545 lines
23 KiB
Python
545 lines
23 KiB
Python
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# 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, 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 ...image_utils import ImageInput
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from ...processing_utils import (
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ImagesKwargs,
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ProcessingKwargs,
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ProcessorMixin,
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TextKwargs,
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Unpack,
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)
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...utils import is_tf_available, is_torch_available
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from ...utils.deprecation import deprecate_kwarg
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if is_torch_available():
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import torch
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if is_tf_available():
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import tensorflow as tf
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IMAGE_TOKEN = "<image>"
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class IdeficsImagesKwargs(ImagesKwargs, total=False):
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transform: Optional[Callable]
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image_size: Optional[dict[str, int]]
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image_mean: Optional[Union[float, list[float]]]
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image_std: Optional[Union[float, list[float]]]
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class IdeficsTextKwargs(TextKwargs, total=False):
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add_eos_token: Optional[bool]
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add_end_of_utterance_token: Optional[bool]
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class IdeficsProcessorKwargs(ProcessingKwargs, total=False):
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text_kwargs: IdeficsTextKwargs
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images_kwargs: IdeficsImagesKwargs
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_defaults = {
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"text_kwargs": {
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"add_special_tokens": False,
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"padding": "longest",
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"add_eos_token": False,
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},
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"images_kwargs": {},
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"common_kwargs": {"return_tensors": "pt"},
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}
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# copied from m4.training.packing
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def incremental_to_binary_attention_mask(incremental_mask, return_tensors, num_classes=-1):
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# Set elements >= num_classes to -1
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if num_classes != -1:
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if return_tensors == "pt":
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incremental_mask[incremental_mask >= num_classes] = -1
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elif return_tensors == "tf":
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incremental_mask = tf.where(incremental_mask >= num_classes, -1, incremental_mask)
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# Create mask for negative values
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if return_tensors == "pt":
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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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elif return_tensors == "tf":
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negatives = tf.equal(incremental_mask, -1)
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incremental_mask = tf.where(negatives, 0, incremental_mask)
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attn_mask = tf.one_hot(incremental_mask, depth=num_classes)
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# Reshape 'negatives' to add an extra dimension, making it [batch_size, seq_length, 1]
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negatives_expanded = tf.expand_dims(negatives, -1)
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attn_mask = tf.where(negatives_expanded, tf.zeros_like(attn_mask), attn_mask)
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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, return_tensors):
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if return_tensors == "pt":
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return image_attention_mask_for_packed_input_ids_pt(input_ids, tokenizer)
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elif return_tensors == "tf":
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return image_attention_mask_for_packed_input_ids_tf(input_ids, tokenizer)
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def image_attention_mask_for_packed_input_ids_pt(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 image_attention_mask_for_packed_input_ids_tf(input_ids, tokenizer):
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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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batch_size = tf.shape(input_ids)[0]
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image_attention_mask = tf.fill(tf.shape(input_ids), -1)
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next_image_attention_mask = tf.fill(tf.shape(input_ids), -1)
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for batch_idx in range(batch_size):
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count = -1
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seen_eod = False
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seq_length = tf.shape(input_ids)[1]
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for idx in range(seq_length - 1, -1, -1):
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token_id = input_ids[batch_idx, idx].numpy()
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if token_id == image_token_id:
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count += 1
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indices = [[batch_idx, idx]]
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updates = [count]
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image_attention_mask = tf.tensor_scatter_nd_update(image_attention_mask, indices, updates)
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next_image_attention_mask = tf.tensor_scatter_nd_update(next_image_attention_mask, indices, updates)
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elif token_id == eod_token_id and not seen_eod:
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seen_eod = True
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count = 0
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indices = [[batch_idx, idx]]
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updates = [count]
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next_image_attention_mask = tf.tensor_scatter_nd_update(next_image_attention_mask, indices, updates)
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if seen_eod and token_id != eod_token_id:
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indices = [[batch_idx, idx]]
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updates = [-1]
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next_image_attention_mask = tf.tensor_scatter_nd_update(next_image_attention_mask, indices, updates)
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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):
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Image size (assuming a square image)
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add_end_of_utterance_token (`str`, *optional*):
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The string representation of token representing end of utterance
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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 = (
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tokenizer.image_token_id
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if hasattr(tokenizer, "image_token")
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else tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
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)
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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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@deprecate_kwarg(old_name="prompts", version="5.0.0", new_name="text", raise_if_both_names=True)
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def __call__(
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self,
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images: Union[ImageInput, list[ImageInput], str, list[str], list[list[str]]] = None,
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text: Union[
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TextInput,
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PreTokenizedInput,
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list[TextInput],
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list[PreTokenizedInput],
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list[list[TextInput]],
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list[list[PreTokenizedInput]],
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] = None,
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audio=None,
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videos=None,
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**kwargs: Unpack[IdeficsProcessorKwargs],
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) -> BatchFeature:
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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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images (`Union[ImageInput, list[ImageInput], str, list[str], list[list[str]]]`):
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either a single image or a batched list of images - can be passed in when text contains only text prompts,
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in order to use the image-text-to-text behavior.
