python/FunASR-XL.git

			@@ -1,17 +1,29 @@
			import math
			import os
			import time
			import torch
			import logging
			from tqdm import tqdm
			from datetime import datetime
			import torch.distributed as dist
			from contextlib import nullcontext
			# from torch.utils.tensorboard import SummaryWriter
			from tensorboardX import SummaryWriter
			from torch.cuda.amp import autocast, GradScaler
			from contextlib import nullcontext, contextmanager
			from pathlib import Path

			from funasr.train_utils.device_funcs import to_device
			from funasr.train_utils.recursive_op import recursive_average
			from funasr.train_utils.average_nbest_models import average_checkpoints
			from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler


			@contextmanager
			def maybe_autocast(enabled):
			if enabled:
			with autocast():
			yield
			else:
			yield


			class Trainer:
			"""
			@@ -28,17 +40,16 @@
			output_dir (str): Directory where model checkpoints will be saved.
			resume (str, optional): Path to a checkpoint to resume training from.
			"""

			def __init__(self, model,
			optim,
			scheduler,
			dataloader_train,
			dataloader_val,
			local_rank,
			use_ddp=False,
			use_fsdp=False,
			output_dir: str="./",
			**kwargs):

			def __init__(
			self,
			local_rank,
			use_ddp: bool = False,
			use_fsdp: bool = False,
			use_fp16: bool = False,
			output_dir: str = "./",
			**kwargs,
			):
			"""
			Initializes the Trainer class with the model, optimizer, scheduler, dataloaders, and other settings.

			@@ -53,24 +64,30 @@
			output_dir (str): The directory where model checkpoints will be saved. Default is './'.
			resume (str, optional): The file path to a checkpoint to resume training from.
			"""

			self.model = model
			self.optim = optim
			self.scheduler = scheduler
			self.dataloader_train = dataloader_train
			self.dataloader_val = dataloader_val

			self.output_dir = output_dir
			self.resume = kwargs.get('resume', True)
			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)
			self.max_epoch = kwargs.get("max_epoch", 100)
			self.local_rank = local_rank
			self.use_ddp = use_ddp
			self.use_fsdp = use_fsdp
			self.device = next(model.parameters()).device
			self.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
			self.kwargs = kwargs


			self.device = kwargs.get("device", "cuda")
			# self.kwargs = kwargs
			self.log_interval = kwargs.get("log_interval", 50)
			self.batch_total = 0
			self.use_fp16 = use_fp16
			self.save_checkpoint_interval = kwargs.get("save_checkpoint_interval", 5000)
			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", 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)

			try:
			rank = dist.get_rank()
			world_size = dist.get_world_size()
			@@ -80,12 +97,29 @@
			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.step_or_epoch = -1
			self.best_step_or_epoch = ""
			self.val_acc_step_or_eoch = {}
			self.val_loss_step_or_eoch = {}

			os.makedirs(os.path.join(self.output_dir, "tensorboard"), exist_ok=True)
			self.writer = SummaryWriter(os.path.join(self.output_dir, "tensorboard")) if rank == 0 else None


			def _save_checkpoint(self, epoch):
			self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)


			def save_checkpoint(
			self,
			epoch,
			step=None,
			model=None,
			optim=None,
			scheduler=None,
			scaler=None,
			):
			"""
			Saves a checkpoint containing the model's state, the optimizer's state,
			and the scheduler's state at the end of the given epoch. This method is
			@@ -94,27 +128,99 @@
			Args:
			epoch (int): The epoch number at which the checkpoint is being saved.
			"""
			state = {
			'epoch': epoch,
			'state_dict': self.model.state_dict(),
			'optimizer': self.optim.state_dict(),
			'scheduler': self.scheduler.state_dict(),
			}
			# Create output directory if it does not exist
			os.makedirs(self.output_dir, exist_ok=True)
			filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
			torch.save(state, filename)

			print(f'Checkpoint saved to {filename}')
			latest = Path(os.path.join(self.output_dir, f'model.pt'))
			try:
			latest.unlink()
			except:
			pass

			latest.symlink_to(filename)

			def _resume_checkpoint(self, resume_path):
			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(),
			"saved_ckpts": self.saved_ckpts,
			"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": self.avg_keep_nbest_models_type,
			}
			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:
			ckpt_name = f"model.pt.ep{epoch}"
			else:
			ckpt_name = f"model.pt.ep{epoch}.{step}"
			filename = os.path.join(self.output_dir, ckpt_name)
			torch.save(state, filename)

