[None][chroe] Polish qwen3-next modeling code. #8902
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📝 WalkthroughWalkthroughThe changes introduce parallel MoE routing execution in Qwen3-next by wrapping routed and shared expert computation in separate functions coordinated via CUDA events, and implement the ChunkGatedDeltaRuleFunction forward method with conditional l2-norm application and autograd-enabling outputs. Changes
Sequence Diagram(s)sequenceDiagram
participant Router as Router Logic
participant Routed as Routed Expert Path
participant Shared as Shared Expert Path
participant Combiner as Output Combination
rect rgb(100, 150, 200)
note over Router,Shared: Before: Sequential Execution
Router->>Router: Compute router logits
Router->>Routed: Route through experts
Routed->>Combiner: Receive routed output
Combiner->>Shared: Compute shared experts
Shared->>Combiner: Combine outputs
end
rect rgb(150, 200, 100)
note over Router,Shared: After: Parallel Execution
Router->>Routed: Route computation (with events)
Router->>Shared: Shared computation (with events)
par Parallel Paths
Routed->>Routed: Execute routed experts
and
Shared->>Shared: Execute shared experts
end
Routed->>Combiner: Synchronize routed output
Shared->>Combiner: Synchronize shared output
Combiner->>Combiner: Combine outputs
end
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes
Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✨ Finishing touches
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⚠️ Outside diff range comments (1)
tensorrt_llm/_torch/models/modeling_qwen3_next.py (1)
373-432: Critical bug: Missingself.aux_streamassignment.The
aux_streamparameter is passed to__init__(line 376) and forwarded tocreate_moe(line 407), but is never stored asself.aux_stream. However, line 481 in theforwardmethod referencesself.aux_stream, which will raise anAttributeErrorat runtime.Apply this diff to fix the issue:
def __init__( self, model_config: ModelConfig[Qwen3NextConfig], aux_stream: torch.cuda.Stream, layer_idx: Optional[int] = None, ): super().__init__() config = model_config.pretrained_config self.model_config = model_config + self.aux_stream = aux_stream self.hidden_dim = config.hidden_size
🧹 Nitpick comments (1)
tensorrt_llm/_torch/models/modeling_qwen3_next.py (1)
1138-1138: Consider removing commented-out code.The commented
has_tplines are explained by the adjacent comments about fusion kernel limitations. However, if this code will not be re-enabled soon, consider removing it to reduce clutter.Also applies to: 1297-1297
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📒 Files selected for processing (2)
tensorrt_llm/_torch/models/modeling_qwen3_next.py(6 hunks)tensorrt_llm/_torch/modules/fla/chunk.py(0 hunks)
💤 Files with no reviewable changes (1)
- tensorrt_llm/_torch/modules/fla/chunk.py
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🧠 Learnings (5)
📓 Common learnings
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.
Learnt from: jhaotingc
Repo: NVIDIA/TensorRT-LLM PR: 7856
File: cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp:159-166
Timestamp: 2025-09-19T21:28:13.751Z
Learning: In TensorRT-LLM blockScaleMoe routing (cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.cu), the DeepSeek routing method performs reinterpret_cast<float*>(routingLogits) at line 89, which could cause issues if routing_logits are BF16. However, Qwen3-FP8 models use RenormalizeNaive routing method and are not affected by this dtype casting issue.
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.
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.
📚 Learning: 2025-09-19T21:28:13.751Z
Learnt from: jhaotingc
Repo: NVIDIA/TensorRT-LLM PR: 7856
File: cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp:159-166
Timestamp: 2025-09-19T21:28:13.751Z
Learning: In TensorRT-LLM blockScaleMoe routing (cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.cu), the DeepSeek routing method performs reinterpret_cast<float*>(routingLogits) at line 89, which could cause issues if routing_logits are BF16. However, Qwen3-FP8 models use RenormalizeNaive routing method and are not affected by this dtype casting issue.
Applied to files:
tensorrt_llm/_torch/models/modeling_qwen3_next.py
📚 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/models/modeling_qwen3_next.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:
tensorrt_llm/_torch/models/modeling_qwen3_next.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/models/modeling_qwen3_next.py
🧬 Code graph analysis (1)
tensorrt_llm/_torch/models/modeling_qwen3_next.py (1)
tensorrt_llm/_torch/modules/multi_stream_utils.py (1)
maybe_execute_in_parallel(35-74)
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🔇 Additional comments (3)
tensorrt_llm/_torch/models/modeling_qwen3_next.py (3)
53-53: LGTM - Imports support parallel MoE execution.The new imports are used correctly to implement parallel routing and shared expert computation.
Also applies to: 56-56
459-482: Well-structured parallel MoE execution pattern.The refactoring successfully parallelizes routed and shared expert computation using closure functions and event-based synchronization. The logic correctly preserves the original behavior: routed experts and shared experts are computed in parallel, then combined.
Note: This depends on fixing the missing
self.aux_streamassignment flagged above.
1079-1083: LGTM - Cleaner dispatch logic.The simplified conditional correctly dispatches to
forward_extendwhen there are prefills andforward_decodeotherwise. This is more readable than the previous branching approach.
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Summary by CodeRabbit
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