Fix per file ruff ignores related to line length (#26262)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/entrypoints/test_chat_utils.py

@@ -947,7 +947,8 @@ def test_parse_chat_messages_placeholder_one_already_in_prompt(
                     {"type": "image_url", "image_url": {"url": image_url}},
                     {
                         "type": "text",
-                        "text": "What's in <|image_1|> and how does it compare to the other one?",  # noqa: E501
+                        "text": "What's in <|image_1|> and how does it compare to "
+                        "the other one?",
                     },
                 ],
             }
@@ -960,8 +961,8 @@ def test_parse_chat_messages_placeholder_one_already_in_prompt(
     assert conversation == [
         {
             "role": "user",
-            "content": "<|image_2|>\nWhat's in <|image_1|> and how does it compare to the "
-            "other one?",
+            "content": "<|image_2|>\nWhat's in <|image_1|> and how does it compare to "
+            "the other one?",
         }
     ]
     _assert_mm_data_is_image_input(mm_data, 2)
@@ -1364,7 +1365,7 @@ def test_parse_chat_messages_multiple_images_multiple_messages_interleave(
     _assert_mm_uuids(mm_uuids, 2, expected_uuids=[None, None])
 
 
-def test_parse_chat_messages_multiple_images_with_uuids_multiple_messages_interleave(  # noqa: E501
+def test_parse_chat_messages_multiple_images_with_uuids_multiple_messages_interleave(
     phi3v_model_config_mm_interleaved,
     phi3v_tokenizer,
     image_url,
@@ -1451,14 +1452,14 @@ def test_parse_chat_messages_multiple_modals_multiple_messages_interleave(
     assert conversation == [
         {
             "role": "user",
-            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
-            "Now listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>",  # noqa: E501
+            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>"
+            "\nNow listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>",
         },
         {"role": "assistant", "content": "Some stuff."},
         {
             "role": "user",
-            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
-            "And what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>",
+            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>"
+            "\nAnd what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>",
         },
     ]
 
@@ -1468,7 +1469,7 @@ def test_parse_chat_messages_multiple_modals_multiple_messages_interleave(
     _assert_mm_uuids(mm_uuids, 1, modality="audio", expected_uuids=[None])
 
 
-def test_parse_chat_messages_multiple_modals_with_uuids_multiple_messages_interleave(  # noqa: E501
+def test_parse_chat_messages_multiple_modals_with_uuids_multiple_messages_interleave(
     qwen25omni_model_config_mm_interleaved,
     qwen25omni_tokenizer,
     image_url,
@@ -1521,14 +1522,14 @@ def test_parse_chat_messages_multiple_modals_with_uuids_multiple_messages_interl
     assert conversation == [
         {
             "role": "user",
-            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
-            "Now listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>",  # noqa: E501
+            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>"
+            "\nNow listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>",
         },
         {"role": "assistant", "content": "Some stuff."},
         {
             "role": "user",
-            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
-            "And what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>",
+            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>"
+            "\nAnd what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>",
         },
     ]
 
@@ -1593,14 +1594,14 @@ def test_parse_chat_messages_multiple_modals_with_uuids_multiple_empty_media_mes
     assert conversation == [
         {
             "role": "user",
-            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
-            "Now listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>",  # noqa: E501
+            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>"
+            "\nNow listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>",
         },
         {"role": "assistant", "content": "Some stuff."},
         {
             "role": "user",
-            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
-            "And what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>",
+            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>"
+            "\nAnd what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>",
         },
     ]
 
@@ -1661,14 +1662,14 @@ def test_parse_chat_messages_multiple_modals_with_partial_uuids_multiple_message
     assert conversation == [
         {
             "role": "user",
-            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
-            "Now listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>",  # noqa: E501
+            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>"
+            "\nNow listen to this audio\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>",
         },
         {"role": "assistant", "content": "Some stuff."},
         {
             "role": "user",
-            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>\n"
-            "And what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>",
+            "content": "What's on this image?\n<|vision_start|><|IMAGE|><|vision_end|>"
+            "\nAnd what's in the video?\n<|vision_start|><|VIDEO|><|vision_end|>",
         },
     ]
 
