python/FunASR-XL.git

			@@ -1,3 +1,4 @@
			import math
			import os
			import time
			import torch
			@@ -61,6 +62,8 @@
			"""

			self.output_dir = output_dir
			if not os.path.exists(self.output_dir):
			os.makedirs(self.output_dir, exist_ok=True)
			self.resume = kwargs.get('resume', True)
			self.start_epoch = 0
			self.max_epoch = kwargs.get('max_epoch', 100)
			@@ -78,6 +81,7 @@
			# scaler = ShardedGradScaler(enabled=use_fp16) if use_fsdp else scaler
			# self.scaler = scaler
			self.save_checkpoint_interval = kwargs.get("save_checkpoint_interval", 5000)
			self.keep_nbest_models = kwargs.get("keep_nbest_models", -1)
			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)
			@@ -93,6 +97,15 @@
			logging.warning("distributed is not initialized, only single shard")
			self.rank = rank
			self.world_size = world_size
			self.train_acc_avg = 0.0
			self.train_loss_avg = 0.0
			self.val_acc_avg = 0.0
			self.val_loss_avg = 0.0
			self.best_acc_idx = 0
			self.saved_ckpts = {}
			self.val_acc_list = []
			self.step_or_epoch = -1




			@@ -112,27 +125,55 @@
			Args:
			epoch (int): The epoch number at which the checkpoint is being saved.
			"""

			if self.rank == 0:
			logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
			self.step_or_epoch += 1
			state = {
			'epoch': epoch,
			'state_dict': model.state_dict(),
			'optimizer': optim.state_dict(),
			'scheduler': scheduler.state_dict(),
			"acc": self.val_acc_list,
			"step_or_epoch": self.step_or_epoch,
			}
			if hasattr(model, "module"):
			state["state_dict"] = model.module.state_dict()

			if scaler:
			state["scaler_state"] = scaler.state_dict()
			# Create output directory if it does not exist
			os.makedirs(self.output_dir, exist_ok=True)
			if step is None:
			filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
			ckpt_name = f'model.pt.ep{epoch}'
			else:
			filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}.{step}')

			ckpt_name = f'model.pt.ep{epoch}.{step}'
			filename = os.path.join(self.output_dir, ckpt_name)
			torch.save(state, filename)

			print(f'\nCheckpoint saved to {filename}\n')
			logging.info(f'\nCheckpoint saved to {filename}\n')
			latest = Path(os.path.join(self.output_dir, f'model.pt'))
			torch.save(state, latest)

			if self.val_acc_list[self.step_or_epoch] >= self.val_acc_list[self.best_acc_idx]:
			self.best_acc_idx = self.step_or_epoch
			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_list[self.best_acc_idx]}, {best_ckpt}")
			else:
			logging.info(f"No improvement in acc: {self.val_acc_list[self.best_acc_idx]}")

			if self.keep_nbest_models > 0:
			self.saved_ckpts[ckpt_name] = self.val_acc_list[-1]
			if len(self.saved_ckpts) > self.keep_nbest_models:

			min_key = min(self.saved_ckpts, key=self.saved_ckpts.get)
			if min_key in self.saved_ckpts:
			del self.saved_ckpts[min_key]
			filename = os.path.join(self.output_dir, min_key)
			logging.info(f"Delete: {filename}")
			if os.path.exists(filename):
			os.remove(filename)

			if self.use_ddp or self.use_fsdp:
			dist.barrier()
			@@ -173,6 +214,10 @@
			scheduler.load_state_dict(checkpoint['scheduler'])
			if scaler is not None and 'scaler_state' in checkpoint:
			scaler.load_state_dict(checkpoint['scaler_state'])

			self.val_acc_list = checkpoint["acc"]
			self.step_or_epoch = checkpoint["step_or_epoch"]

			print(f"Checkpoint loaded successfully from '{ckpt}'")
			else:
			print(f"No checkpoint found at '{ckpt}', does not resume status!")
			@@ -180,51 +225,6 @@
			if self.use_ddp or self.use_fsdp:
			dist.barrier()

			# def train(self):
			# """
			# Starts the training process, iterating over epochs, training the model,
			# and saving checkpoints at the end of each epoch.
			# """
			# if self.resume:
			# self.resume_checkpoint(self.output_dir)
			#
			# for epoch in range(self.start_epoch, self.max_epoch + 1):
			# time1 = time.perf_counter()
			# self.train_epoch(epoch)
			#
			#
			#
			# if self.use_ddp or self.use_fsdp:
			# dist.barrier()
			#
			# self._validate_epoch(epoch)
			#
			# if self.use_ddp or self.use_fsdp:
			# dist.barrier()
			#
			#
			# if self.rank == 0:
			# self._save_checkpoint(epoch)
			#
			# if self.use_ddp or self.use_fsdp:
			# dist.barrier()
			#
			# self.scheduler.step()
			#
			# time2 = time.perf_counter()
			# time_escaped = (time2 - time1)/3600.0
			# print(f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f} hours\n")
			#
			# if self.rank == 0:
			# average_checkpoints(self.output_dir, self.avg_nbest_model)
			#
			# if self.use_ddp or self.use_fsdp:
			# dist.barrier()
			#
			#
			# if writer:
			# writer.close()
			#

			def train_epoch(self,
			model=None,
			@@ -241,8 +241,8 @@
			Args:
			epoch (int): The current epoch number.
			"""
			logging.info(f"Train epoch: {epoch}, rank: {self.local_rank}\n")
			model.train()


