git a/tests/integration/test_lists/test-db/l0_a10.yml b/tests/integration/test_lists/test-db/l0_a10.yml index 31722b3b4873..9b89844efaab 100644 --- a/tests/integration/test_lists/test-db/l0_a10.yml +++ b/tests/integration/test_lists/test-db/l0_a10.yml @@ -53,7 +53,6 @@ l0_a10: - disaggregated/test_disaggregated_single_gpu.py::test_disaggregated_simple_llama[False-True-TinyLlama-1.1B-Chat-v1.0] - disaggregated/test_disaggregated_single_gpu.py::test_disaggregated_simple_llama[True-False-TinyLlama-1.1B-Chat-v1.0] - disaggregated/test_disaggregated_single_gpu.py::test_disaggregated_simple_llama[True-True-TinyLlama-1.1B-Chat-v1.0] - - test_e2e.py::test_openai_chat_guided_decoding - test_e2e.py::test_get_ci_container_port - test_e2e.py::test_openai_chat_multimodal_example ISOLATION - test_e2e.py::test_openai_mmencoder_example @@ -239,8 +238,8 @@ l0_a10: - accuracy/test_cli_flow.py::TestVicuna7B::test_eagle[cuda_graph=True-chunked_context=False-typical_acceptance=True] # 5 mins - accuracy/test_llm_api.py::TestEagleVicuna_7B_v1_3::test_auto_dtype - accuracy/test_llm_api.py::TestEagle2Vicuna_7B_v1_3::test_auto_dtype - - stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-MAX_UTILIZATION-trt-stress-test] - stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-GUARANTEED_NO_EVICT-trt-stress-test] + - stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-MAX_UTILIZATION-pytorch-stress-test] - test_e2e.py::test_gpt3_175b_1layers_build_only # 6 mins - examples/test_chatglm.py::test_llm_glm_4_9b_single_gpu_summary[glm-4-9b-disable_weight_only] - unittest/trt/model/test_mamba.py # 3 mins @@ -260,8 +259,8 @@ l0_a10: stage: post_merge backend: pytorch tests: - - stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-MAX_UTILIZATION-pytorch-stress-test] - stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-GUARANTEED_NO_EVICT-pytorch-stress-test] + - stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-MAX_UTILIZATION-pytorch-stress-test] - condition: ranges: system_gpu_count: diff --git a/tests/integration/test_lists/test-db/l0_b200.yml b/tests/integration/test_lists/test-db/l0_b200.yml index 4e82657d74ec..e89dc8048715 100644 --- a/tests/integration/test_lists/test-db/l0_b200.yml +++ b/tests/integration/test_lists/test-db/l0_b200.yml @@ -50,7 +50,7 @@ l0_b200: - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_trtllm-torch_compile=False] - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_trtllm-torch_compile=True] - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-CUTLASS] - - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-TRITON] + - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a16_mxfp4[latency-TRITON] - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[fp8-latency-TRTLLM] - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-latency-TRTLLM] - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-latency-CUTLASS] diff --git a/tests/integration/test_lists/test-db/l0_h100.yml b/tests/integration/test_lists/test-db/l0_h100.yml index 0e9fc8b8d66f..498cb0edf611 100644 --- a/tests/integration/test_lists/test-db/l0_h100.yml +++ b/tests/integration/test_lists/test-db/l0_h100.yml @@ -27,6 +27,7 @@ l0_h100: - unittest/_torch/speculative -k "not eagle3" - unittest/_torch/thop/parallel - unittest/_torch/thop/serial + - unittest/_torch/flashinfer/test_trtllm_flashinfer_symbol_collision.py # Only key models in H100: llama/mixtral/nemotron/deepseek - unittest/_torch/modeling -k "modeling_llama" - unittest/_torch/modeling -k "modeling_mixtral" @@ -110,6 +111,7 @@ l0_h100: - test_e2e.py::test_trtllm_bench_help_sanity[meta-llama/Llama-3.1-8B] - test_e2e.py::test_openai_chat_harmony - test_e2e.py::test_openai_responses + - test_e2e.py::test_openai_chat_guided_decoding - test_e2e.py::test_trtllm_benchmark_serving[llama-3.1-model/Meta-Llama-3.1-8B] - condition: ranges: diff --git a/tests/integration/test_lists/test-db/l0_sanity_check.yml b/tests/integration/test_lists/test-db/l0_sanity_check.yml index f88ac7733750..21aafd1e97fe 100644 --- a/tests/integration/test_lists/test-db/l0_sanity_check.yml +++ b/tests/integration/test_lists/test-db/l0_sanity_check.yml @@ -1,38 +1,58 @@ version: 0.0.1 l0_sanity_check: - - condition: - ranges: - system_gpu_count: - gte: 1 - lte: 1 - wildcards: - gpu: - - '*b100*' - - '*h100*' - - '*h200*' - - '*l40s*' - - '*a10*' - - '*gb202*' - - '*gb203*' - - '*5080*' - - '*5090*' - linux_distribution_name: ubuntu* - tests: - - llmapi/test_llm_examples.py::test_llmapi_quickstart - - llmapi/test_llm_examples.py::test_llmapi_example_inference - - llmapi/test_llm_examples.py::test_llmapi_example_inference_async - - llmapi/test_llm_examples.py::test_llmapi_example_inference_async_streaming - - llmapi/test_llm_examples.py::test_llmapi_example_multilora - - llmapi/test_llm_examples.py::test_llmapi_example_guided_decoding - - llmapi/test_llm_examples.py::test_llmapi_example_logits_processor - - llmapi/test_llm_examples.py::test_llmapi_speculative_decoding_mtp TIMEOUT (90) - - llmapi/test_llm_examples.py::test_llmapi_speculative_decoding_eagle3 - - llmapi/test_llm_examples.py::test_llmapi_speculative_decoding_ngram - - llmapi/test_llm_examples.py::test_llmapi_sampling - - llmapi/test_llm_examples.py::test_llmapi_runtime - - llmapi/test_llm_examples.py::test_llmapi_tensorrt_engine - - examples/test_llm_api_with_mpi.py::test_llm_api_single_gpu_with_mpirun[TinyLlama-1.1B-Chat-v1.0] ISOLATION - - unittest/others/test_kv_cache_transceiver.py::test_kv_cache_transceiver_single_process[NIXL-mha-ctx_fp16_gen_fp16] - - unittest/others/test_kv_cache_transceiver.py::test_kv_cache_transceiver_single_process[UCX-mha-ctx_fp16_gen_fp16] - - unittest/others/test_kv_cache_transceiver.py::test_cancel_request_in_transmission[mha] - - unittest/others/test_kv_cache_transceiver.py::test_cancel_request_in_transmission[mla] +- condition: + ranges: + system_gpu_count: + gte: 1 + lte: 1 + wildcards: + gpu: + - '*b100*' + - '*h100*' + - '*h200*' + - '*l40s*' + - '*a10*' + - '*gb202*' + - '*gb203*' + - '*5080*' + - '*5090*' + linux_distribution_name: ubuntu* + tests: + - llmapi/test_llm_examples.py::test_llmapi_quickstart + - llmapi/test_llm_examples.py::test_llmapi_example_inference + - llmapi/test_llm_examples.py::test_llmapi_example_inference_async + - llmapi/test_llm_examples.py::test_llmapi_example_inference_async_streaming + - llmapi/test_llm_examples.py::test_llmapi_example_multilora + - llmapi/test_llm_examples.py::test_llmapi_example_guided_decoding + - llmapi/test_llm_examples.py::test_llmapi_example_logits_processor + - llmapi/test_llm_examples.py::test_llmapi_sampling + - llmapi/test_llm_examples.py::test_llmapi_runtime + - llmapi/test_llm_examples.py::test_llmapi_tensorrt_engine + - examples/test_llm_api_with_mpi.py::test_llm_api_single_gpu_with_mpirun[TinyLlama-1.1B-Chat-v1.0] ISOLATION + - unittest/others/test_kv_cache_transceiver.py::test_kv_cache_transceiver_single_process[NIXL-mha-ctx_fp16_gen_fp16] + - unittest/others/test_kv_cache_transceiver.py::test_kv_cache_transceiver_single_process[UCX-mha-ctx_fp16_gen_fp16] + - unittest/others/test_kv_cache_transceiver.py::test_cancel_request_in_transmission[mha] + - unittest/others/test_kv_cache_transceiver.py::test_cancel_request_in_transmission[mla] +- condition: + ranges: + system_gpu_count: + gte: 1 + lte: 1 + wildcards: + gpu: + - '*b100*' + - '*h100*' + - '*h200*' + - '*l40s*' + - '*a10*' + - '*gb202*' + - '*gb203*' + - '*5080*' + - '*5090*' + linux_distribution_name: ubuntu* + cpu: x86_64 + tests: + # Don't run long-running and medium-/large-size model sanity tests on GH200 + - llmapi/test_llm_examples.py::test_llmapi_speculative_decoding_mtp TIMEOUT (90) + - llmapi/test_llm_examples.py::test_llmapi_speculative_decoding_eagle3 + - llmapi/test_llm_examples.py::test_llmapi_speculative_decoding_ngram diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt index e495a02cffba..7a318ad331c3 100644 --- a/tests/integration/test_lists/waives.txt +++ b/tests/integration/test_lists/waives.txt @@ -227,10 +227,7 @@ examples/test_llama.py::test_llm_llama_1gpu_fp4[llama-3.1-70b-instruct-enable_no accuracy/test_llm_api_pytorch.py::TestNemotronNas::test_auto_dtype_tp8 SKIP (https://nvbugs/5673527) unittest/_torch/auto_deploy/unit/multigpu/test_ad_build_small_multi.py::test_build_ad[meta-llama/Meta-Llama-3.1-8B-Instruct-llm_extra_args0-2] SKIP (https://nvbugs/5680755) full:H100_PCIe/unittest/llmapi/test_llm_pytorch.py::test_llama_7b_multi_lora_evict_and_reload_lora_gpu_cache SKIP (https://nvbugs/5682551) -unittest/_torch/speculative/test_draft_len_schedule.py::test_correctness_across_batch_sizes[model_drafter-schedule1] SKIP (https://nvbugs/5680911) accuracy/test_llm_api_pytorch.py::TestSeedOss_36B::test_auto_dtype SKIP (https://nvbugs/5612438) -accuracy/test_llm_api_autodeploy.py::TestNemotronH::test_auto_dtype[True] SKIP (https://nvbugs/5688721) -accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B::test_auto_dtype[False-4] SKIP (https://nvbugs/5769712) test_e2e.py::test_openai_completions_example[trt] SKIP (https://nvbugs/5701450) accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[tp4-fp8kv=False-attn_backend=TRTLLM-torch_compile=False] SKIP (https://nvbugs/5701457) triton_server/test_triton_llm.py::test_llmapi_backend[4-0-disableDecoupleMode-tensorrt_llm] SKIP (https://nvbugs/5701480) @@ -247,32 +244,22 @@ accuracy/test_llm_api_pytorch.py::TestLlama3_3NemotronSuper49Bv1::test_fp8_prequ accuracy/test_llm_api_pytorch.py::TestNemotronUltra::test_fp8_prequantized[tp8ep4-cuda_graph=True] SKIP (https://nvbugs/5707145) accuracy/test_llm_api_pytorch.py::TestNemotronUltra::test_fp8_prequantized[tp8-cuda_graph=True] SKIP (https://nvbugs/5707145) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_chunked_prefill[cutlass-auto] SKIP (https://nvbugs/5596343) -unittest/_torch/speculative/test_spec_gate.py::test_spec_gate_e2e SKIP (https://nvbugs/5710045) -accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=0-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] SKIP (https://nvbugs/5721661) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4[moe_backend=CUTLASS-mtp_nextn=0-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] SKIP (https://nvbugs/5722629) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_2gpus[cutlass-two_model-overlap_scheduler] SKIP (https://nvbugs/5702826) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_4gpus[cutlass-two_model-overlap_scheduler] SKIP (https://nvbugs/5702826) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[llguidance-mtp_nextn=2] SKIP (https://nvbugs/5740075) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[xgrammar-mtp_nextn=2] SKIP (https://nvbugs/5740075) -unittest/_torch/modeling/test_modeling_out_of_tree.py::TestOutOfTree::test_llm_api[False] SKIP (https://nvbugs/5739981) -unittest/_torch/modeling/test_modeling_out_of_tree.py::TestOutOfTree::test_llm_api[True] SKIP (https://nvbugs/5739981) -unittest/_torch/modeling/test_modeling_out_of_tree.py::TestOutOfTree::test_serve[True] SKIP (https://nvbugs/5739981) +full:RTXPro6000D/accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[xgrammar-mtp_nextn=0] SKIP (https://nvbugs/5748600) +full:RTXPro6000D/accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[xgrammar-mtp_nextn=2] SKIP (https://nvbugs/5748600) +full:RTXPro6000D/accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[llguidance-mtp_nextn=0] SKIP (https://nvbugs/5748600) +full:RTXPro6000D/accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[llguidance-mtp_nextn=2] SKIP (https://nvbugs/5748600) full:sm89/accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ctx_pp_gen_tp_asymmetric[MMLU-gen_tp=2-ctx_pp=2] SKIP (https://nvbugs/5596337) full:sm89/accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_tp_pp_symmetric[MMLU-tp2pp2] SKIP (https://nvbugs/5596337) -accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4[latency_moe_trtllm] SKIP (https://nvbugs/5721672) -accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[tp4-fp8kv=True-attn_backend=FLASHINFER-torch_compile=True] SKIP (https://nvbugs/5741304) unittest/_torch/multi_gpu/test_allreduce.py::test_allreduce_fusion_patterns[2-residual_rms_norm_out_quant_fp8-hidden:7168-seqlen:8192] SKIP (https://nvbugs/5741392) unittest/executor/test_rpc.py::TestRpcCorrectness::test_incremental_task_async SKIP (https://nvbugs/5741476) -accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[pp4-fp8kv=True-attn_backend=TRTLLM-torch_compile=False] SKIP (https://nvbugs/5740377) -accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_cutlass-torch_compile=False] SKIP (https://nvbugs/5740377) test_e2e.py::test_trtllm_bench_llmapi_launch[pytorch_backend-llama-v3-llama3-8b] SKIP (https://nvbugs/5744432) -test_e2e.py::test_trtllm_serve_multimodal_example SKIP (https://nvbugs/5747920) -test_e2e.py::test_trtllm_serve_example SKIP (https://nvbugs/5747938) -triton_server/test_triton.py::test_opt[opt] SKIP (https://nvbugs/5739981) -accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus_online_eplb[fp8kv=True-moe_backend=TRTLLM] SKIP (https://nvbugs/5740377) cpp/test_multi_gpu.py::TestDisagg::test_symmetric_executor[gpt-2proc-mpi_kvcache-90] SKIP (https://nvbugs/5755941) examples/test_granite.py::test_llm_granite[granite-3.0-1b-a400m-instruct-bfloat16] SKIP (https://nvbugs/5608979) examples/test_granite.py::test_llm_granite[granite-3.0-2b-instruct-bfloat16] SKIP (https://nvbugs/5608979) +unittest/_torch/speculative/test_dynamic_spec_decode.py::test_dynamic_spec_decode SKIP (https://nvbugs/5758449) unittest/executor/test_base_worker.py::TestWorkerBase SKIP (https://nvbugs/5759698) triton_server/test_triton.py::test_gpt_disaggregated_serving_bls[gpt-disaggregated-serving-bls] SKIP (https://nvbugs/5582118) cpp/test_multi_gpu.py::test_cache_transceiver[8proc-mooncake_kvcache-90] SKIP (https://nvbugs/5760737) @@ -289,16 +276,12 @@ accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4[moe_backend=CUT test_e2e.py::test_ptp_quickstart_advanced_2gpus_sm120[Nemotron-Super-49B-v1-BF16-nemotron-nas/Llama-3_3-Nemotron-Super-49B-v1] SKIP (https://nvbugs/5670469) unittest/_torch/multi_gpu/test_mnnvl_allreduce.py::test_row_linear_residual_norm_fusion[no_fusion-strategy:8-dtype:bfloat16-hidden:8192-seqlen:[15]] SKIP (https://nvbugs/5761364) triton_server/test_triton.py::test_gpt_speculative_decoding[gpt-speculative-decoding] SKIP (https://nvbugs/5762854) -accuracy/test_llm_api_pytorch.py::TestLlama3_1_8B_Instruct_RocketKV::test_auto_dtype SKIP (https://nvbugs/5762822) unittest/_torch/sampler/test_return_logits.py SKIP (https://nvbugs/5764627) -examples/serve/test_serve.py::test_config_file_loading[--config] SKIP (https://nvbugs/5754977) -full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-tp2pp2-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] SKIP (https://nvbugspro.nvidia.com/bug/5794313) examples/test_ray.py::test_ray_disaggregated_serving[tp2] SKIP (https://nvbugs/5612502) unittest/executor/test_rpc_proxy.py SKIP (https://nvbugs/5605741) unittest/executor/test_rpc_worker.py SKIP (https://nvbugs/5605741) cpp/test_e2e.py::test_model[-redrafter-86] SKIP (https://nvbugs/5761642) unittest/_torch/thop/parallel/test_fp8_block_scale_gemm.py::test_deep_gemm_in_subprocess[env2] SKIP (https://nvbugs/5766853) -test_e2e.py::test_openai_responses SKIP (https://nvbugs/5804146) triton_server/test_triton.py::test_gpt_gather_logits[gpt-gather-logits] SKIP (https://nvbugs/5766960) full:sm89/accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ctx_pp_gen_tp_asymmetric[MMLU-gen_tp=1-ctx_pp=2] SKIP (https://nvbugs/5596337) accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ngram SKIP (https://nvbugs/5768068) @@ -315,74 +298,57 @@ accuracy/test_cli_flow.py::TestVicuna7B::test_eagle_2[cuda_graph=True-chunked_co accuracy/test_disaggregated_serving.py::TestGPTOSS::test_auto_dtype[True] SKIP (https://nvbugs/5596343) accuracy/test_disaggregated_serving.py::TestGPTOSS::test_auto_dtype[False] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestLlama3_3_70BInstruct::test_fp8_eagle3_tp8[eagle3_one_model=False-torch_compile=True] SKIP (https://nvbugs/5775326) -accuracy/test_llm_api_pytorch.py::TestLlama3_3_70BInstruct::test_nvfp4_tp4[torch_compile=False] SKIP (https://nvbugs/5794796) -accuracy/test_llm_api_pytorch.py::TestLlama3_3_70BInstruct::test_fp8_tp4[torch_compile=False] SKIP (https://nvbugs/5794796) accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_fp8[throughput_latency] SKIP (https://nvbugs/5775544) triton_server/test_triton.py::test_llava_onevision[llava_onevision] SKIP (https://nvbugs/5775205) triton_server/test_triton.py::test_gpt_ib_lad[gpt-ib-lad] SKIP (https://nvbugs/5775223) -unittest/_torch/modules/test_fused_moe.py::test_fused_moe_fp8_blockwise_cute_dsl_multi_gpu[MoEWeightLoadingMode.FUSED_GATE_UP_PROJ-DefaultMoeRoutingMethod-1] SKIP (https://nvbugs/5775256) -disaggregated/test_disaggregated.py::test_disaggregated_benchmark_on_diff_backends[llama-3.1-8b-instruct-hf-fp8] SKIP (https://nvbugs/5769890) -disaggregated/test_disaggregated.py::test_disaggregated_benchmark_on_diff_backends[DeepSeek-V3-Lite-bf16] SKIP (https://nvbugs/5769890) -disaggregated/test_disaggregated.py::test_disaggregated_benchmark_on_diff_backends[llama-v3-8b-hf] SKIP (https://nvbugs/5769890,https://nvbugs/5748683) -accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_nvfp4_multi_gpus[throughput_pp4_mtp] SKIP (https://nvbugs/5779536) unittest/_torch/attention/test_flashinfer_star_attn.py::TestStarAttention::test_flashinfer_star_attention[num_layers:2-num_heads:32-num_kv_heads:8-head_dim:64-anchor_size:64-block_size:64-dtype:torch.float16] SKIP (https://nvbugs/5781389) unittest/_torch/ray_orchestrator/multi_gpu/test_ops.py::test_reducescatter_pg_op[var_len:True-seqlen:16-hidden:128] SKIP (https://nvbugs/5781383) cpp/test_e2e.py::test_model[-mamba-86] SKIP (https://nvbugs/5781665) unittest/llmapi/test_llm_multi_gpu_pytorch.py::test_tinyllama_logits_processor_tp2pp2 SKIP (https://nvbugs/5781731) accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_tp_pp_symmetric[GSM8K-tp2pp2] SKIP (https://nvbugs/5756008) accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_eagle3[eagle3_one_model=False-overlap_scheduler=False] SKIP (https://nvbugs/5807902) -accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_guided_decoding_4gpus[llguidance] SKIP (https://nvbugs/5756008) accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[tp2pp2-fp8kv=False-attn_backend=TRTLLM-torch_compile=False] SKIP (https://nvbugs/5784526) -unittest/_torch/modules/test_fused_moe.py::test_fused_moe_multi_gpu[1-CUTLASS] SKIP (https://nvbugs/5784543) accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_fp8_4gpus[tp4-fp8kv=False-attn_backend=FLASHINFER-torch_compile=False] SKIP (https://nvbugs/5707359) -accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ctx_pp_gen_tp_asymmetric[GSM8K-gen_tp=1-ctx_pp=2] SKIP (https://nvbugs/5673559) -accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[ep4-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] SKIP (https://nvbugs/5701445) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[llguidance-mtp_nextn=0] SKIP (https://nvbugs/5748600) unittest/_torch/ray_orchestrator/multi_gpu/test_multi_instance.py::test_multi_instance[tp2_2instances] SKIP (https://nvbugs/5784566) -disaggregated/test_auto_scaling.py::test_worker_restart[etcd-round_robin] SKIP (https://nvbugs/5776445) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_vswa_reuse_4gpus[one_model] SKIP (https://nvbugs/5756028) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_vswa_reuse_4gpus[two_model] SKIP (https://nvbugs/5756028) -accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_fp8[latency-torch_compile=False] SKIP (https://nvbugs/5785206) accuracy/test_llm_api_pytorch.py::TestLlama3_2_1B::test_fp8_prequantized SKIP (https://nvbugs/5785465) accuracy/test_llm_api_pytorch.py::TestMinistral8BInstruct::test_fp8 SKIP (https://nvbugs/5785485) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:none-pp1tp2cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:with_padding-pp1tp2cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:none-pp1tp2cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:with_padding-pp1tp2cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:none-pp1tp1cp4] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:with_padding-pp1tp1cp4] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:none-pp1tp1cp4] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:with_padding-pp1tp1cp4] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:none-pp2tp1cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:without_padding-pp2tp1cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[nccl-cudagraph:with_padding-pp2tp1cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:none-pp2tp1cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:without_padding-pp2tp1cp2] SKIP (https://nvbugs/5787836) -accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype_with_helix[fifo-cudagraph:with_padding-pp2tp1cp2] SKIP (https://nvbugs/5787836) -accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_ngram SKIP (https://nvbugs/5769815) -accuracy/test_llm_api_pytorch.py::TestLlama3_3_70BInstruct::test_fp8_eagle3_tp8[eagle3_one_model=True-torch_compile=False] SKIP (https://nvbugs/5787892) accuracy/test_llm_api_pytorch.py::TestLlama3_3_70BInstruct::test_fp8_eagle3_tp8[eagle3_one_model=False-torch_compile=False] SKIP (https://nvbugs/5787892) accuracy/test_llm_api_pytorch.py::TestLlama4MaverickInstruct::test_fp8_chunked_prefill[tp8ep8-cuda_graph=True] SKIP (https://nvbugs/5791839) accuracy/test_llm_api_pytorch.py::TestLlama4MaverickInstruct::test_fp8_chunked_prefill[tp8ep8-cuda_graph=False] SKIP (https://nvbugs/5795918) accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_auto_dtype[mtp_nextn=2-overlap_scheduler=True] SKIP (https://nvbugs/5800591) -accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[tp4-mtp_nextn=0-attention_dp=False-cuda_graph=True-overlap_scheduler=True-torch_compile=True] SKIP (https://nvbugs/5800646) full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] SKIP (https://nvbugs/5800672) full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_4gpus[cutlass-one_model-overlap_scheduler] SKIP (https://nvbugs/5800679) -accuracy/test_llm_api_pytorch.py::TestLlama3_1_8BInstruct::test_bfloat16_4gpus[tp4-attn_backend=FLASHINFER-torch_compile=False] SKIP (https://nvbugs/5741304) examples/test_medusa.py::test_llm_medusa_with_qaunt_base_model_1gpu[fp8-use_cpp_session-medusa-vicuna-7b-v1.3-4-heads-float16-bs1] SKIP (https://nvbugs/5802248) accuracy/test_disaggregated_serving.py::TestGemma3_1BInstruct::test_auto_dtype[False] SKIP (https://nvbugs/5799901) accuracy/test_llm_api_pytorch.py::TestGemma3_1BInstruct::test_auto_dtype SKIP (https://nvbugs/5799901) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_guided_decoding_4gpus[one_model] SKIP (https://nvbugs/5596343) accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_guided_decoding_4gpus[two_model] SKIP (https://nvbugs/5596343) accuracy/test_disaggregated_serving.py::TestGemma3_1BInstruct::test_auto_dtype[True] SKIP (https://nvbugs/5799901) -accuracy/test_disaggregated_serving.py::TestQwen3_30B_A3B::test_mixed_ctx_gen_model[ctxpp2gentp2] SKIP (https://nvbugs/5748664) -accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm_eagle] SKIP (https://nvbugs/5804683) -accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_4gpus[cutlass-one_model-no_overlap_scheduler] SKIP (https://nvbugs/5809169) -accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_27BInstruct::test_fp8_prequantized SKIP (https://nvbugs/5811087) test_e2e.py::test_ptp_quickstart_advanced[Qwen3-30B-A3B-Qwen3/Qwen3-30B-A3B] SKIP (https://nvbugs/5810935) accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_2gpus[target_sparsity_0.5] SKIP (https://nvbugs/5810940) accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B_Instruct_2507::test_skip_softmax_attention_2gpus[target_sparsity_0.9] SKIP (https://nvbugs/5810940) -accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[ep4-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=True] SKIP (https://nvbugs/5800646) -accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[tp4-mtp_nextn=0-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=True] SKIP (https://nvbugs/5800646) accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=2-tp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] SKIP (https://nvbugs/5800672) accuracy/test_llm_api_pytorch.py::TestNemotronV3Super::test_auto_dtype_4gpus[4-4-True-False-True] SKIP (https://nvbugs/5810980) +examples/test_ray.py::test_llm_inference_distributed_ray[tp2pp2] SKIP (https://nvbugs/5781731) +accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[tp4-mtp_nextn=2-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=True] SKIP (https://nvbugs/5800646) +accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[dp4-trtllm-auto] SKIP (https://nvbugs/5651865) +accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus[ep4-mtp_nextn=2-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] SKIP (https://nvbugs/5800646) +accuracy/test_llm_api_pytorch.py::TestLlama4ScoutInstruct::test_fp4[tp4-cuda_graph=True] SKIP (https://nvbugs/5820734) +llmapi/test_llm_examples.py::test_llmapi_tensorrt_engine SKIP (https://nvbugs/5820553) +unittest/_torch/misc/test_autotuner.py::test_autotuner_distributed_strategy[2-DistributedTuningStrategy.INDEPENDENT] SKIP (https://nvbugs/5823194) +accuracy/test_disaggregated_serving.py::TestQwen3_30B_A3B::test_mixed_ctx_gen_model[ctxpp2gentp2] SKIP (https://nvbugs/5748664) +perf/test_perf_sanity.py::test_e2e[aggr_upload-deepseek_r1_fp4_v2_grace_blackwell-r1_fp4_v2_tep4_mtp3_8k1k] SKIP (https://nvbugs/5819053) +unittest/_torch/thop/serial/test_moe.py::TestMoeFp4::test_gptoss_style_nvfp4[limitinf-beta0-alpha0.1-RoutingGPTOSS-512-512-1] SKIP (https://nvbugs/5819042) +accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ctx_pp_gen_tp_asymmetric[MMLU-gen_tp=1-ctx_pp=4] SKIP (https://nvbugs/5845943) +accuracy/test_cli_flow.py::TestGpt2::test_cuda_graph SKIP (https://nvbugs/5860520) +unittest/_torch/flashinfer/test_trtllm_flashinfer_symbol_collision.py::test_flashinfer_fused_moe_matches_torch_moe SKIP (https://nvbugs/5920779) +accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[dp4-cutlass-auto] SKIP (https://nvbugs/5920771) +full:DGX_H100/unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_bmm_sharding.py::test_sharding[1-1] SKIP (https://nvbugs/5936322) +accuracy/test_llm_api_pytorch.py::TestLlama3_1_8B_Instruct_RocketKV::test_auto_dtype SKIP (https://nvbugs/5948493) +accuracy/test_disaggregated_serving.py::TestLlama3_1_8BInstruct::test_ctx_pp_gen_tp_asymmetric[MMLU-gen_tp=2-ctx_pp=4] SKIP (https://nvbugs/5948684) +accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4_4gpus[latency_moe_cutlass] SKIP (https://nvbugs/5948709) +unittest/_torch/modeling -k "modeling_siglip" SKIP (https://nvbugs/5941242) +stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-GUARANTEED_NO_EVICT-pytorch-stress-test] SKIP (https://nvbugs/5955927) +stress_test/stress_test.py::test_run_stress_test[llama-v3-8b-instruct-hf_tp1-stress_time_300s_timeout_450s-MAX_UTILIZATION-pytorch-stress-test] SKIP (https://nvbugs/5955927) diff --git a/tests/microbenchmarks/README.md b/tests/microbenchmarks/README.md index 48bce4ec89b6..884e02004a85 100644 --- a/tests/microbenchmarks/README.md +++ b/tests/microbenchmarks/README.md @@ -16,7 +16,7 @@ python ./build_time_benchmark.py --model "TinyLlama/TinyLlama_v1.1" # no weights python ./build_time_benchmark.py --model "openai-community/gpt2" --load # with weights loading # example 3: benchmark a local download HF model -python ./build_time_benchmark.py --model /home/scratch.trt_llm_data/llm-models/falcon-rw-1b/ +python ./build_time_benchmark.py --model /home/scratch.trt_llm_data_ci/llm-models/falcon-rw-1b/ # example 4: benchmark one model with managed weights option, with verbose option python ./build_time_benchmark.py --model llama2-70b.TP4 --managed_weights -v diff --git a/tests/microbenchmarks/all_reduce.py b/tests/microbenchmarks/all_reduce.py index 9f50b66d3b48..ca9d9a7610c3 100644 --- a/tests/microbenchmarks/all_reduce.py +++ b/tests/microbenchmarks/all_reduce.py @@ -141,6 +141,7 @@ def allreduce_benchmark( logger.set_rank(mapping.rank) AutoTuner.get().setup_distributed_state(mapping) + dist = Distributed.get(mapping) sm_version = get_sm_version() diff --git a/tests/scripts/allreduce_perf/allreduce_heuristic_code_gen.py b/tests/scripts/allreduce_perf/allreduce_heuristic_code_gen.py index e7aeb994b6e3..df45bf111eff 100644 --- a/tests/scripts/allreduce_perf/allreduce_heuristic_code_gen.py +++ b/tests/scripts/allreduce_perf/allreduce_heuristic_code_gen.py @@ -176,7 +176,7 @@ def main(): parser.add_argument("--enable_auto", action="store_true", default=False) args = parser.parse_args() - tp_size_list = [2] + tp_size_list = [2, 4, 8] # Process the benchmark data # combine all the data into one dataframe diff --git a/tests/scripts/perf-sanity/benchmark-serve.sh b/tests/scripts/perf-sanity/benchmark-serve.sh index 4621f571e804..3d2d14284971 100755 --- a/tests/scripts/perf-sanity/benchmark-serve.sh +++ b/tests/scripts/perf-sanity/benchmark-serve.sh @@ -44,7 +44,7 @@ report_head() { run_benchmark_and_parse() { # Run benchmark and parse results in a single Docker container - mount=" -v /home/scratch.trt_llm_data:/home/scratch.trt_llm_data:ro -v $output_dir:$output_dir:rw -v $bench_dir:$bench_dir:ro" + mount=" -v /home/scratch.trt_llm_data_ci:/home/scratch.trt_llm_data_ci:ro -v $output_dir:$output_dir:rw -v $bench_dir:$bench_dir:ro" if [[ -n "$trtllm_dir" && -d "$trtllm_dir" ]]; then mount="$mount -v $trtllm_dir:$trtllm_dir:ro" fi @@ -56,7 +56,7 @@ run_benchmark_and_parse() { ${IMAGE} \ bash -c " echo 'Running benchmarks...' - export LLM_MODELS_ROOT=/home/scratch.trt_llm_data/llm-models + export LLM_MODELS_ROOT=/home/scratch.trt_llm_data_ci/llm-models # Handle trtllm_dir parameter if [[ -n \"$trtllm_dir\" && -d \"$trtllm_dir\" ]]; then diff --git a/tests/scripts/perf-sanity/run_benchmark_serve.py b/tests/scripts/perf-sanity/run_benchmark_serve.py index 627b5d980ddf..8636c815d0e9 100644 --- a/tests/scripts/perf-sanity/run_benchmark_serve.py +++ b/tests/scripts/perf-sanity/run_benchmark_serve.py @@ -166,7 +166,7 @@ def get_gpu_info() -> str: } LLM_MODELS_ROOT = os.environ.get('LLM_MODELS_ROOT', - '/home/scratch.trt_llm_data/llm-models') + '/home/scratch.trt_llm_data_ci/llm-models') # Model path mapping diff --git a/tests/test_common/llm_data.py b/tests/test_common/llm_data.py index 74fd310366e1..a3855b8e8c46 100644 --- a/tests/test_common/llm_data.py +++ b/tests/test_common/llm_data.py @@ -47,7 +47,7 @@ def llm_models_root(check: bool = False) -> Optional[Path]: - root = Path("/home/scratch.trt_llm_data/llm-models/") + root = Path("/home/scratch.trt_llm_data_ci/llm-models/") if "LLM_MODELS_ROOT" in os.environ: root = Path(os.environ.get("LLM_MODELS_ROOT")) @@ -57,7 +57,7 @@ def llm_models_root(check: bool = False) -> Optional[Path]: if check: assert root.exists(), ( - "You must set LLM_MODELS_ROOT env or be able to access /home/scratch.trt_llm_data to run this test" + "You must set LLM_MODELS_ROOT env or be able to access /home/scratch.trt_llm_data_ci to run this test" ) return root if root.exists() else None @@ -99,16 +99,31 @@ def mock_snapshot_download(repo_id: str, **kwargs) -> str: return local_path -def with_mocked_hf_download(func): +def with_mocked_hf_download_for_single_gpu(func): """Decorator to mock huggingface_hub.snapshot_download for tests. When applied, any calls to snapshot_download will be redirected to use local model paths from LLM_MODELS_ROOT instead of downloading from HuggingFace. + + NOTE: We must patch snapshot_download at the location where it's actually imported + with 'from huggingface_hub import snapshot_download', since that creates a + local binding that won't be affected by patching huggingface_hub.snapshot_download. + + Additionally sets HF_HUB_OFFLINE=1 to ensure no network requests are made to + HuggingFace. + + WARNING: This decorator only works for single-GPU tests. For multi-GPU tests, the + mock won't be applied in MPI worker processes. """ @wraps(func) def wrapper(*args, **kwargs): - with patch("huggingface_hub.snapshot_download", side_effect=mock_snapshot_download): + with ( + patch.dict(os.environ, {"HF_HUB_OFFLINE": "1"}), + patch( + "tensorrt_llm.llmapi.utils.snapshot_download", side_effect=mock_snapshot_download + ), + ): return func(*args, **kwargs) return wrapper diff --git a/tests/unittest/_torch/auto_deploy/unit/multigpu/custom_ops/test_mxfp4_moe_ep.py b/tests/unittest/_torch/auto_deploy/unit/multigpu/custom_ops/test_mxfp4_moe_ep.py index 211053a299ce..12ef18218a75 100644 --- a/tests/unittest/_torch/auto_deploy/unit/multigpu/custom_ops/test_mxfp4_moe_ep.py +++ b/tests/unittest/_torch/auto_deploy/unit/multigpu/custom_ops/test_mxfp4_moe_ep.py @@ -4,10 +4,8 @@ import torch import torch.distributed as dist from _dist_test_utils import get_device_counts +from utils.util import skip_no_hopper -from tensorrt_llm._torch.auto_deploy.custom_ops.fused_moe.mxfp4_moe import ( - IS_TRITON_KERNELS_AVAILABLE, -) from tensorrt_llm._torch.auto_deploy.distributed.common import spawn_multiprocess_job @@ -109,10 +107,7 @@ def _rand_scales(shape): torch.testing.assert_close(part_out, ref_out, rtol=5e-2, atol=5e-2, equal_nan=True) -@pytest.mark.skipif( - not IS_TRITON_KERNELS_AVAILABLE, - reason="triton_kernels unavailable", -) +@skip_no_hopper @pytest.mark.parametrize("num_experts", [6, 8]) @pytest.mark.parametrize("topk", [4]) # must be <= num_experts @pytest.mark.parametrize("device_count", get_device_counts()) diff --git a/tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_speculative_decoding.py b/tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_speculative_decoding.py index 81481e8f51dd..cb3655411abb 100644 --- a/tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_speculative_decoding.py +++ b/tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_speculative_decoding.py @@ -16,13 +16,13 @@ import pytest from _model_test_utils import get_small_model_config from build_and_run_ad import ExperimentConfig, main -from test_common.llm_data import with_mocked_hf_download +from test_common.llm_data import with_mocked_hf_download_for_single_gpu from tensorrt_llm.llmapi import DraftTargetDecodingConfig, KvCacheConfig @pytest.mark.parametrize("use_hf_speculative_model", [False, True]) -@with_mocked_hf_download +@with_mocked_hf_download_for_single_gpu def test_ad_speculative_decoding_smoke(use_hf_speculative_model: bool): """Test speculative decoding with AutoDeploy using the build_and_run_ad main().""" diff --git a/tests/unittest/_torch/executor/test_resource_manager.py b/tests/unittest/_torch/executor/test_resource_manager.py index c31c08fa2256..92504469408b 100644 --- a/tests/unittest/_torch/executor/test_resource_manager.py +++ b/tests/unittest/_torch/executor/test_resource_manager.py @@ -19,6 +19,8 @@ from tensorrt_llm.bindings import executor as tllm from tensorrt_llm.bindings.internal.batch_manager import \ PeftTaskNotCachedException +from tensorrt_llm.bindings.internal.testing import \ + simulate_prefill_completion_only_use_for_testing from tensorrt_llm.llmapi.llm_args import KvCacheConfig, PeftCacheConfig from tensorrt_llm.lora_helper import LoraConfig from tensorrt_llm.mapping import Mapping @@ -456,9 +458,11 @@ def test_adjust_window_sizes_for_vswa(self): model_config.num_hidden_layers = len(total_layers) model_config.num_attention_layers = len(total_layers) + model_config.layer_types = [LayerType.ATTENTION + ] * model_config.num_attention_layers kv_factor = 2 - cache_bytes_per_token_per_layer = 8 + cache_bytes_per_token_per_layer = 32 # Define test cases: # (memory_bytes, expected_window_sizes, max_tokens, description) @@ -466,7 +470,7 @@ def test_adjust_window_sizes_for_vswa(self): test_cases = [ ( # Case 1: Limited memory - windows get clamped - cache_bytes_per_token_per_layer * (100 * 9 + 30 * 5) + 4, + cache_bytes_per_token_per_layer * (100 * 4 + 130 * 5) + 4, { 100: [0, 1, 2, 3], 130: [4, 5, 6, 7, 8], @@ -477,7 +481,7 @@ def test_adjust_window_sizes_for_vswa(self): ( # Case 2: Less limited memory - the largest window get clamped cache_bytes_per_token_per_layer * - (100 * 9 + 100 * 5 + 817 * 2) + 4, + (100 * 4 + 200 * 3 + 1017 * 2) + 4, { 100: [0, 1, 2, 3], 200: [4, 5, 6], @@ -510,8 +514,7 @@ def test_adjust_window_sizes_for_vswa(self): ( # Case 5: Less limited memory but max_tokens is given. # memory is enough for 1017 tokens, it will be clamped by max_tokens=134. - cache_bytes_per_token_per_layer * - (100 * 9 + 100 * 5 + 817 * 2) + 4, + cache_bytes_per_token_per_layer * (100 * 4 + 134 * 5) + 4, { 100: [0, 1, 2, 3], 134: [4, 5, 6, 7, 8], @@ -523,8 +526,33 @@ def test_adjust_window_sizes_for_vswa(self): for memory_bytes, expected_window_sizes, expected_max_attention_window_vec, max_tokens, description in test_cases: with self.subTest(case=description, memory_bytes=memory_bytes): - kv_cache_config = tllm.KvCacheConfig(max_tokens=max_tokens) - adjusted, adjusted_max_attention_window_vec = KVCacheManager.adjust_window_sizes_for_vswa( + kv_cache_config_params = { + "max_attention_window": max_attention_window_vec, + "free_gpu_memory_fraction": 1.0, + "host_cache_size": 0, + "max_gpu_total_bytes": memory_bytes, + } + kv_cache_config = TestResourceManager._create_kv_cache_config_for_kv_cache_manager( + kv_cache_config_params) + mapping = Mapping(world_size=1, tp_size=1, pp_size=1) + + manager = KVCacheManager( + kv_cache_config=kv_cache_config, + kv_cache_type=tensorrt_llm.bindings.internal.batch_manager. + CacheType.SELF, + num_layers=model_config.num_attention_layers, + num_kv_heads=8, + head_dim=model_config.head_size, + tokens_per_block=32, + max_seq_len=max(max_attention_window_vec), + max_batch_size=1, + mapping=mapping, + dtype=model_config.data_type, + model_config=model_config, + max_beam_width=1, + ) + + adjusted, adjusted_max_attention_window_vec = manager.adjust_window_sizes_for_vswa( window_size_to_layers=window_size_to_layers, max_attention_window_vec=max_attention_window_vec, model_config=model_config, @@ -581,7 +609,7 @@ def _create_kv_cache_config_for_kv_cache_manager( """ return KvCacheConfig(**params) - def test_calculate_max_num_blocks_from_cpp(self): + def test_calculate_max_num_blocks_for_vswa(self): # Construct a minimal mapping (single-rank, no TP/PP) mapping = Mapping(world_size=1, tp_size=1, pp_size=1) @@ -651,7 +679,7 @@ class MemTestCase(NamedTuple): kv_cache_config_params) with patch('torch.cuda.mem_get_info', return_value=(fixed_free_mem, fixed_total_mem)): - # Create a real KVCacheManager, it will run calculate_max_num_blocks_from_cpp in __init__ + # Create a real KVCacheManager, it will run calculate_max_num_blocks_for_vswa in __init__ manager = KVCacheManager( kv_cache_config=kv_cache_config, kv_cache_type=tensorrt_llm.bindings.internal. @@ -730,6 +758,7 @@ def test_kv_cache_reset_reuse_state(self): stats_initial = kv_cache_manager.get_kv_cache_stats() initial_reused_blocks = stats_initial.reused_blocks + simulate_prefill_completion_only_use_for_testing(req1) kv_cache_manager.free_resources(req1) # Second request with same tokens - should reuse blocks from the reuse tree @@ -744,6 +773,7 @@ def test_kv_cache_reset_reuse_state(self): f"reused_blocks before: {initial_reused_blocks}, after: {stats_after_reuse.reused_blocks}" ) + simulate_prefill_completion_only_use_for_testing(req2) kv_cache_manager.free_resources(req2) # Reset reuse state @@ -762,6 +792,7 @@ def test_kv_cache_reset_reuse_state(self): f"Third request should NOT reuse blocks after reset. " f"reused_blocks after reset: {reused_blocks_after_reset}, after third request: {stats_after_third.reused_blocks}" ) + simulate_prefill_completion_only_use_for_testing(req3) kv_cache_manager.free_resources(req3) def test_kv_cache_manager_with_execution_stream(self): diff --git a/tests/unittest/_torch/attention/test_trtllm_flashinfer_symbol_collision.py b/tests/unittest/_torch/flashinfer/test_trtllm_flashinfer_symbol_collision.py similarity index 100% rename from tests/unittest/_torch/attention/test_trtllm_flashinfer_symbol_collision.py rename to tests/unittest/_torch/flashinfer/test_trtllm_flashinfer_symbol_collision.py diff --git a/tests/unittest/_torch/misc/test_autotuner.py b/tests/unittest/_torch/misc/test_autotuner.py index 7212f491e6ba..c5021ef7f9a0 100644 --- a/tests/unittest/_torch/misc/test_autotuner.py +++ b/tests/unittest/_torch/misc/test_autotuner.py @@ -738,6 +738,8 @@ def _distributed_worker_function(world_size, strategy): runner = DistributedGemmRunner(prefer_tactics=prefer_tactics) config = TuningConfig(distributed_tuning_strategy=strategy) + # Keep temp_dir in function scope to prevent premature garbage collection + temp_dir = None if rank == 0: temp_dir = tempfile.TemporaryDirectory() # rank 0 should broadcast the cache path to all ranks @@ -782,6 +784,7 @@ def _distributed_worker_function(world_size, strategy): else: assert False, f"Unknown strategy: {strategy}" + dist.barrier() return