@@ -2193,7 +2194,8 @@ def test_parse_chat_messages_single_empty_audio_with_uuid(
     assert conversation == [
         {
             "role": "user",
-            "content": "Audio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\nWhat does the audio say?",
+            "content": "Audio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\nWhat does the "
+            "audio say?",
         }
     ]
     _assert_mm_data_inputs(mm_data, {"audio": 1})
@@ -2228,7 +2230,8 @@ async def test_parse_chat_messages_single_empty_audio_with_uuid_async(
     assert conversation == [
         {
             "role": "user",
-            "content": "Audio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\nWhat does the audio say?",
+            "content": "Audio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\nWhat does the "
+            "audio say?",
         }
     ]
     _assert_mm_data_inputs(await mm_future, {"audio": 1})

tests/kernels/attention/test_attention_selector.py

@@ -165,7 +165,7 @@ def test_env(
                             # FlashMLA only supports block_size == 64
                             pytest.skip("FlashMLA only supports block_size 64")
                         else:
-                            from vllm.v1.attention.backends.mla.flashmla import (  # noqa: E501
+                            from vllm.v1.attention.backends.mla.flashmla import (
                                 is_flashmla_supported,
                             )
 

tests/kernels/moe/modular_kernel_tools/common.py

@@ -331,7 +331,8 @@ class WeightTensors:
             in_dtype=config.dtype,
             quant_dtype=config.quant_dtype,
             block_shape=config.quant_block_shape,
-            per_out_ch_quant=config.is_per_act_token_quant,  # or config.is_per_out_ch_quant
+            # or config.is_per_out_ch_quant
+            per_out_ch_quant=config.is_per_act_token_quant,
         )
         return WeightTensors(
             w1=w1, w2=w2, w1_scale=w1_scale, w2_scale=w2_scale, w1_gs=w1_gs, w2_gs=w2_gs

tests/kernels/moe/modular_kernel_tools/make_feature_matrix.py

@@ -124,7 +124,7 @@ def make_feature_matrix(csv_file_path: str):
     results_df: Optional[pd.DataFrame] = None
     for m, k, n, e, topks, dtype, pf_type, experts_type, quant_config in tqdm(
         combinations
-    ):  # noqa: E501
+    ):
         config = Config(
             Ms=[m],
             K=k,

tests/kernels/moe/modular_kernel_tools/mk_objects.py

@@ -10,7 +10,7 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
     BatchedDeepGemmExperts,
 )
-from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import (  # noqa: E501
+from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import (
     BatchedTritonOrDeepGemmExperts,
 )
 from vllm.model_executor.layers.fused_moe.config import (
@@ -196,10 +196,10 @@ register_experts(
 
 # Disable on blackwell for now
 if has_deep_ep() and not current_platform.has_device_capability(100):
-    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (
         DeepEPHTPrepareAndFinalize,
     )
-    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (
         DeepEPLLPrepareAndFinalize,
     )
 
@@ -233,7 +233,7 @@ if has_pplx():
     )
 
 if has_flashinfer_cutlass_fused_moe() and current_platform.has_device_capability(100):
-    from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
         FlashInferExperts,
     )
     from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize import (  # noqa: E501

tests/kernels/moe/parallel_utils.py

@@ -17,10 +17,10 @@ from typing_extensions import Concatenate, ParamSpec
 from vllm.utils import get_open_port, has_deep_ep
 
 if has_deep_ep():
-    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (
         DeepEPHTPrepareAndFinalize,
     )
-    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (
         DeepEPLLPrepareAndFinalize,
     )
 

tests/kernels/moe/test_deepep_deepgemm_moe.py

@@ -30,10 +30,10 @@ from .parallel_utils import ProcessGroupInfo, parallel_launch
 from .utils import make_test_weights
 
 if has_deep_ep():
-    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (
         DeepEPHTPrepareAndFinalize,
     )
-    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (
         DeepEPLLPrepareAndFinalize,
     )
 

tests/kernels/moe/test_deepep_moe.py

@@ -28,10 +28,10 @@ from ...utils import multi_gpu_test
 from .parallel_utils import ProcessGroupInfo, parallel_launch
 
