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Merged
merged 2 commits into from
Sep 12, 2025
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Merged

Improve torch_dtype checks #40808

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cc5af93
Improve torch_dtype checks
cyyever Sep 11, 2025
db69d85
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gante Sep 12, 2025
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10 changes: 8 additions & 2 deletions src/transformers/commands/chat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -289,8 +289,14 @@ class ChatArguments:
def __post_init__(self):
"""Only used for BC `torch_dtype` argument."""
# In this case only the BC torch_dtype was given
if self.torch_dtype is not None and self.dtype == "auto":
self.dtype = self.torch_dtype
if self.torch_dtype is not None:
if self.dtype is None:
self.dtype = self.torch_dtype
elif self.torch_dtype != self.dtype:
raise ValueError(
f"`torch_dtype` {self.torch_dtype} and `dtype` {self.dtype} have different values. `torch_dtype` is deprecated and "
"will be removed in 4.59.0, please set `dtype` instead."
)


def chat_command_factory(args: Namespace):
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10 changes: 8 additions & 2 deletions src/transformers/commands/serving.py
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Expand Up @@ -457,8 +457,14 @@ class ServeArguments:
def __post_init__(self):
"""Only used for BC `torch_dtype` argument."""
# In this case only the BC torch_dtype was given
if self.torch_dtype is not None and self.dtype == "auto":
self.dtype = self.torch_dtype
if self.torch_dtype is not None:
if self.dtype is None:
self.dtype = self.torch_dtype
elif self.torch_dtype != self.dtype:
raise ValueError(
f"`torch_dtype` {self.torch_dtype} and `dtype` {self.dtype} have different values. `torch_dtype` is deprecated and "
"will be removed in 4.59.0, please set `dtype` instead."
)


class ServeCommand(BaseTransformersCLICommand):
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2 changes: 1 addition & 1 deletion src/transformers/pipelines/keypoint_matching.py
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Expand Up @@ -147,7 +147,7 @@ def __call__(
def preprocess(self, images, timeout=None):
images = [load_image(image, timeout=timeout) for image in images]
model_inputs = self.image_processor(images=images, return_tensors=self.framework)
model_inputs = model_inputs.to(self.torch_dtype)
model_inputs = model_inputs.to(self.dtype)
target_sizes = [image.size for image in images]
preprocess_outputs = {"model_inputs": model_inputs, "target_sizes": target_sizes}
return preprocess_outputs
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