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Motivation

Accuracy went to 0 for flashinfer cutlass MoE after recent MoE refactor and improvements.

Modifications

Accuracy Test

python3 -m sglang.launch_server --model-path nvidia/DeepSeek-R1-0528-FP4 --trust-remote-code --quantization modelopt_fp4 --tp 8 --enable-flashinfer-cutlass-moe --enable-ep-moe --ep-size 8
python3 benchmark/gsm8k/bench_sglang.py --num-shots 8 --num-questions 1319 --parallel 1319 --port=30000
Accuracy: 0.961
Invalid: 0.000
Latency: 23.725 s
Output throughput: 6143.771 token/s

Benchmark & Profiling

Checklist

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Summary of Changes

Hello @trevor-m, 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 provides crucial bug fixes for the FlashInfer Cutlass MoE implementation, which was experiencing zero accuracy due to recent refactoring. The changes primarily involve refining the expert mapping logic for this specific MoE type and introducing a compatibility adjustment for expert and tensor parallelism configurations to ensure correct operation.

Highlights

  • FlashInfer Cutlass MoE Fix: I've addressed a critical bug where FlashInfer Cutlass MoE was causing accuracy to drop to zero after a recent MoE refactor. This involved correcting the handling of expert_map_gpu for this specific MoE path, as it was found not to require mapping top-k IDs using the GPU expert map.
  • Expert Parallelism (EP) and Tensor Parallelism (TP) Compatibility: I've implemented a temporary measure to handle the current lack of support for hybrid EP/TP configurations with FlashInfer Cutlass MoE. When both FlashInfer Cutlass MoE and EP MoE are enabled, the expert parallel size (ep_size) is now automatically adjusted to match the tensor parallel size (tp_size), with a warning logged to indicate this adjustment.
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@zhyncs zhyncs added bug Something isn't working high priority labels Jul 31, 2025
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Code Review

This pull request introduces a bugfix for the flashinfer cutlass MoE implementation, which was causing accuracy issues after a recent refactoring. The changes are well-targeted and address two specific issues:

  1. In python/sglang/srt/layers/moe/fused_moe_triton/layer.py, the code now correctly avoids mapping topkid IDs on the GPU when using the flashinfer cutlass MoE path, which is an unnecessary and incorrect operation for this specific kernel.
  2. In python/sglang/srt/server_args.py, a check is added to enforce that the expert parallelism size (ep_size) is equal to the tensor parallelism size (tp_size) when flashinfer cutlass MoE is enabled. This prevents an unsupported hybrid EP/TP configuration.

The fixes appear correct and are consistent with the existing codebase. I have one suggestion to improve the clarity of a warning message for the user.

Comment on lines +441 to +444
self.ep_size = self.tp_size
logger.warning(
f"Flashinfer cutlass MoE and EP MoE are enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
)
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medium

The warning message informs the user about the automatic adjustment. It could be more informative by explaining why the adjustment is necessary. The reason, as mentioned in the PR description, is that hybrid EP/TP is not supported for this path. Adding this context will help users understand the constraint better.

                logger.warning(
                    f"Flashinfer cutlass MoE and EP MoE are enabled. Forcing ep_size=tp_size ({self.tp_size}) because hybrid EP/TP is not supported for this path."
                )

@zhyncs zhyncs merged commit b7170cc into sgl-project:main Jul 31, 2025
51 of 62 checks passed
zminglei pushed a commit to zminglei/sglang that referenced this pull request Jul 31, 2025
huangzhilin-hzl pushed a commit to huangzhilin-hzl/sglang that referenced this pull request Aug 1, 2025
TianQiLin666666 pushed a commit to TianQiLin666666/sglang that referenced this pull request Aug 1, 2025
narutolhy pushed a commit to narutolhy/sglang that referenced this pull request Aug 17, 2025
narutolhy pushed a commit to narutolhy/sglang that referenced this pull request Aug 18, 2025
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4 participants