 if has_deep_ep():
-    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import (
         DeepEPHTPrepareAndFinalize,
     )
-    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (  # noqa: E501
+    from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import (
         DeepEPLLPrepareAndFinalize,
     )
 

tests/kernels/moe/test_modular_kernel_combinations.py

@@ -271,7 +271,7 @@ if __name__ == "__main__":
     parser = make_config_arg_parser(
         description=(
             "Run single prepare-finalize & fused-experts combination test"
-            "Example : python3 -m tests.kernels.moe.test_modular_kernel_combinations "  # noqa: E501
+            "Example : python3 -m tests.kernels.moe.test_modular_kernel_combinations "
             "--pf-type PplxPrepareAndFinalize --experts-type BatchedTritonExperts"
         )
     )

tests/kernels/moe/test_moe.py

@@ -483,8 +483,8 @@ def test_mixtral_moe(
     }
 
     if use_rocm_aiter:
-        # The values of rtol and atol are set based on the tests in ROCM AITER package. # noqa: E501
-        # https://github.com/ROCm/aiter/blob/dfed377f4be7da96ca2d75ac0761f569676f7240/op_tests/test_moe.py#L174  # noqa: E501
+        # The values of rtol and atol are set based on the tests in ROCM AITER package.
+        # https://github.com/ROCm/aiter/blob/dfed377f4be7da96ca2d75ac0761f569676f7240/op_tests/test_moe.py#L174
         torch.testing.assert_close(
             hf_states.flatten(0, 1), vllm_states, rtol=0.01, atol=100
         )

tests/kernels/moe/test_mxfp4_moe.py

@@ -10,11 +10,11 @@ import pytest
 import torch
 from packaging import version
 
-from vllm.model_executor.layers.quantization.quark.quark import (  # noqa: E501
+from vllm.model_executor.layers.quantization.quark.quark import (
     QuarkLinearMethod,
     QuarkW4A4MXFP4,
 )
-from vllm.model_executor.layers.quantization.quark.quark_moe import (  # noqa: E501
+from vllm.model_executor.layers.quantization.quark.quark_moe import (
     QuarkW4A4MXFp4MoEMethod,
 )
 from vllm.platforms import current_platform

tests/lora/test_chatglm3_tp.py

@@ -12,7 +12,7 @@ PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example
 
 EXPECTED_LORA_OUTPUT = [
     "SELECT count(*) FROM singer",
-    "SELECT avg(age) ,  min(age) ,  max(age) FROM singer WHERE country  =  'France'",  # noqa: E501
+    "SELECT avg(age) ,  min(age) ,  max(age) FROM singer WHERE country  =  'France'",
     "SELECT name ,  country ,  age FROM singer ORDER BY age",
 ]
 
@@ -21,10 +21,16 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     prompts = [
         PROMPT_TEMPLATE.format(query="How many singers do we have?"),
         PROMPT_TEMPLATE.format(
-            query="What is the average, minimum, and maximum age of all singers from France?"  # noqa: E501
+            query=(
+                "What is the average, minimum, and maximum "
+                "age of all singers from France?"
+            )
         ),
         PROMPT_TEMPLATE.format(
-            query="Show name, country, age for all singers ordered by age from the oldest to the youngest."  # noqa: E501
+            query=(
+                "Show name, country, age for all singers ordered "
+                "by age from the oldest to the youngest."
+            )
         ),
     ]
     sampling_params = vllm.SamplingParams(temperature=0, max_tokens=32)

tests/lora/test_llama_tp.py

@@ -15,10 +15,10 @@ MODEL_PATH = "meta-llama/Llama-2-7b-hf"
 
