[https://nvbugs/5761391][fix] Include triton-kernels as a packaged dependency #10471
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📝 WalkthroughWalkthroughThe changes make Triton kernels a required dependency by adding Changes
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📒 Files selected for processing (12)
ATTRIBUTIONS-Python.mdexamples/models/core/gpt_oss/README.mdrequirements.txttensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.pytensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.pytensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.pytensorrt_llm/_torch/modules/triton_linear.pytests/integration/defs/accuracy/test_llm_api_pytorch.pytests/unittest/_torch/auto_deploy/unit/multigpu/custom_ops/test_mxfp4_moe_ep.pytests/unittest/_torch/modeling/test_modeling_gpt_oss.pytests/unittest/_torch/modules/test_fused_moe.pytests/unittest/_torch/modules/test_triton_linear.py
💤 Files with no reviewable changes (5)
- tests/integration/defs/accuracy/test_llm_api_pytorch.py
- tests/unittest/_torch/modeling/test_modeling_gpt_oss.py
- tests/unittest/_torch/modules/test_fused_moe.py
- tests/unittest/_torch/auto_deploy/unit/multigpu/custom_ops/test_mxfp4_moe_ep.py
- tests/unittest/_torch/modules/test_triton_linear.py
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📓 Path-based instructions (3)
**/*.{md,rst}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
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ATTRIBUTIONS-Python.mdexamples/models/core/gpt_oss/README.md
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Files:
tensorrt_llm/_torch/modules/triton_linear.pytensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.pytensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.pytensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
**/*.{cpp,cc,cxx,h,hpp,hxx,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
All TensorRT-LLM source files (.cpp, .h, .cu, .py, and other source files) should contain an NVIDIA copyright header with the year of latest meaningful modification
Files:
tensorrt_llm/_torch/modules/triton_linear.pytensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.pytensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.pytensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
🧠 Learnings (12)
📚 Learning: 2025-08-27T17:50:13.264Z
Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.
Applied to files:
examples/models/core/gpt_oss/README.md
📚 Learning: 2025-08-14T23:23:27.449Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4010-4012
Timestamp: 2025-08-14T23:23:27.449Z
Learning: For MOE (Mixture of Experts) code reviews in TensorRT-LLM, avoid repeatedly suggesting finalize fusion validation checks and safety assertions. The user djns99 has indicated these suggestions are repetitive and unwanted across multiple MOE-related changes.
Applied to files:
examples/models/core/gpt_oss/README.mdtensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.pytensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.pytensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
📚 Learning: 2025-08-20T07:43:36.447Z
Learnt from: ChristinaZ
Repo: NVIDIA/TensorRT-LLM PR: 7068
File: cpp/tensorrt_llm/kernels/moeTopKFuncs.cuh:169-172
Timestamp: 2025-08-20T07:43:36.447Z
Learning: In TensorRT-LLM MOE kernels, when processing up to 128 experts across 32 threads, each thread handles at most 4 experts (N < 5 constraint), where N represents candidates per thread rather than total system capacity.
Applied to files:
examples/models/core/gpt_oss/README.md
📚 Learning: 2025-08-21T02:39:12.009Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 7104
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1475-1480
Timestamp: 2025-08-21T02:39:12.009Z
Learning: The min latency mode functionality in TensorRT-LLM MOE kernels (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu) is deprecated and no longer being maintained/updated, as confirmed by djns99. Bug reports and optimization suggestions for the computeStridesTmaWarpSpecializedLowLatencyKernel and related min latency code paths should be deprioritized.
