| | |
|---|
| | | 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) |
|---|
| | |
|---|
| | | 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: |
|---|
| | |
|---|
| | | 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() |
|---|
| | | |
|---|
| | |
|---|
| | | step_in_epoch=self.step_in_epoch, |
|---|
| | | batch_num_epoch=batch_num_epoch, |
|---|
| | | lr=lr, |
|---|
| | | loss=loss.detach().cpu().item(), |
|---|
| | | loss=accum_grad * loss.detach().cpu().item(), |
|---|
| | | speed_stats=speed_stats, |
|---|
| | | stats=stats, |
|---|
| | | writer=writer, |
|---|
