[TRTLLMINF-391][infra] Upgrade public PyTorch to 2.14 and integrate Triton 3.8 by EmmaQiaoCh · Pull Request #19477 · NVIDIA/TensorRT-LLM

EmmaQiaoCh · 2026-09-21T06:25:17Z

Description

Problem

Public installations currently use PyTorch 2.13.0 and Triton 3.7.1. Moving to PyTorch 2.14.0 and Triton 3.8.0 requires updating the dependency constraints and vendored kernels together while retaining compatibility with the NGC 26.08 PyTorch alpha build. Triton 3.8 also requires an explicit microblock size when MXFP4 weight scales are supplied to matmul.

Solution

Upgrade public PyTorch to 2.14.0, pin Triton to 3.8.0 and NCCL to 2.30.7, and keep the NGC PyTorch alpha admissible. Synchronize installation scripts, package-sanity checks, documentation, security-scanning constraints, and Triton attribution.
Re-vendor triton_kernels from upstream v3.8.0 and set b_microblock_size=32 in the five MXFP4 matmul configurations used by Triton linear, fused MoE, and AutoDeploy.
Require CUDA 13.0 or newer, using the CUDA 13.x major version currently covered by TRT-LLM CI as the minimum.
Harden dependency patch validation: apply forward, discard reject files, then require a reverse dry-run with --force so the check cannot switch direction and accept an unapplied patch.

Validation

Validation snapshot for commit cef6629bca6648a6ac6482b68154623afe277372:

Regenerating the vendored package from Triton v3.8.0 produces identical files. Changed Python files and the PyTorch installation shell script pass syntax checks.
GNU patch 2.8 smoke checks pass: pristine and already-patched trees succeed; partially patched trees, missing files, conflicts, and unwritable target directories are rejected.

coderabbitai · 2026-09-21T06:41:53Z

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3rdparty/CMakeLists.txt

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Walkthrough

The pull request raises the minimum CUDA Toolkit version to 13.0, updates PyTorch, Triton, NCCL, and TorchVision versions, and upgrades vendored Triton kernels. The Triton changes add FP4, microscaling, new layouts, configurable reductions, and updated MXFP4 integrations.

Changes

Triton 3.8.0 integration

Layer / File(s)	Summary
Build requirements and dependency pins `cpp/CMakeLists.txt`, `3rdparty/CMakeLists.txt`, `requirements.txt`, `security_scanning/pyproject.toml`, `docker/common/install_pytorch.sh`, `jenkins/L0_Test.groovy`, `ATTRIBUTIONS-Python.md`, `docs/source/installation/installation-guide.md`	The minimum CUDA Toolkit version is 13.0. Dependency, attribution, installation, and patch-validation references were updated.
Matmul and physical layouts `triton_kernels/matmul.py`, `triton_kernels/matmul_details/`, `triton_kernels/tensor.py`, `triton_kernels/tensor_details/`	Matmul supports MXFP4, NVFP4, tensor scales, packed outputs, architecture-specific layouts, and validated storage shapes.
Microscaling and optimization runtime `triton_kernels/numerics_details/mxfp.py`, `triton_kernels/numerics_details/mxfp_details/`, `triton_kernels/matmul_details/opt_flags.py`, `triton_kernels/matmul_details/opt_flags_details/`, `triton_kernels/specialize.py`	Quantization supports configurable microblocks, FP8 or uint8 scales, tensor-scale preconditioning, and FP4 decoding. Optimization selection uses scoped state and architecture-specific constraints.
Reduction and application call sites `triton_kernels/reduce.py`, `triton_kernels/distributed.py`, `triton_kernels/testing.py`, `triton_kernels/target_info.py`, `tensorrt_llm/_torch/...`	Reduction uses adaptive tiled scheduling and configurable MX output settings. Supporting Triton utilities and MXFP4 matmul call sites use the updated interfaces.