 EXPECTED_LORA_OUTPUT = [
     "  SELECT icao FROM table_name_74 WHERE airport = 'lilongwe international airport' ",  # noqa: E501
-    "  SELECT nationality FROM table_name_11 WHERE elector = 'anchero pantaleone' ",  # noqa: E501
+    "  SELECT nationality FROM table_name_11 WHERE elector = 'anchero pantaleone' ",
     "  SELECT one_mora FROM table_name_95 WHERE gloss = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] AND accented_mora = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] ",  # noqa: E501
     "  SELECT sex FROM people WHERE people_id IN (SELECT people_id FROM candidate GROUP BY sex ORDER BY COUNT(people_id) DESC LIMIT 1) ",  # noqa: E501
-    "  SELECT pick FROM table_name_60 WHERE former_wnba_team = 'Minnesota Lynx' ",  # noqa: E501
+    "  SELECT pick FROM table_name_60 WHERE former_wnba_team = 'Minnesota Lynx' ",
     "  SELECT womens_doubles FROM table_28138035_4 WHERE mens_singles = 'Werner Schlager' ",  # noqa: E501
 ]
 

tests/lora/test_llm_with_multi_loras.py

@@ -26,7 +26,7 @@ LORA_RANK = 8
 LORA_TEST_PROMPTS = ["What is GitHub?", "Hi, tell me about you"]
 LORA_TEST_EXPECTED = [
     "GitHub is an open-source platform that provides a way to manage and develop software projects. It allows developers to store and manage code, collaborate on projects, and automate tasks.",  # noqa: E501
-    "I am Alice, an AI assistant developed by GitHub/Charent.",  # noqa: E501
+    "I am Alice, an AI assistant developed by GitHub/Charent.",
 ]
 
 

tests/models/language/generation/test_gemma.py

@@ -16,7 +16,7 @@ def test_dummy_loader(vllm_runner, monkeypatch, model: str) -> None:
         ) as llm:
             if model == "google/gemma-3-4b-it":
                 normalizers = llm.llm.collective_rpc(
-                    lambda self: self.model_runner.model.language_model.model.normalizer.cpu().item()
+                    lambda self: self.model_runner.model.language_model.model.normalizer.cpu().item()  # noqa: E501
                 )
                 config = llm.llm.llm_engine.model_config.hf_config.text_config
             else:

tests/models/language/generation/test_mistral.py

@@ -46,12 +46,13 @@ TOOLS = [
                 "properties": {
                     "city": {
                         "type": "string",
-                        "description": "The city to find the weather for, e.g. 'San Francisco'",
+                        "description": "The city to find the weather for, e.g. "
+                        "'San Francisco'",
                     },
                     "state": {
                         "type": "string",
-                        "description": "the two-letter abbreviation for the state that the city is"
-                        " in, e.g. 'CA' which would mean 'California'",
+                        "description": "the two-letter abbreviation for the state that "
+                        "the city is in, e.g. 'CA' which would mean 'California'",
                     },
                     "unit": {
                         "type": "string",
@@ -85,7 +86,8 @@ MSGS = [
     {"role": "system", "content": "You are an assistant."},
     {
         "role": "user",
-        "content": "Could you please rewrite the below article? \n\n My English needs improvving, maybe I make errors.",  # noqa
+        "content": "Could you please rewrite the below article? \n\n My English needs "
+        "improvving, maybe I make errors.",
     },
     {
         "role": "assistant",
@@ -96,14 +98,16 @@ MSGS = [
                 "type": "function",
                 "function": {
                     "name": "rewrite",
-                    "arguments": '{"text":"My English needs improvving, maybe I make errors."}',  # noqa
+                    "arguments": '{"text":"My English needs improvving, maybe '
+                    'I make errors."}',
                 },
             }
         ],
     },
     {
         "role": "tool",
-        "content": '{"action":"rewrite","outcome":"My English needs improving, maybe I make errors."}',  # noqa
+        "content": '{"action":"rewrite","outcome":"My English needs improving, maybe '
+        'I make errors."}',
         "tool_call_id": "bbc5b7ede",
         "name": "rewrite",
     },

