import argparse import logging import os import sys from io import BytesIO from collections.abc import Sequence import torch import hydra from omegaconf import DictConfig, OmegaConf from funasr.train_utils.set_all_random_seed import set_all_random_seed from funasr.models.lora.utils import mark_only_lora_as_trainable from funasr.optimizers import optim_classes from funasr.schedulers import scheduler_classes from funasr.train_utils.load_pretrained_model import load_pretrained_model from funasr.train_utils.initialize import initialize # from funasr.tokenizer.build_tokenizer import build_tokenizer # from funasr.tokenizer.token_id_converter import TokenIDConverter # from funasr.tokenizer.funtoken import build_tokenizer from funasr.train_utils.trainer import Trainer import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from funasr.download.download_from_hub import download_model from funasr.utils.register import registry_tables @hydra.main(config_name=None, version_base=None) def main_hydra(kwargs: DictConfig): import pdb; pdb.set_trace() assert "model" in kwargs if "model_conf" not in kwargs: logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms"))) kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs) main(**kwargs) def main(**kwargs): # preprocess_config(kwargs) # import pdb; pdb.set_trace() # set random seed registry_tables.print() set_all_random_seed(kwargs.get("seed", 0)) torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled) torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark) torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True) local_rank = int(os.environ.get('LOCAL_RANK', 0)) # Check if we are using DDP or FSDP use_ddp = 'WORLD_SIZE' in os.environ and int(os.environ["WORLD_SIZE"]) > 1 use_fsdp = kwargs.get("use_fsdp", None) if use_ddp or use_fsdp: dist.init_process_group(backend=kwargs.get("backend", "nccl"), init_method='env://') torch.cuda.set_device(local_rank) # save config.yaml if (use_ddp or use_fsdp) and dist.get_rank() == 0 or not (use_ddp or use_fsdp) and local_rank == 0: os.makedirs(kwargs.get("output_dir", "./"), exist_ok=True) yaml_file = os.path.join(kwargs.get("output_dir", "./"), "config.yaml") OmegaConf.save(config=kwargs, f=yaml_file) logging.info("config.yaml is saved to: %s", yaml_file) tokenizer = kwargs.get("tokenizer", None) if tokenizer is not None: tokenizer_class = registry_tables.tokenizer_classes.get(tokenizer.lower()) tokenizer = tokenizer_class(**kwargs["tokenizer_conf"]) kwargs["tokenizer"] = tokenizer # build frontend if frontend is none None frontend = kwargs.get("frontend", None) if frontend is not None: frontend_class = registry_tables.frontend_classes.get(frontend.lower()) frontend = frontend_class(**kwargs["frontend_conf"]) kwargs["frontend"] = frontend kwargs["input_size"] = frontend.output_size() # import pdb; # pdb.set_trace() # build model model_class = registry_tables.model_classes.get(kwargs["model"].lower()) model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list)) # init_param init_param = kwargs.get("init_param", None) if init_param is not None: if not isinstance(init_param, (list, tuple)): init_param = (init_param,) logging.info("init_param is not None: %s", init_param) for p in init_param: logging.info(f"Loading pretrained params from {p}") load_pretrained_model( model=model, init_param=p, ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True), oss_bucket=kwargs.get("oss_bucket", None), ) else: initialize(model, kwargs.get("init", "kaiming_normal")) # freeze_param freeze_param = kwargs.get("freeze_param", None) if freeze_param is not None: freeze_param = eval(freeze_param) if isinstance(freeze_param, Sequence): freeze_param = (freeze_param,) logging.info("freeze_param is not None: %s", freeze_param) for t in freeze_param: for k, p in model.named_parameters(): if k.startswith(t + ".") or k == t: logging.info(f"Setting {k}.requires_grad = False") p.requires_grad = False if use_ddp: model = model.cuda(local_rank) model = DDP(model, device_ids=[local_rank], find_unused_parameters=kwargs.get("train_conf", {}).get("find_unused_parameters", False)) elif use_fsdp: model = FSDP(model).cuda(local_rank) else: model = model.to(device=kwargs.get("device", "cuda")) # optim optim = kwargs.get("optim", "adam") assert optim in optim_classes optim_class = optim_classes.get(optim) optim = optim_class(model.parameters(), **kwargs.get("optim_conf")) # scheduler scheduler = kwargs.get("scheduler", "warmuplr") assert scheduler in scheduler_classes scheduler_class = scheduler_classes.get(scheduler) scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf")) # import pdb; # pdb.set_trace() # dataset dataset_class = registry_tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset").lower()) dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf")) # dataloader batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler") batch_sampler_class = registry_tables.batch_sampler_classes.get(batch_sampler.lower()) batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf")) dataloader_tr = torch.utils.data.DataLoader(dataset_tr, collate_fn=dataset_tr.collator, batch_sampler=batch_sampler, num_workers=kwargs.get("dataset_conf").get("num_workers", 4), pin_memory=True) trainer = Trainer( model=model, optim=optim, scheduler=scheduler, dataloader_train=dataloader_tr, dataloader_val=None, local_rank=local_rank, use_ddp=use_ddp, use_fsdp=use_fsdp, **kwargs.get("train_conf"), ) trainer.run() if use_ddp or use_fsdp: torch.distributed.destroy_process_group() if __name__ == "__main__": main_hydra()