Dev gzf exp (#1672)

zhifu gao

2024-04-28 b7ae3d52681ef4f5611b059762788af7d6a37190

Dev gzf exp (#1672)

* resume from step

* batch

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* batch

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 funasr/bin/train.py

@@ -223,11 +223,13 @@
            torch.cuda.empty_cache()

        trainer.validate_epoch(
            model=model, dataloader_val=dataloader_val, epoch=epoch, writer=writer
            model=model, dataloader_val=dataloader_val, epoch=epoch + 1, writer=writer
        )
        scheduler.step()
        trainer.step_cur_in_epoch = 0
        trainer.save_checkpoint(epoch, model=model, optim=optim, scheduler=scheduler, scaler=scaler)
        trainer.step_in_epoch = 0
        trainer.save_checkpoint(
            epoch + 1, model=model, optim=optim, scheduler=scheduler, scaler=scaler
        )

        time2 = time.perf_counter()
        time_escaped = (time2 - time1) / 3600.0

 funasr/train_utils/trainer.py

@@ -116,7 +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.step_in_epoch = 0
        self.use_wandb = kwargs.get("use_wandb", False)
        if self.use_wandb:
            wandb.login(key=kwargs.get("wandb_token"))
@@ -138,7 +138,7 @@
        optim=None,
        scheduler=None,
        scaler=None,
        step_cur_in_epoch=None,
        step_in_epoch=None,
        **kwargs,
    ):
        """
@@ -150,7 +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
        step_in_epoch = None if step is None else step_in_epoch
        if self.rank == 0:
            logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
            # self.step_or_epoch += 1
@@ -165,12 +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,
                "step_in_epoch": step_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
            step = step_in_epoch
            if hasattr(model, "module"):
                state["state_dict"] = model.module.state_dict()

@@ -204,7 +204,7 @@
                    )
                else:
                    logging.info(
                        f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}"
                        f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
                    )
            elif self.avg_keep_nbest_models_type == "loss":
                if (
@@ -219,7 +219,7 @@
                    )
                else:
                    logging.info(
                        f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}"
                        f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
                    )
            else:
                print("Undo")
@@ -260,7 +260,7 @@
            ckpt = os.path.join(self.output_dir, "model.pt")
            if os.path.isfile(ckpt):
                checkpoint = torch.load(ckpt, map_location="cpu")
                self.start_epoch = checkpoint["epoch"] + 1
                self.start_epoch = checkpoint["epoch"]
                # self.model.load_state_dict(checkpoint['state_dict'])
                src_state = checkpoint["state_dict"]
                dst_state = model.state_dict()
@@ -297,17 +297,15 @@
                    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
                    checkpoint["data_split_i"] if "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
                self.step_cur_in_epoch = (
                    checkpoint["step_cur_in_epoch"] if "step_cur_in_epoch" in checkpoint else 0
                self.step_in_epoch = (
                    checkpoint["step_in_epoch"] if "step_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
                )
                self.step_in_epoch = 0 if self.step_in_epoch is None else self.step_in_epoch

                model.to(self.device)
                print(f"Checkpoint loaded successfully from '{ckpt}'")
@@ -356,7 +354,7 @@
                if iterator_stop > 0:
                    break
            self.batch_total += 1
            self.step_cur_in_epoch += 1
            self.step_in_epoch += 1
            time1 = time.perf_counter()
            speed_stats["data_load"] = f"{time1-time_beg:0.3f}"

@@ -459,7 +457,7 @@
                self.log(
                    epoch,
                    batch_idx,
                    step_cur_in_epoch=self.step_cur_in_epoch,
                    step_in_epoch=self.step_in_epoch,
                    batch_num_epoch=batch_num_epoch,
                    lr=lr,
                    loss=loss.detach().cpu().item(),
@@ -471,17 +469,17 @@
                    data_split_num=kwargs.get("data_split_num", 1),
                )

            if (batch_idx + 1) % self.validate_interval == 0:
            if self.step_in_epoch % self.validate_interval == 0:
                self.validate_epoch(
                    model=model,
                    dataloader_val=dataloader_val,
                    epoch=epoch,
                    writer=writer,
                    step=batch_idx + 1,
                    step_cur_in_epoch=self.step_cur_in_epoch,
                    step_in_epoch=self.step_in_epoch,
                )

            if (batch_idx + 1) % self.save_checkpoint_interval == 0:
            if self.step_in_epoch % self.save_checkpoint_interval == 0:
                self.save_checkpoint(
                    epoch,
                    model=model,
@@ -489,7 +487,7 @@
                    scheduler=scheduler,
                    scaler=scaler,
                    step=batch_idx + 1,
                    step_cur_in_epoch=self.step_cur_in_epoch,
                    step_in_epoch=self.step_in_epoch,
                    data_split_i=kwargs.get("data_split_i", 0),
                    data_split_num=kwargs.get("data_split_num", 1),
                )
@@ -599,10 +597,10 @@
                    iterator_stop.fill_(1)
                    dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)

        if kwargs.get("step_cur_in_epoch", None) is None:
        if kwargs.get("step_in_epoch", None) is None:
            ckpt_name = f"model.pt.ep{epoch}"
        else:
            ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step_cur_in_epoch")}'
            ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step_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()
@@ -615,7 +613,7 @@
        self,
        epoch=0,
        batch_idx=0,
        step_cur_in_epoch=0,
        step_in_epoch=0,
        batch_num_epoch=-1,
        lr=0.0,
        loss=0.0,
@@ -648,9 +646,8 @@
                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"step_in_slice: {batch_idx + 1}/{batch_num_epoch}, step_in_epoch: {step_in_epoch}, total step: {self.batch_total}, "
                f"(loss_avg_rank: {loss:.3f}), "
                f"(loss_avg_epoch: {loss_avg_epoch:.3f}), "
                f"(ppl_avg_epoch: {math.exp(loss_avg_epoch):.3e}), "