| | |
|---|
| | | import torch |
|---|
| | | import os |
|---|
| | | from funasr.train_utils.device_funcs import to_device |
|---|
| | | import logging |
|---|
| | | import time |
|---|
| | | import torch |
|---|
| | | import logging |
|---|
| | | from tqdm import tqdm |
|---|
| | | from contextlib import nullcontext |
|---|
| | | import torch.distributed as dist |
|---|
| | | from contextlib import nullcontext |
|---|
| | | # from torch.utils.tensorboard import SummaryWriter |
|---|
| | | from tensorboardX import SummaryWriter |
|---|
| | | from pathlib import Path |
|---|
| | | |
|---|
| | | from funasr.train_utils.device_funcs import to_device |
|---|
| | | from funasr.train_utils.recursive_op import recursive_average |
|---|
| | | from funasr.train_utils.average_nbest_models import average_checkpoints |
|---|
| | | |
|---|
| | | class Trainer: |
|---|
| | | """ |
|---|
| | | A simple trainer class for training a PyTorch model, saving checkpoints at the end of each epoch, |
|---|
| | | and optionally resuming from a saved checkpoint. |
|---|
| | | """ |
|---|
| | | A simple trainer class for training a PyTorch model, saving checkpoints at the end of each epoch, |
|---|
| | | and optionally resuming from a saved checkpoint. |
|---|
| | | |
|---|
| | | Attributes: |
|---|
| | | max_epoch (int): Maximum number of epochs for training. |
|---|
| | | model (torch.nn.Module): The model to be trained. |
|---|
| | | optim (torch.optim.Optimizer): The optimizer to use for training. |
|---|
| | | scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler. |
|---|
| | | dataloader_train (torch.utils.data.DataLoader): DataLoader for the training dataset. |
|---|
| | | dataloader_val (torch.utils.data.DataLoader): DataLoader for the validation dataset. |
|---|
| | | output_dir (str): Directory where model checkpoints will be saved. |
|---|
| | | resume (str, optional): Path to a checkpoint to resume training from. |
|---|
| | | """ |
|---|
| | | |
|---|
| | | def __init__(self, model, |
|---|
| | | optim, |
|---|
| | | scheduler, |
|---|
| | | dataloader_train, |
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| | | dataloader_val, |
|---|
| | | local_rank, |
|---|
| | | use_ddp=False, |
|---|
| | | use_fsdp=False, |
|---|
| | | **kwargs): |
|---|
| | | """ |
|---|
| | | Initializes the Trainer class with the model, optimizer, scheduler, dataloaders, and other settings. |
|---|
| | | Attributes: |
|---|
| | | max_epoch (int): Maximum number of epochs for training. |
|---|
| | | model (torch.nn.Module): The model to be trained. |
|---|
| | | optim (torch.optim.Optimizer): The optimizer to use for training. |
|---|
| | | scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler. |
|---|
| | | dataloader_train (torch.utils.data.DataLoader): DataLoader for the training dataset. |
|---|
| | | dataloader_val (torch.utils.data.DataLoader): DataLoader for the validation dataset. |
|---|
| | | output_dir (str): Directory where model checkpoints will be saved. |
|---|
| | | resume (str, optional): Path to a checkpoint to resume training from. |
|---|
| | | """ |
|---|
| | | |
|---|
| | | def __init__(self, model, |
|---|
| | | optim, |
|---|
| | | scheduler, |
|---|
| | | dataloader_train, |
|---|
| | | dataloader_val, |
|---|
| | | local_rank, |
|---|
| | | use_ddp=False, |
|---|
| | | use_fsdp=False, |
|---|
| | | output_dir: str="./", |
|---|
| | | **kwargs): |
|---|
| | | """ |
|---|
| | | Initializes the Trainer class with the model, optimizer, scheduler, dataloaders, and other settings. |
|---|
| | | |
|---|
