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fix condition where torch_dtype auto collides with model_kwargs. (#39054)

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* fix condition where torch_dtype auto collides with model_kwargs.

* update tests

* update comment

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
vb
2025-06-26 14:52:57 +02:00
committed by GitHub
parent 44b231671d
commit d973e62fdd
3 changed files with 19 additions and 11 deletions
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8
src/transformers/pipelines/__init__.py
View File

@@ -1005,10 +1005,18 @@ def pipeline(
model_kwargs["device_map"] = device_map model_kwargs["device_map"] = device_map
if torch_dtype is not None: if torch_dtype is not None:
if "torch_dtype" in model_kwargs: if "torch_dtype" in model_kwargs:
# If the user did not explicitly provide `torch_dtype` (i.e. the function default "auto" is still
# present) but a value is supplied inside `model_kwargs`, we silently defer to the latter instead of
# raising. This prevents false positives like providing `torch_dtype` only via `model_kwargs` while the
# top-level argument keeps its default value "auto".
if torch_dtype == "auto":
torch_dtype = None
else:
raise ValueError( raise ValueError(
'You cannot use both `pipeline(... torch_dtype=..., model_kwargs={"torch_dtype":...})` as those' 'You cannot use both `pipeline(... torch_dtype=..., model_kwargs={"torch_dtype":...})` as those'
" arguments might conflict, use only one.)" " arguments might conflict, use only one.)"
) )
if torch_dtype is not None:
if isinstance(torch_dtype, str) and hasattr(torch, torch_dtype): if isinstance(torch_dtype, str) and hasattr(torch, torch_dtype):
torch_dtype = getattr(torch, torch_dtype) torch_dtype = getattr(torch, torch_dtype)
model_kwargs["torch_dtype"] = torch_dtype model_kwargs["torch_dtype"] = torch_dtype

4
tests/pipelines/test_pipelines_image_text_to_text.py
View File

@@ -161,11 +161,11 @@ class ImageTextToTextPipelineTests(unittest.TestCase):
[ [
{ {
"input_text": "<image> What this is? Assistant: This is", "input_text": "<image> What this is? Assistant: This is",
"generated_text": "<image> What this is? Assistant: This is a photo of two cats lying on a pink blanket. The cats are sleeping and appear to be comfortable", "generated_text": "<image> What this is? Assistant: This is a photo of two cats lying on a pink blanket. The cats are facing the camera, and they",
}, },
{ {
"input_text": "<image> What this is? Assistant: This is", "input_text": "<image> What this is? Assistant: This is",
"generated_text": "<image> What this is? Assistant: This is a photo of two cats lying on a pink blanket. The cats are sleeping and appear to be comfortable", "generated_text": "<image> What this is? Assistant: This is a photo of two cats lying on a pink blanket. The cats are facing the camera, and they",
}, },
], ],
) )

4
tests/pipelines/test_pipelines_text_generation.py
View File

@@ -441,11 +441,11 @@ class TextGenerationPipelineTests(unittest.TestCase):
[{"generated_text": ("This is a test test test test test test")}], [{"generated_text": ("This is a test test test test test test")}],
) )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602 # torch_dtype will be automatically set to torch.bfloat16 if not provided - check: https://github.com/huggingface/transformers/pull/38882
pipe = pipeline( pipe = pipeline(
model="hf-internal-testing/tiny-random-bloom", device_map="auto", max_new_tokens=5, do_sample=False model="hf-internal-testing/tiny-random-bloom", device_map="auto", max_new_tokens=5, do_sample=False
) )
self.assertEqual(pipe.model.lm_head.weight.dtype, torch.float32) self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
out = pipe("This is a test") out = pipe("This is a test")
self.assertEqual( self.assertEqual(
out, out,