DeepSeek-VL2/deepseek_vl2/models/processing_deepseek_vl_v2.py

# Copyright (c) 2023-2024 DeepSeek.
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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 = "<image>",
            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 <image> 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