Priority: ⬇️ Low

Estimated code review effort: 5 (Critical) | ~90 minutes

Change: Other

Merge Risk: 🟡 Moderate · up to cef66

The dependency and CUDA 13/Triton 3.8 update still has a reported Release Checks failure during pre-commit, leaving merge readiness uncertain until the failure is fixed or explicitly accepted.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 41.10% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 163 functions across 53 files. (1 skipped…	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly summarizes the primary changes: the public PyTorch upgrade and Triton 3.8 integration. The ticket and type formats are also valid.
Description check	✅ Passed	The description explains the problem, solution, dependency updates, vendored kernel changes, CUDA requirement, patch validation, and validation results. It does not use the template's exact Test Cover…

Full details: Docstring Coverage

Explanation

Docstring coverage is 41.10% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 163 functions across 53 files. (1 skipped: 1 unsupported.)

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Inline comments:
In `@3rdparty/CMakeLists.txt`:
- Line 160: The patch application command must reject every dry-run failure, not
only output containing “FAILED”. Update the patch handling command around
DEP_NAME to check the dry-run exit status and allow only the known fully applied
state, including explicitly rejecting cases such as missing target files or
skipped patches while preserving the existing inconsistent-state message.

In `@cpp/CMakeLists.txt`:
- Around line 349-357: Update the CUDAToolkit minimum version in the
find_package declaration to 12.0, keeping the existing required components
unchanged so CUDA compilation matches the C++20 requirement.

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📥 Commits

Reviewing files that changed from the base of the PR and between eca9e52 and 2676a6d.

📒 Files selected for processing (134)

