# Copyright (c) 2023-2024 DeepSeek. # # Permission is hereby granted, free of charge, to any person obtaining a copy of # this software and associated documentation files (the "Software"), to deal in # the Software without restriction, including without limitation the rights to # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of # the Software, and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS # FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. from dataclasses import dataclass from typing import Dict, Tuple, List, Literal, Optional import math import torch from torch.nn.utils.rnn import pad_sequence import torchvision.transforms as T from transformers import LlamaTokenizerFast from transformers.processing_utils import ProcessorMixin from PIL import Image, ImageOps from .conversation import get_conv_template def select_best_resolution(image_size, candidate_resolutions): # used for cropping original_width, original_height = image_size best_fit = None max_effective_resolution = 0 min_wasted_resolution = float("inf") for width, height in candidate_resolutions: scale = min(width / original_width, height / original_height) downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale) effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height) wasted_resolution = (width * height) - effective_resolution if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution): max_effective_resolution = effective_resolution min_wasted_resolution = wasted_resolution best_fit = (width, height) return best_fit class DictOutput(object): def keys(self): return self.__dict__.keys() def __getitem__(self, item): return self.__dict__[item] def __setitem__(self, key, value): self.__dict__[key] = value # 对于inference sample也可以维护input_ids，反正最后不会用到 @dataclass class VLChatProcessorOutput(DictOutput): sft_format: str input_ids: torch.LongTensor target_ids: torch.LongTensor images: torch.Tensor images_seq_mask: torch.BoolTensor images_spatial_crop: torch.LongTensor num_image_tokens: List[int] def __len__(self): return len(self.input_ids) @dataclass class BatchCollateOutput(DictOutput): sft_format: List[str] input_ids: torch.LongTensor labels: torch.LongTensor images: torch.Tensor attention_mask: torch.Tensor images_seq_mask: torch.BoolTensor images_spatial_crop: torch.LongTensor seq_lens: List[int] def to(self, device, dtype=torch.bfloat16): self.input_ids = self.input_ids.to(device) self.labels = self.labels.to(device) self.attention_mask = self.attention_mask.to(device) self.images_seq_mask = self.images_seq_mask.to(device) self.images_spatial_crop = self.images_spatial_crop.to(device) self.images = self.images.to(device=device, dtype=dtype) return self class ImageTransform(object): def __init__( self, mean: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5), std: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5), normalize: bool = True ): self.mean = mean self.std = std self.normalize = normalize transform_pipelines = [ T.ToTensor() ] if normalize: transform_pipelines.append(T.Normalize(mean, std)) self.transform = T.Compose(transform_pipelines) def __call__(self, pil_img: Image.Image): x = self.transform(pil_img) return x class DeepseekVLV2Processor(ProcessorMixin): tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast") attributes = ["tokenizer"] def __init__( self, tokenizer: LlamaTokenizerFast, candidate_resolutions: Tuple[Tuple[int, int]], patch_size: int, downsample_ratio: int, image_mean: Tuple[float, float, float] = (0.5, 0.5, 0.5), image_std: Tuple[float, float, float] = (0.5, 0.5, 0.5), normalize: bool = True, image_token: str = "", pad_token: str = "<｜▁pad▁｜>", add_special_token: bool = False, sft_format: str = "deepseek", mask_prompt: bool = True, ignore_id: int = -100, **kwargs, ): self.candidate_resolutions = candidate_resolutions self.image_size = candidate_resolutions[0][0] self.patch_size = patch_size self.image_mean = image_mean self.image_std = image_std self.normalize = normalize self.downsample_ratio = downsample_ratio self.image_transform = ImageTransform(mean=image_mean, std=image_std, normalize=normalize) self.tokenizer = tokenizer self.tokenizer.padding_side = 'left' # must set this，padding side with make a difference in batch inference # add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id' if tokenizer.pad_token is None: self.tokenizer.add_special_tokens({'pad_token': pad_token}) print(f"Add