Create processing_phi3_v.py

processing_phi3_v.py

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+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+
+"""
+Processor class for Phi3-V.
+"""
+import re
+from typing import List, Optional, Union
+
+import torch
+
+import transformers
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy
+from transformers.utils import TensorType
+from .image_processing_phi3_v import Phi3VImageProcessor 
+transformers.Phi3VImageProcessor = Phi3VImageProcessor 
+
+class Phi3VProcessor(ProcessorMixin):
+    r"""
+    Constructs a Phi3-V processor which wraps a Phi3-V image processor and a LLaMa tokenizer into a single processor.
+    [`Phi3VProcessor`] offers all the functionalities of [`Phi3VImageProcessor`] and [`LlamaTokenizerFast`]. See the
+    [`~Phi3VProcessor.__call__`] and [`~Phi3VProcessor.decode`] for more information.
+    Args:
+        image_processor ([`Phi3VImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`LlamaTokenizerFast`], *optional*):
+            The tokenizer is a required input.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "Phi3VImageProcessor"
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    special_image_token = "<|image|>"
+
+    def __init__(self, image_processor, tokenizer):
+        self.image_processor = image_processor
+        self.tokenizer = tokenizer
+        self.num_img_tokens = image_processor.num_img_tokens
+        self.img_tokens = [f"<|image_{i+1}|>" for i in range(1000000)]
+
+    def __call__(
+        self,
+        text: Union[TextInput, List[TextInput]],
+        images: ImageInput = None,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length=None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+        Phi3ImageProcessor's [`~Phi3ImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
+        of the above two methods for more information.
+        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).
+            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. Both channels-first and channels-last formats are supported.
+            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
+                Select a strategy to pad the returned sequences (according to the model's padding side and padding
+                index) among:
+                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+                  sequence if provided).
+                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+                  acceptable input length for the model if that argument is not provided.
+                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+                  lengths).
+            max_length (`int`, *optional*):
+                Maximum length of the returned list and optionally padding length (see above).
+            truncation (`bool`, *optional*):
+                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **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`.
+        """
+        if images is not None:
+            image_inputs = self.image_processor(images, return_tensors=return_tensors)
+        else:
+            image_inputs = {}
+        inputs = self._convert_images_texts_to_inputs(image_inputs, text, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors)
+        return inputs
+
+    def calc_num_image_tokens(self, images: ImageInput):
+        """ Calculate the number of image tokens for each image.
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+        """
+        return self.image_processor.calc_num_image_tokens(images)
+        
+    def calc_num_image_tokens_from_image_size(self, width, height):
+        """ Calculate the number of image token for an image with given width and height.
+        Args:
+            width (`int`):
+                Width of the image.
+            height (`int`):
+                Height of the image.
+        """
+        return self.image_processor.calc_num_image_tokens_from_image_size(width, height)
+    
+    
+    @property 
+    def special_image_token_id(self):
+        return self.tokenizer.convert_tokens_to_ids(self.special_image_token)
+
+    def get_special_image_token_id(self):
+        return self.tokenizer.convert_tokens_to_ids(self.special_image_token)
+    
+    def _convert_images_texts_to_inputs(self, images, texts, padding=False, truncation=None, max_length=None, return_tensors=None):
+
+        if not len(images):
+            model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length)
+            return BatchFeature(data={**model_inputs})
+
+        pattern = r"<\|image_\d+\|>"
+        prompt_chunks = [self.tokenizer(chunk).input_ids for chunk in re.split(pattern, texts)] 
+
+        if 'num_img_tokens' in images:
+            num_img_tokens = images['num_img_tokens']
+        else:
+            assert 'num_crops' in images, 'num_crops must be provided in images if num_img_tokens is not provided'
+            num_crops = images['num_crops']
+            num_img_tokens = [_num_crops * self.num_img_tokens for _num_crops in num_crops] 
+
+        images, image_sizes = images['pixel_values'], images['image_sizes']
+
+        # image_tags needs to start from 1 to n
+        image_tags = re.findall(pattern, texts) 
+        # image_ids = [int(s.split("|")[1].split("_")[-1]) * -1 for s in image_tags]
+        # image_ids_pad = [[iid]*num_img_tokens[i] for i, iid in enumerate(image_ids)]
+        image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags]
+        unique_image_ids = sorted(list(set(image_ids)))
+        # image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be [1, 4, 5]
+        # check the condition
+        assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}"
+        # total images must be the same as the number of image tags
+        assert len(unique_image_ids) == len(images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(images)} images"
+
+        image_ids_pad = [[-iid]*num_img_tokens[iid-1] for iid in image_ids]
+
+        def insert_separator(X, sep_list):
+            if len(X) > len(sep_list):
+                sep_list.append([])
+            return [ele for sublist in zip(X, sep_list) for ele in sublist]
+        input_ids = []
+        offset = 0                
+        for x in insert_separator(prompt_chunks, image_ids_pad):
+            input_ids.extend(x[offset:])
+
+        input_ids = torch.tensor(input_ids, dtype=torch.long).unsqueeze(0)
+        attention_mask = (input_ids > -1000000).to(torch.long)
+
+        return BatchFeature(data={"input_ids": input_ids,
+                                  "attention_mask": attention_mask,
+                                  "pixel_values": images, 
+                                  "image_sizes": image_sizes})
+
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))