[Misc] Clean up unnecessary E501 ignore (#26274)

Signed-off-by: Roger Wang <hey@rogerw.io>

vllm/benchmarks/datasets.py

@@ -626,7 +626,7 @@ class RandomDataset(BenchmarkDataset):
         # Decode, then re-encode and truncate to preserve token count invariants
         total_input_len = prefix_len + int(input_len)
         prompt, adjusted_token_sequence, token_mismatch = (
-            gen_prompt_decode_to_target_len(  # noqa: E501
+            gen_prompt_decode_to_target_len(
                 tokenizer=tokenizer,
                 token_sequence=token_sequence,
                 target_token_len=total_input_len,
@@ -2855,7 +2855,7 @@ class PrefixRepetitionRandomDataset(BenchmarkDataset):
             for _ in range(prompts_per_prefix):
                 suffix_tokens, token_mistmatch = _generate_exact_length_tokens(
                     suffix_len
-                )  # noqa: E501
+                )
                 token_mismatch_total += token_mistmatch
                 combined_tokens = prefix_tokens + suffix_tokens
                 prompt = tokenizer.decode(combined_tokens)

vllm/benchmarks/throughput.py

@@ -459,14 +459,14 @@ def validate_args(args):
         ):
             assert args.backend == "vllm-chat", (
                 f"{args.dataset_path} needs to use vllm-chat as the backend."
-            )  # noqa: E501
+            )
         elif args.dataset_path in (
             InstructCoderDataset.SUPPORTED_DATASET_PATHS
             | AIMODataset.SUPPORTED_DATASET_PATHS
         ):
             assert args.backend == "vllm", (
                 f"{args.dataset_path} needs to use vllm as the backend."
-            )  # noqa: E501
+            )
         else:
             raise ValueError(f"{args.dataset_path} is not supported by hf dataset.")
 

vllm/compilation/inductor_pass.py

@@ -19,7 +19,7 @@ if is_torch_equal_or_newer("2.6"):
     from torch._inductor.custom_graph_pass import CustomGraphPass
 else:
     # CustomGraphPass is not present in 2.5 or lower, import our version
-    from .torch25_custom_graph_pass import (  # noqa: E501
+    from .torch25_custom_graph_pass import (
         Torch25CustomGraphPass as CustomGraphPass,
     )
 

vllm/model_executor/layers/quantization/__init__.py

@@ -95,7 +95,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     from .awq_marlin import AWQMarlinConfig
     from .bitblas import BitBLASConfig
     from .bitsandbytes import BitsAndBytesConfig
-    from .compressed_tensors.compressed_tensors import (  # noqa: E501
+    from .compressed_tensors.compressed_tensors import (
         CompressedTensorsConfig,
     )
     from .deepspeedfp import DeepSpeedFPConfig

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

@@ -26,7 +26,7 @@ from vllm.model_executor.layers.linear import (
     UnquantizedLinearMethod,
 )
 from vllm.model_executor.layers.quantization import QuantizationMethods
-from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
+from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
@@ -256,7 +256,7 @@ class CompressedTensorsConfig(QuantizationConfig):
                         )
                     else:
                         target_scheme_map[target]["input_activations"] = (
-                            QuantizationArgs.model_validate(  # noqa: E501
+                            QuantizationArgs.model_validate(
                                 quant_config.get("input_activations")
                             )
                         )

vllm/model_executor/models/gemma3n_mm.py

@@ -176,7 +176,7 @@ class Gemma3nDummyInputsBuilder(BaseDummyInputsBuilder[Gemma3nProcessingInfo]):
         processor = self.info.get_hf_processor()
         audio_feature_extractor: Gemma3nAudioFeatureExtractor = (
             processor.feature_extractor
-        )  # noqa: E501
+        )
         audio_len = audio_feature_extractor.fft_length
         image_processor: SiglipImageProcessorFast = processor.image_processor
         img_width = image_processor.size.get("width", 224)

