import os import io import copy from dataclasses import dataclass, field import json import logging import pathlib from typing import Dict, Optional, Sequence, List import time import torch, gc import glob import transformers import tokenizers import random from blip3o.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_IDX from torch.utils.data import Dataset from blip3o.train.blip3o_trainer import blip3oTrainer from blip3o import conversation as conversation_lib from blip3o.model import * from blip3o.mm_utils import tokenizer_image_token from PIL import Image, ImageFile from datasets import load_dataset, concatenate_datasets from pathlib import Path from datasets.utils.logging import set_verbosity_info from transformers import logging as tf_logging import torchvision.transforms as T from torchvision.transforms.functional import InterpolationMode from transformers import AutoProcessor ImageFile.LOAD_TRUNCATED_IMAGES = True transform_und_images = T.Compose([T.Resize(448, interpolation=InterpolationMode.BICUBIC, antialias=True), T.CenterCrop(448)]) set_verbosity_info() tf_logging.set_verbosity_info() local_rank = None def rank0_print(*args): if local_rank == 0: print(*args) from packaging import version IS_TOKENIZER_GREATER_THAN_0_14 = version.parse(tokenizers.__version__) >= version.parse("0.14") @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="facebook/opt-125m") version: Optional[str] = field(default="v0") freeze_backbone: bool = field(default=True) tune_mm_mlp_adapter: bool = field(default=False) vision_tower: Optional[str] = field(default=None) gen_vision_tower: Optional[str] = field(default=None) mm_vision_select_layer: Optional[int] = field(default=-1) # default to the last layer pretrain_mm_mlp_adapter: Optional[str] = field(default=None) pretrain_gen_mlp_adapter: Optional[str] = field(default=None) vision_tower_pretrained: Optional[str] = field(default=None) mm_projector_type: Optional[str] = field(default="linear") gen_projector_type: Optional[str] = field(default="linear") mm_use_im_start_end: bool = field(default=False) mm_use_im_patch_token: bool = field(default=True) mm_patch_merge_type: Optional[str] = field(default="flat") mm_vision_select_feature: Optional[str] = field(default="patch") n_query: Optional[int] = field(default=729) # clip 576, siglip 729 n_und_query: Optional[int] = field(default=729) # clip 576, siglip 729 gen_pooling: Optional[str] = field(default="all") # options are: pool2d_3, pool2d_9, seq_3, seq_9, seq_27 @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) lazy_preprocess: bool = False is_multimodal: bool = False image_folder: Optional[str] = field(default=None) shortcaption_image_folder: Optional[str] = field(default=None) data_type: Optional[str] = field(default="mix") image_aspect_ratio: str = "square" @dataclass class TrainingArguments(transformers.TrainingArguments): cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") remove_unused_columns: bool = field(default=False) freeze_mm_mlp_adapter: bool = field(default=False) mpt_attn_impl: Optional[str] = field(default="triton") model_max_length: int = field( default=512, metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, ) double_quant: bool = field( default=True, metadata={"help": "Compress the quantization statistics through double quantization."}, ) quant_type: str = field( default="nf4", metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}, ) bits: int = field(default=16, metadata={"help": "How many bits to use."}) lora_enable: bool = False lora_r: int = 64 lora_alpha: int = 16 lora_dropout: float = 0.05 lora_weight_path: str = "" lora_bias: str = "none" mm_projector_lr: Optional[float] = None group_by_modality_length: bool = field(default=False) def maybe_zero_3(param, ignore_status=False, name=None): from deepspeed import zero from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus if hasattr(param, "ds_id"): if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: if not ignore_status: logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}") with zero.GatheredParameters([param]): param = param.data.detach().cpu().clone() else: param = param.detach().cpu().clone() return param # Borrowed from peft.utils.get_peft_model_state_dict def get_peft_state_maybe_zero_3(named_params, bias): if bias == "none": to_return = {k: t for k, t in named_params if "lora_" in k} elif bias == "all": to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k} elif bias == "lora_only": to_return = {} maybe_lora_bias = {} lora_bias_names = set() for k, t in named_params: if "lora_" in k: to_return[k] = t bias_name = k.split("lora_")[0] + "bias" lora_bias_names.add(bias_name) elif "bias" in k: maybe_lora_bias[k] = t for k, t in maybe_lora_bias: if bias_name in lora_bias_names: to_return[bias_name] = t else: raise NotImplementedError to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()} return to_return def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True): to_return = {k: t for k, t in named_params if "lora_" not in k} if require_grad_only: to_return = {k: t for k, t in to_return.items() if t.requires_grad} to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()} return to_return def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)} to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()} return to_return def get_vision_tower_state_maybe_zero_3(named_params, keys_to_match=[""]): to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)} to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()} return to_return def find_all_linear_names(model): cls = torch.nn.Linear lora_module_names = set() multimodal_keywords = ["mm_projector", "vision_tower", "vision_resampler"] for name, module in model.named_modules(): if any(mm_keyword in name for mm_keyword in multimodal_keywords): continue if isinstance(module, cls): names = name.split(".") lora_module_names.add(names[0] if len(names) == 1 else names[-1]) if "lm_head" in lora_module_names: # needed for 16-bit lora_module_names.remove("lm_head") return list(lora_module_names) def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str, vision_tower: str): """Collects the state dict and dump to disk.""" # if getattr(trainer.args, "tune_vision_model", False): if trainer.deepspeed: torch.cuda.synchronize() # Only save Adapter keys_to_match = ["mm_projector"] if getattr(trainer.args, "use_im_start_end", False): keys_to_match.extend(["embed_tokens", "embed_in"]) weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match) trainer.model.config.save_pretrained(output_dir) current_folder = output_dir.split("/")[-1] parent_folder = os.path.dirname(output_dir) if trainer.args.local_rank == 0 or trainer.args.local_rank == -1: if current_folder.startswith("checkpoint-"): mm_projector_folder = os.path.join(parent_folder, "mm_projector") os.makedirs(mm_projector_folder, exist_ok=True) torch.save( weight_to_save, os.path.join(mm_projector_folder, f"{current_folder}.bin"), ) else: torch.save(weight_to_save, os.path.join(output_dir, f"mm_projector.bin")) keys_to_match = ["gen_projector"] if getattr(trainer.args, "use_im_start_end", False): keys_to_match.extend(["embed_tokens", "embed_in"]) weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match) trainer.model.config.save_pretrained(output_dir) current_folder = output_dir.split("/")[-1] parent_folder = os.path.dirname(output_dir) if trainer.args.local_rank == 0 or trainer.args.local_rank == -1: if current_folder.startswith("checkpoint-"): mm_projector_folder = os.path.join(parent_folder, "gen_projector") os.makedirs(mm_projector_folder, exist_ok=True) torch.save( weight_to_save, os.path.join(mm_projector_folder, f"{current_folder}.bin"), ) else: torch.save(weight_to_save, os.path.join(output_dir, f"gen_projector.bin")) if trainer.deepspeed: torch.cuda.synchronize() trainer.save_model(output_dir) return state_dict = trainer.model.state_dict() if trainer.args.should_save: cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()} del state_dict trainer._save(output_dir, state_dict=cpu_state_dict) # noqa def smart_tokenizer_and_embedding_resize( special_tokens_dict: Dict, tokenizer: transformers.PreTrainedTokenizer, model: transformers.PreTrainedModel, ): num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) model.resize_token_embeddings(len(tokenizer)) if num_new_tokens > 0: input_embeddings = model.get_input_embeddings().weight.data input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) input_embeddings[-num_new_tokens:] = input_embeddings_avg def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: """Tokenize a list of strings.""" tokenized_list = [ tokenizer( text, return_tensors="pt", padding="longest", max_length=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(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(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 == "human": target[cur_idx + 2 : cur_idx + tokenized_len] = IGNORE_INDEX cur_idx += tokenized_len def _add_speaker_and_signal(header, source, get_conversation=True): """Add speaker and start/end signal on each round.""" BEGIN_SIGNAL = "### " END_SIGNAL = "\n" conversation = header for sentence in source: from_str = sentence["from"] if from_str.lower() == "human": from_str = conversation_lib.default_conversation.roles[0] elif from_str.lower() == "gpt": from_str = conversation_lib.default_conversation.roles[1] else: from_str = "unknown" sentence["value"] = BEGIN_SIGNAL + from_str + ": " + sentence["value"] + END_SIGNAL if get_conversation: conversation += sentence["value"] conversation += BEGIN_SIGNAL return conversation def preprocess_multimodal(sources: Sequence[str], data_args: DataArguments) -> Dict: is_multimodal = data_args.is_multimodal if not is_multimodal: return sources und_placeholder = "<|vision_start|>" + "<|image_pad|>" * data_args.n_und_query + "<|vision_end|>" gen_placeholder = "" # "[IMG]" + "" * data_args.n_query + "[/IMG]" inst_type = None for source in sources: # [instance] for sentence in source: if sentence["from"] == "human" and "" in sentence["value"]: sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, und_placeholder).strip() inst_type = "und" elif sentence["from"] == "gpt" and "" in sentence["value"]: sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, gen_placeholder).strip() inst_type = "gen" return sources, inst_type def preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict: roles = {"human": "user", "gpt": "assistant"} tokenizer = copy.deepcopy(tokenizer) chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" tokenizer.chat_template = chat_template # Apply prompt templates input_ids, targets = [], [] for i, source in enumerate(sources): if roles[source[0]["from"]] != roles["human"]: source = source[1:] input_id, target = [], [] # New version, use apply chat template # Build system message for each sentence input_id += tokenizer.apply_chat_template([{"role" : "system", "content" : system_message}]) target += [IGNORE_INDEX] * len(input_id) for conv in source: try: role = conv["role"] content = conv["content"] except: role = conv["from"] content = conv["value"] role = roles.get(role, role) conv = [{"role" : role, "content" : content}] encode_id = tokenizer.apply_chat_template(conv) input_id += encode_id if role in ["user", "system"]: target += [IGNORE_INDEX] * len(encode_id) else: target += encode_id assert len(input_id) == len(target), f"{len(input_id)} != {len(target)}" input_ids.append(input_id) targets.append(target) input_ids = torch.tensor(input_ids, dtype=torch.long) targets = torch.tensor(targets, dtype=torch.long) return dict( input_ids=input_ids, # tensor(bs x seq_len) labels=targets, # tensor(bs x seq_len) ) def preprocess_llama3( sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.", ) -> Dict: # roles = {"human": "<|start_header_id|>user<|end_header_id|>", "gpt": "<|start_header_id|>assistant<|end_header_id|>"} roles = {"human": "user", "gpt": "assistant"} # Add image tokens to tokenizer as a special tokens # Use a deepcopy of tokenizer so that we don't modify on the tokenizer tokenizer = copy.deepcopy(tokenizer) # When there is actually an image, we add the image tokens as a special token if has_image: tokenizer.add_tokens([""], special_tokens=True) image_token_index = tokenizer.convert_tokens_to_ids("") bos_token_id = tokenizer.convert_tokens_to_ids("<|begin_of_text|>") start_header_id = tokenizer.convert_tokens_to_ids("<|start_header_id|>") end_header_id = tokenizer.convert_tokens_to_ids("<|end_header_id|>") eot_id = tokenizer.convert_tokens_to_ids("<|eot_id|>") unmask_tokens = ["<|begin_of_text|>", "<|start_header_id|>", "<|end_header_id|>", "<|eot_id|>", "\n\n"] unmask_tokens_idx = [tokenizer.convert_tokens_to_ids(tok) for tok in unmask_tokens] # After update, calling tokenizer of llama3 will # auto add bos id for the tokens. ヽ(｀⌒´)ﾉ def safe_tokenizer_llama3(text): input_ids = tokenizer(text).input_ids if input_ids[0] == bos_token_id: input_ids = input_ids[1:] return input_ids nl_tokens = tokenizer.convert_tokens_to_ids("\n\n") # Apply prompt templates input_ids, targets = [], [] for i, source in enumerate(sources): if roles[source[0]["from"]] != roles["human"]: source = source[1:] input_id, target = [], [] # New version, use apply chat template # Build system message for each sentence input_id += tokenizer.apply_chat_template([{"role" : "system", "content" : system_message}]) target += [IGNORE_INDEX] * len(input_id) for conv in source: try: role = conv["role"] content = conv["content"] except: role = conv["from"] content = conv["value"] role = roles.get(role, role) conv = [{"role" : role, "content" : content}] # First is bos token we don't need here encode_id = tokenizer.apply_chat_template(conv)[1:] input_id += encode_id if role in ["user", "system"]: target += [IGNORE_INDEX] * len(encode_id) else: target += encode_id assert len(input_id) == len(target), f"{len(input_id)} != {len(target)}" for idx, encode_id in enumerate(input_id): if encode_id in unmask_tokens_idx: target[idx] = encode_id if encode_id == image_token_index: input_id[idx] = IMAGE_TOKEN_INDEX input_ids.append(input_id) targets.append(target) input_ids = torch.tensor(input_ids, dtype=torch.long) targets = torch.tensor(targets, dtype=torch.long) return dict( input_ids=input_ids, # tensor(bs x seq_len) labels=targets, # tensor(bs x seq_len) ) def preprocess_plain( sources: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, ) -> Dict: # add end signal and concatenate together conversations = [] for source in sources: assert len(source) == 2 # assert DEFAULT_IMAGE_TOKEN in source[0]['value'] or DEFAULT_IMAGE_TOKEN in source[1]['value'] conversation = source[0]["value"] + source[1]["value"] + conversation_lib.default_conversation.sep conversations.append(conversation) # tokenize conversations input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations] targets = copy.deepcopy(input_ids) for target, source in zip(targets, sources): tokenized_len = len(tokenizer_image_token(source[0]["value"], tokenizer)) target[:tokenized_len] = IGNORE_INDEX return dict(input_ids=input_ids, labels=targets) def preprocess( sources: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, ) -> Dict: """ Given a list of sources, each is a conversation list. This transform: 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; 2. Concatenate conversations together; 3. Tokenize the concatenated conversation; 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. """ if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN: return preprocess_plain(sources, tokenizer) if conversation_lib.default_conversation.version == "llama3": return preprocess_llama3(sources, tokenizer, has_image=has_image) if conversation_lib.default_conversation.version == "qwen": return preprocess_qwen(sources, tokenizer, has_image=has_image) # add end signal and concatenate together conversations = [] for source in sources: header = f"{conversation_lib.default_conversation.system}\n\n" conversation = _add_speaker_and_signal(header, source) conversations.append(conversation) # tokenize conversations def get_tokenize_len(prompts): return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts] if has_image: input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations] else: conversations_tokenized = _tokenize_fn(conversations, tokenizer) input_ids = conversations_tokenized["input_ids"] targets = copy.deepcopy(input_ids) for target, source in zip(targets, sources): if has_image: tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source]) else: tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)["input_ids_lens"] speakers = [sentence["from"] for sentence in source] _mask_targets(target, tokenized_lens, speakers) return dict(input_ids=input_ids, labels=targets) class LazySupervisedMixDataset(Dataset): """Dataset for supervised fine-tuning.""" def __init__( self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, data_args: DataArguments, ): super(LazySupervisedMixDataset, self).__init__() self.data_args = data_args list_data_dict = [] ###################################### text to image ####################################### data_files = glob.glob(os.path.join(self.data_args.image_folder, "*.tar")) ## text to image train_dataset = load_dataset("webdataset", data_files=data_files, split="train", num_proc=128) train_dataset = train_dataset.rename_column("jpg", "image") train_dataset = train_dataset.add_column('type', len(train_dataset) * ['T2I']) train_dataset = train_dataset.add_column('image_path', len(train_dataset) * [None]) train_dataset = train_dataset.remove_columns([col for col in train_dataset.column_names if not col in ( ["image", "txt", "type", "image_path"])]) print(f"finish loading image {len(train_dataset)}") list_data_dict.append(train_dataset) if len(list_data_dict) > 1: list_data_dict = concatenate_datasets(list_data_dict) else: list_data_dict = list_data_dict[0] list_data_dict = list_data_dict.shuffle(seed=42) rank0_print(f"Totoal number of training instance: {len(list_data_dict)}") self.tokenizer = tokenizer self.list_data_dict = list_data_dict def __len__(self): return len(self.list_data_dict) @property def lengths(self): length_list = [] for sample in self.list_data_dict: img_tokens = 128 if "image" in sample else 0 length_list.append(sum(len(conv["value"].split()) for conv in sample["conversations"]) + img_tokens) return length_list @property def modality_lengths(self): length_list = [] for sample in self.list_data_dict: cur_len = sum(len(conv["value"].split()) for conv in sample["conversations"]) cur_len = cur_len if "image" in sample else -cur_len length_list.append(cur_len) return length_list def __getitem__(self, i) -> Dict[str, torch.Tensor]: while True: sources = self.list_data_dict[i] if sources["type"] == "T2I" or sources["type"] == "journeyDB_T2I": sources["conversations"] = [ {"from": "human", "value": f"Please generate image based on the following caption: {sources['txt']}"}, {"from": "gpt", "value": ""}, ] elif sources["type"] == "I2I" or sources["type"] == "journeyDB_I2I": sources["conversations"] = [ { "from": "human", "value": f"\nPlease reconstruct the given image.", }, {"from": "gpt", "value": ""}, ] else: raise ValueError("Unknown source type. Please check the 'type' in 'sources'.") if "image" in sources: def img_process(images, processor, image_aspect_ratio): if image_aspect_ratio == "pad": def expand2square(pil_img, background_color): width, height = pil_img.size if width == height: return pil_img elif width > height: result = Image.new(pil_img.mode, (width, width), background_color) result.paste(pil_img, (0, (width - height) // 2)) return result else: result = Image.new(pil_img.mode, (height, height), background_color) result.paste(pil_img, ((height - width) // 2, 0)) return result images = [expand2square(img, tuple(int(x * 255) for x in processor.image_mean)) for img in images] images = processor.preprocess(images, return_tensors="pt")["pixel_values"] else: images = processor.preprocess(images, return_tensors="pt")["pixel_values"] return images if sources["type"] == "T2I" or sources["type"] == "I2I": image_files = self.list_data_dict[i]["image"] else: image_files = self.list_data_dict[i]["image_path"] if not isinstance(image_files, list): image_files = [image_files] images = [] def read_bin_as_bytesio(bin_file_path): with open(bin_file_path, "rb") as f: return io.BytesIO(f.read()) for img in image_files: try: if sources["type"] == "T2I" or sources["type"] == "I2I": img = img.convert("RGB") elif sources["type"] == "journeyDB_T2I" or sources["type"] == "journeyDB_I2I": if sources["type"] == "journeyDB_T2I" or sources["type"] == "journeyDB_I2I": image_path = os.path.join('/fsx/sfr/data/jiuhai/hub/datasets--JourneyDB--JourneyDB/snapshots/e191aa61ca37e5e4418707ade4df5deb5c6d5d8f/data/train/imgs', img) else: raise ValueError("Unknown source type. Please check the 'type' in 'sources'.") img = Image.open(image_path).convert("RGB") images.append(img) except Exception as e: print(f"Error opening image {img}: {e}") images = None break # Skip to the next image if there's an error if not images is None: try: temp = img_process( images, self.data_args.gen_image_processor, self.data_args.image_aspect_ratio, ) except Exception as e: print(f"Error wrong number of channels: {e}") images = None # If no valid images were found, randomly pick another item if images is None: print(sources) print(f"warning false image!!!!!!") i = random.randint(0, len(self.list_data_dict) - 1) continue sources, inst_type = preprocess_multimodal(copy.deepcopy([sources["conversations"]]), self.data_args) else: sources = copy.deepcopy([sources["conversations"]]) data_dict = preprocess(sources, self.tokenizer, has_image=("image" in self.list_data_dict[i])) if isinstance(i, int): data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0]) # image exist in the data if "image" in self.list_data_dict[i]: if inst_type == "gen": data_dict["gen_image"] = img_process( images, self.data_args.gen_image_processor, self.data_args.image_aspect_ratio, ) elif inst_type == "und": resized_images = [transform_und_images(img) for img in images] image_inputs = self.data_args.image_processor(resized_images, return_tensors="pt") data_dict["und_image"] = image_inputs.pixel_values data_dict["grid_thw"] = image_inputs.image_grid_thw data_dict["gen_image"] = img_process( resized_images, self.data_args.gen_image_processor, self.data_args.image_aspect_ratio, ) elif