tests/models/multimodal/generation/test_common.py

@@ -130,14 +130,14 @@ VLM_TEST_SETTINGS = {
         dtype="bfloat16",
         marks=[
             pytest.mark.skip(reason="vLLM does not support PrefixLM attention mask")
-        ],  # noqa: E501
+        ],
     ),
     "qwen2_5_vl": VLMTestInfo(
         models=["Qwen/Qwen2.5-VL-3B-Instruct"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE, VLMTestType.VIDEO),
         prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",  # noqa: E501
-        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",
+        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>",
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -149,8 +149,8 @@ VLM_TEST_SETTINGS = {
         models=["Qwen/Qwen2.5-Omni-3B"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE, VLMTestType.VIDEO),
         prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<|vision_bos|><|IMAGE|><|vision_eos|>",  # noqa: E501
-        video_idx_to_prompt=lambda idx: "<|vision_bos|><|VIDEO|><|vision_eos|>",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<|vision_bos|><|IMAGE|><|vision_eos|>",
+        video_idx_to_prompt=lambda idx: "<|vision_bos|><|VIDEO|><|vision_eos|>",
         max_model_len=4096,
         max_num_seqs=2,
         num_logprobs=6 if current_platform.is_cpu() else 5,
@@ -181,7 +181,7 @@ VLM_TEST_SETTINGS = {
         max_model_len=16384,
         hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs(
             "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
-        ),  # noqa: E501
+        ),
         auto_cls=AutoModelForImageTextToText,
         vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
         image_size_factors=[(0.25, 0.5, 1.0)],
@@ -213,7 +213,7 @@ VLM_TEST_SETTINGS = {
         models=["Qwen/Qwen2.5-VL-3B-Instruct"],
         test_type=VLMTestType.IMAGE,
         prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -237,10 +237,10 @@ VLM_TEST_SETTINGS = {
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
                 "stop_sign": "<vlm_image>Please describe the image shortly.",
-                "cherry_blossom": "<vlm_image>Please infer the season with reason.",  # noqa: E501
+                "cherry_blossom": "<vlm_image>Please infer the season with reason.",
             }
         ),
-        multi_image_prompt="<vlm_image><vlm_image>Describe the two images shortly.",  # noqa: E501
+        multi_image_prompt="<vlm_image><vlm_image>Describe the two images shortly.",
         stop_str=["<|im_end|>"],
         image_size_factors=[(0.10, 0.15)],
         max_tokens=64,
@@ -252,11 +252,11 @@ VLM_TEST_SETTINGS = {
         prompt_formatter=lambda img_prompt: f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{img_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",  # noqa: E501
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
-                "stop_sign": "<image>What's the content in the center of the image?",  # noqa: E501
-                "cherry_blossom": "<image>What is the season?",  # noqa: E501
+                "stop_sign": "<image>What's the content in the center of the image?",
+                "cherry_blossom": "<image>What is the season?",
             }
         ),
-        multi_image_prompt="<image><image>Describe the two images in detail.",  # noqa: E501
+        multi_image_prompt="<image><image>Describe the two images in detail.",
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -268,11 +268,11 @@ VLM_TEST_SETTINGS = {
         prompt_formatter=lambda img_prompt: f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{img_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",  # noqa: E501
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
-                "stop_sign": "<image>What's the content in the center of the image?",  # noqa: E501
-                "cherry_blossom": "<image>What is the season?",  # noqa: E501
+                "stop_sign": "<image>What's the content in the center of the image?",
+                "cherry_blossom": "<image>What is the season?",
             }
         ),
-        multi_image_prompt="<image><image>Describe the two images in detail.",  # noqa: E501
+        multi_image_prompt="<image><image>Describe the two images in detail.",
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -311,14 +311,14 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
-                "stop_sign": "<image>\nWhat's the content in the center of the image?",  # noqa: E501
+                "stop_sign": "<image>\nWhat's the content in the center of the image?",
                 "cherry_blossom": "<image>\nPlease infer the season with reason in details.",  # noqa: E501
             }
         ),
         multi_image_prompt="image_1:<image>\nimage_2:<image>\nWhich image can we see the car and the tower?",  # noqa: E501
         patch_hf_runner=model_utils.deepseekvl2_patch_hf_runner,
         hf_output_post_proc=model_utils.deepseekvl2_trunc_hf_output,
-        stop_str=["<｜end▁of▁sentence｜>", "<｜begin▁of▁sentence｜>"],  # noqa: E501
+        stop_str=["<｜end▁of▁sentence｜>", "<｜begin▁of▁sentence｜>"],
         image_size_factors=[(), (1.0,), (1.0, 1.0, 1.0), (0.1, 0.5, 1.0)],
     ),
     "fuyu": VLMTestInfo(
@@ -342,7 +342,7 @@ VLM_TEST_SETTINGS = {
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
                 "stop_sign": "<start_of_image>What's the content in the center of the image?",  # noqa: E501
-                "cherry_blossom": "<start_of_image>What is the season?",  # noqa: E501
+                "cherry_blossom": "<start_of_image>What is the season?",
             }
         ),
         multi_image_prompt="<start_of_image><start_of_image>Describe the two images in detail.",  # noqa: E501
@@ -356,7 +356,7 @@ VLM_TEST_SETTINGS = {
     "glm4v": VLMTestInfo(
         models=["zai-org/glm-4v-9b"],
         test_type=VLMTestType.IMAGE,
-        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|assistant|>",  # noqa: E501
+        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|assistant|>",
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
                 "stop_sign": "<|begin_of_image|><|endoftext|><|end_of_image|>What's the content in the center of the image?",  # noqa: E501
@@ -377,9 +377,9 @@ VLM_TEST_SETTINGS = {
     "glm4_1v": VLMTestInfo(
