[Misc] Add unit tests for MoE ModularKernel combinations + Profiling utility

[varun-sundar-rabindranath]

Purpose

The ModularKernel framework is very useful in mix-matching different PrepareFinalize objects with FusedExperts implementations. The catch is that it is hard to test the various combinations of these operations. This PR adds a test_modular_kernel_combinations unit test, that tests various combinations in a multi-gpu (single-node) and single-gpu setting.

Design

• All core functionality is isolated is tests/kernels/moe/modular_kernel_tools
• tests/kernels/moe/modular_kernel_tools/mk_objects.py defines all high-level collections, like all prepare-finalize types, all fused experts types and, all quant configs
• tests/kernels/moe/modular_kernel_tools/common.py defines all high-level utilities. Mainly the functions, make_modular_kernel and run_modular_kernel
• The main test tests/kernels/moe/test_modular_kernel_combinations.py, the profiling code and the feature matrix generator code all leverage the make_modular_kernel / run_modular_kernel functions.

Restrictions

• Note that the tests only test the --data-parallel-size=2 and --tensor-parallel-size=1 case.
• The tests require pplx, deep_ep and deep_gemm packages to run. This is a harsh requirement that can be relaxed.
• The test requires atleast nvidia H100 GPUs to run.

Features

• The test script test_modular_kernel_combinations.py can be run as a standalone script to test specific PrepareAndFinalize and FusedExperts combinations.
• The PR adds a profiling utility to generate ModularKernel torch traces
• The PR adds a feature-matrix generation utility.

Profiling command example:
python3 -m tests.kernels.moe.modular_kernel_tools.profile_modular_kernel --pf-type PplxPrepareAndFinalize --experts-type BatchedTritonExperts" --torch-trace-dir-path /home/varun/code/vllm/torch_trace_files/

Feature Matrix Generation command example:
python3 -m tests.kernels.moe.modular_kernel_tools.make_feature_matrix -f feature_matrices/feature_matrix.csv

feature_matrix.csv

Test Plan

Machine : H100
pytest : test_modular_kernel_combinations.py pass locally

e2e tests:
VLLM_ALL2ALL_BACKEND="deepep_high_throughput" VLLM_USE_DEEP_GEMM=1 vllm serve Qwen/Qwen3-30B-A3B-FP8 --trust-remote-code --enable-expert-parallel --data-parallel-size 2 --port 9010

VLLM_ALL2ALL_BACKEND="pplx" vllm serve deepseek-ai/DeepSeek-V2-Lite --data-parallel-size 2 --enable-expert-parallel --port 9020 --trust-remote-code

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|↑  | 0.28|±  |0.0451|
|     |       |strict-match    |     5|exact_match|↑  | 0.27|±  |0.0446|

(Optional) Documentation Update

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[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 significantly enhances the testing infrastructure for Mixture-of-Experts (MoE) ModularKernels. It introduces a comprehensive unit test suite to validate various combinations of PrepareFinalize and FusedExperts implementations in both single-GPU and multi-GPU (single-node) environments. Additionally, it integrates a utility for generating PyTorch profiler traces, aiding performance analysis and debugging of these MoE configurations.

Highlights

• New MoE Combination Test Suite: A new file test_modular_kernel_combinations.py is added, providing extensive unit tests for different PrepareFinalize and FusedExperts combinations, including various quantization settings (FP8, block-quantized) and topk values. These tests cover both single-GPU and multi-GPU (single-node) scenarios.
• Distributed Test Utility Enhancement: The parallel_utils.py file is updated with parallel_launch_with_config, a new helper function that simplifies launching distributed tests with specific vLLM configurations and environment variables, crucial for testing MoE setups.
• MoE Initialization Refactoring: The FusedMoEMethodBase class in layer.py is refactored to introduce a static method maybe_make_prepare_finalize, centralizing the logic for creating PrepareAndFinalize objects. This simplifies the initialization process and improves modularity.
• Profiling Utility Integration: The new test script includes an option (--do-profile) to generate detailed PyTorch profiler traces for specific MoE kernel executions, enabling in-depth performance analysis.

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[gemini-code-assist]

Code Review

This pull request introduces a comprehensive test suite for MoE ModularKernel combinations, which is a valuable addition for ensuring code quality and correctness. The ability to profile different combinations is also a great feature.

I've found a couple of issues that could affect the reliability of the tests. Specifically, a hardcoded port in the parallel utilities could lead to flaky tests, and there's a potential argument swap in the weight generation logic that could cause incorrect behavior. Addressing these points will help solidify this excellent contribution.

[varun-sundar-rabindranath]

need some help in verifying if there are more quant configs we should consider. cc @robertgshaw2-redhat @tlrmchlsmth @mgoin Thanks 🙌

[robertgshaw2-redhat]

We need to leave placeholders for NVFP4 as well

[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]

cruft : set below as well

[varun-sundar-rabindranath]

match the batched case

[varun-sundar-rabindranath]

refactor : move prepare-finalize init to a staticmethod that can be invoked from the tests.

[varun-sundar-rabindranath]

verify is some combination / config is valid.

[tlrmchlsmth]

Mostly looks good once it's working smoothly -- one thing I ran to running an example from the PR description:

python3 -m tests.kernels.moe.modular_kernel_tools.profile_modular_kernel --pf-type PplxPrepareAndFinalize --experts-type "BatchedTritonExperts"

and hit the following assert:

(EngineCore_1 pid=835) AssertionError: with expert map, -1 id is used for
(EngineCore_1 pid=835)             non-local token; this causes error when casting ids to the
(EngineCore_1 pid=835)             topk_indices_dtype() uint32

...which looks like a good assert to me. Expert maps + pplx kernels shouldn't be combined IMO

[varun-sundar-rabindranath]

Thanks @tlrmchlsmth . The error #20714 should fix it. I have noticed that the expert_map isn't actually being used in any of the implementations. We can remove that argument altogether.