[None][fix] Autotune trtllm moe with same distribution across ranks

[rosenrodt]

Summary by CodeRabbit

• Improvements
  • Enhanced consistency of random number generation during profiling operations to ensure deterministic and reproducible results across all execution units and hardware platforms, providing more reliable performance analysis outcomes.

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Description

Test Coverage

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[coderabbitai[bot]]

📝 Walkthrough

Walkthrough

Adds deterministic seeding at the start of prepare_dummy_topk_and_hook function, resetting both CPU and CUDA seeds to zero to ensure consistent random distributions across EP ranks during AutoTuner profiling. No interface changes.

Changes

Cohort / File(s)	Summary
Deterministic seeding for AutoTuner profiling tensorrt_llm/_torch/custom_ops/trtllm_gen_custom_ops.py	Adds seed initialization (CPU and CUDA seeds to 0) at the start of prepare_dummy_topk_and_hook to ensure reproducible dummy tensor generation across EP ranks

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

• Single file modification with minimal scope
• Straightforward addition of deterministic seeding logic
• Verify that seed values (zero) are appropriate for the profiling context and don't inadvertently affect other parts of the system

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	PR description lacks required content - no title, issue/ticket reference, specific problem statement, solution explanation, or test coverage details provided.	Add a proper PR title following the template format (e.g., [TRTLLM-XXXX][fix] Title), describe the issue and solution in the Description section, and list relevant tests in Test Coverage section.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically describes the main change: fixing AutoTune for TensorRT-LLM MOE by ensuring consistent distribution across ranks through deterministic seeding.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

✨ Finishing touches

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[rosenrodt]

/bot run

[tensorrt-cicd]

PR_Github #25453 [ run ] triggered by Bot. Commit: 1a80cad

[tensorrt-cicd]

PR_Github #25453 [ run ] completed with state FAILURE. Commit: 1a80cad /LLM/main/L0_MergeRequest_PR pipeline #19267 completed with status: 'FAILURE'

[rosenrodt]

/bot run

[tensorrt-cicd]

PR_Github #25481 [ run ] triggered by Bot. Commit: 28ea6e4

[tensorrt-cicd]

PR_Github #25481 [ run ] completed with state SUCCESS. Commit: 28ea6e4 /LLM/main/L0_MergeRequest_PR pipeline #19295 completed with status: 'SUCCESS' Pipeline passed with automatic retried tests. Check the rerun report for details.

[hyukn]

Looks like this bug also reflects some other issues associated with the distribution across ranks https://nvbugspro.nvidia.com/bug/5680133. Maybe you will have some ideas or comments on this @rosenrodt. Thanks a lot for the help