[Doc]: fix typos in Python comments

tests/core/test_scheduler.py

@@ -641,7 +641,7 @@ def test_schedule_decode_blocks_to_copy_update():
     # Nothing is preempted.
     assert output.blocks_to_swap_out == []
     # Since append_slot returns the source -> dist mapping, it should
-    # applied.
+    # be applied.
     assert output.blocks_to_copy == [(2, 3)]
 
 

tests/entrypoints/openai/correctness/test_transcription_api_correctness.py

@@ -32,7 +32,7 @@ def to_bytes(y, sr):
 
 async def transcribe_audio(client, tokenizer, y, sr):
     # Send loaded audio directly instead of loading from disk,
-    # dont account for that time though
+    # don't account for that time though
     with to_bytes(y, sr) as f:
         start_time = time.perf_counter()
         transcription = await client.audio.transcriptions.create(

tests/entrypoints/openai/test_return_token_ids.py

@@ -224,7 +224,7 @@ async def test_comparison_with_prompt_logprobs_and_logprobs(server):
                 logprobs_token_ids.append(token_id)
 
         # When echo=True, the logprobs include both prompt and response tokens
-        # The token_ids field should match the the suffix of response portion
+        # The token_ids field should match the suffix of response portion
         # The prompt_token_ids should match the prompt portion
         assert len(completion.choices[0].token_ids) < len(logprobs_token_ids)
         response_token_ids_length = len(completion.choices[0].token_ids)

tests/entrypoints/openai/test_serving_chat.py

@@ -313,7 +313,7 @@ async def test_serving_chat_did_set_correct_cache_salt(model_type):
         }],
     )
 
-    # By default cache_salt in the engine prompt is not set
+    # By default, cache_salt in the engine prompt is not set
     with suppress(Exception):
         await serving_chat.create_chat_completion(req)
     assert "cache_salt" not in mock_engine.generate.call_args.args[0]

tests/kernels/utils.py

@@ -1236,7 +1236,7 @@ def baseline_scaled_mm(a: torch.Tensor,
                        bias: Optional[torch.Tensor] = None) -> torch.Tensor:
 
     # We treat N-dimensional group scaling as extended numpy-style broadcasting
-    # in numpy simply stretches dimensions with an extent of 1 to match the
+    # in numpy simply stretches dimensions with an extent of 1 to match
     # the target shape by repeating the data along that dimension (broadcasting)
     # , we extend these semantics to say if the extent of a dimension in the
     # source shape is not 1 and does not match the target shape we repeat each

tests/multimodal/test_utils.py

@@ -458,7 +458,7 @@ def run_dp_sharded_vision_model_vs_direct(local_rank: int, world_size: int,
     with torch.inference_mode():
         sharded_output = run_dp_sharded_vision_model(image_input, vision_model)
 
-    # Check that the world size is setup correctly
+    # Check that the world size is set up correctly
     assert get_tensor_model_parallel_world_size() == world_size
 
     # Check that the outputs have the same shape
@@ -642,7 +642,7 @@ def run_dp_sharded_mrope_vision_model_vs_direct(local_rank: int,
                                                            rope_type="rope_3d")
         sharded_output = torch.cat(sharded_output, dim=0)
 
-    # Check that the world size is setup correctly
+    # Check that the world size is set up correctly
     assert get_tensor_model_parallel_world_size() == world_size
 
     # Compare outputs (only on rank 0)

tests/v1/e2e/test_spec_decode.py

@@ -83,7 +83,7 @@ def test_ngram_correctness(
     model_name: str,
 ):
     '''
-    Compare the outputs of a original LLM and a speculative LLM
+    Compare the outputs of an original LLM and a speculative LLM
     should be the same when using ngram speculative decoding.
     '''
     with monkeypatch.context() as m:

tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py

@@ -42,7 +42,7 @@ def test_basic_lifecycle():
     engine_core_outputs = scheduler.update_from_output(scheduler_output,
                                                        model_runner_output)
 
-    # Ensure the request is finished after 1 tokens.
+    # Ensure the request is finished after 1 token.
     assert request.is_finished()
     assert request.status == RequestStatus.FINISHED_LENGTH_CAPPED
     output = engine_core_outputs[0].outputs[0]
@@ -141,7 +141,7 @@ def test_short_prompt_lifecycle():
 
 
 def test_prefix_cache_lifecycle():
-    """Test that remote decode params still works with a prefix cache hit."""
+    """Test that remote decode params still work with a prefix cache hit."""
 
     vllm_config = create_vllm_config()
     scheduler = create_scheduler(vllm_config)

tests/v1/spec_decode/test_tree_attention.py

@@ -187,7 +187,7 @@ def test_tree_attn_correctness() -> None:
                         dtype=torch.bfloat16,
                     )
 
