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[Refactor] Abstract DeepGemm MegaMOE backend - #58053
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Thank you for the work. Once the comment is addressed we can merge to unblock you and I will come up with a comprehensive refactor in subsequent PRs.
| self.num_logical_experts = ( | ||
| num_logical_experts if num_logical_experts is not None else num_experts | ||
| ) | ||
| class HiddenQuantSpec(typing.NamedTuple): |
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Please use QuantKey abstraction
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Updated.
Use QuantKey in DeepGemmMegaMoEBackend and default value is kMxfp8Dynamic.
kMxfp8Dynamic is per-group quant with group size 32, match current sm100 mxfp4 mega moe default quant.
kMxfp8DynamicScale = ScaleDesc(torch.uint8, False, GroupShape(1, 32))
kMxfp8Dynamic = QuantKey(FP8_DTYPE, kMxfp8DynamicScale, symmetric=True)
passing QuantKey to prepare_megamoe_inputs, and will extract groupk and if scale is ue8m0 from QuantKey.
| ) | ||
| set_weight_attrs(self.w2_weight_scale, weight_attrs) | ||
| self.w2_weight_scale.quant_method = "block" | ||
| class DeepGemmMegaMoEBackend: |
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We should put it into a separate file under model_executor/layers/fused_moe/ and create another DeepGemmMegaMoe.py
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DeepGemmMegaMoEBackend , DeepGemmSm100MegaMoEBackend and get_deep_gemm_mega_moe_backend api are moved to new file vllm/model_executor/layers/fused_moe/deep_gemm_mega_moe.py.
Left vllm/models/deepseek_v4/xpu/model.py unchanged in this PR. It has its own MegaMoE path: it imports vllm.third_party.deep_gemm directly instead of going through vllm.utils.deep_gemm. I'm not sure it targets the same DeepGEMM build/API as the CUDA path, so I'd rather not fold it into DeepGemmMegaMoEBackend without input from the XPU maintainers.
@zyongye Thanks for your review! All comments are addressed, please help review again. |
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/ci run |
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❌ This PR is 649 commits behind upstream |
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/ci run |
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✅ Triggered Buildkite CI #92497 for commit |
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Thanks. Could you fix the DCO? |
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This pull request has merge conflicts that must be resolved before it can be |
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Co-authored-by: Hui Shi <kalinshi@tencent.com> Co-authored-by: LuneExplorer <liyihan23@mails.tsinghua.edu.cn> Co-authored-by: ArmageddonKnight <bojian@cs.toronto.edu> Co-authored-by: Claude Signed-off-by: Hui Shi <kalinshi@tencent.com>
…p8Dynamic Signed-off-by: Hui Shi <kalinshi@tencent.com>
…/fused_moe/deep_gemm_mega_moe.py Signed-off-by: Hui Shi <kalinshi@tencent.com>
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PR branch rebased with latest vllm code and fix DCO and other failures in test_deepseek_v4_mega_moe.py on sm100 1. Fix conflict with #59927 Move _finalize_shared_expert_weights change to new file vllm/model_executor/layers/fused_moe/deep_gemm_mega_moe.py 2. Fix failures in The backend refactor caused several regressions in this test file:
Could you please help review again and trigger new CI test? Thanks! |
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Hi @huishi-hs, the pre-commit checks have failed. Please run: uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-filesThen, commit the changes and push to your branch. For future commits, |
…moe.py regressions - Fix UE8M0 scale packing in prepare_megamoe_inputs to reduce over the group axis (axis=1). - Resolve the MegaMoE backend and symm buffer after EPLB mapping in DeepseekV4/KimiK3 MegaMoE experts forward, right before first use. - Pass activation/activation_alpha/activation_beta to deep_gemm only when set, so DeepSeek V4 keeps deep_gemm defaults; merge the duplicated fp8_fp4_mega_moe call paths. - Update MegaMoE tests to stub _ensure_backend instead of the removed _check_runtime_supported. Signed-off-by: Hui Shi <kalinshi@tencent.com>
Signed-off-by: Hui Shi <kalinshi@tencent.com>
@zyongye |
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/ci run |
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✅ Triggered Buildkite CI #93465 for commit |
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Thanks a lot for reviewing and approving this PR! I noticed that one CI job failed: buildkite/ci/pr/nvidia-4xb200-kimi-k3-prefix-cache-shard-1 From the Buildkite log, the job never actually started running. The pod seems to have stayed pending for more than an hour and then timed out. It looks like an infra or resource-scheduling issue on the 4xB200 runners. Could you help confirm whether this failure is a known infra issue? If the community is already working on a fix for the B200 runners, I'm happy to wait until it's resolved. When convenient, could you please re-trigger this job (or the CI) for me? I don't have permission to retry Buildkite jobs. Thanks again for your time and help! |
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/ci retry |
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✅ Queued 2 failed job(s) for retry in Buildkite CI #93465. |
Signed-off-by: Hui Shi <kalinshi@tencent.com> Co-authored-by: LuneExplorer <liyihan23@mails.tsinghua.edu.cn> Co-authored-by: ArmageddonKnight <bojian@cs.toronto.edu>
Purpose
Refactor DeepGemm MegaMoE integration behind a DeepGemmMegaMoEBackend abstraction to unblock non-SM100 backends; no behavior change on SM100. The DeepGEMM MegaMoE kernel is a very high-performance MoE implementation, and is expected to be reused by more models and hardware. For example:
This PR adds an abstract DeepGemmMegaMoEBackend so more models can enable the DeepGEMM MegaMoE kernel quickly, and so external vLLM plugins can support more scenarios flexibly.
Main changes:
Test Plan
Test Result
Run TP4+EP on hopper works correctly.
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.