[Bugfix] Fix broken profile_modular_kernel.py

[x41lakazam]

Purpose

Make tests/kernels/moe/modular_kernel_tools/profile_modular_kernel.py usable again.

Explanation

The profile_modular_kernel utility has been unrunnable on main since the modular-kernel cleanup commit 8ad7285 ([Kernels] Clean up FusedMoeMethodBase and modular kernel setup, #22035). Every invocation fails with TypeError: FusedMoEKernel.apply() got an unexpected keyword argument 'w1_scale', regardless of --pf-type / --experts-type.

The fix mirrors the working call sequence in run_modular_kernel (common.py) / test_modular_kernel_combinations.py:

• Build FusedMoEQuantConfig from the per-rank weights and pass it to make_modular_kernel (the third parameter is quant_config, not weights).
• Update mk_kwargs to the current FusedMoEKernel.apply signature: add activation, drop w1_scale/w2_scale/a1_scale, cast topk_ids with prepare_finalize.topk_indices_dtype() (falling back to the input dtype when the backend has no preference — topk_indices_dtype() can return None).
• Clone hidden_states before the timed loop; some backends mutate the input tensor in place, which would otherwise corrupt warmup iterations 2-N.
• Wrap the profiled call in set_forward_context (required by DP-aware prepare/finalize backends, e.g. DeepEP HT/LL).
• Build num_tokens_across_dp on CPU. DPMetadata indexes it on the host; a CUDA tensor would trigger a device-to-host sync inside the timed region and skew profiler measurements.
• Call init_workspace_manager(...) once per rank (normally done by GPUModelRunner); without it the kernels hit WorkspaceManager not initialized inside _allocate_buffers.
• Pass device positionally to torch.accelerator.synchronize to match call sites elsewhere in vLLM.

The measurement loop (5 warmups + 1 profiled apply + chrome trace export) is unchanged.

Duplicate-PR check

No other open PR addresses profile_modular_kernel.py:

gh pr list --repo vllm-project/vllm --state open --search "profile_modular_kernel"
# returns only this PR.

Test plan

CI smoke test (added in this PR)

tests/kernels/moe/test_profile_modular_kernel.py invokes profile_modular_kernel.run(config) with the simplest universally-supported combination (world_size=1, MoEPrepareAndFinalizeNoDPEPModular + TritonExperts, bf16, tiny shapes), then asserts a chrome trace was emitted. Runs in ~25 s, requires only a single CUDA device. Verified to fail on the unfixed harness (with the 'w1_scale' TypeError) and pass on this branch.

$ python -m pytest tests/kernels/moe/test_profile_modular_kernel.py -v
tests/kernels/moe/test_profile_modular_kernel.py::test_profile_modular_kernel_smoke PASSED
======================= 1 passed, 22 warnings in 23.45s ========================

Manual before/after smoke (single-rank, GB200)

Same invocation, run once with the unchanged file from origin/main and once with the patched file. Single GPU, single rank, no EP.

python -m tests.kernels.moe.modular_kernel_tools.profile_modular_kernel \
  --world-size 1 \
  --pf-type MoEPrepareAndFinalizeNoDPEPModular \
  --experts-type TritonExperts \
  --torch-trace-dir-path /tmp/traces

Before (origin/main):

Running m=64, topk=4 ...
TypeError: FusedMoEKernel.apply() got an unexpected keyword argument 'w1_scale'
Running m=64, topk=1 ...
TypeError: FusedMoEKernel.apply() got an unexpected keyword argument 'w1_scale'
EXIT CODE: 1
TRACES PRODUCED: 0

After (this PR):

Running m=64, topk=4 ...
Running m=64, topk=1 ...
EXIT CODE: 0
TRACES PRODUCED: 1   # m64_0_trace.json, 285 KB

(Both m=64 configs share an output filename — pre-existing TODO (varun): Add a descriptive trace file name. Not addressed in this PR.)

Wider combination matrix

The same fix has been exercised across multi-rank combinations:

• NoEP / DeepEP-HT / DeepEP-LL prepare/finalize backends
• Triton / DeepGEMM / CUTLASS / Batched-Triton / Batched-DeepGEMM / Naive-Batched / Triton-or-DeepGEMM expert kernels
• bf16 / fp8 per-tensor / fp8 block-quant quantization

python -m tests.kernels.moe.modular_kernel_tools.profile_modular_kernel \
  --pf-type <PF> --experts-type <EXPERTS> --quant-dtype <DTYPE> \
  --torch-trace-dir-path /tmp/traces

AI assistance disclosure

This PR was developed with few AI assistance. All changed lines have been reviewed and tested by me

[github-actions]

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[gemini-code-assist]

Code Review

This pull request updates the MoE modular kernel profiling script to incorporate FusedMoEQuantConfig, set_forward_context, and init_workspace_manager, while also ensuring compatibility with PyTorch 2.8+ by modifying torch.accelerator.synchronize calls. Reviewers identified a potential TypeError when casting topk_ids if the backend returns no specific dtype and recommended moving num_tokens_across_dp to the CPU to prevent unnecessary device-to-host synchronizations that could skew profiling results.

[x41lakazam]

@tlrmchlsmth @mgoin - could one of you apply the verified label so CI can run on this small bugfix? It's my first vllm pr

[mgoin]

Thanks! @bnellnm should we add a smoke test in CI for this utility?

[bnellnm]

LGTM

[bnellnm]

Thanks! @bnellnm should we add a smoke test in CI for this utility?

A smoke test would probably be good.

[x41lakazam]

@bnellnm @mgoin Added smoke test to CI

[x41lakazam]

@mgoin can you check ?

[x41lakazam]

@mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye @AndreasKaratzas

[mgoin]

Sorry for delay, thanks 😊