python/FunASR-XL.git

			@@ -79,7 +79,7 @@
			self.validate_interval = kwargs.get("validate_interval", 5000)
			self.keep_nbest_models = kwargs.get("keep_nbest_models", 500)
			self.avg_keep_nbest_models_type = kwargs.get("avg_keep_nbest_models_type", "acc")
			self.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
			self.avg_nbest_model = kwargs.get("avg_nbest_model", 10)
			self.accum_grad = kwargs.get("accum_grad", 1)
			self.grad_clip = kwargs.get("grad_clip", 10.0)
			self.grad_clip_type = kwargs.get("grad_clip_type", 2.0)
			@@ -134,7 +134,7 @@
			"val_acc_step_or_eoch": self.val_acc_step_or_eoch,
			"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": slef.avg_keep_nbest_models_type,
			"avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
			}
			if hasattr(model, "module"):
			state["state_dict"] = model.module.state_dict()
			@@ -161,17 +161,17 @@
			self.best_step_or_epoch = ckpt_name
			best_ckpt = Path(os.path.join(self.output_dir, f'model.pt.best'))
			torch.save(state, best_ckpt)
			logging.info(f"Update best acc: {self.val_acc_step_or_eoch[self.best_step_or_epoch]}, {best_ckpt}")
			logging.info(f"Update best acc: {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {best_ckpt}")
			else:
			logging.info(f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]}")
			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}")
			elif self.avg_keep_nbest_models_type == "loss":
			if self.val_loss_step_or_eoch[ckpt_name] <= self.val_loss_step_or_eoch[self.best_step_or_epoch]:
			self.best_step_or_epoch = ckpt_name
			best_ckpt = Path(os.path.join(self.output_dir, f'model.pt.best'))
			torch.save(state, best_ckpt)
			logging.info(f"Update best loss: {self.val_loss_step_or_eoch[self.best_step_or_epoch]}, {best_ckpt}")
			logging.info(f"Update best loss: {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {best_ckpt}")
			else:
			logging.info(f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]}")
			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}")
			else:
			print("Undo")
			self.saved_ckpts[ckpt_name] = getattr(self, f"val_{self.avg_keep_nbest_models_type}_step_or_eoch")[ckpt_name]
			@@ -233,7 +233,7 @@
			self.saved_ckpts = checkpoint["saved_ckpts"]
			self.val_acc_step_or_eoch = checkpoint["val_acc_step_or_eoch"] if "val_acc_step_or_eoch" in checkpoint else {}
			self.val_loss_step_or_eoch = checkpoint["val_loss_step_or_eoch"] if "val_loss_step_or_eoch" in checkpoint else {}
			self.val_loss_step_or_eoch = checkpoint["best_step_or_epoch"] if "best_step_or_epoch" in checkpoint else ""
			self.best_step_or_epoch = checkpoint["best_step_or_epoch"] if "best_step_or_epoch" in checkpoint else ""
			model.to(self.device)
			print(f"Checkpoint loaded successfully from '{ckpt}'")
			else:
			@@ -252,6 +252,7 @@
			dataloader_val=None,
			epoch=None,
			writer=None,
			**kwargs,
			):
			"""
			Defines the training process for a single epoch with gradient accumulation.
			@@ -268,10 +269,12 @@
			# Initialize the gradient accumulation
			optim.zero_grad()
			speed_stats = {}
			time5 = time.perf_counter()

			iterator_stop = torch.tensor(0).to(self.device)

			dataloader_train.batch_sampler.set_epoch(epoch)
			time_beg = time.perf_counter()
			time5 = time_beg
			for batch_idx, batch in enumerate(dataloader_train):
			if self.use_ddp or self.use_fsdp:
			dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
			@@ -279,11 +282,13 @@
			break
			self.batch_total += 1
			time1 = time.perf_counter()
			speed_stats["data_load"] = f"{time1-time5:0.3f}"
			speed_stats["data_load"] = f"{time1-time_beg:0.3f}"

			batch = to_device(batch, self.device)

			my_context = model.no_sync if batch_idx % accum_grad != 0 else nullcontext

			my_context = nullcontext
			if self.use_ddp or self.use_fsdp:
			my_context = model.no_sync if batch_idx % accum_grad != 0 else my_context
			with my_context():
			time2 = time.perf_counter()
			with maybe_autocast(self.use_fp16):
			@@ -370,6 +375,8 @@
			stats=stats,
			writer=writer,
			tag="train",
			data_split_i=kwargs.get("data_split_i", 0),
			data_split_num=kwargs.get("data_split_num", 1),
			)

			if (batch_idx + 1) % self.validate_interval == 0:
			@@ -377,12 +384,14 @@
			model=model,
			dataloader_val=dataloader_val,
			epoch=epoch,
			writer=writer
			writer=writer,
			step=batch_idx+1,
			)

			if (batch_idx+1) % self.save_checkpoint_interval == 0:
			self.save_checkpoint(epoch, model=model, optim=optim, scheduler=scheduler, scaler=scaler, step=batch_idx+1)

			time_beg = time.perf_counter()
			else:
			if self.use_ddp or self.use_fsdp:
			iterator_stop.fill_(1)
			@@ -501,6 +510,9 @@
			stats=None,
			writer=None,
			tag="train",
			data_split_i=0,
			data_split_num=1,
			**kwargs,
			):

			if (batch_idx + 1) % self.log_interval == 0:
			@@ -520,10 +532,11 @@
			f"{tag}, "
			f"rank: {self.local_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"(loss_avg_rank: {loss:.3f}), "
			f"(loss_avg_epoch: {loss_avg_epoch:.3f}), "
			f"(ppl_avg_epoch: {math.exp(loss_avg_epoch):.3f}), "
			f"(ppl_avg_epoch: {math.exp(loss_avg_epoch):.3e}), "
			f"(acc_avg_epoch: {acc_avg_epoch:.3f}), "
			f"(lr: {lr:.3e}), "
			f"{[(k, round(v.detach().cpu().item(), 3)) for k, v in stats.items()]}, "