-                    # Setup the block table and KV cache for paged KV.
+                    # Set up the block table and KV cache for paged KV.
                     assert max_sequence_length % block_size == 0
                     max_blocks_per_batch = max_sequence_length // block_size
                     kv_cache = torch.randn(
@@ -222,7 +222,7 @@ def test_tree_attn_correctness() -> None:
                                 num_alloc_blocks_per_batch] = block_ids.view(
                                     -1, num_alloc_blocks_per_batch)
 
-                    # Setup the slot mapping for the input KVs.
+                    # Set up the slot mapping for the input KVs.
                     tree_positions = sequence_position + torch.arange(
                         0,
                         tree_size_q,

vllm/lora/utils.py

@@ -239,7 +239,7 @@ def get_adapter_absolute_path(lora_path: str) -> str:
     except (HfHubHTTPError, RepositoryNotFoundError, EntryNotFoundError,
             HFValidationError):
         # Handle errors that may occur during the download
-        # Return original path instead instead of throwing error here
+        # Return original path instead of throwing error here
         logger.exception("Error downloading the HuggingFace model")
         return lora_path
 

vllm/model_executor/layers/quantization/compressed_tensors/utils.py

@@ -94,7 +94,7 @@ def find_matched_target(
     config that a layer corresponds to.
 
     Recall that a compressed-tensors configs has a concept of
-    config_groups, where each layer can be quantized with with a different
+    config_groups, where each layer can be quantized with a different
     scheme.
 
     targets in each config_group will be a list of either layer names

vllm/multimodal/utils.py

@@ -213,7 +213,7 @@ def fetch_image(
         image_mode: str = "RGB",
     ) -> Image.Image:
         """
-        Load a PIL image from a HTTP or base64 data URL.
+        Load a PIL image from an HTTP or base64 data URL.
 
         By default, the image is converted into RGB format.
         """
@@ -237,7 +237,7 @@ async def fetch_image_async(
         image_mode: str = "RGB",
     ) -> Image.Image:
         """
-        Asynchronously load a PIL image from a HTTP or base64 data URL.
+        Asynchronously load a PIL image from an HTTP or base64 data URL.
 
         By default, the image is converted into RGB format.
         """
@@ -261,7 +261,7 @@ def fetch_video(
         image_mode: str = "RGB",
     ) -> tuple[npt.NDArray, dict[str, Any]]:
         """
-        Load video from a HTTP or base64 data URL.
+        Load video from an HTTP or base64 data URL.
         """
         image_io = ImageMediaIO(image_mode=image_mode,
                                 **self.media_io_kwargs.get("image", {}))
@@ -281,7 +281,7 @@ async def fetch_video_async(
         image_mode: str = "RGB",
     ) -> tuple[npt.NDArray, dict[str, Any]]:
         """
-        Asynchronously load video from a HTTP or base64 data URL.
+        Asynchronously load video from an HTTP or base64 data URL.
 
         By default, the image is converted into RGB format.
         """
@@ -370,7 +370,7 @@ def group_mm_inputs_by_modality(
 
     def modality_group_func(
             mm_input: MultiModalKwargsItems) -> Union[str, int]:
-        # If the input has multiple modalities, return a id as the unique key
+        # If the input has multiple modalities, return an id as the unique key
         # for the mm_input input.
         if len(mm_input) > 1:
             return id(mm_input)

vllm/v1/attention/backends/utils.py

@@ -709,7 +709,7 @@ def reorder_batch_to_split_decodes_and_prefills(
 
     for i, req_id in enumerate(input_batch.req_ids):
         num_tokens = scheduler_output.num_scheduled_tokens[req_id]
-        # for now treat 1 scheduled token as "decode" even if its not,
+        # for now treat 1 scheduled token as "decode" even if it's not,
         # we should update this to something like < 8 in the future but
         # currently the TritonMLA._forward_decode only supports
         # num_tokens = 1

vllm/v1/structured_output/utils.py

@@ -65,9 +65,9 @@ def get_outlines_cache_path() -> str:
     elif xdg_cache_home:
         return os.path.join(xdg_cache_home, ".cache", "outlines")
     # If homedir is "/", we may be inside a container, and thus writing to
-    # root would be problematic, so we fallback to using a tempfile.
+    # root would be problematic, so we fall back to using a tempfile.
     # Also validate the path exists, since os.path.expanduser does
-    # not garuntee existence.
+    # not guarantee existence.
     elif os.path.isdir(home_dir) and home_dir != "/":
         # Default Unix fallback: ~/.cache/outlines
         return os.path.join(home_dir, ".cache", "outlines")

vllm/v1/worker/tpu_worker.py

@@ -250,7 +250,7 @@ def execute_model(
         scheduler_output: "SchedulerOutput",
     ) -> Optional[ModelRunnerOutput]:
         output = self.model_runner.execute_model(scheduler_output)
-        # every worker's output is needed when kv_transfer_group is setup
+        # every worker's output is needed when kv_transfer_group is set up
         return output if self.is_driver_worker or has_kv_transfer_group(
         ) else None