| | | Args: |
|---|
| | | model (torch.nn.Module): The model to be trained. |
|---|
| | | optim (torch.optim.Optimizer): The optimizer to use for training. |
|---|
| | | scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler. |
|---|
| | | dataloader_train (torch.utils.data.DataLoader): The DataLoader for the training dataset. |
|---|
| | | dataloader_val (torch.utils.data.DataLoader): The DataLoader for the validation dataset. |
|---|
| | | **kwargs: Additional keyword arguments: |
|---|
| | | max_epoch (int): The maximum number of epochs for training. |
|---|
| | | output_dir (str): The directory where model checkpoints will be saved. Default is './'. |
|---|
| | | resume (str, optional): The file path to a checkpoint to resume training from. |
|---|
| | | """ |
|---|
| | | |
|---|
| | | self.model = model |
|---|
| | | self.optim = optim |
|---|
| | | self.scheduler = scheduler |
|---|
| | | self.dataloader_train = dataloader_train |
|---|
| | | self.dataloader_val = dataloader_val |
|---|
| | | self.output_dir = kwargs.get('output_dir', './') |
|---|
| | | self.resume = kwargs.get('resume', True) |
|---|
| | | self.start_epoch = 1 |
|---|
| | | self.max_epoch = kwargs.get('max_epoch', 100) |
|---|
| | | self.local_rank = local_rank |
|---|
| | | self.use_ddp = use_ddp |
|---|
| | | self.use_fsdp = use_fsdp |
|---|
| | | self.device = next(model.parameters()).device |
|---|
| | | self.kwargs = kwargs |
|---|
| | | |
|---|
| | | if self.resume: |
|---|
| | | self._resume_checkpoint(self.resume) |
|---|
| | | |
|---|
| | | try: |
|---|
| | | rank = dist.get_rank() |
|---|
| | | world_size = dist.get_world_size() |
|---|
| | | except: |
|---|
| | | rank = 0 |
|---|
| | | world_size = 1 |
|---|
| | | logging.warning("distributed is not initialized, only single shard") |
|---|
| | | self.rank = rank |
|---|
| | | self.world_size = world_size |
|---|
| | | |
|---|
| | | def _save_checkpoint(self, epoch): |
|---|
| | | """ |
|---|
| | | Saves a checkpoint containing the model's state, the optimizer's state, |
|---|
| | | and the scheduler's state at the end of the given epoch. This method is |
|---|
| | | intended to be called at the end of each epoch to save the training progress. |
|---|
| | | Args: |
|---|
| | | model (torch.nn.Module): The model to be trained. |
|---|
| | | optim (torch.optim.Optimizer): The optimizer to use for training. |
|---|
| | | scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler. |
|---|
| | | dataloader_train (torch.utils.data.DataLoader): The DataLoader for the training dataset. |
|---|
| | | dataloader_val (torch.utils.data.DataLoader): The DataLoader for the validation dataset. |
|---|
| | | **kwargs: Additional keyword arguments: |
|---|
| | | max_epoch (int): The maximum number of epochs for training. |
|---|
| | | output_dir (str): The directory where model checkpoints will be saved. Default is './'. |
|---|
| | | resume (str, optional): The file path to a checkpoint to resume training from. |
|---|
| | | """ |
|---|
| | | |
|---|
| | | self.model = model |
|---|
| | | self.optim = optim |
|---|
| | | self.scheduler = scheduler |
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| | | self.dataloader_train = dataloader_train |
|---|
| | | self.dataloader_val = dataloader_val |
|---|
| | | self.output_dir = output_dir |
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| | | self.resume = kwargs.get('resume', True) |
|---|
| | | self.start_epoch = 0 |
|---|
| | | self.max_epoch = kwargs.get('max_epoch', 100) |
|---|
| | | self.local_rank = local_rank |
