349 lines
16 KiB
Python
349 lines
16 KiB
Python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Processor class for IDEFICS2.
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"""
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from typing import TYPE_CHECKING, Dict, List, Optional, Union
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from ...feature_extraction_utils import BatchFeature
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from ...image_utils import ImageInput, is_valid_image, load_image
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from ...processing_utils import ProcessorMixin
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from ...tokenization_utils_base import AddedToken, BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
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from ...utils import TensorType, logging
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if TYPE_CHECKING:
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from ...pipelines.conversational import Conversation
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from ...tokenization_utils_base import PreTokenizedInput
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logger = logging.get_logger(__name__)
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def is_url(val) -> bool:
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return isinstance(val, str) and val.startswith("http")
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def is_image_or_image_url(elem):
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return is_url(elem) or is_valid_image(elem)
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class Idefics2Processor(ProcessorMixin):
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r"""
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Constructs a IDEFICS2 processor which wraps a LLama tokenizer and IDEFICS2 image processor into a single processor.
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[`IdeficsProcessor`] offers all the functionalities of [`Idefics2ImageProcessor`] and [`LlamaTokenizerFast`]. See
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the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
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Args:
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image_processor (`Idefics2ImageProcessor`):
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An instance of [`Idefics2ImageProcessor`]. The image processor is a required input.
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tokenizer (`PreTrainedTokenizerBase`, *optional*):
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An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
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image_seq_len (`int`, *optional*, defaults to 64):
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The length of the image sequence i.e. the number of <image> tokens per image in the input.
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This parameter is used to build the string from the input prompt and image tokens and should match the
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config.perceiver_config.resampler_n_latents value for the model used.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "Idefics2ImageProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(self, image_processor, tokenizer=None, image_seq_len: int = 64, **kwargs):
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if image_processor is None:
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raise ValueError("You need to specify an `image_processor`.")
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if tokenizer is None:
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raise ValueError("You need to specify a `tokenizer`.")
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self.fake_image_token = AddedToken("<fake_token_around_image>", normalized=False, special=True)
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self.image_token = AddedToken("<image>", normalized=False, special=True)
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self.end_of_utterance_token = AddedToken("<end_of_utterance>", normalized=False, special=True)
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self.image_seq_len = image_seq_len
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tokens_to_add = {
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"additional_special_tokens": [self.fake_image_token, self.image_token, self.end_of_utterance_token]
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}
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tokenizer.add_special_tokens(tokens_to_add)
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# Stores a Jinja template that formats chat histories into tokenizable strings
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self.chat_template = kwargs.pop("chat_template", None)
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super().__init__(image_processor, tokenizer)
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def _extract_images_from_prompts(self, prompts):
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prompt_images = []
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for prompt in prompts:
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images = []
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for elem in prompt:
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if is_valid_image(elem):
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images.append(elem)
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elif is_url(elem):
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images.append(load_image(elem))
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prompt_images.append(images)
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return prompt_images
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def __call__(
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self,
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text: Union[TextInput, "PreTokenizedInput", List[TextInput], List["PreTokenizedInput"]] = None,
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images: Union[ImageInput, List[ImageInput], List[List[ImageInput]]] = None,
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image_seq_len: Optional[int] = None,
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padding: Union[bool, str, PaddingStrategy] = False,
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truncation: Union[bool, str, TruncationStrategy] = None,
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max_length: Optional[int] = None,
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is_split_into_words: bool = False,
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add_special_tokens: bool = True,
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return_tensors: Optional[Union[str, TensorType]] = None,
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) -> BatchEncoding:
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"""
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Processes the input prompts and returns a BatchEncoding.