True diff --git a/tests/unittest/_torch/modeling/test_modeling_gpt_oss.py b/tests/unittest/_torch/modeling/test_modeling_gpt_oss.py index 0e2b97459315..7d6bcc29e96a 100644 --- a/tests/unittest/_torch/modeling/test_modeling_gpt_oss.py +++ b/tests/unittest/_torch/modeling/test_modeling_gpt_oss.py @@ -5,10 +5,9 @@ import pytest from transformers import AutoTokenizer from utils.llm_data import llm_models_root +from utils.util import skip_no_hopper from tensorrt_llm import LLM, SamplingParams -from tensorrt_llm._torch.modules.fused_moe.fused_moe_triton import \ - IS_TRITON_KERNELS_AVAILABLE from tensorrt_llm.llmapi import CudaGraphConfig, KvCacheConfig, MoeConfig configs = """ @@ -48,11 +47,10 @@ def dump_config_json(dst_dir): json.dump(json_configs, f, indent=2, ensure_ascii=False) -@pytest.mark.parametrize("moe_backend", ["CUTLASS", "TRITON"]) +@pytest.mark.parametrize( + "moe_backend", + ["CUTLASS", pytest.param("TRITON", marks=skip_no_hopper)]) def test_gpt_oss_trtllmgen(moe_backend): - if moe_backend == "TRITON" and not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") - prompts = [ "How are you?", "Hello, my name is", diff --git a/tests/unittest/_torch/modules/test_fused_moe.py b/tests/unittest/_torch/modules/test_fused_moe.py index 773218a40c2a..30e64a16f2f2 100644 --- a/tests/unittest/_torch/modules/test_fused_moe.py +++ b/tests/unittest/_torch/modules/test_fused_moe.py @@ -19,9 +19,8 @@ from mpi4py.futures import MPIPoolExecutor from transformers.configuration_utils import PretrainedConfig from utils.util import (check_accuracy, skip_blackwell, skip_blackwell_geforce, - skip_neither_ada_nor_hopper_unittest, - skip_non_hopper_unittest, skip_pre_blackwell, - skip_pre_hopper) + skip_neither_ada_nor_hopper_unittest, skip_no_hopper, + skip_pre_blackwell, skip_pre_hopper) from tensorrt_llm._torch.autotuner import AutoTuner, autotune from tensorrt_llm._torch.model_config import ModelConfig @@ -41,8 +40,6 @@ from tensorrt_llm._torch.modules.fused_moe.quantization import \ NVFP4CutlassFusedMoEMethod # isort: on -from tensorrt_llm._torch.modules.fused_moe.fused_moe_triton import \ - IS_TRITON_KERNELS_AVAILABLE from tensorrt_llm._torch.modules.gated_mlp import GatedMLP from tensorrt_llm._utils import get_sm_version, mpi_rank from tensorrt_llm.mapping import Mapping @@ -92,8 +89,8 @@ def test_fused_moe(moe_backend, mapping=None): if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") + if get_sm_version() != 90: + pytest.skip("TRITON moe backend is only supported on Hopper") if dtype != torch.bfloat16: pytest.skip("Unsupported for TritonFusedMoE") if routing_cls != RenormalizeMoeRoutingMethod: @@ -192,9 +189,9 @@ def test_fused_moe(moe_backend, # Evaluate outputs torch.cuda.synchronize() # There can be one off mismatch in the outputs due to different kernel implementations - # Here we check 99% of the outputs are within the tolerance + # Here we check most of the outputs are within the tolerance # The CutlassFusedMoE case fails as well without this change on H100 for bf16 - check_accuracy(output, ref_output, rtol=0.2, atol=0.2, percent=0.984) + check_accuracy(output, ref_output, rtol=0.2, atol=0.2, percent=0.975) m //= 2 @@ -514,7 +511,9 @@ def per_rank_test_fused_moe_alltoall(job_id, weights, x_list): @skip_pre_hopper -@pytest.mark.parametrize("moe_backend", ["CUTLASS", "TRITON"]) +@pytest.mark.parametrize( + "moe_backend", + ["CUTLASS", pytest.param("TRITON", marks=skip_no_hopper)]) @pytest.mark.parametrize("routing_cls", [DefaultMoeRoutingMethod, RenormalizeMoeRoutingMethod]) @pytest.mark.parametrize("bias", [True, False]) @@ -522,8 +521,6 @@ def per_rank_test_fused_moe_alltoall(job_id, weights, x_list): def test_fused_moe_fp8(moe_backend, dtype, routing_cls, bias): if moe_backend == "TRITON": - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") if dtype != torch.bfloat16: pytest.skip("Unsupported for TritonFusedMoE") if routing_cls != RenormalizeMoeRoutingMethod: @@ -632,19 +629,30 @@ def test_fused_moe_fp8(moe_backend, dtype, routing_cls, bias): with torch.inference_mode(), autotune(): fused_moe.forward(x, router_logits) - # Explicitly capture context for kernel testing - with AutoTuner.get().capture() as all_tactics, torch.inference_mode(): - output = fused_moe.forward(x, router_logits) - - # Test all kernel tactics - for tactic in all_tactics: - with AutoTuner.get().replay(tactic), torch.inference_mode(): + # TRITON backend uses Triton kernels which don't register with AutoTuner + if moe_backend == "TRITON": + with torch.inference_mode(): + output = fused_moe.forward(x, router_logits) + check_accuracy(output, + ref_output, + rtol=0.04, + atol=0.1, + percent=0.99) + else: + # Explicitly capture context for kernel testing + with AutoTuner.get().capture() as all_tactics, torch.inference_mode( + ): output = fused_moe.forward(x, router_logits) - check_accuracy(output, - ref_output, - rtol=0.04, - atol=0.1, - percent=0.99) + + # Test all kernel tactics + for tactic in all_tactics: + with AutoTuner.get().replay(tactic), torch.inference_mode(): + output = fused_moe.forward(x, router_logits) + check_accuracy(output, + ref_output, + rtol=0.04, + atol=0.1, + percent=0.99) def set_tensor_value_2(x, num_row, num_cols): @@ -1174,7 +1182,7 @@ def test_fused_moe_fp8_blockwise_cute_dsl(dtype, return True -@skip_non_hopper_unittest +@skip_no_hopper @pytest.mark.parametrize( "dtype, num_experts, seq_len, hidden_size, RoutingMethodCls, WeightLoadingMode", product( @@ -1306,7 +1314,7 @@ def test_fused_moe_fp8_blockwise_cutlass(dtype, return True -@skip_non_hopper_unittest +@skip_no_hopper @pytest.mark.skipif(torch.cuda.device_count() < 4, reason="needs 4 GPUs to run this test") @pytest.mark.parametrize("ep_size", [1, 2, 4]) @@ -2526,7 +2534,7 @@ def ref(): check_accuracy(output, ref_output, rtol=1e-2, atol=0.1, percent=0.99) -@skip_pre_hopper +@skip_no_hopper @pytest.mark.parametrize("experts", [8, 128]) @pytest.mark.parametrize( "hidden_size, intermediate_size", @@ -2542,12 +2550,8 @@ def ref(): @pytest.mark.parametrize("dynamic_quant", [True, False]) def test_fused_moe_triton_mxfp4(experts, hidden_size, intermediate_size, fp8_activation, bias, dynamic_quant): - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") - if torch.cuda.get_device_capability()[0] < 10 and fp8_activation: + if fp8_activation: pytest.skip("Latest Triton requires BF16 activation on Hopper") - if torch.cuda.get_device_capability()[0] >= 10 and not fp8_activation: - pytest.skip("Latest Triton requires FP8 activation on Blackwell") mapping = Mapping() mapping.rank = mpi_rank() diff --git a/tests/unittest/_torch/modules/test_triton_linear.py b/tests/unittest/_torch/modules/test_triton_linear.py index 2d5e87ae688b..9b1229cf5d82 100644 --- a/tests/unittest/_torch/modules/test_triton_linear.py +++ b/tests/unittest/_torch/modules/test_triton_linear.py @@ -5,10 +5,8 @@ import pytest import torch from mpi4py import MPI -from utils.util import check_accuracy, skip_pre_hopper +from utils.util import check_accuracy, skip_no_hopper -from tensorrt_llm._torch.modules.fused_moe.fused_moe_triton import \ - IS_TRITON_KERNELS_AVAILABLE from tensorrt_llm._torch.modules.linear import Linear from tensorrt_llm._torch.modules.triton_linear import TritonLinear from tensorrt_llm.models.modeling_utils import QuantAlgo, QuantConfig @@ -21,11 +19,10 @@ ) -@pytest.mark.parametrize("linear_cls", [Linear, TritonLinear]) +@pytest.mark.parametrize( + "linear_cls", + [Linear, pytest.param(TritonLinear, marks=skip_no_hopper)]) def test_linear_unquantized(linear_cls): - if not IS_TRITON_KERNELS_AVAILABLE and linear_cls is TritonLinear: - pytest.skip("Triton kernels are not available") - torch.manual_seed(0) torch.cuda.manual_seed(0) num_tokens = 128 @@ -56,11 +53,10 @@ def test_linear_unquantized(linear_cls): check_accuracy(actual_c, reference_c, atol=0.01, rtol=0.01, percent=0.99) -@pytest.mark.parametrize("linear_cls", [Linear, TritonLinear]) +@pytest.mark.parametrize( + "linear_cls", + [Linear, pytest.param(TritonLinear, marks=skip_no_hopper)]) def test_linear_fp8qdq(linear_cls): - if not IS_TRITON_KERNELS_AVAILABLE and linear_cls is TritonLinear: - pytest.skip("Triton kernels are not available") - torch.manual_seed(0) torch.cuda.manual_seed(0) num_tokens = 128 @@ -100,18 +96,12 @@ def test_linear_fp8qdq(linear_cls): percent=0.99) -@skip_pre_hopper +@skip_no_hopper @pytest.mark.parametrize("activation_dtype", [torch.bfloat16, torch.float8_e4m3fn]) def test_linear_mxfp4(activation_dtype): - if not IS_TRITON_KERNELS_AVAILABLE: - pytest.skip("Triton kernels are not available") - if torch.cuda.get_device_capability( - )[0] < 10 and activation_dtype == torch.float8_e4m3fn: + if activation_dtype == torch.float8_e4m3fn: pytest.skip("Latest Triton requires BF16 activation on Hopper") - if torch.cuda.get_device_capability( - )[0] >= 10 and activation_dtype == torch.bfloat16: - pytest.skip("Latest Triton requires FP8 activation on Blackwell") dtype = torch.bfloat16 num_tokens = 128 diff --git a/tests/unittest/_torch/multi_gpu/test_user_buffers.py b/tests/unittest/_torch/multi_gpu/test_user_buffers.py index 00abc2229b96..eb5051afcb52 100644 --- a/tests/unittest/_torch/multi_gpu/test_user_buffers.py +++ b/tests/unittest/_torch/multi_gpu/test_user_buffers.py @@ -330,35 +330,40 @@ def __init__(self, tp_size, rank, hidden_size, dtype, eps, l0_weight, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.ROW, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.l1 = Linear(in_features=hidden_size, out_features=hidden_size, bias=False, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.COLUMN, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.l2 = Linear(in_features=hidden_size, out_features=hidden_size, bias=False, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.ROW, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.l3 = Linear(in_features=hidden_size, out_features=hidden_size, bias=False, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.COLUMN, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.l4 = Linear(in_features=hidden_size, out_features=hidden_size, bias=False, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.ROW, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.norm0 = RMSNorm(hidden_size=hidden_size, eps=eps, dtype=dtype).cuda() self.norm1 = RMSNorm(hidden_size=hidden_size, eps=eps, @@ -460,7 +465,10 @@ def run_single_rank_ub_pass( # 3 AR_NORM replacement # 3 Scaled MM Prologue # 2 UB Finalize Removal - assert backend.match_count == [3, 0, 2, 0, 0, 0, 0, 3, 0, 3, 0, 2, 0] + # 1 Insert copy for graph output + assert backend.match_count == [ + 3, 0, 2, 0, 0, 0, 0, 0, 3, 0, 3, 0, 2, 0, 1, 0 + ] torch.cuda.synchronize() if rank == 0: @@ -759,7 +767,10 @@ def run_single_rank_ub_mm_add_pass(tensor_parallel_size, num_tokens, # 3 AR_NORM replacement # 3 Prologue # 1 UB Finalize Removal - assert backend.match_count == [3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 1, 0] + # 1 Insert copy for graph output + assert backend.match_count == [ + 3, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 1, 0, 1, 0 + ] torch.cuda.synchronize() if rank == 0: @@ -819,35 +830,40 @@ def __init__(self, tp_size, rank, hidden_size, dtype, eps, l0_weight, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.ROW, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.l1 = Linear(in_features=hidden_size, out_features=hidden_size, bias=False, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.COLUMN, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.l2 = Linear(in_features=hidden_size, out_features=hidden_size, bias=False, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.ROW, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.l3 = Linear(in_features=hidden_size, out_features=hidden_size, bias=False, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.COLUMN, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.l4 = Linear(in_features=hidden_size, out_features=hidden_size, bias=False, dtype=dtype, mapping=mapping, tensor_parallel_mode=TensorParallelMode.ROW, - quant_config=quant_config).cuda() + quant_config=quant_config, + enable_gemm_allreduce_fusion=False).cuda() self.norm0 = RMSNorm(hidden_size=hidden_size, eps=eps, dtype=dtype).cuda() self.norm1 = RMSNorm(hidden_size=hidden_size, eps=eps, @@ -993,7 +1009,10 @@ def block_scale_unswizzled(scale): # 3 AR_NORM replacement # 3 Scaled MM Prologue # 2 UB Finalize Removal - assert backend.match_count == [3, 0, 2, 0, 0, 0, 0, 3, 0, 3, 0, 2, 0] + # 1 Insert copy for graph output + assert backend.match_count == [ + 3, 0, 2, 0, 0, 0, 0, 0, 3, 0, 3, 0, 2, 0, 1, 0 + ] torch.cuda.synchronize() torch.testing.assert_close(output_fused, output_ref, diff --git a/tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py b/tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py index aa5c6c5434dc..a4fbce63477b 100644 --- a/tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py +++ b/tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py @@ -1,3 +1,4 @@ +import copy import json import os import time @@ -11,8 +12,10 @@ from tensorrt_llm import MultimodalEncoder from tensorrt_llm._torch.shared_tensor import SharedTensorContainer +from tensorrt_llm._utils import get_sm_version from tensorrt_llm.inputs import default_multimodal_input_loader -from tensorrt_llm.llmapi import CacheTransceiverConfig, KvCacheConfig +from tensorrt_llm.llmapi import (CacheTransceiverConfig, DisaggregatedParams, + KvCacheConfig, MoeConfig) from tensorrt_llm.llmapi.llm import LLM, SamplingParams test_data_root = Path( @@ -26,6 +29,67 @@ _LLAVA_DIR = llm_models_root() / "multimodals" / "llava-v1.6-mistral-7b-hf" _QWEN_2_5_VL_DIR = llm_models_root() / "Qwen2.5-VL-3B-Instruct" _QWEN_3_VL_DIR = llm_models_root() / "Qwen3" / "Qwen3-VL-2B-Instruct" +_QWEN_3_VL_30B_A3B_FP8_DIR = llm_models_root( +) / "Qwen3" / "Qwen3-VL-30B-A3B-Instruct-FP8" + +_FAKE_QWEN3_VL_30B_A3B_FP8_SENTINEL = "qwen3_vl_30b_a3b_fp8_fake" +_FAKE_CHECKPOINT_MARKER = ".tllm_fake_checkpoint" + + +# Unlike the other models, we cannot fit a multimodal encoder + 2 copies of the LLM on a single +# H100 GPU in CI. We therefore resort to creating a slimmed down version of the model with less +# layers. +def _get_fake_qwen3_vl_30b_a3b_config() -> dict: + config_path = _QWEN_3_VL_30B_A3B_FP8_DIR / "config.json" + if not config_path.exists(): + pytest.skip(f"Qwen3-VL-30B-A3B config not found: {config_path}") + with open(config_path, "r") as f: + config = json.load(f) + config = copy.deepcopy(config) + config["text_config"]["num_hidden_layers"] = 2 + return config + + +def _create_fake_qwen3_vl_30b_a3b_fp8_dir( + tmp_path_factory: pytest.TempPathFactory, + assets_dir: Path, +) -> Path: + if not assets_dir.exists(): + pytest.skip(f"Base model dir not found: {assets_dir}") + + fake_dir = tmp_path_factory.mktemp("qwen3_vl_30b_a3b_fp8_fake") + + for item in assets_dir.iterdir(): + if item.name == "config.json": + continue + target = fake_dir / item.name + if target.exists(): + continue + os.symlink(item, target, target_is_directory=item.is_dir()) + + config_path = fake_dir / "config.json" + with open(config_path, "w") as f: + json.dump(_get_fake_qwen3_vl_30b_a3b_config(), f, indent=2) + + (fake_dir / + _FAKE_CHECKPOINT_MARKER).write_text("Synthetic checkpoint for CI tests.\n") + return fake_dir + + +def _get_fake_checkpoint_kwargs(model_dir: Path) -> dict: + if (model_dir / _FAKE_CHECKPOINT_MARKER).exists(): + return {"load_format": "dummy"} + return {} + + +def _is_fake_checkpoint(model_dir: Path) -> bool: + return (model_dir / _FAKE_CHECKPOINT_MARKER).exists() + + +def _get_moe_config_for_blackwell() -> MoeConfig: + if get_sm_version() >= 100: + return MoeConfig(backend="DEEPGEMM") + return MoeConfig() @pytest.mark.parametrize( @@ -66,10 +130,12 @@ def test_kv_event_mm_keys_with_reuse(prompts, expected_num_duplicates): free_gpu_memory_fraction=free_gpu_memory_fraction, event_buffer_max_size=1024, # Enable KV cache events ) + moe_config = _get_moe_config_for_blackwell() llm = LLM(model=encoder_model_dir, backend='pytorch', kv_cache_config=kv_cache_config, + moe_config=moe_config, max_batch_size=1) inputs = _load_inputs(llm, prompts, media) @@ -99,10 +165,20 @@ def test_kv_event_mm_keys_with_reuse(prompts, expected_num_duplicates): f"got {num_duplicates}. Offsets: {mm_keys_offsets}") -@pytest.fixture(scope="module", - params=[_LLAVA_DIR, _QWEN_2_5_VL_DIR, _QWEN_3_VL_DIR], - ids=["llava_7b", "qwen2.5_3b", "qwen3_2b"]) -def model_dir(request) -> Path: +@pytest.fixture( + scope="module", + params=[ + pytest.param(_LLAVA_DIR, id="llava_7b"), + pytest.param(_QWEN_2_5_VL_DIR, id="qwen2.5_3b"), + pytest.param(_QWEN_3_VL_DIR, id="qwen3_2b"), + pytest.param(_FAKE_QWEN3_VL_30B_A3B_FP8_SENTINEL, + id="qwen3_30b_a3b_fp8"), + ], +) +def model_dir(request, tmp_path_factory: pytest.TempPathFactory) -> Path: + if request.param == _FAKE_QWEN3_VL_30B_A3B_FP8_SENTINEL: + return _create_fake_qwen3_vl_30b_a3b_fp8_dir(tmp_path_factory, + _QWEN_3_VL_DIR) return request.param @@ -124,14 +200,18 @@ def llms(model_dir: Path, free_gpu_memory_fraction=free_gpu_memory_fraction, ) + load_kwargs = _get_fake_checkpoint_kwargs(model_dir) + moe_config = _get_moe_config_for_blackwell() llm = LLM( model=model_dir, backend='pytorch', kv_cache_config=kv_cache_config, + moe_config=moe_config, trust_remote_code=True, cache_transceiver_config=cache_transceiver_cfg, disable_overlap_scheduler=disable_overlap_scheduler, max_batch_size=1, # fix batch size to reduce non-determinism in tests + **load_kwargs, ) with llm: if pd_disagg: @@ -139,8 +219,10 @@ def llms(model_dir: Path, model=model_dir, backend='pytorch', kv_cache_config=kv_cache_config, + moe_config=moe_config, trust_remote_code=True, cache_transceiver_config=cache_transceiver_cfg, + **load_kwargs, ) with llm_decode: yield (llm, llm_decode) @@ -216,7 +298,9 @@ def test_single_image_chat( # Prepare inputs for llm (pass mm_embeddings) # Process multimodal data using encoder (pass mm_embeddings) - encoder = MultimodalEncoder(model=model_dir, max_batch_size=max_batch_size) + encoder = MultimodalEncoder(model=model_dir, + max_batch_size=max_batch_size, + **_get_fake_checkpoint_kwargs(model_dir)) with encoder: encoder_outputs = encoder.generate(inputs) @@ -283,6 +367,58 @@ def test_single_image_chat( f"Log probabilities don't match for output {i}, generation {j}" +@pytest.mark.parametrize("model_dir", [_QWEN_3_VL_DIR], indirect=True) +@pytest.mark.parametrize("pd_disagg", [True], indirect=True) +@pytest.mark.threadleak(enabled=False) +def test_pd_disagg_with_image_input( + model_dir: Path, + pd_disagg: bool, + llms: tuple[LLM, LLM | None], +): + """Test P/D disagg with image input.""" + llm, llm_decode = llms + assert llm_decode is not None, "Disaggregated decode worker required." + + prompts = ["Describe the image."] + media = [example_images[-1]] + sampling_params = SamplingParams(max_tokens=32, temperature=0) + + # Reference outputs: use desired `max_tokens`. + inputs = _load_inputs(llm, prompts, media) + outputs_ref = llm.generate(inputs, sampling_params=sampling_params) + assert outputs_ref is not None and len(outputs_ref) == len(prompts) + + # Prefill: `max_tokens=0`. + prefill_disagg_params = DisaggregatedParams(request_type="context_only") + outputs = llm.generate(inputs, + sampling_params=SamplingParams(max_tokens=0, + temperature=0), + disaggregated_params=prefill_disagg_params) + assert len(outputs) == 1 + pd_disaggregated_params = outputs[0].disaggregated_params + pd_disaggregated_params.request_type = "generation_only" + + # Decode: use desired `max_tokens`. + decode_inputs = [{ + "prompt": inputs[0]["prompt"], + "multi_modal_data": None, + "prompt_token_ids": outputs[0].prompt_token_ids, + }] + outputs_pd = llm_decode.generate( + decode_inputs, + sampling_params=sampling_params, + disaggregated_params=pd_disaggregated_params) + + assert len(outputs_pd) == len(prompts) + for i, (ref_output, test_output) in enumerate(zip(outputs_ref, outputs_pd)): + assert len(ref_output.outputs) == len(test_output.outputs), \ + f"Number of generated outputs don't match for output {i}: {len(ref_output.outputs)} vs {len(test_output.outputs)}" + for j, (ref_gen, test_gen) in enumerate( + zip(ref_output.outputs, test_output.outputs)): + assert ref_gen.text == test_gen.text, \ + f"Generated text doesn't match for output {i}, generation {j}:\nReference: {ref_gen.text!r}\nTest: {test_gen.text!r}" + + @pytest.mark.parametrize("use_mm_embeddings,pass_embeddings_through_loader", product([False, True], [False, True])) @pytest.mark.threadleak(enabled=False) @@ -298,13 +434,15 @@ def test_multi_request_batch_chat( embeddings alongside the prompt ("multi_modal_embeddings"), as well as the embedding handling within default_multimodal_input_loader. """ - if use_mm_embeddings and model_dir in [_QWEN_2_5_VL_DIR, _QWEN_3_VL_DIR]: + if use_mm_embeddings and (model_dir in [_QWEN_2_5_VL_DIR, _QWEN_3_VL_DIR] + or _is_fake_checkpoint(model_dir)): pytest.skip("Qwen does not implement attach_multimodal_embeddings") # Qwen2.5/3 VL's vision encoder seems to output different embeddings based on this value. # The test only passes with this set to 1. - encoder_max_batch_size = (1 if model_dir - in [_QWEN_2_5_VL_DIR, _QWEN_3_VL_DIR] else 3) + encoder_max_batch_size = (1 if + model_dir in [_QWEN_2_5_VL_DIR, _QWEN_3_VL_DIR] + or _is_fake_checkpoint(model_dir) else 3) llm, llm_decode = llms if llm_decode is not None: @@ -335,7 +473,8 @@ def test_multi_request_batch_chat( ) > 0, f"Reference generation has no output text for input {i}" encoder = MultimodalEncoder(model=model_dir, - max_batch_size=encoder_max_batch_size) + max_batch_size=encoder_max_batch_size, + **_get_fake_checkpoint_kwargs(model_dir)) with encoder: # Encoder path encoder_outputs = encoder.generate(inputs) diff --git a/tests/unittest/_torch/ray_orchestrator/multi_gpu/test_accuracy_with_allreduce_strategy.py b/tests/unittest/_torch/ray_orchestrator/multi_gpu/test_accuracy_with_allreduce_strategy.py index 765bd7f5f40b..90e8029c6f96 100644 --- a/tests/unittest/_torch/ray_orchestrator/multi_gpu/test_accuracy_with_allreduce_strategy.py +++ b/tests/unittest/_torch/ray_orchestrator/multi_gpu/test_accuracy_with_allreduce_strategy.py @@ -282,15 +282,12 @@ def compare_logprobs(logprobs_list, ref_new_token_logprobs_list): def test_accuracy_with_allreduce_strategy(model_dir, sampler_type, allreduce_strategy): """Test accuracy with different allreduce strategies. - The default allreduce_strategy (AUTO) produced wrong logprobs with large batch size, - causing VeRL integration to fail to converge. There may be an issue with the - customAllReduce kernels. - - Tracked: NVBug (https://nvbugs/5727691) + This test validates that both NCCL and AUTO allreduce strategies produce + correct logprobs compared to HuggingFace reference implementation. Expected behavior: - allreduce_strategy="NCCL": Accuracy assertion PASSES - - allreduce_strategy="AUTO": Accuracy assertion FAILS + - allreduce_strategy="AUTO": Accuracy assertion PASSES """ model_dir = str(llm_models_root() / model_dir) @@ -401,8 +398,4 @@ async def process_all_chunks(hf_models: List[HFModel]): torch.cuda.empty_cache() # Compare LLM logprobs vs HF reference - if allreduce_strategy == "AUTO": - with pytest.raises(AssertionError, match=r"Final Min diff: .* is below threshold -2\.30"): - compare_logprobs(llm_logprobs, ref_new_token_logprobs) - else: - compare_logprobs(llm_logprobs, ref_new_token_logprobs) + compare_logprobs(llm_logprobs, ref_new_token_logprobs) diff --git a/tests/unittest/_torch/ray_orchestrator/single_gpu/test_llm_update_weights.py b/tests/unittest/_torch/ray_orchestrator/single_gpu/test_llm_update_weights.py index 96e882261222..9914913c2ffa 100644 --- a/tests/unittest/_torch/ray_orchestrator/single_gpu/test_llm_update_weights.py +++ b/tests/unittest/_torch/ray_orchestrator/single_gpu/test_llm_update_weights.py @@ -1,3 +1,5 @@ +import base64 +import pickle from typing import Callable, List, Optional import pytest @@ -71,6 +73,40 @@ def get_weight_ipc_handles( return ret + def get_weight_ipc_handles_serialized( + self, + cuda_device: Optional[List[int]] = None, + weight_filter: Optional[Callable[[str], bool]] = None, + ): + """ + Get base64-encoded serialized IPC handles for model weights. + + Args: + cuda_device: List of CUDA device indices to get weights from + weight_filter: Optional function that takes weight name and returns True if weight should be included + + Returns: + ret: Dictionary mapping device UUIDs to base64-encoded pickled handles + """ + ret = {} + device_list = list(range(torch.cuda.device_count())) if cuda_device is None else cuda_device + + for device in device_list: + all_handles = [] + for item in self.all_weights[device]: + name, p = item + # Apply filter if provided + if weight_filter is not None and not weight_filter(name): + continue + handle = reduce_tensor(p) + all_handles.append((name, handle)) + + # Serialize with base64-encoded pickle + serialized = base64.b64encode(pickle.dumps(all_handles)).decode("utf-8") + ret[self.device_uuid[device]] = serialized + + return ret + def generate_batch_incremental( self, original_prompts: List[str], generated_token_ids_list: List[List[int]] ): @@ -153,11 +189,13 @@ def run_generate(llm, hf_model, prompts, sampling_params): return llm_logits, ref_logits +@pytest.mark.parametrize("use_serialized_handles", [True, False]) @pytest.mark.parametrize( "model_dir", ["Qwen2.5-0.5B-Instruct", "Qwen3/Qwen3-8B", "llama-models-v2/TinyLlama-1.1B-Chat-v1.0"], ) -def test_llm_update_weights(model_dir): +def test_llm_update_weights(model_dir, use_serialized_handles): + """Test LLM update_weights with both serialized and direct IPC handle formats.""" model_dir = str(llm_models_root() / model_dir) kv_cache_config = KvCacheConfig(enable_block_reuse=True, free_gpu_memory_fraction=0.1) @@ -182,7 +220,11 @@ def test_llm_update_weights(model_dir): sampling_params = SamplingParams(temperature=0, return_generation_logits=True) - ipc_handles = hf_model.get_weight_ipc_handles([0]) + # Get IPC handles in either serialized or direct format + if use_serialized_handles: + ipc_handles = hf_model.get_weight_ipc_handles_serialized([0]) + else: + ipc_handles = hf_model.get_weight_ipc_handles([0]) llm._collective_rpc("update_weights", (ipc_handles,)) # Finalize the update weights diff --git a/tests/unittest/_torch/sampler/test_torch_sampler.py b/tests/unittest/_torch/sampler/test_torch_sampler.py index db518e608593..b9efcaaa7fa6 100644 --- a/tests/unittest/_torch/sampler/test_torch_sampler.py +++ b/tests/unittest/_torch/sampler/test_torch_sampler.py @@ -643,10 +643,13 @@ def __init__( finish_reasons: list[FinishReason], max_new_tokens: int = MAX_NEW_TOKENS, end_id: Optional[int] = None, + num_draft_tokens: int | None = None, stop_words_list: Optional[list[list[int]]] = None, ): seq_slot = self.seq_slots.pop() # random seq slot in MAX_NUM_SEQUENCES self.prompt = prompt + if num_draft_tokens is None: + num_draft_tokens = len(new_tokens) - 1 self.request = LlmRequest( request_id=seq_slot, seq_slot=seq_slot, @@ -658,7 +661,7 @@ def __init__( end_id=end_id, sampling_config=SamplingConfig(), is_streaming=False, - draft_tokens=new_tokens[:-1], + draft_tokens=new_tokens[:num_draft_tokens], ) assert len(new_tokens) == len(finish_reasons) self.new_tokens = new_tokens @@ -731,6 +734,15 @@ def test_write_finish_reasons(): new_tokens=[12, 13, 60], finish_reasons=[NOT_FINISHED, STOP_WORDS, NOT_FINISHED], ), + RequestCase( + prompt=[7, 8, 6], + stop_words_list=[[12, 13]], + new_tokens=[60, 12, 13], + # The request has stop words, but no draft is created + # Tokens at indices greater than 0 should be ignored + num_draft_tokens=0, + finish_reasons=[NOT_FINISHED, NOT_FINISHED, NOT_FINISHED], + ), RequestCase( prompt=[1, 2, 3, 4], end_id=99, diff --git a/tests/unittest/_torch/speculative/test_draft_len_schedule.py b/tests/unittest/_torch/speculative/test_draft_len_schedule.py index e64ca7fa538c..32c491460f34 100644 --- a/tests/unittest/_torch/speculative/test_draft_len_schedule.py +++ b/tests/unittest/_torch/speculative/test_draft_len_schedule.py @@ -13,23 +13,18 @@ # # ============================================================================ -# # Fixture: Force single-worker mode for all tests in this module +# # Fixture: Force single-worker mode (only for tests that use mocking) # # ============================================================================ -@pytest.fixture(scope="module", autouse=True) -def enforce_single_worker(): - """Force single-worker mode for all tests in this module.""" - import os - - os.environ["TLLM_WORKER_USE_SINGLE_PROCESS"] = "1" +@pytest.fixture(scope="function") +def enforce_single_worker(monkeypatch): + """Mock functions don't work with multiple processes, so we enforce single worker.""" + monkeypatch.setenv("TLLM_WORKER_USE_SINGLE_PROCESS", "1") yield - if "TLLM_WORKER_USE_SINGLE_PROCESS" in os.environ: - del os.environ["TLLM_WORKER_USE_SINGLE_PROCESS"] # # ============================================================================ # # test 1: Generation correctness check # # ============================================================================ -@pytest.mark.skip("https://nvbugspro.nvidia.com/bug/5680911") @pytest.mark.parametrize( "drafter_type,schedule", [ @@ -151,8 +146,9 @@ def test_correctness_across_batch_sizes(drafter_type: str, schedule: dict): ], ) @pytest.mark.high_cuda_memory -@pytest.mark.skip("https://nvbugspro.nvidia.com/bug/5680911") -def test_draft_len_schedule_functionality(drafter_type: str, draft_schedule: dict): +def test_draft_len_schedule_functionality( + enforce_single_worker, drafter_type: str, draft_schedule: dict +): if not torch.cuda.is_available(): pytest.skip("CUDA not available") diff --git a/tests/unittest/_torch/speculative/test_dynamic_spec_decode.py b/tests/unittest/_torch/speculative/test_dynamic_spec_decode.py index eaa215c81e08..29b1016810f1 100644 --- a/tests/unittest/_torch/speculative/test_dynamic_spec_decode.py +++ b/tests/unittest/_torch/speculative/test_dynamic_spec_decode.py @@ -21,6 +21,7 @@ def enforce_single_worker(monkeypatch): yield +@pytest.mark.skip("https://nvbugs/5758449") @pytest.mark.parametrize("disable_overlap_scheduler", [True, False]) @pytest.mark.high_cuda_memory def test_dynamic_spec_decode(enforce_single_worker, diff --git a/tests/unittest/_torch/speculative/test_eagle3.py b/tests/unittest/_torch/speculative/test_eagle3.py index c2d4cf50f4c1..7ef69a00be48 100644 --- a/tests/unittest/_torch/speculative/test_eagle3.py +++ b/tests/unittest/_torch/speculative/test_eagle3.py @@ -8,7 +8,7 @@ import pytest import torch -from test_common.llm_data import with_mocked_hf_download +from test_common.llm_data import with_mocked_hf_download_for_single_gpu from utils.llm_data import llm_models_root from tensorrt_llm import LLM, SamplingParams @@ -150,7 +150,7 @@ def test_kv_lens_runtime_with_eagle3_one_model(): [False, "TRTLLM", True, False, False, False, True, False, False, True], ]) @pytest.mark.high_cuda_memory -@with_mocked_hf_download +@with_mocked_hf_download_for_single_gpu def test_llama_eagle3(use_cuda_graph: bool, attn_backend: str, disable_overlap_scheduler: bool, enable_block_reuse: bool, use_one_model: bool, enable_chunked_prefill: bool, diff --git a/tests/unittest/_torch/speculative/test_spec_gate.py b/tests/unittest/_torch/speculative/test_spec_gate.py index a99654a9c633..4a859c7fc019 100644 --- a/tests/unittest/_torch/speculative/test_spec_gate.py +++ b/tests/unittest/_torch/speculative/test_spec_gate.py @@ -1,28 +1,34 @@ import os import sys import unittest +from unittest.mock import patch import pytest import torch from utils.llm_data import llm_models_root -from utils.util import similar, skip_blackwell from tensorrt_llm import LLM, SamplingParams from tensorrt_llm._torch.speculative.speculation_gate import SpeculationGate from tensorrt_llm.llmapi import (CudaGraphConfig, EagleDecodingConfig, KvCacheConfig) +from tensorrt_llm.logger import logger sys.path.append(os.path.join(os.path.dirname(__file__), '..')) -# It tests the end-to-end functionality of the SpeculationGate, -# which will turn off spec decode when the average acceptance length is below the threshold. -# It is set with acceptance window and acceptance threshold in spec_config. -# This test set the max_concurrency to a large value to prevent spec decode turned off due to number of effective requests > max_concurrency, -# So that we can only focus on the turning off effect from the SpeculationGate. -@skip_blackwell # TODO: Remove after fixing TRTLLM-GEN FMHA segfault on Blackwell. NVBugs: https://nvbugspro.nvidia.com/bug/5698292 +@pytest.fixture(scope="function") +def enforce_single_worker(monkeypatch): + """Mock functions don't work with multiple processes, so we enforce single worker.""" + monkeypatch.setenv("TLLM_WORKER_USE_SINGLE_PROCESS", "1") + yield + + +# Tests that the SpeculationGate correctly disables speculative decoding +# when the average acceptance rate drops below the threshold. +# This test uses a mock to simulate low acceptance rates and verifies +# that the spec gate triggers and disables speculation. @pytest.mark.high_cuda_memory -def test_spec_gate_e2e(): +def test_spec_gate_e2e(enforce_single_worker): total_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9 if total_mem_gb < 35: pytest.skip("Not enough memory to load target + draft model") @@ -32,6 +38,8 @@ def test_spec_gate_e2e(): max_batch_size = 2 max_draft_len = 4 + acceptance_window = 3 + acceptance_threshold = 0.6 kv_cache_config = KvCacheConfig(enable_block_reuse=True, max_tokens=8192) cuda_graph_config = CudaGraphConfig(batch_sizes=[1]) @@ -48,39 +56,88 @@ def test_spec_gate_e2e(): spec_config = EagleDecodingConfig( max_draft_len=max_draft_len, speculative_model=eagle_model_dir, - # Llama 3 does not support one model eagle. eagle3_one_model=False, max_concurrency=10000, - acceptance_window=5, - acceptance_length_threshold=0.6, + acceptance_window=acceptance_window, + acceptance_length_threshold=acceptance_threshold, ) - llm_spec = LLM(**llm_common_config, speculative_config=spec_config) - # Output tests prompts = [ "The capital of France is", "The president of the United States is", "What is the capital of Australia?", - "Explain in one sentence why the sky is blue.", - "Who wrote the book 'Pride and Prejudice'?", - "List three U.S. national holidays in the year 2025.", - "What is the currency of Japan?", - "How many players are on a basketball court for one team?", - "List three primary colors.", ] - sampling_params = SamplingParams(max_tokens=32, temperature=0) + sampling_params = SamplingParams(max_tokens=20, temperature=0) + + # Track calls to record_avg_decoded and the disabled state + gate_state = {"record_calls": [], "gate_disabled": False} + + original_record_avg_decoded = SpeculationGate.record_avg_decoded + + def mock_record_avg_decoded(self, + avg_decoded_tokens_per_iter, + request_id=None): + """ + Mock that simulates low acceptance rate (1.2 tokens/iter = 0.2 accepted). + This is below the threshold of 0.6, so the gate should trigger after the window fills. + """ + # Simulate low acceptance: avg_decoded = 1.2 means accepted_len = 0.2 + # This is below threshold (0.6), so gate should trigger + simulated_low_avg = 1.2 + disabled_now, avg = original_record_avg_decoded(self, simulated_low_avg, + request_id) + + gate_state["record_calls"].append({ + "original_avg": avg_decoded_tokens_per_iter, + "simulated_avg": simulated_low_avg, + "disabled_now": disabled_now, + "avg_accept": avg, + "request_id": request_id, + }) + if disabled_now: + gate_state["gate_disabled"] = True + + return disabled_now, avg - results_spec = llm_spec.generate(prompts, sampling_params) - generated_text_spec = [result.outputs[0].text for result in results_spec] - llm_spec.shutdown() - - llm_ref = LLM(**llm_common_config) - results_ref = llm_ref.generate(prompts, sampling_params) - generated_text_ref = [result.outputs[0].text for result in results_ref] - llm_ref.shutdown() + llm_spec = LLM(**llm_common_config, speculative_config=spec_config) - for text_spec, text_ref in zip(generated_text_spec, generated_text_ref): - assert similar(text_spec, text_ref) + try: + with patch.object(SpeculationGate, 'record_avg_decoded', + mock_record_avg_decoded): + llm_spec.generate(prompts, sampling_params) + + # Verify the mock was called (requests completed) + assert len(gate_state["record_calls"] + ) > 0, "record_avg_decoded should have been called" + + # Verify the gate was disabled after enough requests with low acceptance + assert gate_state["gate_disabled"], \ + f"Gate should have been disabled with simulated low acceptance. Calls: {gate_state['record_calls']}" + + # Verify the gate triggered at the right time (after window is filled) + # The gate should trigger on the `acceptance_window`-th call (index = window - 1) + disable_indices = [ + i for i, call in enumerate(gate_state["record_calls"]) + if call["disabled_now"] + ] + assert len(disable_indices) == 1, \ + f"Gate should have triggered exactly once, but triggered at indices: {disable_indices}" + assert disable_indices[0] >= acceptance_window - 1, \ + f"Gate should trigger after window ({acceptance_window}) is filled, but triggered at index {disable_indices[0]}" + + # Verify the average acceptance was below threshold when disabled + disable_call = gate_state["record_calls"][disable_indices[0]] + assert disable_call["avg_accept"] is not None + assert disable_call["avg_accept"] < acceptance_threshold, \ + f"Avg acceptance ({disable_call['avg_accept']}) should be below threshold ({acceptance_threshold})" + + logger.debug( + f"Gate correctly triggered after {disable_indices[0] + 1} requests") + logger.debug( + f"Final avg acceptance: {disable_call['avg_accept']:.3f} < threshold {acceptance_threshold}" + ) + finally: + llm_spec.shutdown() def test_returns_none_until_window_and_enabled_when_above_threshold(): diff --git a/tests/unittest/disaggregated/test_remoteDictionary.py b/tests/unittest/disaggregated/test_remoteDictionary.py index c9909de88a90..bdd465aabd30 100644 --- a/tests/unittest/disaggregated/test_remoteDictionary.py +++ b/tests/unittest/disaggregated/test_remoteDictionary.py @@ -5,9 +5,7 @@ import time import unittest -import etcd3 - -from tensorrt_llm.serve.metadata_server import EtcdDictionary +from tensorrt_llm.serve.metadata_server import EtcdDictionary, etcd3 def wait_for_port(host, port, timeout=15): diff --git a/tests/unittest/llmapi/_run_mpi_comm_task.py b/tests/unittest/llmapi/_run_mpi_comm_task.py index b60b7a1efdc1..94ca4d386596 100644 --- a/tests/unittest/llmapi/_run_mpi_comm_task.py +++ b/tests/unittest/llmapi/_run_mpi_comm_task.py @@ -3,6 +3,7 @@ import click +from tensorrt_llm.executor.utils import get_spawn_proxy_process_ipc_hmac_key_env from tensorrt_llm.llmapi.mpi_session import RemoteMpiCommSessionClient from tensorrt_llm.llmapi.utils import print_colored @@ -15,8 +16,9 @@ def main(task_type: Literal["submit", "submit_sync"]): tasks = [0] assert os.environ[ 'TLLM_SPAWN_PROXY_PROCESS_IPC_ADDR'] is not None, "TLLM_SPAWN_PROXY_PROCESS_IPC_ADDR is not set" + hmac_key = get_spawn_proxy_process_ipc_hmac_key_env() client = RemoteMpiCommSessionClient( - os.environ['TLLM_SPAWN_PROXY_PROCESS_IPC_ADDR']) + os.environ['TLLM_SPAWN_PROXY_PROCESS_IPC_ADDR'], hmac_key=hmac_key) for task in tasks: if task_type == "submit": client.submit(print_colored, f"{task}\n", "green") diff --git a/tests/unittest/llmapi/_run_multi_mpi_comm_tasks.py b/tests/unittest/llmapi/_run_multi_mpi_comm_tasks.py index 5b50df94f2da..440d07149cbb 100644 --- a/tests/unittest/llmapi/_run_multi_mpi_comm_tasks.py +++ b/tests/unittest/llmapi/_run_multi_mpi_comm_tasks.py @@ -3,7 +3,8 @@ import click -from tensorrt_llm.executor.utils import LlmLauncherEnvs +from tensorrt_llm.executor.utils import ( + LlmLauncherEnvs, get_spawn_proxy_process_ipc_hmac_key_env) from tensorrt_llm.llmapi.mpi_session import RemoteMpiCommSessionClient from tensorrt_llm.llmapi.utils import print_colored @@ -13,8 +14,10 @@ def run_task(task_type: Literal["submit", "submit_sync"]): assert os.environ[ LlmLauncherEnvs. TLLM_SPAWN_PROXY_PROCESS_IPC_ADDR] is not None, "TLLM_SPAWN_PROXY_PROCESS_IPC_ADDR is not set" + hmac_key = get_spawn_proxy_process_ipc_hmac_key_env() client = RemoteMpiCommSessionClient( - os.environ[LlmLauncherEnvs.TLLM_SPAWN_PROXY_PROCESS_IPC_ADDR]) + os.environ[LlmLauncherEnvs.TLLM_SPAWN_PROXY_PROCESS_IPC_ADDR], + hmac_key=hmac_key) for task in tasks: if task_type == "submit": diff --git a/tests/unittest/llmapi/apps/_test_disagg_serving_multi_nodes.py b/tests/unittest/llmapi/apps/_test_disagg_serving_multi_nodes.py index ff9dd92e0caa..c14027434607 100644 --- a/tests/unittest/llmapi/apps/_test_disagg_serving_multi_nodes.py +++ b/tests/unittest/llmapi/apps/_test_disagg_serving_multi_nodes.py @@ -197,7 +197,7 @@ def test_completion(client: openai.OpenAI, # allow 5ms leniency when comparing the time points from disagg and ctx/gen servers validate_timing_metrics(perf_metrics, "multinode test_completion", - time_leniency_seconds=0.005) + time_tolerance_seconds=0.005) # sleep 10 seconds to ensure a successful wait_for_endpoint_ready on rank1 time.sleep(10) disagg_server.terminate() diff --git a/tests/unittest/llmapi/apps/_test_openai_chat_harmony.py b/tests/unittest/llmapi/apps/_test_openai_chat_harmony.py index fc550f082472..e247cb21d9aa 100644 --- a/tests/unittest/llmapi/apps/_test_openai_chat_harmony.py +++ b/tests/unittest/llmapi/apps/_test_openai_chat_harmony.py @@ -160,16 +160,31 @@ async def test_streaming(client: openai.AsyncOpenAI, model: str): "content": "Explain the theory of relativity in brief." }], stream=True, + extra_body={"top_k": 1}, ) - collected_chunks = [] collected_messages = [] + first_iteration = True async for chunk in response: - # Last streaming response will only contains usage info - if len(chunk.choices) <= 0: - continue - - collected_chunks.append(chunk) - collected_messages.append(chunk.choices[0].delta) + if chunk.choices: + delta = chunk.choices[0].delta + if first_iteration: + assert delta.role == "assistant", ValueError( + "Expected role 'assistant' for first iteration") + collected_messages.append(delta) + first_iteration = False + else: + assert delta.role is None, ValueError( + "Expected no role except for first iteration") + collected_messages.append(delta) + else: + assert hasattr(chunk, "usage"), ValueError( + "Expected usage info in last streaming response") + assert chunk.usage.prompt_tokens is not None + assert chunk.usage.completion_tokens is not None + assert chunk.usage.total_tokens is not None + assert chunk.usage.prompt_tokens > 0 + assert chunk.usage.completion_tokens > 0 + assert chunk.usage.total_tokens > 0 full_response = "".join([ m.content for m in collected_messages diff --git a/tests/unittest/llmapi/apps/_test_openai_responses.py b/tests/unittest/llmapi/apps/_test_openai_responses.py index 08b53490b15f..67ae21cd913f 100644 --- a/tests/unittest/llmapi/apps/_test_openai_responses.py +++ b/tests/unittest/llmapi/apps/_test_openai_responses.py @@ -13,10 +13,7 @@ @pytest.fixture(scope="module", - params=[ - "gpt_oss/gpt-oss-20b", "DeepSeek-R1-Distill-Qwen-1.5B", - "Qwen3/Qwen3-0.6B" - ]) + params=["gpt_oss/gpt-oss-20b", "Qwen3/Qwen3-0.6B"]) def model(request): return request.param @@ -35,10 +32,6 @@ def server(model: str, num_postprocess_workers: int): args = ["--num_postprocess_workers", f"{num_postprocess_workers}"] if model.startswith("Qwen3"): args.extend(["--reasoning_parser", "qwen3"]) - elif model.startswith("DeepSeek-R1"): - args.extend(["--reasoning_parser", "deepseek-r1"]) - - if not model.startswith("gpt_oss"): args.extend(["--tool_parser", "qwen3"]) with RemoteOpenAIServer(model_path, args) as remote_server: @@ -167,8 +160,8 @@ def get_current_weather(location: str, format: str = "celsius") -> dict: @pytest.mark.asyncio(loop_scope="module") async def test_tool_calls(client: openai.AsyncOpenAI, model: str): - if model.startswith("DeepSeek-R1"): - pytest.skip("DeepSeek-R1 does not support tool calls") + if model.startswith("Qwen3"): + pytest.skip("Qwen3 tool call is not stable") tool_get_current_weather = { "type": "function", @@ -241,8 +234,8 @@ async def test_streaming(client: openai.AsyncOpenAI, model: str): @pytest.mark.asyncio(loop_scope="module") async def test_streaming_tool_call(client: openai.AsyncOpenAI, model: str): - if model.startswith("DeepSeek-R1"): - pytest.skip("DeepSeek-R1 does not support tool calls") + if model.startswith("Qwen3"): + pytest.skip("Qwen3 tool call is not stable") tool_get_current_weather = { "type": "function", diff --git a/tests/unittest/llmapi/apps/_test_trtllm_serve_multimodal_example.py b/tests/unittest/llmapi/apps/_test_trtllm_serve_multimodal_example.py index 490ccfb232de..30c2b9d2c24a 100644 --- a/tests/unittest/llmapi/apps/_test_trtllm_serve_multimodal_example.py +++ b/tests/unittest/llmapi/apps/_test_trtllm_serve_multimodal_example.py @@ -61,12 +61,15 @@ def example_root(): @pytest.mark.parametrize("exe, script", [("python3", "openai_chat_client_for_multimodal.py"), ("bash", "aiperf_client_for_multimodal.sh")]) -def test_trtllm_serve_examples(exe: str, script: str, +def test_trtllm_serve_examples(exe: str, script: str, model_name: str, server: RemoteOpenAIServer, example_root: str): client_script = os.path.join(example_root, script) + custom_env = os.environ.copy() + custom_env["AIPERF_TOKENIZER_PATH"] = get_model_path(model_name) # CalledProcessError will be raised if any errors occur subprocess.run([exe, client_script], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, - check=True) + check=True, + env=custom_env) diff --git a/tests/unittest/llmapi/apps/test_harmony_channel_validation.py b/tests/unittest/llmapi/apps/test_harmony_channel_validation.py index 6e6046a3ac25..bb16cb4bc974 100644 --- a/tests/unittest/llmapi/apps/test_harmony_channel_validation.py +++ b/tests/unittest/llmapi/apps/test_harmony_channel_validation.py @@ -362,6 +362,7 @@ def test_handle_streaming_response_aborts_on_invalid_channels(self, mock_result) request_id=request_id, done=False, # Not done yet, still streaming num_prompt_tokens=10, + first_iteration=True, ) # CRITICAL ASSERTION: result.abort() should be called @@ -424,6 +425,7 @@ def test_handle_streaming_response_continues_on_valid_channels(self, mock_result request_id=request_id, done=False, num_prompt_tokens=10, + first_iteration=True, ) # CRITICAL ASSERTION: result.abort() should NOT be called diff --git a/tests/unittest/llmapi/test_llm_kv_cache_events.py b/tests/unittest/llmapi/test_llm_kv_cache_events.py index ee5da20c437d..01f11d2ed024 100644 --- a/tests/unittest/llmapi/test_llm_kv_cache_events.py +++ b/tests/unittest/llmapi/test_llm_kv_cache_events.py @@ -9,6 +9,8 @@ from tensorrt_llm._torch.pyexecutor.llm_request import LlmRequest from tensorrt_llm._torch.pyexecutor.resource_manager import KVCacheManager from tensorrt_llm._utils import KVCacheEventSerializer +from tensorrt_llm.bindings.internal.testing import \ + simulate_prefill_completion_only_use_for_testing from tensorrt_llm.llmapi import KvCacheConfig from tensorrt_llm.mapping import Mapping from tensorrt_llm.sampling_params import SamplingParams @@ -83,6 +85,7 @@ def test_kv_cache_event_data_serialization(): req = create_llm_request(0, [1, 2, 3, 4, 5]) kv_cache_manager.impl.add_sequence(req.py_request_id, req.prompt_len, 1, req) + simulate_prefill_completion_only_use_for_testing(req) kv_cache_manager.free_resources(req) flush_events(kv_cache_manager) @@ -100,6 +103,7 @@ def test_kv_cache_event_data_serialization(): req2 = create_llm_request(1, [1, 2, 3, 4, 5]) kv_cache_manager.impl.add_sequence(req2.py_request_id, req2.prompt_len, 1, req2) + simulate_prefill_completion_only_use_for_testing(req2) kv_cache_manager.free_resources(req2) flush_events(kv_cache_manager) diff --git a/tests/unittest/llmapi/test_llm_pytorch.py b/tests/unittest/llmapi/test_llm_pytorch.py index 86f48d31266e..e0fcc96497ae 100644 --- a/tests/unittest/llmapi/test_llm_pytorch.py +++ b/tests/unittest/llmapi/test_llm_pytorch.py @@ -29,8 +29,8 @@ prompts, run_llm_abort_request, run_llm_with_postprocess_parallel_and_result_handler, tinyllama_logits_processor_test_harness) -from utils.util import (force_ampere, similar, skip_fp8_pre_ada, - skip_gpu_memory_less_than_40gb, +from utils.util import (force_ampere, similar, similarity_score, + skip_fp8_pre_ada, skip_gpu_memory_less_than_40gb, skip_gpu_memory_less_than_80gb, skip_gpu_memory_less_than_138gb, skip_ray) from utils.llm_data import llm_models_root @@ -568,6 +568,42 @@ def test_llama_3_1_8b_fp8_with_bf16_lora() -> None: assert similar(output.outputs[0].text, reference) +@skip_ray # https://nvbugs/5682551 +@skip_gpu_memory_less_than_80gb +def test_llama_3_3_70b_fp8_with_squad_lora_tp2() -> None: + skip_fp8_pre_ada(use_fp8=True) + + model_dir = f"{llm_models_root()}/llama-3.3-models/Llama-3.3-70B-Instruct-FP8" + lora_dir = f"{llm_models_root()}/llama-3.3-models/Llama-3.3-70B-Instruct-FP8-lora-adapter_NIM_r8" + + prompt = "What is the capital of the United States?" + expected_output = " Washington, D.C.\nWhat is the capital of the United States? Washington, D.C." + + lora_config = LoraConfig(lora_dir=[lora_dir], + max_lora_rank=8, + max_loras=2, + max_cpu_loras=2) + lora_req = LoRARequest("squad-lora", 0, lora_dir) + + llm = LLM(model_dir, + tensor_parallel_size=2, + lora_config=lora_config, + cuda_graph_config=None) + + try: + output = llm.generate(prompt, + SamplingParams(max_tokens=50, temperature=0.0), + lora_request=[lora_req]) + generated_text = output.outputs[0].text + print(f"Generated output: {repr(generated_text)}") + + similarity = similarity_score(generated_text, expected_output) + assert similar(generated_text, expected_output, threshold=0.8), \ + f"Output similarity too low (similarity={similarity:.2%})!\nExpected: {repr(expected_output)}\nGot: {repr(generated_text)}" + finally: + llm.shutdown() + + @skip_gpu_memory_less_than_80gb @pytest.mark.part2 def test_bielik_11b_v2_2_instruct_multi_lora() -> None: diff --git a/tests/unittest/others/test_llama_conversion.sh b/tests/unittest/others/test_llama_conversion.sh index 49140fc9b248..ed5883e78696 100755 --- a/tests/unittest/others/test_llama_conversion.sh +++ b/tests/unittest/others/test_llama_conversion.sh @@ -1,7 +1,7 @@ set -ex export PATH=~/.local/bin/:$PATH # trtllm-build is inside ~/.local/bin -export MODEL=/home/scratch.trt_llm_data/llm-models/llama-models/llama-7b-hf/ +export MODEL=/home/scratch.trt_llm_data_ci/llm-models/llama-models/llama-7b-hf/ test_fake_config() { python3 convert_checkpoint.py --dtype float16 --n_layer 2 --output_dir ./c-model/llama-7b/fp16 @@ -13,12 +13,12 @@ test_fake_config() { } test_meta() { - python convert_checkpoint.py --meta_ckpt_dir /home/scratch.trt_llm_data/llm-models/llama-models-v2/7B/ --output_dir ./tllm_checkpoint/llama-v2-7b-ckpt-from-meta --tp_size 2 + python convert_checkpoint.py --meta_ckpt_dir /home/scratch.trt_llm_data_ci/llm-models/llama-models-v2/7B/ --output_dir ./tllm_checkpoint/llama-v2-7b-ckpt-from-meta --tp_size 2 trtllm-build --checkpoint_dir ./tllm_checkpoint/llama-v2-7b-ckpt-from-meta --output_dir ./trt_engines/llama-v2-7b-engine-tp2-meta --gemm_plugin float16 mpirun -n 2 --allow-run-as-root \ python ../summarize.py --test_trt_llm \ --tensorrt_llm_rouge1_threshold 18 \ - --hf_model_dir /home/scratch.trt_llm_data/llm-models/llama-models-v2/llama-v2-7b-hf/ \ + --hf_model_dir /home/scratch.trt_llm_data_ci/llm-models/llama-models-v2/llama-v2-7b-hf/ \ --data_type fp16 \ --engine_dir ./trt_engines/llama-v2-7b-engine-tp2-meta \ --test_hf @@ -80,7 +80,7 @@ test_gptq() { python convert_checkpoint.py --model_dir ${MODEL} \ --output_dir ./tllm_checkpoint/2gpu_gptq \ --dtype float16 \ - --quant_ckpt_path /home/scratch.trt_llm_data/llm-models/int4-quantized-gptq-awq/llama-7b-4bit-gs128.safetensors \ + --quant_ckpt_path /home/scratch.trt_llm_data_ci/llm-models/int4-quantized-gptq-awq/llama-7b-4bit-gs128.safetensors \ --use_weight_only \ --weight_only_precision int4_gptq \ --per_group \ @@ -100,8 +100,8 @@ test_gptq() { } test_lora() { - lora_dir=/home/scratch.trt_llm_data/llm-models/llama-models-v2/chinese-llama-2-lora-13b - python convert_checkpoint.py --model_dir /home/scratch.trt_llm_data/llm-models/llama-models-v2/llama-v2-13b-hf \ + lora_dir=/home/scratch.trt_llm_data_ci/llm-models/llama-models-v2/chinese-llama-2-lora-13b + python convert_checkpoint.py --model_dir /home/scratch.trt_llm_data_ci/llm-models/llama-models-v2/llama-v2-13b-hf \ --output_dir ./tllm_checkpoint/2gpu_lora \ --dtype float16 \ --tp_size 2 @@ -126,7 +126,7 @@ test_lora() { } test_mixtral() { - python convert_checkpoint.py --model_dir /home/scratch.trt_llm_data/llm-models/Mixtral-8x7B-v0.1/ \ + python convert_checkpoint.py --model_dir /home/scratch.trt_llm_data_ci/llm-models/Mixtral-8x7B-v0.1/ \ --output_dir ./tllm_checkpoint/mixtral_2gpu \ --dtype float16 \ --pp_size 2 \ @@ -137,7 +137,7 @@ test_mixtral() { } test_long_alpaca_rope_scaling() { - python convert_checkpoint.py --model_dir /home/scratch.trt_llm_data/llm-models/LongAlpaca-7B/ \ + python convert_checkpoint.py --model_dir /home/scratch.trt_llm_data_ci/llm-models/LongAlpaca-7B/ \ --output_dir ./tllm_checkpoint/long_alpaca_tp2 \ --dtype float16 \ --tp_size 2 @@ -152,12 +152,12 @@ test_long_alpaca_rope_scaling() { --max_input_length 32768 \ --input_file ../../tests/integration/test_input_files/pg64317_sanitized.txt \ --engine_dir ./trt_engines/long_alpaca_tp2 \ - --tokenizer_dir /home/scratch.trt_llm_data/llm-models/LongAlpaca-7B/ + --tokenizer_dir /home/scratch.trt_llm_data_ci/llm-models/LongAlpaca-7B/ } test_llava() { python ../llama/convert_checkpoint.py \ - --model_dir /home/scratch.trt_llm_data/llm-models/llava-1.5-7b-hf/ \ + --model_dir /home/scratch.trt_llm_data_ci/llm-models/llava-1.5-7b-hf/ \ --output_dir ./trt_checkpoint/llava-1gpu \ --dtype float16 @@ -172,7 +172,7 @@ test_llava() { } test_bfloat16() { - python convert_checkpoint.py --output_dir ./tllm_checkpoint/llama_v2-summarization/bfloat16/1-gpu --dtype=bfloat16 --tp_size=1 --pp_size=1 --model_dir /home/scratch.trt_llm_data/llm-models/llama-models-v2/llama-v2-7b-hf + python convert_checkpoint.py --output_dir ./tllm_checkpoint/llama_v2-summarization/bfloat16/1-gpu --dtype=bfloat16 --tp_size=1 --pp_size=1 --model_dir /home/scratch.trt_llm_data_ci/llm-models/llama-models-v2/llama-v2-7b-hf } test_all() diff --git a/tests/unittest/others/test_triton_kernels_vendoring.py b/tests/unittest/others/test_triton_kernels_vendoring.py new file mode 100644 index 000000000000..8a88baeb2f6e --- /dev/null +++ b/tests/unittest/others/test_triton_kernels_vendoring.py @@ -0,0 +1,73 @@ +# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for vendored triton_kernels package. + +The triton_kernels package is vendored from the Triton project to provide +optimized kernels. These tests verify that the vendoring mechanism works +correctly and that our version takes precedence over any external installation. +""" + +import unittest +from pathlib import Path + + +class TestTritonKernelsVendoring(unittest.TestCase): + def test_triton_kernels_is_vendored_version(self): + """Verify we're using the vendored version (has VERSION and LICENSE files).""" + import tensorrt_llm # noqa: F401, I001 + import triton_kernels + + triton_kernels_path = Path(triton_kernels.__file__).parent + + # VERSION file is added by our vendor script and not present in external installations + version_file = triton_kernels_path / "VERSION" + self.assertTrue( + version_file.exists(), + f"VERSION file not found at {version_file}. " + "This suggests an external triton_kernels is being used instead of our vendored version.", + ) + + # LICENSE file should also be present for compliance + license_file = triton_kernels_path / "LICENSE" + self.assertTrue(license_file.exists(), f"LICENSE file not found at {license_file}.") + + def test_version_matches_requirements(self): + """Verify vendored triton_kernels VERSION matches triton version in requirements.txt.""" + import re + + repo_root = Path(__file__).parent.parent.parent.parent + + version_file = repo_root / "triton_kernels" / "VERSION" + vendored_version = version_file.read_text().strip().split()[0].lstrip("v") + + requirements_file = repo_root / "requirements.txt" + requirements_text = requirements_file.read_text() + + match = re.search(r"^triton==([^\s#]+)", requirements_text, re.MULTILINE) + self.assertIsNotNone(match, "Could not find triton version in requirements.txt") + requirements_version = match.group(1) + + self.assertEqual( + vendored_version, + requirements_version, + f"Vendored triton_kernels version ({vendored_version}) does not match " + f"triton version in requirements.txt ({requirements_version}). " + "To update the vendored triton_kernels, run: python scripts/vendor_triton_kernels.py " + f"--tag v{requirements_version}", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/unittest/test_pip_install.py b/tests/unittest/test_pip_install.py index 346b5051ac38..5b2c9b97e67f 100644 --- a/tests/unittest/test_pip_install.py +++ b/tests/unittest/test_pip_install.py @@ -183,8 +183,11 @@ def test_pip_install(): subprocess.check_call("apt-get -y install libzmq3-dev", shell=True) subprocess.check_call("apt-get -y install python3-pip", shell=True) - subprocess.check_call("pip3 install --upgrade pip || true", shell=True) - subprocess.check_call("pip3 install --upgrade setuptools || true", + subprocess.check_call("pip3 install --ignore-installed pip || true", + shell=True) + subprocess.check_call("pip3 install --ignore-installed setuptools || true", + shell=True) + subprocess.check_call("pip3 install --ignore-installed wheel || true", shell=True) download_wheel(args) diff --git a/tests/unittest/utils/util.py b/tests/unittest/utils/util.py index 2c731e9110b1..828bf08ea55b 100644 --- a/tests/unittest/utils/util.py +++ b/tests/unittest/utils/util.py @@ -106,6 +106,9 @@ def isSM100Family(): skip_pre_hopper = pytest.mark.skipif( getSMVersion() < 90, reason="This test is not supported in pre-Hopper architecture") +skip_no_hopper = pytest.mark.skipif( + getSMVersion() != 90, + reason="This test is only supported in Hopper architecture") skip_pre_blackwell = pytest.mark.skipif( getSMVersion() < 100, reason="This test is not supported in pre-Blackwell architecture") diff --git a/triton_backend/all_models/multimodal/Deprecation_notice.md b/triton_backend/all_models/multimodal/Deprecation_notice.md index ba4ce112acd4..15fe93a413c9 100644 --- a/triton_backend/all_models/multimodal/Deprecation_notice.md +++ b/triton_backend/all_models/multimodal/Deprecation_notice.md @@ -2,6 +2,6 @@ Trition backend with multimodal is deprecated and is no longer maintained. **End of Life:** TensorRT-LLM-V1.2 \ -**Reason:** Retiring support for older multimodal models and migrating to [Dynamo + PyTorch backend](https://docs.nvidia.com/dynamo/latest/backends/trtllm/multimodal_support.html) for newer ones. \ +**Reason:** Retiring support for older multimodal models and migrating to [Dynamo + PyTorch backend](https://docs.dynamo.nvidia.com/dynamo/latest/backends/tensor-rt-llm/reference-guide#multimodal-support) for newer ones. \ **Action:** Please use `PyTorch LLM backend` instead. \ Check here for the [Supporting Matrix](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/models/supported-models.md#multimodal-feature-support-matrix-pytorch-backend). diff --git a/triton_backend/ci/L0_backend_trtllm/base_metrics_verification_tests.py b/triton_backend/ci/L0_backend_trtllm/base_metrics_verification_tests.py index 2d6f2ba55706..5cae15823621 100644 --- a/triton_backend/ci/L0_backend_trtllm/base_metrics_verification_tests.py +++ b/triton_backend/ci/L0_backend_trtllm/base_metrics_verification_tests.py @@ -81,6 +81,8 @@ def _run_infer(self, client, prompts, output_lens, beam_width_value=1): utils.prepare_tensor("max_tokens", output0_len, "http"), utils.prepare_tensor("bad_words", bad_words_list, "http"), utils.prepare_tensor("stop_words", stop_words_list, "http"), + utils.prepare_tensor("streaming", np.array([[False]], + dtype=bool), "http"), ] if beam_width_value > 1: beam_width = np.ones_like(input0).astype( diff --git a/triton_backend/requirements.txt b/triton_backend/requirements.txt index 7daa868ed48b..4375447772cd 100644 --- a/triton_backend/requirements.txt +++ b/triton_backend/requirements.txt @@ -1,7 +1,7 @@ regex fire tritonclient[all] -transformers==4.57.1 +transformers==4.57.3 pandas tabulate flash_attn diff --git a/triton_kernels/LICENSE b/triton_kernels/LICENSE new file mode 100644 index 000000000000..1d0238e86b1b --- /dev/null +++ b/triton_kernels/LICENSE @@ -0,0 +1,23 @@ +/* +* Copyright 2018-2020 Philippe Tillet +* Copyright 2020-2022 OpenAI +* +* Permission is hereby granted, free of charge, to any person obtaining +* a copy of this software and associated documentation files +* (the "Software"), to deal in the Software without restriction, +* including without limitation the rights to use, copy, modify, merge, +* publish, distribute, sublicense, and/or sell copies of the Software, +* and to permit persons to whom the Software is furnished to do so, +* subject to the following conditions: +* +* The above copyright notice and this permission notice shall be +* included in all copies or substantial portions of the Software. +* +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +*/ diff --git a/triton_kernels/README.md b/triton_kernels/README.md new file mode 100644 index 000000000000..4d6005b9f4b0 --- /dev/null +++ b/triton_kernels/README.md @@ -0,0 +1,24 @@ +# Vendored triton_kernels + +This directory contains code vendored from the [Triton](https://github.com/triton-lang/triton) project. + +| | | +|---|---| +| **Copyright** | The Triton Authors | +| **License** | MIT (see [LICENSE](LICENSE) file in this directory) | +| **Source** | https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels | +| **Version** | `v3.5.1` | + +## Attribution + +This code is the work of the Triton authors and is included here under the MIT License. +Each Python file includes an attribution header indicating its origin. + +## Do Not Edit + +This code is vendored verbatim and should not be modified directly. +To update to a newer version, run: + +```bash +python scripts/vendor_triton_kernels.py --tag +``` diff --git a/triton_kernels/VERSION b/triton_kernels/VERSION new file mode 100644 index 000000000000..c63f79d80cb2 --- /dev/null +++ b/triton_kernels/VERSION @@ -0,0 +1,3 @@ +v3.5.1 +# This file tracks the version of triton-kernels that was vendored. +# To update, run: python scripts/vendor_triton_kernels.py --tag diff --git a/triton_kernels/__init__.py b/triton_kernels/__init__.py new file mode 100644 index 000000000000..7676bb8f765e --- /dev/null +++ b/triton_kernels/__init__.py @@ -0,0 +1,3 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/__init__.py +# Triton is licensed under the MIT License. diff --git a/triton_kernels/compaction.py b/triton_kernels/compaction.py new file mode 100644 index 000000000000..7b0ed10c35a0 --- /dev/null +++ b/triton_kernels/compaction.py @@ -0,0 +1,73 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/compaction.py +# Triton is licensed under the MIT License. + +import torch +from .compaction_details._masked_compaction import _masked_compaction +from .tensor import Bitmatrix + + +def compaction(yv, yi, bitmask, sentinel=-1): + """ + Return compacted copies of *yv* and *yi* based on a per-row bitmask. + + Only the elements whose index appears among the active bits of *bitmask* + are kept; the rest are replaced by *sentinel*. Kept elements preserve + their original left-to-right order. + + Parameters + ---------- + yv : torch.Tensor, shape (B, K) + Values tensor. + yi : torch.Tensor, shape (B, K), dtype torch.long + Integer indices (0 ≤ index < 32) associated with *yv*. + bitmask : torch.Tensor, shape (B,) **or** (B, 32) + Per-row mask of active indices. See the in-place version for details. + sentinel : int, default -1 + Value written into dropped positions of the returned tensors. + + Returns + ------- + (yv_out, yi_out) : Tuple[torch.Tensor, torch.Tensor], each shape (B, K) + New tensors with the same dtype/device as the inputs. + + """ + + n_rows, n_cols = yi.shape + ret_yv = torch.empty_like(yv) + ret_yi = torch.empty_like(yi) + if isinstance(bitmask, Bitmatrix): + bitmask = bitmask.storage.data + + _masked_compaction[(n_rows, )]( + yv, yi, bitmask, bitmask.stride(0), bitmask.stride(1), # inputs + ret_yv, ret_yi, # outputs + sentinel, # sentinel + K=n_cols # constants + ) + return ret_yv, ret_yi + + +def compaction_torch(yv: torch.Tensor, yi: torch.Tensor, bitmask: torch.Tensor, sentinel=-1): + """ + reference implementation of `masked_compact` + """ + B, K = yi.shape + device = yi.device + # Expand bitmask to a boolean matrix of active bits (B, 32) + w = (1 << torch.arange(32, device=device, dtype=bitmask.dtype)) + bits = (bitmask.unsqueeze(-1) & w) != 0 + mask = bits.flatten(start_dim=-2) # or bits.reshape(B, -1) + # For every yi element decide whether it should be kept + keep = mask.gather(1, yi.long()) + # Build a stable permutation that brings all "keep" items forward + # False→0, True→1 ==> invert so kept==0, dropped==1, then argsort + order = (~keep).to(torch.int).argsort(dim=1, stable=True) + # Re‑order tensors according to above permutation + yi_sorted = yi.gather(1, order) + yv_sorted = yv.gather(1, order) + # fill relevant positions with sentinel + keep_sorted = keep.gather(1, order) + yi_sorted[~keep_sorted] = sentinel + yv_sorted[~keep_sorted] = sentinel + return yv_sorted, yi_sorted diff --git a/triton_kernels/compaction_details/__init__.py b/triton_kernels/compaction_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/compaction_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/compaction_details/_masked_compaction.py b/triton_kernels/compaction_details/_masked_compaction.py new file mode 100644 index 000000000000..1432644d4471 --- /dev/null +++ b/triton_kernels/compaction_details/_masked_compaction.py @@ -0,0 +1,24 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/compaction_details/_masked_compaction.py +# Triton is licensed under the MIT License. + +import triton +import triton.language as tl + + +@triton.jit +def _masked_compaction(Yv, Yi, BitMask, stride_bm, stride_bn, RetYv, RetYi, sentinel, K: tl.constexpr): + pid_m = tl.program_id(0) + yv = tl.load(Yv + pid_m * K + tl.arange(0, K)) + yi = tl.load(Yi + pid_m * K + tl.arange(0, K)) + div = yi // 32 + rem = yi % 32 + active_bits = (tl.load(BitMask + pid_m * stride_bm + div * stride_bn) >> rem) & 1 + exc_cumsum = tl.cumsum(active_bits, 0) - active_bits + active_flags = active_bits.to(tl.int1) + rev_arange = tl.where(active_flags, 0, K - 1 - tl.arange(0, K)) + write_indx = exc_cumsum + rev_arange + yv = tl.where(active_flags, yv, sentinel) + yi = tl.where(active_flags, yi, sentinel) + tl.store(RetYv + pid_m * K + write_indx, yv) + tl.store(RetYi + pid_m * K + write_indx, yi) diff --git a/triton_kernels/matmul_ogs.py b/triton_kernels/matmul_ogs.py new file mode 100644 index 000000000000..b2cc83b2b542 --- /dev/null +++ b/triton_kernels/matmul_ogs.py @@ -0,0 +1,613 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/matmul_ogs.py +# Triton is licensed under the MIT License. + +# isort: off +# fmt: off +from dataclasses import dataclass +import itertools +import sys +import torch +import triton +from enum import Enum, auto +import math +# utilities +from triton_kernels import target_info +from triton_kernels.numerics import InFlexData, OutFlexData +from triton_kernels.routing import GatherIndx, RoutingData, ScatterIndx +from triton_kernels.target_info import is_cuda +# details +from .matmul_ogs_details._matmul_ogs import _matmul_ogs +from .matmul_ogs_details._p_matmul_ogs import _p_matmul_ogs, get_per_device_per_stream_alloc_fn +from .matmul_ogs_details._reduce_grouped import _reduce_grouped +from .numerics_details.mxfp import MXFP_BLOCK_SIZE +from .matmul_ogs_details.opt_flags import make_opt_flags, update_opt_flags_constraints, InapplicableConstraint +from .specialize import specialize +from .tensor import Storage, Tensor, FP4, bitwidth, wrap_torch_tensor + + +@dataclass(frozen=True) +class FnSpecs: + name: str + fn: "triton.runtime.jit.JITFunction" + fn_arg_names: tuple[str] + fn_arg_do_not_specialize: tuple[str] = tuple() + + @staticmethod + def default(): + return FnSpecs("dflt", None, tuple()) + + +@dataclass(frozen=True) +class FusedActivation: + specs: FnSpecs = FnSpecs.default() + fn_args: tuple[object] = tuple() + reduction_n: int = 1 + + +@dataclass(frozen=True) +class Epilogue: + specs: FnSpecs = FnSpecs.default() + fn_arg_values_matmul: tuple[object] = tuple() + fn_arg_values_finalize: tuple[object] = tuple() + effective_itemsize: float = None + +class FnName(Enum): + QUANTIZE_MXFP8 = auto() + + +EpilogueSpecs = FnSpecs # TODO: remove this alias when callers are updated + +_kernels = dict() + + +def get_kernels(epilogue: FnSpecs = FnSpecs.default(), fused_activation: FnSpecs = FnSpecs.default()): + global _kernels + key = (fused_activation.name, epilogue.name) + if key in _kernels: + return _kernels[key] + spec_constants = { + "ACTIVATION_FN": fused_activation.fn, + "EPILOGUE_FN": epilogue.fn, + } + spec_tuples = { + "activation_fn_args": fused_activation.fn_arg_names, + "epilogue_fn_args": epilogue.fn_arg_names, + } + do_not_specialize = fused_activation.fn_arg_do_not_specialize + epilogue.fn_arg_do_not_specialize + import types + + module = types.ModuleType(f"matmul_ogs_{'_'.join(key)}") + sys.modules[module.__name__] = module + module._matmul_ogs = specialize(_matmul_ogs, module, spec_constants, spec_tuples, + do_not_specialize=do_not_specialize) + module._p_matmul_ogs = specialize(_p_matmul_ogs, module, spec_constants, spec_tuples, + do_not_specialize=do_not_specialize) + module._reduce_grouped = specialize(_reduce_grouped, module, spec_constants, spec_tuples, + do_not_specialize=do_not_specialize) + _kernels[key] = module + return module + + +# ----------------------------------------------------------------------------- +# Matrix Multiplication + Outer Gather/Scatter +# ----------------------------------------------------------------------------- + + +def can_overflow_int32(tensor: torch.Tensor): + max_int32 = (1 << 31) - 1 + offset = 0 + for i in range(tensor.ndim): + offset += (tensor.shape[i] - 1) * tensor.stride(i) + return offset > max_int32 + + +def should_upcast_indices(*args): + return any(tensor is not None and can_overflow_int32(tensor) for tensor in args) + + +# --------------------- +# Numerics +# --------------------- + +# fmt: off + +@dataclass(frozen=True) +class FlexCtx: + lhs_data: InFlexData = InFlexData() + rhs_data: InFlexData = InFlexData() + out_data: OutFlexData = OutFlexData() + +@dataclass +class PrecisionConfig: + max_num_imprecise_acc: int = None + allow_tf32: bool = True + flex_ctx: FlexCtx = FlexCtx() + acc_scale: int = 1.0 + flexpoint_saturate_inf: bool = False + report_quantization_err_fn: callable = None + act_scale: Tensor | None = None + weight_scale: Tensor| None = None + out_scale: Tensor | None = None + out_dtype: torch.dtype = None + enforce_bitwise_invariance: bool = False + + +# TODO: merge in opt_flags +def get_swap_xw(precision_config, opt_flags): + if target_info.cuda_capability_geq(10, 0): + return precision_config.weight_scale is not None and opt_flags.block_m <= 64 and opt_flags.is_persistent + return False + +# --------------------- +# Allocation +# --------------------- + +@dataclass +class MatmulAllocation: + device: str + output: tuple[tuple[int], torch.dtype] + scratchpads: dict[str, tuple] + +def init_allocation(x, w, precision_config, fused_activation, routing_data, gather_indx, scatter_indx, opt_flags): + # ---- output ------ + N = w.shape[-1] + # by default - M is number of rows in the activations + M = x.shape[-2] + # if the activations are gathered, then M is number of gather indices + if gather_indx is not None: + M = gather_indx.src_indx.shape[0] + # final output + if routing_data.n_expts_act == 1 or scatter_indx is None: + y_rows = M + else: + Mc = scatter_indx.src_indx.shape[0] // routing_data.n_expts_act # compressed number of rows + y_rows = Mc + batch_dim = x.shape[0] if x.ndim == 3 else 1 + out_shape = (batch_dim, y_rows, N // fused_activation.reduction_n) + out_dtype = precision_config.out_dtype or x.dtype + output = (out_shape, out_dtype) + # ---- scratchpad -----# + scratchpad = dict() + if opt_flags.split_k > 1 or (scatter_indx is not None and not opt_flags.fused_scatter): + scratch_out_dtype = torch.float32 if opt_flags.split_k > 1 else out_dtype + scratchpad["matmul"] = ((opt_flags.split_k, 1, M, N), scratch_out_dtype) + if "matmul" in scratchpad and precision_config.out_scale is not None: + scratchpad["mx_out_scale"] = ((opt_flags.split_k, 1, M, triton.cdiv(N, MXFP_BLOCK_SIZE)), torch.uint8) + return MatmulAllocation(x.device, output, scratchpad) + +def apply_allocation(allocation: MatmulAllocation, output): + ret = dict() + if output is None: + output = torch.empty(allocation.output[0], device=allocation.device, dtype=allocation.output[1]) + else: + assert output.shape == allocation.output[0] + ret["output"] = output[None, :, :] + ret["scratchpad"] = { + k: torch.empty(v[0], device=allocation.device, dtype=v[1]) + for k, v in allocation.scratchpads.items() + } + return ret + +# ----------------------------------------------------------------------------- +# Canonicalize +# ----------------------------------------------------------------------------- +# the `matmul_ogs` kernel can operate on 2D or 3D inputs depending on the mode being used +# we can canonicalize storages to make the implementation more uniform + +def _canonicalize_storage(storage, out_ndim, flex_data): + assert out_ndim >= storage.data.ndim + # Need to use as_strided instead of view because for a tensor with + # shape[-2] == 1 can have ambuiguity related to col-wise. Fo example, + # > t = torch.randn(2, 5, 1).mT + # > t_view = t.view(t.shape) + # > t.stride(), t_view.stride() + # ((5, 1, 1), (5, 5, 1)) + # Our check t_view is col-wise fails since t_view.stride(-2) != 1 + # This case is covered by (m, n, k) == (1000, 700, 2) in test_matmul.py + new_storage_shape = [1] * (out_ndim - storage.data.ndim) + list(storage.data.shape) + new_storage_view = storage.data.view(new_storage_shape) + new_storage_stride = [new_storage_view.stride(0)] * (out_ndim - storage.data.ndim) + list(storage.data.stride()) + new_storage_data = storage.data.as_strided(new_storage_shape, new_storage_stride) + if flex_data is not None: + new_storage_data = flex_data.reinterpret(new_storage_data) + return Storage(new_storage_data, storage.layout) + +# + +def reduce_grouped(x: torch.Tensor, indx: torch.Tensor, out: torch.Tensor, out_mx_scale: torch.Tensor, + fused_activation, epilogue, + x_flex: InFlexData | None = None, + out_flex: OutFlexData | None = None, x_mx_scale: torch.Tensor | None = None, + out_dtype: bool = None, flexpoint_saturate_inf: bool = False): + """ + In-place grouped row reduction. + + Arguments + - x: Tensor[AnyFloat] of shape [(num_groups * K), N] + - indx: Tensor[Int] of shape [num_groups, K] + + Description + For each group g in [0, num_groups), this routine sums the K rows of `x` + specified by `indx[g, :]` and overwrites the row corresponding to the first + valid (non-negative) index with the per-group sum. Accumulation is performed + in float32 for numerical stability, and the result is written back in the + dtype of `x`. + + Behavior and edge cases + - Invalid (-1) entries are skipped during accumulation and do not generate + memory traffic. If a group has no valid entries, nothing is written for + that group. + - Reduction is performed tile-by-tile along the N dimension within a single + kernel launch (persistent along N) to minimize launch overhead. + + Performance notes + - Memory traffic per group is approximately (valid_rows_read + 1) * N * sizeof(x), + plus index reads. With no invalid entries, this becomes (K + 1) reads/writes + of length N per group. + + Returns + - The input tensor `x` (modified in place). + """ + if indx is None and x.shape[0] == 1: + return x.squeeze(0), None + if indx is not None: + num_groups = indx.shape[0] + else: + num_groups = x.shape[-2] + if x_flex is None: + x_flex = InFlexData() + if out_flex is None: + out_flex = OutFlexData() + K = 1 if indx is None else indx.shape[1] + out_dtype = x.dtype if out_dtype is None else out_dtype + assert x.shape[-1] % fused_activation.reduction_n == 0 + BLOCK_N = 512 + # Resolve scalar flex scales (may be None) + x_expected_scale = None if x_flex is None else x_flex.scale + out_expected_scale = None if out_flex is None else out_flex.expected_scale + out_actual_scale = None if out_flex is None else out_flex.actual_scale + out_checksum_scale = None if out_flex is None else out_flex.checksum_scale + # Resolve MXFP output scale row stride + stride_mxb = 0 if x_mx_scale is None else x_mx_scale.stride(0) + stride_mxs = 0 if x_mx_scale is None else x_mx_scale.stride(1) + stride_omxs = 0 if out_mx_scale is None else out_mx_scale.stride(0) + kernels = get_kernels(epilogue.specs, fused_activation.specs) + kernels._reduce_grouped[(num_groups, )]( + x_flex.reinterpret(x), x.stride(0), x.stride(2), x.stride(3), # + x_expected_scale, # scalar input scale + out_flex.reinterpret(out), out.stride(1), out.stride(2), # + out_expected_scale, out_actual_scale, out_checksum_scale, indx, # + x.shape[0], x.shape[-1], # + x_mx_scale, stride_mxb, stride_mxs, # + out_mx_scale, stride_omxs, # + *fused_activation.fn_args, fused_activation.reduction_n, + *epilogue.fn_arg_values_finalize, + HAS_IN_MX_SCALE=x_mx_scale is not None, HAS_OUT_MX_SCALE=out_mx_scale is not None, + FLEXPOINT_SATURATE_INF=flexpoint_saturate_inf, # + BLOCK_N=BLOCK_N, K=K, # + num_warps=1, # + ) + return out, out_mx_scale + +# ----------------------------------------------------------------------------- +# Triton Implementation +# ----------------------------------------------------------------------------- + +def matmul_ogs_set_idle_sms(num_idle_sms): + """ + persistent kernels will leave `num_idle_sms` idle + """ + update_opt_flags_constraints({"idle_sms": num_idle_sms}) + +def matmul_ogs(x, w, bias, + routing_data: RoutingData | None = None, + gather_indx: GatherIndx | None = None, + scatter_indx: ScatterIndx | None = None, + precision_config: PrecisionConfig | None = None, + betas: torch.Tensor | None = None, + gammas: torch.Tensor | None = None, + out_alpha: float | None = None, + y: torch.Tensor | None = None, + fused_activation: FusedActivation | None = None, + epilogue: Epilogue | None = None, + ): + """ + Y[:, :] = 0. + for e in num_experts: + Y[idxs_y_m(e), :] += matmul(X[idxs_x_m(e), :], W[e, :, :]) + """ + is_input_batched = x.ndim == 3 + if is_input_batched: + assert gather_indx is None, "gather not supported in batched mode" + assert scatter_indx is None, "scatter not supported in batched mode" + assert routing_data is None, "routing not supported in batched mode" + assert w.ndim == 3 and w.shape[0] == x.shape[0] + # canonicalize inputs + if precision_config is None: + precision_config = PrecisionConfig() + if fused_activation is None: + fused_activation = FusedActivation(FnSpecs.default(), tuple(), 1) + if epilogue is None: + epilogue = Epilogue(FnSpecs.default(), tuple(), tuple(), False) + if routing_data is None: + routing_data = RoutingData(None, None, max(1, w.shape[0]), 1) + # unpack scales + w_scale = precision_config.weight_scale + w_has_mx = w_scale is not None + is_hopper_fp8 = is_cuda() and not target_info.cuda_capability_geq(10, 0) and bitwidth(w.dtype) == 8 + if is_hopper_fp8: assert w.stride(-2) == 1, "`w` must be column-major when it has data-type FP8 on capability < 10" + if not isinstance(w, Tensor): + # TODO: remove this code path; using uint8 for mxfp4 weight will bite us when we want to support uint8 for real + dtype = FP4 if w.dtype == torch.uint8 else w.dtype + w = wrap_torch_tensor(w, dtype=dtype) + if w_scale is not None and not isinstance(w_scale, Tensor): + w_scale = Tensor(w_scale) + if w_scale is not None: + w_scale.storage.data = w_scale.data.view(torch.uint8) + w_scale.dtype = torch.uint8 + x_scale = precision_config.act_scale + x_has_mx = x_scale is not None + if x_has_mx: assert x.stride(-1) == 1, "'x' must be row-major when it has data-type mxfp" + if x_scale is not None and not isinstance(x_scale, Tensor): + x_scale = Tensor(x_scale) + if not isinstance(x, Tensor): + x = Tensor(x, dtype=x.dtype) + # determine shapes + has_gather = gather_indx is not None + has_scatter = scatter_indx is not None + is_ragged = routing_data.expt_hist is not None + M = x.shape[-2] if gather_indx is None else gather_indx.src_indx.shape[0] + batch_size = w.shape[0] if routing_data.expt_hist is None and w.ndim == 3 else 1 + K, N = w.shape[-2:] + assert K == x.shape[-1] + if x.ndim == 3 and w.ndim == 3: + assert x.shape[0] == w.shape[0] + # compute optimization flags + out_dtype = precision_config.out_dtype or x.dtype + can_use_tma = x.numel() > 0 and x.storage.is_tma_compliant() and \ + w.numel() > 0 and w.storage.is_tma_compliant() and \ + (w_scale is None or w_scale.storage.is_tma_compliant()) + # hopper w/ mxfp4 doesn't support TMA + can_use_tma = can_use_tma and (torch.cuda.get_device_capability()[0] > 9 or bitwidth(w.dtype) != 4) + can_use_fused_scatter = has_scatter and (fused_activation.specs.fn is None) and (epilogue.specs.fn is None) and (routing_data.n_expts_act == 1) + opt_flags = make_opt_flags(out_dtype, x.dtype, w.dtype, precision_config, + M, N, K, routing_data, can_use_tma, can_use_fused_scatter, epilogue.effective_itemsize, + ) + if not can_use_fused_scatter and opt_flags.fused_scatter: + raise InapplicableConstraint("Fused scatter is not supported") + if w_scale is not None and opt_flags.is_persistent and not target_info.has_native_mxfp(): + raise NotImplementedError("Must use non-persistent kernel for simulated MXFP") + if w_scale is not None and w_scale.storage.layout.name is not None and not opt_flags.is_persistent and target_info.has_native_mxfp(): + raise NotImplementedError("Must use persistent kernel and be TMA-compliant for native MXFP") + # fused activation + matmul_fused_activation = fused_activation + reduce_fused_activation = FusedActivation() + if opt_flags.split_k > 