finetune

游雁

2024-03-24 16a976a01d110d3969759be7720cae2b6b0664f7

 funasr/train_utils/trainer.py

@@ -239,6 +239,8 @@
        Args:
            epoch (int): The current epoch number.
        """
        if self.use_ddp or self.use_fsdp:
            dist.barrier()
        logging.info(f"Train epoch: {epoch}, rank: {self.local_rank}\n")
        model.train()

@@ -248,8 +250,14 @@
        optim.zero_grad()
        speed_stats = {}
        time5 = time.perf_counter()
        
        # iterator_stop = torch.tensor(0).to(self.device)

        dataloader_train.batch_sampler.set_epoch(epoch)
        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}"
@@ -290,13 +298,13 @@
                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
                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
@@ -356,7 +364,11 @@
            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)

        
        # 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()
        
@@ -376,6 +388,8 @@
        Args:
            epoch (int): The current epoch number.
        """
        if self.use_ddp or self.use_fsdp:
            dist.barrier()
        logging.info(f"Validate epoch: {epoch}, rank: {self.local_rank}\n")
        model.eval()
        
@@ -383,7 +397,15 @@
            
            speed_stats = {}
            time5 = time.perf_counter()
            # iterator_stop = torch.tensor(0).to(self.device)

            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 epoch >= 1:
                #         print(f"iterator_stop: {iterator_stop}\n")
                #     if iterator_stop > 0:
                #         break
                time1 = time.perf_counter()
                speed_stats["data_load"] = f"{time1 - time5:0.3f}"
                batch = to_device(batch, self.device)
@@ -397,7 +419,7 @@
                    # 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
@@ -412,13 +434,13 @@
                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
                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__"):
@@ -433,9 +455,14 @@
                         tag="val",
                         )

            # else:
            #     if self.use_ddp or self.use_fsdp:
            #         iterator_stop.fill_(1)
            #         dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
                    
        self.val_acc_list.append(self.val_acc_avg)
        model.train()
        

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