[Doc]: fix typos in Python comments (#24077)

Signed-off-by: Didier Durand <durand.didier@gmail.com>

tests/async_engine/test_api_server.py

@@ -98,7 +98,7 @@ def test_api_server(api_server, distributed_executor_backend: str):
         pool.join()
 
         # check cancellation stats
-        # give it some times to update the stats
+        # give it some time to update the stats
         time.sleep(1)
 
         num_aborted_requests = requests.get(

tests/core/block/e2e/test_correctness.py

@@ -439,10 +439,10 @@ def test_auto_prefix_caching_with_preemption(baseline_llm_generator,
 @pytest.mark.parametrize("seed", [1])
 def test_auto_prefix_caching_after_eviction_start(baseline_llm_generator,
                                                   test_llm_generator):
-    """Verify block manager v2 with auto prefix caching could works normal
+    """Verify block manager v2 with auto prefix caching could work normally
     even when eviction started.
     With APC enabled, all blocks are held by native block at the beginning.
-    Then blocks are managed by evictor instead. If cache hit at the evitor's
+    Then blocks are managed by evictor instead. If cache hit at the evictor's
     block, then it could be reused, or we need to recompute its kv cache.
     """
     output_len = 10

tests/engine/test_arg_utils.py

@@ -167,7 +167,7 @@ def test_get_kwargs():
     # dict should have json tip in help
     json_tip = "Should either be a valid JSON string or JSON keys"
     assert json_tip in kwargs["json_tip"]["help"]
-    # nested config should should construct the nested config
+    # nested config should construct the nested config
     assert kwargs["nested_config"]["type"]('{"field": 2}') == NestedConfig(2)
 
 

tests/kernels/moe/test_deepep_deepgemm_moe.py

@@ -282,7 +282,7 @@ def triton_impl(a: torch.Tensor, topk_ids: torch.Tensor,
         a1_scale=a1_scale,
         block_shape=block_shape,
         # Make sure this is set to False so we
-        # dont end up comparing the same implementation.
+        # don't end up comparing the same implementation.
         allow_deep_gemm=False)
 
 

tests/lora/test_add_lora.py

@@ -59,10 +59,10 @@ async def requests_processing_time(llm,
 @pytest.mark.asyncio
 async def test_add_lora(chatglm3_lora_files):
     """ 
-    The add_lora function is used to pre-load some LoRA adapters into the
+    The add_lora function is used to preload some LoRA adapters into the
     engine in anticipation of future requests using these adapters. To test
     this functionality, we use the async engine to process some requests - We
-    do it twice, once with add_lora() pre-loading and once without.
+    do it twice, once with add_lora() preloading and once without.
 
     We measure the request processing time in both cases and expect the time 
     to be lesser in the case with add_lora() calls.

tests/lora/test_lora_allowed_token_ids.py

@@ -18,7 +18,7 @@ def test_allowed_token_ids_with_lora_vocab(llama_2_7b_base_huggingface_id,
     adapters that define additional tokens.
     """
 
-    # Setup a base model compatible with the sql_lora_files adapter and
+    # Set up a base model compatible with the sql_lora_files adapter and
     # a known number of tokens in the base model.
     model_config = ModelConfig(
         model=llama_2_7b_base_huggingface_id,
@@ -84,7 +84,7 @@ def test_allowed_token_ids_with_lora_adapter_no_vocab(
     adapters that do not define additional tokens.
     """
 
-    # Setup a base model compatible with the qwen25vl_lora_files adapter and
+    # Set up a base model compatible with the qwen25vl_lora_files adapter and
     # a known number of tokens in the base model.
     model_config = ModelConfig(
         model=qwen25vl_base_huggingface_id,

tests/models/language/generation/test_common.py

@@ -13,7 +13,7 @@ from ...registry import HF_EXAMPLE_MODELS
 from ...utils import check_logprobs_close
 
