409 lines
18 KiB
Python
409 lines
18 KiB
Python
# coding=utf-8
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# Copyright 2022 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 IDEFICS.
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"""
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from typing import Callable, List, Optional, Union
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from urllib.parse import urlparse
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from ...feature_extraction_utils import BatchFeature
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from ...processing_utils import ProcessorMixin
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from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
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from ...utils import TensorType, is_torch_available
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if is_torch_available():
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import torch
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IMAGE_TOKEN = "<image>"
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# copied from m4.training.packing
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def incremental_to_binary_attention_mask(incremental_mask, num_classes=-1):
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# This function converts: [-1, 0, 1] => [[0, 0], [1, 0], [0, 1]]
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# If any of images index are more than num_classes, set them to -1.
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# Words after the max number of images allowed have been seen don't attend on anything
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if num_classes != -1:
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incremental_mask[incremental_mask >= num_classes] = -1
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negatives = incremental_mask == -1
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incremental_mask[negatives] = 0
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attn_mask = torch.nn.functional.one_hot(incremental_mask, num_classes=num_classes)
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attn_mask[negatives, :] = 0
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return attn_mask
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# copied from m4.training.packing
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def image_attention_mask_for_packed_input_ids(input_ids, tokenizer):
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image_attention_mask = torch.full_like(input_ids, fill_value=-1)
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next_image_attention_mask = torch.full_like(input_ids, fill_value=-1)
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image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
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eod_token_id = tokenizer.eos_token_id
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for batch_idx in range(input_ids.size(0)):
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count = -1
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seen_eod = False
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for idx, token_id in enumerate(input_ids[batch_idx]):
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if token_id == image_token_id:
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count += 1
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image_attention_mask[batch_idx][idx] = count
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seen_eod = False
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else:
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image_attention_mask[batch_idx][idx] = count
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if seen_eod:
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image_attention_mask[batch_idx][idx] = -1
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if token_id == eod_token_id:
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seen_eod = True
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for batch_idx in range(input_ids.size(0)):
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count = -1
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seen_eod = False
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for idx in range(input_ids[batch_idx].size(0) - 1, -1, -1):
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token_id = input_ids[batch_idx][idx]
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if token_id == image_token_id:
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count += 1
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next_image_attention_mask[batch_idx][idx] = count
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seen_eod = False
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else:
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next_image_attention_mask[batch_idx][idx] = count
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if token_id == eod_token_id:
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seen_eod = True
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if seen_eod:
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next_image_attention_mask[batch_idx][idx] = -1
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non_negative_indices = next_image_attention_mask[batch_idx] != -1
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next_image_attention_mask[batch_idx][non_negative_indices] -= count
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next_image_attention_mask[batch_idx][non_negative_indices] *= -1
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return image_attention_mask, next_image_attention_mask
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def is_url(string):
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"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
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invalidated the url"""
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if " " in string:
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return False
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result = urlparse(string)
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return all([result.scheme, result.netloc])
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class IdeficsProcessor(ProcessorMixin):
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r"""
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Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
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[`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] 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 (`IdeficsImageProcessor`):
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An instance of [`IdeficsImageProcessor`]. The image processor is a required input.
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tokenizer (`LlamaTokenizerFast`):
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An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
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image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "IdeficsImageProcessor"
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tokenizer_class = "LlamaTokenizerFast"
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def __init__(self, image_processor, tokenizer=None, image_size=224, add_end_of_utterance_token=None, **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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super().__init__(image_processor, tokenizer)
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self.current_processor = self.image_processor
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self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
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self.default_image_dims = (
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self.image_processor.image_num_channels,
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self.image_processor.image_size,
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self.image_processor.image_size,
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)
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self.tokenizer_was_trained_with_end_of_utterance_token = (
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True
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if "<end_of_utterance>" in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
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else False
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)
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def __call__(
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self,
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prompts: Union[List[TextInput], List[List[TextInput]]],
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padding: Union[bool, str, PaddingStrategy] = "longest",
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truncation: Union[bool, str, TruncationStrategy] = None,
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max_length: Optional[int] = None,
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transform: Callable = None,
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add_eos_token=False,
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add_end_of_utterance_token=None,
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debug=False,
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return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
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) -> BatchEncoding:
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"""This method takes batched or non-batched prompts made of text and images and converts them into prompts that
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the model was trained on and prepares the image pixel values for the model to process.
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Args:
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prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
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either a single prompt or a batched list of prompts - see the detailed description immediately after
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the end of the arguments doc section.
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padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `"longest"`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding
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index) among:
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- `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
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sequence if provided).
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- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
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acceptable input length for the model if that argument is not provided.
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- `False` or `'do_not_pad'`: No padding. This will raise an error if the input sequences are of different
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lengths.
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Note: Unlike most processors, which set padding=`False` by default, `IdeficsProcessor` sets `padding="longest"`
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by default. See https://github.com/huggingface/transformers/pull/29449#pullrequestreview-1925576061 for why.
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max_length (`int`, *optional*):
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Maximum length of the returned list and optionally padding length (see above).
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truncation (`bool`, *optional*):
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Activates truncation to cut input sequences longer than `max_length` to `max_length`.