vllm/model_executor/models/registry.py

@@ -120,7 +120,7 @@ _TEXT_GENERATION_MODELS = {
     "JambaForCausalLM": ("jamba", "JambaForCausalLM"),
     "Lfm2ForCausalLM": ("lfm2", "Lfm2ForCausalLM"),
     "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
-    "Llama4ForCausalLM": ("llama4", "Llama4ForCausalLM"),  # noqa: E501
+    "Llama4ForCausalLM": ("llama4", "Llama4ForCausalLM"),
     # For decapoda-research/llama-*
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
     "LongcatFlashForCausalLM": ("longcat_flash", "LongcatFlashForCausalLM"),
@@ -204,7 +204,7 @@ _EMBEDDING_MODELS = {
     "LlavaNextForConditionalGeneration": (
         "llava_next",
         "LlavaNextForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
     "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),  # noqa: E501
     # Technically Terratorch models work on images, both in
@@ -240,46 +240,46 @@ _MULTIMODAL_MODELS = {
     "AyaVisionForConditionalGeneration": (
         "aya_vision",
         "AyaVisionForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"),
     "ChameleonForConditionalGeneration": (
         "chameleon",
         "ChameleonForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Cohere2VisionForConditionalGeneration": (
         "cohere2_vision",
         "Cohere2VisionForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
     "DotsOCRForCausalLM": ("dots_ocr", "DotsOCRForCausalLM"),
     "Ernie4_5_VLMoeForConditionalGeneration": (
         "ernie45_vl",
         "Ernie4_5_VLMoeForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
     "Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"),  # noqa: E501
     "Gemma3nForConditionalGeneration": (
         "gemma3n_mm",
         "Gemma3nForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "GLM4VForCausalLM": ("glm4v", "GLM4VForCausalLM"),
     "Glm4vForConditionalGeneration": ("glm4_1v", "Glm4vForConditionalGeneration"),  # noqa: E501
     "Glm4vMoeForConditionalGeneration": ("glm4_1v", "Glm4vMoeForConditionalGeneration"),  # noqa: E501
     "GraniteSpeechForConditionalGeneration": (
         "granite_speech",
         "GraniteSpeechForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "InternVLChatModel": ("internvl", "InternVLChatModel"),
     "NemotronH_Nano_VL_V2": ("nano_nemotron_vl", "NemotronH_Nano_VL_V2"),
     "InternS1ForConditionalGeneration": (
         "interns1",
         "InternS1ForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "InternVLForConditionalGeneration": (
         "interns1",
         "InternS1ForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Idefics3ForConditionalGeneration": (
         "idefics3",
         "Idefics3ForConditionalGeneration",
@@ -289,7 +289,7 @@ _MULTIMODAL_MODELS = {
     "KeyeVL1_5ForConditionalGeneration": (
         "keye_vl1_5",
         "KeyeVL1_5ForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "RForConditionalGeneration": ("rvl", "RForConditionalGeneration"),
     "KimiVLForConditionalGeneration": ("kimi_vl", "KimiVLForConditionalGeneration"),  # noqa: E501
     "Llama_Nemotron_Nano_VL": ("nemotron_vl", "LlamaNemotronVLChatModel"),
@@ -298,27 +298,27 @@ _MULTIMODAL_MODELS = {
     "LlavaNextForConditionalGeneration": (
         "llava_next",
         "LlavaNextForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "LlavaNextVideoForConditionalGeneration": (
         "llava_next_video",
         "LlavaNextVideoForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "LlavaOnevisionForConditionalGeneration": (
         "llava_onevision",
         "LlavaOnevisionForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "MantisForConditionalGeneration": ("llava", "MantisForConditionalGeneration"),  # noqa: E501
