[Misc] ModularKernel : Perform WeightAndReduce inside TritonExperts & DeepGemmExperts

[varun-sundar-rabindranath]

Purpose

Perform weight-application and reduction inside the TritonExperts and DeepGemmExperts. This helps save memory. For example please refer to #20228

Changes:

• Add topk_weights and apply_router_weight_on_input args to FusedMoEPermuteExpertsUnpermute::apply functions - so the implementations can perform topk-weight application if they wish to.
• Adjust workspace reuse in TritonExperts and DeepGemmExperts to accommodate weight-application and reduction.

Test Plan

pytest : pytest -s tests/kernels/moe/test_modular_kernel_combinations.py

e2e tests:
Using TritonOrDeepGemmExperts

 VLLM_ALL2ALL_BACKEND="deepep_high_throughput" VLLM_USE_DEEP_GEMM=1  canhazgpu run -g 2 --  vllm serve Qwen/Qwen3-30B-A3B-FP8  --trust-remote-code --enable-expert-parallel --data-parallel-size 2 --port 9010

Using only TritonExperts

 VLLM_ALL2ALL_BACKEND="deepep_high_throughput" VLLM_USE_DEEP_GEMM=0  canhazgpu run -g 2 --  vllm serve Qwen/Qwen3-30B-A3B-FP8  --trust-remote-code --enable-expert-parallel --data-parallel-size 2 --port 9010

lm-eval command : lm_eval --model local-completions --tasks gsm8k --model_args model=Qwen/Qwen3-30B-A3B-FP8,base_url=http://127.0.0.1:9010/v1/completions,num_concurrent=30,max_retries=3 --limit 100

Test Result

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  | 0.86|±  |0.0349|
|     |       |strict-match    |     5|exact_match|↑  | 0.92|±  |0.0273|

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  | 0.80|±  |0.0402|
|     |       |strict-match    |     5|exact_match|↑  | 0.91|±  |0.0288|

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @varun-sundar-rabindranath.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[gemini-code-assist]

Summary of Changes

Hello @varun-sundar-rabindranath, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the FusedMoEPermuteExpertsUnpermute module by enabling weight application and reduction within the fused operation. This is achieved through the introduction of the TopKWeightAndReduce abstraction, which allows implementations to specify how the finalize() method should behave. The PR also standardizes weight application and reduction implementations, improving code organization and maintainability.

Highlights

• MoE Reduction: Adds the ability to perform MoE reduction within the FusedMoEPermuteExpertsUnpermute operation, allowing for memory footprint reduction.
• TopKWeightAndReduce Abstraction: Introduces the TopKWeightAndReduce abstraction to standardize weight application and reduction implementations, providing flexibility in the finalize() method.
• Standardization: Consolidates all weight-application and reduction implementations into a single location for better maintainability.

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[gemini-code-assist]

Code Review

This pull request introduces a significant and well-designed refactoring to make MoE kernels more modular. By abstracting the weight application and reduction logic into a WeightAndReduce class, it allows different FusedMoEPermuteExpertsUnpermute implementations to either perform this step themselves or delegate it to the finalize stage. This is a great improvement for code clarity, reusability, and will help in reducing memory footprint as intended.

The changes are well-implemented across the affected files. My feedback focuses on a few areas where code can be made more concise and consistent with the established API contracts. These are minor points in an otherwise excellent PR.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @varun-sundar-rabindranath.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[varun-sundar-rabindranath]

rearrage how workspaces are used to make space for perm_out - note that perm_out cannot use workspace13 as workspace13 may be used as the output tensor (

vllm/vllm/model_executor/layers/fused_moe/modular_kernel.py

Line 486 in 5923ab9

fused_out = _resize_cache(workspace13, fused_out_shape)

)

[varun-sundar-rabindranath]

rearrage how workspaces are used to make space for intermediate_cache3 - note that intermediate_cache3 cannot use workspace13 as workspace13 may be used as the output tensor

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @varun-sundar-rabindranath.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[tlrmchlsmth]

Confirm that without this PR, I cannot run a full sequence length DeepSeekV3 across 16 H200s and with it I see:

GPU KV cache size: 236,736 tokens