python/FunASR-XL.git

			@@ -169,6 +169,8 @@
			"data_split_i": kwargs.get("data_split_i", 0),
			"data_split_num": kwargs.get("data_split_num", 1),
			"batch_total": self.batch_total,
			"train_loss_avg": kwargs.get("train_loss_avg", 0),
			"train_acc_avg": kwargs.get("train_acc_avg", 0),
			}
			step = step_in_epoch
			if hasattr(model, "module"):
			@@ -306,7 +308,13 @@
			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

			print(checkpoint["train_acc_avg"])
			self.train_acc_avg = (
			checkpoint["train_acc_avg"] if "train_acc_avg" in checkpoint else 0
			)
			self.train_loss_avg = (
			checkpoint["train_loss_avg"] if "train_loss_avg" in checkpoint else 0
			)
			model.to(self.device)
			print(f"Checkpoint loaded successfully from '{ckpt}'")
			else:
			@@ -374,8 +382,6 @@
			):
			torch.cuda.empty_cache()

			time3 = time.perf_counter()
			speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
			loss, stats, weight = retval
			stats = {k: v for k, v in stats.items() if v is not None}
			if self.use_ddp or self.use_fsdp:
			@@ -390,33 +396,28 @@
			# Multiply world_size because DistributedDataParallel
			# automatically normalizes the gradient by world_size.
			loss *= self.world_size
			# loss *= self.world_size
			# Scale the loss since we're not updating for every mini-batch
			loss = loss / accum_grad

			time3 = time.perf_counter()
			speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
			if self.use_fp16:
			scaler.scale(loss).backward()
			else:
			loss.backward()
			time4 = time.perf_counter()
			speed_stats["backward_time"] = f"{time4 - time3:0.3f}"
			speed_stats["backward_and_AllReaduce_time"] = f"{time4 - time3:0.3f}"

			self.train_loss_avg = (
			self.train_loss_avg * batch_idx + loss.detach().cpu().item()
			) / (batch_idx + 1)
			self.train_loss_avg * (batch_idx + kwargs.get("start_step", 0))
			+ loss.detach().cpu().item()
			) / (batch_idx + kwargs.get("start_step", 0) + 1)
			if "acc" in stats:
			self.train_acc_avg = (
			self.train_acc_avg * batch_idx + stats["acc"].detach().cpu().item()
			) / (batch_idx + 1)
			if self.use_ddp or self.use_fsdp:
			train_loss_avg = torch.tensor(self.train_loss_avg, dtype=torch.float32).to(
			self.device
			)
			train_acc_avg = torch.tensor(self.train_acc_avg, dtype=torch.float32).to(
			self.device
			)
			dist.all_reduce(train_loss_avg, op=dist.ReduceOp.SUM)
			dist.all_reduce(train_acc_avg, op=dist.ReduceOp.SUM)
			self.train_loss_avg = train_loss_avg.detach().cpu().item() / self.world_size
			self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size
			self.train_acc_avg * (batch_idx + kwargs.get("start_step", 0))
			+ stats["acc"].detach().cpu().item()
			) / (batch_idx + kwargs.get("start_step", 0) + 1)

			# Perform an optimizer step only after accumulating enough gradients
			if (batch_idx + 1) % accum_grad == 0:
			@@ -445,8 +446,22 @@
			scheduler.step()
			# Clear gradients for the next accumulation stage
			optim.zero_grad(set_to_none=True)
			total_time = f"{time.perf_counter() - time5:0.3f}"

			if self.use_ddp or self.use_fsdp:
			train_loss_avg = torch.tensor(self.train_loss_avg, dtype=torch.float32).to(
			self.device
			)
			train_acc_avg = torch.tensor(self.train_acc_avg, dtype=torch.float32).to(
			self.device
			)
			dist.all_reduce(train_loss_avg, op=dist.ReduceOp.SUM)
			dist.all_reduce(train_acc_avg, op=dist.ReduceOp.SUM)
			self.train_loss_avg = train_loss_avg.detach().cpu().item() / self.world_size
			self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size

			total_time = f"{(time.perf_counter() - time5)/accum_grad:0.3f}"
			time5 = time.perf_counter()

			speed_stats["optim_time"] = f"{time5 - time4:0.3f}"

			speed_stats["total_time"] = total_time
			@@ -457,6 +472,7 @@
			self.log(
			epoch,
			batch_idx,
			log_step=batch_idx + kwargs.get("start_step", 0),
			step_in_epoch=self.step_in_epoch,
			batch_num_epoch=batch_num_epoch,
			lr=lr,
			@@ -490,6 +506,8 @@
			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),
			train_loss_avg=self.train_loss_avg,
			train_acc_avg=self.train_acc_avg,
			)

			time_beg = time.perf_counter()
			@@ -623,11 +641,12 @@
			tag="train",
			data_split_i=0,
			data_split_num=1,
			log_step=None,
			**kwargs,
			):

			if (batch_idx + 1) % self.log_interval == 0:

			batch_idx = log_step if log_step is not None else batch_idx
			gpu_info = (
			"GPU, memory: usage: {:.3f} GB, "
			"peak: {:.3f} GB, "
			@@ -649,9 +668,9 @@
			f"data_slice: {data_split_i}/{data_split_num}, "
			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}), "
			f"(acc_avg_epoch: {acc_avg_epoch:.3f}), "
			f"(loss_avg_slice: {loss_avg_epoch:.3f}), "
			f"(ppl_avg_slice: {math.exp(loss_avg_epoch):.3e}), "
			f"(acc_avg_slice: {acc_avg_epoch:.3f}), "
			f"(lr: {lr:.3e}), "
			f"{[(k, round(v.detach().cpu().item(), 3)) for k, v in stats.items()]}, "
			f"{speed_stats}, "