|---|
| | | self.use_ddp = use_ddp |
|---|
| | | self.use_fsdp = use_fsdp |
|---|
| | | self.device = next(model.parameters()).device |
|---|
| | | self.avg_nbest_model = kwargs.get("avg_nbest_model", 5) |
|---|
| | | self.kwargs = kwargs |
|---|
| | | |
|---|
| | | |
|---|
| | | try: |
|---|
| | | rank = dist.get_rank() |
|---|
| | | world_size = dist.get_world_size() |
|---|
| | | except: |
|---|
| | | rank = 0 |
|---|
| | | world_size = 1 |
|---|
| | | logging.warning("distributed is not initialized, only single shard") |
|---|
| | | self.rank = rank |
|---|
| | | self.world_size = world_size |
|---|
| | | |
|---|
| | | os.makedirs(os.path.join(self.output_dir, "tensorboard"), exist_ok=True) |
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| | | self.writer = SummaryWriter(os.path.join(self.output_dir, "tensorboard")) if rank == 0 else None |
|---|
| | | |
|---|
| | | |
|---|
| | | def _save_checkpoint(self, epoch): |
|---|
| | | """ |
|---|
| | | Saves a checkpoint containing the model's state, the optimizer's state, |
|---|
| | | and the scheduler's state at the end of the given epoch. This method is |
|---|
| | | intended to be called at the end of each epoch to save the training progress. |
|---|
| | | |
|---|
| | | Args: |
|---|
| | | epoch (int): The epoch number at which the checkpoint is being saved. |
|---|
| | | """ |
|---|
| | | state = { |
|---|
| | | 'epoch': epoch, |
|---|
| | | 'state_dict': self.model.state_dict(), |
|---|
| | | 'optimizer': self.optim.state_dict(), |
|---|
| | | 'scheduler': self.scheduler.state_dict(), |
|---|
| | | } |
|---|
| | | # Create output directory if it does not exist |
|---|
| | | os.makedirs(self.output_dir, exist_ok=True) |
|---|
| | | filename = os.path.join(self.output_dir, f'model.e{epoch}.pb') |
|---|
| | | torch.save(state, filename) |
|---|
| | | print(f'Checkpoint saved to {filename}') |
|---|
| | | |
|---|
| | | def _resume_checkpoint(self, resume_path): |
|---|
| | | """ |
|---|
| | | Resumes training from a checkpoint at the given file path. |
|---|
| | | Loads the model's state, the optimizer's state, and the scheduler's state. |
|---|
| | | Args: |
|---|
| | | epoch (int): The epoch number at which the checkpoint is being saved. |
|---|
| | | """ |
|---|
| | | state = { |
|---|
| | | 'epoch': epoch, |
|---|
| | | 'state_dict': self.model.state_dict(), |
|---|
| | | 'optimizer': self.optim.state_dict(), |
|---|
| | | 'scheduler': self.scheduler.state_dict(), |
|---|
| | | } |
|---|
| | | # Create output directory if it does not exist |
|---|
| | | os.makedirs(self.output_dir, exist_ok=True) |
|---|
| | | filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}') |
|---|
| | | torch.save(state, filename) |
|---|
| | | |
|---|
| | | print(f'Checkpoint saved to {filename}') |
|---|
| | | latest = Path(os.path.join(self.output_dir, f'model.pt')) |
|---|
| | | try: |
|---|
| | | latest.unlink() |
|---|
| | | except: |
|---|
| | | pass |
|---|
| | | |
|---|
| | | Args: |
|---|
| | | resume_path (str): The file path to the checkpoint to resume from. |
|---|
| | | """ |
|---|
| | | if os.path.isfile(resume_path): |
|---|
| | | checkpoint = torch.load(resume_path) |
|---|
| | | self.start_epoch = checkpoint['epoch'] + 1 |
|---|
| | | self.model.load_state_dict(checkpoint['state_dict']) |
|---|
| | | self.optim.load_state_dict(checkpoint['optimizer']) |
|---|
| | | self.scheduler.load_state_dict(checkpoint['scheduler']) |
|---|
| | | print(f"Checkpoint loaded successfully from '{resume_path}' at (epoch {checkpoint['epoch']})") |
|---|