			# Set the number of steps for gradient accumulation
			accum_grad = self.accum_grad
			@@ -289,6 +289,18 @@
			time4 = time.perf_counter()
			speed_stats["backward_time"] = f"{time4 - time3:0.3f}"

			self.train_loss_avg = (self.train_loss_avg*batch_idx + loss.detach().cpu().item())/(batch_idx+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


			# Perform an optimizer step only after accumulating enough gradients
			if (batch_idx + 1) % accum_grad == 0:
			# Perform gradient clipping if it is set
			@@ -322,9 +334,11 @@

			speed_stats["total_time"] = total_time
			lr = scheduler.get_last_lr()[0]

			batch_num_epoch = -1
			if hasattr(dataloader_train, "__len__"):
			batch_num_epoch = len(dataloader_train)
			self.log(epoch, batch_idx,
			batch_num_epoch=len(dataloader_train),
			batch_num_epoch=batch_num_epoch,
			lr=lr,
			loss=loss.detach().cpu().item(),
			speed_stats=speed_stats,
			@@ -341,7 +355,7 @@
			writer=writer
			)

			if (batch_idx+1) % self.save_checkpoint_interval == 0 and self.rank == 0:
			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)


			@@ -364,6 +378,7 @@
			Args:
			epoch (int): The current epoch number.
			"""
			logging.info(f"Validate epoch: {epoch}, rank: {self.local_rank}\n")
			model.eval()

			with torch.no_grad():
			@@ -394,18 +409,35 @@
			loss = loss
			time4 = time.perf_counter()

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

			batch_num_epoch = -1
			if hasattr(dataloader_val, "__len__"):
			batch_num_epoch = len(dataloader_val)
			self.log(epoch, batch_idx,
			batch_num_epoch=len(dataloader_val),
			batch_num_epoch=batch_num_epoch,
			lr=0.0,
			loss=loss.detach().cpu().item(),
			speed_stats=speed_stats,
			stats=stats,
			writer=writer,
			tag="train",
			tag="val",
			)

			self.val_acc_list.append(self.val_acc_avg)
			model.train()

			if self.use_ddp or self.use_fsdp:
			dist.barrier()


			def log(self,
			@@ -422,39 +454,47 @@

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

			gpu_info = "GPU, memory: {:.3f} GB, " \
			"{:.3f} GB, " \
			"{:.3f} GB, " \
			"{:.3f} GB".format(torch.cuda.memory_allocated() / 1024 / 1024 / 1024,
			gpu_info = "GPU, memory: usage: {:.3f} GB, " \
			"peak: {:.3f} GB, " \
			"cache: {:.3f} GB, " \
			"cache_peak: {:.3f} GB".format(torch.cuda.memory_allocated() / 1024 / 1024 / 1024,
			torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024,
			torch.cuda.memory_reserved() / 1024 / 1024 / 1024,
			torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024,
			)

			time_now = datetime.now()
			time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
			loss_avg_epoch = getattr(self, f"{tag}_loss_avg")
			acc_avg_epoch = getattr(self, f"{tag}_acc_avg")
			description = (
			f"{time_now}, "
			f"{tag}, "
			f"rank: {self.local_rank}, "
			f"epoch: {epoch}/{self.max_epoch}, "
			f"step: {batch_idx + 1}/{batch_num_epoch}, total step: {self.batch_total}, "
			f"(loss: {loss:.3f}), "
			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"(acc_avg_epoch: {acc_avg_epoch:.3f}), "
			f"(lr: {lr:.3e}), "
			f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
			f"{[(k, round(v.detach().cpu().item(), 3)) for k, v in stats.items()]}, "
			f"{speed_stats}, "
			f"{gpu_info}"
			)
			logging.info(description)

			if writer is not None:
			writer.add_scalar(f'rank{self.local_rank}_Loss/{tag}', loss, self.batch_total)
			writer.add_scalar(f'rank{self.local_rank}_loss/{tag}', loss, self.batch_total)
			writer.add_scalar(f'rank{self.local_rank}_lr/{tag}', lr, self.batch_total)
			writer.add_scalar(f'rank{self.local_rank}_lr/{tag}', lr, self.batch_total)
			for key, var in stats.items():
			writer.add_scalar(f'rank{self.local_rank}_{key}/{tag}', var.item(), self.batch_total)
			writer.add_scalar(f'stats_rank{self.local_rank}_{key}/{tag}', var.item(), self.batch_total)
			for key, var in speed_stats.items():
			writer.add_scalar(f'rank{self.local_rank}_{key}/{tag}', eval(var), self.batch_total)
			writer.add_scalar(f'stats_rank{self.local_rank}_{key}/{tag}', eval(var), self.batch_total)

			def close(self, writer=None):

			if self.use_ddp or self.use_fsdp:
			dist.barrier()

			if writer is not None:
			writer.close()