[None][fix] Avoid write-write race for async pp send. #10488
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Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
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tensorrt_llm/_torch/distributed/communicator.py (1)
828-828: Remove unusedtensor_ready_eventattribute.After replacing event-based synchronization with stream-based synchronization at lines 841-842,
tensor_ready_eventis no longer referenced anywhere in the class. This attribute should be removed to eliminate dead code.🔎 Proposed fix
- self.tensor_ready_event = torch.cuda.Event() self.send_stream = torch.cuda.Stream()
🤖 Fix all issues with AI agents
In @tensorrt_llm/_torch/distributed/communicator.py:
- Around line 841-842: Add an inline comment immediately before the
tensor.clone() call in the send path (the block that uses
self.send_stream.wait_stream(torch.cuda.current_stream())) explaining that the
clone creates an independent copy to avoid write-write races: the async NCCL
send operates on the cloned tensor in the dedicated send_stream so the caller
can safely modify the original tensor while the send is in progress. This
comment should reference tensor.clone() and the
send_stream.wait_stream(torch.cuda.current_stream()) call so future readers
understand why the copy is required.
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**/*.py
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Files:
tensorrt_llm/_torch/distributed/communicator.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/distributed/communicator.py
🧠 Learnings (2)
📓 Common learnings
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.
Learnt from: pcastonguay
Repo: NVIDIA/TensorRT-LLM PR: 7455
File: tensorrt_llm/_torch/pyexecutor/py_executor.py:1852-1860
Timestamp: 2025-09-02T13:42:44.885Z
Learning: In MPI communication within TensorRT-LLM pipeline parallelism, different communication types (tokens, logits, termination sync) must use disjoint tag namespaces to avoid message routing collisions when using the same source/destination patterns.
📚 Learning: 2025-09-02T13:42:44.885Z
Learnt from: pcastonguay
Repo: NVIDIA/TensorRT-LLM PR: 7455
File: tensorrt_llm/_torch/pyexecutor/py_executor.py:1852-1860
Timestamp: 2025-09-02T13:42:44.885Z
Learning: In MPI communication within TensorRT-LLM pipeline parallelism, different communication types (tokens, logits, termination sync) must use disjoint tag namespaces to avoid message routing collisions when using the same source/destination patterns.
Applied to files:
tensorrt_llm/_torch/distributed/communicator.py
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