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[Zero-shot image classification pipeline] Remove tokenizer_kwargs #33174

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[Zero-shot image classification pipeline] Remove tokenizer_kwargs #33174

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First draft
NielsRogge Aug 28, 2024
abdf5c5
Pass attribute to from_pretrained
NielsRogge Aug 29, 2024
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2 changes: 2 additions & 0 deletions src/transformers/models/blip_2/convert_blip_2_original_to_pytorch.py
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Expand Up @@ -155,6 +155,8 @@ def convert_blip2_checkpoint(
else:
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")

tokenizer.model_input_names = ["input_ids", "attention_mask"]

@amyeroberts amyeroberts Aug 29, 2024

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This is pretty hacky. It should at the very least only be done in the BertTokenizer branch to make explicit why this is needed (including an explanatory comment)

@NielsRogge NielsRogge Aug 29, 2024

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I don't see what's hacky about this, model_input_names is a genuine attribute of PretrainedTokenizer that has been there since BERT/GPT-2 came out (it's not used a lot). As can be seen here, a tokenizer will return token_type_ids if it's present in the model_input_names.

A less "hacky" way would be to pass model_input_names directly in the from_pretrained method, which the Transformers library also supports.

@NielsRogge NielsRogge Aug 29, 2024 •
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What I'm trying to say is that one can avoid lines like these by appropriately setting the model_input_names attribute of the tokenizer, which is something we can still do for the Blip2ImageTextRetrieval models on the hub as they are just added and not yet in a stable release.

@amyeroberts amyeroberts Aug 29, 2024

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Ah, yes, I see. Although both modifying model_input_names and return_token_ids are somewhat hacky: they modify a class attribute which might be depended upon for other behaviours / assumed to have certain properties. Passing in model_input_names to the init is better, as this is more likely to correctly propagate any changes to any other dependant attributes / variables.


if "itm" in model_name:
eos_token_id = None
else:
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12 changes: 2 additions & 10 deletions src/transformers/pipelines/zero_shot_image_classification.py
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Expand Up @@ -97,9 +97,6 @@ def __call__(self, images: Union[str, List[str], "Image", List["Image"]], **kwar
The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
the call may block forever.

tokenizer_kwargs (`dict`, *optional*):
Additional dictionary of keyword arguments passed along to the tokenizer.

Comment on lines -100 to -102

@amyeroberts amyeroberts Aug 29, 2024

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I don't think this should be removed. tokenizer_kwargs is a fairly standard input to _sanitize_parameters as a way to control tokenizer behaviour. It would also be breaking for anyone using this in their pipelines.

We might be able to remove in the tests but it should stay here

@NielsRogge NielsRogge Aug 29, 2024

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Hmm could you clarify? tokenizer_kwargs was added in #29261 which is not yet in a stable release. Hence removing this argument wouldn't break anything

@amyeroberts amyeroberts Aug 29, 2024

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Ah, OK, in this case we can remove!

Return:
A list of dictionaries containing one entry per proposed label. Each dictionary contains the
following keys:
Expand All @@ -109,16 +106,14 @@ def __call__(self, images: Union[str, List[str], "Image", List["Image"]], **kwar
"""
return super().__call__(images, **kwargs)

def _sanitize_parameters(self, tokenizer_kwargs=None, **kwargs):
def _sanitize_parameters(self, **kwargs):
preprocess_params = {}
if "candidate_labels" in kwargs:
preprocess_params["candidate_labels"] = kwargs["candidate_labels"]
if "timeout" in kwargs:
preprocess_params["timeout"] = kwargs["timeout"]
if "hypothesis_template" in kwargs:
preprocess_params["hypothesis_template"] = kwargs["hypothesis_template"]
if tokenizer_kwargs is not None:
preprocess_params["tokenizer_kwargs"] = tokenizer_kwargs

return preprocess_params, {}, {}

Expand All @@ -128,18 +123,15 @@ def preprocess(
candidate_labels=None,
hypothesis_template="This is a photo of {}.",
timeout=None,
tokenizer_kwargs=None,
):
if tokenizer_kwargs is None:
tokenizer_kwargs = {}
image = load_image(image, timeout=timeout)
inputs = self.image_processor(images=[image], return_tensors=self.framework)
if self.framework == "pt":
inputs = inputs.to(self.torch_dtype)
inputs["candidate_labels"] = candidate_labels
sequences = [hypothesis_template.format(x) for x in candidate_labels]
padding = "max_length" if self.model.config.model_type == "siglip" else True
text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=padding, **tokenizer_kwargs)
text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=padding)
inputs["text_inputs"] = [text_inputs]
return inputs

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2 changes: 0 additions & 2 deletions tests/pipelines/test_pipelines_zero_shot_image_classification.py
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Expand Up @@ -292,7 +292,6 @@ def test_blip2_model_pt(self):
output = image_classifier(
image,
candidate_labels=["2 cats", "a plane", "a remote"],
tokenizer_kwargs={"return_token_type_ids": False},
)

self.assertEqual(
Expand All @@ -308,7 +307,6 @@ def test_blip2_model_pt(self):
[image] * 5,
candidate_labels=["2 cats", "a plane", "a remote"],
batch_size=2,
tokenizer_kwargs={"return_token_type_ids": False},
)

self.assertEqual(
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