[Bugfix][CI] fix typos (#34934)

Signed-off-by: 1195343015 <1195343015@qq.com>
Signed-off-by: Jiayi Yan <66017932+1195343015@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

docs/design/cuda_graphs.md

@@ -98,7 +98,7 @@ The goal of this structure is to uniquely identify a (padded) batch with minimal
 
 ### `CudagraphDispatcher`
 
-The [CudagraphDispatcher][vllm.v1.cudagraph_dispatcher.CudagraphDispatcher] takes responsibility for maintaining two sets of valid dispatching keys, one set for `FULL` runtime mode and one set for `PIECEWISE` runtime mode, and dispatches the correct runtime mode and the dispatching keys before executing the model's forwards. It will take in the initial key (a rough batch_descriptor for the padded input) and return the selected runtime mode and the final batch_descriptor, then tell the CUDAGraphWarpper instances that decision through forward contexts. Notice that `CudagraphDispatcher` is the only source of truth for available CUDA Graph keys and `CUDAGraphWrapper` instances can blindly trust the forward context on what CUDA Graphs to dispatch to. This lets us simplify the wrapper code and centralize the logic in the dispatcher.
+The [CudagraphDispatcher][vllm.v1.cudagraph_dispatcher.CudagraphDispatcher] takes responsibility for maintaining two sets of valid dispatching keys, one set for `FULL` runtime mode and one set for `PIECEWISE` runtime mode, and dispatches the correct runtime mode and the dispatching keys before executing the model's forwards. It will take in the initial key (a rough batch_descriptor for the padded input) and return the selected runtime mode and the final batch_descriptor, then tell the CUDAGraphWrapper instances that decision through forward contexts. Notice that `CudagraphDispatcher` is the only source of truth for available CUDA Graph keys and `CUDAGraphWrapper` instances can blindly trust the forward context on what CUDA Graphs to dispatch to. This lets us simplify the wrapper code and centralize the logic in the dispatcher.
 
 The dispatching keys are initialized through the dispatcher's `initialize_cudagraph_keys` method, which is called by the gpu_model_runner after all possible attention backends are initialized. This is where we can get much fancier in the future and “prepare” all kinds of CUDA Graphs combinations. For now, we just append available keys based on the valid combos of `decode_mode`/`mixed_mode` of `cudagraph_mode` and `cudagraph_capture_sizes` in the compilation config.
 

docs/design/fused_moe_modular_kernel.md

@@ -47,7 +47,7 @@ The TopK Weight Application and Reduction components happen right after the Unpe
 Please find the implementations of TopKWeightAndReduce [here](../../vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py).
 
 `FusedMoEPrepareAndFinalizeModular::finalize()` method accepts a `TopKWeightAndReduce` argument that is invoked inside the method.
-The `FusedMoEModularKernel` acts as a bridge between the `FusedMoEExpertsModular` and `FusedMoEPerpareAndFinalize` implementations to determine where the TopK Weight Application and Reduction happens.
+The `FusedMoEModularKernel` acts as a bridge between the `FusedMoEExpertsModular` and `FusedMoEPrepareAndFinalize` implementations to determine where the TopK Weight Application and Reduction happens.
 
 * `FusedMoEExpertsModular::finalize_weight_and_reduce_impl` method returns `TopKWeightAndReduceNoOp` if the `FusedMoEExpertsModular` implementation does the weight application and reduction itself.
 * `FusedMoEExpertsModular::finalize_weight_and_reduce_impl` method returns `TopKWeightAndReduceContiguous` / `TopKWeightAndReduceNaiveBatched` / `TopKWeightAndReduceDelegate` if the `FusedMoEExpertsModular` implementation needs the `FusedMoEPrepareAndFinalizeModular::finalize()` to do the weight application and reduction.

docs/design/logits_processors.md

@@ -352,7 +352,7 @@ The `BatchUpdate` abstraction models the persistent batch as a list of requests,
         (s, d, UNIDIRECTIONAL or SWAP)
         ```
 
-    * If the Move specifies `UNIDRECTIONAL`:
+    * If the Move specifies `UNIDIRECTIONAL`:
 
         * The request at index `s` is moved to index `d`; index `s` becomes an empty slot
 

docs/design/plugin_system.md

@@ -141,7 +141,7 @@ Every plugin has three parts:
     - triton ops
       Custom way doesn't work for triton ops now.
 
-7. (optional) Implement other plugable modules, such as lora, graph backend, quantization, mamba attention backend, etc.
+7. (optional) Implement other pluggable modules, such as lora, graph backend, quantization, mamba attention backend, etc.
 
 ## Compatibility Guarantee
 

docs/models/pooling_models.md

@@ -641,7 +641,7 @@ Then you obtain the sparse embeddings like this:
 curl -s http://localhost:8000/pooling -H "Content-Type: application/json" -d '{
      "model": "BAAI/bge-m3",
      "task": "token_classify",
-     "input": ["What is BGE M3?", "Defination of BM25"]
+     "input": ["What is BGE M3?", "Definition of BM25"]
 }'
 ```
 
@@ -657,7 +657,7 @@ You can obtain the colbert embeddings like this:
 curl -s http://localhost:8000/pooling -H "Content-Type: application/json" -d '{
      "model": "BAAI/bge-m3",
      "task": "token_embed",
-     "input": ["What is BGE M3?", "Defination of BM25"]
+     "input": ["What is BGE M3?", "Definition of BM25"]
 }'
 ```