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transform (`Callable`, *optional*):
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A custom transform function that accepts a single image can be passed for training. For example,
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`torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
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set of transforms will be applied to the images
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add_eos_token (`bool`, *optional*, defaults to `False`):
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Adds `eos_token` at the end of the final prompt if True`
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add_end_of_utterance_token (`bool`, *optional*)
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Whether to automatically add `<end_of_utterance>` after each prompt's text input (unless followed by an
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image). If `None` the tokenizer will be checked instead and if this token is found in
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`additional_special_tokens` then the value will be `True`.
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debug (`bool`, *optional*, defaults to `False`):
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`True` value will help debug prompt generation by dumping useful information
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return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
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The type of tensors to return. Can be one of:
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- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
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Returns:
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a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
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directly passed to `model.generate`
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Detailed explanation:
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Each entry in `prompts` is either a text to be passed as is or an image that will be processed.
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An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.
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When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
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entry into the prompt.
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Example:
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```python
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checkpoint = "HuggingFaceM4/idefics-9b"
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processor = AutoProcessor.from_pretrained(checkpoint)
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url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
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img = processor.image_processor.fetch_images([url])[0]
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prompts = [
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"User:",
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img,
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"Describe this image.\nAssistant: An image of two kittens in grass.\n",
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"User:",
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"https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
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"Describe this image.\nAssistant:",
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]
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inputs = processor(prompts, return_tensors="pt")
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generated_ids = model.generate(**inputs, max_length=100)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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In this example the `prompts` will be converted into:
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```
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<s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
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Assistant: An image of two kittens in grass.
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User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
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Assistant:'
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```
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and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
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`pixel_values` dict entry of the return value.
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This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
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first image is passed as object and the second one as a url.
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To do training do:
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```python
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image_transform = transforms.Compose(
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[
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transforms.RandomResizedCrop(
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(w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
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),
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transforms.ToTensor(),
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transforms.Normalize(mean=self.image_mean, std=self.image_std),
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]
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)
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inputs = processor(prompts, transform=image_transform, return_tensors="pt")
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```
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In order to help debug prompt generation enable `debug=True` which will show you what's happening.
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"""
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# if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
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if add_end_of_utterance_token is None:
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add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token
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# turn non-batched prompts into batched
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if not any(isinstance(i, list) for i in prompts):
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prompts = [prompts]
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fake_token = "<fake_token_around_image>"
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image_token = "<image>"
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end_of_utterance_token = "<end_of_utterance>"
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def image_tokens(last_was_image):
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if last_was_image:
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return image_token + fake_token
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else:
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return fake_token + image_token + fake_token
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all_prompts = []
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all_images = []
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for sample in prompts:
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# the model was trained on samples starting with <s>
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full_text = f"{self.tokenizer.bos_token}"
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# an image can either be an image object in the item or the url, everything else is a verbatim prompt text
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image_objects = []
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last_was_image = False
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last_was_text = False
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for i, item in enumerate(sample):
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if i > 0:
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last_was_text = True if not last_was_image else False
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if isinstance(item, str):
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item = item.strip(" ")
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if is_url(item):
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image = self.image_processor.fetch_images(item)
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full_text += image_tokens(last_was_image)
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image_objects.append(image)
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last_was_image = True
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else:
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# we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
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if add_end_of_utterance_token and last_was_text:
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full_text += end_of_utterance_token
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full_text += item
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last_was_image = False
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else:
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# must be an image obj
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full_text += image_tokens(last_was_image)
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image_objects.append(item)
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last_was_image = True
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if add_eos_token:
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full_text += self.tokenizer.eos_token
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if debug is True:
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print(f"{full_text=}")
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image_objects = self.image_processor(image_objects, transform=transform)
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all_prompts.append(full_text)
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all_images.append(image_objects)
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text_encoding = self.tokenizer(
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text=all_prompts,
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add_special_tokens=False,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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)
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all_texts = text_encoding["input_ids"]
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all_attention_masks = text_encoding["attention_mask"]
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# max_num_images has to be at least 1 even when there are no images
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max_num_images = max(len(x) for x in all_images)
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max_num_images = max(1, max_num_images)
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at_least_one_image = sum(len(x) for x in all_images) > 0
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output_input_ids = []
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output_images = []
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output_attention_masks = []
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for text, attention_mask, images in zip(all_texts, all_attention_masks, all_images):
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padded_input_ids = text
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image_count = padded_input_ids.count(self.image_token_id)
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local_max_num_images = min(image_count, max_num_images)
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current_images = images[:local_max_num_images]
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if len(current_images) > 0:
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padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
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padded_image_tensor[: current_images.size(0)] = current_images
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else:
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padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)
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output_images.append(padded_image_tensor)
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output_input_ids.append(torch.tensor(padded_input_ids))
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output_attention_masks.append(torch.tensor(attention_mask))
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output_input_ids = torch.stack(output_input_ids)
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output_images = torch.stack(output_images)
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output_attention_masks = torch.stack(output_attention_masks)
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if at_least_one_image:
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image_attention_mask, _ = image_attention_mask_for_packed_input_ids(output_input_ids, self.tokenizer)
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image_attention_mask = incremental_to_binary_attention_mask(
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image_attention_mask, num_classes=max_num_images
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)
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else:
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# in full language mode we set the image mask to all-0s
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image_attention_mask = torch.zeros(
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output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool
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)
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return BatchFeature(
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data={
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"input_ids": output_input_ids,
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"attention_mask": output_attention_masks,
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"pixel_values": output_images,
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"image_attention_mask": image_attention_mask,
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}
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)
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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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