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| | | from funasr.train_utils.trainer_ds import Trainer |
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| | | from funasr.schedulers import scheduler_classes |
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| | | from funasr.train_utils.initialize import initialize |
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| | | from funasr.download.download_from_hub import download_model |
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| | | from funasr.download.download_model_from_hub import download_model |
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| | | from funasr.models.lora.utils import mark_only_lora_as_trainable |
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| | | from funasr.train_utils.set_all_random_seed import set_all_random_seed |
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| | | from funasr.train_utils.load_pretrained_model import load_pretrained_model |
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| | |
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| | | # open tf32 |
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| | | torch.backends.cuda.matmul.allow_tf32 = kwargs.get("enable_tf32", True) |
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| | | |
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| | | rank = int(os.environ.get("RANK", 0)) |
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| | | local_rank = int(os.environ.get("LOCAL_RANK", 0)) |
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| | | world_size = int(os.environ.get("WORLD_SIZE", 1)) |
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| | | |
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| | | deepspeed.init_distributed(dist_backend=kwargs.get("backend", "nccl")) |
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| | | elif use_ddp or use_fsdp: |
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| | | logging.info(f"use_ddp: {use_ddp}, use_fsdp: {use_fsdp}") |
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| | | dist.init_process_group(backend=kwargs.get("backend", "nccl"), init_method="env://") |
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| | | dist.init_process_group( |
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| | | backend=kwargs.get("backend", "nccl"), |
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| | | init_method="env://", |
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| | | ) |
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| | | torch.cuda.set_device(local_rank) |
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| | | |
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| | | rank = dist.get_rank() |
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| | | # rank = dist.get_rank() |
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| | | |
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| | | logging.info("Build model, frontend, tokenizer") |
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| | | device = kwargs.get("device", "cuda") |
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| | | **kwargs.get("train_conf"), |
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| | | ) |
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| | | |
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| | | model = trainer.warp_model(model) |
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| | | model = trainer.warp_model(model, **kwargs) |
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| | | |
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| | | kwargs["device"] = int(os.environ.get("LOCAL_RANK", 0)) |
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| | | trainer.device = int(os.environ.get("LOCAL_RANK", 0)) |
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| | | dataloader = dataloader_class(**kwargs) |
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| | | # dataloader_tr, dataloader_val = dataloader_class(**kwargs) |
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| | | |
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| | | scaler = GradScaler(enabled=trainer.use_fp16) if trainer.use_fp16 else None |
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| | | scaler = GradScaler(enabled=True) if trainer.use_fp16 or trainer.use_bf16 else None |
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| | | scaler = ShardedGradScaler(enabled=trainer.use_fp16) if trainer.use_fsdp else scaler |
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| | | |
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| | | trainer.resume_checkpoint( |
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