Applied to files:
examples/models/core/gpt_oss/README.mdtensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.pytensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.pytensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
📚 Learning: 2025-10-20T17:09:21.560Z
Learnt from: nvchenghaoz
Repo: NVIDIA/TensorRT-LLM PR: 8469
File: tensorrt_llm/_torch/auto_deploy/transform/library/rms_norm.py:180-182
Timestamp: 2025-10-20T17:09:21.560Z
Learning: In tensorrt_llm/_torch/auto_deploy/transform/library/rms_norm.py, the _gated_rmsnorm_replacement function does not need to cast the output of torch.ops.auto_deploy.torch_rmsnorm_gated back to the input dtype, even though the custom op returns fp32. The dtype handling is managed elsewhere or the fp32 output is acceptable for downstream consumers.
Applied to files:
tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.py
📚 Learning: 2025-08-19T12:45:11.997Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.
Applied to files:
tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.pytensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.pytensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
📚 Learning: 2025-12-19T06:31:54.973Z
Learnt from: nvyocox
Repo: NVIDIA/TensorRT-LLM PR: 10117
File: tensorrt_llm/_torch/auto_deploy/transform/library/fuse_rope_attention.py:336-339
Timestamp: 2025-12-19T06:31:54.973Z
Learning: In tensorrt_llm/_torch/auto_deploy/transform/library/fuse_rope_attention.py, the cast to torch.float16 for qkv_node before creating the AttentionPlugin is intentional and required because DriveOS LLM expects float16 dtype specifically. This should not be changed to preserve original dtype or made configurable for bfloat16 models in the DriveOS LLM ONNX export path.
Applied to files:
tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.py
📚 Learning: 2025-08-19T12:45:35.429Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:2086-2092
Timestamp: 2025-08-19T12:45:35.429Z
Learning: DoRA (Delta Orthogonal Rank Adaptation) functionality has been removed from the PyTorch flow in tensorrt_llm/_torch/pyexecutor/model_engine.py. The is_dora field is computed but not used downstream in the PyTorch flow, so converting it to a tensor would be wasteful overhead.
Applied to files:
tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py
📚 Learning: 2025-08-08T22:03:40.707Z
Learnt from: sklevtsov-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1198-1209
Timestamp: 2025-08-08T22:03:40.707Z
Learning: In the CUTLASS MoE kernels (cpp/tensorrt_llm/cutlass_extensions), when `layout_info.fusion` is set to `TmaWarpSpecializedGroupedGemmInput::EpilogueFusion::FINALIZE`, the `router_scales` parameter must be non-null by design. The fused finalize kernel epilogue does not perform nullptr checks and requires valid router scales to function correctly. This is an implicit contract that callers must satisfy when enabling the FINALIZE fusion mode.
Applied to files:
tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.pytensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
📚 Learning: 2025-10-20T16:54:09.824Z
Learnt from: nvchenghaoz
Repo: NVIDIA/TensorRT-LLM PR: 8469
File: tensorrt_llm/_torch/auto_deploy/custom_ops/rms_norm.py:6-6
Timestamp: 2025-10-20T16:54:09.824Z
Learning: In tensorrt_llm/_torch/auto_deploy/custom_ops/rms_norm.py, the import `from ...modules.mamba.layernorm_gated import _layer_norm_fwd` is correct and should not be changed to modules.fla.layernorm_gated. The _layer_norm_fwd function exists in both modules/mamba/layernorm_gated.py and modules/fla/layernorm_gated.py, but the mamba version is the intended implementation for this use case.
Applied to files:
tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
📚 Learning: 2025-11-14T11:22:03.729Z
Learnt from: nzmora-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 9163
File: tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py:107-113
Timestamp: 2025-11-14T11:22:03.729Z
Learning: In TensorRT-LLM AutoDeploy custom ops, when adding hardware capability checks to select between kernel implementations (e.g., cuBLAS vs. CUDA kernel), use descriptive variable names that identify the specific GPU architectures or families being targeted (e.g., `is_blackwell_geforce_or_ada`) rather than generic names like `enable_cuda_core`. This makes it clear that the code is selecting an implementation path based on hardware capabilities, not enabling/disabling hardware features.
