[Model]Force use triton compressed_tensor_moe instead of cutlass

[access2rohit]

… this improves performance for llama4

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

This PR is to improve performance of LLama 4 by force use triton based compressed tensor MOE kernel with the help of a flagh. Changed the following files:

envs.py: added environment varaibles
compressed_tensors_moe.py: logic to force override triton kernel 

Test Plan

an with a uploaded scout based eagle to test E2E
Example cmd

CUDA_VISIBLE_DEVICES=4,5,6,7 VLLM_USE_V1=1 python examples/offline_inference/spec_decode.py --num_spec_tokens 7 --num_prompts 1 --method eagle --model_dir /home/$USER/local/models/scout_base_HF_20250605_201140 --eagle_dir /home/$USER/local/models/scout_draft_HF_20250605_202942 --tp 4

unit test: python -m pytest tests/v1/e2e/test_spec_decode.py

vllm serve + benchmarking
EAGLE server cmd

#!/bin/bash

Configuration of environment variables

export CUDA_VISIBLE_DEVICES=4,5,6,7
export VLLM_USE_V1=1

Command to run the vllm server

spec_dec_config='{"method": "eagle", "model": "/home/$USER/local/models/scout_draft_HF_20250605_202942", "prefill_token_shift": false, "num_speculative_tokens": 3, "draft_tensor_parallel_size": 4, "max_model_len": 32768}'
vllm serve /home/$USER/local/models/scout_base_HF_20250605_201140 --disable-log-requests
-tp 4
--max-num-seqs 128
--max_num_batched_tokens=80000
--max-model-len=32768
--no-enable-prefix-caching
--trust-remote-code
--speculative-config="$spec_dec_config"
--num-lookahead-slots=3
2>&1 | tee /data/users/$USER/logs/server/vllm_17b16e_vllm_serving$(date +%Y%m%d_%H%M%S).log

base cmd = eagle server cmd, removing --speculative-config="$spec_dec_config" \

benchmarking

python benchmarks/benchmark_serving.py --backend vllm --model /home/$USER/local/models/scout_base_HF_20250605_201140 --dataset-name hf --dataset-path philschmid/mt-bench --seed 0 --max-concurrency 16 2>&1 | tee /data/users/$USER/tmp/vllm_17b16e_vllm_loadgen$(date +%Y%m%d_%H%M%S).log\n

Test Result

[WIP]

(Optional) Documentation Update

[gemini-code-assist]

Code Review

This pull request introduces a new environment variable, VLLM_TRITON_COMPRESSED_TENSORS_MOE_KERNEL, to allow forcing the use of the Triton-based kernel for compressed tensor Mixture of Experts (MoE) layers. This is intended to improve performance for certain models. The implementation correctly adds the environment variable and uses it to control the kernel selection logic. My main feedback concerns the robustness of parsing this new environment variable, as the current method can lead to a ValueError if an invalid string is provided, causing a crash. I've suggested a safer parsing method to prevent this.

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[yewentao256]

Thanks for the work!
Could you show more data about performance improvement? Not sure if this is actually needed

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @access2rohit.

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

[chenxi-yang]

Hi @access2rohit , I had a similar purpose PR here: #23442
May I ask why you want to use fused moe? Did you see performance gain compared with cutlass? Thank you!

[github-actions]

This pull request has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this pull request should remain open. Thank you!

[access2rohit]

This change is no longer needed since vllm automatically defaults to triton instead of cutlass. Perhaps the next stage could be to simply run perf benchamrks to see if triton still outperforms cutlass or not

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @access2rohit.

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

[hmellor]

Thanks for the contribution! As you noted, this change is no longer needed since vLLM now automatically defaults to triton instead of cutlass. We're closing this PR accordingly. Thank you for your work on improving Llama 4 performance!