3rdparty/CMakeLists.txt
3rdparty/fetch_content.json
3rdparty/patches/xgrammar_constexpr.patch
3rdparty/patches/xgrammar_cxx20.patch
ATTRIBUTIONS-Python.md
cpp/CMakeLists.txt
cpp/include/tensorrt_llm/batch_manager/microBatchScheduler.h
cpp/kernels/fmha_v2/setup.py
cpp/kernels/fmha_v2/src/fmha/fragment.h
cpp/kernels/fmha_v2/src/fmha/gmem_tile_o.h
cpp/kernels/fmha_v2/src/fmha/gmem_tile_o_packed.h
cpp/kernels/fmha_v2/src/fmha/gmem_tile_ps.h
cpp/kernels/fmha_v2/src/fmha/gmem_tile_qkv.h
cpp/kernels/fmha_v2/src/fmha/gmem_tile_qkv_packed.h
cpp/kernels/fmha_v2/src/fmha/hopper/compute_tile.h
cpp/kernels/fmha_v2/src/fmha/hopper/fragment.h
cpp/kernels/fmha_v2/src/fmha/hopper/gmem_tile_o_packed.h
cpp/kernels/fmha_v2/src/fmha/hopper/gmem_tile_qkv_packed.h
cpp/kernels/fmha_v2/src/fmha/hopper/gmma_descriptor.h
cpp/kernels/fmha_v2/src/fmha/hopper/kernel_traits.h
cpp/kernels/fmha_v2/src/fmha/hopper/smem_tile.h
cpp/kernels/fmha_v2/src/fmha/hopper/smem_tile_o.h
cpp/kernels/fmha_v2/src/fmha/kernel_traits.h
cpp/kernels/fmha_v2/src/fmha/mask.h
cpp/kernels/fmha_v2/src/fmha/smem_tile.h
cpp/kernels/fmha_v2/src/fmha/smem_tile_o.h
cpp/kernels/fmha_v2/src/fmha/smem_tile_qkv.h
cpp/kernels/fmha_v2/src/fmha/smem_tile_v.h
cpp/kernels/fmha_v2/src/fmha/softmax.h
cpp/kernels/fmha_v2/src/fmha/traits.h
cpp/kernels/fmha_v2/src/fmha/utils.h
cpp/kernels/fmha_v2/src/fmha/warpspec/compute.h
cpp/kernels/fmha_v2/src/fmha/warpspec/dma.h
cpp/kernels/fmha_v2/src/fmha/warpspec/epilogue.h
cpp/kernels/fmha_v2/src/fmha/warpspec/kernel_traits.h
cpp/kernels/fmha_v2/src/fmha/warpspec_sm120/compute_sync_mma.h
cpp/kernels/fmha_v2/src/fmha/warpspec_sm120/dma_sync_mma.h
cpp/kernels/fmha_v2/src/fmha/warpspec_sm120/kernel_traits.h
cpp/kernels/fmha_v2/src/fused_multihead_attention_kernel_1xN.h
cpp/kernels/fmha_v2/src/fused_multihead_attention_kernel_1xN_multi_cta.h
cpp/kernels/fmha_v2/src/fused_multihead_attention_kernel_1xN_noloop.h
cpp/kernels/fmha_v2/src/fused_multihead_attention_kernel_2x2.h
cpp/kernels/fmha_v2/src/fused_multihead_attention_kernel_4x1_hopper.h
cpp/kernels/fmha_v2/src/fused_multihead_attention_kernel_4x1_hopper_noloop.h
cpp/kernels/fmha_v2/src/fused_multihead_attention_kernel_4xN_hopper.h
cpp/kernels/fmha_v2/src/fused_multihead_attention_kernel_4xN_hopper_noloop.h
cpp/kernels/fmha_v2/src/fused_multihead_cross_attention_kernel_1xN.h
cpp/kernels/fmha_v2/src/fused_multihead_cross_attention_kernel_1xN_noloop.h
cpp/kernels/fmha_v2/src/fused_multihead_flash_attention_kernel.h
cpp/kernels/fmha_v2/src/fused_multihead_flash_attention_kernel_noloop.h
cpp/kernels/fmha_v2/src/fused_multihead_flash_attention_kernel_noloop_tiled.h
cpp/kernels/fmha_v2/src/softmax_impl.h
cpp/kernels/fmha_v2/train_ops/CMakeLists.txt
cpp/kernels/xqa/CMakeLists.txt
cpp/kernels/xqa/gen_cubins.py
cpp/micro_benchmarks/CMakeLists.txt
cpp/tensorrt_llm/CMakeLists.txt
cpp/tensorrt_llm/batch_manager/CMakeLists.txt
cpp/tensorrt_llm/common/cudaTypeUtils.cuh
cpp/tensorrt_llm/deep_ep/CMakeLists.txt
cpp/tensorrt_llm/deep_gemm/CMakeLists.txt
cpp/tensorrt_llm/executor/CMakeLists.txt
cpp/tensorrt_llm/executor/cache_transmission/ucx_utils/CMakeLists.txt
cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttention/decoderMaskedMultiheadAttentionTemplate.h
cpp/tensorrt_llm/kernels/decoderMaskedMultiheadAttentionUtils.h
cpp/tensorrt_llm/kernels/llama4MinLatencyKernels/llama4Fp8Bf16GemmAttnScalingPerBlockTemplate.cuh
cpp/tensorrt_llm/kernels/llama4MinLatencyKernels/llama4Fp8Bf16GemmPerBlockTemplate.cuh
cpp/tensorrt_llm/kernels/llama4MinLatencyKernels/llama4Fp8Bf16GemmPerWarpTemplate.cuh
cpp/tensorrt_llm/kernels/llama4MinLatencyKernels/llama4Fp8Fp8GemmSwiGLUPerBlockTemplate.cuh
cpp/tensorrt_llm/kernels/llama4MinLatencyKernels/llama4MinLatencyMoEOp.cu
cpp/tensorrt_llm/kernels/llama4MinLatencyKernels/llama4Utils.cuh
cpp/tensorrt_llm/kernels/mamba2MTPSSMCache/CMakeLists.txt
cpp/tensorrt_llm/kernels/moe/cutlass/launchers/moe_gemm_tma_ws_launcher.inl
cpp/tensorrt_llm/kernels/selectiveScan/Poly.h
cpp/tensorrt_llm/thop/CMakeLists.txt
cpp/tests/CMakeLists.txt
cpp/tests/unit_tests/kernels/moe/mixtureOfExpertsTest.cu
cpp/tests/unit_tests/runtime/localizationTest.cu
docker/common/install_pytorch.sh
docs/source/installation/installation-guide.md
jenkins/L0_MergeRequest.groovy
jenkins/L0_Test.groovy
requirements.txt
scripts/build_wheel.py
security_scanning/pyproject.toml
triton_kernels/README.md
triton_kernels/VERSION
triton_kernels/__init__.py
triton_kernels/compaction.py
triton_kernels/compaction_details/_masked_compaction.py
triton_kernels/distributed.py
triton_kernels/distributed_details/mesh.py
triton_kernels/matmul.py
triton_kernels/matmul_details/_common.py
triton_kernels/matmul_details/_matmul.py
triton_kernels/matmul_details/_p_matmul.py
triton_kernels/matmul_details/opt_flags.py
triton_kernels/matmul_details/opt_flags_details/opt_flags_amd.py
triton_kernels/matmul_details/opt_flags_details/opt_flags_nvidia.py
triton_kernels/meta.py
triton_kernels/numerics.py
triton_kernels/numerics_details/__init__.py
triton_kernels/numerics_details/flexpoint.py
triton_kernels/numerics_details/mxfp.py
triton_kernels/numerics_details/mxfp_details/_downcast_to_mxfp.py
triton_kernels/numerics_details/mxfp_details/_upcast_from_mxfp.py
triton_kernels/proton_opts.py
triton_kernels/reduce.py
triton_kernels/roofline.py
triton_kernels/specialize.py
triton_kernels/swiglu.py
triton_kernels/swiglu_details/_swiglu.py
triton_kernels/target_info.py
triton_kernels/tensor.py
triton_kernels/tensor_details/bitmatrix.py
triton_kernels/tensor_details/bitmatrix_details/sum_bitmatrix_rows.py
triton_kernels/tensor_details/dtype.py
triton_kernels/tensor_details/layout.py
triton_kernels/tensor_details/layout_details/base.py
triton_kernels/tensor_details/layout_details/blackwell_scale.py
triton_kernels/tensor_details/layout_details/blackwell_value.py
triton_kernels/tensor_details/layout_details/blackwell_value_shuffled.py
triton_kernels/tensor_details/layout_details/cdna4_scale.py
triton_kernels/tensor_details/layout_details/gfx1250_scale.py
triton_kernels/tensor_details/layout_details/hopper_scale.py
triton_kernels/tensor_details/layout_details/hopper_value.py
triton_kernels/tensor_details/layout_details/strided.py
triton_kernels/tensor_details/layout_details/torch_utils.py
triton_kernels/tensor_details/ragged_tensor.py
triton_kernels/testing.py
triton_kernels/topk.py
triton_kernels/topk_details/__init__.py
triton_kernels/topk_details/_topk_backward.py
triton_kernels/topk_details/_topk_forward.py