Applied to files:
tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
📚 Learning: 2025-08-19T03:35:20.866Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4616-4626
Timestamp: 2025-08-19T03:35:20.866Z
Learning: In the MOE profiler TMA workspace preparation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu), the overlapping of TMA WS regions for NONE and FINALIZE variants is deliberate design to save memory space, as confirmed by djns99. The comment "reuse the same pointers to save space" reflects this intentional behavior.
Applied to files:
tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py
🧬 Code graph analysis (4)
tensorrt_llm/_torch/modules/triton_linear.py (1)
tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py (1)
maybe_update_stride(176-179)
tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.py (3)
tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (1)
get(1247-1249)tensorrt_llm/_torch/auto_deploy/transform/interface.py (1)
get(539-541)tensorrt_llm/_torch/auto_deploy/models/quant_config_reader.py (1)
get(74-77)
tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py (1)
tensorrt_llm/_torch/models/modeling_gpt_oss.py (1)
swiglu(204-212)
tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py (1)
tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py (1)
TritonEPRouter(111-173)
🪛 markdownlint-cli2 (0.18.1)
ATTRIBUTIONS-Python.md
62425-62425: Unordered list indentation
Expected: 0; Actual: 2
(MD007, ul-indent)
62426-62426: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
62451-62451: Unordered list indentation
Expected: 0; Actual: 2
(MD007, ul-indent)
62451-62451: Bare URL used
(MD034, no-bare-urls)
🔇 Additional comments (7)
tensorrt_llm/_torch/auto_deploy/transform/library/mxfp4_moe.py (1)
222-222: LGTM! Simplifies gating logic by removing Triton kernel availability check.The transform now proceeds whenever the quantization method is "mxfp4", without requiring a runtime check for Triton kernel availability. This aligns with the PR's goal of making triton-kernels a required dependency.
tensorrt_llm/_torch/modules/triton_linear.py (2)
7-8: LGTM! Unconditional imports reflect triton-kernels as required dependency.The Triton kernel imports are now unconditional, consistent with triton-kernels being a packaged dependency rather than an optional component.
14-14: LGTM! Consolidates Triton-related imports.The import of
maybe_update_strideis now part of the unconditional top-level imports, simplifying the module structure.tensorrt_llm/_torch/modules/fused_moe/fused_moe_triton.py (1)
10-16: LGTM! Comprehensive move to unconditional Triton imports.All Triton kernel imports (swiglu, matmul_ogs, routing, numerics, tensor utilities) are now unconditionally imported at the top level. This reflects the architectural change to make triton-kernels a required dependency.
tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/mxfp4_moe.py (1)
7-15: LGTM! Triton imports now unconditional across custom ops.All Triton kernel imports and the TritonEPRouter are now imported unconditionally, enabling the MXFP4 MoE custom ops to always use Triton-based implementations.
requirements.txt (1)
69-71: The triton-kernels git dependency is correctly configured.The tag
v3.5.0exists in the triton repository and thepython/triton_kernelssubdirectory is present at that tag, making the git+https installation method valid.examples/models/core/gpt_oss/README.md (1)
111-118: Documentation update aligns with simplified Triton MoE setup and follows guidelines.The changes correctly simplify the documentation to reflect Triton kernels as a packaged dependency, removing the need for complex manual setup. The use of
--configflag in Line 113 follows the coding guidelines for TensorRT-LLM CLI tool documentation. The YAML configuration snippet is clear and concise.
| License: `MIT License` | ||
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Description
Currently, users need to clone Triton and build it from source in order to use the Triton MoE kernels. This change adds
triton-kernelsas a dependency to eliminate this extra step.Test Coverage
This functionality is covered by the config database tests added in #9382, which include H200 tests that use the Triton MoE backend (e.g. https://github.com/NVIDIA/TensorRT-LLM/blob/main/tests/scripts/perf-sanity/config_database_h200_sxm.yaml#L192)
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
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