1 or (scatter_indx is not None and not opt_flags.fused_scatter): + matmul_fused_activation, reduce_fused_activation = reduce_fused_activation, matmul_fused_activation + # allocate output/scratchpad memory + allocation = init_allocation(x, w, precision_config, fused_activation, + routing_data, gather_indx, scatter_indx, opt_flags) + memory = apply_allocation(allocation, y) + # early exit + if batch_size * M * N == 0: + ret = memory["output"].squeeze(0) + if not is_input_batched: + ret = ret.squeeze(0) + return ret + # TMA descriptors require a global memory allocation + if opt_flags.is_persistent: + triton.set_allocator(get_per_device_per_stream_alloc_fn(x.device)) + # Intermediate tensors and postprocess kernels for each situation + has_scratchpad = "matmul" in memory["scratchpad"] + # Canonical output tensor (matmul scratchpad if present, otherwise final output tensor) + out_matmul = memory["scratchpad"].get("matmul", memory["output"]) + out_matmul_flex = OutFlexData() if out_matmul.dtype == torch.float32 else precision_config.flex_ctx.out_data + # Unified mx-scale pointer; when scratchpad exists, prefer its mx buffer + out_matmul_scale = precision_config.out_scale + if out_matmul_scale is not None: + out_matmul_scale = out_matmul_scale.data.view(torch.uint8) + if has_scratchpad and "mx_out_scale" in memory["scratchpad"]: + out_matmul_scale = memory["scratchpad"]["mx_out_scale"] + out_matmul_has_mx = out_matmul_scale is not None and out_matmul.element_size() == 1 + # matrix multiplication + flex = precision_config.flex_ctx + bias_stride = None if bias is None else bias.stride(0) + num_indx = None if scatter_indx is None else scatter_indx.src_indx.shape[0] + # moe metadata + expt_data = routing_data.expt_data + block_m = opt_flags.block_m + expt_hist = None if expt_data is None else expt_data.hist + expt_hist_sum = None if expt_data is None else expt_data.token_offs_pad[block_m][-1] + expt_token_offs_raw = None if expt_data is None else expt_data.token_offs_raw + expt_block_pid_map = None if expt_data is None else expt_data.block_pid_map[block_m] + # spmd grid + grid_m = triton.cdiv(M, opt_flags.block_m) + if expt_block_pid_map is not None: + grid_m = routing_data.n_blocks(M, opt_flags.block_m) + grid_n = triton.cdiv(N, opt_flags.block_n) + max_grid = batch_size * grid_m * grid_n * opt_flags.split_k + grid = min(target_info.num_sms() - opt_flags.idle_sms, max_grid) if opt_flags.is_persistent else max_grid + # canonicalize storage + has_gather_tma = has_gather and target_info.has_tma_gather() + has_scatter_tma = opt_flags.fused_scatter and target_info.has_tma_gather() + y = wrap_torch_tensor(out_matmul.view(math.prod(out_matmul.shape[:-1]), out_matmul.shape[-1]) if opt_flags.fused_scatter else out_matmul.view(math.prod(out_matmul.shape[:-2]), *out_matmul.shape[-2:])) + x_storage = _canonicalize_storage(x.storage, 2 if has_gather_tma else 3, flex.lhs_data) + w_storage = _canonicalize_storage(w.storage, 3, flex.rhs_data) + y_storage = _canonicalize_storage(y.storage, 2 if has_scatter_tma else 3, flex.out_data) + # create tma descriptor for x + x_has_tma = opt_flags.is_persistent and (has_gather_tma or not has_gather) + x_tma_block_size = [1, opt_flags.block_k] if has_gather_tma else [1, opt_flags.block_m, opt_flags.block_k] + x_tma_mode = None if not x_has_tma else "ragged" if is_ragged and not has_gather_tma else "dense" + x_tensor_or_tma = x_storage.make_tma(x_tma_block_size, x_tma_mode) if x_has_tma else x_storage.data + # create tma descriptor for y + y_has_tma = opt_flags.is_persistent and (has_scatter_tma or not opt_flags.fused_scatter) + block_n = opt_flags.block_n // opt_flags.epilogue_subtile // matmul_fused_activation.reduction_n + y_tma_block_size = [1, block_n] if has_scatter_tma else [1, opt_flags.block_m, block_n] + y_tma_mode = None if not y_has_tma else "ragged" if is_ragged and not has_scatter_tma else "dense" + y_tensor_or_tma = y_storage.make_tma(y_tma_block_size, y_tma_mode) if y_has_tma else y_storage.data + # create tma descriptor for w + w_has_tma = opt_flags.is_persistent + w_tensor_or_tma = w_storage.make_tma([1, opt_flags.block_k, opt_flags.block_n], "dense") if w_has_tma else w_storage.data + # create tma descriptor for w_scale + w_scale_tensor_or_tma = w_scale + w_scale_has_tma = opt_flags.is_persistent and w_scale is not None + w_scale_tensor_or_tma = w_scale.storage.make_tma([opt_flags.block_n, opt_flags.block_k], "dense") if w_scale_has_tma else w_scale + # canonicalize strides + x_strides = [0]*(3 - x_storage.data.ndim) + list(x_storage.data.stride()) + x_scale_strides = x_scale.stride() if x_has_mx else (None, None, None) + x_scale_strides = (0, ) * (3 - len(x_scale_strides)) + x_scale_strides + w_scale_strides = w_scale.stride() if w_has_mx and not w_scale_has_tma else (None, None, None) + w_scale_strides = (0, ) * (3 - len(w_scale_strides)) + w_scale_strides + out_matmul_scale_strides = out_matmul_scale.stride() if out_matmul_has_mx else (None, None, None, None) + out_matmul_scale_strides = (0, ) * (3 - len(out_matmul_scale_strides)) + out_matmul_scale_strides + # launch kernel + kernels = get_kernels(epilogue.specs, matmul_fused_activation.specs) + # When stride(-2) == stride(-1) == 1, it's ambiguous whether W is transposed + # (i.e. col-wise). Since this matters when w_has_mx is True and w_transpose + # is True the fast code path, stride(-2) == 1 takes precedence, e.g., vs. + # w_transpose = w_storage.data.stride()[-1] != 1 + w_transpose = w_storage.data.stride()[-2] == 1 + (kernels._p_matmul_ogs if opt_flags.is_persistent else kernels._matmul_ogs)[(grid,)]( + y_tensor_or_tma, y_storage.data, *out_matmul.stride(), + *((None, out_matmul_scale, None) if out_matmul_has_mx else out_matmul_flex), + *out_matmul_scale_strides[-3:], + x_tensor_or_tma, x_storage.data, *x_strides, + flex.lhs_data.scale, + None if x_scale is None else x_scale.data.view(torch.uint8), *x_scale_strides, + w_tensor_or_tma, w_storage.data, *w_storage.data.stride(), w_transpose, + flex.rhs_data.scale, + w_scale_tensor_or_tma, *w_scale_strides, + bias, bias_stride, + x.shape[-2], + x.shape[-2] if routing_data.expt_hist is None else None, + N, K, + betas, gammas, + None if gather_indx is None else gather_indx.src_indx, + None if scatter_indx is None else scatter_indx.src_indx, + num_indx, + None if not opt_flags.fused_scatter else scatter_indx.dst_indx, + None if not opt_flags.fused_scatter else scatter_indx.dst_indx.shape[0], + expt_hist, expt_token_offs_raw, expt_hist_sum, expt_block_pid_map, + batch_size, grid_m, grid_n, + out_alpha, + *matmul_fused_activation.fn_args, matmul_fused_activation.reduction_n, + *epilogue.fn_arg_values_matmul, + routing_data.n_expts_tot, routing_data.n_expts_act, + precision_config.max_num_imprecise_acc, + precision_config.allow_tf32, + precision_config.flexpoint_saturate_inf, + flex.rhs_data.is_per_batch, + opt_flags.block_m, + opt_flags.block_n, + opt_flags.block_k, + opt_flags.group_m, + XCD_SWIZZLE=opt_flags.xcd_swizzle, + SWIZZLE_MX_VALUE=w.storage.layout.name, + SWIZZLE_MX_SCALE=None if w_scale is None else w_scale.storage.layout.name, + EPILOGUE_SUBTILE=opt_flags.epilogue_subtile, + SPLIT_K=opt_flags.split_k, + EVEN_K=K % opt_flags.block_k == 0, + W_CACHE_MODIFIER=opt_flags.w_cache_modifier, + TOKENS_PER_EXPT_FOR_ANNOTATION=routing_data.expected_tokens_per_expt, + num_warps=opt_flags.num_warps, + num_stages=opt_flags.num_stages, + arch=opt_flags.arch, + UPCAST_INDICES=should_upcast_indices(x, w, out_matmul), + X_TMA_MODE=x_tma_mode, + Y_TMA_MODE=y_tma_mode, + SWAP_XW=get_swap_xw(precision_config, opt_flags), + IS_EPILOGUE_QUANT_MXFP8=epilogue.specs.name == FnName.QUANTIZE_MXFP8.name, + NUM_SMS = grid if opt_flags.is_persistent else 0, + **opt_flags.target_kernel_kwargs) + # Build grouped reduction inputs in a uniform way + group_indx = None if scatter_indx is None or opt_flags.fused_scatter else scatter_indx.src_indx.view(-1, routing_data.n_expts_act) + out_final, out_final_mx_scale = reduce_grouped( + out_matmul, + group_indx, + memory["output"].squeeze(0), + precision_config.out_scale, + reduce_fused_activation, + epilogue, + x_flex=InFlexData(dtype=out_matmul_flex.dtype, scale=out_matmul_flex.expected_scale), + out_flex=precision_config.flex_ctx.out_data, + x_mx_scale=out_matmul_scale.squeeze(1) if out_matmul_has_mx else None, + out_dtype=memory["output"].dtype, + flexpoint_saturate_inf=precision_config.flexpoint_saturate_inf, + ) + if not is_input_batched: + out_final = out_final.squeeze(0) + if out_final_mx_scale is not None: + precision_config.out_scale = out_final_mx_scale + return out_final + +# ----------------------------------------------------------------------------- +# Reference Implementation +# ----------------------------------------------------------------------------- + +def matmul_ogs_torch(x, w, bias, + routing_data: RoutingData = None, + gather_indx: GatherIndx = None, + scatter_indx: ScatterIndx = None, + precision_config: PrecisionConfig = None, + betas = None, + gammas = None, + round_x = None, round_y = None, + ): + is_input_batched = x.ndim == 3 + assert x.dtype.itemsize > 1 + assert w.dtype.itemsize > 1 + if is_input_batched: + assert gather_indx is None, "gather not supported in batched mode" + assert scatter_indx is None, "scatter not supported in batched mode" + assert routing_data is None, "routing not supported in batched mode" + assert w.ndim == 3 and w.shape[0] == x.shape[0] + if round_x is None: + round_x = lambda x, idx: x + if round_y is None: + round_y = lambda x: x + if bias is not None and bias.ndim == 1: + bias = bias.view(1, *bias.shape) + if w.ndim == 2: + w = w.view(1, *w.shape) + if x.ndim == 2: + x = x.view(1, *x.shape) + if routing_data is None: + routing_data = RoutingData(None, None, w.shape[0], 1) + n_expts_act = routing_data.n_expts_act + # memory offsets + if routing_data.n_expts_tot > 1 and not is_input_batched: + sizes = routing_data.expt_hist + off = torch.zeros(sizes.shape[0] + 1, dtype=torch.int32) + off[1:] = torch.cumsum(sizes, 0) + offs = list(itertools.pairwise(off)) + else: + offs = [[0, x.shape[1]] for _ in range(w.shape[0])] + # compute + n_rows = x.shape[1] if gather_indx is None else gather_indx.dst_indx.shape[0] + y = torch.zeros((x.shape[0], n_rows, w.shape[-1]), device=x.device, dtype=x.dtype) + for i, (lo, hi) in enumerate(offs): + if gather_indx is None: + idx = torch.arange(lo, hi, device=x.device) + else: + idx = gather_indx.src_indx[lo:hi] // n_expts_act + batch = i if is_input_batched else 0 + out = torch.matmul(round_x(x[batch, idx, :], torch.arange(lo, hi, device="cuda")).float(), + w[i].float()) + if bias is not None: + out += bias[i, :] if betas is None else bias[i, :] * betas[lo:hi, None] + if gammas is not None: + out *= gammas[lo:hi, None] + y[batch, lo:hi, :] = round_y(out) + if not is_input_batched: + y = y.view(y.shape[1], y.shape[2]) + if scatter_indx is None: + return y + # accumulate output from all experts + n_rows = y.shape[0] // n_expts_act + out = torch.zeros((n_rows, y.shape[-1]), dtype=torch.float32, device=x.device) + for i, (lo, hi) in enumerate(offs): + dst_idx = scatter_indx.dst_indx[lo:hi] // n_expts_act + msk = dst_idx != -1 + out[dst_idx[msk], :] += y[lo:hi, :][msk, :].float() + return out diff --git a/triton_kernels/matmul_ogs_details/__init__.py b/triton_kernels/matmul_ogs_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/matmul_ogs_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/matmul_ogs_details/_common.py b/triton_kernels/matmul_ogs_details/_common.py new file mode 100644 index 000000000000..c149dcf10efb --- /dev/null +++ b/triton_kernels/matmul_ogs_details/_common.py @@ -0,0 +1,169 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/matmul_ogs_details/_common.py +# Triton is licensed under the MIT License. + +import torch + +import triton +import triton.language as tl + +# ----------------------------------------------------------------------------- +# Utilities +# ----------------------------------------------------------------------------- + + +@triton.constexpr_function +def get_scaled_dot_format_string(dtype: tl.dtype): + mapping = { + tl.float16: "fp16", + tl.bfloat16: "bf16", + tl.uint8: "e2m1", + tl.float8e4nv: "e4m3", + tl.float8e5: "e5m2", + } + return mapping[dtype] + + +@triton.jit +def xcd_swizzle(pid, domain_size, XCD_SWIZZLE: tl.constexpr): + """ + Swizzle the program id based on integer XCD_SWIZZLE. + This is useful for reording how blocks are ordered. A scheduler may, for example, + assign sequential blocks 0, 1, 2, 3, ..., 8, 9, 10.. to its 8 hardware units 0, 1, 2, 3, ..., 0, 1, 2. + This pattern may not be ideal for memory access, and it may be better to swizzle so the assignment + becomes 0, 0, 0, 0, ..., 1, 1, 1, ... In the swizzled arrangement, sequential blocks are assigned to + the same hardware unit. + """ + # Number of pids per group in the new arrangement + pids_per_group = domain_size // XCD_SWIZZLE + extra_pid_groups = domain_size % XCD_SWIZZLE + + # Compute current current and local pid within the group + group = pid % XCD_SWIZZLE + local_pid = pid // XCD_SWIZZLE + + # Calculate new pid based on the new grouping + new_pid = group * pids_per_group + min(group, extra_pid_groups) + local_pid + return new_pid + + +@triton.jit +def swizzle2d(pid, grid_m, grid_n, GROUP_M: tl.constexpr): + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + tl.assume(group_size >= 0) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + return pid_m, pid_n + + +def make_matmul_repr(base_name, order): + + def matmul_repr(specialization): + signature = specialization.signature + constants = specialization.constants + reorder = lambda L: [L[i] for i in order] + layout = lambda stride: "N" if stride in constants else "T" + + def convert_dtype(dtype): + if "tensordesc" in dtype: + ret = convert_dtype(dtype.split("<")[1].split("[")[0]) + return ret + elif "u8" in dtype: + return "mxfp4" + elif dtype[0] == "*": + return dtype[1:] + else: + return dtype + + dtypes = "x".join([convert_dtype(f"{signature[i]}") for i in reorder(["Y", "X", "W"])]) + layouts = "".join([f"{layout(i)}" for i in reorder(["stride_y_n", "stride_x_k", "stride_w_n"])]) + blocks = "x".join([f"{constants[i]}" for i in ["BLOCK_M", "BLOCK_N", "BLOCK_K", "SPLIT_K"]]) + # mode = [] + # if "GatherIndx" not in constants: + # mode += ['g'] + # if "ScatterSrcIndx" not in constants: + # mode += ['s'] + # suffix = "" if not mode else "_o" + (''.join(mode)) + # if base_name.startswith("_p"): + # suffix += "_ptma" + return f"{base_name}_{layouts}_{dtypes}_{blocks}" + + return matmul_repr + + +def matmul_launch_metadata(grid, kernel, args): + from ..proton_opts import launch_metadata_allow_sync + + ret = dict() + M, N, K = args["M"], args["N"], args["K"] + Y, X, W = args["YPtr"], args["XPtr"], args["WPtr"] + tokens_per_expt = args.get("TOKENS_PER_EXPT_FOR_ANNOTATION") + hist = args["ExptHist"] + if hist is not None: + # If annotation is given, use that to generate name for profiling. + if tokens_per_expt is not None: + n_rows = f"{tokens_per_expt}*" + elif launch_metadata_allow_sync(): + n_rows = int(hist.float().mean()) + else: + n_rows = "unknown" + + if launch_metadata_allow_sync(): + n_tokens = float(hist.sum()) + n_w_bytes = (W.numel() * W.element_size() // hist.numel()) * (hist > 0).sum() + elif tokens_per_expt is not None: + n_tokens = tokens_per_expt * args["N_EXPTS_TOT"] + # This may not be totally correct (e.g., we might not be using all experts) + # but it's better than nothing. + n_w_bytes = W.numel() * W.element_size() + else: + n_tokens = None + n_w_bytes = 0 + + # If annotation is given, use that to generate name for profiling. + tokens_per_expt = args.get("TOKENS_PER_EXPT_FOR_ANNOTATION") + n_rows = f"{tokens_per_expt}*" if tokens_per_expt is not None else n_rows + else: + n_tokens = None + n_w_bytes = W.numel() * W.element_size() + repr = lambda s, x: f"{s} = {x}" if x is not None else f"E_{len(hist)}({s}) = {n_rows}" + nbits = X.dtype.itemsize * 8 + batch_repr = "" + if "batch_size" in args and args["batch_size"] > 1: + batch_repr = repr("B", args["batch_size"]) + ", " + ret["name"] = f"{kernel.name} [{batch_repr}{repr('M', M)}, {repr('N', N)}, {repr('K', K)}] stg{kernel.num_stages}" + ep_subtile = args["EPILOGUE_SUBTILE"] + if ep_subtile is not None and ep_subtile > 1: + ret["name"] += f" ep/{ep_subtile}" + + if hist is not None and n_tokens is None: + return ret # Don't fill metadata because we can't compute them properly. + + fM = M if M is not None else n_tokens + fK = K if K is not None else n_tokens + ret[f"flops{nbits}"] = 2.0 * fM * N * fK + + gindx = args.get("GatherIndx", None) + # sindx = args.get("WriteBackIndx", None) + n_x_bytes = X.numel() * X.element_size() + n_y_bytes = Y.numel() * Y.element_size() + if hist is not None: + assert n_tokens is not None + n_expts_act = args["N_EXPTS_ACT"] + + if (gindx is not None) and launch_metadata_allow_sync(): + # recreate inverse GatherIndx. + dst = torch.full_like(gindx, -1) + idx = torch.arange(len(gindx), device=gindx.device, dtype=torch.int32) + mask = (gindx != -1) + dst[gindx[mask]] = idx[mask] + n_read_rows = (dst.view((-1, n_expts_act)) != -1).any(dim=1).sum() + else: + n_read_rows = n_tokens + n_x_bytes = n_read_rows * X.shape[-1] * X.element_size() + n_y_bytes = n_tokens * Y.shape[-1] * Y.element_size() + ret["bytes"] = int(n_x_bytes + n_y_bytes + n_w_bytes) + + return ret diff --git a/triton_kernels/matmul_ogs_details/_matmul_ogs.py b/triton_kernels/matmul_ogs_details/_matmul_ogs.py new file mode 100644 index 000000000000..9fcffec8ef08 --- /dev/null +++ b/triton_kernels/matmul_ogs_details/_matmul_ogs.py @@ -0,0 +1,433 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/matmul_ogs_details/_matmul_ogs.py +# Triton is licensed under the MIT License. + +# isort: off +# fmt: off +import triton +import triton.language as tl +from triton_kernels.tensor_details.layout_details.blackwell_scale import unswizzle_mx_scale_bw +from triton_kernels.tensor_details.layout_details.hopper_scale import unswizzle_mxfp4_scale_hopper +from triton_kernels.tensor_details.layout_details.hopper_value import mxfp4_to_bf16_triton +from triton_kernels.tensor_details.layout_details.cdna4_scale import unswizzle_mx_scale_cdna4 +from triton_kernels.numerics_details.flexpoint import float_to_flex, load_scale +from triton_kernels.numerics_details.mxfp_details._downcast_to_mxfp import MXFP_BLOCK_SIZE +from ._common import make_matmul_repr, matmul_launch_metadata, swizzle2d, xcd_swizzle, get_scaled_dot_format_string + + +@triton.jit +def _zero_masked_rows( + pid_m, pid_n, + Y, stride_y_m, stride_y_n, + N, + ScatterSrcIndx, num_idxs, + BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): + offs_m = BLOCK_M * pid_m.to(tl.int64) + tl.arange(0, BLOCK_M) + offs_n = BLOCK_N * pid_n + tl.arange(0, BLOCK_N) + src_idx = tl.load(ScatterSrcIndx + offs_m, mask=offs_m < num_idxs, other=0) + YPtrs = Y + offs_m[:, None] * stride_y_m + offs_n[None, :] * stride_y_n + mask_n = offs_n < N + mask = (src_idx == -1)[:, None] & mask_n[None, :] + tl.store(YPtrs, tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32), mask=mask) + + +_matmul_ogs_repr = make_matmul_repr("_matmul_ogs", [0, 1, 2]) +@triton.jit(do_not_specialize=["TOKENS_PER_EXPT_FOR_ANNOTATION"], + repr=_matmul_ogs_repr, launch_metadata=matmul_launch_metadata) +def _matmul_ogs( + Y, YPtr, stride_y_k, stride_y_z, stride_y_m, stride_y_n, + YExpectedScale, YActualScale, YChecksumScale, + stride_y_mx_z, stride_y_mx_m, stride_y_mx_n, + X, XPtr, stride_x_z, stride_x_m, stride_x_k, + XScale, + XMxScale, stride_x_mx_z, stride_x_mx_m, stride_x_mx_k, + W, WPtr, stride_w_e, stride_w_k, stride_w_n, W_TRANSPOSE: tl.constexpr, + WScale, + WMxScale, stride_w_mx_e, stride_w_mx_k, stride_w_mx_n, + B, stride_b_e, # Bias + NRows, M, N, K, # shapes + # expt data + Betas, Gammas, + GatherIndx, + ScatterSrcIndx, num_idxs, + WriteBackIndx, writeback_size, + ExptHist, ExptOffs, ExptOffsSum, ExptData, + # true grid size + batch_size, grid_m, grid_n, + # Out scale + out_alpha, + # fused activation function + ACTIVATION_FN: tl.constexpr, activation_fn_args, ACTIVATION_REDUCTION_N: tl.constexpr, + # epilogue transform + EPILOGUE_FN: tl.constexpr, epilogue_fn_args, + # MoE config + N_EXPTS_TOT: tl.constexpr, N_EXPTS_ACT: tl.constexpr, + # precision config + MAX_NUM_IMPRECISE_ACC: tl.constexpr, ALLOW_TF32: tl.constexpr, + FLEXPOINT_SATURATE_INF: tl.constexpr, + PER_BATCH_SCALE: tl.constexpr, + # optimization config + BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, + GROUP_M: tl.constexpr, XCD_SWIZZLE: tl.constexpr, + # One of ["HOPPER", "BLACKWELL", None] + SWIZZLE_MX_VALUE: tl.constexpr, + # One of ["HOPPER", "BLACKWELL", None] + SWIZZLE_MX_SCALE: tl.constexpr, + EPILOGUE_SUBTILE: tl.constexpr, + EVEN_K: tl.constexpr, SPLIT_K: tl.constexpr, + W_CACHE_MODIFIER: tl.constexpr, + NUM_SMS: tl.constexpr, + X_TMA_MODE: tl.constexpr, + Y_TMA_MODE: tl.constexpr, + TOKENS_PER_EXPT_FOR_ANNOTATION=None, + UPCAST_INDICES: tl.constexpr = False, + SWAP_XW: tl.constexpr = False, + IS_EPILOGUE_QUANT_MXFP8: tl.constexpr = False): + + tl.assume(stride_y_k >= 0) + tl.assume(stride_y_z >= 0) + tl.assume(stride_y_m >= 0) + tl.assume(stride_y_n >= 0) + tl.assume(stride_x_z >= 0) + tl.assume(stride_x_m >= 0) + tl.assume(stride_x_k >= 0) + tl.assume(stride_w_e >= 0) + tl.assume(stride_w_k >= 0) + tl.assume(stride_w_n >= 0) + if stride_w_mx_e is not None: + tl.assume(stride_w_mx_e >= 0) + if stride_w_mx_k is not None: + tl.assume(stride_w_mx_k >= 0) + if stride_w_mx_n is not None: + tl.assume(stride_w_mx_n >= 0) + if B is not None: + tl.assume(stride_b_e >= 0) + tl.assume(batch_size >= 0) + tl.assume(grid_m >= 0) + tl.assume(grid_n >= 0) + + is_w_microscaled: tl.constexpr = WMxScale is not None + MX_PACK_DIVISOR: tl.constexpr = MXFP_BLOCK_SIZE + if is_w_microscaled: + w_type: tl.constexpr = W.dtype.element_ty + is_mxfp4: tl.constexpr = w_type == tl.uint8 + tl.static_assert(w_type == tl.uint8 or (w_type == tl.float8e4nv or w_type == tl.float8e5), + "mx_weight_ptr must be uint8 or fp8") + tl.static_assert(WMxScale.dtype.element_ty == tl.uint8, "mx_scale_ptr must be uint8") + tl.static_assert(BLOCK_K % MX_PACK_DIVISOR == 0, "BLOCK_K must be a multiple of MX_PACK_DIVISOR") + tl.static_assert(SWIZZLE_MX_VALUE == "HOPPER_VALUE" or SWIZZLE_MX_VALUE is None, "Only Hopper swizzling is supported for values") + else: + tl.static_assert(SWIZZLE_MX_VALUE is None) + tl.static_assert(SWIZZLE_MX_SCALE is None) + is_x_microscaled: tl.constexpr = XMxScale is not None + if is_x_microscaled: + x_type: tl.constexpr = X.dtype.element_ty + tl.static_assert(is_w_microscaled) + tl.static_assert(x_type == tl.float8e4nv, "mx_act_ptr must be float8e4nv") + tl.static_assert(XMxScale.dtype.element_ty == tl.uint8, "mx_scale_ptr must be uint8") + tl.static_assert(BLOCK_K % MX_PACK_DIVISOR == 0, "BLOCK_K must be a multiple of MX_PACK_DIVISOR") + is_out_microscaled: tl.constexpr = stride_y_mx_z is not None + + OUT_BLOCK_N: tl.constexpr = BLOCK_N // ACTIVATION_REDUCTION_N + yN = N // ACTIVATION_REDUCTION_N + + pid = tl.program_id(0) + if ExptOffsSum is not None and XCD_SWIZZLE > 1: + # Determine how much padding there is on the expert data. This allows us to + # know the true grid size and avoid processing padding tiles. + padding_m = grid_m - tl.load(ExptOffsSum) + else: + padding_m: tl.constexpr = 0 + + HAS_FUSED_SCATTER: tl.constexpr = WriteBackIndx is not None + index_type: tl.constexpr = tl.int64 if UPCAST_INDICES else tl.int32 + + unpadded_m = grid_m - padding_m + tl.assume(unpadded_m >= 0) + total_actual_tiles = batch_size * unpadded_m * grid_n * SPLIT_K + if padding_m > 0 and pid >= total_actual_tiles: + tl.device_assert(batch_size == 0) + pid_mn = pid - total_actual_tiles + if pid_mn < padding_m * grid_n: + pid_m, pid_n = swizzle2d(pid_mn, padding_m, grid_n, GROUP_M) + + # set masked out rows to 0 + if HAS_FUSED_SCATTER and N_EXPTS_ACT == 1: + _zero_masked_rows(pid_m, pid_n, Y, stride_y_m, stride_y_n, yN, ScatterSrcIndx, num_idxs, BLOCK_M, OUT_BLOCK_N) + return + + # swizzle program ids + pid_emnk = pid + if XCD_SWIZZLE != 1: + pid_emnk = xcd_swizzle(pid_emnk, total_actual_tiles, XCD_SWIZZLE) + pid_e = pid_emnk // (unpadded_m * grid_n * SPLIT_K) + pid_mnk = pid_emnk % (unpadded_m * grid_n * SPLIT_K) + pid_k = pid_mnk % SPLIT_K + pid_mn = pid_mnk // SPLIT_K + pid_m, pid_n = swizzle2d(pid_mn, unpadded_m, grid_n, GROUP_M) + # For split-k, advance to the output k slice + if SPLIT_K > 1: + Y += pid_k.to( index_type) * stride_y_k + if is_out_microscaled: + YActualScale += pid_k.to(index_type) * stride_x_mx_k + # set masked out rows to 0 + if HAS_FUSED_SCATTER and N_EXPTS_ACT == 1: + _zero_masked_rows(pid_m, pid_n, Y, stride_y_m, stride_y_n, yN, ScatterSrcIndx, num_idxs, BLOCK_M, OUT_BLOCK_N) + # unpack expert data + if ExptData is None: + tl.static_assert(M is not None) + expt_id, start_z, start_m, block_id = pid_e, pid_e, 0, pid_m + else: + tl.static_assert(M is None) + expt_data = tl.load(ExptData + pid_m) + if expt_data == -1: + return + expt_id = expt_data & 0x0000FFFF + block_id = expt_data >> 16 + M = tl.load(ExptHist + expt_id) + start_m = tl.load(ExptOffs + expt_id) + start_z = 0 + expt_id, block_id = expt_id.to(index_type), block_id.to(index_type) + start_m, start_z = start_m.to(index_type), start_z.to(index_type) + pid_n, pid_k = pid_n.to(index_type), pid_k.to(index_type) + # A pointers + offs_x_m = BLOCK_M * block_id + tl.arange(0, BLOCK_M) + offs_x_m = tl.max_contiguous(tl.multiple_of(offs_x_m % M, BLOCK_M), BLOCK_M) + X += start_z * stride_x_z + if GatherIndx is None: + X += start_m * stride_x_m + else: + GatherIndx += start_m + # no needs to bounds-check here because `offs_x_m` wraps around M dim + offs_x_m = tl.load(GatherIndx + offs_x_m) // N_EXPTS_ACT + offs_k = BLOCK_K * pid_k + tl.arange(0, BLOCK_K) + XPtrs = X + offs_x_m.to(index_type)[:, None] * stride_x_m + offs_k.to(index_type)[None, :] * stride_x_k + + # TODO: refactor if/else when triton front end improves + if is_w_microscaled: + if SWIZZLE_MX_VALUE == "HOPPER_VALUE": + tl.static_assert(is_mxfp4, "Only mxfp4 is supported for HOPPER swizzling") + tl.static_assert(not is_x_microscaled) + # We have pack 2 fp4 values in a byte but we divide the dimension by 2 + # when swizzling + W_K_DIVISOR: tl.constexpr = 1 + W_K_MULTIPLIER: tl.constexpr = 2 + W_N_DIVISOR: tl.constexpr = 4 + else: + # We have pack 2 fp4 values in a byte + W_K_DIVISOR: tl.constexpr = 2 if is_mxfp4 else 1 + W_K_MULTIPLIER: tl.constexpr = 1 + W_N_DIVISOR: tl.constexpr = 1 + + if W_TRANSPOSE: + # When weight is transposed, 2 fp4 values are packed per Byte along + # the contiguous dimension, K. + PACKED_BLOCK_K_W: tl.constexpr = (BLOCK_K // W_K_DIVISOR) * W_K_MULTIPLIER + PACKED_BLOCK_N_W: tl.constexpr = BLOCK_N // W_N_DIVISOR + else: + # When weight is not transposed, fp4 values are *not* packed along + # the contiguous dimension, N. + PACKED_BLOCK_K_W: tl.constexpr = BLOCK_K + PACKED_BLOCK_N_W: tl.constexpr = BLOCK_N // W_K_DIVISOR + MX_SCALE_BLOCK_K: tl.constexpr = BLOCK_K // MX_PACK_DIVISOR + + WMxScale += expt_id * stride_w_mx_e + + if SWIZZLE_MX_SCALE == "BLACKWELL_SCALE": + # TODO: support non W_TRANSPOSE with blackwell swizzling + tl.static_assert(W_TRANSPOSE) + tl.static_assert(BLOCK_N % 128 == 0) + tl.static_assert(MX_SCALE_BLOCK_K % 4 == 0) + PACKED_MX_BLOCK: tl.constexpr = (MX_SCALE_BLOCK_K // 4) * 32 * 4 * 4 + SCALE_BLOCK_N: tl.constexpr = BLOCK_N // 128 + stride_scale_k: tl.constexpr = 1 + elif SWIZZLE_MX_SCALE == "HOPPER_SCALE": + # TODO: support non W_TRANSPOSE with Hopper swizzling + tl.static_assert(W_TRANSPOSE) + n_warps: tl.constexpr = tl.extra.cuda.num_warps() + tl.static_assert(BLOCK_N % (2 * n_warps * 2 * 8) == 0) + tl.static_assert(MX_SCALE_BLOCK_K % 2 == 0) + PACKED_MX_BLOCK: tl.constexpr = MX_SCALE_BLOCK_K * 32 + SCALE_BLOCK_N: tl.constexpr = BLOCK_N // 32 + stride_scale_k = stride_w_mx_k + elif SWIZZLE_MX_SCALE == "CDNA4_SCALE": + tl.static_assert(stride_w_mx_k is not None) + tl.static_assert(stride_w_mx_n is not None) + NON_K_PRESHUFFLE_BLOCK_SIZE: tl.constexpr = 32 + PACKED_MX_BLOCK: tl.constexpr = MX_SCALE_BLOCK_K * NON_K_PRESHUFFLE_BLOCK_SIZE + SCALE_BLOCK_N: tl.constexpr = BLOCK_N // NON_K_PRESHUFFLE_BLOCK_SIZE + stride_scale_k = stride_w_mx_k + else: + PACKED_MX_BLOCK: tl.constexpr = MX_SCALE_BLOCK_K + SCALE_BLOCK_N: tl.constexpr = BLOCK_N + stride_scale_k = stride_w_mx_k + offs_n_scale = (pid_n * SCALE_BLOCK_N + tl.arange(0, SCALE_BLOCK_N)) % N + offs_n_scale = tl.max_contiguous(tl.multiple_of(offs_n_scale, SCALE_BLOCK_N), SCALE_BLOCK_N) + # K dimension must be the last dimension for the scales + offs_k_scale = PACKED_MX_BLOCK * pid_k + tl.arange(0, PACKED_MX_BLOCK) + WMxScalePtrs = WMxScale + offs_k_scale.to(index_type)[None, :] * stride_scale_k + offs_n_scale.to(index_type)[:, None] * stride_w_mx_n + else: + WMxScalePtrs = None + offs_k_scale = None + W_K_DIVISOR: tl.constexpr = 1 + W_K_MULTIPLIER: tl.constexpr = 1 + W_N_DIVISOR: tl.constexpr = 1 + PACKED_BLOCK_K_W: tl.constexpr = BLOCK_K + PACKED_BLOCK_N_W: tl.constexpr = BLOCK_N + + # B pointers + offs_w_n = pid_n * PACKED_BLOCK_N_W + tl.arange(0, PACKED_BLOCK_N_W) + offs_w_n = tl.max_contiguous(tl.multiple_of(offs_w_n % (N // W_N_DIVISOR), PACKED_BLOCK_N_W), PACKED_BLOCK_N_W) + + if is_x_microscaled: + XMxScale += start_z.to(index_type) * stride_x_mx_z + if GatherIndx is None: + XMxScale += start_m * stride_x_mx_m + offs_x_k_scale = MX_SCALE_BLOCK_K * pid_k + tl.arange(0, MX_SCALE_BLOCK_K) + XMxScalePtrs = XMxScale + offs_x_m.to(index_type)[:, None] * stride_x_mx_m + offs_x_k_scale.to(index_type)[None, :] * stride_x_mx_k + else: + XMxScalePtrs = None + + offs_w_k = PACKED_BLOCK_K_W * pid_k + tl.arange(0, PACKED_BLOCK_K_W) + W += expt_id * stride_w_e + WPtrs = W + (offs_w_k.to(index_type)[:, None] * stride_w_k + offs_w_n.to(index_type)[None, :] * stride_w_n) + # compute output + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) + for k in range(K, BLOCK_K * pid_k, -(BLOCK_K * SPLIT_K)): + if EVEN_K: + mask_k = tl.full([BLOCK_K], True, dtype=tl.int1) + mask_k_w = tl.full([PACKED_BLOCK_K_W], True, dtype=tl.int1) + if is_w_microscaled and SWIZZLE_MX_SCALE is None: + mask_k_scale = tl.full([PACKED_MX_BLOCK], True, dtype=tl.int1) + if is_x_microscaled: + mask_x_k_scale = tl.full([MX_SCALE_BLOCK_K], True, dtype=tl.int1) + else: + mask_k = offs_k < k + mask_k_w = offs_w_k < ((k // (W_K_DIVISOR if W_TRANSPOSE else 1)) * W_K_MULTIPLIER) + if is_w_microscaled and SWIZZLE_MX_SCALE is None: + mask_k_scale = offs_k_scale * MX_PACK_DIVISOR < k + if is_x_microscaled: + mask_x_k_scale = offs_x_k_scale * MX_PACK_DIVISOR < k + + x = tl.load(XPtrs, mask=mask_k[None, :], other=0.0) + w = tl.load(WPtrs, mask=mask_k_w[:, None], other=0.0, cache_modifier=W_CACHE_MODIFIER) + if is_w_microscaled: + x_format: tl.constexpr = get_scaled_dot_format_string(x.dtype) + w_format: tl.constexpr = get_scaled_dot_format_string(w.dtype) + + if is_x_microscaled: + x_scales = tl.load(XMxScalePtrs, mask=mask_x_k_scale[None, :]) + elif x_format == "fp16" or x_format == "bf16": + x_scales: tl.constexpr = None + else: + # Scale of 1 in E8M0 format + x_scales = tl.full((BLOCK_M, MX_SCALE_BLOCK_K), 127, dtype=tl.uint8) + + if SWIZZLE_MX_SCALE == "BLACKWELL_SCALE": + w_scales = unswizzle_mx_scale_bw(tl.load(WMxScalePtrs)) + elif SWIZZLE_MX_SCALE == "HOPPER_SCALE": + # Handshake with the swizzling code + num_warps: tl.constexpr = tl.extra.cuda.num_warps() + w_scales = unswizzle_mxfp4_scale_hopper(tl.load(WMxScalePtrs), mx_axis=1, num_warps=num_warps) + elif SWIZZLE_MX_SCALE == "CDNA4_SCALE": + w_scales = unswizzle_mx_scale_cdna4(tl.load(WMxScalePtrs), BLOCK_N, MX_SCALE_BLOCK_K) + else: + w_scales = tl.load(WMxScalePtrs, mask=mask_k_scale[None, :]) + + if SWIZZLE_MX_VALUE == "HOPPER_VALUE": + # Handshake with the swizzling code + tl.static_assert(x_format == "bf16") + tl.static_assert(w_format == "e2m1") + w = mxfp4_to_bf16_triton(w.trans(), w_scales, 1) + tl.static_assert(w.dtype == tl.bfloat16) + acc = acc.trans() + x = x.trans() + # w = w.trans() + acc = tl.dot(w, x, acc, max_num_imprecise_acc=MAX_NUM_IMPRECISE_ACC, allow_tf32=ALLOW_TF32) + acc = acc.trans() + else: + rhs_k_pack: tl.constexpr = W_TRANSPOSE or not is_w_microscaled or W_K_DIVISOR != 2 + acc = tl.dot_scaled(x, x_scales, x_format, w, w_scales, w_format, acc=acc, fast_math=True, rhs_k_pack=rhs_k_pack) + if SWIZZLE_MX_SCALE == "BLACKWELL_SCALE": + WMxScalePtrs += (MX_SCALE_BLOCK_K // 4 * SPLIT_K) * stride_w_mx_k + else: + WMxScalePtrs += (PACKED_MX_BLOCK * SPLIT_K) * stride_w_mx_k + if is_x_microscaled: + XMxScalePtrs += (MX_SCALE_BLOCK_K * SPLIT_K) * stride_x_mx_k + else: + acc = tl.dot(x, w, acc, max_num_imprecise_acc=MAX_NUM_IMPRECISE_ACC, allow_tf32=ALLOW_TF32) + XPtrs += (BLOCK_K * SPLIT_K) * stride_x_k + WPtrs += (PACKED_BLOCK_K_W * SPLIT_K) * stride_w_k + # bias + scale + offs_m = BLOCK_M * block_id + tl.arange(0, BLOCK_M) + offs_y_n = BLOCK_N * pid_n + tl.arange(0, BLOCK_N) + mask_m = offs_m < M + mask_n = offs_y_n < N + if B is not None: + BPtrs = B + expt_id * stride_b_e + offs_y_n + if pid_k == 0: + bias = tl.load(BPtrs, mask=mask_n, other=0) + else: + bias = tl.full([BLOCK_N], 0, dtype=tl.float32) + else: + bias = tl.full([BLOCK_N], 0, dtype=tl.float32) + if Betas is not None: + betas = tl.load(Betas + start_m + offs_m, mask=mask_m, other=0.0) + else: + betas = tl.full([BLOCK_M], 1, dtype=tl.float32) + if Gammas is not None: + gammas = tl.load(Gammas + start_m + offs_m, mask=mask_m, other=0.0) + else: + gammas = tl.full([BLOCK_M], 1, dtype=tl.float32) + # flexpoint + x_scale = load_scale(XScale) + if PER_BATCH_SCALE: + w_scale = load_scale(WScale + expt_id) + else: + w_scale = load_scale(WScale) + acc *= x_scale * w_scale + acc = acc + bias[None, :] * betas[:, None] + if out_alpha is not None: + acc *= out_alpha + if ACTIVATION_FN is not None: + out = ACTIVATION_FN(acc, *activation_fn_args) + tl.static_assert(out.shape[1] == OUT_BLOCK_N, f"Activation fn out.shape[1] ({out.shape[1]}) doesn't match computed OUT_BLOCK_N ({OUT_BLOCK_N})") + offs_y_n = OUT_BLOCK_N * pid_n + tl.arange(0, OUT_BLOCK_N) + mask_n = offs_y_n < yN + else: + tl.static_assert(ACTIVATION_REDUCTION_N == 1, "Activation reduction must be 1 if no activation fn is provided") + out = acc + out *= gammas[:, None] + # write-back + Y += start_z.to(index_type) * stride_y_z + if WriteBackIndx is not None: + WriteBackIndx += start_m + dst_idx = tl.load(WriteBackIndx + offs_m, mask=start_m + offs_m < writeback_size, other=-1) + mask_m = mask_m & (dst_idx != -1) + offs_y_m = dst_idx + else: + Y += start_m * stride_y_m + offs_y_m = offs_m + + YPtrs = Y + offs_y_m.to(index_type)[:, None] * stride_y_m + offs_y_n.to(index_type)[None, :] * stride_y_n + mask = mask_m[:, None] & mask_n[None, :] + if is_out_microscaled: + MX_SCALE_BLOCK_N: tl.constexpr = BLOCK_N // MXFP_BLOCK_SIZE + N_MX_BLOCK: tl.constexpr = tl.cdiv(N, MXFP_BLOCK_SIZE) + tl.static_assert(EPILOGUE_FN is not None) + out, out_scale = EPILOGUE_FN(out, mask, *epilogue_fn_args) + tl.static_assert(BLOCK_N % MX_SCALE_BLOCK_N == 0, "") + offs_y_n_scale = MX_SCALE_BLOCK_N * pid_n + tl.arange(0, MX_SCALE_BLOCK_N) + mask_n_scale = offs_y_n_scale < N_MX_BLOCK + YActualScale += start_z.to(index_type) * stride_y_mx_z + if WriteBackIndx is None: + YActualScale += start_m * stride_y_mx_m + YActualScalePtrs = YActualScale + offs_y_m.to(index_type)[:, None] * stride_y_mx_m + offs_y_n_scale.to(index_type)[None, :] * stride_y_mx_n + else: + YActualScalePtrs = YActualScale + (offs_y_m - NRows).to(index_type)[:, None] * stride_y_mx_m + offs_y_n_scale.to(index_type)[None, :] * stride_y_mx_n + tl.store(YActualScalePtrs, out_scale, mask=mask_m[:, None] & mask_n_scale[None, :]) + else: + out = float_to_flex(out, YExpectedScale, YActualScale, YChecksumScale, mask, Y, FLEXPOINT_SATURATE_INF) + if EPILOGUE_FN is not None and not IS_EPILOGUE_QUANT_MXFP8: + out = EPILOGUE_FN(out, *epilogue_fn_args, target_dtype=YPtrs.dtype.element_ty) + tl.store(YPtrs, out, mask=mask) diff --git a/triton_kernels/matmul_ogs_details/_p_matmul_ogs.py b/triton_kernels/matmul_ogs_details/_p_matmul_ogs.py new file mode 100644 index 000000000000..ac5ae147fc1a --- /dev/null +++ b/triton_kernels/matmul_ogs_details/_p_matmul_ogs.py @@ -0,0 +1,475 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/matmul_ogs_details/_p_matmul_ogs.py +# Triton is licensed under the MIT License. + +# isort: off +# fmt: off +import torch +import triton +import triton.language as tl +from triton.tools.ragged_tma import load_ragged, store_ragged +from triton_kernels import target_info +from triton_kernels.tensor_details.layout_details.blackwell_scale import unswizzle_mx_scale_bw +from triton_kernels.numerics_details.flexpoint import ( + float_to_flex, + load_scale, + nan_propagating_absmax_reduce, + compute_scale, +) +from triton_kernels.numerics_details.mxfp_details._downcast_to_mxfp import MXFP_BLOCK_SIZE +from ._common import make_matmul_repr, matmul_launch_metadata, swizzle2d, xcd_swizzle, get_scaled_dot_format_string + + +@triton.constexpr_function +def cuda_capability_geq(major, minor): + return target_info.cuda_capability_geq(major, minor) + +@triton.constexpr_function +def get_dtype(tensor_or_desc: tl.tensor | tl.tensor_descriptor) -> tl.dtype: + if isinstance(tensor_or_desc, tl.tensor): + return tensor_or_desc.dtype.element_ty + elif isinstance(tensor_or_desc, tl.tensor_descriptor): + return tensor_or_desc.dtype + else: + raise ValueError(f"Invalid type: {type(tensor_or_desc)}") + +@triton.jit +def _load_tile_attrs( + tile_id, num_tiles, grid_m, grid_n, padding_m, + M, ExptData, ExptHist, ExptOffs, + BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, SPLIT_K: tl.constexpr, + GROUP_M: tl.constexpr, XCD_SWIZZLE: tl.constexpr): + # unpack and swizzle program ids + pid_emnk = tile_id + if XCD_SWIZZLE != 1: + pid_emnk = xcd_swizzle(pid_emnk, num_tiles // SPLIT_K, XCD_SWIZZLE) + pid_e = pid_emnk // ((grid_m - padding_m) * grid_n * SPLIT_K) + pid_mnk = pid_emnk % ((grid_m - padding_m) * grid_n * SPLIT_K) + if SPLIT_K > 1: + pid_k = pid_mnk % SPLIT_K + pid_mn = pid_mnk // SPLIT_K + else: + pid_k: tl.constexpr = 0 + pid_mn = pid_mnk + pid_m, pid_n = swizzle2d(pid_mn, (grid_m - padding_m), grid_n, GROUP_M) + + # unpack expert data + if ExptData is None: + tl.static_assert(M is not None) + expt_id, start_z, start_m, block_id, eM = pid_e, pid_e, 0, pid_m, -1 + else: + tl.static_assert(M is None) + expt_data = tl.load(ExptData + pid_m) + expt_id = expt_data & 0x0000FFFF + block_id = expt_data >> 16 + eM = tl.load(ExptHist + expt_id) + start_m = tl.load(ExptOffs + expt_id) + start_z = 0 + + off_m = BLOCK_M * block_id + off_n = BLOCK_N * pid_n + + return expt_id, start_z, start_m, eM, off_m, off_n, pid_k + +@triton.jit +def _load_writeback_idx_and_mask(WriteBackIndx, writeback_size, offs, mask): + mask = mask & (offs < writeback_size) + offs = tl.load(WriteBackIndx + offs, mask=mask, other=-1) + mask = offs != -1 + return (offs, mask) + + +_matmul_ogs_repr = make_matmul_repr("_p_matmul_ogs", [0, 1, 2]) +@triton.jit(do_not_specialize=["TOKENS_PER_EXPT_FOR_ANNOTATION"], + repr=_matmul_ogs_repr, launch_metadata=matmul_launch_metadata) +def _p_matmul_ogs( + Y, YPtr, stride_y_k, stride_y_z, stride_y_m, stride_y_n, + YExpectedScale, YActualScale, YChecksumScale, + stride_y_mx_z, stride_y_mx_m, stride_y_mx_n, + X, XPtr, stride_x_z, stride_x_m, stride_x_k, + XScale, + XMxScale, stride_x_mx_z, stride_x_mx_m, stride_x_mx_k, + W, WPtr, stride_w_e, stride_w_k, stride_w_n, W_TRANSPOSE: tl.constexpr, + WScale, + MxScale, stride_mx_e, stride_mx_k, stride_mx_n, + B, stride_b_e, # Bias + NRows, M, N, K, # shapes + # expt data + Betas, Gammas, + GatherIndx, + ScatterSrcIndx, num_idxs, + WriteBackIndx, writeback_size, + ExptHist, ExptOffs, ExptOffsSum, ExptData, + # true grid size + batch_size, grid_m, grid_n, + # Out scale + out_alpha, + # fused activation function + ACTIVATION_FN: tl.constexpr, activation_fn_args, ACTIVATION_REDUCTION_N: tl.constexpr, + # epilogue transform + EPILOGUE_FN: tl.constexpr, epilogue_fn_args, + # MoE config + N_EXPTS_TOT: tl.constexpr, N_EXPTS_ACT: tl.constexpr, + # precision config + MAX_NUM_IMPRECISE_ACC: tl.constexpr, ALLOW_TF32: tl.constexpr, + FLEXPOINT_SATURATE_INF: tl.constexpr, + PER_BATCH_SCALE: tl.constexpr, + # optimization config + BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, + GROUP_M: tl.constexpr, XCD_SWIZZLE: tl.constexpr, + # NYI: Must be None + SWIZZLE_MX_VALUE: tl.constexpr, + # One of ["BLACKWELL", None] + SWIZZLE_MX_SCALE: tl.constexpr, + EPILOGUE_SUBTILE: tl.constexpr, + EVEN_K: tl.constexpr, SPLIT_K: tl.constexpr, + W_CACHE_MODIFIER: tl.constexpr, + NUM_SMS: tl.constexpr, + X_TMA_MODE: tl.constexpr, + Y_TMA_MODE: tl.constexpr, + TOKENS_PER_EXPT_FOR_ANNOTATION=None, + UPCAST_INDICES:tl.constexpr=False, + SWAP_XW: tl.constexpr = False, + IS_EPILOGUE_QUANT_MXFP8: tl.constexpr = False): + # tl.static_assert(SWIZZLE_MX_VALUE is None, "NYI. Value swizzling") + + # why is this faster than using host-side tensor descriptor?! + if Y_TMA_MODE is not None: + Y = tl.make_tensor_descriptor(YPtr, Y.shape, Y.strides[:-1] + (1,), Y.block_shape) + + is_microscaled_format: tl.constexpr = MxScale is not None + tl.static_assert(not is_microscaled_format or W_TRANSPOSE, "NYI. Non-transposed mxfp4 weights") + MX_PACK_DIVISOR: tl.constexpr = MXFP_BLOCK_SIZE + if is_microscaled_format: + w_type: tl.constexpr = get_dtype(W) + tl.static_assert(w_type == tl.uint8 or (w_type == tl.float8e4nv or w_type == tl.float8e5), + "mx_weight_ptr must be uint8") + tl.static_assert(get_dtype(MxScale) == tl.uint8, "mx_scale_ptr must be uint8") + tl.static_assert(BLOCK_K % MX_PACK_DIVISOR == 0, "BLOCK_K must be a multiple of MX_PACK_DIVISOR") + tl.static_assert(SWIZZLE_MX_SCALE == "BLACKWELL_SCALE" or SWIZZLE_MX_SCALE is None, "Only Blackwell swizzling is supported for scales") + + # We have pack 2 fp4 values in a byte + W_PACK_DIVISOR: tl.constexpr = 2 if w_type == tl.uint8 else 1 + PACKED_BLOCK_K_W: tl.constexpr = BLOCK_K // W_PACK_DIVISOR + MX_SCALE_BLOCK_K: tl.constexpr = BLOCK_K // MX_PACK_DIVISOR + else: + W_PACK_DIVISOR: tl.constexpr = 1 + MX_SCALE_BLOCK_K: tl.constexpr = 1 + PACKED_BLOCK_K_W: tl.constexpr = BLOCK_K + tl.static_assert(SWIZZLE_MX_SCALE is None) + + if ExptOffsSum is not None: + # Determine how much padding there is on the expert data. This allows us to + # know the true grid size and avoid processing padding tiles. + padding_m = grid_m - tl.load(ExptOffsSum) + else: + padding_m: tl.constexpr = 0 + + index_type: tl.constexpr = tl.int64 + + USE_FLEXPOINT_SCALE: tl.constexpr = YActualScale is not None or YChecksumScale is not None + HAS_SCATTER: tl.constexpr = WriteBackIndx is not None + HAS_GATHER: tl.constexpr = GatherIndx is not None + USE_GATHER_TMA: tl.constexpr = HAS_GATHER and X_TMA_MODE == "dense" + USE_SCATTER_TMA: tl.constexpr = HAS_SCATTER and Y_TMA_MODE == "dense" + + if EPILOGUE_SUBTILE is None: + SUBTILE_FACTOR: tl.constexpr = 1 + else: + SUBTILE_FACTOR: tl.constexpr = EPILOGUE_SUBTILE + EPILOGUE_BLOCK_N: tl.constexpr = BLOCK_N // SUBTILE_FACTOR + OUT_BLOCK_N: tl.constexpr = EPILOGUE_BLOCK_N // ACTIVATION_REDUCTION_N + yN = N // ACTIVATION_REDUCTION_N + + # set masked out rows to 0 + if HAS_SCATTER and N_EXPTS_ACT == 1: + # Iterate with reversed pids so that later pids will get more tiles if the number of + # tiles isn't evenly divisible by the number of SMs. + # The main loop after this iterates in the forward direction such that earlier + # pids get more tiles if the number of tiles isn't evenly divisible. + # This helps balance the work across the SMs. + for pid_mnk in range(NUM_SMS - tl.program_id(0) - 1, batch_size * grid_m * grid_n * SPLIT_K, NUM_SMS): + pid_k = pid_mnk % SPLIT_K + pid_mn = pid_mnk // SPLIT_K + pid_m, pid_n = swizzle2d(pid_mn, grid_m, grid_n, GROUP_M) + + z = tl.zeros([BLOCK_M, BLOCK_N // ACTIVATION_REDUCTION_N], dtype=tl.float32) + offs_m = z.shape[0] * pid_m + tl.arange(0, z.shape[0]) + offs_n = z.shape[1] * pid_n + tl.arange(0, z.shape[1]) + src_idx = tl.load(ScatterSrcIndx + offs_m, mask=offs_m < num_idxs, other=0) + YPtrs = YPtr + offs_m.to(index_type)[:, None] * stride_y_m + offs_n[None, :] * stride_y_n + mask_n = offs_n < yN + mask = (src_idx == -1)[:, None] & mask_n[None, :] + tl.store(YPtrs + pid_k * stride_y_k, z, mask=mask) + + + k_tiles = tl.cdiv(K, BLOCK_K * SPLIT_K) + num_tiles = batch_size * (grid_m - padding_m) * grid_n * SPLIT_K + + # If true, do not share loop-carried variables between the prologue and the + # epilogue to enable better pipelining with mmav5 + INDEPENDENT_EPILOGUE: tl.constexpr = cuda_capability_geq(10, 0) + + # start negative; will be incremented at the top of the loop + if INDEPENDENT_EPILOGUE: + tile_id1 = tl.program_id(0) - NUM_SMS + + # Keep track of local max for updating flexpoint scales. + THREADS_PER_BLOCK: tl.constexpr = tl.extra.cuda.num_threads() + local_absmax = tl.full([THREADS_PER_BLOCK], 0.0, tl.uint32) + + DISALLOW_ACC_MULTI_BUFFER: tl.constexpr = is_microscaled_format and BLOCK_M * BLOCK_N >= 128 * 256 + + for tile_id in tl.range(tl.program_id(0), num_tiles, NUM_SMS, flatten=True, disallow_acc_multi_buffer=DISALLOW_ACC_MULTI_BUFFER, warp_specialize=True): + expt_id, start_z, start_m, eM, off_m, off_n, pid_k = _load_tile_attrs( + tile_id, num_tiles, grid_m, grid_n, padding_m, + M, ExptData, ExptHist, ExptOffs, + BLOCK_M, BLOCK_N, SPLIT_K, + GROUP_M, XCD_SWIZZLE) + + # Base pointers and offsets. + if X_TMA_MODE is None: + XBase = X + start_z.to(index_type) * stride_x_z + offs_x_k = tl.arange(0, BLOCK_K)[None, :] * stride_x_k + if SPLIT_K > 1: + offs_x_k += pid_k.to(index_type) * BLOCK_K * stride_x_k + + if USE_GATHER_TMA: + offs_m = off_m + tl.arange(0, BLOCK_M) + mask_m = offs_m < (M if M is not None else eM) + if ExptData is None: + offs_x_m = tl.load(GatherIndx + start_m.to(index_type) + offs_m, mask=mask_m) + # Bump rows to account for the Z offset. + offs_x_m += start_z * (stride_x_z // stride_x_m) + offs_x_m = tl.where(mask_m, offs_x_m, -1) + else: + offs_x_m = tl.load(GatherIndx + start_m.to(index_type) + offs_m, + mask=mask_m, other=-N_EXPTS_ACT) // N_EXPTS_ACT + elif X_TMA_MODE is None: + tl.static_assert(HAS_GATHER) + offs_m = off_m + tl.arange(0, BLOCK_M) + if M is not None: + offs_m = tl.max_contiguous(tl.multiple_of(offs_m % M, BLOCK_M), BLOCK_M) + else: + offs_m = tl.max_contiguous(tl.multiple_of(offs_m % eM, BLOCK_M), BLOCK_M) + # no needs to bounds-check here because `offs_m` wraps around M dim + offs_m = tl.load(GatherIndx + start_m.to(index_type) + offs_m) // N_EXPTS_ACT + offs_x_m = offs_m.to(index_type)[:, None] * stride_x_m + + + acc = tl.zeros((BLOCK_N, BLOCK_M) if SWAP_XW else (BLOCK_M, BLOCK_N), dtype=tl.float32) + for ki in tl.range(k_tiles, disallow_acc_multi_buffer=DISALLOW_ACC_MULTI_BUFFER): + off_k = pid_k * BLOCK_K + ki * BLOCK_K * SPLIT_K + off_k_w = pid_k * PACKED_BLOCK_K_W + ki * PACKED_BLOCK_K_W * SPLIT_K + off_k_mx = pid_k * MX_SCALE_BLOCK_K + ki * MX_SCALE_BLOCK_K * SPLIT_K + + # --- load x --- + if USE_GATHER_TMA: + x = X.gather(offs_x_m, off_k) + elif X_TMA_MODE == "dense": + x = X.load([start_z, start_m + off_m, off_k]) + x = x.reshape(BLOCK_M, BLOCK_K) + elif X_TMA_MODE == "ragged": + x = load_ragged(X, start_m, eM, [start_z, off_m, off_k], ragged_dim=1) + x = x.reshape(BLOCK_M, BLOCK_K) + else: + tl.static_assert(X_TMA_MODE is None) + XPtrs = XBase + offs_x_m + offs_x_k + XBase += BLOCK_K * SPLIT_K * stride_x_k + mask_k = tl.arange(0, BLOCK_K) < K - off_k + if EVEN_K: + if SPLIT_K > 1: + x = tl.load(XPtrs, mask=mask_k[None, :], other=0.0) + else: + x = tl.load(XPtrs) + else: + x = tl.load(XPtrs, mask=mask_k[None, :], other=0.0) + + # --- load w --- + if W_TRANSPOSE: + w = tl.reshape(W.load([expt_id, off_n, off_k_w]), W.block_shape[1:]).T + else: + w = tl.reshape(W.load([expt_id, off_k_w, off_n]), W.block_shape[1:]) + + # --- load w_scale --- + if is_microscaled_format: + x_format: tl.constexpr = get_scaled_dot_format_string(x.dtype) + mx_format: tl.constexpr = get_scaled_dot_format_string(w.dtype) + if x_format == "fp16" or x_format == "bf16": + x_scales: tl.constexpr = None + else: + x_scales = tl.full((BLOCK_M, BLOCK_K // MX_PACK_DIVISOR), 127, dtype=tl.uint8) + if SWIZZLE_MX_SCALE == "BLACKWELL_SCALE": + flattened_expt_n_idx = expt_id * ((N + 127) // 128) + (off_n // 128) + w_scales = MxScale.load([0, flattened_expt_n_idx, pid_k * MX_SCALE_BLOCK_K // 4 + ki * (MX_SCALE_BLOCK_K // 4 * SPLIT_K), 0, 0]) + w_scales = w_scales.reshape((w_scales.shape[1], w_scales.shape[2] * w_scales.shape[-2] * w_scales.shape[-1])) + w_scales = unswizzle_mx_scale_bw(w_scales) + else: + w_scales = MxScale.load([expt_id, off_k_mx, off_n]) + w_scales = tl.reshape(w_scales, *w_scales.shape[1:]).T + + # --- update accumulator --- + if is_microscaled_format: + if SWAP_XW: + acc = tl.dot_scaled(w.T, w_scales, mx_format, x.T, x_scales, x_format, acc=acc, fast_math=True) + else: + acc = tl.dot_scaled(x, x_scales, x_format, w, w_scales, mx_format, acc=acc, fast_math=True) + else: + if SWAP_XW: + acc = tl.dot(w.T, x.T, acc, max_num_imprecise_acc=MAX_NUM_IMPRECISE_ACC, allow_tf32=ALLOW_TF32) + else: + acc = tl.dot(x, w, acc, max_num_imprecise_acc=MAX_NUM_IMPRECISE_ACC, allow_tf32=ALLOW_TF32) + + if INDEPENDENT_EPILOGUE: + tile_id1 += NUM_SMS + expt_id1, start_z1, start_m1, eM1, off_m1, off_n1, pid_k1 = _load_tile_attrs( + tile_id1, num_tiles, grid_m, grid_n, padding_m, + M, ExptData, ExptHist, ExptOffs, + BLOCK_M, BLOCK_N, SPLIT_K, + GROUP_M, XCD_SWIZZLE) + else: + tile_id1, expt_id1, start_z1, start_m1, eM1 = tile_id, expt_id, start_z, start_m, eM + off_m1, off_n1, pid_k1 = off_m, off_n, pid_k + + offs_m = off_m1 + tl.arange(0, BLOCK_M) + mask_m = offs_m < (M if M is not None else eM1) + if USE_SCATTER_TMA: + offs_y_m, mask_m = _load_writeback_idx_and_mask(WriteBackIndx, writeback_size, start_m1 + offs_m, mask_m) + MASK_ACC: tl.constexpr = USE_FLEXPOINT_SCALE + if SPLIT_K > 1: + # Compute the split k offset in number of rows, and add it to offs_y_m. + # This allows us to write to the correct slice in the output tensor while using + # a 2D TMA scatter. + tl.device_assert(stride_y_k // stride_y_m == tl.cdiv(stride_y_k, stride_y_m)) + split_k_row_offs = pid_k1 * (stride_y_k // stride_y_m) + offs_y_m = tl.where(mask_m, offs_y_m + split_k_row_offs, offs_y_m) + elif Y_TMA_MODE is None: + tl.static_assert(HAS_SCATTER) + offs_y_m, mask_m = _load_writeback_idx_and_mask(WriteBackIndx, writeback_size, start_m1 + offs_m, mask_m) + MASK_ACC: tl.constexpr = USE_FLEXPOINT_SCALE + else: + offs_y_m = start_m1 + offs_m + MASK_ACC = False if USE_GATHER_TMA else USE_FLEXPOINT_SCALE + + # bias + scale + offs_y_n = off_n1 + tl.arange(0, BLOCK_N) + mask_n = offs_y_n < N + if B is not None: + BPtrs = B + expt_id1 * stride_b_e + offs_y_n + if pid_k1 == 0: + bias = tl.load(BPtrs, mask=mask_n, other=0) + else: + bias = tl.full([BLOCK_N], 0, dtype=tl.float32) + else: + bias = tl.full([BLOCK_N], 0, dtype=tl.float32) + if Betas is not None: + betas = tl.load(Betas + start_m1 + offs_m, mask=mask_m, other=0.0) + else: + betas = tl.full([BLOCK_M], 1, dtype=tl.float32) + if Gammas is not None: + gammas = tl.load(Gammas + start_m1 + offs_m, mask=mask_m, other=0.0) + else: + gammas = tl.full([BLOCK_M], 1, dtype=tl.float32) + x_scale = load_scale(XScale) + if PER_BATCH_SCALE: + w_scale = load_scale(WScale + expt_id1) + else: + w_scale = load_scale(WScale) + + accs = (acc,) + biases = (bias,) + + if SUBTILE_FACTOR >= 2: + acc0, acc1 = acc.reshape(BLOCK_M, 2, BLOCK_N // 2).permute(0, 2, 1).split() + accs = (acc0, acc1) + bias0, bias1 = bias.reshape(2, BLOCK_N // 2).permute(1, 0).split() + biases = (bias0, bias1) + + if SUBTILE_FACTOR >= 4: + acc00, acc01 = acc0.reshape(BLOCK_M, 2, BLOCK_N // 4).permute(0, 2, 1).split() + acc10, acc11 = acc1.reshape(BLOCK_M, 2, BLOCK_N // 4).permute(0, 2, 1).split() + accs = (acc00, acc01, acc10, acc11) + bias00, bias01 = bias0.reshape(2, BLOCK_N // 4).permute(1, 0).split() + bias10, bias11 = bias1.reshape(2, BLOCK_N // 4).permute(1, 0).split() + biases = (bias00, bias01, bias10, bias11) + + tl.static_assert(EPILOGUE_BLOCK_N == BLOCK_N // SUBTILE_FACTOR) + tl.static_assert(len(accs) == SUBTILE_FACTOR) + + for a_i in tl.static_range(len(accs)): + acc_tile = accs[a_i] + acc_tile *= x_scale * w_scale + + if SWAP_XW: + acc_tile = acc_tile.T + + acc_tile = acc_tile + biases[a_i][None, :] * betas[:, None] + if out_alpha is not None: + acc_tile *= out_alpha + + if ACTIVATION_FN is not None: + out = ACTIVATION_FN(acc_tile, *activation_fn_args) + tl.static_assert(out.shape[1] == OUT_BLOCK_N, f"Activation fn out.shape[1] ({out.shape[1]}) doesn't match computed OUT_BLOCK_N ({OUT_BLOCK_N})") + else: + tl.static_assert(ACTIVATION_REDUCTION_N == 1, "Activation reduction must be 1 if no activation fn is provided") + out = acc_tile + + out *= gammas[:, None] + + if MASK_ACC: + out = tl.where(mask_m[:, None], out, 0.0) + # Flexpoint + out_view = tl.reshape(out, [out.numel // THREADS_PER_BLOCK, THREADS_PER_BLOCK], can_reorder=True) + local_absmax = tl.maximum(local_absmax, nan_propagating_absmax_reduce(out_view, axis=0)) + out = float_to_flex( + out, YExpectedScale, + None, # ActualScale: local absmax is tracked and updated after the loop + YChecksumScale, + None, # mask: out is manually masked to 0 + YPtr, FLEXPOINT_SATURATE_INF + ) + if EPILOGUE_FN is not None: + out = EPILOGUE_FN(out, *epilogue_fn_args, target_dtype=YPtr.dtype.element_ty, pid=len(accs)*tile_id1 + a_i) + + out_off_n = off_n1 // ACTIVATION_REDUCTION_N + a_i * OUT_BLOCK_N + out = out.to(YPtr.dtype.element_ty) + if USE_SCATTER_TMA: + # Convert -1 offsets to INT_MAX. We do this by clearing the leading bit. Note that + # there shouldn't be any other negative values. + offs_y_m = (offs_y_m.to(tl.uint32, bitcast=True) & 0x7FFFFFFF).to(tl.int32, bitcast=True) + Y.scatter(out, offs_y_m, out_off_n) + elif Y_TMA_MODE == "dense": + out = tl.reshape(out, [1] + out.shape) + off_kz = pid_k * batch_size + start_z1 + Y.store([off_kz, off_m1, out_off_n], out) + elif Y_TMA_MODE == "ragged": + out = tl.reshape(out, [1] + out.shape) + store_ragged(Y, start_m1, eM1, [pid_k, off_m1, out_off_n], out, ragged_dim=1) + else: + tl.static_assert(Y_TMA_MODE is None) + offs_y_n = out_off_n + tl.arange(0, OUT_BLOCK_N) + mask_n = offs_y_n < yN + + YPtrs = YPtr + pid_k1.to(index_type) * stride_y_k + start_z1.to(index_type) * stride_y_z + offs_y_m.to(index_type)[:, None] * stride_y_m + offs_y_n[None, :] * stride_y_n + mask = mask_m[:, None] & mask_n[None, :] + tl.store(YPtrs, out, mask=mask) + + + # Update the flexpoint scales + if YActualScale is not None: + tl.atomic_max(YActualScale, compute_scale(local_absmax.to(tl.float32, bitcast=True), YPtr), sem="relaxed") + + +_per_device_alloc_fns = {} +def get_per_device_per_stream_alloc_fn(device): + if device not in _per_device_alloc_fns: + _per_stream_tensors = {} + def alloc_fn(size: int, alignment: int, stream): + assert alignment == 128 + if stream not in _per_stream_tensors or _per_stream_tensors[stream].numel() < size: + _per_stream_tensors[stream] = torch.empty(size, device=device, dtype=torch.int8) + _per_stream_tensors[stream].__hibernate__ = {"type": "ignore"} + return _per_stream_tensors[stream] + + _per_device_alloc_fns[device] = alloc_fn + return _per_device_alloc_fns[device] diff --git a/triton_kernels/matmul_ogs_details/_reduce_grouped.py b/triton_kernels/matmul_ogs_details/_reduce_grouped.py new file mode 100644 index 000000000000..7fe29bd40ab4 --- /dev/null +++ b/triton_kernels/matmul_ogs_details/_reduce_grouped.py @@ -0,0 +1,98 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/matmul_ogs_details/_reduce_grouped.py +# Triton is licensed under the MIT License. + +from triton_kernels.numerics_details.flexpoint import float_to_flex, load_scale +from triton_kernels.numerics_details.mxfp import quantize_mxfp8_fn +import triton +import triton.language as tl + + +@triton.jit +def _reduce_grouped(X, stride_xb: tl.uint64, stride_xm: tl.uint64, stride_xn, # + XScale, # input scalar flex scale + Out, stride_om: tl.uint64, stride_on, # output tensor + OutExpectedScale, OutActualScale, OutChecksumScale, # output scalar flex scales + InIndx, B, N, # + XMxScale, stride_mxb: tl.uint64, + stride_mxs: tl.uint64, # optional per-32-col output MXFP scales (uint8) + OutMxScale, stride_omxs: tl.uint64, # optional per-32-col output MXFP scales (uint8) + # fused activation function + ACTIVATION_FN: tl.constexpr, activation_fn_args, ACTIVATION_REDUCTION_N: tl.constexpr, + # epilogue transform + EPILOGUE_FN: tl.constexpr, epilogue_fn_args, + # + HAS_IN_MX_SCALE: tl.constexpr, HAS_OUT_MX_SCALE: tl.constexpr, FLEXPOINT_SATURATE_INF: tl.constexpr, + K: tl.constexpr, BLOCK_N: tl.constexpr): + pid_t = tl.program_id(0) + BLOCK_N_OUT: tl.constexpr = BLOCK_N // ACTIVATION_REDUCTION_N + # persistent along N: single program on N, iterate tiles of size BLOCK_N + start = pid_t * K + # load indices into a tuple + if InIndx is None: + indxs = (pid_t, ) + else: + indxs = () + for i in tl.static_range(0, K): + indxs = indxs + (tl.load(InIndx + start + i), ) + # determine first valid topk row + fi = indxs[(K - 1)] + for i in tl.static_range(K - 2, -1, -1): + fi = tl.where(indxs[i] != -1, indxs[i], fi) + # record overwritten row index (may be -1 if none) + XPtrs = X + tl.arange(0, BLOCK_N) * stride_xn + OutPtrs = Out + tl.arange(0, BLOCK_N_OUT) * stride_on + if HAS_IN_MX_SCALE: + XScalePtrs = XMxScale + tl.arange(0, BLOCK_N // 32) * stride_xn + if HAS_OUT_MX_SCALE: + OutScalePtrs = OutMxScale + tl.arange(0, BLOCK_N_OUT // 32) * stride_on + x_scale = load_scale(XScale) + for n_curr in tl.range(0, N, BLOCK_N, num_stages=4): + acc = tl.zeros([BLOCK_N_OUT], dtype=tl.float32) + x_n_mask = tl.arange(0, BLOCK_N) < N - n_curr + x_n_mask_scale = tl.arange(0, BLOCK_N // 32) < tl.cdiv(N - n_curr, 32) + # accumulate contributions for this tile + for i in tl.static_range(0, K): + curr = tl.zeros([BLOCK_N], dtype=tl.float32) + # iterate over split_k partial values + for b in tl.range(0, B): + is_valid = indxs[i] != -1 + x_row_ptr = XPtrs + indxs[i] * stride_xm + b * stride_xb + vals = tl.load(x_row_ptr, mask=x_n_mask & is_valid, other=0.0) + vals = vals.to(tl.float32) + if HAS_IN_MX_SCALE: + scale_row_ptr = XScalePtrs + indxs[i] * stride_mxs + b * stride_mxb + scale = tl.load(scale_row_ptr, mask=x_n_mask_scale & is_valid, other=0.) + scale = (scale.to(tl.uint32) << 23).to(tl.float32, bitcast=True) + vals = vals.reshape([BLOCK_N // 32, 32]) + vals = (scale[:, None] * vals).reshape([BLOCK_N]) + curr += vals + # apply nonlinearity to split-k output + if ACTIVATION_FN is not None: + curr = ACTIVATION_FN(curr[None, :], *activation_fn_args) + curr = tl.reshape(curr, [curr.shape[-1]]) + # update final accumulator + acc += curr + acc *= x_scale + # Compute per-32-col MXFP scales for this tile if requested + Nrem = (N - n_curr) // ACTIVATION_REDUCTION_N + out_n_mask = tl.arange(0, BLOCK_N_OUT) < Nrem + out_n_mask_scale = tl.arange(0, BLOCK_N_OUT // 32) < tl.cdiv(Nrem, 32) + if HAS_OUT_MX_SCALE: + acc, acc_scale = quantize_mxfp8_fn(acc[None, :], out_n_mask[None, :]) + acc = tl.reshape(acc, [acc.shape[-1]]) + acc_scale = tl.reshape(acc_scale, [acc_scale.shape[-1]]) + # Convert to flexpoint output if configured (scalar scales) + acc = float_to_flex(acc, OutExpectedScale, OutActualScale, OutChecksumScale, None, Out, FLEXPOINT_SATURATE_INF) + # write-back for this tile + out_ptr = OutPtrs + pid_t * stride_om + tl.store(out_ptr, acc, mask=out_n_mask) + if HAS_OUT_MX_SCALE: + out_scale_ptr = OutScalePtrs + pid_t * stride_omxs + tl.store(out_scale_ptr, acc_scale, mask=out_n_mask_scale) + XPtrs += BLOCK_N * stride_xn + OutPtrs += BLOCK_N_OUT * stride_on + if HAS_IN_MX_SCALE: + XScalePtrs += BLOCK_N // 32 * stride_xn + if HAS_OUT_MX_SCALE: + OutScalePtrs += BLOCK_N_OUT // 32 * stride_xn diff --git a/triton_kernels/matmul_ogs_details/opt_flags.py b/triton_kernels/matmul_ogs_details/opt_flags.py new file mode 100644 index 000000000000..3d870d6427f2 --- /dev/null +++ b/triton_kernels/matmul_ogs_details/opt_flags.py @@ -0,0 +1,307 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/matmul_ogs_details/opt_flags.py +# Triton is licensed under the MIT License. + +# isort: off +# fmt: off +from dataclasses import dataclass +import triton +from triton_kernels.target_info import get_cdna_version +import torch +from .opt_flags_details import opt_flags_amd, opt_flags_nvidia + + +@dataclass +class OptFlags: + block_m: int + block_n: int + block_k: int + num_warps: int + num_stages: int + group_m: int + xcd_swizzle: int + w_cache_modifier: str + split_k: int + is_persistent: bool + fused_scatter: bool + idle_sms: int + epilogue_subtile: int | None + arch: str + target_kernel_kwargs: dict + + def __post_init__(self): + if self.fused_scatter and self.split_k != 1: + raise ValueError("Not supported") + + +def make_default_opt_flags_amd( + out_dtype, + lhs_dtype, + rhs_dtype, + precision_config, + m, + n, + k, + routing_data, + can_use_persistent_tma, + can_use_fused_scatter, + enforce_bitwise_invariance, + epilogue_effective_itemsize, + constraints, +): + constraints_supported = ["block_m", "block_n", "block_k", "split_k", "fused_scatter", "is_persistent", "epilogue_subtile"] + assert not any([c not in constraints_supported for c in constraints]), constraints.keys() + # tokens per expert + if routing_data is None: + tokens_per_expt = m + elif routing_data.expected_tokens_per_expt is None: + tokens_per_expt = max(1, m // routing_data.n_expts_tot) + else: + tokens_per_expt = routing_data.expected_tokens_per_expt + + is_cdna4 = get_cdna_version() == 4 + # block_m + if constraints.get("block_m", None): + block_m = constraints["block_m"] + elif enforce_bitwise_invariance: + block_m = 256 if is_cdna4 else 128 + elif tokens_per_expt >= 512 and n >= 2048: + block_m = 256 if is_cdna4 else 128 + elif is_cdna4 and m >= 512: + block_m = 128 + else: + block_m = max(32, min(triton.next_power_of_2(tokens_per_expt), 64)) + + if routing_data is not None: + grid_m = routing_data.n_blocks(m, block_m) + else: + grid_m = triton.cdiv(m, block_m) + # group_m: + group_m = 4 + # number of xcds + num_xcds = 8 + xcd_swizzle = num_xcds + # block_nk: + block_n, block_k = opt_flags_amd.compute_block_nk( + n, block_m, grid_m, num_xcds, lhs_dtype, rhs_dtype, precision_config + ) + # Replace block_k if provided in constraints. + # TODO: Does opt_flags_amd.compute_block_nk need to be refactored? + if constraints.get("block_k", None) is not None: + block_k = constraints["block_k"] + if constraints.get("block_n", None) is not None: + block_n = constraints["block_n"] + is_persistent = constraints.get("is_persistent", False) + # split_k: + if constraints.get("split_k", None) is not None: + split_k = constraints["split_k"] + elif is_persistent or enforce_bitwise_invariance: + split_k = 1 + else: + grid_size = grid_m * ((n + block_n - 1) // block_n) + n_cu = torch.cuda.get_device_properties(0).multi_processor_count + split_k = max(1, n_cu // grid_size) + # w_cache_modifier: + w_cache_modifier = ".cg" if block_m <= 32 else None + # num_warps, num_stages + num_warps = 2 if (m is not None and m <= 16) else 8 + num_stages = 2 + # AMD-specific + target_kernel_kwargs = {"waves_per_eu": 0, "matrix_instr_nonkdim": 16, "kpack": 1} + epilogue_subtile = constraints.get('epilogue_subtile', None) + if epilogue_subtile is None: + epilogue_subtile = 1 + ret = OptFlags( + block_m=block_m, + block_n=block_n, + block_k=block_k, + num_warps=num_warps, + num_stages=num_stages, + group_m=group_m, + xcd_swizzle=xcd_swizzle, + w_cache_modifier=w_cache_modifier, + split_k=split_k, + is_persistent=is_persistent, + fused_scatter=constraints.get('fused_scatter', False), + idle_sms=0, + epilogue_subtile=epilogue_subtile, + arch=None, + target_kernel_kwargs=target_kernel_kwargs, + ) + # check constraints + assert all(getattr(ret, ck) == cv for ck, cv in constraints.items() if cv is not None), f"{ret} != {constraints}" + return ret + +def make_default_opt_flags_nvidia( + out_dtype, + lhs_dtype, + rhs_dtype, + precision_config, + m, + n, + k, + routing_data, + can_use_persistent_tma, + can_use_fused_scatter, + enforce_bitwise_invariance, + epilogue_effective_itemsize, + constraints, +): + constraints_supported = ["block_m", "block_k", "split_k", "is_persistent", "fused_scatter", "epilogue_subtile", "num_stages", "idle_sms"] + assert not any([c not in constraints_supported for c in constraints]), constraints.keys() + # tokens per expert + if routing_data is None: + tokens_per_expt = m + elif routing_data.expected_tokens_per_expt is None: + tokens_per_expt = max(1, m // routing_data.n_expts_tot) + else: + tokens_per_expt = routing_data.expected_tokens_per_expt + # pid swizzling + group_m = 8 + xcd_swizzle = 1 + # block_m + if constraints.get("block_m", None): + block_m = constraints["block_m"] + elif enforce_bitwise_invariance: + block_m = 128 + else: + block_m = max(16, min(triton.next_power_of_2(tokens_per_expt), 128)) + # block n + arch = None + block_n = opt_flags_nvidia.compute_block_n(n, arch, precision_config) + # is_persistent + grid_size = opt_flags_nvidia.compute_grid_size(routing_data, m, n, block_m, block_n) + n_sms = torch.cuda.get_device_properties(0).multi_processor_count + tiles_per_sm = grid_size / n_sms + supports_persistent = can_use_persistent_tma and (arch is None or int(arch[2:-1]) >= 9) + if constraints.get("is_persistent", None) is not None: + is_persistent = constraints["is_persistent"] + else: + has_simple_epilogue = precision_config.max_num_imprecise_acc is None + is_persistent = supports_persistent and has_simple_epilogue and (tiles_per_sm >= 2.0 or lhs_dtype.itemsize <= 1) and out_dtype.itemsize < 4 + # TEMP CHANGE + if precision_config.act_scale is not None or precision_config.out_scale is not None: + is_persistent = False + # block k + if constraints.get("block_k", None) is not None: + block_k = constraints["block_k"] + else: + block_k = opt_flags_nvidia.compute_block_k(m, k, is_persistent, lhs_dtype, rhs_dtype, precision_config) + # split_k + if constraints.get("split_k", None) is not None: + split_k = constraints["split_k"] + elif is_persistent or enforce_bitwise_invariance or precision_config.act_scale is not None or precision_config.out_scale is not None: + split_k = 1 + else: + estimated_actual_grid_size = opt_flags_nvidia.compute_grid_size(None, m, n, block_m, block_n) + split_k = opt_flags_nvidia.compute_split_k(block_k, k, estimated_actual_grid_size) + if split_k > 1: + # With split_k, results are written in f32. Use that for the following computations. + out_dtype = torch.float32 + compute_num_stages_args = ( + precision_config, + is_persistent, + + block_m, + block_n, + block_k, + out_dtype, + lhs_dtype, + rhs_dtype, + ) + + if constraints.get("epilogue_subtile", None) is not None: + subtiles_to_check = [constraints["epilogue_subtile"]] + else: + subtiles_to_check = [1, 2, 4] + num_stages = -1 + for ep in subtiles_to_check: + ns = opt_flags_nvidia.compute_num_stages(*compute_num_stages_args, ep, epilogue_effective_itemsize) + if ns > num_stages: + epilogue_subtile, num_stages = ep, ns + assert num_stages >= 1 + if constraints.get("num_stages", None): + num_stages = constraints["num_stages"] + # fused scatter scratchpad + if constraints.get("fused_scatter", None) is not None: + fused_scatter = constraints["fused_scatter"] + else: + fused_scatter = can_use_fused_scatter and split_k == 1 + # Handshake with the HBM swizzling + num_warps = opt_flags_nvidia.compute_num_warps(block_m, block_n, precision_config) + ret = OptFlags( + block_m=block_m, + block_n=block_n, + block_k=block_k, + num_warps=num_warps, + num_stages=num_stages, + fused_scatter=fused_scatter, + group_m=group_m, + xcd_swizzle=xcd_swizzle, + w_cache_modifier=None, + split_k=split_k, + is_persistent=is_persistent, + epilogue_subtile=epilogue_subtile, + arch=arch, + target_kernel_kwargs=dict(), + idle_sms=constraints.get("idle_sms", 0), + ) + # check constraints + assert all(getattr(ret, ck) == cv for ck, cv in constraints.items() if cv is not None), f"{ret} != {constraints}" + return ret + +# -------------- +# User Interface +# -------------- + +_opt_flags_constraints: dict = dict() +_opt_flags: OptFlags | None = None + +def update_opt_flags_constraints(constraints: dict[str, int]): + global _opt_flags_constraints + _opt_flags_constraints.update(constraints) + +def reset_opt_flags_constraints(): + global _opt_flags_constraints + _opt_flags_constraints = dict() + +def set_opt_flags(opt_flags: OptFlags): + global _opt_flags + assert not _opt_flags_constraints, "setting constraints is incompatible with manual flags override" + assert not _opt_flags, "opt_flags already set; please reset to None first" + _opt_flags = opt_flags + +class InapplicableConstraint(Exception): + pass + +def make_opt_flags( + out_dtype, + lhs_dtype, + rhs_dtype, + precision_config, + m, + n, + k, + routing_data, + can_use_persistent_tma, + can_use_fused_scatter, + epilogue_effective_itemsize, +): + if _opt_flags_constraints.get("is_persistent", False) and not can_use_persistent_tma: + raise InapplicableConstraint("cannot enforce `is_persistent=True` constraint") + if _opt_flags_constraints.get("fused_scatter", False) and not can_use_fused_scatter: + raise InapplicableConstraint("cannot enforce `fused_scatter=True` constraint") + enforce_bitwise_invariance = precision_config.enforce_bitwise_invariance + if _opt_flags is not None: + assert not _opt_flags_constraints + return _opt_flags + args = [out_dtype, lhs_dtype, rhs_dtype, precision_config, m, n, k, + routing_data, can_use_persistent_tma, can_use_fused_scatter, + enforce_bitwise_invariance, epilogue_effective_itemsize, + _opt_flags_constraints] + backend = triton.runtime.driver.active.get_current_target().backend + if backend == "hip": + return make_default_opt_flags_amd(*args) + if backend == "cuda": + return make_default_opt_flags_nvidia(*args) + assert False diff --git a/triton_kernels/matmul_ogs_details/opt_flags_details/__init__.py b/triton_kernels/matmul_ogs_details/opt_flags_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/matmul_ogs_details/opt_flags_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/matmul_ogs_details/opt_flags_details/opt_flags_amd.py b/triton_kernels/matmul_ogs_details/opt_flags_details/opt_flags_amd.py new file mode 100644 index 000000000000..11d8102d47d3 --- /dev/null +++ b/triton_kernels/matmul_ogs_details/opt_flags_details/opt_flags_amd.py @@ -0,0 +1,37 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/matmul_ogs_details/opt_flags_details/opt_flags_amd.py +# Triton is licensed under the MIT License. + +import torch +import triton +from triton_kernels.target_info import get_cdna_version +from triton_kernels.tensor import bitwidth + + +def compute_block_nk(n, block_m, grid_m, num_xcds, lhs_dtype, rhs_dtype, precision_config): + lhs_width = bitwidth(lhs_dtype) / 8 + rhs_width = bitwidth(rhs_dtype) / 8 + + # block_n: + n_cu = torch.cuda.get_device_properties(0).multi_processor_count + if n is not None: + if n <= 128 and (n & (n - 1)) == 0: + block_n = n + else: + block_n = max(32, min(256, triton.next_power_of_2(grid_m * n * num_xcds // n_cu))) + elif block_m > 64: + block_n = 256 + else: + block_n = 128 + + if get_cdna_version() == 4 and block_m == 128: + block_n = 512 + + # block_k needs to match the cacheline size (128B) + block_k = int(128 // min(lhs_width, rhs_width)) + + # TODO: block_k = 128 seems to work better for now. + # perhaps due to increased number of k loops to pipeline + if precision_config.weight_scale is not None and get_cdna_version() != 4: + block_k = 128 + return block_n, block_k diff --git a/triton_kernels/matmul_ogs_details/opt_flags_details/opt_flags_nvidia.py b/triton_kernels/matmul_ogs_details/opt_flags_details/opt_flags_nvidia.py new file mode 100644 index 000000000000..9ae84646c93e --- /dev/null +++ b/triton_kernels/matmul_ogs_details/opt_flags_details/opt_flags_nvidia.py @@ -0,0 +1,115 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/matmul_ogs_details/opt_flags_details/opt_flags_nvidia.py +# Triton is licensed under the MIT License. + +import torch +import triton +from triton_kernels import target_info +from triton_kernels.tensor import get_layout, bitwidth, FP4 +from triton_kernels.tensor_details.layout import HopperMXScaleLayout +from triton_kernels.numerics_details.mxfp_details._downcast_to_mxfp import MXFP_BLOCK_SIZE + + +def compute_grid_size(routing_data, m, n, block_m, block_n): + if routing_data is not None: + grid_m = routing_data.n_blocks(m, block_m) + else: + grid_m = triton.cdiv(m, block_m) + grid_n = (n + block_n - 1) // block_n + return grid_m * grid_n + + +def compute_block_n(n: int, arch, precision_config): + # block_n: + layout = get_layout(precision_config.weight_scale) + if isinstance(layout, HopperMXScaleLayout) and layout.num_warps == 4: + return 128 + elif precision_config.max_num_imprecise_acc is None and n > 128: + return 256 + else: + return max(16, min(128, triton.next_power_of_2(n))) + + +def compute_block_k(m: int, k: int | None, is_persistent: bool, lhs_dtype, rhs_dtype, precision_config): + lhs_width = bitwidth(lhs_dtype) + rhs_width = bitwidth(rhs_dtype) + # block_k needs to match the cacheline size (1024 bits) + block_k = int(1024 // min(lhs_width, rhs_width)) + has_native_mxfp = target_info.cuda_capability_geq(10, 0) + if rhs_width == 4 and not has_native_mxfp: + block_k = 128 + elif k is not None: + block_k = max(32, min(triton.next_power_of_2(k), block_k)) + has_mx_weight_scale = precision_config is not None and precision_config.weight_scale is not None + if has_native_mxfp and is_persistent and has_mx_weight_scale: + block_k = min(block_k, 128) + return block_k + + +def compute_split_k(block_k: int, k: int | None, grid_size: int) -> int: + device_props = torch.cuda.get_device_properties(0) + n_sms = device_props.multi_processor_count + split_k = n_sms // grid_size + if k is not None: + # avoid split_k for small k + num_block_k = triton.cdiv(k, block_k) + split_k = min(split_k, num_block_k // 4) + split_k = max(split_k, 1) + return split_k + + +def compute_num_warps(block_m, block_n, precision_config): + layout = get_layout(precision_config.weight_scale) + if isinstance(layout, HopperMXScaleLayout): + return layout.num_warps + return max(block_m * block_n // 4096, 4) + + +def compute_num_stages( + precision_config, + is_persistent, + block_m, + block_n, + block_k, + out_dtype, + lhs_dtype, + rhs_dtype, + epilogue_subtile, + epilogue_effective_itemsize, +): + if precision_config.max_num_imprecise_acc is not None: + return 3 + weight_size = bitwidth(rhs_dtype) / 8 + stage_size = block_m * block_k * lhs_dtype.itemsize + block_k * block_n * weight_size + device_props = torch.cuda.get_device_properties(0) + smem_capacity = device_props.shared_memory_per_block_optin + has_native_mxfp = target_info.cuda_capability_geq(10, 0) + if has_native_mxfp and getattr(precision_config, "weight_scale", None) is not None: + if rhs_dtype == FP4: + # 4-bit e2m1 weights are padded 2x + # https://docs.nvidia.com/cuda/parallel-thread-execution/#packing-format-used-for-matrix-a-and-b-by-kind-mxf8f6f4-in-shared-memory + stage_size += block_k * block_n * weight_size + + if is_persistent: + # Per-stage wait barrier + stage_size += 8 + if target_info.cuda_capability_geq(10, 0): + acc_size = epilogue_effective_itemsize or out_dtype.itemsize + else: + acc_size = out_dtype.itemsize + if target_info.cuda_capability_geq(10, 0) and epilogue_subtile is not None: + acc_block_n = block_n // epilogue_subtile + else: + acc_block_n = block_n + # pipelined TMA store local to global, or + # pipelined layout conversion before store of the accumulator + # note: layout conversion has some padding + smem_capacity -= int((block_m + 4) * acc_block_n * acc_size) + if precision_config.weight_scale is not None: + # mx scales + stage_size += block_n * (block_k // int(MXFP_BLOCK_SIZE)) + elif has_native_mxfp: + # mx scales + stage_size += block_n * (block_k // int(MXFP_BLOCK_SIZE)) + num_stages = min(4, smem_capacity // int(stage_size)) + return num_stages diff --git a/triton_kernels/numerics.py b/triton_kernels/numerics.py new file mode 100644 index 000000000000..0bbe6b1b6389 --- /dev/null +++ b/triton_kernels/numerics.py @@ -0,0 +1,46 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/numerics.py +# Triton is licensed under the MIT License. + +import torch +from dataclasses import dataclass + +MAX_FINITE_FLOAT8E5 = 57344.0 +MAX_FINITE_FLOAT8E4NV = 448.0 +MAX_FINITE_FLOAT8E4B8 = 240.0 + + +@dataclass(frozen=True) +class BaseFlexData: + dtype: torch.dtype | None = None + + def view(self, x: torch.Tensor): + if self.dtype is None: + return x + return x.view(self.dtype) + + def reinterpret(self, x): + if self.dtype is None or x.dtype.itemsize > 1: + return x + return x.view(self.dtype) + + +@dataclass(frozen=True) +class InFlexData(BaseFlexData): + scale: torch.Tensor | None = None + + @property + def is_per_batch(self): + return False if self.scale is None else len(self.scale) > 1 + + +@dataclass(frozen=True) +class OutFlexData(BaseFlexData): + expected_scale: torch.Tensor | None = None + actual_scale: torch.Tensor | None = None + checksum_scale: torch.Tensor | None = None + + def __iter__(self): + yield self.expected_scale + yield self.actual_scale + yield self.checksum_scale diff --git a/triton_kernels/numerics_details/__init__.py b/triton_kernels/numerics_details/__init__.py new file mode 100644 index 000000000000..06cad1246f25 --- /dev/null +++ b/triton_kernels/numerics_details/__init__.py @@ -0,0 +1,3 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/numerics_details/__init__.py +# Triton is licensed under the MIT License. diff --git a/triton_kernels/numerics_details/flexpoint.py b/triton_kernels/numerics_details/flexpoint.py new file mode 100644 index 000000000000..ad905d009d0e --- /dev/null +++ b/triton_kernels/numerics_details/flexpoint.py @@ -0,0 +1,194 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/numerics_details/flexpoint.py +# Triton is licensed under the MIT License. + +from ..numerics import MAX_FINITE_FLOAT8E4B8, MAX_FINITE_FLOAT8E4NV, MAX_FINITE_FLOAT8E5 +import triton +import triton.language as tl +from triton_kernels.target_info import cuda_capability_geq + +# ------------------------------- +# Kernels stuff +# ------------------------------- + +TL_MAX_FINITE_FLOAT8E5 = tl.constexpr(MAX_FINITE_FLOAT8E5) +TL_MAX_FINITE_FLOAT8E4NV = tl.constexpr(MAX_FINITE_FLOAT8E4NV) +TL_MAX_FINITE_FLOAT8E4B8 = tl.constexpr(MAX_FINITE_FLOAT8E4B8) +TL_MAX_FINITE_FLOAT8E4B15 = tl.constexpr(1.750) +TL_MAX_FINITE_FLOAT16 = tl.constexpr(65472.0) + +TL_RCP_MAX_FINITE_FLOAT8E5 = tl.constexpr(0x37924925) # 0x1.24924Ap-16 +TL_RCP_MAX_FINITE_FLOAT8E4NV = tl.constexpr(0x3B124925) # 0x1.24924Ap-9 +TL_RCP_MAX_FINITE_FLOAT8E4B8 = tl.constexpr(0x3B888889) # 0x1.111112p-8 +TL_RCP_MAX_FINITE_FLOAT8E4B15 = tl.constexpr(0x3F124925) # 0x1.24924Ap-1 +TL_RCP_MAX_FINITE_FLOAT16 = tl.constexpr(0x37802008) # 0x1.004010p-16 + + +@triton.jit +def max_finite(dtype): + if dtype == tl.constexpr(tl.float8e5): + return TL_MAX_FINITE_FLOAT8E5 + elif dtype == tl.constexpr(tl.float8e4nv): + return TL_MAX_FINITE_FLOAT8E4NV + elif dtype == tl.constexpr(tl.float8e4b8): + return TL_MAX_FINITE_FLOAT8E4B8 + elif dtype == tl.constexpr(tl.float8e4b15): + return TL_MAX_FINITE_FLOAT8E4B15 + elif dtype == tl.constexpr(tl.float16): + return TL_MAX_FINITE_FLOAT16 + else: + tl.static_assert(tl.constexpr(False), f"{dtype} not supported in flexpoint") + + +@triton.jit +def rcp_max_finite(dtype): + if dtype == tl.constexpr(tl.float8e5): + return TL_RCP_MAX_FINITE_FLOAT8E5 + elif dtype == tl.constexpr(tl.float8e4nv): + return TL_RCP_MAX_FINITE_FLOAT8E4NV + elif dtype == tl.constexpr(tl.float8e4b8): + return TL_RCP_MAX_FINITE_FLOAT8E4B8 + elif dtype == tl.constexpr(tl.float8e4b15): + return TL_RCP_MAX_FINITE_FLOAT8E4B15 + elif dtype == tl.constexpr(tl.float16): + return TL_RCP_MAX_FINITE_FLOAT16 + else: + tl.static_assert(tl.constexpr(False), f"{dtype} not supported in flexpoint") + + +@triton.jit +def sm86_min_nan_xorsign_abs_f32(a, b): + """Wrapper for min.NaN.xorsign.abs.f32 PTX instruction. + + Computes the minimum of the absolute values of the two inputs and sets its sign to the XOR of the signs of the inputs. + NaN inputs are propagated to the output. + + Requires CUDA compute capability 8.6+ (A100 and A30 Ampere GPUs don't support it, but A40/A16/A10/A2, Ada, and Hopper GPUs do). + """ + tl.static_assert(cuda_capability_geq(8, 6), "min.NaN.xorsign.abs.f32 requires CUDA compute capability 8.6+") + tl.static_assert(a.dtype == tl.float32, "min.NaN.xorsign.abs.f32 requires float32 inputs") + tl.static_assert(b.dtype == tl.float32, "min.NaN.xorsign.abs.f32 requires float32 inputs") + + return tl.inline_asm_elementwise( + """{ + min.NaN.xorsign.abs.f32 $0, $1, $2; + }""", + "=r,r,r", + [a, b], + dtype=tl.float32, + is_pure=True, + pack=1, + ) + + +@triton.jit +def sm86_max_nan_xorsign_abs_f32(a, b): + """Wrapper for max.NaN.xorsign.abs.f32 PTX instruction. + + Computes the maximum of the absolute values of the two inputs and sets its sign to the XOR of the signs of the inputs. + NaN inputs are propagated to the output. + + Requires CUDA compute capability 8.6+ (A100 and A30 Ampere GPUs don't support it, but A40/A16/A10/A2, Ada, and Hopper GPUs do). + """ + tl.static_assert(cuda_capability_geq(8, 6), "max.NaN.xorsign.abs.f32 requires CUDA compute capability 8.6+") + tl.static_assert(a.dtype == tl.float32, "max.NaN.xorsign.abs.f32 requires float32 inputs") + tl.static_assert(b.dtype == tl.float32, "max.NaN.xorsign.abs.f32 requires float32 inputs") + + return tl.inline_asm_elementwise( + """{ + max.NaN.xorsign.abs.f32 $0, $1, $2; + }""", + "=r,r,r", + [a, b], + dtype=tl.float32, + is_pure=True, + pack=1, + ) + + +@triton.jit +def load_scale(scale_ptr): + return 1.0 if scale_ptr is None else tl.load(scale_ptr) + + +@triton.jit +def flex_to_float(x, scale_ptr): + scale = load_scale(scale_ptr) + return x.to(tl.float32) * scale + + +@triton.jit +def clip(x, limit): + res = tl.minimum(x, limit) + res = tl.maximum(-limit, res) + return res + + +@triton.jit +def nan_propagating_absmax_reduce(x, axis=None): + if cuda_capability_geq(8, 6): + # abs-max-reduce as floating-point if `max.NaN.xorsign.abs.f32` is supported. + x_absmax = tl.reduce(x, axis, sm86_max_nan_xorsign_abs_f32) + # Note: sign of reduction result is the xor of signs of all inputs, explicitly clear the sign bit to fix it. + x_absmax = x_absmax.to(tl.uint32, bitcast=True) & 0x7FFFFFFF + else: + # Clear the sign bit, max-reduce as integer (same as NaN-propagating max-reduce as float) + masked_abs_x = x.to(tl.uint32, bitcast=True) & 0x7FFFFFFF + x_absmax = tl.max(masked_abs_x, axis) + + return x_absmax + + +@triton.jit +def compute_scale(x, Out): + x_absmax = nan_propagating_absmax_reduce(tl.ravel(x, can_reorder=True)) + + # atomic_max does not propagate NaNs, so we replace them with +inf (0x7f800000). + # We use integer minimum because NaNs are above +inf in integer representation. + x_absmax = tl.minimum(x_absmax, 0x7F800000).to(tl.float32, bitcast=True) + RCP_MAX_VALUE = rcp_max_finite(Out.dtype.element_ty) + return tl.fma(x_absmax, RCP_MAX_VALUE.to(tl.float32, bitcast=True), 1.0e-30) + + +@triton.jit +def update_scale(x, scale_ptr, Out) -> None: + if scale_ptr is not None: + scale = compute_scale(x, Out) + tl.atomic_max(scale_ptr, scale, sem="relaxed") + + +@triton.jit +def float_to_flex( + x, + expected_scale_ptr_or_val, + actual_scale_ptr, + checksum_scale_ptr, + mask, + Out, + saturate_infs: tl.constexpr, +): + if expected_scale_ptr_or_val is not None: + if expected_scale_ptr_or_val.dtype.is_ptr(): + invscale = 1.0 / tl.load(expected_scale_ptr_or_val) + else: + invscale = 1.0 / expected_scale_ptr_or_val + else: + invscale = 1.0 + if checksum_scale_ptr is not None: + x_int32 = x.to(tl.int32, bitcast=True) + zero = tl.cast(0.0, tl.int32) + if mask is not None: + x_int32 = tl.where(mask, x_int32, zero) + checksum_local = tl.xor_sum(tl.ravel(x_int32, can_reorder=True), 0) + tl.atomic_add(checksum_scale_ptr, checksum_local) + if mask is not None: + if actual_scale_ptr is not None: + x = tl.where(mask, x, 0.0) + update_scale(x, actual_scale_ptr, Out) + x = x * invscale + # if expected_scale_ptr is not None, we applied flexpoint scale. We only want to clip in this case. + if expected_scale_ptr_or_val is not None: + if saturate_infs: + CLIP_VALUE = max_finite(Out.dtype.element_ty) + x = clip(x, CLIP_VALUE) + return x diff --git a/triton_kernels/numerics_details/mxfp.py b/triton_kernels/numerics_details/mxfp.py new file mode 100644 index 000000000000..bd4fca8fc813 --- /dev/null +++ b/triton_kernels/numerics_details/mxfp.py @@ -0,0 +1,307 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/numerics_details/mxfp.py +# Triton is licensed under the MIT License. + +# isort: off +# fmt: off +from enum import Enum +import triton +import torch +import torch.nn.functional as F +from .mxfp_details._upcast_from_mxfp import _upcast_from_mxfp +from .mxfp_details._downcast_to_mxfp import _downcast_to_mxfp, MXFP_BLOCK_SIZE, _quantize_mxfp8_fn + +# ----------------------------------------------------------------------------- +# Dequantization / Quantization Utilities +# ----------------------------------------------------------------------------- + + +class DequantScaleRoundingMode(Enum): + ROUND_UP = 0 + ROUND_DOWN = 1 + + +def downcast_to_mxfp(src_tensor: torch.Tensor, out_quant_type: torch.dtype, axis: int, + DEQUANT_SCALE_ROUNDING_MODE: DequantScaleRoundingMode = DequantScaleRoundingMode.ROUND_UP): + """ + Convert the src weights to mx format. The src weight is quantized along the axis dimension. + + If weight_quant_type is torch.uint8, we output mxfp4 where two e2m1 values are packed into a single byte. + Note that this means the k_dim of the tensor will be half of the logical k_dim. + + If weight_quant_type is torch.float8_e4m3fn or torch.float8_e5m2, we output mxfp8 with the float8s are stored + in their respective formats. + """ + ndim = src_tensor.ndim + assert -ndim <= axis < ndim, f"Invalid axis {axis=}" + axis = axis if axis >= 0 else axis + ndim + # downcast + src_tensor = src_tensor.transpose(axis, src_tensor.ndim - 1) + is_fp4 = out_quant_type == torch.uint8 + is_fp8 = out_quant_type in (torch.float8_e4m3fn, torch.float8_e5m2) + assert is_fp4 or is_fp8 + divisor = 2 if is_fp4 else 1 + L = src_tensor.shape[-1] + if is_fp4: + assert L % 2 == 0, f"axis dim must be divisible by 2 for e2m1. Got {L}" + out_shape = src_tensor.shape[:-1] + (L // divisor, ) + out_scale_shape = src_tensor.shape[:-1] + (triton.cdiv(L, MXFP_BLOCK_SIZE), ) + + out_quant_tensor = src_tensor.new_empty(out_shape, dtype=out_quant_type) + out_scale = src_tensor.new_empty(out_scale_shape, dtype=torch.uint8) + + if src_tensor.numel() > 0: + kernel_src_tensor = src_tensor.reshape(-1, src_tensor.shape[-1]) + kernel_quant_tensor = out_quant_tensor.view(-1, out_quant_tensor.shape[-1]) + kernel_scale = out_scale.view(-1, out_scale.shape[-1]) + + BLOCK_OUT_DIM = 128 + BLOCK_QUANT_DIM = MXFP_BLOCK_SIZE.value + grid_out = triton.cdiv(kernel_src_tensor.shape[0], BLOCK_OUT_DIM) + grid_quant = triton.cdiv(kernel_src_tensor.shape[1], BLOCK_QUANT_DIM) + + _downcast_to_mxfp[(grid_out, grid_quant)](kernel_quant_tensor, *kernel_quant_tensor.stride(), kernel_scale, + *kernel_scale.stride(), kernel_src_tensor, *kernel_src_tensor.stride(), + *kernel_src_tensor.shape, BLOCK_OUT_DIM, BLOCK_QUANT_DIM, + DEQUANT_SCALE_ROUNDING_MODE.value, num_warps=8) + + out_quant_tensor = out_quant_tensor.transpose(axis, src_tensor.ndim - 1) + out_scale = out_scale.transpose(axis, src_tensor.ndim - 1) + return out_quant_tensor, out_scale + + +def upcast_from_mxfp(tensor: torch.Tensor, scale: torch.Tensor, target_dtype: torch.dtype, axis: int): + """ + Upcasts an mxfp (packed) weight tensor back to float16 or bfloat16. + + The function assumes that the tensors were quantized along the given axis. + It permutes the tensor so that the quantized axis is last, reshapes to 2D, + launches the Triton upcast kernel, and then unpermutes back to the original order. + """ + ndim = tensor.ndim + assert -ndim <= axis < ndim, f"Invalid axis {axis=}" + axis = axis if axis >= 0 else axis + ndim + assert tensor.ndim == scale.ndim, (f"Weight and scale must have the same number of dimensions. " + f"Got {tensor.ndim=} and {scale.ndim=}") + # dtype checks + assert tensor.dtype in {torch.uint8, torch.float8_e5m2, torch.float8_e4m3fn}, \ + f"Invalid tensor dtype {tensor.dtype=}" + assert scale.dtype == torch.uint8, f"Invalid scale dtype {scale.dtype=}" + assert target_dtype in (torch.float16, torch.bfloat16, torch.float32), f"Invalid output dtype {target_dtype=}" + # upcast + logical_quant_dim = tensor.shape[axis] * (2 if tensor.dtype == torch.uint8 else 1) + tensor = tensor.transpose(axis, tensor.ndim - 1).contiguous() + scale = scale.transpose(axis, scale.ndim - 1).contiguous() + out = torch.empty((*tensor.shape[:-1], logical_quant_dim), dtype=target_dtype, device=tensor.device) + reshaped_out = out.view(-1, out.shape[-1]) + reshaped_tensor = tensor.view(-1, tensor.shape[-1]) + reshaped_scale = scale.view(-1, scale.shape[-1]) + BLOCK_OUT_DIM = 128 + BLOCK_QUANT_DIM = MXFP_BLOCK_SIZE.value + blocks_out_dim = triton.cdiv(reshaped_out.shape[0], BLOCK_OUT_DIM) + blocks_quant_dim = triton.cdiv(reshaped_out.shape[1], BLOCK_QUANT_DIM) + _upcast_from_mxfp[(blocks_out_dim, blocks_quant_dim)](reshaped_out, *reshaped_out.stride(), reshaped_scale, + *reshaped_scale.stride(), reshaped_tensor, + *reshaped_tensor.stride(), *reshaped_out.shape, BLOCK_OUT_DIM, + BLOCK_QUANT_DIM, num_warps=8) + out = out.transpose(axis, scale.ndim - 1).contiguous() + return out + + +# ------------ + + +def right_shift_unsigned(x, shift): + # CUDA torch does not support bit ops on uint32, so we need to mask to get unsigned right shift + return (x >> shift) & ((1 << (32 - shift)) - 1) + + +def get_max_quant_val(dtype: torch.dtype): + d = {torch.uint8: 6.0, torch.float8_e5m2: 57344.0, torch.float8_e4m3fn: 448.0} + assert dtype in d + return d[dtype] + + +def downcast_to_mxfp_torch(src_tensor: torch.Tensor, out_quant_type: torch.dtype, axis: int, + DEQUANT_SCALE_ROUNDING_MODE: DequantScaleRoundingMode = DequantScaleRoundingMode.ROUND_UP): + """ + Converts the src tensor to the output format specified by out_quant_type. + axis: The axis along which the tensors are contiguous and quantization is applied. + DEQUANT_SCALE_ROUNDING_MODE: 0 for ROUND_UP, 1 for ROUND_DOWN. + + Returns: + out_quant_tensor: Quantized tensor in mx format. + • For mxfp8, the output has the same shape as src_tensor. + • For mxfp4, the size along the axis is halved, and the tensor is returned as a torch.uint8. + scale: Scale tensor (stored as uint8) computed per group of 32 elements along the axis. + Its shape is the same as src_tensor except that the axis is replaced by ceil(L/32), + where L is the original length along that axis. + """ + # This should probably be packed into its own tiny class + ndim = src_tensor.ndim + assert -ndim <= axis < ndim, f"Invalid axis {axis=}" + assert src_tensor.dtype in {torch.float32, torch.bfloat16, + torch.float16}, f"Invalid input tensor dtype {src_tensor.dtype}" + + axis = axis if axis >= 0 else axis + ndim + is_fp4 = out_quant_type == torch.uint8 + is_fp8 = "float8" in str(out_quant_type) + assert is_fp4 or is_fp8, f"Invalid input tensor dtype {out_quant_type}" + + device = src_tensor.device + + # For mxfp4 conversion, we assume the contiguous axis length is even. + if is_fp4: + axis_shape = src_tensor.size(axis) + assert axis_shape % 2 == 0, "For mxfp4 conversion the contiguous axis length must be even." + + # Permute the tensor so that the contiguous axis becomes the last dimension. + src = src_tensor.transpose(axis, src_tensor.ndim - 1).to(torch.float32) + axis_shape = src.shape[-1] + + # Pad the axis to be divisible by 32, in case it is not. + next_multiple = triton.cdiv(axis_shape, MXFP_BLOCK_SIZE) * MXFP_BLOCK_SIZE + pad_amount = next_multiple - axis_shape + padded_src = F.pad(src, (0, pad_amount)) + valid_mask = F.pad(torch.ones_like(src, dtype=torch.bool), (0, pad_amount)) + padded_axis_shape = padded_src.size(-1) # now divisible by 32 + + # --- Compute per-group maximums for scale --- + # Set padded entries to -1 so they don’t affect the max. + abs_f = torch.abs(padded_src) + abs_f = torch.where(valid_mask, abs_f, torch.tensor(-1.0, device=device, dtype=padded_src.dtype)) + # Reshape the last dimension into groups of 32. + new_shape = padded_src.shape[:-1] + (padded_axis_shape // MXFP_BLOCK_SIZE, MXFP_BLOCK_SIZE) + abs_groups = abs_f.view(*new_shape) + # Compute maximum along the group dimension (of size 32). + max_val, _ = abs_groups.max(dim=-1, keepdim=True) + + # Choose a max quantization value depending on type. + max_quant_val = get_max_quant_val(out_quant_type) + dequant_scale = max_val / max_quant_val # shape: (..., padded_axis_shape//32, 1) + + # Convert to int to round the FP32 scale, prior to quantization! + ds_int = dequant_scale.view(torch.int32) + if DEQUANT_SCALE_ROUNDING_MODE == DequantScaleRoundingMode.ROUND_UP: + ds_int_rounded = (ds_int + 0x007FFFFF) & 0x7F800000 + else: + ds_int_rounded = ds_int & 0x7F800000 + # Reinterpret back as float32. + dequant_scale_rounded = ds_int_rounded.view(torch.float32) + + # Compute the quantization scale. + quant_scale = torch.where(dequant_scale_rounded == 0, torch.tensor(0.0, device=device), 1.0 / dequant_scale_rounded) + + # Quantize the tensor + orig_padded_shape = padded_src.shape + padded_src_groups = padded_src.view(*new_shape) + quant_tensor = padded_src_groups * quant_scale + # Reshape back to the original shape and trim padding + quant_tensor = quant_tensor.view(orig_padded_shape) + quant_tensor = quant_tensor[..., :axis_shape] + + # Finally, convert the quantized tensor to the target format + if is_fp8: + # Conversion must use satfinite PTX, so clamp before the conversion in torch to emulate this behavior + quant_tensor = torch.clamp(quant_tensor, -max_quant_val, max_quant_val) + out_weight = quant_tensor.to(out_quant_type) + else: + assert is_fp4, f"Invalid output quantization type {out_quant_type}" + # For mxfp4, perform bit-level manipulation and pack two 4-bit values per uint8. + # First, reinterpret the quantized tensor bits. + q_int = quant_tensor.contiguous().view(torch.int32) + # Extract sign, exponent, and mantissa. + signs = q_int & 0x80000000 + exponents = right_shift_unsigned(q_int, 23) & 0xFF + mantissas = q_int & 0x7FFFFF + + E8_BIAS = 127 + E2_BIAS = 1 + # Adjust mantissas for subnormals. + mantissas = torch.where(exponents < E8_BIAS, (0x400000 | right_shift_unsigned(mantissas, 1)) >> + (E8_BIAS - exponents - 1), mantissas) + exponents = torch.maximum(exponents, torch.tensor(E8_BIAS - E2_BIAS, device=device)) - (E8_BIAS - E2_BIAS) + e2m1_tmp = right_shift_unsigned(((exponents << 2) | right_shift_unsigned(mantissas, 21)) + 1, 1) + e2m1_tmp = torch.minimum(e2m1_tmp, torch.tensor(0x7, device=device)) + e2m1_value = (right_shift_unsigned(signs, 28) | e2m1_tmp).to(torch.uint8) # shape: (..., even_axis_shape) + + # Pack pairs of 4-bit values along the last dimension. + e2m1_value = e2m1_value.view(*e2m1_value.shape[:-1], axis_shape // 2, 2) + evens = e2m1_value[..., 0] + odds = e2m1_value[..., 1] + out_weight = evens | (odds << 4) # shape: (..., axis_shape//2) + + # --- Process and output the scale --- + dq_scale = (ds_int_rounded.view(*dequant_scale.shape) >> 23).to(torch.uint8) # shape: (..., axis_shape//32, 1) + dq_scale = dq_scale.squeeze(-1) + out_weight = out_weight.transpose(axis, src_tensor.ndim - 1) + dq_scale = dq_scale.transpose(axis, src_tensor.ndim - 1) + return out_weight, dq_scale + + +def cvt_e2m1_to_fp32(input_tensor): + assert input_tensor.dtype == torch.uint8 + + input_tensor = input_tensor.to(torch.int32) + evens = input_tensor & 0xF + odds = (input_tensor >> 4) & 0xF + + vals = [0.0, 0.5, 1, 1.5, 2, 3, 4, 6] + outputs = torch.tensor(vals, dtype=torch.float32, device=input_tensor.device) + outputs = torch.cat([outputs, -outputs]) + + even_floats = outputs[evens] + odd_floats = outputs[odds] + output_tensor = torch.stack([even_floats, odd_floats], dim=-1) + output_tensor = output_tensor.view(*input_tensor.shape[:-1], -1) + return output_tensor + + +def upcast_from_mxfp_torch(tensor: torch.Tensor, scale: torch.Tensor, target_dtype: torch.dtype, axis: int): + """ + Converts the mxfp4/mxfp8 tensor to the target format specified by target_dtype. + axis: The axis along which dequantization is applied. + + Returns: + out_weight: Tensor in the target format. + """ + + ndim = tensor.ndim + assert -ndim <= axis < ndim, f"Invalid axis {axis=}" + is_fp8 = tensor.dtype == torch.float8_e4m3fn or tensor.dtype == torch.float8_e5m2 + assert is_fp8 or tensor.dtype == torch.uint8, f"Invalid input quantization type {tensor.dtype}" + + # Permute the tensor and scale so that the quantization axis becomes the last dimension + axis = axis if axis >= 0 else axis + ndim + scale = scale.transpose(axis, scale.ndim - 1) + tensor = tensor.transpose(axis, tensor.ndim - 1) + + dq_scale = (scale.to(torch.int32) << 23).view(torch.float32) # Shift to the exponent and bitcast to fp32 + if tensor.dtype == torch.uint8: + fp32_tensor = cvt_e2m1_to_fp32(tensor) + else: + fp32_tensor = tensor.to(torch.float32) + + logical_quant_dim = tensor.shape[-1] * (2 if tensor.dtype == torch.uint8 else 1) + axis_shape = fp32_tensor.size(-1) + padded_axis_shape = triton.cdiv(logical_quant_dim, MXFP_BLOCK_SIZE) * MXFP_BLOCK_SIZE + pad_size = padded_axis_shape - axis_shape + padded_tensor = F.pad(fp32_tensor, (0, pad_size)) + + new_axis_shape = padded_tensor.shape[-1] + new_shape = padded_tensor.shape[:-1] + (new_axis_shape // MXFP_BLOCK_SIZE, MXFP_BLOCK_SIZE) + padded_tensor = padded_tensor.view(*new_shape) + dq_scale_padded = dq_scale.unsqueeze(-1) # shape: [..., ceil(axis_shape/32), 1] + out_padded = padded_tensor * dq_scale_padded + + # Flatten back and remove the padded tail + out_padded = out_padded.view(*fp32_tensor.shape[:-1], new_axis_shape) + out_tensor = out_padded[..., :axis_shape] + + out_tensor = out_tensor.to(target_dtype).contiguous() + out_tensor = out_tensor.transpose(axis, tensor.ndim - 1) + + return out_tensor + + +quantize_mxfp8_fn = _quantize_mxfp8_fn diff --git a/triton_kernels/numerics_details/mxfp_details/__init__.py b/triton_kernels/numerics_details/mxfp_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/numerics_details/mxfp_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/numerics_details/mxfp_details/_downcast_to_mxfp.py b/triton_kernels/numerics_details/mxfp_details/_downcast_to_mxfp.py new file mode 100644 index 000000000000..616dfd0dac64 --- /dev/null +++ b/triton_kernels/numerics_details/mxfp_details/_downcast_to_mxfp.py @@ -0,0 +1,162 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/numerics_details/mxfp_details/_downcast_to_mxfp.py +# Triton is licensed under the MIT License. + +import triton +import triton.language as tl + +# fmt: off + + +MXFP_BLOCK_SIZE = tl.constexpr(32) + + +@triton.jit +def _get_max_quant_val(dtype: tl.constexpr): + if dtype == tl.uint8: + return 6.0 + elif dtype == tl.float8e5: + return 57344.0 + elif dtype == tl.float8e4nv: + return 448.0 + else: + tl.static_assert(False, f"Invalid {dtype=}") + +@triton.jit +def _compute_quant_and_scale(src_tensor, valid_src_mask, mx_tensor_dtype: tl.constexpr, + DEQUANT_SCALE_ROUNDING_MODE: tl.constexpr = 0): + is_fp8: tl.constexpr = mx_tensor_dtype == tl.float8e4nv or mx_tensor_dtype == tl.float8e5 + BLOCK_SIZE_OUT_DIM: tl.constexpr = src_tensor.shape[0] + BLOCK_SIZE_QUANT_DIM: tl.constexpr = src_tensor.shape[1] + BLOCK_SIZE_QUANT_MX_SCALE: tl.constexpr = src_tensor.shape[1] // MXFP_BLOCK_SIZE + + # Explicit cast to fp32 since most ops are not supported on bfloat16. We avoid needless conversions to and from bf16 + f32_tensor = src_tensor.to(tl.float32) + abs_tensor = tl.abs(f32_tensor) + abs_tensor = tl.where(valid_src_mask, abs_tensor, -1.0) # Don't consider padding tensors in scale computation + abs_tensor = tl.reshape(abs_tensor, [BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_MX_SCALE, MXFP_BLOCK_SIZE]) + max_val = tl.max(abs_tensor, axis=2, keep_dims=True) + dequant_scale = max_val / _get_max_quant_val(mx_tensor_dtype) + if DEQUANT_SCALE_ROUNDING_MODE == 0: + # DequantScaleRoundingMode.ROUND_UP + # compute 2 ** ceil(log2(dequant_scale)) + # Adding 0x007FFFFF adds exponent by 1 unless mantissa is all zeros + # A corner case: exponent is 0xFF that will overflow but that's already + # NaN so assume we don't care. + dequant_scale_exponent = (dequant_scale.to(tl.uint32, bitcast=True) + 0x007FFFFF) & 0x7F800000 + else: + # DequantScaleRoundingMode.ROUND_DOWN + # compute 2 ** floor(log2(dequant_scale)) + assert DEQUANT_SCALE_ROUNDING_MODE == 1 + dequant_scale_exponent = dequant_scale.to(tl.uint32, bitcast=True) & 0x7F800000 + dequant_scale_rounded = dequant_scale_exponent.to(tl.float32, bitcast=True) + quant_scale = tl.where(dequant_scale_rounded == 0, 0, 1.0 / dequant_scale_rounded) + + f32_tensor = tl.reshape(f32_tensor, [BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_MX_SCALE, MXFP_BLOCK_SIZE]) + quant_tensor = f32_tensor * quant_scale + + # Reshape the tensors after scaling + quant_tensor = quant_tensor.reshape([BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_DIM]) + # Set the invalid portions of the tensor to 0. This will ensure that any padding tensors are 0 in the mx format. + quant_tensor = tl.where(valid_src_mask, quant_tensor, 0) + dequant_scale_exponent = dequant_scale_exponent.reshape([BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_MX_SCALE]) + + # First, we simply extract the exponent part of the scales and store the result + dequant_scale_exponent = (dequant_scale_exponent >> 23).to(tl.uint8) + # Now we must convert the tensors to the mx format. + if is_fp8: + out_tensor = quant_tensor.to(mx_tensor_dtype) + else: + quant_tensor = quant_tensor.to(tl.uint32, bitcast=True) + signs = quant_tensor & 0x80000000 + exponents = (quant_tensor >> 23) & 0xFF + mantissas = (quant_tensor & 0x7FFFFF) + + # 0.25 <= x < 0.75 maps to 0.5, a denormal number + E8_BIAS = 127 + E2_BIAS = 1 + # Move implicit bit 1 at the beginning to mantissa for denormals + adjusted_exponents = tl.core.sub(E8_BIAS, exponents + 1, sanitize_overflow=False) + mantissas = tl.where(exponents < E8_BIAS, (0x400000 | (mantissas >> 1)) >> adjusted_exponents, mantissas) + + # For normal numbers, we change the bias from 127 to 1, and for subnormals, we keep exponent as 0. + exponents = tl.maximum(exponents, E8_BIAS - E2_BIAS) - (E8_BIAS - E2_BIAS) + + # Combine sign, exponent, and mantissa, while saturating + # rounding nearest with tie breaking up by adding +1 to one bit right of the LSB, then shift right + e2m1_tmp = tl.minimum((((exponents << 2) | (mantissas >> 21)) + 1) >> 1, 0x7) + e2m1_value = ((signs >> 28) | e2m1_tmp).to(tl.uint8) + + e2m1_value = tl.reshape(e2m1_value, [BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_DIM // 2, 2]) + evens, odds = tl.split(e2m1_value) + out_tensor = evens | (odds << 4) + + return out_tensor, dequant_scale_exponent + +@triton.jit +def _downcast_to_mxfp(mx_tensor_ptr, stride_mxt_outer, stride_mxt_quant: tl.constexpr, + mx_scale_ptr, stride_mx_scale_outer, stride_mx_scale_quant, + src_ptr, stride_src_outer, stride_src_quant, + outer_dim, quant_dim, + BLOCK_SIZE_OUT_DIM: tl.constexpr, BLOCK_SIZE_QUANT_DIM: tl.constexpr, + DEQUANT_SCALE_ROUNDING_MODE: tl.constexpr): + + tl.static_assert(stride_mxt_quant == 1, f"Output stride, {stride_mxt_quant=} must be 1.") + tl.static_assert(BLOCK_SIZE_QUANT_DIM % MXFP_BLOCK_SIZE == 0, f"{BLOCK_SIZE_QUANT_DIM=} must be a multiple of 32") + + # uint8 signifies two fp4 e2m1 values packed into a single byte + mx_tensor_dtype: tl.constexpr = mx_tensor_ptr.dtype.element_ty + tl.static_assert(mx_tensor_dtype == tl.uint8 or (mx_tensor_dtype == tl.float8e4nv or mx_tensor_dtype == tl.float8e5), + f"Invalid {mx_tensor_dtype=}. Must be uint8 or float8.") + + src_dtype: tl.constexpr = src_ptr.dtype.element_ty + tl.static_assert(mx_scale_ptr.dtype.element_ty == tl.uint8, f"{mx_scale_ptr.dtype.element_ty=} must be uint8") + tl.static_assert((src_dtype == tl.bfloat16) or (src_dtype == tl.float16) or (src_dtype == tl.float32), f"{src_dtype=} must be bfloat16 or float16 or float32") + is_fp4: tl.constexpr = mx_tensor_dtype == tl.uint8 + + outer_block = tl.program_id(0).to(tl.int64) + quant_block = tl.program_id(1).to(tl.int64) + + K_DIVISOR: tl.constexpr = 2 if is_fp4 else 1 + BLOCK_SIZE_QUANT_MX_SCALE: tl.constexpr = BLOCK_SIZE_QUANT_DIM // MXFP_BLOCK_SIZE + BLOCK_SIZE_QUANT_MX_TENSOR: tl.constexpr = BLOCK_SIZE_QUANT_DIM // K_DIVISOR + + start_src_quant = quant_block * BLOCK_SIZE_QUANT_DIM + start_mx_scale_quant = quant_block * BLOCK_SIZE_QUANT_MX_SCALE + start_mx_quant = quant_block * BLOCK_SIZE_QUANT_MX_TENSOR + start_out = outer_block * BLOCK_SIZE_OUT_DIM + + src_ptr += start_src_quant * stride_src_quant + start_out * stride_src_outer + mx_scale_ptr += start_mx_scale_quant * stride_mx_scale_quant + start_out * stride_mx_scale_outer + mx_tensor_ptr += start_mx_quant * stride_mxt_quant + start_out * stride_mxt_outer + + offs_src_quant = tl.arange(0, BLOCK_SIZE_QUANT_DIM)[None, :].to(tl.int64) + offs_mxt_quant = tl.arange(0, BLOCK_SIZE_QUANT_MX_TENSOR)[None, :].to(tl.int64) + offs_scale_quant = tl.arange(0, BLOCK_SIZE_QUANT_MX_SCALE)[None, :].to(tl.int64) + offs_outer = tl.arange(0, BLOCK_SIZE_OUT_DIM)[:, None].to(tl.int64) + + mask_src_quant = start_src_quant + offs_src_quant < quant_dim + mask_n = start_out + offs_outer < outer_dim + full_mask_src = mask_src_quant & mask_n + + mask_mxt_quant = start_mx_quant + offs_mxt_quant < tl.cdiv(quant_dim, K_DIVISOR) + full_mask_mxt = mask_mxt_quant & mask_n + + scale_mask_k = start_mx_scale_quant + offs_scale_quant < tl.cdiv(quant_dim, MXFP_BLOCK_SIZE) + full_scale_mask = scale_mask_k & mask_n + + src_tensor_offsets = offs_src_quant * stride_src_quant + offs_outer * stride_src_outer + mx_scale_offsets = offs_scale_quant * stride_mx_scale_quant + offs_outer * stride_mx_scale_outer + mx_tensor_offsets = offs_mxt_quant * stride_mxt_quant + offs_outer * stride_mxt_outer + src_tensor = tl.load(src_ptr + src_tensor_offsets, mask=full_mask_src) + + out_tensor, scale_tensor = _compute_quant_and_scale(src_tensor, full_mask_src, mx_tensor_dtype, + DEQUANT_SCALE_ROUNDING_MODE) + + tl.store(mx_scale_ptr + mx_scale_offsets, scale_tensor, mask=full_scale_mask) + tl.store(mx_tensor_ptr + mx_tensor_offsets, out_tensor, mask=full_mask_mxt) + + +@triton.jit(repr=lambda _: "_dequantize_mxfp8") +def _quantize_mxfp8_fn(input, mask, pid=None): + return _compute_quant_and_scale(input, mask, tl.float8e4nv) diff --git a/triton_kernels/numerics_details/mxfp_details/_upcast_from_mxfp.py b/triton_kernels/numerics_details/mxfp_details/_upcast_from_mxfp.py new file mode 100644 index 000000000000..58e368972b8b --- /dev/null +++ b/triton_kernels/numerics_details/mxfp_details/_upcast_from_mxfp.py @@ -0,0 +1,129 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/numerics_details/mxfp_details/_upcast_from_mxfp.py +# Triton is licensed under the MIT License. + +import triton +import triton.language as tl + +from ._downcast_to_mxfp import MXFP_BLOCK_SIZE + + +# fmt: off +@triton.jit +def _upcast_from_mxfp(out_ptr, stride_o_outer, stride_o_quant: tl.constexpr, mx_scale_ptr, stride_scale_outer, + stride_scale_quant, mx_tensor_ptr, stride_tensor_outer, stride_tensor_quant: tl.constexpr, + outer_dim, quant_dim, BLOCK_SIZE_OUT_DIM: tl.constexpr, BLOCK_SIZE_QUANT_DIM: tl.constexpr): + + tl.static_assert(stride_o_quant == 1, "the weight must be contiguous in the k dimension for mx") + tl.static_assert(BLOCK_SIZE_QUANT_DIM % MXFP_BLOCK_SIZE == 0, "BLOCK_SIZE_K must be a multiple of 32") + # uint8 signifies two fp4 e2m1 values packed into a single byte + mx_tensor_dtype: tl.constexpr = mx_tensor_ptr.dtype.element_ty + dst_dtype: tl.constexpr = out_ptr.dtype.element_ty + tl.static_assert(dst_dtype == tl.float16 or dst_dtype == tl.bfloat16 or dst_dtype == tl.float32) + tl.static_assert( + mx_tensor_dtype == tl.uint8 + or ((mx_tensor_dtype == tl.float8e4nv or mx_tensor_dtype == tl.float8e5) or mx_tensor_dtype == dst_dtype), + "mx_tensor_ptr must be uint8 or float8 or dst_dtype") + tl.static_assert(mx_scale_ptr.dtype.element_ty == tl.uint8, "mx_scale_ptr must be uint8") + + # Determine if we are dealing with fp8 types. + is_fp4: tl.constexpr = mx_tensor_dtype == tl.uint8 + is_fp8: tl.constexpr = mx_tensor_dtype == tl.float8e4nv or mx_tensor_dtype == tl.float8e5 + K_DIVISOR: tl.constexpr = 2 if is_fp4 else 1 + BLOCK_SIZE_QUANT_MX_SCALE: tl.constexpr = BLOCK_SIZE_QUANT_DIM // MXFP_BLOCK_SIZE + BLOCK_SIZE_QUANT_MX_TENSOR: tl.constexpr = BLOCK_SIZE_QUANT_DIM // K_DIVISOR + + # Compute starting indices for the quantized (packed) dimension and the outer dimension. + outer_block = tl.program_id(0).to(tl.int64) + quant_block = tl.program_id(1).to(tl.int64) + + start_mxt_quant = quant_block * BLOCK_SIZE_QUANT_MX_TENSOR + start_out_quant = quant_block * BLOCK_SIZE_QUANT_DIM + start_mx_scale_quant = quant_block * BLOCK_SIZE_QUANT_MX_SCALE + start_out = outer_block * BLOCK_SIZE_OUT_DIM + + mx_tensor_ptr += start_mxt_quant * stride_tensor_quant + start_out * stride_tensor_outer + mx_scale_ptr += start_mx_scale_quant * stride_scale_quant + start_out * stride_scale_outer + out_ptr += start_out * stride_o_outer + start_out_quant * stride_o_quant + + # Compute offsets and masks. + offs_src_quant = tl.arange(0, BLOCK_SIZE_QUANT_MX_TENSOR)[None, :].to(tl.int64) + offs_out_quant = tl.arange(0, BLOCK_SIZE_QUANT_DIM)[None, :].to(tl.int64) + offs_outer = tl.arange(0, BLOCK_SIZE_OUT_DIM)[:, None].to(tl.int64) + offs_scale = tl.arange(0, BLOCK_SIZE_QUANT_MX_SCALE)[None, :].to(tl.int64) + + mask_outer = start_out + offs_outer < outer_dim + mask_out_quant = start_out_quant + offs_out_quant < quant_dim + full_mask_out = mask_out_quant & mask_outer + + mask_src_quant = start_mxt_quant + offs_src_quant < tl.cdiv(quant_dim, K_DIVISOR) + full_mask_src = mask_src_quant & mask_outer + + mask_scale = start_mx_scale_quant + offs_scale < tl.cdiv(quant_dim, MXFP_BLOCK_SIZE) + full_scale_mask = mask_scale & mask_outer + + tensor_offsets = offs_src_quant * stride_tensor_quant + offs_outer * stride_tensor_outer + scale_offsets = offs_scale * stride_scale_quant + offs_outer * stride_scale_outer + out_offsets = offs_out_quant * stride_o_quant + offs_outer * stride_o_outer + + # Load the packed tensor and scale. + tensor = tl.load(mx_tensor_ptr + tensor_offsets, mask=full_mask_src) + scale = tl.load(mx_scale_ptr + scale_offsets, mask=full_scale_mask) + + # Upcast the scale to the destination type. + if dst_dtype == tl.bfloat16: + dst_scale = (scale.to(tl.uint16) << 7).to(dst_dtype, bitcast=True) + else: + dst_scale = (scale.to(tl.uint32) << 23).to(tl.float32, bitcast=True) + if dst_dtype == tl.float16: + dst_scale = dst_scale.to(tl.float16) + + # Now upcast the tensor. + intermediate_dtype: tl.constexpr = tl.bfloat16 if dst_dtype == tl.float32 else dst_dtype + if is_fp8: + dst_tensor = tensor.to(intermediate_dtype) + if tensor.dtype == tl.float8e5: + from_e_bits: tl.constexpr = 5 + from_m_bits: tl.constexpr = 2 + to_e_bits: tl.constexpr = 8 if intermediate_dtype == tl.bfloat16 else 5 + to_m_bits: tl.constexpr = 7 if intermediate_dtype == tl.bfloat16 else 10 + + # Preserve infs and nans. FIXME Fp8E5M2_to_Bf16 doesn't preserve them! + non_finite_mask_src: tl.constexpr = ((1 << from_e_bits) - 1) << from_m_bits + non_finite_mask_dst: tl.constexpr = ((1 << to_e_bits) - 1) << to_m_bits + dst_tensor = tl.where( + (tensor.to(tl.uint8, bitcast=True) & non_finite_mask_src) == non_finite_mask_src, + (dst_tensor.to(tl.uint16, bitcast=True) | non_finite_mask_dst).to(intermediate_dtype, bitcast=True), + dst_tensor, + ) + else: + assert is_fp4 + dst_bias: tl.constexpr = 127 if intermediate_dtype == tl.bfloat16 else 15 + dst_0p5: tl.constexpr = 16128 if intermediate_dtype == tl.bfloat16 else 0x3800 + dst_m_bits: tl.constexpr = 7 if intermediate_dtype == tl.bfloat16 else 10 + # e2m1 + em0 = tensor & 0x07 + em1 = tensor & 0x70 + x0 = (em0.to(tl.uint16) << (dst_m_bits - 1)) | ((tensor & 0x08).to(tl.uint16) << 12) + x1 = (em1.to(tl.uint16) << (dst_m_bits - 5)) | ((tensor & 0x80).to(tl.uint16) << 8) + # Three cases: + # 1) x is normal and non-zero: Correct bias + x0 = tl.where((em0 & 0x06) != 0, x0 + ((dst_bias - 1) << dst_m_bits), x0) + x1 = tl.where((em1 & 0x60) != 0, x1 + ((dst_bias - 1) << dst_m_bits), x1) + # 2) x is subnormal (x == 0bs001 where s is the sign): Map to +-0.5 in the dst type + x0 = tl.where(em0 == 0x01, dst_0p5 | (x0 & 0x8000), x0) + x1 = tl.where(em1 == 0x10, dst_0p5 | (x1 & 0x8000), x1) + # 3) x is zero, do nothing + dst_tensor = tl.interleave(x0, x1).to(intermediate_dtype, bitcast=True) + dst_tensor = dst_tensor.to(dst_dtype) + + # Reshape for proper broadcasting: the scale was stored with a 32‐sized “inner” grouping. + dst_tensor = dst_tensor.reshape([BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_MX_SCALE, MXFP_BLOCK_SIZE]) + dst_scale = dst_scale.reshape([BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_MX_SCALE, 1]) + scale = scale.reshape(dst_scale.shape) + + out_tensor = dst_tensor * dst_scale + # Correct any NaNs encoded via the scale. + out_tensor = tl.where(scale == 0xFF, float("nan"), out_tensor) + out_tensor = out_tensor.reshape([BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_DIM]) + tl.store(out_ptr + out_offsets, out_tensor, mask=full_mask_out) diff --git a/triton_kernels/proton_opts.py b/triton_kernels/proton_opts.py new file mode 100644 index 000000000000..befa2f6ce267 --- /dev/null +++ b/triton_kernels/proton_opts.py @@ -0,0 +1,21 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/proton_opts.py +# Triton is licensed under the MIT License. + +# proton options + +import os + +_launch_metadata_allow_sync = None + + +def launch_metadata_allow_sync(): + global _launch_metadata_allow_sync + if _launch_metadata_allow_sync is None: + _launch_metadata_allow_sync = not (os.getenv("PROTON_LAUNCH_METADATA_NOSYNC") == "1") + return _launch_metadata_allow_sync + + +def set_launch_metadata_allow_sync(allow_sync: bool): + global _launch_metadata_allow_sync + _launch_metadata_allow_sync = allow_sync diff --git a/triton_kernels/reduction_details/__init__.py b/triton_kernels/reduction_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/reduction_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/reduction_details/reduce_bitmatrix.py b/triton_kernels/reduction_details/reduce_bitmatrix.py new file mode 100644 index 000000000000..4f84e73ac08f --- /dev/null +++ b/triton_kernels/reduction_details/reduce_bitmatrix.py @@ -0,0 +1,115 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/reduction_details/reduce_bitmatrix.py +# Triton is licensed under the MIT License. + +import torch +import triton +import triton.language as tl + + +@triton.jit +def vpopc(x): + """ + Vertical popcount + Input x : uint32[..., N] + Output y : uint32[..., 32] + semantics : y[..., i] = sum_j((x[..., j] >> i) & 1) + credits: @apgoucher + """ + + tl.static_assert(x.dtype == tl.uint32, "x should consist of 32-bit unsigned integers") + + BLOCK_N: tl.constexpr = x.shape[-1] # summation axis + BATCHES: tl.constexpr = x.numel // BLOCK_N # number of batches + if BLOCK_N >= 8: + sa1: tl.constexpr = 8 + else: + sa1: tl.constexpr = BLOCK_N + # create 8-way sums in 4-bit fields: + y = tl.reshape(x, [BATCHES, BLOCK_N // sa1, sa1, 1]) + y = (y >> tl.arange(0, 4)[None, None, None, :]) & 0x11111111 + y = tl.sum(y, 2) # [BATCHES, BLOCK_N // sa1, 4] + if BLOCK_N >= 128: + sa2: tl.constexpr = 16 + else: + sa2: tl.constexpr = BLOCK_N // sa1 + # create 128-way sums in 8-bit fields: + y = tl.reshape(y, [BATCHES, BLOCK_N // (sa1 * sa2), sa2, 1, 4]) + y = (y >> (4 * tl.arange(0, 2))[None, None, None, :, None]) & 0x0f0f0f0f + y = tl.sum(y, 2) # [BATCHES, BLOCK_N // (sa1 * sa2), 2, 4] + sa3: tl.constexpr = BLOCK_N // (sa1 * sa2) + # create N-way sums in 32-bit fields: + y = tl.reshape(y, [BATCHES, 1, sa3, 8]) + y = (y >> (8 * tl.arange(0, 4))[None, :, None, None]) & 0x000000ff + y = tl.sum(y, 2) # [BATCHES, 4, 8] + y = tl.reshape(y, x.shape[:-1] + [32]) + return y + + +@triton.jit +def _sum_bitmatrix_memset(Ret, BLOCK: tl.constexpr): + pid = tl.program_id(0) + offs = pid * BLOCK + tl.arange(0, BLOCK) + tl.store(Ret + offs, 0) + + +@triton.jit +def _sum_bitmatrix_rows(B, shape_bm, stride_bm: tl.constexpr, stride_bn: tl.constexpr, # input bitmatrix + Ret, Partials, stride_pm: tl.constexpr, stride_pn, shape_pn, # outputs + BLOCK_MM: tl.constexpr, BLOCK_M: tl.constexpr): + + tl.static_assert(BLOCK_MM % BLOCK_M == 0) + TILE_SIZE: tl.constexpr = BLOCK_MM // BLOCK_M + if isinstance(shape_bm, tl.tensor) and shape_bm.dtype.is_ptr(): + shape_bm = tl.load(shape_bm) + pid_m = tl.program_id(0) + pid_n = tl.program_id(1) + offs_m = pid_m * BLOCK_MM + tl.arange(0, BLOCK_MM) + offs_n = pid_n * 32 + tl.arange(0, 32) + n_rows = shape_bm + bits = tl.load(B + pid_n * stride_bn + offs_m * stride_bm, mask=offs_m < n_rows, other=0) + bits = tl.reshape(bits, [TILE_SIZE, BLOCK_M]) + ret = vpopc(bits) # [TILE_SIZE, 32] + + offs_t = pid_m * TILE_SIZE + tl.arange(0, TILE_SIZE) + + tl.atomic_add(Ret + offs_n, tl.sum(ret, 0), sem="relaxed") + tl.store(Partials + offs_t[:, None] * stride_pm + offs_n[None, :] * stride_pn, ret) + + +def clear_sums(n_cols, device, MEMSET_BLOCK=512): + cdiv = triton.cdiv + blocks = cdiv(n_cols, MEMSET_BLOCK) + out_ret = torch.empty((blocks * MEMSET_BLOCK, ), device=device, dtype=torch.int32) + _sum_bitmatrix_memset[(blocks, )](out_ret, MEMSET_BLOCK) + return out_ret + + +def sum_bitmatrix_rows(x, out_ret, partials_block_size=None): + assert partials_block_size is not None + cdiv = triton.cdiv + PARTIALS_BLOCK_M = partials_block_size + n_rows, n_cols = x.shape + n_rows_max = x.shape_max[0] + assert out_ret.shape == (n_cols, ) + + TILE_SIZE = max(1, 128 // PARTIALS_BLOCK_M) + BLOCK_MM = PARTIALS_BLOCK_M * TILE_SIZE + + pids_x = cdiv(n_rows_max, BLOCK_MM) + pids_y = cdiv(n_cols, 32) + out_partials = torch.empty((pids_y * 32, pids_x * TILE_SIZE), device=out_ret.device, dtype=torch.int32) + out_partials = torch.transpose(out_partials, 0, 1) + + # output tensors + _sum_bitmatrix_rows[(pids_x, pids_y)]( + x.storage.data, n_rows, x.stride(0), x.stride(1), # input + out_ret, # output [final reduction] + out_partials, out_partials.stride(0), out_partials.stride(1), + out_partials.shape[1], # output [partial reductions] + BLOCK_M=PARTIALS_BLOCK_M, BLOCK_MM=BLOCK_MM, # constants + num_warps=8) + + out_partials = out_partials[:cdiv(n_rows_max, PARTIALS_BLOCK_M), :] + + return out_ret, out_partials diff --git a/triton_kernels/routing.py b/triton_kernels/routing.py new file mode 100644 index 000000000000..f59a6a001a2c --- /dev/null +++ b/triton_kernels/routing.py @@ -0,0 +1,396 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/routing.py +# Triton is licensed under the MIT License. + +import torch +import triton +from dataclasses import dataclass, field +from .routing_details._routing_compute import _combined_routing_compute +from .routing_details._routing_compute import _combined_routing_memset +from .routing_details._routing_compute import _routing_clear_bitmatrix +from .routing_details._expt_data import _expt_data_memset +from .routing_details._expt_data import _expt_data_compute +from .target_info import is_hip + + +@dataclass +class GatherIndx: + """ + Indices for an operation that performs: + Y = X[src_idx, :] + """ + # array such that `dst_idx[src_idx] = arange(0, N)` + src_indx: torch.Tensor + dst_indx: torch.Tensor + + +@dataclass +class ScatterIndx: + """ + Indices for an operation that performs: + Y[dst_idx, :] = X + """ + # array such that `dst_idx[src_idx] = arange(0, N)` + src_indx: torch.Tensor + dst_indx: torch.Tensor + + +@dataclass +class ExptData: + # hist[i] is the number of tokens routed to expert i + hist: torch.Tensor + # token_offs_raw[i] is the offset of the first token routed + # to expert i in an expert-sorted array + token_offs_raw: torch.Tensor + # token_offs_pad[block][i] is the offset of the first token routed + # to expert i in an expert-sorted array, assuming histogram + # rounded to the next multiple of `block` + token_offs_pad: dict[int, torch.Tensor] + # block_id_map[block] contain one value for each `pid`` launched by + # the matrix multiplication kernel launched with BLOCK_M=block: + # - the value is -1 if the `pid` has no work to do + # - otherwise, the value is two int16 (packed as an int32) that + # correspond respectively to (1) the expert assigned to + # the tokens processed by this pid; (2) the block assigned to the + # tokens processed by this pid (think `pid_m` in a regular matmul) + # see `test_routing.py` for a reference implementation and more details + block_pid_map: dict[int, torch.Tensor] + + def __post_init__(self): + if self.hist is not None: + assert self.hist.dtype == torch.int32 + if self.token_offs_raw is not None: + assert self.token_offs_raw.dtype == torch.int32 + if self.token_offs_pad is not None: + for v in self.token_offs_pad.values(): + assert v.dtype == torch.int32 + if self.block_pid_map is not None: + for v in self.block_pid_map.values(): + assert v.dtype == torch.int32 + + +@dataclass +class RoutingData: + gate_scal: torch.Tensor = field() + expt_hist: torch.Tensor = field() + n_expts_tot: int = field() + n_expts_act: int = field() + expt_data: ExptData = None + + # Used to make perf annotation cleaner: when we use expert sharding, we can + # use this to tell the "expected" number of local tokens per expert, because + # the actual number can vary per each input. + expected_tokens_per_expt: int = field(default=None) + + def n_blocks(self, n_rows, block_m): + if n_rows <= self.n_expts_tot: + return n_rows + else: + return triton.cdiv(max(n_rows - self.n_expts_tot + 1, 0), block_m) + self.n_expts_tot - 1 + + +# -------------------------- +# sort tokens by expert +# -------------------------- + + +class SortTokens(torch.autograd.Function): + + @staticmethod + def forward(ctx, expt_scal, expt_indx, n_expts_tot, bitmatrix): + HIST_BLOCK_M = 32 + INDX_OFFS_BLOCK_M = 512 + MEMSET_BLOCK = 1024 + cdiv = triton.cdiv + + device = expt_scal.device + dtype = expt_scal.dtype + n_tokens_raw, _ = bitmatrix.shape + n_tokens_pad, n_expts_act = expt_scal.shape + n_gates_pad = n_tokens_pad * n_expts_act + + hist, partial_hist = bitmatrix.sum(partials_block_size=HIST_BLOCK_M) + hist = hist[:n_expts_tot] + assert hist.dtype == torch.int32 + # scratchpad + expt_offs = torch.empty(n_expts_tot, dtype=torch.int32, device=device) + combined_indx = torch.empty(n_gates_pad * 2, dtype=torch.int32, device=device) + # output + topk_indx = combined_indx[:n_gates_pad] + gate_indx = combined_indx[n_gates_pad:] + gate_scal = torch.empty(n_gates_pad, dtype=dtype, device=device) + + token_offs_combined, token_offs_raw, token_offs_pad, block_pid_map, blocks1a, blocks2a, MEMSET_BLOCK_A, HIST2_BLOCK_M, block_m_log2_start, block_m_num = _compute_expt_data_internal( + hist, n_expts_tot, n_gates_pad) + + blocks1b = cdiv(n_gates_pad * 2, MEMSET_BLOCK) + n_expts_tot + 1 + blocks2b = cdiv(n_tokens_pad, HIST_BLOCK_M) + + _combined_routing_memset[(blocks1a + blocks1b, )]( + combined_indx, n_gates_pad * 2, -1, MEMSET_BLOCK, hist, # + expt_offs, hist.shape[0], n_expts_tot, partial_hist, # inputs + partial_hist.shape[0], partial_hist.stride(0), partial_hist.stride(1), # outputs + token_offs_combined, token_offs_combined.stride(0), # + blocks1a, block_pid_map, # + block_m_log2_start, SIZES=block_m_num, BLOCK_A=MEMSET_BLOCK_A, # optimization parameters + BLOCK_N=512, BLOCK_M=INDX_OFFS_BLOCK_M, # tunable parameters + ) + + indx_offs = partial_hist + + _combined_routing_compute[(blocks2a + blocks2b, )]( + topk_indx, gate_indx, gate_scal, # outputs + expt_scal, expt_indx, indx_offs, indx_offs.stride(0), indx_offs.stride(1), # inputs + expt_offs, n_tokens_raw, # input shape + HIST_BLOCK_M, n_expts_act, # constants + hist, token_offs_pad, token_offs_pad.stride(0), block_pid_map, block_pid_map.stride(0), # outputs + block_m_log2_start, block_m_num, HIST2_BLOCK_M, blocks2a, # etc. + ) + + ctx.n_tokens_raw = n_tokens_raw + ctx.n_tokens_pad = n_tokens_pad + ctx.n_expts_act = n_expts_act + ctx.save_for_backward(gate_indx) + return hist, topk_indx, gate_indx, gate_scal, token_offs_raw, token_offs_pad, block_pid_map + + @staticmethod + def backward(ctx, _0, _1, _2, dgate_scal, _3, _4, _5): + (gate_indx, ) = ctx.saved_tensors + dgate_scal = dgate_scal[gate_indx] + dgate_scal = dgate_scal.reshape(ctx.n_tokens_pad, ctx.n_expts_act) + return dgate_scal, None, None, None + + +def sort_tokens(expt_scal, expt_indx, n_expts_tot, bitmatrix): + return SortTokens.apply(expt_scal, expt_indx, n_expts_tot, bitmatrix) + + +# -------------------------- +# prune routing +# -------------------------- + + +class PruneRouting(torch.autograd.Function): + + @staticmethod + def forward(ctx, expt_scal, expt_indx, bitmatrix, n_expts_tot, simulated_ep): + from .compaction import compaction + n_tokens_pad = expt_scal.shape[0] + assert n_expts_tot % simulated_ep == 0 + _routing_clear_bitmatrix[(n_tokens_pad, )]( + bitmatrix.storage.data, + bitmatrix.storage.data.stride(0), + bitmatrix.storage.data.stride(1), + bitmatrix.storage.data.shape[1], + n_expts_tot // simulated_ep, + BLOCK_N=512, + ) + # perform compaction to update expt_scal / expt_indx + expt_scal, expt_indx = compaction(expt_scal, expt_indx, bitmatrix) + n_expts_tot = n_expts_tot // simulated_ep + bitmatrix.shape[-1] = n_expts_tot + return expt_scal, expt_indx, bitmatrix + + +def prune_routing(expt_scal, expt_indx, bitmatrix, n_expts_tot, simulated_ep): + return PruneRouting.apply(expt_scal, expt_indx, bitmatrix, n_expts_tot, simulated_ep) + + +# -------------------------- +# expt_data +# -------------------------- + + +def log2_power_of_two(x): + assert x > 0 and (x & (x - 1)) == 0, "x must be a power of two" + return x.bit_length() - 1 + + +block_m_log2_start = 4 + + +def _compute_expt_data_internal(expt_hist, n_expts_tot, n_gates): + + MEMSET_BLOCK = 512 + HIST2_BLOCK_M = 512 + device = expt_hist.device + n_expts_tot = n_expts_tot + cdiv = triton.cdiv + # block_ms are all powers-of-two between 16 and 128 (inclusive) + block_m_log2_end = 9 if is_hip() else 8 + block_m_num = block_m_log2_end - block_m_log2_start + if n_gates <= n_expts_tot: + max_n_tiles = n_gates + else: + max_n_tiles = n_expts_tot - 1 - ((n_expts_tot - n_gates - 1) // 2**block_m_log2_start) + # allocate memory + pad = lambda x: cdiv(x, MEMSET_BLOCK) * MEMSET_BLOCK + dtype = torch.int32 + + token_offs_combined = torch.empty((block_m_num + 1, pad(n_expts_tot + 1)), dtype=dtype, device=device) + + token_offs_raw = token_offs_combined[0][:n_expts_tot + 1] + token_offs_pad = token_offs_combined[1:] + + block_pid_map = torch.empty((block_m_num, pad(max_n_tiles)), dtype=dtype, device=device) + memset_grid = torch.numel(block_pid_map) // MEMSET_BLOCK # exact division + # compute outputs + token_offs_pad = token_offs_pad[:, :n_expts_tot + 1] + block_pid_map = block_pid_map[:, :max_n_tiles] + + blocks1 = memset_grid + block_m_num + 1 + blocks2 = n_expts_tot * block_m_num + return token_offs_combined, token_offs_raw, token_offs_pad, block_pid_map, blocks1, blocks2, MEMSET_BLOCK, HIST2_BLOCK_M, block_m_log2_start, block_m_num + + +def _unpack_into_dict(x): + + block_m_log2_end = block_m_log2_start + x.shape[0] + x = {2**j: x[i, :] for i, j in enumerate(range(block_m_log2_start, block_m_log2_end))} + return x + + +def compute_expt_data(expt_hist, n_expts_tot, n_gates): + + if expt_hist is None: + return ExptData(None, None, None, None) + + # this just computes the kernel arguments: + token_offs_combined, token_offs_raw, token_offs_pad, block_pid_map, blocks1, blocks2, MEMSET_BLOCK, HIST2_BLOCK_M, block_m_log2_start, block_m_num = _compute_expt_data_internal( + expt_hist, n_expts_tot, n_gates) + + _expt_data_memset[(blocks1, )]( + expt_hist, n_expts_tot, # + token_offs_combined, token_offs_combined.stride(0), # + block_pid_map, # + block_m_log2_start, SIZES=block_m_num, BLOCK=MEMSET_BLOCK, # optimization parameters + num_warps=4) + _expt_data_compute[(blocks2, )]( + expt_hist, token_offs_pad, token_offs_pad.stride(0), block_pid_map, block_pid_map.stride(0), # outputs + block_m_log2_start, SIZES=block_m_num, BLOCK=HIST2_BLOCK_M, # optimization parameters + num_warps=4) + + token_offs_pad = _unpack_into_dict(token_offs_pad) + block_pid_map = _unpack_into_dict(block_pid_map) + return ExptData(expt_hist, token_offs_raw, token_offs_pad, block_pid_map) + + +# -------------------------- +# routing +# -------------------------- + + +def routing_from_bitmatrix(bitmatrix, expt_scal, expt_indx, n_expts_tot, n_expts_act): + hist, topk_indx, gate_indx, gate_scal, token_offs_raw, token_offs_pad, block_pid_map = sort_tokens( + expt_scal, expt_indx, n_expts_tot, bitmatrix) + token_offs_pad = _unpack_into_dict(token_offs_pad) + block_pid_map = _unpack_into_dict(block_pid_map) + expt_data = ExptData(hist, token_offs_raw, token_offs_pad, block_pid_map) + + # pack the matmul data structure + gather_indx = GatherIndx(src_indx=topk_indx, dst_indx=gate_indx) + scatter_indx = ScatterIndx(src_indx=gate_indx, dst_indx=topk_indx) + return RoutingData(gate_scal, hist, n_expts_tot, n_expts_act, expt_data), gather_indx, scatter_indx + + +def routing(logits, n_expts_act, sm_first=False, expt_indx=None, simulated_ep=1, n_rows=None): + from .topk import topk + if sm_first: + logits = torch.softmax(logits, dim=-1) + expt_scal, expt_indx, bitmatrix = topk(logits, n_expts_act, # + apply_softmax=not sm_first, y_indx=expt_indx, n_rows=n_rows) + n_expts_tot = logits.shape[-1] // simulated_ep + # mutate bitmatrix + if simulated_ep > 1: + expt_scal, expt_indx, bitmatrix = prune_routing(expt_scal, expt_indx, bitmatrix, logits.shape[-1], simulated_ep) + + return routing_from_bitmatrix(bitmatrix, expt_scal, expt_indx, n_expts_tot, n_expts_act) + + +# -------------------------- +# torch reference +# -------------------------- + + +def compute_expt_data_torch(hist, n_expts_tot, n_gates): + # offset for each experts + device = hist.device + token_offs_raw = torch.cumsum(hist, dim=0) + token_offs_raw = torch.cat((torch.zeros(1, device=device), token_offs_raw)) + token_offs_raw = token_offs_raw.int() + # maximum number of tiles for all values of `block_m` considered + block_ms = [16, 32, 64, 128] + if is_hip(): + block_ms.append(256) + if n_gates <= n_expts_tot: + max_n_tiles = n_gates + else: + # ceil_div(n_gates - n_experts + 1, d_tile) + n_experts - 1 + # ceil_div(x, y): -(-x // y) + max_n_tiles = n_expts_tot - 1 - ((n_expts_tot - n_gates - 1) // min(block_ms)) + # fill up tile offset/infos for each block + token_offs_pad = dict() + block_pid_map = dict() + for block_m in block_ms: + n_tiles = (hist + block_m - 1) // block_m # matmul blocks needed + token_offs_pad[block_m] = torch.cumsum(n_tiles, dim=0) + token_offs_pad[block_m] = torch.cat((torch.zeros(1, device=device), token_offs_pad[block_m])) + token_offs_pad[block_m] = token_offs_pad[block_m].int() + # compute data required to drive ragged batch matmul + block_pid_map[block_m] = -torch.ones(max_n_tiles, dtype=torch.int32, device=device) + + # for e in range(n_expts_tot): + # offset = token_offs_pad[block_m][e] + # for b in range(n_tiles[e]): + # block_pid_map[block_m][offset + b] = (b << 16) + e + + col = torch.arange(max_n_tiles, device=device) + map_vals = torch.arange(n_expts_tot, device=device)[:, None] + (col << 16)[None, :] + map_idxs = token_offs_pad[block_m][:-1, None] + col[None, :] + mask = col[None, :] < n_tiles[:, None] + block_pid_map[block_m].index_put_((map_idxs[mask], ), map_vals.int()[mask]) + return ExptData(hist, token_offs_raw, token_offs_pad, block_pid_map) + + +def topk_torch(vals, k, expt_indx, has_user_provided_indx=False): + # topk of experts + if has_user_provided_indx: + tk_indx = expt_indx + else: + tk_indx = torch.argsort(-vals, dim=1, stable=True)[:, :k] + tk_indx = tk_indx.long() + tk_val = torch.take_along_dim(vals, tk_indx, dim=1) + tk_indx = tk_indx.int() + return tk_val, tk_indx + + +def routing_torch(logits, n_expts_act, sm_first=False, expt_indx=None, n_rows=None): + has_user_provided_indx = expt_indx is not None + n_gates_pad = logits.shape[0] * n_expts_act + + if n_rows is not None: + logits = logits[:n_rows, :] + _, n_expts_tot = logits.shape + if sm_first: + logits = torch.softmax(logits, dim=-1) + expt_scal, expt_indx = topk_torch(logits, n_expts_act, expt_indx, has_user_provided_indx=has_user_provided_indx) + if not sm_first: + expt_scal = torch.softmax(expt_scal, dim=-1) + # sort each token's selections by expert + if not has_user_provided_indx: + expt_indx, sort_indices = torch.sort(expt_indx, dim=1) + expt_scal = torch.gather(expt_scal, 1, sort_indices) + # flatten topk data + expt_scal = expt_scal.reshape(-1) + expt_indx = expt_indx.reshape(-1).to(torch.int32) + # sort by expert_id so experts are contiguous for the matmul + topk_indx = torch.argsort(expt_indx, stable=True) + gate_indx = torch.argsort(topk_indx, stable=True) + gate_scal = expt_scal[topk_indx] + hist = torch.histc(expt_indx, bins=n_expts_tot, max=n_expts_tot - 1).int() # histogram of tokens over experts + # pack the matmul data structure + gather_indx = GatherIndx(src_indx=topk_indx.int(), dst_indx=gate_indx.int()) + scatter_indx = ScatterIndx(src_indx=gate_indx.int(), dst_indx=topk_indx.int()) + # compute expt_data + expt_data = compute_expt_data_torch(hist, n_expts_tot, n_gates_pad) + return RoutingData(gate_scal, hist, n_expts_tot, n_expts_act, expt_data), gather_indx, scatter_indx diff --git a/triton_kernels/routing_details/__init__.py b/triton_kernels/routing_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/routing_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/routing_details/_expt_data.py b/triton_kernels/routing_details/_expt_data.py new file mode 100644 index 000000000000..d5b736a3747b --- /dev/null +++ b/triton_kernels/routing_details/_expt_data.py @@ -0,0 +1,68 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/routing_details/_expt_data.py +# Triton is licensed under the MIT License. + +import triton +import triton.language as tl + + +@triton.jit +def _cdiv_pow2(n, log2_k): + return (n + ((1 << log2_k) - 1)) >> log2_k + + +@triton.jit +def _expt_data_memset(Hist, n_expts_tot, MDStarts, tile_starts_stridem, MDTileInfo, first_tile_dim_log2, + SIZES: tl.constexpr, BLOCK: tl.constexpr): + + pid = tl.program_id(0) + + if pid <= SIZES: + + MDStarts += pid * tile_starts_stridem + x_tile = tl.zeros([BLOCK], dtype=MDStarts.dtype.element_ty) + Tile_ptrs = MDStarts + tl.arange(0, BLOCK) + tile_dim_log2 = tl.where(pid == 0, 0, pid + first_tile_dim_log2 - 1) + + for i in range(0, n_expts_tot + 1, BLOCK): + + offs_n = tl.arange(0, BLOCK) + i + mask_n0 = offs_n < n_expts_tot + hist_tok = tl.load(Hist + offs_n, mask=mask_n0, other=0) + hist_tile = _cdiv_pow2(hist_tok, tile_dim_log2) + + tile_starts = tl.cumsum(hist_tile, 0) + x_tile + x_tile += tl.sum(hist_tile, 0).to(MDStarts.dtype.element_ty) + tl.store(Tile_ptrs, tile_starts - hist_tile) + Tile_ptrs += BLOCK + + else: + + pid -= (SIZES + 1) + TileInfoOut = MDTileInfo + pid * BLOCK + tl.arange(0, BLOCK) + tl.store(TileInfoOut, 0xffffffff) + + +@triton.jit +def _expt_data_compute(Hist, MDTileStarts, tile_starts_stridem, MDTileInfo, tile_info_stridem, first_tile_dim_log2, + SIZES: tl.constexpr, BLOCK: tl.constexpr): + + pid = tl.program_id(0) + + expt_id = pid // SIZES + buff_id = pid % SIZES + + MDTileStarts += buff_id * tile_starts_stridem + MDTileInfo += buff_id * tile_info_stridem + + n_tokens = tl.load(Hist + expt_id) + tile_dim_log2 = first_tile_dim_log2 + buff_id + n_blocks = _cdiv_pow2(n_tokens, tile_dim_log2) + + tile_off = tl.load(MDTileStarts + expt_id) + MDTileInfo += tile_off + + for block_off in range(0, n_blocks, BLOCK): + block_offs = block_off + tl.arange(0, BLOCK) + data = (block_offs << 16) + expt_id + tl.store(MDTileInfo + block_offs, data, mask=block_offs < n_blocks) diff --git a/triton_kernels/routing_details/_routing_compute.py b/triton_kernels/routing_details/_routing_compute.py new file mode 100644 index 000000000000..595d842878d1 --- /dev/null +++ b/triton_kernels/routing_details/_routing_compute.py @@ -0,0 +1,152 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/routing_details/_routing_compute.py +# Triton is licensed under the MIT License. + +import triton +import triton.language as tl + +from ._expt_data import _expt_data_compute, _expt_data_memset + + +@triton.jit +def _routing_compute_expt_offs(ExpertHist, FinalExpertOffs, hist_size, # histogram + BLOCK_N: tl.constexpr): + loop_iterations = (hist_size + BLOCK_N - 1) // BLOCK_N + x = tl.zeros([BLOCK_N], ExpertHist.dtype.element_ty) + for i in range(loop_iterations): + offs_n = i * BLOCK_N + tl.arange(0, BLOCK_N) + mask_n = offs_n < hist_size + hist2 = tl.load(ExpertHist + offs_n, mask=mask_n) + tok_starts = tl.cumsum(hist2, 0) - hist2 + x + x += tl.sum(hist2, 0) + tl.store(FinalExpertOffs + offs_n, tok_starts, mask=mask_n) + offs_n += BLOCK_N + + +@triton.jit +def _routing_compute_indx_offs(PartialHist, shape_pm, stride_pm, stride_pn, BLOCK_M: tl.constexpr, expt_id): + offs_m = tl.arange(0, BLOCK_M) + # iterate over input data + curr_sum = 0 + for _ in range(0, shape_pm, BLOCK_M): + offs = offs_m * stride_pm + expt_id * stride_pn + curr = tl.load(PartialHist + offs, mask=offs_m < shape_pm) + out = tl.cumsum(curr, 0) + curr_sum + curr_sum += tl.sum(curr, 0) + tl.store(PartialHist + offs, out - curr, mask=offs_m < shape_pm) + offs_m += BLOCK_M + + +@triton.jit +def _keyed_add(x, y): + + # we keep the key in the upper 16 bits of a uint32: + key_mask: tl.constexpr = 0xffff0000 + + kx = x & key_mask + ky = y & key_mask + z = tl.where(kx == ky, x + y - kx, y) + return z + + +@triton.jit +def _routing_compute_indx(pid_m, GatherIndx, ScatterIndx, GateScal, ExptScal, ExptIndx, PartialOffs, stride_pm, + stride_pn, TokensStart, n_tokens, BLOCK_M: tl.constexpr, N_EXPTS_ACT: tl.constexpr): + + if isinstance(n_tokens, tl.tensor) and n_tokens.dtype.is_ptr(): + n_tokens = tl.load(n_tokens) + n_gates = n_tokens * N_EXPTS_ACT + + tl.static_assert(N_EXPTS_ACT * BLOCK_M <= 32768) + + local_offs = tl.arange(0, N_EXPTS_ACT * BLOCK_M) + offs = pid_m * BLOCK_M * N_EXPTS_ACT + local_offs + expert = tl.load(ExptIndx + offs, mask=(offs < n_gates), other=-1).to(tl.uint32) + + # stable-sort by expert ID: + kv_pairs = ((expert << 16) | local_offs).to(tl.uint32) + kv_pairs = tl.sort(kv_pairs, 0) + expert = kv_pairs >> 16 + offs = pid_m * BLOCK_M * N_EXPTS_ACT + (kv_pairs & 0xffff) + mask = expert != 0xffff + gate_scal = tl.load(ExptScal + offs, mask=mask) + + # compute run lengths in expert-sorted order: + x = (kv_pairs & 0xffff0000 | 0x00000001) + expts_and_inclusive_run_lengths = tl.associative_scan(x, 0, _keyed_add) + exclusive_run_lengths = (expts_and_inclusive_run_lengths - 1) & 0xffff + + gates = tl.load(PartialOffs + pid_m * stride_pm + expert * stride_pn, mask=mask) + gates += tl.load(TokensStart + expert, mask=mask) + gates += exclusive_run_lengths + + tl.store(ScatterIndx + offs, gates, mask=mask) + tl.store(GatherIndx + gates, offs, mask=mask) + tl.store(GateScal + gates, gate_scal, mask=mask) + + +@triton.jit +def _combined_routing_compute(GatherIndx, ScatterIndx, GateScal, ExptScal, ExptIndx, PartialOffs, stride_pm, stride_pn, + TokensStart, n_tokens, BLOCK_M: tl.constexpr, N_EXPTS_ACT: tl.constexpr, Hist, + MDTileStarts, tile_starts_stridem, MDTileInfo, tile_info_stridem, first_tile_dim_log2, + SIZES: tl.constexpr, BLOCK: tl.constexpr, blocks2a): + + pid = tl.program_id(0) + if pid < blocks2a: + _expt_data_compute(Hist, MDTileStarts, tile_starts_stridem, MDTileInfo, tile_info_stridem, first_tile_dim_log2, + SIZES, BLOCK) + else: + pid -= blocks2a + _routing_compute_indx(pid, GatherIndx, ScatterIndx, GateScal, ExptScal, ExptIndx, PartialOffs, stride_pm, + stride_pn, TokensStart, n_tokens, BLOCK_M, N_EXPTS_ACT) + + +@triton.jit +def _routing_clear_bitmatrix(Bitmatrix, stride_bm, stride_bn, shape_bn, cutoff, BLOCK_N: tl.constexpr): + pid_m = tl.program_id(0) + cutoff_word = cutoff // 32 + cutoff_bit = cutoff % 32 + cutoff_mask = (1 << (cutoff_bit)) - 1 + for start_n in range(0, shape_bn, BLOCK_N): + offs_n = start_n + tl.arange(0, BLOCK_N) + values = tl.load(Bitmatrix + pid_m * stride_bm + offs_n * stride_bn, mask=offs_n < shape_bn) + values = tl.where(offs_n == cutoff_word, values & cutoff_mask, values) + values = tl.where(offs_n > cutoff_word, 0, values) + tl.store(Bitmatrix + pid_m * stride_bm + offs_n * stride_bn, values, mask=offs_n < shape_bn) + + +@triton.jit +def _combined_routing_memset(Indx, size, sentinel, BLOCK: tl.constexpr, ExpertHist, FinalExpertOffs, hist_size, + n_expts_tot, PartialHist, shape_pm, stride_pm, stride_pn, MDStarts, tile_starts_stridem, + blocks1a, MDTileInfo, first_tile_dim_log2, SIZES: tl.constexpr, BLOCK_A: tl.constexpr, + BLOCK_N: tl.constexpr, BLOCK_M: tl.constexpr): + """ + This kernel essentially combines 6 different pieces of functionality, + statically branching on the value of tl.program_id(0) to decide which + codepath to take. + + pid == 0: create the token cumsum + 1 <= pid <= SIZES: create a tile cumsum + SIZES < pid < blocks1a: initialise MDTileInfo to 0xffffffff + blocks1a <= pid < blocks1a + n_expts_tot: compute_indx_offs + pid == blocks1a + n_expts_tot: compute_expt_offs + pid > blocks1a + n_expts_tot: initialise Indx to sentinel + + As each of these is a relatively trivial workload, launching them from + this single trampoline is beneficial as they can execute on different + streaming multiprocesses in parallel. + """ + + pid = tl.program_id(0) + + if pid < blocks1a: + _expt_data_memset(ExpertHist, n_expts_tot, MDStarts, tile_starts_stridem, MDTileInfo, first_tile_dim_log2, + SIZES, BLOCK_A) + elif pid == n_expts_tot + blocks1a: + _routing_compute_expt_offs(ExpertHist, FinalExpertOffs, hist_size, BLOCK_N) + elif pid < n_expts_tot + blocks1a: + _routing_compute_indx_offs(PartialHist, shape_pm, stride_pm, stride_pn, BLOCK_M, pid - blocks1a) + else: + offs = (pid - n_expts_tot - blocks1a - 1) * BLOCK + tl.arange(0, BLOCK) + mask = offs < size + tl.store(Indx + offs, sentinel, mask=mask) diff --git a/triton_kernels/specialize.py b/triton_kernels/specialize.py new file mode 100644 index 000000000000..9fcb857b29d1 --- /dev/null +++ b/triton_kernels/specialize.py @@ -0,0 +1,139 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/specialize.py +# Triton is licensed under the MIT License. + +import inspect +import re +import textwrap +import types +import triton + + +def cacheable(f): + """ + A decorator that allow you to write something of the form: + + @cacheable + def my_kernel(): return (expression dynamically defining a kernel) + + such that it interacts gracefully with triton cache and preload. + """ + + g = f() + g.fn.__name__ = f.__name__ + g.fn.__module__ = f.__module__ + g.fn.__qualname__ = f.__qualname__ + g.__name__ = f.__name__ + g.__module__ = f.__module__ + g.__qualname__ = f.__qualname__ + g._fn_name = f"{f.__module__}.{f.__qualname__}" + return g + + +def define_kernel(src, module, attrs=None, **extra_globals): + """ + Dynamically create a Triton function or kernel from a src string, + linking any symbols in the kernel to objects specified by extra_globals. + """ + + # create templace function + def _empty_fn(): + pass + + gdict = dict(**(_empty_fn.__globals__)) + gdict.update(extra_globals) + f = types.FunctionType(_empty_fn.__code__, gdict) + f.__module__ = module.__name__ + + src = textwrap.dedent(src) + src = src[src.find("def "):] + + stored_functions = [] + function_name = src[4:].split("(")[0].strip() + + exec_globals = gdict + exec_globals.update({"stored_functions": stored_functions}) + exec(src + "\n\nstored_functions.append(" + function_name + ")\n", exec_globals) + + f.__signature__ = inspect.signature(stored_functions[0]) + f.__name__ = function_name + f.__doc__ = stored_functions[0].__doc__ + + if attrs is None: + attrs = dict() + f = triton.JITFunction(f, **attrs) + f._unsafe_update_src(src) + return f + + +def specialize(fn, module, constants, tuples, name=None, do_not_specialize=tuple()): + assert isinstance(fn, triton.runtime.jit.JITFunction) + if name is None: + name = f"{fn.__name__}" + # Get original source code + src = inspect.getsource(fn.fn) + src = textwrap.dedent(src) + lines = src.split("\n") + # Skip decorator and def line + def_idx = next(i for i, line in enumerate(lines) if line.strip().startswith("def")) + # separate header vs body LOC + header_end = def_idx + while not lines[header_end].rstrip().endswith(":"): + header_end += 1 + body_lines = lines[header_end + 1:] + header_lines = lines[def_idx:header_end + 1] + # clean-up header + header_clean = [ + l.split("#", 1)[0].strip() # keep code, discard comment + for l in header_lines + if l.split("#", 1)[0].strip() # skip blank‑after‑comment lines + ] + # decompose arguments + header_src = " ".join(header_clean) # turn it into a single line + m = re.search(r"\((.*)\)\s*:", header_src) + if not m: + raise ValueError("Could not parse function header") + args_str = m.group(1) + args = [arg.strip() for arg in args_str.split(",") if arg.strip()] + non_specialized_args = [] + for arg in args: + arg_key = arg.split(":")[0].split("=")[0].strip() + new_args = tuples.get(arg_key, [arg]) + if arg_key not in constants: + non_specialized_args += new_args + # add global symbols + spec_fns = {v.__name__: v for k, v in constants.items() if isinstance(v, triton.runtime.jit.JITFunction)} + globals = spec_fns | fn.get_capture_scope() + # build new source code and define kernel dynamically + new_signature = f"def {name}({', '.join(non_specialized_args)}):" + constexpr_lines = [ + f" {key}: tl.constexpr = {value.__name__ if callable(value) else value}" for key, value in constants.items() + ] + tuple_lines = [ + f" {key} = {'(' + ','.join(value) + (',' if len(value)>=1 else '') + ')'}" for key, value in tuples.items() + ] + new_src = "\n".join(["@triton.jit", new_signature] + constexpr_lines + tuple_lines + body_lines) + # find function parameters + sig = inspect.signature(triton.runtime.jit.JITFunction.__init__) + params = list(sig.parameters.values())[2:] + attrs = {param.name: getattr(fn, param.name, param.default) for param in params} + + # make a new repr which appends the repr of the specialized functions. + base_repr = attrs["repr"] + + def new_repr(specialization): + ret = base_repr(specialization) + for spec_fn in spec_fns.values(): + spec_repr = spec_fn.repr(None) + if spec_repr: + spec_repr = spec_repr.strip("_") + if spec_repr: + ret += f"_{spec_repr}" + return ret + + attrs["repr"] = new_repr + + if do_not_specialize: + attrs["do_not_specialize"] = do_not_specialize + ret = define_kernel(new_src, module, attrs, **globals) + return ret diff --git a/triton_kernels/swiglu.py b/triton_kernels/swiglu.py new file mode 100644 index 000000000000..c1baee40f95b --- /dev/null +++ b/triton_kernels/swiglu.py @@ -0,0 +1,104 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/swiglu.py +# Triton is licensed under the MIT License. + +from dataclasses import dataclass +from triton_kernels.numerics import InFlexData, OutFlexData +import torch +import triton +from .swiglu_details._swiglu import _swiglu, _swiglu_fn +from triton_kernels import target_info + + +@dataclass(frozen=True) +class FlexCtx: + out_data: OutFlexData = OutFlexData() + inp_data: InFlexData = InFlexData() + saturate_inf: bool = False + + +@dataclass(frozen=True) +class PrecisionConfig: + limit: float + flex_ctx: FlexCtx = FlexCtx() + + +swiglu_fn = _swiglu_fn + + +class SwiGLU(torch.autograd.Function): + + @staticmethod + def forward(ctx, a, alpha, precision_config, routing_data): + N = a.shape[-1] + M = a.numel() // N + assert a.stride()[-1] == 1 + assert a.shape[-1] % 2 == 0 + out = torch.empty(size=(M, N // 2), dtype=a.dtype, device=a.device) + flex_ctx = precision_config.flex_ctx + # optimization hyperparameters + BLOCK_M, BLOCK_N = 32 // a.itemsize, 128 + num_warps = 4 + kwargs = {'maxnreg': 64} if not target_info.is_hip() else {} + # launch semi-persistent kernel + N_BLOCKS = triton.cdiv(N // 2, BLOCK_N) + num_sms = target_info.num_sms() + if routing_data is not None: + waves_per_sm = 32 if target_info.is_hip() else 128 + num_pid = num_sms * (waves_per_sm // num_warps) + M_BLOCKS = max(1, triton.cdiv(num_pid, N_BLOCKS)) + grid = (min(M_BLOCKS * N_BLOCKS, 4 * num_sms), ) + else: + M_BLOCKS = triton.cdiv(M, BLOCK_M) + if M_BLOCKS * N_BLOCKS >= 8 * num_sms: + grid = (8 * num_sms, ) + else: + grid = (min(M_BLOCKS * N_BLOCKS, 4 * num_sms), ) + n_tokens = None + if routing_data is not None: + n_tokens = routing_data.expt_data.token_offs_raw[routing_data.n_expts_tot] + _swiglu[grid]( + flex_ctx.out_data.reinterpret(out), + flex_ctx.out_data.expected_scale, + flex_ctx.out_data.actual_scale, + flex_ctx.out_data.checksum_scale, + flex_ctx.inp_data.reinterpret(a), + flex_ctx.inp_data.scale, + alpha, + M, + N // 2, + a.shape[-1], + 1, + out.shape[-1], + 1, + precision_config.limit, + n_tokens, + BLOCK_M=BLOCK_M, + BLOCK_N=BLOCK_N, + EVEN_N=(N // 2) % BLOCK_N == 0, + M_BLOCKS=M_BLOCKS, + N_BLOCKS=N_BLOCKS, + flexpoint_saturate_inf=flex_ctx.saturate_inf, + num_warps=num_warps, + **kwargs, + ) + out = out.view(a.shape[:-1] + out.shape[-1:]) + return out + + +def swiglu(a, alpha, precision_config, routing_data=None): + return SwiGLU.apply(a, alpha, precision_config, routing_data) + + +def swiglu_torch(a, alpha, precision_config): + limit = precision_config.limit + a_gelu = a[..., ::2] + if limit is not None: + a_gelu = a_gelu.clamp(max=limit) + a_linear = a[..., 1::2] + if limit is not None: + a_linear = a_linear.clamp(min=-limit, max=limit) + + out_gelu = a_gelu * torch.sigmoid(alpha * a_gelu) + out = out_gelu * (a_linear + 1) + return out diff --git a/triton_kernels/swiglu_details/__init__.py b/triton_kernels/swiglu_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/swiglu_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/swiglu_details/_swiglu.py b/triton_kernels/swiglu_details/_swiglu.py new file mode 100644 index 000000000000..b2a907e07448 --- /dev/null +++ b/triton_kernels/swiglu_details/_swiglu.py @@ -0,0 +1,106 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/swiglu_details/_swiglu.py +# Triton is licensed under the MIT License. + +from triton_kernels.numerics_details.flexpoint import load_scale, float_to_flex, update_scale +import triton +import triton.language as tl + + +@triton.jit +def clip(x, limit, clip_lower: tl.constexpr): + res = tl.minimum(x, limit) + if clip_lower: + res = tl.maximum(-limit, res) + return res + + +@triton.jit +def thread_local_absmax(x, BLOCK_SIZE: tl.constexpr, NUM_THREADS: tl.constexpr): + return tl.max(tl.reshape(tl.abs(x), [NUM_THREADS, BLOCK_SIZE // NUM_THREADS], can_reorder=True), axis=1) + + +def swiglu_repr(specialization): + signature = specialization.signature + constants = specialization.constants + convert_dtype = lambda dtype: "mxfp4" if "u8" in dtype else dtype + dtypes = "x".join([convert_dtype(f"{signature[i][1:]}") for i in ["Out", "A"]]) + blocks = "x".join([f"{constants[i]}" for i in ["BLOCK_M", "BLOCK_N"]]) + return f"_swiglu_{dtypes}_{blocks}" + + +def swiglu_launch_metadata(grid, kernel, args): + M, N = args["M"], args["N"] + ret = dict() + ret["name"] = f"{kernel.name} [M = {M}, N = {N}]" + A, Out = args["A"], args["Out"] + ret["bytes"] = Out.numel() * Out.element_size() + A.numel() * A.element_size() + return ret + + +@triton.jit +def compute_swiglu(gelu, linear, scale, alpha, limit): + gelu = gelu.to(tl.float32) * scale + if limit is not None: + gelu = clip(gelu, limit, clip_lower=False) + linear = linear.to(tl.float32) * scale + if limit is not None: + linear = clip(linear, limit, clip_lower=True) + s = gelu / (1 + tl.exp(-alpha * gelu)) + return tl.fma(s, linear, s) # (s * (linear + 1)) + + +@triton.jit(repr=lambda _: "_swiglu") +def _swiglu_fn(input, alpha, limit): + gelu, linear = tl.split(tl.reshape(input, (input.shape[0], input.shape[1] // 2, 2))) + return compute_swiglu(gelu, linear, 1.0, alpha, limit) + + +@triton.jit(repr=swiglu_repr, launch_metadata=swiglu_launch_metadata) +def _swiglu(Out, OutExpectedScale, OutActualScale, OutChecksumScale, A, AScale, alpha, M, N, stride_am, stride_an, + stride_outm, stride_outn, limit: tl.constexpr, NTokens, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, + EVEN_N: tl.constexpr, M_BLOCKS, N_BLOCKS, flexpoint_saturate_inf: tl.constexpr): + if NTokens is not None: + M = tl.load(NTokens) + M_BLOCKS = (M + BLOCK_M - 1) // BLOCK_M + + local_max = tl.full([tl.extra.cuda.num_threads()], 0.0, tl.float32) + + a_scale = load_scale(AScale) + out_expected_scale = load_scale(OutExpectedScale) + + for pid in tl.range(tl.program_id(0), M_BLOCKS * N_BLOCKS, tl.num_programs(0), num_stages=2): + pid_m = (pid // N_BLOCKS) + pid_n = (pid % N_BLOCKS) + off_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + off_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + mask_m = off_m < M + mask_n = off_n < N + packed_off_n = pid_n * BLOCK_N + tl.arange(0, 2 * BLOCK_N) // 2 + packed_mask_n = packed_off_n < N + packed_mask_n = tl.max_constancy(packed_mask_n, [16]) + # load a + packed_off_n = pid_n * 2 * BLOCK_N + tl.arange(0, 2 * BLOCK_N) + packed_offs = off_m[:, None] * stride_am + packed_off_n[None, :] * stride_an + if EVEN_N: + a_packed = tl.load(A + packed_offs, mask=mask_m[:, None], other=0.) + else: + if pid_n * BLOCK_N + BLOCK_N <= N: + a_packed = tl.load(A + packed_offs, mask=mask_m[:, None], other=0.) + else: + packed_mask = mask_m[:, None] & packed_mask_n[None, :] + a_packed = tl.load(A + packed_offs, mask=packed_mask, other=0.) + a_gelu, a_linear = tl.split(tl.reshape(a_packed, (BLOCK_M, BLOCK_N, 2))) + out = compute_swiglu(a_gelu, a_linear, a_scale, alpha, limit) + # update flexpoint stats and divide by scale + # we don't need masking because of the `other` when loading `A` + if OutActualScale is not None: + absmax = thread_local_absmax(out, out.numel, tl.extra.cuda.num_threads()) + local_max = tl.maximum(local_max, absmax) + out = float_to_flex(out, out_expected_scale, + None, # ActualScale: local absmax is tracked and updated after the loop + OutChecksumScale, None, Out, flexpoint_saturate_inf) + mask = mask_m[:, None] if EVEN_N else mask_m[:, None] & mask_n[None, :] + tl.store(Out + off_m[:, None] * stride_outm + off_n[None, :] * stride_outn, out, mask) + + update_scale(local_max, OutActualScale, Out) diff --git a/triton_kernels/target_info.py b/triton_kernels/target_info.py new file mode 100644 index 000000000000..6966f63d6282 --- /dev/null +++ b/triton_kernels/target_info.py @@ -0,0 +1,58 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/target_info.py +# Triton is licensed under the MIT License. + +import torch +import triton +import triton.language as tl + +from triton.language.target_info import ( + cuda_capability_geq, + is_cuda, + is_hip, + is_hip_cdna3, + is_hip_cdna4, +) + +__all__ = [ + "cuda_capability_geq", + "get_cdna_version", + "has_tma_gather", + "has_native_mxfp", + "is_cuda", + "is_hip", + "is_hip_cdna3", + "is_hip_cdna4", + "num_sms", +] + + +@triton.constexpr_function +def get_cdna_version(): + """ + Gets the AMD architecture version, i.e. CDNA3 or CDNA4, currently + only supports 3 (gfx942) or 4 (gfx950). Returns -1 if it is not AMD + hardware or unsupported architecture + """ + target = tl.target_info.current_target() + if target.backend != 'hip': + return -1 + if target.arch == 'gfx942': + return 3 + if target.arch == 'gfx950': + return 4 + return -1 + + +@triton.constexpr_function +def has_tma_gather(): + return cuda_capability_geq(10, 0) + + +@triton.constexpr_function +def has_native_mxfp(): + return cuda_capability_geq(10, 0) + + +def num_sms(): + return torch.cuda.get_device_properties(0).multi_processor_count diff --git a/triton_kernels/tensor.py b/triton_kernels/tensor.py new file mode 100644 index 000000000000..ae68a898963d --- /dev/null +++ b/triton_kernels/tensor.py @@ -0,0 +1,219 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor.py +# Triton is licensed under the MIT License. + +from dataclasses import dataclass, fields +from typing import Type + +import torch +from triton.tools.tensor_descriptor import TensorDescriptor +from triton.tools.ragged_tma import create_ragged_descriptor + +from .reduction_details.reduce_bitmatrix import clear_sums, sum_bitmatrix_rows +from .target_info import cuda_capability_geq +from .tensor_details.layout import Layout, StridedLayout + + +@dataclass +class Storage: + data: torch.Tensor + layout: Layout = None + + def __post_init__(self): + assert isinstance(self.data, torch.Tensor) + if self.layout is None: + self.layout = StridedLayout(self.data.shape) + + @property + def device(self): + return self.data.device + + def is_tma_compliant(self): + # TMAs didn't exist until Hopper + if not cuda_capability_geq(9, 0): + return False + # TMAs only exist for 2D, 3D, 5D inputs + if len(self.data.shape) not in [2, 3, 5]: + return False + # TMAs need at most one stride equal to 1 + # and all other strides divisble by 16 + strides = list(self.data.stride()) + try: + major_dim = strides.index(1) + except ValueError: + major_dim = -1 + ndim = self.data.ndim + bitwidth = 4 if self.data.dtype == torch.uint8 else self.data.element_size() * 8 + compliant = [strides[i] * bitwidth % 128 == 0 for i in range(ndim) if i != major_dim] + return all(compliant) + + def make_dense_tma(self, block_shape, transpose=False): + strides = list(self.data.stride()) + shape = list(self.data.shape) + transpose = self.data.stride()[-1] != 1 + if transpose: + block_shape = block_shape[:-2] + [block_shape[-1], block_shape[-2]] + shape = shape[:-2] + [shape[-1], shape[-2]] + strides = strides[:-2] + [strides[-1], strides[-2]] + if self.data.dtype == torch.uint8 and self.layout.name == "BLACKWELL_VALUE": + indx = strides.index(1) + block_shape[indx] = block_shape[indx] // 2 + if shape[-1] % 128 != 0: + raise ValueError("inner shape need to be multiple of 128 for " + "mxfp4 (CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B) TMAs.") + block_shape = self.layout.swizzle_block_shape(block_shape) + return TensorDescriptor(self.data, shape, strides, block_shape) + + def make_tma(self, block_shape, mode, transpose=False): + if mode in ["dense", "gather", "scatter"]: + return self.make_dense_tma(block_shape, transpose) + assert mode == "ragged" + ragged_dim = len(self.data.shape) - 2 + return create_ragged_descriptor(self.data, block_shape, ragged_dim=ragged_dim) + + +@dataclass +class IntegerType: + bitwidth: int + + +@dataclass +class FloatType: + bitwidth_exponent: int + bitwidth_mantissa: int + is_signed: bool + + def __post_init__(self): + self.bitwidth = int(self.is_signed) + self.bitwidth_exponent + self.bitwidth_mantissa + + +BIT = IntegerType(1) +FP4 = FloatType(bitwidth_exponent=2, bitwidth_mantissa=1, is_signed=True) + + +def bitwidth(type: IntegerType | FloatType | torch.dtype): + if isinstance(type, torch.dtype): + return type.itemsize * 8 + return type.bitwidth + + +@dataclass +class Tensor: + storage: Storage | torch.Tensor + dtype: IntegerType | FloatType | torch.dtype = None + shape: list[int] | None = None + shape_max: list[int] | None = None + + def __post_init__(self): + # set storage + if isinstance(self.storage, torch.Tensor): + self.storage = Storage(self.storage) + # initialize dtype + if self.dtype is None: + self.dtype = self.storage.data.dtype + if bitwidth(self.dtype) < 8 and self.shape is None: + raise ValueError("shape must be provided for sub-byte types") + # initialize shape + if self.shape is None: + self.shape = list(self.storage.data.shape) + # validate shape: all elements must be `int` or numel-1 `torch.Tensor` + is_int = lambda s: isinstance(s, int) + is_item = lambda s: hasattr(s, "numel") and s.numel() == 1 + assert all(map(lambda s: is_int(s) or is_item(s), self.shape)) + # initialize shape_max + if self.shape_max is None: + self.shape_max = [None] * len(self.shape) + for i, (s, smax) in enumerate(zip(self.shape, self.shape_max)): + if smax is not None and not is_int(smax): + raise ValueError(f"shape_max[{i}] must be `int` or `None`; got {type(smax)}") + if smax is None: + self.shape_max[i] = s + # validate shape_max: all elements must be `int` + assert all(map(is_int, self.shape_max)) + + # torch compatibility layer + @property + def ndim(self): + return len(self.shape) + + @property + def device(self): + return self.storage.device + + def stride(self, i=None): + return self.storage.data.stride() if i is None else self.storage.data.stride(i) + + def data_ptr(self): + return self.storage.data.data_ptr() + + def numel(self): + return self.storage.data.numel() + + def element_size(self): + return bitwidth(self.dtype) // 8 + + @property + def data(self): + t = self.storage + return t.data if isinstance(t, Storage) else t + + def dim(self): + return self.ndim + + def size(self, i=None): + if i is None: + return self.shape + return self.shape[i] + + +@dataclass +class Bitmatrix(Tensor): + """ + Represents a boolean matrix in a packed format where each element occupies + a single bit of memory. + + _scratchpad is either None or an all-zero array of size >= shape[-1]; we pass it along + with the actual bitmatrix to avoid having to launch a separate memset + kernel when we call Bitmatrix::sum(). + """ + + scratchpad: torch.Tensor = None + + def __init__(self, storage, shape, shape_max=None, scratchpad=None): + super().__init__(storage, dtype=BIT, shape=shape, shape_max=shape_max) + self.scratchpad = scratchpad + + def sum(self, partials_block_size): + _, n_cols = self.shape + dev = self.device + if self.scratchpad is None: + self.scratchpad = clear_sums(n_cols, dev) + out_ret = self.scratchpad[:n_cols] + self.scratchpad = None # throw error if we try to sum again + return sum_bitmatrix_rows(self, out_ret, partials_block_size) + + +def get_layout(tensor: torch.Tensor | Tensor | None): + if tensor is None: + return None + if isinstance(tensor, Tensor): + return tensor.storage.layout + return StridedLayout + + +def wrap_torch_tensor(torch_tensor, dtype=None): + if dtype is None: + dtype = torch_tensor.dtype + shape = list(torch_tensor.shape) + shape[torch_tensor.stride().index(1)] *= bitwidth(torch_tensor.dtype) // bitwidth(dtype) + return Tensor(Storage(torch_tensor), dtype=dtype, shape=shape) + + +def convert_layout(tensor: Tensor, layout_cls: Type[Layout], **layout_kwargs): + assert isinstance(tensor, Tensor) + old_storage = tensor.storage + old_data = old_storage.layout.unswizzle_data(old_storage.data) + new_layout = layout_cls(old_data.shape, **layout_kwargs) + new_data = new_layout.swizzle_data(old_data) + attrs = {k.name: getattr(tensor, k.name) for k in fields(tensor) if k.name != "storage"} + return Tensor(Storage(new_data, new_layout), **attrs) diff --git a/triton_kernels/tensor_details/__init__.py b/triton_kernels/tensor_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/tensor_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/tensor_details/layout.py b/triton_kernels/tensor_details/layout.py new file mode 100644 index 000000000000..9670c096adb5 --- /dev/null +++ b/triton_kernels/tensor_details/layout.py @@ -0,0 +1,44 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor_details/layout.py +# Triton is licensed under the MIT License. + +from .layout_details.base import Layout +from .layout_details.blackwell_scale import BlackwellMXScaleLayout +from .layout_details.blackwell_value import BlackwellMXValueLayout +from .layout_details.hopper_scale import HopperMXScaleLayout +from .layout_details.hopper_value import HopperMXValueLayout +from .layout_details.cdna4_scale import CDNA4MXScaleLayout +from .layout_details.strided import StridedLayout +from ..target_info import cuda_capability_geq, is_hip_cdna4 + +__all__ = [ + "Layout", + "BlackwellMXValueLayout", + "BlackwellMXScaleLayout", + "HopperMXScaleLayout", + "HopperMXValueLayout", + "CDNA4MXScaleLayout", + "StridedLayout", +] + + +def make_default_matmul_mxfp4_w_layout(mx_axis: int): + if cuda_capability_geq(10): + # return StridedLayout, dict() + return BlackwellMXValueLayout, dict() + elif cuda_capability_geq(9): + return HopperMXValueLayout, {"mx_axis": mx_axis} + else: + return StridedLayout, dict() + + +def make_default_matmul_mxfp4_w_scale_layout(mx_axis: int, num_warps: int = 8): + if is_hip_cdna4(): + return CDNA4MXScaleLayout, dict() + else: + if cuda_capability_geq(10): + return BlackwellMXScaleLayout, dict() + elif cuda_capability_geq(9): + return HopperMXScaleLayout, {"mx_axis": mx_axis, "num_warps": num_warps} + + return StridedLayout, dict() diff --git a/triton_kernels/tensor_details/layout_details/__init__.py b/triton_kernels/tensor_details/layout_details/__init__.py new file mode 100644 index 000000000000..2d460d330a98 --- /dev/null +++ b/triton_kernels/tensor_details/layout_details/__init__.py @@ -0,0 +1 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. diff --git a/triton_kernels/tensor_details/layout_details/base.py b/triton_kernels/tensor_details/layout_details/base.py new file mode 100644 index 000000000000..3d725ac923d5 --- /dev/null +++ b/triton_kernels/tensor_details/layout_details/base.py @@ -0,0 +1,23 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor_details/layout_details/base.py +# Triton is licensed under the MIT License. + +from abc import ABC, abstractmethod + + +class Layout(ABC): + + def __init__(self, shape) -> None: + self.initial_shape = shape + + @abstractmethod + def swizzle_data(self, data): + pass + + @abstractmethod + def unswizzle_data(self, data): + pass + + @abstractmethod + def swizzle_block_shape(self, block_shape): + pass diff --git a/triton_kernels/tensor_details/layout_details/blackwell_scale.py b/triton_kernels/tensor_details/layout_details/blackwell_scale.py new file mode 100644 index 000000000000..3ff602577ae8 --- /dev/null +++ b/triton_kernels/tensor_details/layout_details/blackwell_scale.py @@ -0,0 +1,62 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor_details/layout_details/blackwell_scale.py +# Triton is licensed under the MIT License. + +import math +import triton +import triton.language as tl +import torch +from .base import Layout + +SWIZZLE_ALIGN_INNER = 8 +SWIZZLE_SIZE_INNER = 4 +SWIZZLE_SIZE_OUTER = 128 + + +class BlackwellMXScaleLayout(Layout): + name: str = "BLACKWELL_SCALE" + + def __init__(self, shape) -> None: + super().