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text (`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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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 `text` 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(text=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 exemplifies 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(text=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 images is None and text is None:
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raise ValueError("You need to specify either `text` or `images` and `text`.")
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if images is None:
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# assuming the user wants to use the old behavior with prompts as the only argument
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prompts = text
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elif text is not None:
|
||
|
|
# Assuming image-text-to-text behavior:
|
||
|
|
# Check if batched images are provided
|
||
|
|
if not isinstance(images, (list, tuple)):
|
||
|
|
images = [images]
|
||
|
|
if isinstance(text, str):
|
||
|
|
text = [text]
|
||
|
|
# Check if batched images and text are in the correct format
|
||
|
|
if isinstance(text, (list, tuple)) and len(text) != len(images):
|
||
|
|
raise ValueError(
|
||
|
|
"When providing both images and text arguments, the number of text prompts should be the same as the number of images."
|
||
|
|
"If you want to have several images per prompt, images should be nested as such: images=[[img1, img2], [img3, img4], ...] for text=[prompt1, prompt2, ...]."
|
||
|
|
)
|
||
|
|
# Check that only text is present in the prompts
|
||
|
|
if not all(isinstance(i, str) for i in text):
|
||
|
|
raise ValueError("When using the image-text-to-text behavior, the prompts should only contain text.")
|
||
|
|
if isinstance(images[0], (list, tuple)):
|
||
|
|
# if nested images, nest text as well
|
||
|
|
text = [[i] for i in text]
|
||
|
|
prompts = list(zip(images, text))
|
||
|
|
|
||
|
|
output_kwargs = self._merge_kwargs(
|
||
|
|
IdeficsProcessorKwargs,
|
||
|
|
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
add_eos_token = output_kwargs["text_kwargs"].pop("add_eos_token", False)
|
||
|
|
add_end_of_utterance_token = output_kwargs["text_kwargs"].pop("add_end_of_utterance_token", None)
|
||
|
|
|
||
|
|
# if the value isn't overridden by the user, check if the tokenizer was trained with this token and then use it
|
||
|
|
if add_end_of_utterance_token is None:
|
||
|
|
add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token
|
||
|
|
# turn non-batched prompts into batched
|
||
|
|
if not any(isinstance(i, (list, tuple)) for i in prompts):
|
||
|
|
prompts = [prompts]
|
||
|
|
|
||
|
|
fake_token = "<fake_token_around_image>"
|
||
|
|
image_token = "<image>"
|
||
|
|
end_of_utterance_token = "<end_of_utterance>"
|
||
|
|
|
||
|
|
def image_tokens(last_was_image):
|
||
|
|
if last_was_image:
|
||
|
|
return image_token + fake_token
|
||
|
|
else:
|
||
|
|
return fake_token + image_token + fake_token
|
||
|
|
|
||
|
|
all_prompts = []
|
||
|
|
all_images = []
|
||
|
|
for sample in prompts:
|
||
|
|
# the model was trained on samples starting with <s>
|
||
|
|
full_text = f"{self.tokenizer.bos_token}"
|
||
|
|
|
||
|
|
# an image can either be an image object in the item or the url, everything else is a verbatim prompt text
|
||
|
|
image_objects = []
|
||
|
|
last_was_image = False
|
||
|
|
last_was_text = False
|
||
|
|
for i, item in enumerate(sample):
|
||
|
|
if i > 0:
|
||
|
|
last_was_text = True if not last_was_image else False
|
||
|
|
|
||
|
|
if isinstance(item, str):
|
||
|
|
item = item.strip(" ")
|
||
|
|
if is_url(item):
|
||
|
|
image = self.image_processor.fetch_images(item)
|
||
|
|
full_text += image_tokens(last_was_image)
|
||
|
|
image_objects.append(image)
|
||
|
|
last_was_image = True
|
||
|
|
else:
|
||
|
|
# we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
|
||
|
|
if add_end_of_utterance_token and last_was_text:
|
||
|
|
full_text += end_of_utterance_token
|
||
|
|
full_text += item
|
||
|
|
last_was_image = False
|
||
|
|
else:
|
||
|
|
# must be an image obj
|
||
|
|
full_text += image_tokens(last_was_image)
|
||
|
|
image_objects.append(item)
|
||
|
|
last_was_image = True
|
||
|
|
|
||
|
|
if add_eos_token:
|
||
|
|
full_text += self.tokenizer.eos_token
|
||
|
|
|
||
|
|
image_objects = self.image_processor(image_objects, **output_kwargs["images_kwargs"])
|
||
|
|
|
