# Copyright 2024 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional, Union from transformers.models.paligemma.processing_paligemma import IMAGE_TOKEN, PaliGemmaProcessor, build_string_from_input from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput, make_flat_list_of_images from ...processing_utils import ProcessingKwargs, Unpack from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...utils import is_torch_available, logging if is_torch_available(): import torch logger = logging.get_logger(__name__) class ColPaliProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "text_kwargs": { "padding": "longest", }, "images_kwargs": { "data_format": "channels_first", "do_convert_rgb": True, }, "common_kwargs": {"return_tensors": "pt"}, } class ColPaliProcessor(PaliGemmaProcessor): def __init__( self, image_processor=None, tokenizer=None, chat_template=None, visual_prompt_prefix: str = "Describe the image.", query_prefix: str = "Question: ", ): r""" visual_prompt_prefix (`str`, *optional*, defaults to `"Describe the image."`): A string that gets tokenized and prepended to the image tokens. query_prefix (`str`, *optional*, defaults to `"Question: "`): A prefix to be used for the query. """ self.visual_prompt_prefix = visual_prompt_prefix self.query_prefix = query_prefix super().__init__(image_processor=image_processor, tokenizer=tokenizer, chat_template=chat_template) @property def query_augmentation_token(self) -> str: """ Return the query augmentation token. Query augmentation buffers are used as reasoning buffers during inference. """ return self.tokenizer.pad_token def __call__( self, images: ImageInput | None = None, text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, **kwargs: Unpack[ColPaliProcessorKwargs], ) -> BatchFeature: r""" Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. """ output_kwargs = self._merge_kwargs( ColPaliProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) suffix = output_kwargs["text_kwargs"].pop("suffix", None) return_token_type_ids = True if text is None and images is None: raise ValueError("Either text or images must be provided") if text is not None and images is not None: raise ValueError("Only one of text or images can be processed at a time") if images is not None: images = self.image_processor.fetch_images(images) images = make_flat_list_of_images(images) texts_doc = [self.visual_prompt_prefix] * len(images) images = [image.convert("RGB") for image in images] input_strings = [ build_string_from_input( prompt=prompt, bos_token=self.tokenizer.bos_token, image_seq_len=self.image_seq_length, image_token=IMAGE_TOKEN, num_images=len(image_list) if isinstance(image_list, list) else 1, ) for prompt, image_list in zip(texts_doc, images) ] pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"] # max_length has to account for the image tokens if output_kwargs["text_kwargs"].get("max_length", None) is not None: output_kwargs["text_kwargs"]["max_length"] += self.image_seq_length inputs = self.tokenizer( input_strings, return_token_type_ids=return_token_type_ids, **output_kwargs["text_kwargs"], ) return_data = {**inputs, "pixel_values": pixel_values} if return_token_type_ids: labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100) return_data.update({"labels": labels}) return BatchFeature(data=return_data) elif text is not None: if isinstance(text, str): text = [text] elif not (isinstance(text, list) and isinstance(text[0], str)): raise ValueError("Text must be a string or a list of strings") if suffix is None: suffix = self.query_augmentation_token * 10 texts_query: list[str] = [] for query in text: query = self.tokenizer.bos_token + self.query_prefix + query + suffix + "\n" texts_query.append(query) output_kwargs["text_kwargs"]["max_length"] = output_kwargs["text_kwargs"].get("max_length", 50) batch_query = self.tokenizer( texts_query, return_token_type_ids=return_token_type_ids, **output_kwargs["text_kwargs"], ) return batch_query def process_images( self, images: ImageInput | None = None, **kwargs: Unpack[ColPaliProcessorKwargs], ) -> BatchFeature: """ Prepare for the model one or several image(s). This method is a wrapper around the `__call__` method of the ColPaliProcessor's [`ColPaliProcessor.__call__`]. This method forwards the `images` and `kwargs` arguments to the image processor. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. """ return self.__call__(images=images, **kwargs) def process_queries( self, text: TextInput | list[TextInput], **kwargs: Unpack[ColPaliProcessorKwargs], ) -> BatchFeature: """ Prepare for the model one or several texts. This method is a wrapper around the `__call__` method of the ColPaliProcessor's [`ColPaliProcessor.__call__`]. This method forwards the `text` and `kwargs` arguments to the tokenizer. Args: text (`str`, `list[str]`, `list[list[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). """ return self.__call__(text=text, **kwargs) def score_retrieval( self, query_embeddings: Union["torch.Tensor", list["torch.Tensor"]], passage_embeddings: Union["torch.Tensor", list["torch.Tensor"]], batch_size: int = 128, output_dtype: Optional["torch.dtype"] = None, output_device: Union["torch.device", str] = "cpu", ) -> "torch.Tensor": """ Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector query embeddings (`qs`) and passage embeddings (`ps`). For ColPali, a passage is the image of a document page. Because the embedding tensors are multi-vector and can thus have different shapes, they should be fed as: (1) a list of tensors, where the i-th tensor is of shape (sequence_length_i, embedding_dim) (2) a single tensor of shape (n_passages, max_sequence_length, embedding_dim) -> usually obtained by padding the list of tensors. Args: query_embeddings (`Union[torch.Tensor, list[torch.Tensor]`): Query embeddings. passage_embeddings (`Union[torch.Tensor, list[torch.Tensor]`): Passage embeddings. batch_size (`int`, *optional*, defaults to 128): Batch size for computing scores. output_dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): The dtype of the output tensor. If `None`, the dtype of the input embeddings is used. output_device (`torch.device` or `str`, *optional*, defaults to "cpu"): The device of the output tensor. Returns: `torch.Tensor`: A tensor of shape `(n_queries, n_passages)` containing the scores. The score tensor is saved on the "cpu" device. """ if len(query_embeddings) == 0: raise ValueError("No queries provided") if len(passage_embeddings) == 0: raise ValueError("No passages provided") if query_embeddings[0].device != passage_embeddings[0].device: raise ValueError("Queries and passages must be on the same device") if query_embeddings[0].dtype != passage_embeddings[0].dtype: raise ValueError("Queries and passages must have the same dtype") if output_dtype is None: output_dtype = query_embeddings[0].dtype scores: list[torch.Tensor] = [] for i in range(0, len(query_embeddings), batch_size): batch_scores: list[torch.Tensor] = [] batch_queries = torch.nn.utils.rnn.pad_sequence( query_embeddings[i : i + batch_size], batch_first=True, padding_value=0 ) for j in range(0, len(passage_embeddings), batch_size): batch_passages = torch.nn.utils.rnn.pad_sequence( passage_embeddings[j : j + batch_size], batch_first=True, padding_value=0 ) batch_scores.append( torch.einsum("bnd,csd->bcns", batch_queries, batch_passages).max(dim=3)[0].sum(dim=2) ) scores.append(torch.cat(batch_scores, dim=1).to(output_dtype).to(output_device)) return torch.cat(scores, dim=0) __all__ = [ "ColPaliProcessor", ]