[None][feat] Add ExpertStatistic and DUMMY_ALLREDUCE for configurable_moe#10401
[None][feat] Add ExpertStatistic and DUMMY_ALLREDUCE for configurable_moe#10401syuoni merged 7 commits intoNVIDIA:mainfrom
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…le_moe. Signed-off-by: Xianjie <5410381+qiaoxj07@users.noreply.github.com>
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📝 WalkthroughWalkthroughAdds debugging and profiling capabilities to a MOE module by introducing an environment-controlled dummy all-reduce operation for load balancing during timing analysis, alongside integration of ExpertStatistic metrics collection at key computation points in the forward pass. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes 🚥 Pre-merge checks | ✅ 2 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (2 passed)
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Actionable comments posted: 1
🧹 Nitpick comments (2)
tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py (2)
226-227: Consider more robust environment variable parsing.The current string comparison approach works but only accepts exactly "1" to enable. Consider using a more flexible approach that handles common truthy values:
🔎 Proposed refactor for more robust parsing
- # Debug function for eliminating imbalance during performance analysis. - self.enable_dummy_allreduce = os.environ.get("TRTLLM_ENABLE_DUMMY_ALLREDUCE", "0") == "1" + # Debug function for eliminating imbalance during performance analysis. + self.enable_dummy_allreduce = os.environ.get("TRTLLM_ENABLE_DUMMY_ALLREDUCE", "0").lower() in ("1", "true", "yes")
243-251: Consider adding device availability check.The method directly creates a CUDA tensor without checking device availability. While this is a debug feature, adding a safety check could prevent confusing errors if CUDA is not available.
🔎 Proposed enhancement for device safety
def dummy_allreduce(self): """ Debug function for eliminating imbalance during performance analysis. Creates a small dummy tensor and performs allreduce to synchronize processes and eliminate timing imbalances for more accurate profiling measurements. """ + assert torch.cuda.is_available(), "CUDA must be available for dummy_allreduce" dummy_tensor = torch.zeros(4, dtype=torch.float32, device="cuda") dummy_tensor = self.all_reduce(dummy_tensor) return dummy_tensor
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tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py
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📓 Path-based instructions (2)
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tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py
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tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py
🧠 Learnings (2)
📓 Common learnings
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.
📚 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:
tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py
🧬 Code graph analysis (1)
tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py (2)
tensorrt_llm/_torch/expert_statistic.py (3)
ExpertStatistic(10-101)set_layer(43-45)maybe_add_info(48-52)tensorrt_llm/_torch/autotuner.py (1)
get(628-631)
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- GitHub Check: Pre-commit Check
🔇 Additional comments (2)
tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py (2)
31-31: LGTM: Import additions follow coding guidelines.The import additions are correctly placed and follow the project's Python coding guidelines for maintaining namespace and import ordering.
Also applies to: 36-36
668-670: LGTM: Strategic placement for profiling synchronization.The conditional dummy_allreduce calls are well-placed before key communication operations to eliminate timing imbalances during profiling. The feature is properly gated behind the environment variable flag, ensuring zero overhead when not enabled.
Also applies to: 735-736
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Signed-off-by: Xianjie <5410381+qiaoxj07@users.noreply.github.com>
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…_moe (NVIDIA#10401) Signed-off-by: Xianjie <5410381+qiaoxj07@users.noreply.github.com> Signed-off-by: Daniil Kulko <kulkodaniil@gmail.com>
4 participants
…le_moe.
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