Dev gzf exp (#1661)

zhifu gao

2024-04-25 fc68b5ffe453235294a561737d8e84bb6c1689a4

 funasr/train_utils/trainer.py

@@ -15,6 +15,11 @@
from funasr.train_utils.average_nbest_models import average_checkpoints
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

try:
    import wandb
except:
    wandb = None


@contextmanager
def maybe_autocast(enabled):
@@ -107,9 +112,22 @@
        self.best_step_or_epoch = ""
        self.val_acc_step_or_eoch = {}
        self.val_loss_step_or_eoch = {}
        
        self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)

        self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)
        self.start_data_split_i = 0
        self.start_step = 0
        self.use_wandb = kwargs.get("use_wandb", False)
        if self.use_wandb:
            wandb.login(key=kwargs.get("wandb_token"))
            wandb.init(
                config=kwargs,
                project=kwargs.get("wandb_project", "my_project"),
                entity=kwargs.get("wandb_team", "my_team"),
                name=kwargs.get("wandb_exp_name", "my_exp"),
                dir=output_dir,
                job_type="training",
                reinit=True,
            )

    def save_checkpoint(
        self,
@@ -142,6 +160,7 @@
                "val_loss_step_or_eoch": self.val_loss_step_or_eoch,
                "best_step_or_epoch": self.best_step_or_epoch,
                "avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
                "step": step,
            }
            if hasattr(model, "module"):
                state["state_dict"] = model.module.state_dict()
@@ -268,6 +287,13 @@
                self.best_step_or_epoch = (
                    checkpoint["best_step_or_epoch"] if "best_step_or_epoch" in checkpoint else ""
                )
                self.start_data_split_i = (
                    checkpoint["start_data_split_i"] if "start_data_split_i" in checkpoint else 0
                )
                self.batch_total = checkpoint["batch_total"] if "batch_total" in checkpoint else 0
                self.start_step = checkpoint["step"] if "step" in checkpoint else 0
                self.start_step = 0 if self.start_step is None else self.start_step

                model.to(self.device)
                print(f"Checkpoint loaded successfully from '{ckpt}'")
            else:
@@ -327,7 +353,7 @@
                time2 = time.perf_counter()
                with maybe_autocast(self.use_fp16):
                    retval = model(**batch)
                    

                    if (
                        self.reset_gpu_cache
                        and (torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024) > 70
@@ -598,7 +624,7 @@
            acc_avg_epoch = getattr(self, f"{tag}_acc_avg")
            description = (
                f"{tag}, "
                f"rank: {self.local_rank}, "
                f"rank: {self.rank}, "
                f"epoch: {epoch}/{self.max_epoch}, "
                f"data_slice: {data_split_i}/{data_split_num}, "
                f"step: {batch_idx + 1}/{batch_num_epoch}, total step: {self.batch_total}, "
@@ -613,18 +639,29 @@
            )
            logging.info(description)

            description_dict = {
                f"rank{self.rank}_loss/{tag}": loss,
                f"rank{self.rank}_lr/{tag}": lr,
            }

            if writer is not None:
                writer.add_scalar(f"rank{self.local_rank}_loss/{tag}", loss, self.batch_total)
                writer.add_scalar(f"rank{self.local_rank}_lr/{tag}", lr, self.batch_total)
                writer.add_scalar(f"rank{self.local_rank}_lr/{tag}", lr, self.batch_total)
                writer.add_scalar(f"rank{self.rank}_loss/{tag}", loss, self.batch_total)
                writer.add_scalar(f"rank{self.rank}_lr/{tag}", lr, self.batch_total)
                for key, var in stats.items():
                    writer.add_scalar(
                        f"stats_rank{self.local_rank}_{key}/{tag}", var.item(), self.batch_total
                        f"stats_rank{self.rank}_{key}/{tag}", var.item(), self.batch_total
                    )
                    description_dict[f"stats_rank{self.rank}_{key}/{tag}"] = var.item()
                for key, var in speed_stats.items():
                    writer.add_scalar(
                        f"stats_rank{self.local_rank}_{key}/{tag}", eval(var), self.batch_total
                        f"stats_rank{self.rank}_{key}/{tag}", eval(var), self.batch_total
                    )
                    description_dict[f"stats_rank{self.rank}_{key}/{tag}"] = eval(var)
            if self.use_wandb and wandb is not None:
                wandb.log(
                    description_dict,
                    setp=self.batch_total,
                )

    def close(self, writer=None):