| | | else: |
|---|
| | | print(f"No checkpoint found at '{resume_path}', starting from scratch") |
|---|
| | | |
|---|
| | | def run(self): |
|---|
| | | """ |
|---|
| | | Starts the training process, iterating over epochs, training the model, |
|---|
| | | and saving checkpoints at the end of each epoch. |
|---|
| | | """ |
|---|
| | | for epoch in range(self.start_epoch, self.max_epoch + 1): |
|---|
| | | self._train_epoch(epoch) |
|---|
| | | # self._validate_epoch(epoch) |
|---|
| | | if dist.get_rank() == 0: |
|---|
| | | self._save_checkpoint(epoch) |
|---|
| | | self.scheduler.step() |
|---|
| | | break |
|---|
| | | |
|---|
| | | def _train_epoch(self, epoch): |
|---|
| | | """ |
|---|
| | | Defines the training process for a single epoch with gradient accumulation. |
|---|
| | | Args: |
|---|
| | | epoch (int): The current epoch number. |
|---|
| | | """ |
|---|
| | | self.model.train() |
|---|
| | | pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_train), |
|---|
| | | dynamic_ncols=True) |
|---|
| | | |
|---|
| | | # Set the number of steps for gradient accumulation |
|---|
| | | accum_grad = self.kwargs.get("accum_grad", 1) |
|---|
| | | # Initialize the gradient accumulation |
|---|
| | | self.optim.zero_grad() |
|---|
| | | speed_stats = {} |
|---|
| | | time5 = time.perf_counter() |
|---|
| | | for batch_idx, batch in enumerate(self.dataloader_train): |
|---|
| | | time1 = time.perf_counter() |
|---|
| | | speed_stats["data_load"] = f"{time1-time5:0.3f}" |
|---|
| | | # import pdb; |
|---|
| | | # pdb.set_trace() |
|---|
| | | batch = to_device(batch, self.device) |
|---|
| | | |
|---|
| | | 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) |
|---|
| | | time3 = time.perf_counter() |
|---|
| | | speed_stats["forward_time"] = f"{time3 - time2:0.3f}" |
|---|
| | | loss, stats, weight = retval |
|---|
| | | stats = {k: v for k, v in stats.items() if v is not None} |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | # 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) |
|---|
| | | # Now weight is summation over all workers |
|---|
| | | loss /= weight |
|---|
| | | # Multiply world_size because DistributedDataParallel |
|---|
| | | # automatically normalizes the gradient by world_size. |
|---|
| | | loss *= self.world_size |
|---|
| | | # Scale the loss since we're not updating for every mini-batch |
|---|
| | | loss = loss / accum_grad |
|---|
| | | loss.backward() |
|---|
| | | time4 = time.perf_counter() |
|---|
| | | speed_stats["backward_time"] = f"{time4 - time3:0.3f}" |
|---|
| | | |
|---|
| | | # Perform an optimizer step only after accumulating enough gradients |
|---|
| | | if (batch_idx + 1) % accum_grad == 0 or (batch_idx + 1) == len(self.dataloader_train): |
|---|
| | | # Perform gradient clipping if it is set |
|---|
| | | if self.kwargs.get("grad_clip", None) is not None: |
|---|
| | | grad_norm = torch.nn.utils.clip_grad_norm_( |
|---|
| | | self.model.parameters(), |
|---|
| | | max_norm=self.kwargs.get("grad_clip", 10.0), |
|---|
| | | norm_type=self.kwargs.get("grad_clip_type", 2.0), |
|---|
| | | ) |
|---|
| | | if not torch.isfinite(grad_norm): |
|---|
| | | logging.warning( |
|---|
| | | f"The grad norm is {grad_norm}. Skipping updating the model." |
|---|
| | | ) |
|---|
| | | self.optim.zero_grad() # Reset gradients |
|---|
| | | continue |
|---|
| | | |
|---|
| | | # Execute an optimization step (update model parameters) |
|---|
| | | self.optim.step() |
|---|
| | | self.scheduler.step() |
|---|
| | | # Clear gradients for the next accumulation stage |
