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• generate_num_tokens_per_expert caps rank-local assignments at num_tokens * min(top_k, num_local_experts).
• generate_token_selected_experts validates count length, per-expert bounds, and total capacity. It skips zero-count experts, samples local expert assignments independently, repairs per-token overflow while preserving counts and avoiding duplicates, and applies local_expert_offset when creating the output tensor.
• The file also reformats imports across unrelated CuteDSL components. No API declarations change. Test results were not provided.

QA Engineer Review

No test changes.

Per-File QA Perspective

• tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py: Verify zero-count experts receive no assignments; output counts match requested local histograms; no token exceeds top_k or repeats an expert; offsets affect only output expert IDs; and invalid counts or unrepairable assignments raise the documented errors.

Description

Fix two issues in GroupedGemmInputsHelper.generate_token_selected_experts, which constructs token-to-expert mappings for CuteDSL MoE autotuning inputs:

• Do not assign an expert when its allocated token count is zero, preventing ghost token assignments.
• Build the synthetic routing assignment from rank-local expert counts instead of applying a global-expert completion heuristic to rank-local state:
  • Independently sample the requested number of distinct tokens for each local expert.
  • Repair any per-token overflow without changing the exact per-expert histogram or introducing duplicate expert selections.
  • Apply local_expert_offset only when materializing the final expert IDs, so the normalized layout is invariant to the EP rank offset.
  • Cap the local assignment count at num_tokens * min(top_k, num_local_experts).

The previous prioritization condition assumes that routing state from all earlier global experts is available. Under expert parallelism, the helper starts from empty rank-local state and only iterates the current shard, so substituting a global expert index does not restore that missing state and instead makes the generated layout depend on the shard offset.

Fixes #14146

Test Coverage

No test-file changes are included in this PR.

Validated with syntax, formatting, and lint checks plus randomized property checks covering the reported zero-count case, exact expert histograms, per-token uniqueness and capacity, deterministic output, unchanged global RNG state, EP offset invariance, and num_local_experts < top_k.

PR Checklist

• Please check this after reviewing the above items as appropriate for this PR.

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Walkthrough

The change caps estimated rank-local token counts and revises expert assignment generation to validate requested counts, preserve per-expert allocations, enforce the per-token top_k limit, and pad unused slots with -1.

Changes

CuteDSL expert token assignment

Layer / File(s)	Summary
Bound counts and generate assignments tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py	The rank-local token-count ceiling now accounts for both top_k and the number of local experts. Assignment generation validates counts and total capacity, samples each expert’s tokens, and repairs assignments that exceed the per-token limit. Invalid inputs raise ValueError; unsuccessful repair raises RuntimeError.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~20 minutes

Change: Bug fix

Suggested reviewers: bowenfu

Merge Risk: 🟡 Moderate · up to 14ab0

The routing fix lacks a regression test that would catch the original zero-count assignment bug. Add focused coverage before merging.

Pre-merge checks | 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check		The title clearly identifies the ticket, fix type, and main change: correcting CuteDSL MoE autotune routing inputs.
Description check		The description includes the required Description, Test Coverage, and PR Checklist sections. It explains the issues, the implementation approach, and the reported validation coverage. No test-file cha…
Linked Issues check		The change addresses both coding requirements in [#14146]. generate_token_selected_experts skips zero counts and rejects negative counts before assignment. It samples distinct tokens per local exper…
Out of Scope Changes check		The diff changes only the related expert-count cap and token-to-expert assignment logic in cute_dsl_custom_ops.py. Validation, overflow repair, and deterministic local sampling support the [#14146] …
Docstring Coverage		No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0…

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Actionable comments posted: 1

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🤖 Prompt to fix review comments

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Inline comments:
Review comments at @tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py:
- Around line 348-376: Add a CPU-only parametrized regression test in the
requested test module for the assignment generator, using one EP-offset case
with a zero-count expert and overflow repair; assert the exact histogram,
absence of zero-count and duplicate experts, valid offset IDs, and -1 padding.
Cover wrong-length, negative, out-of-range, and over-capacity counts, and test
num_local_experts < top_k through generate_num_tokens_per_expert.

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@NVIDIA/trt-llm-ci-approvers Could someone please add the ci: full pre-merge approved label? Both x86 #8840 and SBSA #7838 finished SUCCESS. Main CI #63612 is UNSTABLE because [Test-SBSA-Multi-GPU] Blocked detected that the label is absent. The rerun report also contains one unrelated ARM CPU flake in test_mpi_session_basic ([2, 2, 2, 3] once, then passed on rerun); this PR only changes tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py.

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Friendly follow-up: the current head dc6a275 is conflict-free. The x86 and SBSA pipelines completed successfully; L0 pipeline #63612 was UNSTABLE because the ci: full pre-merge approved label was missing, with the unrelated ARM test_mpi_session_basic flake passing on rerun. Could @NVIDIA/trt-llm-runtime-devs please review/approve, and could @NVIDIA/trt-llm-ci-approvers add the full pre-merge label? I will rerun /bot run once the label is present.

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