         models=["zai-org/GLM-4.1V-9B-Thinking"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
-        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|assistant|>",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<|begin_of_image|><|image|><|end_of_image|>",  # noqa: E501
-        video_idx_to_prompt=lambda idx: "<|begin_of_video|><|video|><|end_of_video|>",  # noqa: E501
+        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|assistant|>",
+        img_idx_to_prompt=lambda idx: "<|begin_of_image|><|image|><|end_of_image|>",
+        video_idx_to_prompt=lambda idx: "<|begin_of_video|><|video|><|end_of_video|>",
         max_model_len=2048,
         max_num_seqs=2,
         get_stop_token_ids=lambda tok: [151329, 151336, 151338],
@@ -410,10 +410,10 @@ VLM_TEST_SETTINGS = {
             "h2oai/h2ovl-mississippi-2b",
         ],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
-        prompt_formatter=lambda img_prompt: f"<|prompt|>{img_prompt}<|end|><|answer|>",  # noqa: E501
+        prompt_formatter=lambda img_prompt: f"<|prompt|>{img_prompt}<|end|><|answer|>",
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
-                "stop_sign": "<image>\nWhat's the content in the center of the image?",  # noqa: E501
+                "stop_sign": "<image>\nWhat's the content in the center of the image?",
                 "cherry_blossom": "<image>\nWhat is the season?",
             }
         ),
@@ -444,7 +444,7 @@ VLM_TEST_SETTINGS = {
         prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n",  # noqa: E501
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
-                "stop_sign": "<image>\nWhat's the content in the center of the image?",  # noqa: E501
+                "stop_sign": "<image>\nWhat's the content in the center of the image?",
                 "cherry_blossom": "<image>\nWhat is the season?",
             }
         ),
@@ -529,7 +529,7 @@ VLM_TEST_SETTINGS = {
         max_model_len=16384,
         hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs(
             "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
-        ),  # noqa: E501
+        ),
         auto_cls=AutoModelForImageTextToText,
         vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
         custom_test_opts=[
@@ -583,7 +583,7 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(
             ["<|im_end|>", "<|endoftext|>"]
-        ),  # noqa: E501
+        ),
         hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
         patch_hf_runner=model_utils.minicpmo_26_patch_hf_runner,
         # FIXME: https://huggingface.co/openbmb/MiniCPM-o-2_6/discussions/49
@@ -598,7 +598,7 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(
             ["<|im_end|>", "<|endoftext|>"]
-        ),  # noqa: E501
+        ),
         hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
         patch_hf_runner=model_utils.minicpmv_26_patch_hf_runner,
     ),
@@ -627,7 +627,7 @@ VLM_TEST_SETTINGS = {
         models=["AIDC-AI/Ovis1.6-Gemma2-9B"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
         prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<image>\n",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<image>\n",
         max_model_len=4096,
         max_num_seqs=2,
         dtype="half",
@@ -640,7 +640,7 @@ VLM_TEST_SETTINGS = {
         models=["AIDC-AI/Ovis2-1B"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
         prompt_formatter=lambda img_prompt: f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<image>\n",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<image>\n",
         max_model_len=4096,
         max_num_seqs=2,
         dtype="half",
@@ -652,7 +652,7 @@ VLM_TEST_SETTINGS = {
         models=["AIDC-AI/Ovis2.5-2B"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE, VLMTestType.VIDEO),
         prompt_formatter=lambda img_prompt: f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<image>\n",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<image>\n",
         video_idx_to_prompt=lambda idx: "<video>\n",
         max_model_len=4096,
         max_num_seqs=2,
@@ -701,8 +701,8 @@ VLM_TEST_SETTINGS = {
         models=["Qwen/Qwen2-VL-2B-Instruct"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE, VLMTestType.VIDEO),
         prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",  # noqa: E501
-        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",
+        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>",
         multi_image_prompt="Picture 1: <vlm_image>\nPicture 2: <vlm_image>\nDescribe these two images with one paragraph respectively.",  # noqa: E501
         max_model_len=4096,
         max_num_seqs=2,
@@ -717,11 +717,11 @@ VLM_TEST_SETTINGS = {
         prompt_formatter=lambda img_prompt: f"<｜begin▁of▁sentence｜><｜User｜>\n{img_prompt}<｜Assistant｜><think>\n",  # noqa: E501
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
-                "stop_sign": "<image>\nWhat's the content in the center of the image?",  # noqa: E501
+                "stop_sign": "<image>\nWhat's the content in the center of the image?",
                 "cherry_blossom": "<image>\nWhat is the season?",
             }
         ),
-        multi_image_prompt="<image>\n<image>\nDescribe the two images in short.",  # noqa: E501
+        multi_image_prompt="<image>\n<image>\nDescribe the two images in short.",
         max_model_len=4096,
         use_tokenizer_eos=True,
         patch_hf_runner=model_utils.skyworkr1v_patch_hf_runner,
@@ -754,8 +754,8 @@ VLM_TEST_SETTINGS = {
             VLMTestType.VIDEO,
         ),
         prompt_formatter=lambda img_prompt: f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",  # noqa: E501
-        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>",  # noqa: E501
+        img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",
+        video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>",
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -816,7 +816,7 @@ VLM_TEST_SETTINGS = {
         auto_cls=AutoModelForImageTextToText,
         hf_model_kwargs=model_utils.llava_onevision_hf_model_kwargs(
             "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
-        ),  # noqa: E501
+        ),
         vllm_output_post_proc=model_utils.llava_onevision_vllm_to_hf_output,
         custom_test_opts=[
             CustomTestOptions(

