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3dddbf1f25
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn> Signed-off-by: Isotr0py <2037008807@qq.com>
364 lines
14 KiB
Python
364 lines
14 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# yapf: disable
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# ruff: noqa: E501
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# coding=utf-8
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# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/ff23960c5cf9e6874b44be38af930cfb0ccbb620/deepseek_vl2/models/processing_deepseek_vl_v2.py
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# Copyright (c) 2023-2024 DeepSeek.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy of
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# this software and associated documentation files (the "Software"), to deal in
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# the Software without restriction, including without limitation the rights to
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# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
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# the Software, and to permit persons to whom the Software is furnished to do so,
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# subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
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# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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import math
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import torch
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import torchvision.transforms as T
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from PIL import Image, ImageOps
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from transformers import AutoProcessor, BatchFeature, LlamaTokenizerFast
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from transformers.processing_utils import ProcessorMixin
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class ImageTransform:
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def __init__(self,
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mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
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std: tuple[float, float, float] = (0.5, 0.5, 0.5),
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normalize: bool = True):
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self.mean = mean
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self.std = std
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self.normalize = normalize
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transform_pipelines = [T.ToTensor()]
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if normalize:
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transform_pipelines.append(T.Normalize(mean, std))
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self.transform = T.Compose(transform_pipelines)
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def __call__(self, pil_img: Image.Image):
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x = self.transform(pil_img)
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return x
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class DeepseekVLV2Processor(ProcessorMixin):
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tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
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attributes = ["tokenizer"]
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def __init__(
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self,
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tokenizer: LlamaTokenizerFast,
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candidate_resolutions: tuple[tuple[int, int]],
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patch_size: int,
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downsample_ratio: int,
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image_mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
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image_std: tuple[float, float, float] = (0.5, 0.5, 0.5),
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normalize: bool = True,
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image_token: str = "<image>",
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pad_token: str = "<｜▁pad▁｜>",
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add_special_token: bool = False,
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sft_format: str = "deepseek",
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mask_prompt: bool = True,
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ignore_id: int = -100,
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**kwargs,
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):
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self.candidate_resolutions = candidate_resolutions
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self.image_size = candidate_resolutions[0][0]
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self.patch_size = patch_size
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self.image_mean = image_mean
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self.image_std = image_std
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self.normalize = normalize
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self.downsample_ratio = downsample_ratio
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self.image_transform = ImageTransform(mean=image_mean, std=image_std, normalize=normalize)
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self.tokenizer = tokenizer
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self.tokenizer.padding_side = 'left' # must set this，padding side with make a difference in batch inference
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# add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id'
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if tokenizer.pad_token is None:
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self.tokenizer.add_special_tokens({'pad_token': pad_token})
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# add image token
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image_token_id = self.tokenizer.vocab.get(image_token)
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if image_token_id is None:
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special_tokens = [image_token]
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special_tokens_dict = {"additional_special_tokens": special_tokens}
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self.tokenizer.add_special_tokens(special_tokens_dict)
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self.image_token_id = self.tokenizer.vocab.get(image_token)
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# add five special tokens for grounding-related tasks
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# <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|>
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special_tokens = ['<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>']
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special_tokens_dict = {"additional_special_tokens": special_tokens}
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self.tokenizer.add_special_tokens(special_tokens_dict)
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# add special tokens for SFT data
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special_tokens = ["<|User|>", "<|Assistant|>"]
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special_tokens_dict = {"additional_special_tokens": special_tokens}
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self.tokenizer.add_special_tokens(special_tokens_dict)
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self.image_token = image_token
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self.pad_token = pad_token
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self.add_special_token = add_special_token
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self.sft_format = sft_format
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self.mask_prompt = mask_prompt
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self.ignore_id = ignore_id
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super().__init__(
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tokenizer,
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**kwargs,
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)
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def select_best_resolution(self, image_size):
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# used for cropping
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original_width, original_height = image_size
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best_fit = None
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max_effective_resolution = 0
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min_wasted_resolution = float("inf")
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for width, height in self.candidate_resolutions:
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scale = min(width / original_width, height / original_height)
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downscaled_width, downscaled_height = int(
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original_width * scale), int(original_height * scale)
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effective_resolution = min(downscaled_width * downscaled_height,
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original_width * original_height)
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wasted_resolution = (width * height) - effective_resolution
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if effective_resolution > max_effective_resolution or (
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effective_resolution == max_effective_resolution
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and wasted_resolution < min_wasted_resolution):
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max_effective_resolution = effective_resolution
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min_wasted_resolution = wasted_resolution
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best_fit = (width, height)
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return best_fit
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@property
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def bos_id(self):
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return self.tokenizer.bos_token_id
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@property
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def eos_id(self):
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return self.tokenizer.eos_token_id
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@property
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def pad_id(self):
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return self.tokenizer.pad_token_id
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def encode(self, text: str, bos: bool = True, eos: bool = False):
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t = self.tokenizer.encode(text, add_special_tokens=False)
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if bos:
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t = [self.bos_id] + t
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if eos:
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t = t + [self.eos_id]
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return t
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def decode(self, t: list[int], **kwargs) -> str:
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return self.tokenizer.decode(t, **kwargs)
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def process_one(
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self,
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prompt: str,
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images: list[Image.Image],
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inference_mode: bool = True,
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**kwargs,
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):
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"""
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Args:
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prompt (str): the formatted prompt;
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conversations (list[dict]): conversations with a list of messages;
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images (list[ImageType]): the list of images;
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inference_mode (bool): if True, then remove the last eos token;
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system_prompt (str): the system prompt;
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**kwargs:
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Returns:
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outputs (BaseProcessorOutput): the output of the processor,
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- input_ids (torch.LongTensor): [N + image tokens]
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- target_ids (torch.LongTensor): [N + image tokens]
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- pixel_values (torch.FloatTensor): [n_patches, 3, H, W]
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- image_id (int): the id of the image token
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- num_image_tokens (list[int]): the number of image tokens
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"""
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assert (prompt is not None and images is not None
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), "prompt and images must be used at the same time."
