[GPTOSS][DP/EP][Marlin] Enable GPTOSS DP/EP using Marlin kernels #25488
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Code Review
This pull request enables GPT-OSS DP/EP with Marlin kernels by introducing a MarlinExperts modular kernel and integrating it into the mxfp4 quantization backend. The changes correctly refactor the MoE logic to use the modular kernel framework. However, I've found a high-severity issue in the MarlinExperts implementation where workspace management is incorrect, which could lead to performance degradation due to repeated memory allocations. I've provided a suggestion to fix this.
bnellnm
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Sep 30, 2025
bnellnm
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Sep 30, 2025
bnellnm
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Sep 30, 2025
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Looks good to me. Just had a few minor comments. Will PR interfere with #21166? |
Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
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Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
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| def _is_marlin_mxfp4_w4an(quant_config: Optional[FusedMoEQuantConfig] = None): |
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nit: call it _is_marlin_mxfp4_w4aN to make it more clear
| Given Marlin packed weight matrices w1_packed, and w2_packed, | ||
| return the MoE intermediate size N | ||
| """ | ||
| marlin_tile_size = 16 |
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What does this tile size actually correspond to? Would be good to leave a note
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…m-project#25488) Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Co-authored-by: mgoin <mgoin64@gmail.com> Signed-off-by: Tomer Asida <57313761+tomeras91@users.noreply.github.com>
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…m-project#25488) Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Co-authored-by: mgoin <mgoin64@gmail.com> Signed-off-by: Karan Goel <3261985+karan@users.noreply.github.com>
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…m-project#25488) Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Co-authored-by: mgoin <mgoin64@gmail.com>
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Purpose
Enable GPTOSS DP/EP for DeepEPHighThroughput All2All using Marlin codepath. This could be an alternative to matmul_ogs from Triton
Example serving command :
VLLM_MXFP4_USE_MARLIN=1 VLLM_ALL2ALL_BACKEND="deepep_high_throughput" canhazgpu run -g2 -- vllm serve openai/gpt-oss-120b --tensor-parallel-size 1 --data-parallel-size 2 --enable-expert-parallel --no-enable-prefix-caching --port 9010Test Plan
GPT-OSS evals
server command :
VLLM_MXFP4_USE_MARLIN=1 VLLM_ALL2ALL_BACKEND="deepep_high_throughput" vllm serve openai/gpt-oss-120b --tensor-parallel-size 1 --data-parallel-size 2 --enable-expert-parallel --no-enable-prefix-caching --port 9010command:
OPENAI_API_KEY=empty python -m gpt_oss.evals --model openai/gpt-oss-120b --eval gpqa --n-threads 128 --base-url http://localhost:9010/v1Test Result
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Benchmarks
Please find results here
TLDR;
comparing with TP : Better TTFT; Bad TPOT as deepep_high_throughput enforces eager mode
comparing with OAITritonExperts : Worse TTFT and TPOT