💤 Files with no reviewable changes (3)

3rdparty/patches/xgrammar_constexpr.patch
cpp/include/tensorrt_llm/batch_manager/microBatchScheduler.h
cpp/tensorrt_llm/thop/CMakeLists.txt

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EmmaQiaoCh · 2026-09-21T09:25:17Z

/bot run --disable-fail-fast

tensorrt-cicd · 2026-09-21T09:32:24Z

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nv-guomingz

LGTM for doc part

tensorrt-cicd · 2026-09-22T11:14:53Z

PR_Github #74993 [ run ] completed with state SUCCESS. Commit: 22f6c33
/LLM/main/L0_MergeRequest_PR pipeline #61751 completed with status: 'FAILURE'

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chzblych · 2026-09-22T15:39:31Z

/bot run --disable-fail-fast

tensorrt-cicd · 2026-09-22T15:45:27Z

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chzblych · 2026-09-22T23:23:47Z

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EmmaQiaoCh requested review from a team as code owners September 21, 2026 06:25

EmmaQiaoCh requested review from BowenFu, JunyiXu-nv, arysef, chenfeiz0326, dhansen-nvidia, kaiyux, kris1025, pengbowang-nv, schetlur-nv, sunnyqgg, tburt-nv, xxi-nv, yuanjingx87 and zhaoyangwang-nvidia September 21, 2026 06:25

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EmmaQiaoCh force-pushed the emma/update_torch_214 branch from 2676a6d to 70c84f1 Compare September 21, 2026 09:22

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chzblych changed the title ~~[TRTLLMINF-391][infra] Update public torch to 2.14 and triton to 3.8.0~~ [TRTLLMINF-391][infra] Upgrade public PyTorch to 2.14 and integrate Triton 3.8 Sep 22, 2026

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chzblych merged commit adea5c0 into NVIDIA:main Sep 23, 2026
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StanleySun639 mentioned this pull request Sep 29, 2026

[None][test] Add coverage for TritonMXFP4LinearMethod.apply #19675

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