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sft_format = prompt
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tokenized_str, images_list, images_seq_mask, images_spatial_crop, num_image_tokens = self.tokenize_with_images(
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sft_format, images, bos=True, eos=True, cropping=len(images) <= 2)
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masked_tokenized_str = []
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for token_index in tokenized_str:
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if token_index != self.image_token_id:
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masked_tokenized_str.append(token_index)
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else:
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masked_tokenized_str.append(self.ignore_id)
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assert len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str), \
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(f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
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f"imags_seq_mask's length {len(images_seq_mask)}, are not equal")
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input_ids = torch.LongTensor(tokenized_str)
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target_ids = torch.LongTensor(masked_tokenized_str)
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images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
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# set input_ids < 0 | input_ids == self.image_token_id as ignore_id
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target_ids[(input_ids < 0) |
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(input_ids == self.image_token_id)] = self.ignore_id
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input_ids[input_ids < 0] = self.pad_id
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if inference_mode:
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# Remove the ending eos token
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assert input_ids[-1] == self.eos_id
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input_ids = input_ids[:-1]
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target_ids = target_ids[:-1]
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images_seq_mask = images_seq_mask[:-1]
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if len(images_list) == 0:
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pixel_values = torch.zeros((1, 3, self.image_size, self.image_size))
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images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
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else:
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pixel_values = torch.stack(images_list, dim=0)
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images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
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input_ids = input_ids.unsqueeze(0)
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prepare = BatchFeature(
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data=dict(
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input_ids=input_ids,
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pixel_values=pixel_values,
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images_seq_mask=images_seq_mask,
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images_spatial_crop=images_spatial_crop,
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num_image_tokens=num_image_tokens,
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),
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tensor_type="pt",
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)
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return prepare
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def __call__(
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self,
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*,
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text: str,
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images: list[Image.Image],
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inference_mode: bool = True,
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**kwargs,
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):
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"""
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Args:
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text (str): the formatted prompt;
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images (list[ImageType]): the list of images;
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inference_mode (bool): if True, then remove the last eos token;
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**kwargs:
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Returns:
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outputs (BaseProcessorOutput): the output of the processor,
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- input_ids (torch.LongTensor): [N + image tokens]
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- images (torch.FloatTensor): [n_images, 3, H, W]
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- image_id (int): the id of the image token
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- num_image_tokens (list[int]): the number of image tokens
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"""
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prepare = self.process_one(
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prompt=text,
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images=images,
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inference_mode=inference_mode,
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)
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return prepare
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def tokenize_with_images(
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self,
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conversation: str,
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images: list[Image.Image],
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bos: bool = True,
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eos: bool = True,
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cropping: bool = True,
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):
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"""Tokenize text with <image> tags."""
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assert conversation.count(self.image_token) == len(images)
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text_splits = conversation.split(self.image_token)
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images_list, images_seq_mask, images_spatial_crop = [], [], []
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num_image_tokens = []
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tokenized_str = []
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for text_sep, image in zip(text_splits, images):
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"""encode text_sep"""
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tokenized_sep = self.encode(text_sep, bos=False, eos=False)
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tokenized_str += tokenized_sep
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images_seq_mask += [False] * len(tokenized_sep)
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"""select best resolution for anyres"""
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if cropping:
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best_width, best_height = self.select_best_resolution(image.size)
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else:
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best_width, best_height = self.image_size, self.image_size
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"""process the global view"""
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global_view = ImageOps.pad(image, (self.image_size, self.image_size),
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color=tuple(int(x * 255) for x in self.image_transform.mean))
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images_list.append(self.image_transform(global_view))
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"""process the local views"""
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local_view = ImageOps.pad(image, (best_width, best_height),
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color=tuple(int(x * 255) for x in self.image_transform.mean))
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for i in range(0, best_height, self.image_size):
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for j in range(0, best_width, self.image_size):
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images_list.append(
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self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size))))
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"""record height / width crop num"""
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num_width_tiles, num_height_tiles = best_width // self.image_size, best_height // self.image_size
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images_spatial_crop.append([num_width_tiles, num_height_tiles])
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"""add image tokens"""
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h = w = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio)
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# global views tokens h * (w + 1), 1 is for line separator
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tokenized_image = [self.image_token_id] * h * (w + 1)
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# add a separator between global and local views
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tokenized_image += [self.image_token_id]
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# local views tokens, (num_height_tiles * h) * (num_width_tiles * w + 1)
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tokenized_image += [self.image_token_id] * (num_height_tiles * h) * (num_width_tiles * w + 1)
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tokenized_str += tokenized_image
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images_seq_mask += [True] * len(tokenized_image)
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num_image_tokens.append(len(tokenized_image))
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"""process the last text split"""
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tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
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tokenized_str += tokenized_sep
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images_seq_mask += [False] * len(tokenized_sep)
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"""add the bos and eos tokens"""
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if bos:
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tokenized_str = [self.bos_id] + tokenized_str
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images_seq_mask = [False] + images_seq_mask
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if eos:
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tokenized_str = tokenized_str + [self.eos_id]
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images_seq_mask = images_seq_mask + [False]
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assert len(tokenized_str) == len(
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images_seq_mask), f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
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return tokenized_str, images_list, images_seq_mask, images_spatial_crop, num_image_tokens
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AutoProcessor.register("DeepseekVLV2Processor", DeepseekVLV2Processor)
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