Dev gzf exp (#1670)

zhifu gao

2024-04-28 93ef505e2d426b6aa1e58c0b4721999de789ff8e

 funasr/train_utils/trainer.py

@@ -116,6 +116,7 @@
        self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)
        self.start_data_split_i = 0
        self.start_step = 0
        self.step_cur_in_epoch = 0
        self.use_wandb = kwargs.get("use_wandb", False)
        if self.use_wandb:
            wandb.login(key=kwargs.get("wandb_token"))
@@ -137,6 +138,8 @@
        optim=None,
        scheduler=None,
        scaler=None,
        step_cur_in_epoch=None,
        **kwargs,
    ):
        """
        Saves a checkpoint containing the model's state, the optimizer's state,
@@ -147,6 +150,7 @@
            epoch (int): The epoch number at which the checkpoint is being saved.
        """

        step_cur_in_epoch = None if step is None else step_cur_in_epoch
        if self.rank == 0:
            logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
            # self.step_or_epoch += 1
@@ -161,7 +165,12 @@
                "best_step_or_epoch": self.best_step_or_epoch,
                "avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
                "step": step,
                "step_cur_in_epoch": step_cur_in_epoch,
                "data_split_i": kwargs.get("data_split_i", 0),
                "data_split_num": kwargs.get("data_split_num", 1),
                "batch_total": self.batch_total,
            }
            step = step_cur_in_epoch
            if hasattr(model, "module"):
                state["state_dict"] = model.module.state_dict()

@@ -293,6 +302,12 @@
                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
                self.step_cur_in_epoch = (
                    checkpoint["step_cur_in_epoch"] if "step_cur_in_epoch" in checkpoint else 0
                )
                self.step_cur_in_epoch = (
                    0 if self.step_cur_in_epoch is None else self.step_cur_in_epoch
                )

                model.to(self.device)
                print(f"Checkpoint loaded successfully from '{ckpt}'")
@@ -321,7 +336,7 @@
        """
        if self.use_ddp or self.use_fsdp:
            dist.barrier()
        logging.info(f"Train epoch: {epoch}, rank: {self.local_rank}\n")
        logging.info(f"Train epoch: {epoch}, rank: {self.rank}\n")
        model.train()

        # Set the number of steps for gradient accumulation
@@ -341,6 +356,7 @@
                if iterator_stop > 0:
                    break
            self.batch_total += 1
            self.step_cur_in_epoch += 1
            time1 = time.perf_counter()
            speed_stats["data_load"] = f"{time1-time_beg:0.3f}"

@@ -443,6 +459,7 @@
                self.log(
                    epoch,
                    batch_idx,
                    step_cur_in_epoch=self.step_cur_in_epoch,
                    batch_num_epoch=batch_num_epoch,
                    lr=lr,
                    loss=loss.detach().cpu().item(),
@@ -461,6 +478,7 @@
                    epoch=epoch,
                    writer=writer,
                    step=batch_idx + 1,
                    step_cur_in_epoch=self.step_cur_in_epoch,
                )

            if (batch_idx + 1) % self.save_checkpoint_interval == 0:
@@ -471,6 +489,9 @@
                    scheduler=scheduler,
                    scaler=scaler,
                    step=batch_idx + 1,
                    step_cur_in_epoch=self.step_cur_in_epoch,
                    data_split_i=kwargs.get("data_split_i", 0),
                    data_split_num=kwargs.get("data_split_num", 1),
                )

            time_beg = time.perf_counter()
@@ -500,7 +521,7 @@
        """
        if self.use_ddp or self.use_fsdp:
            dist.barrier()
        logging.info(f"Validate epoch: {epoch}, rank: {self.local_rank}\n")
        logging.info(f"Validate epoch: {epoch}, rank: {self.rank}\n")
        model.eval()

        with torch.no_grad():
@@ -578,10 +599,10 @@
                    iterator_stop.fill_(1)
                    dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)

        if kwargs.get("step", None) is None:
        if kwargs.get("step_cur_in_epoch", None) is None:
            ckpt_name = f"model.pt.ep{epoch}"
        else:
            ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step")}'
            ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step_cur_in_epoch")}'
        self.val_acc_step_or_eoch[ckpt_name] = self.val_acc_avg
        self.val_loss_step_or_eoch[ckpt_name] = self.val_loss_avg
        model.train()
@@ -594,6 +615,7 @@
        self,
        epoch=0,
        batch_idx=0,
        step_cur_in_epoch=0,
        batch_num_epoch=-1,
        lr=0.0,
        loss=0.0,
@@ -626,6 +648,7 @@
                f"{tag}, "
                f"rank: {self.rank}, "
                f"epoch: {epoch}/{self.max_epoch}, "
                f"step_cur_in_epoch: {step_cur_in_epoch}, "
                f"data_slice: {data_split_i}/{data_split_num}, "
                f"step: {batch_idx + 1}/{batch_num_epoch}, total step: {self.batch_total}, "
                f"(loss_avg_rank: {loss:.3f}), "