tests/models/multimodal/generation/test_ultravox.py

@@ -170,7 +170,7 @@ async def test_online_serving(client, audio_assets: AudioTestAssets):
                 ],
                 {
                     "type": "text",
-                    "text": f"What's happening in these {len(audio_assets)} audio clips?",
+                    "text": f"What's happening in these {len(audio_assets)} audio clips?",  # noqa: E501
                 },
             ],
         }

tests/models/multimodal/generation/test_voxtral.py

@@ -101,16 +101,11 @@ async def test_online_serving(client, audio_assets: AudioTestAssets):
         return audio_dict
 
     audio_chunks = [asset_to_chunk(asset) for asset in audio_assets]
+    text = f"What's happening in these {len(audio_assets)} audio clips?"
     messages = [
         {
             "role": "user",
-            "content": [
-                *audio_chunks,
-                {
-                    "type": "text",
-                    "text": f"What's happening in these {len(audio_assets)} audio clips?",
-                },
-            ],
+            "content": [*audio_chunks, {"type": "text", "text": text}],
         }
     ]
 

tests/models/multimodal/generation/vlm_utils/custom_inputs.py

@@ -102,8 +102,8 @@ def multi_video_multi_aspect_ratio_inputs(
 def different_patch_input_cases_internvl():
     images = [asset.pil_image.resize((896, 896)) for asset in IMAGE_ASSETS]
     formatter = (
-        lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n"
-    )  # noqa: E501
+        lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n"  # noqa: E501
+    )
     single_img_prompts = [
         "<image>\nWhat's the content in the center of the image?",
         "<image>\nWhat is the season?",