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Example:
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```python
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>>> import requests
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>>> from transformers import Idefics2Processor
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>>> from transformers.image_utils import load_image
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>>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2)
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>>> processor.image_processor.do_image_splitting = False # Force as False to simplify the example
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>>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
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>>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg"
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>>> image1, image2 = load_image(url1), load_image(url2)
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>>> images = [[image1], [image2]]
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>>> text = [
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... "<image>In this image, we see",
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... "bla bla bla<image>",
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... ]
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>>> outputs = processor(text=text, images=images, return_tensors="pt", padding=True)
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>>> input_ids = outputs.input_ids
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>>> input_tokens = processor.tokenizer.batch_decode(input_ids)
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>>> print(input_tokens)
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['<s><fake_token_around_image><image><image><fake_token_around_image> In this image, we see', '<s> bla bla bla<fake_token_around_image><image><image><fake_token_around_image>']
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```
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Args:
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text (`Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]`, *optional*):
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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Wherever an image token, `<image>` is encountered it is expanded to
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`<fake_token_around_image>` + `<image>` * `image_seq_len` * <fake_token_around_image>`.
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images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*):
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The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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tensor. If is of type `List[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1.
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image_seq_len (`int`, *optional*):
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The length of the image sequence. If not provided, the default value is used.
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padding (`Union[bool, str, PaddingStrategy]`, *optional*, defaults to `False`):
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Padding strategy applied to the input ids. See [`PreTrainedTokenizerFast.pad`] for more information.
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truncation (`Union[bool, str, TruncationStrategy]`, *optional*):
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Truncation strategy applied to the input ids. See [`PreTrainedTokenizerFast.truncate`] for more information.
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max_length (`int`, *optional*):
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Maximum length of the returned list and optionally padding/truncation length. See
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[`PreTrainedTokenizerFast.__call__`] for more information.
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is_split_into_words (`bool`, *optional*, defaults to `False`):
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Whether the input text is split into words or not. If set to `True`, the tokenizer will skip the
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tokenization process and assume the input is already tokenized.
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add_special_tokens (`bool`, *optional*, defaults to `True`):
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Whether to add special tokens or not. See [`PreTrainedTokenizerFast.__call__`] for more information.
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return_tensors (`Union[str, TensorType]`, *optional*):
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If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more
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information.
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"""
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image_seq_len = image_seq_len if image_seq_len is not None else self.image_seq_len
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n_images_in_text = []
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inputs = BatchFeature()
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if text is not None:
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if isinstance(text, str):
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text = [text]
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elif not isinstance(text, list) and not isinstance(text[0], str):
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raise ValueError("Invalid input text. Please provide a string, or a list of strings")
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# Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
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fake_image_token = self.fake_image_token.content
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image_token = self.image_token.content
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image_str = f"{fake_image_token}{image_token * image_seq_len}{fake_image_token}"
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if self.image_processor.do_image_splitting:
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# A single image token is split into 4 patches + 1 original image
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image_str = image_str * 5
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prompt_strings = []
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for sample in text:
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n_images_in_text.append(sample.count(image_token))
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sample = sample.replace(image_token, image_str)
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# Remove any double fake tokens if images are adjacent
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sample = sample.replace(f"{fake_image_token}{fake_image_token}", f"{fake_image_token}")
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prompt_strings.append(sample)
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text_inputs = self.tokenizer(
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text=prompt_strings,
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add_special_tokens=add_special_tokens,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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is_split_into_words=is_split_into_words,
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return_tensors=return_tensors,
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)
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inputs.update(text_inputs)
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if images is not None:
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if is_image_or_image_url(images):
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images = [[images]]
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elif isinstance(images, list) and is_image_or_image_url(images[0]):
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images = [images]
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elif (
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not isinstance(images, list)
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and not isinstance(images[0], list)
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and not is_image_or_image_url(images[0][0])
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):
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raise ValueError(
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"Invalid input images. Please provide a single image or a list of images or a list of list of images."
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)
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n_images_in_images = [len(sample) for sample in images]
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if text is not None and not n_images_in_images == n_images_in_text:
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raise ValueError(
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f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same."