||
|
|
all_prompts.append(full_text)
|
||
|
|
all_images.append(image_objects)
|
||
|
|
|
||
|
|
# For BC
|
||
|
|
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", "pt")
|
||
|
|
text_encoding = self.tokenizer(all_prompts, **output_kwargs["text_kwargs"])
|
||
|
|
all_texts = text_encoding["input_ids"]
|
||
|
|
all_attention_masks = text_encoding["attention_mask"]
|
||
|
|
|
||
|
|
# max_num_images has to be at least 1 even when there are no images
|
||
|
|
max_num_images = max(len(x) for x in all_images)
|
||
|
|
max_num_images = max(1, max_num_images)
|
||
|
|
|
||
|
|
at_least_one_image = sum(len(x) for x in all_images) > 0
|
||
|
|
output_input_ids = []
|
||
|
|
output_images = []
|
||
|
|
output_attention_masks = []
|
||
|
|
|
||
|
|
for text_single, attention_mask, extracted_images in zip(all_texts, all_attention_masks, all_images):
|
||
|
|
padded_input_ids = text_single
|
||
|
|
image_count = padded_input_ids.count(self.image_token_id)
|
||
|
|
local_max_num_images = min(image_count, max_num_images)
|
||
|
|
|
||
|
|
current_images = extracted_images[:local_max_num_images]
|
||
|
|
|
||
|
|
if len(current_images) > 0:
|
||
|
|
if return_tensors == "pt":
|
||
|
|
padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
|
||
|
|
padded_image_tensor[: current_images.size(0)] = current_images
|
||
|
|
elif return_tensors == "tf":
|
||
|
|
# Assuming current_images is a TensorFlow tensor
|
||
|
|
# Get the shape of current_images, excluding the first dimension
|
||
|
|
image_shape = tf.shape(current_images)[1:]
|
||
|
|
# Create a shape for the padded_image_tensor
|
||
|
|
padded_shape = tf.concat([[max_num_images], image_shape], axis=0)
|
||
|
|
# Create the padded_image_tensor of zeros
|
||
|
|
padded_image_tensor = tf.zeros(padded_shape, dtype=current_images.dtype)
|
||
|
|
# Get the number of images (assuming current_images has shape [num_images, height, width, channels])
|
||
|
|
num_images = tf.shape(current_images)[0]
|
||
|
|
# Update the padded_image_tensor with the values from current_images
|
||
|
|
indices = tf.reshape(tf.range(num_images), (-1, 1))
|
||
|
|
updates = current_images
|
||
|
|
padded_image_tensor = tf.tensor_scatter_nd_update(padded_image_tensor, indices, updates)
|
||
|
|
else:
|
||
|
|
if return_tensors == "pt":
|
||
|
|
padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)
|
||
|
|
elif return_tensors == "tf":
|
||
|
|
padded_image_tensor = tf.zeros((max_num_images, *self.default_image_dims))
|
||
|
|
|
||
|
|
output_images.append(padded_image_tensor)
|
||
|
|
if return_tensors == "pt":
|
||
|
|
output_input_ids.append(torch.tensor(padded_input_ids))
|
||
|
|
output_attention_masks.append(torch.tensor(attention_mask))
|
||
|
|
elif return_tensors == "tf":
|
||
|
|
output_input_ids.append(tf.convert_to_tensor(padded_input_ids, dtype=tf.int32))
|
||
|
|
output_attention_masks.append(attention_mask)
|
||
|
|
|
||
|
|
if return_tensors == "pt":
|
||
|
|
output_input_ids = torch.stack(output_input_ids)
|
||
|
|
output_images = torch.stack(output_images)
|
||
|
|
output_attention_masks = torch.stack(output_attention_masks)
|
||
|
|
elif return_tensors == "tf":
|
||
|
|
output_input_ids = tf.stack(output_input_ids)
|
||
|
|
output_images = tf.stack(output_images)
|
||
|
|
output_attention_masks = tf.stack(output_attention_masks)
|
||
|
|
|
||
|
|
if at_least_one_image:
|
||
|
|
image_attention_mask, _ = image_attention_mask_for_packed_input_ids(
|
||
|
|
output_input_ids, self.tokenizer, return_tensors
|
||
|
|
)
|
||
|
|
image_attention_mask = incremental_to_binary_attention_mask(
|
||
|
|
image_attention_mask, return_tensors, num_classes=max_num_images
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
# in full language mode we set the image mask to all-0s
|
||
|
|
if return_tensors == "pt":
|
||
|
|
image_attention_mask = torch.zeros(
|
||
|
|
output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool
|
||
|
|
)
|
||
|
|
elif return_tensors == "tf":
|
||
|
|
image_attention_mask = tf.zeros(
|
||
|
|
(output_input_ids.shape[0], output_input_ids.shape[1], 1), dtype=tf.bool
|
||
|
|
)
|
||
|
|
return BatchFeature(
|
||
|
|
data={
|
||
|
|
"input_ids": output_input_ids,
|
||
|
|
"attention_mask": output_attention_masks,
|
||
|
|
"pixel_values": output_images,
|
||
|
|
"image_attention_mask": image_attention_mask,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
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
|
||
|
|
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))
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = ["IdeficsProcessor"]
|