self.data_args.is_multimodal: crop_size = self.data_args.image_processor.crop_size data_dict["image"] = torch.zeros(3, crop_size["height"], crop_size["width"]) data_dict["ids"] = self.list_data_dict[i]["id"] if "id" in self.list_data_dict[i] else "unk" return data_dict @dataclass class DataCollatorForSupervisedDataset(object): """Collate examples for supervised fine-tuning.""" tokenizer: transformers.PreTrainedTokenizer def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: input_ids, labels, ids = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels", "ids")) multi_input_ids = [] multi_labels = [] i_s_pos = [] for input_id, label in zip(input_ids, labels): input_id = input_id[: self.tokenizer.model_max_length - 65] label = label[: self.tokenizer.model_max_length - 65] i_s_pos.append(input_id.shape[0]+1) img_id = torch.full((65,), IMAGE_TOKEN_IDX, dtype=input_id.dtype, device=input_id.device) img_id[0] = 151665 input_id = torch.cat([input_id, img_id]) img_label = torch.full((65,), IMAGE_TOKEN_IDX, dtype=label.dtype, device=label.device) img_label[0] = 151665 label = torch.cat([label, img_label]) multi_input_ids.append(input_id) multi_labels.append(label) input_ids = multi_input_ids labels = multi_labels input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id) labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) if input_ids.shape[1] > self.tokenizer.model_max_length: print(f"Warning input with length {input_ids.shape[1]} is longer than max length {self.tokenizer.model_max_length}") input_ids = input_ids[:, : self.tokenizer.model_max_length] labels = labels[:, : self.tokenizer.model_max_length] batch = dict( input_ids=input_ids, labels=labels, attention_mask=input_ids.ne(self.tokenizer.pad_token_id), ) batch_gen_images = [] batch_und_images = [] batch_grid_thw = [] for instance in instances: if "gen_image" in instance: batch_gen_images.append(instance["gen_image"]) if len(batch_gen_images) > 0: if all(x is not None and y.shape == batch_gen_images[0][0].shape for x in batch_gen_images for y in x): batch["gen_image"] = torch.cat([images for images in batch_gen_images], dim=0) else: batch["gen_image"] = batch_gen_images else: batch["gen_image"] = None for instance in instances: if "und_image" in instance: batch_und_images.append(instance["und_image"].unsqueeze(0)) ## 1*1024*1176 batch_grid_thw.append(instance["grid_thw"]) ## 1*3 # print(f"batch_und_images {batch_und_images}") if len(batch_und_images) > 0: batch["und_image"] = torch.cat([images for images in batch_und_images], dim=0) batch["grid_thw"] = torch.cat([images for images in batch_grid_thw], dim=0) else: batch["und_image"] = None batch["grid_thw"] = None batch["ids"] = ids batch["i_s_pos"] = i_s_pos return batch def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) -> Dict: if data_args.data_type == "mix": train_dataset = LazySupervisedMixDataset(tokenizer=tokenizer, data_path=data_args.data_path, data_args=data_args) else: raise ValueError("Unknown data type. Please check the Dataloader type.") data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) return dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator) def unlock_vit(training_args, model_args, vision_tower): for n, p in vision_tower.named_parameters(): p.requires_grad = True def train(attn_implementation=None): global local_rank parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() print(model_args, data_args, training_args) local_rank = training_args.local_rank compute_dtype = torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32) bnb_model_from_pretrained_args = {} if training_args.bits in [4, 8]: from transformers import BitsAndBytesConfig bnb_model_from_pretrained_args.update( dict( device_map={"": training_args.device}, load_in_4bit=training_args.bits == 4, load_in_8bit=training_args.bits == 8, quantization_config=BitsAndBytesConfig( load_in_4bit=training_args.bits == 4, load_in_8bit=training_args.bits == 8, llm_int8_skip_modules=["mm_projector"], llm_int8_threshold=6.0, llm_int8_has_fp16_weight=False, bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=training_args.double_quant, bnb_4bit_quant_type=training_args.quant_type, # {'fp4', 'nf4'} ), ) ) if model_args.vision_tower is not None: model = blip3oLlamaForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, attn_implementation=attn_implementation, torch_dtype=(torch.bfloat16 if training_args.bf16 else None), **bnb_model_from_pretrained_args, ) else: if "Qwen" in