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[Bugfix] Fix extra whitespace in strings caused by newline (#23272)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-08-21 13:03:00 +08:00
committed by GitHub
parent f571ff8eb6
commit 0c31e28e95
7 changed files with 30 additions and 27 deletions
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6
benchmarks/benchmark_dataset.py
View File

@ -958,8 +958,10 @@ class InstructCoderDataset(HuggingFaceDataset):
for i, item in enumerate(self.data):
if len(sampled_requests) >= num_requests:
break
prompt = f"{item['input']}\n\n{item['instruction']} Just output \
the code, do not include any explanation."
prompt = (
f"{item['input']}\n\n{item['instruction']} Just output "
"the code, do not include any explanation."
)
# apply template
prompt = tokenizer.apply_chat_template(

15
examples/offline_inference/vision_language.py
View File

@ -283,8 +283,10 @@ def run_glm4v(questions: list[str], modality: str) -> ModelRequestData:
)
prompts = [
f"<|user|>\n<|begin_of_image|><|endoftext|><|end_of_image|>\
{question}<|assistant|>"
(
"<|user|>\n<|begin_of_image|><|endoftext|><|end_of_image|>"
f"{question}<|assistant|>"
)
for question in questions
]
@ -767,15 +769,13 @@ def run_llava_next_video(questions: list[str], modality: str) -> ModelRequestDat
def run_llava_onevision(questions: list[str], modality: str) -> ModelRequestData:
if modality == "video":
prompts = [
f"<|im_start|>user <video>\n{question}<|im_end|> \
<|im_start|>assistant\n"
f"<|im_start|>user <video>\n{question}<|im_end|><|im_start|>assistant\n"
for question in questions
]
elif modality == "image":
prompts = [
f"<|im_start|>user <image>\n{question}<|im_end|> \
<|im_start|>assistant\n"
f"<|im_start|>user <image>\n{question}<|im_end|><|im_start|>assistant\n"
for question in questions
]
@ -998,8 +998,7 @@ def run_molmo(questions: list[str], modality: str) -> ModelRequestData:
)
prompts = [
f"<|im_start|>user <image>\n{question}<|im_end|> \
<|im_start|>assistant\n"
f"<|im_start|>user <image>\n{question}<|im_end|><|im_start|>assistant\n"
for question in questions
]

6
vllm/benchmarks/datasets.py
View File

@ -1289,8 +1289,10 @@ class InstructCoderDataset(HuggingFaceDataset):
for i, item in enumerate(self.data):
if len(sampled_requests) >= num_requests:
break
prompt = f"{item['input']}\n\n{item['instruction']} Just output \
the code, do not include any explanation."
prompt = (
f"{item['input']}\n\n{item['instruction']} Just output "
"the code, do not include any explanation."
)
# apply template
prompt = tokenizer.apply_chat_template(

11
vllm/model_executor/model_loader/tpu.py
View File

@ -98,14 +98,15 @@ class TPUModelLoader(DefaultModelLoader):
# Check parameters
for name, param in model.named_parameters():
assert param.device.type == device_type, f"Parameter {name} is on \
{param.device.type} instead of {device_type}"
assert param.device.type == device_type, (
f"Parameter {name} is on {param.device.type} "
f"instead of {device_type}")
# Check buffers
for name, buffer in model.named_buffers():
assert buffer.device.type == device_type, \
f"Buffer {name} is on {buffer.device.type} instead of \
{device_type}"
assert buffer.device.type == device_type, (
f"Buffer {name} is on {buffer.device.type} "
f"instead of {device_type}")
for module in model.modules():
if (mesh is not None) and (get_fqn(module) == 'QKVParallelLinear'):

9
vllm/model_executor/models/hyperclovax_vision.py
View File

@ -929,8 +929,8 @@ class HCXVisionForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
target_group_size = 0
elif video_group_size < target_group_size:
raise RuntimeError(f"video_group_size < target_group_size!! \
[{video_group_size} < {target_group_size}]")
raise RuntimeError(
f"{video_group_size=} < {target_group_size=}")
assert len(target_features
) == 0, f"target_features is not empty!! {target_features}"
@ -1114,9 +1114,8 @@ def reshape_and_unpad_image_features(
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
assert (height * width == base_image_feature.shape[0]
), f"height: {height}, width: {width}, \
base_image_feature.shape[0]: {base_image_feature.shape[0]}"
assert height * width == base_image_feature.shape[0], (
f"{height=} * {width=} != {base_image_feature.shape[0]=}")
num_patch_width, num_patch_height = get_anyres_image_grid_shape(
image_size, possible_resolutions, grid_size)

6
vllm/model_executor/models/phi4mm.py
View File

@ -262,9 +262,9 @@ class Phi4MMImageEncoder(nn.Module):
img_features.shape[1]))
assert base_feat_height == base_feat_height_target \
and base_feat_width == base_feat_height_target, \
f'base_feat_height: {base_feat_height},"\
f" base_feat_width: {base_feat_width}, "\
f"expect {base_feat_height_target} features for hd transform'
(f"base_feat_height: {base_feat_height}, "
f"base_feat_width: {base_feat_width}, "
f"expect {base_feat_height_target} features for hd transform")
# bs x max_num_crops x (24x24) x C
img_features = img_features.view(bs, -1,

4
vllm/transformers_utils/configs/eagle.py
View File

@ -61,8 +61,8 @@ class EAGLEConfig(PretrainedConfig):
else f"Eagle3{arch}" for arch in self.model.architectures
]
else:
raise ValueError(f"Invalid method {method}. \
Supported methods are eagle and eagle3.")
raise ValueError(f"Invalid method {method}. "
"Supported methods are eagle and eagle3.")
super().__init__(**kwargs)