|---|
| | | self.optim.zero_grad() |
|---|
| | | total_time = f"{time.perf_counter() - time5:0.3f}" |
|---|
| | | time5 = time.perf_counter() |
|---|
| | | speed_stats["optim_time"] = f"{time5 - time4:0.3f}" |
|---|
| | | |
|---|
| | | speed_stats["total_time"] = total_time |
|---|
| | | |
|---|
| | | |
|---|
| | | pbar.update(1) |
|---|
| | | if self.local_rank == 0: |
|---|
| | | description = ( |
|---|
| | | f"Epoch: {epoch + 1}/{self.max_epoch}, " |
|---|
| | | f"step {batch_idx}/{len(self.dataloader_train)}, " |
|---|
| | | f"{speed_stats}, " |
|---|
| | | f"(loss: {loss.detach().float():.3f}), " |
|---|
| | | f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}" |
|---|
| | | ) |
|---|
| | | pbar.set_description(description) |
|---|
| | | |
|---|
| | | if batch_idx == 2: |
|---|
| | | break |
|---|
| | | pbar.close() |
|---|
| | | latest.symlink_to(filename) |
|---|
| | | |
|---|
| | | def _resume_checkpoint(self, resume_path): |
|---|
| | | """ |
|---|
| | | Resumes training from a checkpoint at the given file path. |
|---|
| | | Loads the model's state, the optimizer's state, and the scheduler's state. |
|---|
| | | |
|---|
| | | def _validate_epoch(self, epoch): |
|---|
| | | """ |
|---|
| | | Defines the validation process for a single epoch. |
|---|
| | | Should be implemented with the actual model validation steps. |
|---|
| | | |
|---|
| | | Args: |
|---|
| | | epoch (int): The current epoch number. |
|---|
| | | """ |
|---|
| | | self.model.eval() |
|---|
| | | with torch.no_grad(): |
|---|
| | | for data, target in self.dataloader_val: |
|---|
| | | # Implement the model validation steps here |
|---|
| | | pass |
|---|
| | | Args: |
|---|
| | | resume_path (str): The file path to the checkpoint to resume from. |
|---|
| | | """ |
|---|
| | | ckpt = os.path.join(resume_path, "model.pt") |
|---|
| | | if os.path.isfile(ckpt): |
|---|
| | | checkpoint = torch.load(ckpt) |
|---|
| | | self.start_epoch = checkpoint['epoch'] + 1 |
|---|
| | | self.model.load_state_dict(checkpoint['state_dict']) |
|---|
| | | self.optim.load_state_dict(checkpoint['optimizer']) |
|---|
| | | self.scheduler.load_state_dict(checkpoint['scheduler']) |
|---|
| | | print(f"Checkpoint loaded successfully from '{ckpt}'") |
|---|
| | | else: |
|---|
| | | print(f"No checkpoint found at '{ckpt}', starting from scratch") |
|---|
| | | |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | dist.barrier() |
|---|
| | | |
|---|
| | | def run(self): |
|---|
| | | """ |
|---|
| | | Starts the training process, iterating over epochs, training the model, |
|---|
| | | and saving checkpoints at the end of each epoch. |
|---|
| | | """ |
|---|
| | | if self.resume: |
|---|
| | | self._resume_checkpoint(self.output_dir) |
|---|
| | | |
|---|
| | | for epoch in range(self.start_epoch, self.max_epoch + 1): |
|---|
| | | |
|---|
| | | self._train_epoch(epoch) |
|---|
| | | |
|---|
| | | |
|---|
| | | |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | dist.barrier() |
|---|
| | | |
|---|
| | | self._validate_epoch(epoch) |
|---|
| | | |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | dist.barrier() |
|---|
| | | |
|---|
| | | |
|---|
| | | if self.rank == 0: |
|---|
| | | self._save_checkpoint(epoch) |
|---|
| | | |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | dist.barrier() |
|---|
| | | |
|---|
| | | self.scheduler.step() |
|---|
| | | |
|---|
| | | |
|---|
| | | if self.rank == 0: |
|---|
| | | average_checkpoints(self.output_dir, self.avg_nbest_model) |
|---|
