Phi-3-vision-128k-instruct / 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))