python/FunASR-XL.git

			@@ -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_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_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_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
			@@ -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_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_in_epoch
			if hasattr(model, "module"):
			state["state_dict"] = model.module.state_dict()

			@@ -195,7 +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 (
			@@ -210,7 +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")
			@@ -251,7 +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()
			@@ -288,11 +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_in_epoch = (
			checkpoint["step_in_epoch"] if "step_in_epoch" in checkpoint else 0
			)
			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}'")
			@@ -321,7 +334,7 @@
			"""
			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 +354,7 @@
			if iterator_stop > 0:
			break
			self.batch_total += 1
			self.step_in_epoch += 1
			time1 = time.perf_counter()
			speed_stats["data_load"] = f"{time1-time_beg:0.3f}"

			@@ -443,6 +457,7 @@
			self.log(
			epoch,
			batch_idx,
			step_in_epoch=self.step_in_epoch,
			batch_num_epoch=batch_num_epoch,
			lr=lr,
			loss=loss.detach().cpu().item(),
			@@ -454,16 +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_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,
			@@ -471,6 +487,9 @@
			scheduler=scheduler,
			scaler=scaler,
			step=batch_idx + 1,
			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),
			)

			time_beg = time.perf_counter()
			@@ -500,7 +519,7 @@
			"""
			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 +597,10 @@
			iterator_stop.fill_(1)
			dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)

			if kwargs.get("step", 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")}'
			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()
			@@ -594,6 +613,7 @@
			self,
			epoch=0,
			batch_idx=0,
			step_in_epoch=0,
			batch_num_epoch=-1,
			lr=0.0,
			loss=0.0,
			@@ -627,7 +647,7 @@
			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}, "
			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}), "