| | | |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | dist.barrier() |
|---|
| | | |
|---|
| | | |
|---|
| | | if self.writer: |
|---|
| | | self.writer.close() |
|---|
| | | |
|---|
| | | |
|---|
| | | def _train_epoch(self, epoch): |
|---|
| | | """ |
|---|
| | | Defines the training process for a single epoch with gradient accumulation. |
|---|
| | | Args: |
|---|
| | | epoch (int): The current epoch number. |
|---|
| | | """ |
|---|
| | | self.model.train() |
|---|
| | | pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_train), |
|---|
| | | dynamic_ncols=True) |
|---|
| | | |
|---|
| | | # Set the number of steps for gradient accumulation |
|---|
| | | accum_grad = self.kwargs.get("accum_grad", 1) |
|---|
| | | # Initialize the gradient accumulation |
|---|
| | | self.optim.zero_grad() |
|---|
| | | speed_stats = {} |
|---|
| | | time5 = time.perf_counter() |
|---|
| | | for batch_idx, batch in enumerate(self.dataloader_train): |
|---|
| | | time1 = time.perf_counter() |
|---|
| | | speed_stats["data_load"] = f"{time1-time5:0.3f}" |
|---|
| | | |
|---|
| | | batch = to_device(batch, self.device) |
|---|
| | | |
|---|
| | | my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext |
|---|
| | | with my_context(): |
|---|
| | | time2 = time.perf_counter() |
|---|
| | | # print("before, 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, |
|---|
| | | # )) |
|---|
| | | |
|---|
| | | retval = self.model(**batch) |
|---|
| | | torch.cuda.empty_cache() |
|---|
| | | # print("after, 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, |
|---|
| | | # )) |
|---|
| | | time3 = time.perf_counter() |
|---|
| | | speed_stats["forward_time"] = f"{time3 - time2:0.3f}" |
|---|
| | | loss, stats, weight = retval |
|---|
| | | stats = {k: v for k, v in stats.items() if v is not None} |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | # 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) |
|---|
| | | # Now weight is summation over all workers |
|---|
| | | loss /= weight |
|---|
| | | # Multiply world_size because DistributedDataParallel |
|---|
| | | # automatically normalizes the gradient by world_size. |
|---|
| | | loss *= self.world_size |
|---|
| | | # Scale the loss since we're not updating for every mini-batch |
|---|
| | | loss = loss / accum_grad |
|---|
| | | loss.backward() |
|---|
| | | time4 = time.perf_counter() |
|---|
| | | speed_stats["backward_time"] = f"{time4 - time3:0.3f}" |
|---|
| | | |
|---|
| | | # Perform an optimizer step only after accumulating enough gradients |
|---|
| | | if (batch_idx + 1) % accum_grad == 0 or (batch_idx + 1) == len(self.dataloader_train): |
|---|
| | | # Perform gradient clipping if it is set |
|---|
| | | if self.kwargs.get("grad_clip", None) is not None: |
|---|
| | | grad_norm = torch.nn.utils.clip_grad_norm_( |
|---|
| | | self.model.parameters(), |
|---|
| | | max_norm=self.kwargs.get("grad_clip", 10.0), |
|---|
| | | norm_type=self.kwargs.get("grad_clip_type", 2.0), |
|---|
| | | ) |
|---|
| | | if not torch.isfinite(grad_norm): |
|---|
| | | logging.warning( |
|---|
| | | f"The grad norm is {grad_norm}. Skipping updating the model." |
|---|
| | | ) |
|---|
| | | self.optim.zero_grad() # Reset gradients |
|---|
| | | continue |
|---|
| | | |
|---|
| | | # Execute an optimization step (update model parameters) |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | dist.barrier() |
|---|
| | | self.optim.step() |
|---|
| | | self.scheduler.step() |
|---|
| | | # Clear gradients for the next accumulation stage |
|---|
| | | self.optim.zero_grad() |
|---|
