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[Doc]: fix typos in various files (#28567)

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Signed-off-by: Didier Durand <durand.didier@gmail.com>
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Didier Durand
2025-11-15 20:27:50 +01:00
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docs/design/moe_kernel_features.md
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@@ -68,7 +68,7 @@ Modular kernels are supported by the following `FusedMoEMethodBase` classes.
## Fused MoE Experts Kernels
The 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 adatpers so they can be used with compatible all2all backends. This table lists each experts kernel and its particular properties.
The 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`, `DeepEPLLPrepareAndFinalize`.