pad token = ['{pad_token}'] to the tokenizer\n" f"{pad_token}:{tokenizer.encode(pad_token, add_special_tokens=False)[0]}") # add image token image_token_id = self.tokenizer.vocab.get(image_token) if image_token_id is None: special_tokens = [image_token] special_tokens_dict = {"additional_special_tokens": special_tokens} self.tokenizer.add_special_tokens(special_tokens_dict) self.image_token_id = self.tokenizer.vocab.get(image_token) print(f"Add image token = ['{image_token}'] to the tokenizer\n" f"{image_token}:{tokenizer.encode(image_token, add_special_tokens=False)[0]}") # add five special tokens for grounding-related tasks # <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|> special_tokens = ['<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>'] special_tokens_dict = {"additional_special_tokens": special_tokens} self.tokenizer.add_special_tokens(special_tokens_dict) print(f"Add grounding-related tokens = {special_tokens} to the tokenizer with input_ids\n" f"<|ref|>:{tokenizer.encode('<|ref|>', add_special_tokens=False)[0]}\n" f"<|/ref|>:{tokenizer.encode('<|/ref|>', add_special_tokens=False)[0]}\n" f"<|det|>:{tokenizer.encode('<|det|>', add_special_tokens=False)[0]}\n" f"<|/det|>:{tokenizer.encode('<|/det|>', add_special_tokens=False)[0]}\n" f"<|grounding|>:{tokenizer.encode('<|grounding|>', add_special_tokens=False)[0]}") # add special tokens for SFT data special_tokens = ["<|User|>", "<|Assistant|>"] special_tokens_dict = {"additional_special_tokens": special_tokens} self.tokenizer.add_special_tokens(special_tokens_dict) print(f"Add chat tokens = {special_tokens} to the tokenizer with input_ids\n" f"<|User|>:{tokenizer.encode('<|User|>', add_special_tokens=False)[0]}\n" f"<|Assistant|>:{tokenizer.encode('<|Assistant|>', add_special_tokens=False)[0]}\n") self.image_token = image_token self.pad_token = pad_token self.add_special_token = add_special_token self.sft_format = sft_format self.mask_prompt = mask_prompt self.ignore_id = ignore_id super().__init__( tokenizer, **kwargs, ) def new_chat_template(self): conv = get_conv_template(self.sft_format) return conv def format_messages( self, conversations: List[Dict[str, str]], sft_format: str = "deepseek", system_prompt: str = "", ): """ Applies the SFT template to conversation. Args: conversations (List[Dict]): A List of messages. sft_format (str, optional): The format of the SFT template to use. Defaults to "deepseek". system_prompt (str, optional): The system prompt to use in the SFT template. Defaults to "". Returns: sft_prompt (str): The formatted text. """ conv = get_conv_template(sft_format) conv.set_system_message(system_prompt) for message in conversations: conv.append_message(message["role"], message["content"].strip()) sft_prompt = conv.get_prompt().strip() return sft_prompt def format_messages_v2(self, messages, pil_images, systems=None): """play the role of format_messages_v2 and get_images_info in the last version""" tokenized_data = [] masked_tokenized_data = [] # labels images_list = [] images_seq_mask = [] images_spatial_crop = [] num_image_tokens = [] image_index = 0 conv = get_conv_template(self.sft_format) conv_system_message = conv.system_message for idx, message in enumerate(messages): if idx == 0: tokenized_data += [self.bos_id] masked_tokenized_data += [self.bos_id] images_seq_mask += [False] conv.system_message = conv_system_message else: conv.system_message = '' if message['role'] == conv.roles[0] or message['role'] == "user": conv.reset_message() conv.append_message(conv.roles[0], str(message['content']).strip()) conv.append_message(conv.roles[1], '') formatted_question = conv.get_prompt() tokenized_str, images, seq_mask, spatial_crop, n_image_tokens = self.tokenize_with_images( formatted_question, pil_images[image_index: image_index + formatted_question.count(self.image_token)], bos=False, eos=False, cropping=len(pil_images) <= 2 ) image_index += formatted_question.count(self.image_token) tokenized_data += tokenized_str if self.mask_prompt: masked_tokenized_data += [self.ignore_id] * len(tokenized_str) else: masked_tokenized_data += tokenized_str images_list += images images_seq_mask += seq_mask images_spatial_crop += spatial_crop num_image_tokens += n_image_tokens elif message['role'] == conv.roles[1] or message['role'] == "assistant": formatted_answer = message['content'].strip() assert