| | | total_time = f"{time.perf_counter() - time5:0.3f}" |
|---|
| | | time5 = time.perf_counter() |
|---|
| | | speed_stats["optim_time"] = f"{time5 - time4:0.3f}" |
|---|
| | | |
|---|
| | | speed_stats["total_time"] = total_time |
|---|
| | | |
|---|
| | | |
|---|
| | | pbar.update(1) |
|---|
| | | if self.local_rank == 0: |
|---|
| | | 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"(loss: {loss.detach().cpu().item():.3f}), " |
|---|
| | | f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}" |
|---|
| | | f"{gpu_info}" |
|---|
| | | ) |
|---|
| | | pbar.set_description(description) |
|---|
| | | if self.writer: |
|---|
| | | self.writer.add_scalar('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(), |
|---|
| | | epoch * len(self.dataloader_train) + batch_idx) |
|---|
| | | for key, var in speed_stats.items(): |
|---|
| | | self.writer.add_scalar(f'{key}/train', eval(var), |
|---|
| | | epoch * len(self.dataloader_train) + batch_idx) |
|---|
| | | |
|---|
| | | # if batch_idx == 2: |
|---|
| | | # break |
|---|
| | | pbar.close() |
|---|
| | | |
|---|
| | | def _validate_epoch(self, epoch): |
|---|
| | | """ |
|---|
| | | Defines the validation process for a single epoch. |
|---|
| | | Should be implemented with the actual model validation steps. |
|---|
| | | |
|---|
| | | Args: |
|---|
| | | epoch (int): The current epoch number. |
|---|
| | | """ |
|---|
| | | self.model.eval() |
|---|
| | | with torch.no_grad(): |
|---|
| | | pbar = tqdm(colour="red", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_val), |
|---|
| | | dynamic_ncols=True) |
|---|
| | | speed_stats = {} |
|---|
| | | time5 = time.perf_counter() |
|---|
| | | for batch_idx, batch in enumerate(self.dataloader_val): |
|---|
| | | time1 = time.perf_counter() |
|---|
| | | speed_stats["data_load"] = f"{time1 - time5:0.3f}" |
|---|
| | | batch = to_device(batch, self.device) |
|---|
| | | time2 = time.perf_counter() |
|---|
| | | retval = self.model(**batch) |
|---|
| | | time3 = time.perf_counter() |
|---|
| | | speed_stats["forward_time"] = f"{time3 - time2:0.3f}" |
|---|
| | | loss, stats, weight = retval |
|---|
| | | stats = {k: v for k, v in stats.items() if v is not None} |
|---|
| | | if self.use_ddp or self.use_fsdp: |
|---|
| | | # 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) |
|---|
| | | # Now weight is summation over all workers |
|---|
| | | loss /= weight |
|---|
| | | # Multiply world_size because DistributedDataParallel |
|---|
| | | # automatically normalizes the gradient by world_size. |
|---|
| | | loss *= self.world_size |
|---|
| | | # Scale the loss since we're not updating for every mini-batch |
|---|
| | | loss = loss |
|---|
| | | time4 = time.perf_counter() |
|---|
| | | |
|---|
| | | pbar.update(1) |
|---|
| | | if self.local_rank == 0: |
|---|
| | | description = ( |
|---|
| | | f"validation epoch: {epoch}/{self.max_epoch}, " |
|---|
| | | f"step {batch_idx}/{len(self.dataloader_train)}, " |
|---|
| | | f"{speed_stats}, " |
|---|
| | | f"(loss: {loss.detach().cpu().item():.3f}), " |
|---|
| | | f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}" |
|---|
| | | ) |
|---|
| | | pbar.set_description(description) |
|---|
| | | if self.writer: |
|---|
| | | self.writer.add_scalar('Loss/val', loss.item(), |
|---|
| | | epoch*len(self.dataloader_train) + batch_idx) |
|---|
| | | for key, var in stats.items(): |
|---|
| | | self.writer.add_scalar(f'{key}/val', var.item(), |
|---|
| | | epoch * len(self.dataloader_train) + 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) |
|---|