model_args.model_name_or_path or "qwen" in model_args.model_name_or_path : model = blip3oQwenForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, attn_implementation=attn_implementation, torch_dtype=(torch.bfloat16 if training_args.bf16 else None), **bnb_model_from_pretrained_args, ) else: model = transformers.LlamaForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, attn_implementation=attn_implementation, torch_dtype=(torch.bfloat16 if training_args.bf16 else None), **bnb_model_from_pretrained_args, ) model.config.use_cache = False if model_args.freeze_backbone: for (n, p) in model.get_model().named_parameters(): p.requires_grad = False for (n, p) in model.visual.named_parameters(): p.requires_grad = False for (n, p) in model.lm_head.named_parameters(): p.requires_grad = False if training_args.gradient_checkpointing: if hasattr(model, "enable_input_require_grads"): model.enable_input_require_grads() else: def make_inputs_require_grad(module, input, output): output.requires_grad_(True) model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) if "Qwen" in model_args.model_name_or_path or "qwen" in model_args.model_name_or_path: tokenizer = AutoProcessor.from_pretrained(model_args.model_name_or_path).tokenizer tokenizer.model_max_length = training_args.model_max_length else: tokenizer = transformers.AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, model_max_length=training_args.model_max_length, padding_side="right", use_fast=False, ) # tokenizer.pad_token = tokenizer.unk_token if tokenizer.pad_token is None: smart_tokenizer_and_embedding_resize( special_tokens_dict=dict( pad_token="", additional_special_tokens=["[IMG]", "[/IMG]", ""], ), tokenizer=tokenizer, model=model, ) elif not "" in tokenizer.get_added_vocab(): smart_tokenizer_and_embedding_resize( special_tokens_dict=dict(additional_special_tokens=["[IMG]", "[/IMG]", ""]), tokenizer=tokenizer, model=model, ) if model_args.version in conversation_lib.conv_templates: conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version] else: conversation_lib.default_conversation = conversation_lib.conv_templates["llama3"] rank0_print(f"Using conversation format: {conversation_lib.default_conversation.version}") # if model_args.vision_tower is not None: model.get_model().initialize_vision_modules(model_args=model_args, fsdp=training_args.fsdp) ## generation vision tower gen_vision_tower = model.get_gen_vision_tower() gen_vision_tower.to( dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device, ) gen_vision_tower.requires_grad_(False) data_args.gen_image_processor = gen_vision_tower.image_processor data_args.image_processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct").image_processor data_args.is_multimodal = True data_args.n_query = model_args.n_query data_args.n_und_query = model_args.n_und_query model.config.image_aspect_ratio = data_args.image_aspect_ratio model.config.tokenizer_padding_side = tokenizer.padding_side model.config.tokenizer_model_max_length = tokenizer.model_max_length model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter # Calculate total parameters and trainable parameters total_params = sum(p.numel() for p in model.get_model().parameters()) trainable_params = sum(p.numel() for p in model.get_model().parameters() if p.requires_grad) print(f"Total parameters: {total_params}") print(f"Trainable parameters: {trainable_params}") model.config.mm_use_im_start_end = data_args.mm_use_im_start_end = model_args.mm_use_im_start_end model.config.mm_projector_lr = training_args.mm_projector_lr training_args.use_im_start_end = model_args.mm_use_im_start_end model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer) model.config.pad_token_id = tokenizer.pad_token_id data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args) trainer = blip3oTrainer( model=model, tokenizer=tokenizer, args=training_args, **data_module, ) from tabulate import tabulate if trainer.is_world_process_zero(): stat = [] for i, (n, p) in enumerate(trainer.model.named_parameters()): stat.append([i, n, p.shape, p.requires_grad]) print(tabulate(stat, headers=["idx", "name", "shape", "trainable"])) if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")): trainer.train(resume_from_checkpoint=True) else: trainer.train() trainer.save_state() model.config.use_cache = True safe_save_model_for_hf_trainer( trainer=trainer, output_dir=training_args.output_dir, vision_tower=model_args.vision_tower, ) if __name__ == "__main__": train()