__init__(shape) + *self.leading_shape, self.K, self.N, = shape + self.B = math.prod(self.leading_shape) + self.ALIGN_K = 8 + self.ALIGN_N = 128 + self.SWIZZLE_K = 4 + self.K_pad = (self.K + self.ALIGN_K - 1) // self.ALIGN_K * self.ALIGN_K + self.N_pad = (self.N + self.ALIGN_N - 1) // self.ALIGN_N * self.ALIGN_N + + def swizzle_data(self, data): + data = torch.nn.functional.pad(data, (0, self.N_pad - self.N, 0, self.K_pad - self.K)) + data = data.transpose(-1, -2).contiguous() + data = data.reshape(self.B, self.N_pad // self.ALIGN_N, self.ALIGN_N // 32, 32, self.K_pad // self.SWIZZLE_K, + self.SWIZZLE_K) + data = data.transpose(2, 4).contiguous() + data = data.view(1, self.B * self.N_pad // 128, self.K_pad // 4, 2, 256) + return data + + def unswizzle_data(self, data): + data = data.reshape(self.B, self.N_pad // self.ALIGN_N, self.K_pad // self.SWIZZLE_K, 32, self.ALIGN_N // 32, + self.SWIZZLE_K) + data = data.transpose(2, 4) + data = data.reshape(*self.leading_shape, self.N_pad, self.K_pad) + data = data.transpose(-1, -2) + return data[..., :self.K, :self.N] + + def swizzle_block_shape(self, block_shape): + MX_PACK_DIVISOR = 32 + MX_SCALE_BLOCK_K = block_shape[1] // MX_PACK_DIVISOR + return [1, block_shape[0] // 128, MX_SCALE_BLOCK_K // 4, 2, 256] + + +@triton.jit +def unswizzle_mx_scale_bw(x, SIZE_OUTER: tl.constexpr = SWIZZLE_SIZE_OUTER, + SIZE_INNER: tl.constexpr = SWIZZLE_SIZE_INNER, + ALIGN_INNER: tl.constexpr = SWIZZLE_ALIGN_INNER): + shape_0: tl.constexpr = x.shape[0] + shape_1: tl.constexpr = x.shape[1] + tl.static_assert(shape_1 % SIZE_OUTER == 0) + tl.static_assert(shape_1 // SIZE_OUTER <= ALIGN_INNER) + x = x.reshape(shape_0, (shape_1 // SIZE_OUTER) // SIZE_INNER, 32, SIZE_OUTER // 32, SIZE_INNER) + x = x.trans(0, 3, 2, 1, 4).reshape(shape_0 * SIZE_OUTER, shape_1 // SIZE_OUTER) + return x diff --git a/triton_kernels/tensor_details/layout_details/blackwell_value.py b/triton_kernels/tensor_details/layout_details/blackwell_value.py new file mode 100644 index 000000000000..15a0e4783b62 --- /dev/null +++ b/triton_kernels/tensor_details/layout_details/blackwell_value.py @@ -0,0 +1,37 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor_details/layout_details/blackwell_value.py +# Triton is licensed under the MIT License. + +import torch +from .base import Layout + + +class BlackwellMXValueLayout(Layout): + name: str = "BLACKWELL_VALUE" + + def __init__(self, shape) -> None: + super().__init__(shape) + self.shape = shape + + def swizzle_data(self, data): + # permutation needed to make `data` row major + to_row_major = sorted(range(data.ndim), key=lambda d: (data.stride(d), d))[::-1] + # permutation needed to retrieve original order + inv = [0] * data.ndim + for i, d in enumerate(to_row_major): + inv[d] = i + # leading dimension must be padded to be aligned to 128 + align_dim = lambda x: (x + 128 - 1) // 128 * 128 + major_dim = data.stride().index(1) + pad = align_dim(data.shape[major_dim]) - data.shape[major_dim] + data = torch.nn.functional.pad(data.permute(to_row_major), (0, pad)).permute(inv) + return data + + def unswizzle_data(self, data: torch.Tensor): + # Trim padding along all dims back to the original shape recorded at init. + assert data.ndim == len(self.shape), "Rank mismatch between data and recorded shape" + sizes = [min(data.size(i), self.shape[i]) for i in range(data.ndim)] + return data[tuple(slice(0, s) for s in sizes)] + + def swizzle_block_shape(self, block_shape): + return block_shape diff --git a/triton_kernels/tensor_details/layout_details/cdna4_scale.py b/triton_kernels/tensor_details/layout_details/cdna4_scale.py new file mode 100644 index 000000000000..87dbd6b37723 --- /dev/null +++ b/triton_kernels/tensor_details/layout_details/cdna4_scale.py @@ -0,0 +1,48 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor_details/layout_details/cdna4_scale.py +# Triton is licensed under the MIT License. + +import triton +import triton.language as tl +from .base import Layout + +NON_K_PRESHUFFLE_BLOCK_SIZE = 32 + + +class CDNA4MXScaleLayout(Layout): + name: str = "CDNA4_SCALE" + + def __init__(self, shape) -> None: + super().__init__(shape) + + def swizzle_data(self, data): + block_shape = data.shape + SCALE_K = block_shape[-2] + N = block_shape[-1] + data = data.transpose(-1, -2) + data = data.view(-1, N // NON_K_PRESHUFFLE_BLOCK_SIZE, 2, 16, SCALE_K // 8, 2, 4, 1) + data = data.permute(0, 1, 4, 6, 3, 5, 2, 7).contiguous() + if len(block_shape) == 3: + E = block_shape[0] + data = data.reshape(E, N // 32, SCALE_K * 32) + else: + assert len(block_shape) == 2 + data = data.reshape(N // 32, SCALE_K * 32) + return data.transpose(-1, -2) + + def unswizzle_data(self, data): + raise NotImplementedError() + + def swizzle_block_shape(self, block_shape): + SCALE_K = block_shape[-2] + N = block_shape[-1] + return block_shape[:-2] + [N // 32, SCALE_K * 32] + + +@triton.jit +def unswizzle_mx_scale_cdna4(x, BLOCK_N: tl.constexpr, MX_SCALE_BLOCK_K: tl.constexpr, + N_PRESHUFFLE_FACTOR: tl.constexpr = NON_K_PRESHUFFLE_BLOCK_SIZE): + x = x.reshape(BLOCK_N // N_PRESHUFFLE_FACTOR, MX_SCALE_BLOCK_K // 8, 4, 16, 2, 2, 1) + x = x.permute(0, 5, 3, 1, 4, 2, 6) + x = x.reshape(BLOCK_N, MX_SCALE_BLOCK_K) + return x diff --git a/triton_kernels/tensor_details/layout_details/hopper_scale.py b/triton_kernels/tensor_details/layout_details/hopper_scale.py new file mode 100644 index 000000000000..feb342fc8894 --- /dev/null +++ b/triton_kernels/tensor_details/layout_details/hopper_scale.py @@ -0,0 +1,84 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor_details/layout_details/hopper_scale.py +# Triton is licensed under the MIT License. + +import torch +import triton +import triton.language as tl +from .base import Layout + + +class HopperMXScaleLayout(Layout): + name: str = "HOPPER_SCALE" + + def __init__(self, shape, mx_axis, num_warps=8) -> None: + assert num_warps & (num_warps - 1) == 0, "warps_n must be a power of 2" + super().__init__(shape) + self.mx_axis = mx_axis + self.num_warps = num_warps + *self.leading_shape, _, _ = shape + + def _maybe_mT(self, data): + if self.mx_axis == len(self.leading_shape): + return data.contiguous().mT + return data + + def swizzle_data(self, data): + data = self._maybe_mT(data).contiguous() + *batch, M, K = data.shape + SWIZZLE_ALIGN_M = 2 * self.num_warps * 2 * 8 + SWIZZLE_ALIGN_K = 2 + pad_m = (SWIZZLE_ALIGN_M - (M % SWIZZLE_ALIGN_M)) % SWIZZLE_ALIGN_M + pad_k = (SWIZZLE_ALIGN_K - (K % SWIZZLE_ALIGN_K)) % SWIZZLE_ALIGN_K + data = torch.nn.functional.pad(data, (0, pad_k, 0, pad_m)) + *batch, M, K = data.shape + assert data.is_contiguous() + assert M % ( + 2 * self.num_warps * 2 * + 8) == 0 and K % 2 == 0, f"Input tensor must have a subtile of shape (..., {2 * self.num_warps * 2 * 8}, 2)" + b = len(batch) + data = data.reshape(*batch, M // (2 * self.num_warps * 2 * 8), 2, self.num_warps, 2, 8, K // 2, 2) + perm = [0, 2, 5, 1, 4, 6, 3] + perm = list(range(b)) + [b + p for p in perm] + data = data.permute(*perm) + data = data.flatten(-5, -1) + data = data.flatten(-3, -2) + assert data.shape[-2] == M // 32 + assert data.shape[-1] == K * 32 + data = self._maybe_mT(data) + return data + + def unswizzle_data(self, data): + data = self._maybe_mT(data) + *batch, M, K = data.shape + b = len(batch) + data = data.reshape(*batch, M // self.num_warps, self.num_warps, K // 64, 2, 8, 2, 2) + perm = [0, 3, 1, 6, 4, 2, 5] + perm = list(range(b)) + [b + p for p in perm] + data = data.permute(*perm) + data = data.reshape(*batch, M * 32, K // 32) + data = self._maybe_mT(data) + return data + + def swizzle_block_shape(self, block_shape): + return block_shape + + +@triton.jit +def unswizzle_mxfp4_scale_hopper(x, mx_axis: tl.constexpr, num_warps: tl.constexpr): + """ + Triton inverse of swizzle_mxfp4_scale_hopper + """ + tl.static_assert(len(x.shape) == 2, "NYI") + # implementation assumes mxfp data is packed along the last dimension + x = x.trans() if mx_axis == 0 else x + M: tl.constexpr = x.shape[0] + K: tl.constexpr = x.shape[1] + tl.static_assert(M % num_warps == 0, f"M must be divisible by {num_warps}. Got {M}") + tl.static_assert(K % 64 == 0, f"K must be divisible by 64. Got {K}") + x = x.reshape(M // num_warps, num_warps, K // 64, 2, 8, 2, 2) + x = x.trans(0, 3, 1, 6, 4, 2, 5) + x = x.reshape(M * 32, K // 32) + # implementation assumed mxfp data is packed along the last dimension + x = x.trans() if mx_axis == 0 else x + return x diff --git a/triton_kernels/tensor_details/layout_details/hopper_value.py b/triton_kernels/tensor_details/layout_details/hopper_value.py new file mode 100644 index 000000000000..bf38b71b518c --- /dev/null +++ b/triton_kernels/tensor_details/layout_details/hopper_value.py @@ -0,0 +1,327 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor_details/layout_details/hopper_value.py +# Triton is licensed under the MIT License. + +import torch +import triton +import triton.language as tl +from .base import Layout + + +def right_shift_unsigned(x, shift): + return (x >> shift) & ((1 << (32 - shift)) - 1) + + +# ----------------------------------------------------------------------- +# Interleave the bits of four consecutive fp4 values (i.e. 16-bits) as: +# 1000000111000000 (first fp4) +# 1000000111000000 (second fp4) +# 1000000111000000 (third fp4) +# 0110110000000000 (fourth fp4) +# This is done so that dequantization can be done in 14 SASS instructions +# ----------------------------------------------------------------------- + + +def _compress_fp4(x): + x = x.to(torch.int32) + return ((x & 0x8) << 12) | ((x & 0x7) << 6) + + +def _compress_fourth(x): + x = x.to(torch.int32) + return ((x & 0x8) << 11) | ((x & 0x6) << 9) | ((x & 0x1) << 13) + + +def _pack_bits(x: torch.Tensor, mx_axis: int): + x = x.contiguous() + assert x.shape[-1] % 4 == 0, "Input tensor must have a last dimension divisible by 4" + x = x.reshape(x.shape[:-1] + (x.shape[-1] // 4, 4)) + first = _compress_fp4(x[..., 0]) | (_compress_fp4(x[..., 0] >> 4) << 16) + second = _compress_fp4(x[..., 1]) | (_compress_fp4(x[..., 1] >> 4) << 16) + third = _compress_fp4(x[..., 2]) | (_compress_fp4(x[..., 2] >> 4) << 16) + fourth = _compress_fourth(x[..., 3]) | (_compress_fourth(x[..., 3] >> 4) << 16) + x = first | right_shift_unsigned(second, 3) | right_shift_unsigned(third, 6) | fourth + assert x.is_contiguous() + x = x.view(torch.uint8) + return x + + +# ----------------------------------------------------------------------- +# inverse operation of _pack_bits +# ----------------------------------------------------------------------- + + +def _bf16_to_fp4e2m1(x): + # 0bAxxxxxxBCDxxxxxx (int16) -> 0b0000ABCD (uint8) + assert x.dtype == torch.int16 + s = (right_shift_unsigned(x, 15) & 0x1) << 3 + em = right_shift_unsigned(x, 6) & 0x7 + return (s | em).to(torch.uint8) + + +def _bf16x2_to_fp4e2m1x2(x): + # 0bAxxxxxxBCDxxxxxx_0bExxxxxxFGHxxxxxx (int32) -> 0bABCD_EFGH (uint8) + assert x.dtype == torch.int32 + lo = (x & 0xFFFF).to(torch.int16) + hi = (right_shift_unsigned(x, 16) & 0xFFFF).to(torch.int16) + ret_lo = _bf16_to_fp4e2m1(lo) + ret_hi = _bf16_to_fp4e2m1(hi) + return ret_lo | (ret_hi << 4) + + +def _unpack_bits(x, mx_axis: int): + x = x.view(torch.int32) + m = 0b10000001110000001000000111000000 + a = (x << 1) & 0b10000000000000001000000000000000 + b = right_shift_unsigned(x, 3) & 0b00000001100000000000000110000000 + c = right_shift_unsigned(x, 7) & 0b00000000010000000000000001000000 + unpacked = [x & m, (x << 3) & m, (x << 6) & m, (a | b) | c] + x = torch.stack(unpacked, dim=-1) + x = x.flatten(-2, -1) + x = _bf16x2_to_fp4e2m1x2(x) + return x + + +# ----------------------------------------------------------------------- + + +class HopperMXValueLayout(Layout): + name: str = "HOPPER_VALUE" + + def __init__(self, shape, mx_axis, mma_version=3): + super().__init__(shape) + assert mx_axis in range(len(shape)) + self.mx_axis = mx_axis + self.mma_version = mma_version + *self.leading_shape, self.K, self.N, = shape + + def _maybe_mT(self, data): + if self.mx_axis == len(self.leading_shape): + return data.mT + return data + + def swizzle_data(self, data): + """ + Given a uint8 tensor of shape (*, M, K), returns a tensor of shape + (*, M // 4, K * 4) such that: + + 1) Groups contiguously all the elements owned by the same thread of 4 + mma tiles along the K axis. The following animation shows a similar + grouping for 2 tiles along M and 2 tiles along K rather than 4 along K + as done here: + https://neuralmagic.com/wp-content/uploads/2024/10/animation_4.gif + + 2) Moves the elements belonging to thread 4-7 to be contiguous with those + from thread 0-3. This is done to get a full cache line when loading them + from HBM. + + mx_axis selects the lhs or rhs of the matmul. + + WARNING: Assumes that the matmul will be done in bf16 or fp16! + Implementing it for fp8 is as easy as making the tile size (8, 8) + """ + batch = data.ndim - 2 + assert batch >= 0 + assert self.mma_version in (2, 3) + data = self._maybe_mT(data) + init_shape = data.shape + + # We are loading 8 bf16 elements per thread to use ld.global.v4 + # Every u8 represents 2 mxfp4 elements + u8_kwidth = 8 // 2 if self.mma_version == 2 else 1 + + # Pack the 4 // u8_kwidth subtiles of an mma into a u4x8 + contig = (1, u8_kwidth) + scott_trick = (2, 1) + threads = (4, 4) + warp_tile = (2, 2) + k_tile = (1, 4 // u8_kwidth) + + sizes = list(data.shape[:-2]) + pads = [] + # [rest, K, tile, threads] per dimension + for i, (a, b, c, s, d) in enumerate(zip(k_tile, warp_tile, threads, scott_trick, contig)): + pack = a * b * c * s * d + size = data.shape[batch + i] + pad = (pack - size % pack) % pack + pads += [(0, pad)] + sizes.append((size + pad) // pack) + sizes += [a, b, c, s, d] + + pads = tuple(x for t in pads[::-1] for x in t) + data = torch.nn.functional.pad(data, pads) + init_shape = data.shape + # 0: rest[0] + # 1: k_tile[0] + # 2: warp_tile[0] + # 3: threads[0] + # 4: scott_trick[0] + # 5: contig[0] + # 6: rest[1] + # 7: k_tile[1] + # 8: warp_tile[1] + # 9: threads[1] + # 10: scott_trick[1] + # 11: contig[1] + data = data.view(*sizes) + # Want [rest[0], threads[0], rest[1], scott_trick[0], scott_trick[0], threads[1], contig[1], contig[0], k_tile[1], k_tile[0], warp_tile[1], warp_tile[0]] + perm = [0, 3, 6, 10, 4, 9, 7, 1, 8, 2, 5, 11] + perm = list(range(batch)) + [batch + p for p in perm] + data = data.permute(*perm).contiguous() + # These are views + data = data.flatten(-10, -1) + data = data.flatten(-3, -2) + assert data.is_contiguous() + assert data.shape[-2] == init_shape[-2] // 4 + assert data.shape[-1] == init_shape[-1] * 4 + # twiddle the bits + data = _pack_bits(data, self.mx_axis) + data = self._maybe_mT(data) + return data + + def unswizzle_data(self, data): + data = self._maybe_mT(data) + data = _unpack_bits(data, self.mx_axis) + *batch, M, K = data.shape + # We have two times the elements if we already upcasted to bfloat16 + mult = 2 if data.dtype == torch.bfloat16 else 1 + assert M % 4 == 0, "M must be divisible by 4" + assert K % (4 * 8 * 2 * 2 * mult) == 0, f"K must be divisible by {4 * 8 * 2 * 2 * mult}" + # We are loading 8 bf16 elements per thread to use ld.global.v4 + # Every u8 represents 2 mxfp4 elements + u8_kwidth = 8 // 2 if self.mma_version == 2 else 1 + data = data.reshape(*batch, M // 4, 4, K // (4 * 8 * 2 * 2 * mult), 2, 4, 8 // u8_kwidth, 2, u8_kwidth * mult) + b = len(batch) + perm = [0, 6, 1, 3, 2, 5, 4, 7] + perm = list(range(b)) + [b + p for p in perm] + data = data.permute(*perm) + data = data.reshape(*batch, M * 4, K // 4) + data = self._maybe_mT(data) + return data[..., :self.K, :self.N] + + def swizzle_block_shape(self, block_shape): + return block_shape + + +@triton.jit +def _unshuffle_triton(x, mma_version: tl.constexpr): + """ + Triton inverse of swizzle_mxfp4_value_hopper + """ + tl.static_assert(mma_version == 2 or mma_version == 3, "mma_version must be 2 or 3") + # if mx_axis == 0: + # x = x.trans() + + # We have two times the elements if we already upcasted to bfloat16 + mult: tl.constexpr = 2 if x.dtype == tl.bfloat16 else 1 + M: tl.constexpr = x.shape[0] + K: tl.constexpr = x.shape[1] + tl.static_assert(M % 4 == 0, "M must be divisible by 4") + tl.static_assert(K % (4 * 8 * 2 * 2 * mult) == 0, f"K must be divisible by {4 * 8 * 2 * 2 * mult}") + + # We are loading 8 bf16 elements per thread to use ld.global.v4 + # Every u8 represents 2 mxfp4 elements + u8_kwidth: tl.constexpr = 8 // 2 if mma_version == 2 else 1 + x = x.reshape(M // 4, 4, K // (4 * 8 * 2 * 2 * mult), 2, 4, 8 // u8_kwidth, 2, u8_kwidth * mult) + x = x.trans(0, 6, 1, 3, 2, 5, 4, 7) + x = x.reshape(M * 4, K // 4) + # if mx_axis == 0: + # x = x.trans() + return x + + +@triton.jit +def _unpack_fp4_to_bf16_triton(x): + # For now we implement just H100 support (mul.bf16x2) + # A100 support is possible via fma + r0, r1 = tl.inline_asm_elementwise( + r""" + { + .reg .b32 b, c, d<7>, scale; + .reg .b32 bias; + mov.b32 bias, 0x7e807e80; // 2 ** 126 == 2 ** (bias_bf16 - bias_fp2) + // We add the missing bias to the scale directly + and.b32 $0, $4, 0b10000001110000001000000111000000; + mul.bf16x2 $0, $0, bias; + shl.b32 b, $4, 3; + and.b32 $1, b, 0b10000001110000001000000111000000; + mul.bf16x2 $1, $1, bias; + shl.b32 c, $4, 6; + and.b32 $2, c, 0b10000001110000001000000111000000; + mul.bf16x2 $2, $2, bias; + // Unpack last two elements + shl.b32 d0, $4, 1; + and.b32 d1, d0, 0b10000000000000001000000000000000; + shr.b32 d2, $4, 3; + and.b32 d3, d2, 0b00000001100000000000000110000000; + or.b32 d4, d1, d3; + shr.b32 d5, $4, 7; + and.b32 d6, d5, 0b00000000010000000000000001000000; + or.b32 $3, d4, d6; + mul.bf16x2 $3, $3, bias; + } + """, + constraints="=r,=r,=r,=r,r", + args=[x], + dtype=(tl.bfloat16, tl.bfloat16), + is_pure=True, + pack=4, + ) + # Concat each pack of 4 + x = tl.join(r0, r1) + x = x.reshape(x.shape[0], x.shape[1] // 4, 4, x.shape[2]) + x = x.trans(0, 1, 3, 2) + x = x.reshape(x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]) + return x + + +@triton.jit +def mxfp4_to_bf16_triton(x, scale, mx_axis: tl.constexpr): + """ + Implements the bit-untwiddling of a 32-bit integer (8 mxfp4 elements): + (x << 0) & 0b1000000111000000 + (x << 3) & 0b1000000111000000 + (x << 6) & 0b1000000111000000 + ((x << 1) & 0b1000000000000000) | ((x >> 3) & 0b0000000110000000) | ((x >> 7) & 0b0000000001000000) + """ + # upcast values to bfloat16 + tl.static_assert(len(x.shape) == 2) + tl.static_assert(mx_axis == 0 or mx_axis == 1, "mx_axis must be 0 or 1") + tl.static_assert(x.shape[1] % 4 == 0) + tl.static_assert(x.dtype == tl.uint8) + if mx_axis == 0: + x = x.trans() + x = _unpack_fp4_to_bf16_triton(x) + x = _unshuffle_triton(x, mma_version=3) + if mx_axis == 0: + x = x.trans() + + # upcast scale to bfloat16 + # Add bias missing from the bf16 upcasting sequence + # triton / LLVM generates terrible code for this sequence + # scale = scale.to(tl.uint16) + # scale = scale << 7 + # scale = scale.to(tl.bfloat16, bitcast=True) + scale = tl.inline_asm_elementwise( + r""" + { + prmt.b32 $0, $2, 0, 0x5140; + shl.b32 $0, $0, 7; + prmt.b32 $1, $2, 0, 0x7362; + shl.b32 $1, $1, 7; + } + """, + constraints="=r,=r,r", + args=[scale], + dtype=tl.bfloat16, + is_pure=True, + pack=4, + ) + # Broadcast scale + scale = scale.expand_dims(mx_axis + 1) + scale = scale.broadcast_to(scale.shape[:mx_axis + 1] + [32] + scale.shape[mx_axis + 2:]) + scale = scale.reshape(x.shape) + + # Combine scale and x + x = x * scale + return x diff --git a/triton_kernels/tensor_details/layout_details/strided.py b/triton_kernels/tensor_details/layout_details/strided.py new file mode 100644 index 000000000000..9a8d643504a8 --- /dev/null +++ b/triton_kernels/tensor_details/layout_details/strided.py @@ -0,0 +1,21 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/tensor_details/layout_details/strided.py +# Triton is licensed under the MIT License. + +from .base import Layout + + +class StridedLayout(Layout): + name: str = None + + def __init__(self, shape) -> None: + super().__init__(shape) + + def swizzle_data(self, data): + return data + + def unswizzle_data(self, data): + return data + + def swizzle_block_shape(self, block_shape): + return block_shape diff --git a/triton_kernels/testing.py b/triton_kernels/testing.py new file mode 100644 index 000000000000..c5ef8f2dcecb --- /dev/null +++ b/triton_kernels/testing.py @@ -0,0 +1,199 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/testing.py +# Triton is licensed under the MIT License. + +import enum +import functools +import os +import subprocess +import sys +import torch +from triton_kernels.numerics import MAX_FINITE_FLOAT8E4B8, MAX_FINITE_FLOAT8E4NV, MAX_FINITE_FLOAT8E5 + + +def assert_equal(ref, tri): + if isinstance(ref, torch.Tensor): + assert torch.all(ref == tri) + else: + assert ref == tri + + +def assert_close(ref, tri, maxtol=None, rmstol=None, description="--", verbose=True): + if tri.dtype.itemsize == 1: + ref_as_type = ref.to(tri.dtype) + if ref.dtype == tri.dtype: + assert torch.all(ref_as_type == tri) + return + ref = ref_as_type + + if ref.numel() == 0: + return + + if maxtol is None: + maxtol = 2e-2 + if rmstol is None: + rmstol = 4e-3 + """ + Compare reference values against obtained values. + """ + + # cast to float32: + ref = ref.to(torch.float32).detach() + tri = tri.to(torch.float32).detach() + assert ref.shape == tri.shape, f"Tensors must have same size {ref.shape=} {tri.shape=}" + + # deal with infinite elements: + inf_mask_ref = torch.isinf(ref) + inf_mask_tri = torch.isinf(tri) + assert torch.equal(inf_mask_ref, inf_mask_tri), "Tensor must have same infinite elements" + refn = torch.where(inf_mask_ref, 0, ref) + trin = torch.where(inf_mask_tri, 0, tri) + + # normalise so that RMS calculation doesn't overflow: + eps = 1.0e-30 + multiplier = 1.0 / (torch.max(torch.abs(refn)) + eps) + refn *= multiplier + trin *= multiplier + + ref_rms = torch.sqrt(torch.square(refn).mean()) + eps + + rel_err = torch.abs(refn - trin) / torch.maximum(ref_rms, torch.abs(refn)) + max_err = torch.max(rel_err).item() + rms_err = torch.sqrt(torch.square(rel_err).mean()).item() + + if verbose: + print("%s maximum relative error = %s (threshold = %s)" % (description, max_err, maxtol)) + print("%s RMS relative error = %s (threshold = %s)" % (description, rms_err, rmstol)) + + if max_err > maxtol: + bad_idxs = torch.nonzero(rel_err > maxtol) + num_nonzero = bad_idxs.size(0) + bad_idxs = bad_idxs[:1000] + print("%d / %d mismatched elements (shape = %s) at coords %s" % + (num_nonzero, rel_err.numel(), tuple(rel_err.shape), bad_idxs.tolist())) + + bad_idxs = bad_idxs.unbind(-1) + print("ref values: ", ref[tuple(bad_idxs)].cpu()) + print("tri values: ", tri[tuple(bad_idxs)].cpu()) + + assert max_err <= maxtol + assert rms_err <= rmstol + + +class ComputeSanitizerTool(enum.Enum): + MEMCHECK = "memcheck" + RACECHECK = "racecheck" + SYNCCHECK = "synccheck" + INITCHECK = "initcheck" + + +def compute_sanitizer(**target_kwargs): + """ + Decorator to run a test with compute sanitizer enabled and pytorch caching allocator disabled, + to expose potential memory access errors. + This decorator requires the `request` fixture to be present. + If `run_sanitizer` argument is present and set to False, the sanitizer is not run. + Running tests under compute sanitizer requires launching subprocess and is slow, + so use sparingly + """ + + def decorator(test_fn): + + @functools.wraps(test_fn) + def wrapper(*args, **kwargs): + if os.environ.get("SKIP_COMPUTE_SANITIZER") == "1": + test_fn(*args, **kwargs) + return + + import psutil + + if target_kwargs.pop("clear_torch_cache", False): + # If we don't pop clear_torch_cache, it won't pass + # target_kwargs.items() <= kwargs.items() condition below. + torch.cuda.empty_cache() + tools_to_check = target_kwargs.pop("tools_to_check", [ComputeSanitizerTool.MEMCHECK]) + assert isinstance(tools_to_check, list), f"{tools_to_check=}" + assert all(tool in ComputeSanitizerTool for tool in tools_to_check), ( + f"{(tool for tool in tools_to_check if tool not in ComputeSanitizerTool)=}") + + ppid_name = psutil.Process(os.getppid()).exe() + run_compute_sanitizer = target_kwargs.items() <= kwargs.items() + if "run_sanitizer" in kwargs: + run_compute_sanitizer &= kwargs["run_sanitizer"] + if run_compute_sanitizer and "compute-sanitizer" not in ppid_name: + for tool in tools_to_check: + path = os.path.realpath(test_fn.__globals__["__file__"]) + # get path of current file + env = { + "PATH": os.environ["PATH"], + "PYTORCH_NO_CUDA_MEMORY_CACHING": "1", + "TORCH_SHOW_CPP_STACKTRACES": "1", + "CUDA_LAUNCH_BLOCKING": "1", + } + if "CUDA_VISIBLE_DEVICES" in os.environ: + env["CUDA_VISIBLE_DEVICES"] = os.environ["CUDA_VISIBLE_DEVICES"] + assert "request_fixture" in kwargs, ( + "memcheck'ed test must have a (possibly unused) `request` fixture") + test_id = kwargs["request_fixture"].node.callspec.id + cmd = f"{path}::{test_fn.__name__}[{test_id}]" + cmd = [ + "compute-sanitizer", + "--target-processes=application-only", + "--destroy-on-device-error=context", + f"--tool={tool.value}", + sys.executable, + "-m", + "pytest", + "-vsx", + cmd, + ] + for opt in ["--update_checksum", "--ignore_checksum_error"]: + if opt in sys.argv: + cmd.append(opt) + out = subprocess.run( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + env=env, + ) + sanitizer_ok = "ERROR SUMMARY: 0 errors" in str( + out.stdout) or "RACECHECK SUMMARY: 0 hazards displayed" in str(out.stdout) + test_output = out.stdout + if type(test_output) is bytes: + test_output = test_output.decode() + + fail = False + if not sanitizer_ok: + print("compute-sanitizer returned an error") + fail = True + elif out.returncode != 0: + print( + "The test failed due to some other reason: consider running without compute-sanitizer to verify." + ) + print(f"{out.returncode=}") + fail = True + + if fail: + print("*****************************************************") + print("******************** TEST OUTPUT ********************") + print("*****************************************************") + print(test_output) + print("*****************************************************") + print("****************** TEST OUTPUT END ******************") + print("*****************************************************") + assert None + else: + test_fn(*args, **kwargs) + + return wrapper + + return decorator + + +def compute_actual_scale(x, dtype): + max_finite = { + torch.float8_e5m2: MAX_FINITE_FLOAT8E5, + torch.float8_e4m3fn: MAX_FINITE_FLOAT8E4NV, + torch.float8_e4m3fnuz: MAX_FINITE_FLOAT8E4B8, + }[dtype] + return x.abs().max() / max_finite diff --git a/triton_kernels/topk.py b/triton_kernels/topk.py new file mode 100644 index 000000000000..22140cf41a05 --- /dev/null +++ b/triton_kernels/topk.py @@ -0,0 +1,128 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/topk.py +# Triton is licensed under the MIT License. + +import torch +import triton +from triton_kernels.topk_details._topk_forward import _topk_forward +from triton_kernels.topk_details._topk_backward import _topk_backward +from triton_kernels.tensor import Tensor, Bitmatrix +from typing import Optional, Union + + +def topk_forward(x, k, apply_softmax=True, dim=1, return_bitmatrix=True, y_indx=None, n_rows=None): + if not isinstance(x, Tensor): + x_shape = [x.shape[0] if n_rows is None else n_rows, x.shape[1]] + x_shape_max = [x.shape[0], x.shape[1]] + x = Tensor(x, shape=x_shape, shape_max=x_shape_max) + cdiv = lambda a, b: (a + b - 1) // b + BLOCK_M = 32 + BLOCK_N = 32 + BLOCK_S = 128 + assert len(x.shape) == 2 + assert x.shape_max[-1] < 32768 + assert dim == 1 + assert return_bitmatrix + n_rows, n_cols = x.shape + n_rows_max, _ = x.shape_max + dev = x.device + # scratchpad tensors + # NOTE: these are not returned + y_vals = torch.empty((n_rows_max, k), dtype=x.dtype, device=dev) + if y_indx is not None: + use_provided_indx = True + else: + y_indx = torch.empty((n_rows_max, k), dtype=torch.int16, device=dev) + use_provided_indx = False + # create bitmatrix in transposed memory layout: + n_cols_pad = cdiv(n_cols, BLOCK_N) * BLOCK_N + n_cols_words = n_cols_pad // 32 + bitmatrix = torch.empty((n_cols_words, cdiv(n_rows_max, 32) * 32), dtype=torch.uint32, device=dev) + bitmatrix = torch.transpose(bitmatrix, 0, 1)[:n_rows_max] + s_blocks = cdiv(n_cols, BLOCK_S) + s_cols = s_blocks * BLOCK_S + scratchpad = torch.empty((s_cols, ), dtype=torch.int32, device=dev) + pids = max(cdiv(n_rows_max, BLOCK_M), s_blocks) + _topk_forward[(pids, )]( + x, x.stride(0), # inputs + y_vals, y_indx, y_vals.stride(0), use_provided_indx, # output [topk] + bitmatrix, bitmatrix.stride(0), bitmatrix.stride(1), # output [bitmatrix] + n_rows, n_cols, # shapes + scratchpad, BLOCK_S, s_blocks, # thing to memset to zero + BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, # tunable parameter + APPLY_SOFTMAX=apply_softmax, N_EXPTS_PAD=n_cols_pad, N_EXPTS_ACT=k, # constants + ) + bitmatrix_shape = [n_rows, n_cols_words * 32] + bitmatrix_shape_max = [n_rows_max, None] + bitmatrix = Bitmatrix(bitmatrix, shape=bitmatrix_shape, shape_max=bitmatrix_shape_max, scratchpad=scratchpad) + return y_vals, y_indx, bitmatrix + + +def topk_backward(x, y_indx, dy_vals, k, n_rows, apply_softmax): + assert dy_vals.shape[-1] == k + n_expts_pad = triton.next_power_of_2(x.shape[-1]) + dx = torch.empty_like(x) + _topk_backward[(dy_vals.shape[0], )]( + y_indx, y_indx.stride(0), dy_vals, dy_vals.stride(0), x, x.stride(0), # inputs + dx, # outputs + dx.stride(0), x.shape[0], n_rows, x.shape[-1], APPLY_SOFTMAX=apply_softmax, N_EXPTS_ACT=k, + N_EXPTS_PAD=n_expts_pad) + return dx + + +class TopK(torch.autograd.Function): + + @staticmethod + def forward(ctx, x, k, apply_softmax, dim, return_bitmatrix, y_indx, n_rows): + y_vals, y_indx, bitmatrix = topk_forward(x, k, apply_softmax, dim, return_bitmatrix, y_indx, n_rows) + ctx.save_for_backward(x, y_indx) + ctx.apply_softmax = apply_softmax + ctx.k = k + ctx.n_rows = n_rows + return y_vals, y_indx, bitmatrix + + @staticmethod + def backward(ctx, dy_vals, _0, _1): + x, y_indx = ctx.saved_tensors + dx = topk_backward(x, y_indx, dy_vals, ctx.k, ctx.n_rows, ctx.apply_softmax) + return dx, None, None, None, None, None, None + + +def topk( + x: Union[Tensor, torch.Tensor], + k: int, + apply_softmax: bool = True, + dim: int = 1, + return_bitmatrix: bool = True, + y_indx: Optional[torch.Tensor] = None, + n_rows: Optional[int] = None, +): + """ + Computes the top-k values and indices along a specified dimension of a tensor. + Note that the input can be either a `Tensor` or a `torch.Tensor`, but the output will always be a `torch.Tensor`. + + Parameters + ---------- + x : Union[triton_kernels.Tensor, torch.Tensor] + Input tensor of shape (n_tokens, n_expts). + k : int + Number of top elements to retrieve. + apply_softmax : bool, default True + Whether to apply softmax to the input tensor before computing top-k. + dim : int, default 1 + Dimension along which to compute top-k. + return_bitmatrix : bool, default True + A bitmatrix of shape (n_tokens, cdiv(n_expts, 32)). + Each bit on [t, b] indicates whether the b-th expert was selected for the t-th token. + y_indx : torch.Tensor, optional + Pre-allocated tensor for storing indices of top-k elements with shape (n_tokens, k). + If provided, we skip the computation of top-k indices and use this tensor instead. + n_rows : int, optional + Number of rows to apply top-k on. If None, we consider all rows in `x`. + + Returns + ------- + (expt_scal, expt_indx, bitmatrix) : Tuple[torch.Tensor, torch.Tensor, Bitmatrix] + """ + ret = TopK.apply(x, k, apply_softmax, dim, return_bitmatrix, y_indx, n_rows) + return ret diff --git a/triton_kernels/topk_details/__init__.py b/triton_kernels/topk_details/__init__.py new file mode 100644 index 000000000000..6a41f2b81adb --- /dev/null +++ b/triton_kernels/topk_details/__init__.py @@ -0,0 +1,3 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/topk_details/__init__.py +# Triton is licensed under the MIT License. diff --git a/triton_kernels/topk_details/_topk_backward.py b/triton_kernels/topk_details/_topk_backward.py new file mode 100644 index 000000000000..f03166fb9ba6 --- /dev/null +++ b/triton_kernels/topk_details/_topk_backward.py @@ -0,0 +1,55 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/topk_details/_topk_backward.py +# Triton is licensed under the MIT License. + +import triton +import triton.language as tl + + +@triton.jit +def _topk_backward( + Yi, + stride_ym, # topk indices + DY, + stride_dym, # output gradient values + X, + stride_xm, # input values + DX, + stride_dxm, # input gradient values + n_rows, + NRows, + n_expts_tot, + APPLY_SOFTMAX: tl.constexpr, + N_EXPTS_ACT: tl.constexpr, + N_EXPTS_PAD: tl.constexpr, +): + pid_m = tl.program_id(0) + if NRows is not None: + n_rows = tl.load(NRows) + if pid_m >= n_rows: + return + Yi += pid_m * stride_ym + DY += pid_m * stride_dym + X += pid_m * stride_xm + DX += pid_m * stride_dxm + # -- + offs_xn = tl.arange(0, N_EXPTS_PAD) + offs_yn = tl.arange(0, N_EXPTS_ACT) + mask_xn = offs_xn < n_expts_tot + # recompute softmax + y_indx = tl.load(Yi + offs_yn) + x = tl.load(X + y_indx) + x = x.to(tl.float32) + y = tl.softmax(x) + # compute input-gradient + dy = tl.load(DY + offs_yn) + dy = dy.to(tl.float32) + s = tl.sum(y * dy, 0) + # write-back input gradient + tl.store(DX + offs_xn, 0, mask=mask_xn) + tl.debug_barrier() + if APPLY_SOFTMAX: + dx = y * (dy - s) + else: + dx = dy + tl.store(DX + y_indx, dx) diff --git a/triton_kernels/topk_details/_topk_forward.py b/triton_kernels/topk_details/_topk_forward.py new file mode 100644 index 000000000000..4ccdbd20833c --- /dev/null +++ b/triton_kernels/topk_details/_topk_forward.py @@ -0,0 +1,150 @@ +# This file is vendored from the Triton project. DO NOT EDIT THIS FILE DIRECTLY. +# Source: https://github.com/triton-lang/triton/tree/v3.5.1/python/triton_kernels/triton_kernels/topk_details/_topk_forward.py +# Triton is licensed under the MIT License. + +import triton +import triton.language as tl + + +@triton.jit +def get_topmask_and_fullmask(x): + tl.static_assert(x.dtype.is_int_unsigned(), "floating-point value must be passed as bits") + tm: tl.constexpr = 1 << (-1 + x.dtype.primitive_bitwidth) + fm: tl.constexpr = (1 << x.dtype.primitive_bitwidth) - 1 + tm_arr = tl.full(x.shape, tm, dtype=x.dtype) + fm_arr = tl.full(x.shape, fm, dtype=x.dtype) + return tm_arr, fm_arr + + +@triton.jit +def fpval_to_key(x): + tm, fm = get_topmask_and_fullmask(x) + return x ^ tl.where((x & tm) != 0, fm, tm) + + +@triton.jit +def key_to_fpval(x): + tm, fm = get_topmask_and_fullmask(x) + return x ^ tl.where((x & tm) == 0, fm, tm) + + +# stable top-k tie-breaks to value with smaller index +@triton.jit +def indx_to_key(indx, N_EXPTS_PAD: tl.constexpr): + return N_EXPTS_PAD - indx + + +@triton.jit +def key_to_indx(indx, N_EXPTS_PAD: tl.constexpr): + return N_EXPTS_PAD - indx + + +@triton.jit +def streaming_topk(X, stride_xm, n_expts_tot, offs_m, mask_m, N_EXPTS_PAD: tl.constexpr, N_EXPTS_ACT: tl.constexpr, + BLOCK_N: tl.constexpr): + x_nbits: tl.constexpr = X.dtype.element_ty.primitive_bitwidth + x_utype: tl.constexpr = tl.dtype(f"uint{x_nbits}") + if x_nbits < 16: + # this ensures that we leave at least 16 bits for expert index + # even if the input dtype is smaller than 16 bits: + y_nbits: tl.constexpr = 32 + else: + y_nbits: tl.constexpr = x_nbits * 2 + x_ultype: tl.constexpr = tl.dtype(f"uint{y_nbits}") + x_dtype: tl.constexpr = X.dtype.element_ty + + # subtract 1 from loop iterations because we peel the first (masked) iteration: + loop_iterations: tl.constexpr = N_EXPTS_PAD // BLOCK_N - 1 + offs_x_n = loop_iterations * BLOCK_N + tl.arange(0, BLOCK_N) + mask_n = offs_x_n[None, :] < n_expts_tot + + # first iteration: + X_ptrs = X + offs_m[:, None] * stride_xm + offs_x_n[None, :] + x = tl.load(X_ptrs, mask=(mask_m & mask_n), other=float("-inf")) + x = fpval_to_key(x.to(x_utype, bitcast=True)) + x = (x.to(x_ultype) << 16) | indx_to_key(offs_x_n, N_EXPTS_PAD)[None, :] + acc = tl.topk(x, N_EXPTS_ACT, dim=1) + + # subsequent iterations: + for _i in (tl.static_range if loop_iterations <= 4 else range)(loop_iterations): + acc = tl.bitonic_merge(acc) # ensure sorted ascending for the merge + X_ptrs -= BLOCK_N + offs_x_n -= BLOCK_N + x = tl.load(X_ptrs, mask=mask_m, other=float("-inf")) + x = fpval_to_key(x.to(x_utype, bitcast=True)) + x = (x.to(x_ultype) << 16) | indx_to_key(offs_x_n, N_EXPTS_PAD)[None, :] + acc = tl.maximum(acc, tl.topk(x, N_EXPTS_ACT, dim=1)) + + # rotate expert index into upper 16 bits: + # 0000vvvvvvvviiii --> iiii0000vvvvvvvv + acc = (acc << (y_nbits - 16)) | (acc >> 16) + # sort in ascending order of expert (descending order of key) + acc = tl.sort(acc, dim=1, descending=True) + # iiii0000vvvvvvvv --> 0000iiii: + y_indices_raw = (acc >> (y_nbits - 16)).to(tl.uint32) + y_indices = key_to_indx(y_indices_raw, N_EXPTS_PAD) + # iiii0000vvvvvvvv --> vvvvvvvv: + y_values_raw = acc.to(x_utype) + y_values = key_to_fpval(y_values_raw).to(x_dtype, bitcast=True) + + return y_values, y_indices + + +@triton.jit +def _topk_forward(X, stride_xm, # inputs + Yv, Yi, stride_ym, # topk values/indices + USE_PROVIDED_INDX: tl.constexpr, Bits, stride_rm: tl.constexpr, stride_rn: tl.constexpr, # bitmatrix + n_rows, n_expts_tot, # shape + S, BLOCK_S: tl.constexpr, s_blocks, # thing to memset + APPLY_SOFTMAX: tl.constexpr, # constant + BLOCK_M: tl.constexpr, N_EXPTS_PAD: tl.constexpr, N_EXPTS_ACT: tl.constexpr, BLOCK_N: tl.constexpr): + + pid = tl.program_id(0) + if isinstance(n_rows, tl.tensor) and n_rows.dtype.is_ptr(): + n_rows = tl.load(n_rows) + + if pid < s_blocks: + tl.store(S + BLOCK_S * pid + tl.arange(0, BLOCK_S), tl.zeros([BLOCK_S], tl.int32)) + + if pid * BLOCK_M >= n_rows: + # early exit: + return + + tl.static_assert(BLOCK_N % 32 == 0) + tl.static_assert(N_EXPTS_PAD % BLOCK_N == 0) + x_dtype: tl.constexpr = X.dtype.element_ty + + # load logits + offs_m = pid * BLOCK_M + tl.arange(0, BLOCK_M) + offs_y_n = tl.arange(0, N_EXPTS_ACT) + mask_m = offs_m[:, None] < n_rows + if USE_PROVIDED_INDX: + Yi_ptrs = Yi + offs_m[:, None] * stride_ym + offs_y_n[None, :] + y_indices = tl.load(Yi_ptrs, mask=mask_m) + Xv_ptrs = X + offs_m[:, None] * stride_xm + y_indices + y_values = tl.load(Xv_ptrs, mask=mask_m) + else: + y_values, y_indices = streaming_topk(X, stride_xm, n_expts_tot, offs_m, mask_m, # + N_EXPTS_PAD, N_EXPTS_ACT, BLOCK_N) + + # normalize selected values + if APPLY_SOFTMAX: + y_values = tl.softmax(y_values.to(tl.float32), dim=1, keep_dims=True).to(x_dtype) + + # write back + Yv_ptrs = Yv + offs_m[:, None] * stride_ym + offs_y_n[None, :] + tl.store(Yv_ptrs, y_values, mask=mask_m) + if not USE_PROVIDED_INDX: + Yi_ptrs = Yi + offs_m[:, None] * stride_ym + offs_y_n[None, :] + tl.store(Yi_ptrs, y_indices, mask=mask_m) + + # pack into bitmatrix + y_div = y_indices // 32 + y_rem = y_indices % 32 + loop_iterations = N_EXPTS_PAD // BLOCK_N + for i in range(loop_iterations): + offs_r_n = tl.arange(0, BLOCK_N // 32) + i * (BLOCK_N // 32) + y2 = tl.where(y_div[:, :, None] == offs_r_n[None, None, :], (1 << y_rem)[:, :, None], 0) + r = tl.reduce_or(y2, axis=1) + BitsPtrs = Bits + offs_m[:, None] * stride_rm + offs_r_n[None, :] * stride_rn + tl.store(BitsPtrs, r, mask=mask_m)