# ### Setting up experimental environment. """ pip install modelscope pip install numpy pandas matplotlib scikit-learn pip install transformers datasets conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia pip install tqdm tensorboard torchmetrics sentencepiece charset_normalizer pip install accelerate transformers_stream_generator pip install numpy -U # Resolve torchmetrics dependencies and update numpy """ from _common import * @dataclass class Arguments: device: str = '0,1' # e.g. '-1'; '0'; '0,1' seed: int = 42 model_type: str = field( default='baichuan-7b', metadata={ 'choices': ['baichuan-7b', 'baichuan-13b', 'chatglm2', 'llama2-7b'] }) data_sample: Optional[int] = None # lora_target_modules: Optional[List[str]] = None lora_rank: int = 8 lora_alpha: int = 32 lora_dropout_p: float = 0.1 # gradient_checkpoint: bool = True batch_size: int = 1 max_epochs: int = 1 eval_interval: int = 500 learning_rate: float = 1e-4 weight_decay: float = 0.01 n_accumulate_grad: int = 16 grad_clip_norm: float = 1. warmup_iters: int = 200 last_max_checkpoint_num: int = 1 best_max_checkpoint_num: int = 1 # logging_interval: int = 5 tb_interval: int = 5 def __post_init__(self): if self.lora_target_modules is None: if self.model_type in {'baichuan-7b', 'baichuan-13b'}: self.lora_target_modules = ['W_pack'] elif self.model_type == 'chatglm2': self.lora_target_modules = ['query_key_value'] elif self.model_type == 'llama2-7b': self.lora_target_modules = ['q_proj', 'k_proj', 'v_proj'] else: raise ValueError(f'model_type: {self.model_type}') def parse_args() -> Arguments: args, = HfArgumentParser([Arguments]).parse_args_into_dataclasses() return args args = parse_args() logger.info(args) select_device(args.device) seed_everything(args.seed) # ### Loading Model and Tokenizer if args.model_type == 'baichuan-7b': model_dir = snapshot_download('baichuan-inc/baichuan-7B', 'v1.0.5') model, tokenizer = get_baichuan_model_tokenizer(model_dir) elif args.model_type == 'baichuan-13b': model_dir = snapshot_download('baichuan-inc/Baichuan-13B-Base', 'v1.0.2') model, tokenizer = get_baichuan_model_tokenizer(model_dir) elif args.model_type == 'chatglm2': model_dir = snapshot_download('ZhipuAI/chatglm2-6b', 'v1.0.6') model, tokenizer = get_chatglm2_model_tokenizer(model_dir) elif args.model_type == 'llama2-7b': model_dir = snapshot_download('modelscope/Llama-2-7b-ms', 'v1.0.0') model, tokenizer = get_llama2_model_tokenizer(model_dir) else: raise ValueError(f'model_type: {args.model_type}') # if args.gradient_checkpoint: # baichuan13B does not implement the `get_input_embeddings` function if args.model_type == 'baichuan-13b': def get_input_embeddings(self): return self.model.embed_tokens model.__class__.get_input_embeddings = get_input_embeddings.__get__( model) model.gradient_checkpointing_enable() model.enable_input_require_grads() # ### Preparing lora lora_config = LoRAConfig( replace_modules=args.lora_target_modules, rank=args.lora_rank, lora_alpha=args.lora_alpha, lora_dropout=args.lora_dropout_p) logger.info(f'lora_config: {lora_config}') Swift.prepare_model(model, lora_config) # show_freeze_layers(model) print_model_info(model) _p: Parameter = list(model.parameters())[100] logger.info(f'device: {_p.device}, dtype: {_p.dtype}') model.bfloat16() # ### Loading Dataset tokenize_function = partial(tokenize_function, tokenizer=tokenizer) train_dataset, val_dataset = get_alpaca_en_zh_dataset( tokenize_function, split_seed=42, data_sample=args.data_sample) # Data analysis stat_dataset(train_dataset) stat_dataset(val_dataset) data_collate_fn = partial(data_collate_fn, tokenizer=tokenizer) print_example(train_dataset[0], tokenizer) # ### Setting Config cfg_file = os.path.join(model_dir, 'configuration.json') # T_max = get_T_max(len(train_dataset), args.batch_size, args.max_epochs, True) work_dir = get_work_dir(f'runs/{args.model_type}') config = Config({ 'train': { 'dataloader': { 'batch_size_per_gpu': args.batch_size, 'workers_per_gpu': 1, 'shuffle': True, 'drop_last': True, 'pin_memory': True }, 'max_epochs': args.max_epochs, 'work_dir': work_dir, 'optimizer': { 'type': 'AdamW', 'lr': args.learning_rate, 'weight_decay': args.weight_decay, 'options': { 'cumulative_iters': args.n_accumulate_grad, 'grad_clip': { 'norm_type': 2, 'max_norm': args.grad_clip_norm } } }, 'lr_scheduler': { 'type': 'CosineAnnealingLR', 'T_max': T_max, 'eta_min': 0, 'options': { 'by_epoch': False, 'warmup': { 'type': 'LinearWarmup', 'warmup_ratio': 0.1, 'warmup_iters': args.warmup_iters } } }, 'hooks': [ { 'type': 'CheckpointHook', 'by_epoch': False, 'interval': args.eval_interval, 'max_checkpoint_num': args.last_max_checkpoint_num }, { 'type': 'EvaluationHook', 'by_epoch': False, 'interval': args.eval_interval }, { 'type': 'BestCkptSaverHook', 'metric_key': 'loss', 'save_best': True, 'rule': 'min', 'max_checkpoint_num': args.best_max_checkpoint_num }, { 'type': 'TextLoggerHook', 'by_epoch': True, # Whether EpochBasedTrainer is used 'interval': args.logging_interval }, { 'type': 'TensorboardHook', 'by_epoch': False, 'interval': args.tb_interval } ] }, 'evaluation': { 'dataloader': { 'batch_size_per_gpu': args.batch_size, 'workers_per_gpu': 1, 'shuffle': False, 'drop_last': False, 'pin_memory': True }, 'metrics': [{ 'type': 'my_metric', 'vocab_size': tokenizer.vocab_size }] } }) # ### Finetuning def cfg_modify_fn(cfg: Config) -> Config: cfg.update(config) return cfg trainer = EpochBasedTrainer( model=model, cfg_file=cfg_file, data_collator=data_collate_fn, train_dataset=train_dataset, eval_dataset=val_dataset, remove_unused_data=True, seed=42, device='cpu', # No placement for model, leave the model to `device_map` cfg_modify_fn=cfg_modify_fn, ) trainer.train() # ### Visualization tb_dir = os.path.join(work_dir, 'tensorboard_output') plot_image(tb_dir, ['loss'], 0.9)