conversation_dataset_qwen.py

import io
import os
import copy
import json
import logging
import torch
import random

from typing import List, Optional, Tuple, Union, Dict, Sequence
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

from vary.data.base_dataset import BaseDataset
from vary.utils.constants import *
from vary.utils import conversation as conversation_lib

class ConversationDataset(BaseDataset):
    """Conversation format dataset stage2 fine-tuning."""

    def __init__(self, datasets, tokenizer, multimodal_cfg):
        super(ConversationDataset, self).__init__(datasets, tokenizer, multimodal_cfg)
        # v0 version format conversation
        conversation_lib.default_conversation = conversation_lib.conv_templates["mpt"]
        logging.warning("Formatting inputs into conversation type: mpt-fixed")
        logging.warning("Loading data...")

        list_data_dict = []
        list_image_path = []


        for name in datasets.split("+"):
            # for name in vary_data_dict[name_all]:
            dataset = CONVERSATION_DATA[name]

            data_path = dataset['annotations']
            data = json.load(open(data_path, "r"))

            list_data_dict.extend(data)

            image_path = dataset['images']

            list_image_path.extend([image_path] * len(data))

            logging.warning(f"Data from {data_path} provide {len(data)} conversations.")

        assert len(list_data_dict) == len(list_image_path)
        logging.warning(f"{len(list_data_dict)} conversations in total.")
        a_new_list = list(zip(list_data_dict, list_image_path))
        random.shuffle(a_new_list)
        list_data_dict_new, list_image_path_new = zip(*a_new_list)
        self.list_data_dict = list_data_dict_new
        self.list_image_path = list_image_path_new

        self.im_patch_token = 151859

        self.im_start_token = 151857

        self.im_end_token = 151858
    
    def multimodal_processor(self, sources):
        for source in sources:
            if self.multimodal_cfg['sep_image_conv_front']:
                assert DEFAULT_IMAGE_TOKEN in source[0]['value']
                source[0]['value'] = source[0]['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
                source[0]['value'] = DEFAULT_IMAGE_TOKEN + conversation_lib.default_conversation.sep + conversation_lib.default_conversation.roles[0] + ": " + source[0]['value']
            for sentence in source:
                replace_token = DEFAULT_IMAGE_PATCH_TOKEN * self.multimodal_cfg['image_token_len']
                # if self.multimodal_cfg['use_im_start_end']:
                replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
                sentence["value"] = str(sentence["value"]).replace(DEFAULT_IMAGE_TOKEN, replace_token)
        return sources

    def _tokenize_fn(self, strings):
        """Tokenize a list of strings."""
        tokenized_list = [
            self.tokenizer(
                text,
                return_tensors="pt",
                padding="longest",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
            ) for text in strings
        ]
        input_ids = labels = [
            tokenized.input_ids[0] for tokenized in tokenized_list
        ]
        input_ids_lens = labels_lens = [
            tokenized.input_ids.ne(self.tokenizer.pad_token_id).sum().item()
            for tokenized in tokenized_list
        ]
        return dict(
            input_ids=input_ids,
            labels=labels,
            input_ids_lens=input_ids_lens,
            labels_lens=labels_lens,
        )

    def _mask_targets(self, target, tokenized_lens, speakers):
        # cur_idx = 0
        cur_idx = tokenized_lens[0]
        tokenized_lens = tokenized_lens[1:]
        target[:cur_idx] = IGNORE_INDEX
        for tokenized_len, speaker in zip(tokenized_lens, speakers):
            if speaker.lower() == "human":
                target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
            cur_idx += tokenized_len

    def token_processor(self, sources):
        conv = conversation_lib.default_conversation.copy()
        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}

        # Apply prompt templates
        conversations = []
        for i, source in enumerate(sources):
            if roles[source[0]["from"]] != conv.roles[0]:
                # Skip the first one if it is not from human
                source = source[1:]

            conv.messages = []
            for j, sentence in enumerate(source):
                role = roles[sentence["from"]]
                assert role == conv.roles[j % 2], f"{i}"
                conv.append_message(role, sentence["value"])
            conversations.append(conv.get_prompt())

        # Tokenize conversations


        input_ids = self.tokenizer(
            conversations,
            return_tensors="pt",
            padding="longest",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
        ).input_ids

        # input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
        targets = input_ids.clone()
        assert conv.sep_style == conversation_lib.SeparatorStyle.MPT

        # Mask targets
        sep = conv.sep + conv.roles[1]
        for conversation, target in zip(conversations, targets):
            total_len = int(target.ne(self.tokenizer.pad_token_id).sum())

            rounds = conversation.split(conv.sep)
            re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt
            for conv_idx in range(3, len(rounds), 2):
                re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx+2]))    # user + gpt
            cur_len = 0
            target[:cur_len] = IGNORE_INDEX
            for i, rou in enumerate(re_rounds):
                if rou == "":
                    break

                parts = rou.split(sep)
                if len(parts) != 2:
                    break
                parts[0] += sep
                round_len = len(self.tokenizer(rou).input_ids) + len(self.tokenizer(conv.sep).input_ids)

                instruction_len = len(self.tokenizer(parts[0]).input_ids)
                target[cur_len : cur_len + instruction_len] = IGNORE_INDEX

                cur_len += round_len
            target[cur_len:] = IGNORE_INDEX

            if cur_len < self.tokenizer.model_max_length:
                if cur_len != total_len:
                    target[:] = IGNORE_INDEX
                    print(
                        f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
                        f" (ignored)"
                    )

        return dict(
            input_ids=input_ids,
            labels=targets,
        )

    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
        # data = self.list_data_dict[i]
        data = copy.deepcopy(self.list_data_dict[i])

        if isinstance(data, dict):
            if 'image' in data:
                image_path = self.list_image_path[i]
                image_file = data['image']

                try:
                    image = Image.open(image_path + image_file).convert('RGB')
                except:
                    print(f'cannot identify image file {image_path + image_file}.')
                    return self.__getitem__(0)

                try:
                    image, image_1 = self.image_processor(image)
                except:
                    print(f'image {image_file} are broken or grayscale! we thus select 0-th sample instead!')
                    return self.__getitem__(0)

            conversations = self.multimodal_processor([data["conversations"]])

        else:
            conversations = [data]

        # align with fastchat & llava here, put the conversation into a list for tokenization
        data_dict = self.token_processor(conversations)
        data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0])
        
        if isinstance(data, dict) and 'image' in data:
            data_dict['image'] = [image]
            data_dict['image_high'] = [image_1]
        else:
            crop_size = self.multimodal_cfg['image_processor'].crop_size
            data_dict['image'] = [torch.zeros(3, crop_size['height'], crop_size['width'])]
            data_dict['image_high'] = [torch.zeros(3, 1024, 1024)]
        return data_dict