[Docs] Fix warnings in `mkdocs build` (continued) (#23743)

Signed-off-by: Zerohertz <ohg3417@gmail.com>
Signed-off-by: Hyogeun Oh (오효근) <ohg3417@gmail.com>

vllm/v1/structured_output/backend_types.py

@@ -110,7 +110,7 @@ class StructuredOutputBackend(ABC):
 
         Args:
             request_type (StructuredOutputOptions): The type of structured
-              output request.
+                output request.
             grammar_spec (str): The grammar specification to compile.
 
         Returns:
@@ -124,7 +124,7 @@ class StructuredOutputBackend(ABC):
 
         Args:
             max_num_seqs (int): The maximum number of sequences for which
-              to allocate the bitmask.
+                to allocate the bitmask.
         """
 
     @abstractmethod

vllm/v1/worker/gpu_input_batch.py

@@ -525,9 +525,6 @@ class InputBatch:
         Any consecutive empty indices at the very end of the list are not
         filled.
 
-        Args:
-          empty_req_indices: empty indices which may be filled.
-
         Returns:
           swaps: list of (from,to) swap tuples for moved requests
           empty_req_indices: indices not filled by condensation

vllm/v1/worker/gpu_model_runner.py

@@ -2955,7 +2955,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         Args:
             kv_cache_config: The KV cache config
             kv_cache_raw_tensors: The KV cache buffer of each layer, with
-            correct size but uninitialized shape.
+                correct size but uninitialized shape.
         Returns:
             Dict[str, torch.Tensor]: A map between layer names to their
             corresponding memory buffer for KV cache.

vllm/v1/worker/tpu_model_runner.py

@@ -552,7 +552,7 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         return kv_cache_spec
 
     def _get_slot_mapping_metadata(self, num_reqs,
-                                   num_scheduled_tokens_per_req):
+                                   num_scheduled_tokens_per_req) -> np.ndarray:
         """
         Computes metadata for mapping slots to blocks in the key-value (KV)
         cache for a batch of requests.
@@ -565,15 +565,15 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         Args:
             num_reqs (int): Number of requests in the current batch.
             num_scheduled_tokens_per_req (int or np.ndarray): Number of tokens
-            to be scheduled for each request.
+                to be scheduled for each request.
 
         Returns:
             np.ndarray: A 2D array of shape (total_block_len, 3), where each row
-            contains:
+                contains:
                 - kv_cache_start_index (int): The starting index in the KV cache
-                    for the corresponding slice.
+                  for the corresponding slice.
                 - new_kv_start_index (int): The starting index in the new KV
-                    cache for the corresponding slice.
+                  cache for the corresponding slice.
                 - slice_len (int): The length of the slice.
         """
         slices_start = self.input_batch.num_computed_tokens_cpu[:num_reqs]

vllm/v1/worker/utils.py

@@ -172,10 +172,10 @@ def scatter_mm_placeholders(
 
     Args:
         embeds: The multimodal embeddings.
-          Shape: `(num_embeds, embed_dim)`
+            Shape: `(num_embeds, embed_dim)`
         is_embed: A boolean mask indicating which positions in the placeholder
-          tokens need to be filled with multimodal embeddings.
-          Shape: `(num_placeholders, num_embeds)`
+            tokens need to be filled with multimodal embeddings.
+            Shape: `(num_placeholders, num_embeds)`
     """
     if is_embed is None:
         return embeds
@@ -278,7 +278,7 @@ def bind_kv_cache(
     Args:
         kv_caches: The allocated kv_caches with layer names as keys.
         forward_context: The global forward context containing all Attention
-        layers with layer names as keys.
+            layers with layer names as keys.
         runner_kv_caches: The kv_cache declared by ModelRunner.
     """
     # Bind kv_caches to ModelRunner

vllm/v1/worker/worker_base.py

@@ -36,8 +36,8 @@ class WorkerBase(WorkerBaseV0):
             local_rank: Local device index
             rank: Global rank in distributed setup
             distributed_init_method: Distributed initialization method
-            is_driver_worker: Whether this worker handles driver 
-            responsibilities
+            is_driver_worker: Whether this worker handles driver
+                responsibilities
         """
         # Configuration storage
         super().__init__(vllm_config=vllm_config)