			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.best_step_or_epoch == "":
			self.best_step_or_epoch = ckpt_name

			if self.avg_keep_nbest_models_type == "acc":
			if (
			self.val_acc_step_or_eoch[ckpt_name]
			>= self.val_acc_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 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]:.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]:.4f}, {best_ckpt}"
			)
			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}"
			)
			else:
			print("Undo")
			self.saved_ckpts[ckpt_name] = getattr(
			self, f"val_{self.avg_keep_nbest_models_type}_step_or_eoch"
			)[ckpt_name]
			if self.keep_nbest_models > 0:
			if len(self.saved_ckpts) > self.keep_nbest_models:
			if self.avg_keep_nbest_models_type == "acc":
			key = min(self.saved_ckpts, key=self.saved_ckpts.get)
			else:
			key = max(self.saved_ckpts, key=self.saved_ckpts.get)
			if key in self.saved_ckpts:
			del self.saved_ckpts[key]
			filename = os.path.join(self.output_dir, key)
			logging.info(f"Delete: {filename}")
			if os.path.exists(filename):
			os.remove(filename)

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

			def resume_checkpoint(
			self,
			model=None,
			optim=None,
			scheduler=None,
			scaler=None,
			):
			"""
			Resumes training from a checkpoint at the given file path.
			Loads the model's state, the optimizer's state, and the scheduler's state.
			@@ -122,89 +228,112 @@
			Args:
			resume_path (str): The file path to the checkpoint to resume from.
			"""
			ckpt = os.path.join(resume_path, "model.pt")
			if os.path.isfile(ckpt):
			checkpoint = torch.load(ckpt)
			self.start_epoch = checkpoint['epoch'] + 1
			self.model.load_state_dict(checkpoint['state_dict'])
			self.optim.load_state_dict(checkpoint['optimizer'])
			self.scheduler.load_state_dict(checkpoint['scheduler'])
			print(f"Checkpoint loaded successfully from '{ckpt}'")
			else:
			print(f"No checkpoint found at '{ckpt}', starting from scratch")

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

			def run(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):

			self._train_epoch(epoch)
			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.model.load_state_dict(checkpoint['state_dict'])
			src_state = checkpoint["state_dict"]
			dst_state = model.state_dict()
			for k in dst_state.keys():
			if not k.startswith("module.") and "module." + k in src_state.keys():
			k_ddp = "module." + k
			elif k.startswith("module.") and "module." + k not in src_state.keys():
			k_ddp = k.replace("module.", "", 1)
			else:
			k_ddp = k
			if k_ddp in src_state.keys():
			dst_state[k] = src_state[k_ddp]
			else:
			print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}")

			model.load_state_dict(dst_state)
			optim.load_state_dict(checkpoint["optimizer"])
			scheduler.load_state_dict(checkpoint["scheduler"])
			if scaler is not None and "scaler_state" in checkpoint:
			scaler.load_state_dict(checkpoint["scaler_state"])


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

			self._validate_epoch(epoch)
			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.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:
			print(f"No checkpoint found at '{ckpt}', does not resume status!")

			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()


			if self.rank == 0:
			average_checkpoints(self.output_dir, self.avg_nbest_model)

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


			if self.writer:
			self.writer.close()


			def _train_epoch(self, epoch):
			def train_epoch(
			self,
			model=None,
			optim=None,
			scheduler=None,
			scaler=None,
			dataloader_train=None,
			dataloader_val=None,
			epoch=None,
			writer=None,
			**kwargs,
			):
			"""
			Defines the training process for a single epoch with gradient accumulation.
			Args:
			epoch (int): The current epoch number.
			"""
			self.model.train()
			pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
			dynamic_ncols=True)

			if self.use_ddp or self.use_fsdp:
			dist.barrier()
			logging.info(f"Train epoch: {epoch}, rank: {self.local_rank}\n")
			model.train()

			# Set the number of steps for gradient accumulation
			accum_grad = self.kwargs.get("accum_grad", 1)
			accum_grad = self.accum_grad
			# Initialize the gradient accumulation
			self.optim.zero_grad()
			optim.zero_grad()
			speed_stats = {}
			time5 = time.perf_counter()
			for batch_idx, batch in enumerate(self.dataloader_train):

