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游雁

2024-06-06 d6fedbfa77abee8b31340dc1a7aa344b84c8134c

 funasr/train_utils/trainer.py

@@ -85,7 +85,12 @@
        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.validate_interval = kwargs.get("validate_interval", -1)
        if self.validate_interval < 0:
            self.validate_interval = self.save_checkpoint_interval
        assert (
            self.save_checkpoint_interval == self.validate_interval
        ), f"save_checkpoint_interval must equal to validate_interval"
        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)
@@ -410,24 +415,14 @@
                speed_stats["backward_and_AllReaduce_time"] = f"{time4 - time3:0.3f}"

                self.train_loss_avg = (
                    self.train_loss_avg * (self.step_in_epoch - 1) + loss.detach().cpu().item()
                ) / self.step_in_epoch
                    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 * (self.step_in_epoch - 1)
                        self.train_acc_avg * (batch_idx + kwargs.get("start_step", 0))
                        + stats["acc"].detach().cpu().item()
                    ) / self.step_in_epoch
                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
                    ) / (batch_idx + kwargs.get("start_step", 0) + 1)

            # Perform an optimizer step only after accumulating enough gradients
            if (batch_idx + 1) % accum_grad == 0:
@@ -456,6 +451,19 @@
                scheduler.step()
                # Clear gradients for the next accumulation stage
                optim.zero_grad(set_to_none=True)

                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()