autotuner: use torch.ones for e_score_correction_bias when ep_size==1

[lishicheng1996]

When there is no expert parallelism (local_num_experts == num_experts), use torch.ones instead of torch.randn for the dummy e_score_correction_bias tensor in prepare_dummy_topk_and_hook for DeepSeekV3 routing.

Summary by CodeRabbit

• New Features
  • Added support for specifying a local number of experts in MoE generation, enabling dummy top‑k and routing behavior to adapt to local vs. global expert counts.
  • Introduced deterministic routing bias when local experts match global experts, improving consistency; preserves prior randomized behavior otherwise.
  • Ensures all MoE entry points leverage the new setting for consistent behavior across configurations.

Description

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[coderabbitai]

📝 Walkthrough

Walkthrough

Adds an optional local_num_experts parameter to prepare_dummy_topk_and_hook() function. This parameter enables conditional routing bias generation for DeepSeekV3: when local_num_experts equals num_experts, a ones tensor is used instead of random values. The parameter is propagated through all Moe runner call sites.

Changes

Cohort / File(s)	Summary
MoE Custom Operations tensorrt_llm/_torch/custom_ops/trtllm_gen_custom_ops.py	Added optional local_num_experts parameter to prepare_dummy_topk_and_hook(). Modified DeepSeekV3 routing bias generation to use ones tensor when local_num_experts == num_experts, otherwise preserve random behavior. Updated all Moe runner entry points to pass local_num_experts.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

🚥 Pre-merge checks | ✅ 1 | ❌ 2

❌ Failed checks (1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 14.29% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.
Description check	❓ Inconclusive	PR description is minimal and lacks required sections; it only states the code change without explaining the issue, solution rationale, or test coverage.	Complete the Description and Test Coverage sections to explain why this change is needed, what problem it solves, and which tests validate the fix.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title is specific and directly related to the main change: using torch.ones instead of torch.randn for e_score_correction_bias when ep_size==1.

_{✏️ Tip: You can configure your own custom pre-merge checks in the settings.}

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