			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)
			if iterator_stop > 0:
			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 = self.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()
			retval = self.model(**batch)
			with maybe_autocast(self.use_fp16):
			retval = model(**batch)

			if (
			self.reset_gpu_cache
			and (torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024) > 70
			):
			torch.cuda.empty_cache()

			time3 = time.perf_counter()
			speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
			loss, stats, weight = retval
			@@ -213,92 +342,157 @@
			# Apply weighted averaging for loss and stats
			loss = (loss * weight.type(loss.dtype)).sum()
			# if distributed, this method can also apply all_reduce()
			stats, weight = recursive_average(stats, weight, distributed=True)
			# stats, weight = recursive_average(stats, weight, distributed=True)
			if self.use_ddp or self.use_fsdp:
			dist.all_reduce(weight, op=dist.ReduceOp.SUM)
			# Now weight is summation over all workers
			loss /= weight
			loss /= weight.sum() # shape:[1] -> shape:[]
			# Multiply world_size because DistributedDataParallel
			# automatically normalizes the gradient by world_size.
			loss *= self.world_size
			# Scale the loss since we're not updating for every mini-batch
			loss = loss / accum_grad
			loss.backward()
			if self.use_fp16:
			scaler.scale(loss).backward()
			else:
			loss.backward()
			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 or (batch_idx + 1) == len(self.dataloader_train):
			if (batch_idx + 1) % accum_grad == 0:
			# Perform gradient clipping if it is set
			if self.kwargs.get("grad_clip", None) is not None:
			if self.grad_clip > 0:
			grad_norm = torch.nn.utils.clip_grad_norm_(
			self.model.parameters(),
			max_norm=self.kwargs.get("grad_clip", 10.0),
			norm_type=self.kwargs.get("grad_clip_type", 2.0),
			model.parameters(),
			max_norm=self.grad_clip,
			norm_type=self.grad_clip_type,
			)
			if not torch.isfinite(grad_norm):
			logging.warning(
			f"The grad norm is {grad_norm}. Skipping updating the model."
			)
			self.optim.zero_grad() # Reset gradients
			optim.zero_grad() # Reset gradients
			continue


			# Execute an optimization step (update model parameters)
			if self.use_ddp or self.use_fsdp:
			dist.barrier()
			self.optim.step()
			self.scheduler.step()
			if self.use_fp16:
			scaler.step(optim)
			scaler.update()
			else:
			optim.step()
			scheduler.step()
			# Clear gradients for the next accumulation stage
			self.optim.zero_grad()
			optim.zero_grad(set_to_none=True)
			total_time = f"{time.perf_counter() - time5:0.3f}"
			time5 = time.perf_counter()
			speed_stats["optim_time"] = f"{time5 - time4:0.3f}"


			speed_stats["total_time"] = total_time


			pbar.update(1)
			if self.local_rank == 0:
			description = (
			f"Train epoch: {epoch}/{self.max_epoch}, "
			f"step {batch_idx}/{len(self.dataloader_train)}, "
			f"{speed_stats}, "
			f"(loss: {loss.detach().cpu().item():.3f}), "
			f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
			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=batch_num_epoch,
			lr=lr,
			loss=loss.detach().cpu().item(),
			speed_stats=speed_stats,
			stats=stats,
			writer=writer,
			tag="train",
			data_split_i=kwargs.get("data_split_i", 0),
			data_split_num=kwargs.get("data_split_num", 1),
			)
			pbar.set_description(description)
			if self.writer:
			self.writer.add_scalar('Loss/train', loss.item(),
			epoch*len(self.dataloader_train) + batch_idx)
			for key, var in stats.items():
			self.writer.add_scalar(f'{key}/train', var.item(),
			epoch * len(self.dataloader_train) + batch_idx)
			for key, var in speed_stats.items():
			self.writer.add_scalar(f'{key}/train', eval(var),
			epoch * len(self.dataloader_train) + batch_idx)

			# if batch_idx == 2:
			# break
			pbar.close()

			def _validate_epoch(self, epoch):
			if (batch_idx + 1) % self.validate_interval == 0:
			self.validate_epoch(
			model=model,
			dataloader_val=dataloader_val,
			epoch=epoch,
			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)
			dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)

			if self.use_ddp or self.use_fsdp:
			dist.barrier()
			iterator_stop = torch.tensor(0).to(self.device)

			def validate_epoch(
			self,
			model=None,
			dataloader_val=None,
			epoch=None,
			writer=None,
			**kwargs,
			):
			"""
			Defines the validation process for a single epoch.
			Should be implemented with the actual model validation steps.


