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2024-02-21 eeccdc9a5d72f496f5e7b2a0e3dd381bebcc6ff9

 funasr/train_utils/trainer.py

@@ -69,6 +69,8 @@
        self.device = next(model.parameters()).device
        self.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
        self.kwargs = kwargs
        self.log_interval = kwargs.get("log_interval", 50)
        self.batch_total = 0
        
    
        try:
@@ -186,7 +188,7 @@
            epoch (int): The current epoch number.
        """
        self.model.train()
        pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
        pbar = tqdm(colour="blue", desc=f"rank: {self.local_rank}, Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
                    dynamic_ncols=True)
        
        # Set the number of steps for gradient accumulation
@@ -195,7 +197,9 @@
        self.optim.zero_grad()
        speed_stats = {}
        time5 = time.perf_counter()
        
        for batch_idx, batch in enumerate(self.dataloader_train):
            self.batch_total += 1
            time1 = time.perf_counter()
            speed_stats["data_load"] = f"{time1-time5:0.3f}"

@@ -204,7 +208,10 @@
            my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
            with my_context():
                time2 = time.perf_counter()

                retval = self.model(**batch)
                torch.cuda.empty_cache()

                time3 = time.perf_counter()
                speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
                loss, stats, weight = retval
@@ -255,24 +262,35 @@
                speed_stats["total_time"] = total_time


            pbar.update(1)
            if self.local_rank == 0:
            
            if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_train):
                pbar.update(self.log_interval)
                gpu_info = "GPU, memory: {:.3f} GB, " \
                           "{:.3f} GB, "\
                           "{:.3f} GB, "\
                           "{:.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,
                                             )
                description = (
                    f"Train epoch: {epoch}/{self.max_epoch}, "
                    f"step {batch_idx}/{len(self.dataloader_train)}, "
                    f"{speed_stats}, "
                    f"rank: {self.local_rank}, "
                    f"epoch: {epoch}/{self.max_epoch}, "
                    f"step: {batch_idx}/{len(self.dataloader_train)}, total: {self.batch_total}, "
                    f"(loss: {loss.detach().cpu().item():.3f}), "
                    f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
                    f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
                    f"{speed_stats}, "
                    f"{gpu_info}"
                )
                pbar.set_description(description)
                if self.writer:
                    self.writer.add_scalar('Loss/train', loss.item(),
                    self.writer.add_scalar(f'rank{self.local_rank}_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(),
                        self.writer.add_scalar(f'rank{self.local_rank}_{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),
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var),
                                               epoch * len(self.dataloader_train) + batch_idx)
                    
            # if batch_idx == 2:
@@ -289,7 +307,7 @@
        """
        self.model.eval()
        with torch.no_grad():
            pbar = tqdm(colour="red", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_val),
            pbar = tqdm(colour="red", desc=f"rank: {self.local_rank}, Validation Epoch: {epoch + 1}", total=len(self.dataloader_val),
                        dynamic_ncols=True)
            speed_stats = {}
            time5 = time.perf_counter()
@@ -317,22 +335,24 @@
                loss = loss
                time4 = time.perf_counter()

                pbar.update(1)
                if self.local_rank == 0:
                
                if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_val):
                    pbar.update(self.log_interval)
                    description = (
                        f"rank: {self.local_rank}, "
                        f"validation epoch: {epoch}/{self.max_epoch}, "
                        f"step {batch_idx}/{len(self.dataloader_train)}, "
                        f"{speed_stats}, "
                        f"step: {batch_idx}/{len(self.dataloader_val)}, "
                        f"(loss: {loss.detach().cpu().item():.3f}), "
                        f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
                        f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
                        f"{speed_stats}, "
                    )
                    pbar.set_description(description)
                    if self.writer:
                        self.writer.add_scalar('Loss/val', loss.item(),
                                               epoch*len(self.dataloader_train) + batch_idx)
                        self.writer.add_scalar(f"rank{self.local_rank}_Loss/val", loss.item(),
                                               epoch*len(self.dataloader_val) + batch_idx)
                        for key, var in stats.items():
                            self.writer.add_scalar(f'{key}/val', var.item(),
                                                   epoch * len(self.dataloader_train) + batch_idx)
                            self.writer.add_scalar(f'rank{self.local_rank}_{key}/val', var.item(),
                                                   epoch * len(self.dataloader_val) + 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)
                            self.writer.add_scalar(f'rank{self.local_rank}_{key}/val', eval(var),
                                                   epoch * len(self.dataloader_val) + batch_idx)