formatted_answer.count( self.image_token) == 0, f"there should be no {self.image_token} in the assistant's reply, but got {messages}" tokenized_str, images, seq_mask, spatial_crop, n_image_tokens = self.tokenize_with_images( formatted_answer, [], bos=False, eos=True, cropping=len(pil_images) <= 2) tokenized_data += tokenized_str masked_tokenized_data += tokenized_str images_seq_mask += seq_mask elif message['role'] == 'system' or message['role'] == 'deepseekapi-sys': # 如果message里面有system，那就只允许出现在message的第一句，同时conv原本的system就会失效 assert idx == 0, 'system information should only exist in the begining of the conversation' formatted_system = message['content'].strip() tokenized_str = self.encode(formatted_system, bos=False, eos=False) tokenized_data += tokenized_str if self.mask_prompt: masked_tokenized_data += [self.ignore_id] * len(tokenized_str) else: masked_tokenized_data += tokenized_str seq_mask = [False] * len(tokenized_str) images_seq_mask += seq_mask else: assert False, f"Unknown role: {message['role']}" assert len(tokenized_data) == len( images_seq_mask), f"format_messages_v2: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}" assert len(images_spatial_crop) == len(num_image_tokens), f"image number should be compatible" return tokenized_data, masked_tokenized_data, images_list, images_seq_mask, images_spatial_crop, num_image_tokens def format_prompts( self, prompts: str, sft_format: str = "deepseek", system_prompt: str = "", ): """ Applies the SFT template to prompts. Args: prompts (str): the non-sft formatted prompt; sft_format (str, optional): The format of the SFT template to use. Defaults to "deepseek". system_prompt (str, optional): The system prompt to use in the SFT template. Defaults to "". Returns: sft_prompt (str): The formatted text. """ conv = get_conv_template(sft_format) conv.set_system_message(system_prompt) conv.append_message(conv.roles[0], prompts.strip()) conv.append_message(conv.roles[1], "") sft_prompt = conv.get_prompt().strip() return sft_prompt @property def bos_id(self): return self.tokenizer.bos_token_id @property def eos_id(self): return self.tokenizer.eos_token_id @property def pad_id(self): return self.tokenizer.pad_token_id def encode(self, text: str, bos: bool = True, eos: bool = False): t = self.tokenizer.encode(text, add_special_tokens=False) if bos: t = [self.bos_id] + t if eos: t = t + [self.eos_id] return t def decode(self, t: List[int], **kwargs) -> str: return self.tokenizer.decode(t, **kwargs) def process_one( self, prompt: str = None, conversations: List[Dict[str, str]] = None, images: List[Image.Image] = None, apply_sft_format: bool = False, inference_mode: bool = True, system_prompt: str = "", **kwargs, ): """ Args: prompt (str): the formatted prompt; conversations (List[Dict]): conversations with a list of messages; images (List[ImageType]): the list of images; apply_sft_format (bool): if prompt is not None, then apply the SFT format to prompt; if conversations is not None, then it will always apply the SFT format to conversations; inference_mode (bool): if True, then remove the last eos token; system_prompt (str): the system prompt; **kwargs: Returns: outputs (BaseProcessorOutput): the output of the processor, - input_ids (torch.LongTensor): [N + image tokens] - target_ids (torch.LongTensor): [N + image tokens] - images (torch.FloatTensor): [n_images, 3, H, W] - image_id (int): the id of the image token - num_image_tokens (List[int]): the number of image tokens """ assert ( prompt is None or conversations is None ), "prompt and conversations cannot be used at the same time." if prompt is None: # apply sft format sft_format = self.format_messages( conversations=conversations, sft_format=self.sft_format, system_prompt=system_prompt, ) tokenized_str, masked_tokenized_str, images_list, images_seq_mask, images_spatial_crop, num_image_tokens = self.format_messages_v2( conversations, images) else: if apply_sft_format: sft_format = self.format_prompts( prompts=prompt, sft_format=self.sft_format, system_prompt=system_prompt ) else: sft_format = prompt tokenized_str, images_list, images_seq_mask, images_spatial_crop, num_image_tokens = self.tokenize_with_images( sft_format, images, bos=True, eos=True, cropping=len(images) <= 2) masked_tokenized_str = [] for token_index in tokenized_str: if token_index != self.image_token_id: masked_tokenized_str.append(token_index) else: masked_tokenized_str.append(self.ignore_id) assert len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str), \ (f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, " f"imags_seq_mask's length {len(images_seq_mask)}, are not equal") input_ids = torch.LongTensor(tokenized_str) target_ids = torch.LongTensor(masked_tokenized_str) images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool) # set input_ids < 0 | input_ids == self.image_token_id as ignore_id target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = self.ignore_id input_ids[input_ids < 0] = self.pad_id if inference_mode: # 去掉结尾的eos token assert input_ids[-1] == self.eos_id input_ids = input_ids[:-1] target_ids = target_ids[:-1] images_seq_mask = images_seq_mask[:-1] if len(images_list) == 0: images = torch.zeros((1, 3, self.image_size, self.image_size)) images_spatial_crop = torch.zeros((1, 2), dtype=torch.long) else: images = torch.stack(images_list, dim=0) images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long) prepare = VLChatProcessorOutput( sft_format=sft_format, input_ids=input_ids, target_ids=target_ids, images=images, images_seq_mask=images_seq_mask, images_spatial_crop=images_spatial_crop, num_image_tokens=num_image_tokens ) return prepare def __call__( self, *, prompt: str = None, conversations: List[Dict[str, str]] = None, images: List[Image.Image] = None, apply_sft_format: bool = False, force_batchify: bool = True, inference_mode: bool = True, system_prompt: str = "", **kwargs, ): """ Args: prompt (str): the formatted prompt; conversations (List[Dict]): conversations with a list of messages; images (List[ImageType]): the list of images; apply_sft_format (bool): if prompt is not None, then apply the SFT format to prompt; if conversations is not None, then it will always apply the SFT format to conversations; force_batchify (bool): force batchify the inputs; inference_mode (bool): if True, then remove the last eos token; system_prompt (str): the system prompt; **kwargs: Returns: outputs (BaseProcessorOutput): the output of the processor, - input_ids (torch.LongTensor): [N + image tokens] - images (torch.FloatTensor): [n_images, 3, H, W] - image_id (int): the id of the image token - num_image_tokens (List[int]): the number of image tokens """ prepare = self.process_one( prompt=prompt, conversations=conversations, images=images, apply_sft_format=apply_sft_format, inference_mode=inference_mode, system_prompt=system_prompt ) if force_batchify: prepare = self.batchify([prepare]) return prepare def tokenize_with_images( self, conversation: str, images: List[Image.Image], bos: bool = True, eos: bool = True, cropping: bool = True, ): """Tokenize text with tags.""" assert conversation.count(self.image_token) == len(images) text_splits = conversation.split(self.image_token) images_list, images_seq_mask, images_spatial_crop = [], [], [] num_image_tokens = [] tokenized_str = [] for text_sep, image in zip(text_splits, images): """encode text_sep""" tokenized_sep = self.encode(text_sep, bos=False, eos=False) tokenized_str += tokenized_sep images_seq_mask += [False] * len(tokenized_sep) """select best resolution for anyres""" if cropping: best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions) else: best_width, best_height = self.image_size, self.image_size # print(image.size, (best_width, best_height)) # check the select_best_resolutions func """process the global view""" global_view = ImageOps.pad(image, (self.image_size, self.image_size), color=tuple(int(x * 255) for x in self.image_transform.mean)) images_list.append(self.image_transform(global_view)) """process the local views""" local_view = ImageOps.pad(image, (best_width, best_height), color=tuple(int(x * 255) for x in self.image_transform.mean)) for i in range(0, best_height, self.image_size): for j in range(0, best_width, self.image_size): images_list.append( self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size)))) """record height / width crop num""" num_width_tiles, num_height_tiles = best_width // self.image_size, best_height // self.image_size images_spatial_crop.append([num_width_tiles, num_height_tiles]) """add image tokens""" h = w = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio) # global views tokens h * (w + 1), 1 is for line seperator tokenized_image = [self.image_token_id] * h * (w + 1) # add a seperator between global and local views tokenized_image += [self.image_token_id] # local views tokens, (num_height_tiles * h) * (num_width_tiles * w + 1) tokenized_image += [self.image_token_id] * (num_height_tiles * h) * (num_width_tiles * w + 1) tokenized_str += tokenized_image images_seq_mask += [True] * len(tokenized_image) num_image_tokens.append(len(tokenized_image)) # print(width_crop_num, height_crop_num, len(tokenized_image)) # test the correctness of the number of image-related tokens """process the last text split""" tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False) tokenized_str += tokenized_sep images_seq_mask += [False] * len(tokenized_sep) """add the bos and eos tokens""" if bos: tokenized_str = [self.bos_id] + tokenized_str images_seq_mask = [False] + images_seq_mask if eos: tokenized_str = tokenized_str + [self.eos_id] images_seq_mask = images_seq_mask + [False] assert len(tokenized_str) == len( 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)}" return tokenized_str, images_list, images_seq_mask, images_spatial_crop, num_image_tokens def batchify( self, sample_list: List[VLChatProcessorOutput], padding: Literal["left", "right"] = "left" ) -> BatchCollateOutput: """ Preprocesses the inputs for multimodal inference. Args: sample_list (List[VLChatProcessorOutput]): A list of VLChatProcessorOutput. padding (str): The padding method. Defaults to "left". Returns: BatchCollateOutput: A dictionary of the inputs to use for multimodal inference. """ batched_sft_format = [sample.sft_format for sample in sample_list] batched_input_ids = [sample.input_ids for sample in sample_list] batched_labels = [sample.target_ids for sample in sample_list] batched_images_seq_mask = [sample["images_seq_mask"] for sample in sample_list] seq_lens = [len(sample) for sample in sample_list] """padding input_ids and images_seq_mask""" if padding == "left": # the tokenizer is default to pad at left ## TODO, You're using a LlamaTokenizerFast tokenizer. # Please note that with a fast tokenizer, using the `__call__` method is faster than # using a method to encode the text followed by a call to the `pad` method to get a padded encoding. padded_input_ids = self.tokenizer.pad({"input_ids": batched_input_ids}) batched_input_ids, batched_attention_mask = padded_input_ids["input_ids"], padded_input_ids[ "attention_mask"].bool() batched_labels = self.tokenizer.pad({"input_ids": batched_labels})["input_ids"] batched_labels[batched_labels == self.pad_id] = self.ignore_id # labels正常不会出现pad_id，无需额外保护 batched_images_seq_mask = self.tokenizer.pad({"input_ids": batched_images_seq_mask})["input_ids"] batched_images_seq_mask[batched_images_seq_mask == self.pad_id] = False else: batched_input_ids = pad_sequence(batched_input_ids, batch_first=True, padding_value=self.pad_id) batched_labels = pad_sequence(batched_labels, batch_first=True, padding_value=self.ignore_id) batched_images_seq_mask = pad_sequence(batched_images_seq_mask, batch_first=True, padding_value=0) batched_attention_mask = batched_input_ids != self.pad_id """padding images to max_patch_num""" max_n_patches = max(sample["images"].shape[0] for sample in sample_list) batched_images = [] for sample in sample_list: images = sample["images"] n_pads = max_n_patches - images.shape[0] if n_pads > 0: pad_images = torch.zeros((n_pads, *images.shape[1:]), dtype=images.dtype) images = torch.cat([images, pad_images], dim=0) batched_images.append(images) batched_images = torch.stack(batched_images, dim=0) """padding images_spatial_crop to max_n_images""" max_n_images = max(sample["images_spatial_crop"].shape[0] for sample in sample_list) batched_images_spatial_crop = [] for sample in sample_list: images_spatial_crop = sample["images_spatial_crop"] n_pads = max_n_images - sample["images_spatial_crop"].shape[0] if n_pads > 0: pad_images_spatial_crop = torch.full((n_pads, 2), 0, dtype=images_spatial_crop.dtype) images_spatial_crop = torch.cat([images_spatial_crop, pad_images_spatial_crop], dim=0) batched_images_spatial_crop.append(images_spatial_crop) batched_images_spatial_crop = torch.stack(batched_images_spatial_crop, dim=0) batched_samples = BatchCollateOutput( input_ids=batched_input_ids, attention_mask=batched_attention_mask, labels=batched_labels, images=batched_images, images_seq_mask=batched_images_seq_mask, images_spatial_crop=batched_images_spatial_crop, sft_format=batched_sft_format, seq_lens=seq_lens ) return batched_samples