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[WideEP] Remove pplx all2all backend (#33724)

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Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Tyler Michael Smith
2026-02-26 17:30:10 -05:00
committed by GitHub
parent 0f2f24c8b2
commit eb19955c37
39 changed files with 107 additions and 2069 deletions
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5
docs/design/moe_kernel_features.md
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@@ -33,7 +33,6 @@ th {
| Backend | Output act. format | Quant. types | Quant. format | Async | Apply Weight On Input | Subclass |
|---------|--------------------|--------------|---------------|-------|-----------------------|-----------|
| naive | standard | all<sup>1</sup> | G,A,T | N | <sup>6</sup> | [layer.py][vllm.model_executor.layers.fused_moe.layer.FusedMoE] |
| pplx | batched | fp8,int8 | G,A,T | Y | Y | [`PplxPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.pplx_prepare_finalize.PplxPrepareAndFinalize] |
| deepep_high_throughput | standard | fp8 | G(128),A,T<sup>2</sup> | Y | Y | [`DeepEPHTPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize.DeepEPHTPrepareAndFinalize] |
| deepep_low_latency | batched | fp8 | G(128),A,T<sup>3</sup> | Y | Y | [`DeepEPLLPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize.DeepEPLLPrepareAndFinalize] |
| flashinfer_all2allv | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferA2APrepareAndFinalize`][vllm.model_executor.layers.fused_moe.flashinfer_a2a_prepare_finalize.FlashInferA2APrepareAndFinalize] |
@@ -68,7 +67,7 @@ Modular kernels are supported by the following `FusedMoEMethodBase` classes.
There are a number of MoE experts kernel implementations for different quantization types and architectures. Most follow the general API of the base Triton [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts] function. Many have modular kernel adapters, so they can be used with compatible all2all backends. This table lists each experts kernel and its particular properties.
Each kernel must be provided with one of the supported input activation formats. Some flavors of kernels support both standard and batched formats through different entry points, e.g. `TritonExperts` and `BatchedTritonExperts`. Batched format kernels are currently only needed for matching with certain all2all backends, e.g. `pplx` and `DeepEPLLPrepareAndFinalize`.
Each kernel must be provided with one of the supported input activation formats. Some flavors of kernels support both standard and batched formats through different entry points, e.g. `TritonExperts` and `BatchedTritonExperts`. Batched format kernels are currently only needed for matching with certain all2all backends, e.g. `DeepEPLLPrepareAndFinalize`.
Similar to the backend kernels, each experts kernel only supports certain quantization formats. For non-modular experts, the activations will be in the original type and quantized internally by the kernel. Modular experts will expect the activations to already be in the quantized format. Both types of experts will yield outputs in the original activation type.
@@ -110,5 +109,5 @@ The following table shows "families" of modular kernels that are intended to wor
| backend | `FusedMoEPrepareAndFinalize` subclasses | `FusedMoEPermuteExpertsUnpermute` subclasses |
|---------|-----------------------------------------|----------------------------------------------|
| deepep_high_throughput | `DeepEPHTPrepareAndFinalize` | `DeepGemmExperts`,</br>`TritonExperts`,</br>`TritonOrDeepGemmExperts`,</br>`CutlassExpertsFp8`, </br>`MarlinExperts` |
| deepep_low_latency,</br>pplx | `DeepEPLLPrepareAndFinalize`,</br>`PplxPrepareAndFinalize` | `BatchedDeepGemmExperts`,</br>`BatchedTritonExperts`,</br>`CutlassBatchedExpertsFp8`,</br>`BatchedMarlinExperts` |
| deepep_low_latency | `DeepEPLLPrepareAndFinalize` | `BatchedDeepGemmExperts`,</br>`BatchedTritonExperts`,</br>`CutlassBatchedExpertsFp8`,</br>`BatchedMarlinExperts` |
| flashinfer | `FlashInferCutlassMoEPrepareAndFinalize` | `FlashInferExperts` |