update

游雁

2024-03-22 6ce4909ecc39b990e253d692afc4b28451cde33e

 funasr/train_utils/trainer.py

@@ -198,6 +198,8 @@
                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():
@@ -288,13 +290,13 @@
                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
@@ -410,13 +412,13 @@
                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__"):