     "MiDashengLMModel": ("midashenglm", "MiDashengLMModel"),
     "MiniMaxVL01ForConditionalGeneration": (
         "minimax_vl_01",
         "MiniMaxVL01ForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "MiniCPMO": ("minicpmo", "MiniCPMO"),
     "MiniCPMV": ("minicpmv", "MiniCPMV"),
     "Mistral3ForConditionalGeneration": (
         "mistral3",
         "Mistral3ForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "MolmoForCausalLM": ("molmo", "MolmoForCausalLM"),
     "NVLM_D": ("nvlm_d", "NVLM_D_Model"),
     "Ovis": ("ovis", "Ovis"),
@@ -326,7 +326,7 @@ _MULTIMODAL_MODELS = {
     "PaliGemmaForConditionalGeneration": (
         "paligemma",
         "PaliGemmaForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
     "Phi4MMForCausalLM": ("phi4mm", "Phi4MMForCausalLM"),
     "Phi4MultimodalForCausalLM": ("phi4_multimodal", "Phi4MultimodalForCausalLM"),  # noqa: E501
@@ -336,31 +336,31 @@ _MULTIMODAL_MODELS = {
     "Qwen2_5_VLForConditionalGeneration": (
         "qwen2_5_vl",
         "Qwen2_5_VLForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Qwen2AudioForConditionalGeneration": (
         "qwen2_audio",
         "Qwen2AudioForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Qwen2_5OmniModel": (
         "qwen2_5_omni_thinker",
         "Qwen2_5OmniThinkerForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Qwen2_5OmniForConditionalGeneration": (
         "qwen2_5_omni_thinker",
         "Qwen2_5OmniThinkerForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "Qwen3VLForConditionalGeneration": ("qwen3_vl", "Qwen3VLForConditionalGeneration"),  # noqa: E501
     "Qwen3VLMoeForConditionalGeneration": (
         "qwen3_vl_moe",
         "Qwen3VLMoeForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "SkyworkR1VChatModel": ("skyworkr1v", "SkyworkR1VChatModel"),
     "Step3VLForConditionalGeneration": ("step3_vl", "Step3VLForConditionalGeneration"),  # noqa: E501
     "TarsierForConditionalGeneration": ("tarsier", "TarsierForConditionalGeneration"),  # noqa: E501
     "Tarsier2ForConditionalGeneration": (
         "qwen2_vl",
         "Tarsier2ForConditionalGeneration",
-    ),  # noqa: E501
+    ),
     "UltravoxModel": ("ultravox", "UltravoxModel"),
     "VoxtralForConditionalGeneration": ("voxtral", "VoxtralForConditionalGeneration"),  # noqa: E501
     # [Encoder-decoder]
@@ -401,23 +401,23 @@ _TRANSFORMERS_BACKEND_MODELS = {
     "TransformersMoEForMultimodalLM": (
         "transformers_moe",
         "TransformersMoEForMultimodalLM",
-    ),  # noqa: E501
+    ),
     "TransformersEmbeddingModel": (
         "transformers_pooling",
         "TransformersEmbeddingModel",
-    ),  # noqa: E501
+    ),
     "TransformersForSequenceClassification": (
         "transformers_pooling",
         "TransformersForSequenceClassification",
-    ),  # noqa: E501
+    ),
     "TransformersMoEForSequenceClassification": (
         "transformers_pooling",
         "TransformersMoEForSequenceClassification",
-    ),  # noqa: E501
+    ),
     "TransformersMoEEmbeddingModel": (
         "transformers_pooling",
         "TransformersMoEEmbeddingModel",
-    ),  # noqa: E501
+    ),
 }
 
 _VLLM_MODELS = {

vllm/v1/attention/backends/gdn_attn.py

@@ -79,7 +79,7 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
         self.speculative_config = vllm_config.speculative_config
         self.kv_cache_spec = kv_cache_spec
         if self.speculative_config:
-            self.num_spec = self.speculative_config.num_speculative_tokens  # noqa: E501
+            self.num_spec = self.speculative_config.num_speculative_tokens
         else:
             self.num_spec = 0
         self.use_spec_decode = self.num_spec > 0