 # These have unsupported head_dim for FA. We do not
-# not have a clean way to fall back, so we fail with
+# have a clean way to fall back, so we fail with
 # a clear msg when it happens.
 # https://github.com/vllm-project/vllm/issues/14524
 REQUIRES_V0 = ["microsoft/phi-2", "stabilityai/stablelm-3b-4e1t"]

tests/models/language/generation/test_mistral.py

@@ -20,7 +20,7 @@ MISTRAL_FORMAT_MODELS = [
     "mistralai/Mistral-7B-Instruct-v0.3",
     # uses the v3-Tekken tokenizer
     "mistralai/Ministral-8B-Instruct-2410",
-    # Mistral-Nemo is to big for CI, but passes locally
+    # Mistral-Nemo is too big for CI, but passes locally
     # "mistralai/Mistral-Nemo-Instruct-2407"
 ]
 
@@ -273,7 +273,7 @@ def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
 
 
 def test_mistral_function_call_nested_json():
-    """Ensure that the function-name regex captures the entire outer-most
+    """Ensure that the function-name regex captures the entire outermost
     JSON block, including nested braces."""
 
     # Create a minimal stub tokenizer that provides the few attributes the

tests/models/multimodal/generation/test_qwen2_vl.py

@@ -154,7 +154,7 @@ def batch_make_image_embeddings(
         embed_counter += cur_batch_embed_len
         image_counter += cur_batch_image_count
 
-    # ensure we don't lost any images or embeddings
+    # ensure we don't lose any images or embeddings
     assert embed_counter == image_embeds.size(0)
     assert image_counter == image_grid_thw.size(0)
     assert len(image_batches) == len(result)
@@ -238,7 +238,7 @@ def batch_make_video_embeddings(
         embed_counter += cur_batch_embed_len
         video_counter += cur_batch_video_count
 
-    # ensure we don't lost any videos or embeddings
+    # ensure we don't lose any videos or embeddings
     assert embed_counter == video_embeds.size(0)
     assert video_counter == video_grid_thw.size(0)
     assert len(video_batches) == len(result)

tests/v1/core/test_kv_cache_utils.py

@@ -247,7 +247,7 @@ def test_free_kv_cache_block_queue_append_n():
 
 def test_free_kv_cache_block_queue_popleft_n():
     blocks = [KVCacheBlock(block_id=i) for i in range(6)]
-    # Create a empty FreeKVCacheBlockQueue with these blocks
+    # Create an empty FreeKVCacheBlockQueue with these blocks
     queue = FreeKVCacheBlockQueue(
         [blocks[1], blocks[3], blocks[5], blocks[4], blocks[0], blocks[2]])
     assert queue.num_free_blocks == 6

tests/v1/executor/test_executor.py

@@ -27,7 +27,7 @@ class CustomMultiprocExecutor(MultiprocExecutor):
                        kwargs: Optional[dict] = None,
                        non_block: bool = False,
                        unique_reply_rank: Optional[int] = None) -> list[Any]:
-        # Drop marker to show that this was ran
+        # Drop marker to show that this was run
         with open(".marker", "w"):
             ...
         return super().collective_rpc(method, timeout, args, kwargs)

tests/v1/spec_decode/test_eagle.py

@@ -183,7 +183,7 @@ def test_load_model(mock_get_model, mock_get_layers, mock_get_pp_group, method,
     mock_pp_group.world_size = pp_size
     mock_get_pp_group.return_value = mock_pp_group
 
-    # Setup the target model mock with a custom class so that
+    # Set up the target model mock with a custom class so that
     # isinstance() checks match the expected type.
     class _TargetModelStub(LlamaForCausalLM):
         model: mock.MagicMock

tests/v1/test_kv_sharing.py

@@ -30,7 +30,7 @@ def test_initialize_kv_cache_for_kv_sharing_different_attn_groups():
     }
 
     # Layers 0 and 1 both belong in KV cache group 0
-    # However, if they have have different attention backends, they will be
+    # However, if they have different attention backends, they will be
     # placed in different attention groups for KV cache group 0
     kv_cache_groups = [
         KVCacheGroupSpec(["model.layers.0", "model.layers.1"],

tests/v1/worker/test_gpu_model_runner.py

@@ -702,7 +702,7 @@ def test_hybrid_attention_mamba_tensor_shapes(monkeypatch):
     KVCacheTensors for the attention and mamba layers
     (via _reshape_kv_cache_tensors function). This test verifies
     that the views are compatible: writing a mamba block
-    will not corrupt an attention block and vice-versa
+    will not corrupt an attention block and vice versa
     '''
 
     current_platform.seed_everything(42)