			Args:
			epoch (int): The current epoch number.
			"""
			self.model.eval()
			if self.use_ddp or self.use_fsdp:
			dist.barrier()
			logging.info(f"Validate epoch: {epoch}, rank: {self.local_rank}\n")
			model.eval()

			with torch.no_grad():
			pbar = tqdm(colour="red", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_val),
			dynamic_ncols=True)

			speed_stats = {}
			time5 = time.perf_counter()
			for batch_idx, batch in enumerate(self.dataloader_val):
			iterator_stop = torch.tensor(0).to(self.device)
			dataloader_val.batch_sampler.set_epoch(epoch)
			for batch_idx, batch in enumerate(dataloader_val):
			if self.use_ddp or self.use_fsdp:
			dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
			if iterator_stop > 0:
			break
			time1 = time.perf_counter()
			speed_stats["data_load"] = f"{time1 - time5:0.3f}"
			batch = to_device(batch, self.device)
			time2 = time.perf_counter()
			retval = self.model(**batch)
			retval = model(**batch)
			time3 = time.perf_counter()
			speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
			loss, stats, weight = retval
			@@ -307,9 +501,11 @@
			# Apply weighted averaging for loss and stats
			loss = (loss * weight.type(loss.dtype)).sum()
			# if distributed, this method can also apply all_reduce()
			stats, weight = recursive_average(stats, weight, distributed=True)
			# stats, weight = recursive_average(stats, weight, distributed=True)
			if self.use_ddp or self.use_fsdp:
			dist.all_reduce(weight, op=dist.ReduceOp.SUM)
			# Now weight is summation over all workers
			loss /= weight
			loss /= weight.sum() # shape:[1] -> shape:[]
			# Multiply world_size because DistributedDataParallel
			# automatically normalizes the gradient by world_size.
			loss *= self.world_size
			@@ -317,22 +513,126 @@
			loss = loss
			time4 = time.perf_counter()

			pbar.update(1)
			if self.local_rank == 0:
			description = (
			f"validation epoch: {epoch}/{self.max_epoch}, "
			f"step {batch_idx}/{len(self.dataloader_train)}, "
			f"{speed_stats}, "
			f"(loss: {loss.detach().cpu().item():.3f}), "
			f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
			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
			)
			pbar.set_description(description)
			if self.writer:
			self.writer.add_scalar('Loss/val', loss.item(),
			epoch*len(self.dataloader_train) + batch_idx)
			for key, var in stats.items():
			self.writer.add_scalar(f'{key}/val', var.item(),
			epoch * len(self.dataloader_train) + batch_idx)
			for key, var in speed_stats.items():
			self.writer.add_scalar(f'{key}/val', eval(var),
			epoch * len(self.dataloader_train) + batch_idx)
			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
			time5 = time.perf_counter()
			batch_num_epoch = 1
			if hasattr(dataloader_val, "__len__"):
			batch_num_epoch = len(dataloader_val)
			self.log(
			epoch,
			batch_idx,
			batch_num_epoch=batch_num_epoch,
			lr=0.0,
			loss=loss.detach().cpu().item(),
			speed_stats=speed_stats,
			stats=stats,
			writer=writer,
			tag="val",
			)

			else:
			if self.use_ddp or self.use_fsdp:
			iterator_stop.fill_(1)
			dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)

			if kwargs.get("step", None) is None:
			ckpt_name = f"model.pt.ep{epoch}"
			else:
			ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step")}'
			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()

			if self.use_ddp or self.use_fsdp:
			dist.barrier()
			iterator_stop = torch.tensor(0).to(self.device)

			def log(
			self,
			epoch=0,
			batch_idx=0,
			batch_num_epoch=-1,
			lr=0.0,
			loss=0.0,
			speed_stats=None,
			stats=None,
			writer=None,
			tag="train",
			data_split_i=0,
			data_split_num=1,
			**kwargs,
			):

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

			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,
			)
			)

			loss_avg_epoch = getattr(self, f"{tag}_loss_avg")
			acc_avg_epoch = getattr(self, f"{tag}_acc_avg")
			description = (
			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):.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()]}, "
			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}_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"stats_rank{self.local_rank}_{key}/{tag}", var.item(), self.batch_total
			)
			for key, var in speed_stats.items():
			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()

			if self.use_ddp or self.use_fsdp:
			torch.distributed.destroy_process_group()