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)
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# Load images if they are URLs
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images = [[load_image(im) for im in sample] for sample in images]
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image_inputs = self.image_processor(images, return_tensors=return_tensors)
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inputs.update(image_inputs)
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return inputs
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
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the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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def apply_chat_template(
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self,
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conversation: Union[List[Dict[str, str]], "Conversation"],
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chat_template: Optional[str] = None,
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tokenize: bool = False,
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**kwargs,
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) -> str:
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"""
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Overrides the tokenizer's `apply_chat_template` method to apply the IDEFICS2 chat template by default
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if no chat template is provided.
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By default, the output isn't tokenized. This is because the IDEFICS2 chat template is designed to insert
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the image token <image> into the sequence according to the message, but does not handle expanding the image
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tokens to the sequence length or adding the surrounding tokens e.g. <fake_image_token>.
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Args:
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conversation (`Union[List[Dict, str, str], "Conversation"]`):
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The conversation to format.
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chat_template (`Optional[str]`, *optional*):
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The Jinja template to use for formatting the conversation. If not provided, the default chat template
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is used.
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tokenize (`bool`, *optional*, defaults to `False`):
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Whether to tokenize the output or not.
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**kwargs:
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Additional keyword arguments for the tokenizer's `apply_chat_template` method.
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"""
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if chat_template is None:
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if self.chat_template is not None:
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chat_template = self.chat_template
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else:
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chat_template = self.default_chat_template
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return self.tokenizer.apply_chat_template(
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conversation, chat_template=chat_template, tokenize=tokenize, **kwargs
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)
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@property
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def default_chat_template(self):
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"""
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This template formats inputs in the form of a chat history. For each message in the chat history:
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* the template will output the role of the speaker followed by the content of the message.
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* content can be a single string or a list of strings and images.
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* If the content element is an image, the template will output a sequence of <image> tokens and <fake_token_around_image> token before and after each image
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* The template will output an <end_of_utterance> token at the end of each message.
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Example:
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```python
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messages = [{
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"role": "user",
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"content": [
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{"type": "text", "text": "What’s in this image?"},
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{"type": "image"},
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{"type": "image"},
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],
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},
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{
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"role": "assistant",
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"content": [{"type": "text", "text": "This picture depicts Idefix, the dog of Obelix in Asterix and Obelix. Idefix is running on the ground."},]
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}]
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```
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Will create outputs like:
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```
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User: What is in this Image?<image><image><end_of_utterance>
|
|||
|
|
Assistant: This picture depicts Idefix, the dog of Obelix in Asterix and Obelix. Idefix is running on the ground.<end_of_utterance>
|
|||
|
|
```
|
|||
|
|
"""
|
|||
|
|
# fmt: off
|
|||
|
|
return (
|
|||
|
|
"{% for message in messages %}"
|
|||
|
|
"{{message['role'].capitalize()}}"
|
|||
|
|
"{% if message['content'][0]['type'] == 'image' %}"
|
|||
|
|
"{{':'}}"
|
|||
|
|
"{% else %}"
|
|||
|
|
"{{': '}}"
|
|||
|
|
"{% endif %}"
|
|||
|
|
"{% for line in message['content'] %}"
|
|||
|
|
"{% if line['type'] == 'text' %}"
|
|||
|
|
"{{line['text']}}"
|
|||
|
|
"{% elif line['type'] == 'image' %}"
|
|||
|
|
"{{ '<image>' }}"
|
|||
|
|
"{% endif %}"
|
|||
|
|
"{% endfor %}"
|
|||
|
|
"<end_of_utterance>\n"
|
|||
|
|
"{% endfor %}"
|
|||
|
|
|
|||
|
|
"{% if add_generation_prompt %}"
|
|||
|
|
"{{ 'Assistant:' }}"
|
|||
|
|
"{% endif %}"
|
|||
|
|
)
|
|||
|
|
# fmt: on
|