Spaces:
Runtime error
Runtime error
# coding=utf-8 | |
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. | |
# | |
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX | |
# and OPT implementations in this library. It has been modified from its | |
# original forms to accommodate minor architectural differences compared | |
# to GPT-NeoX and OPT used by the Meta AI team that trained the model. | |
# | |
# 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 Qwen2-VL. | |
""" | |
from typing import List, Optional, Union | |
from transformers.feature_extraction_utils import BatchFeature | |
from transformers.image_utils import ImageInput, VideoInput | |
from transformers.processing_utils import ProcessorMixin | |
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy | |
from transformers.utils import TensorType, logging | |
logger = logging.get_logger(__name__) | |
class Qwen2VLProcessor(ProcessorMixin): | |
r""" | |
Constructs a Qwen2-VL processor which wraps a Qwen2-VL image processor and a Qwen2 tokenizer into a single processor. | |
[`Qwen2VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the | |
[`~Qwen2VLProcessor.__call__`] and [`~Qwen2VLProcessor.decode`] for more information. | |
Args: | |
image_processor ([`Qwen2VLImageProcessor`], *optional*): | |
The image processor is a required input. | |
tokenizer ([`Qwen2TokenizerFast`], *optional*): | |
The tokenizer is a required input. | |
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages | |
in a chat into a tokenizable string. | |
""" | |
attributes = ["image_processor", "tokenizer"] | |
valid_kwargs = ["chat_template"] | |
image_processor_class = "Qwen2VLImageProcessor" | |
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast") | |
def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): | |
super().__init__(image_processor, tokenizer, chat_template=chat_template) | |
def __call__( | |
self, | |
images: ImageInput = None, | |
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, | |
videos: VideoInput = None, | |
padding: Union[bool, str, PaddingStrategy] = False, | |
truncation: Union[bool, str, TruncationStrategy] = None, | |
max_length: int = 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 Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode | |
the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to | |
Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`. | |
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. Both channels-first and channels-last formats are supported. | |
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). | |
videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): | |
The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch | |
tensor, or a nested list of 3D frames. 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`. | |
- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. | |
- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. | |
- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. | |
""" | |
if images is not None: | |
image_inputs = self.image_processor(images=images, videos=None, return_tensors=return_tensors) | |
image_grid_thw = image_inputs["image_grid_thw"] | |
else: | |
image_inputs = {} | |
image_grid_thw = None | |
if videos is not None: | |
videos_inputs = self.image_processor(images=None, videos=videos, return_tensors=return_tensors) | |
video_grid_thw = videos_inputs["video_grid_thw"] | |
else: | |
videos_inputs = {} | |
video_grid_thw = None | |
if not isinstance(text, list): | |
text = [text] | |
if image_grid_thw is not None: | |
merge_length = self.image_processor.merge_size**2 | |
index = 0 | |
for i in range(len(text)): | |
while "<|image_pad|>" in text[i]: | |
text[i] = text[i].replace( | |
"<|image_pad|>", "<|placeholder|>" * (image_grid_thw[index].prod() // merge_length), 1 | |
) | |
index += 1 | |
text[i] = text[i].replace("<|placeholder|>", "<|image_pad|>") | |
if video_grid_thw is not None: | |
merge_length = self.image_processor.merge_size**2 | |
index = 0 | |
for i in range(len(text)): | |
while "<|video_pad|>" in text[i]: | |
text[i] = text[i].replace( | |
"<|video_pad|>", "<|placeholder|>" * (video_grid_thw[index].prod() // merge_length), 1 | |
) | |
index += 1 | |
text[i] = text[i].replace("<|placeholder|>", "<|video_pad|>") | |
text_inputs = self.tokenizer( | |
text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length | |
) | |
return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}) | |
def batch_decode(self, *args, **kwargs): | |
""" | |
This method forwards all its arguments to Qwen2TokenizerFast'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 Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to | |
the docstring of this method for more information. | |
""" | |
return self.tokenizer.decode(*args, **kwargs) | |
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)) | |