import random from typing import Callable, List, Tuple, Union import numpy as np import torch import torch.distributed as dist import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO from colossalai.cluster import DistCoordinator from colossalai.interface import ModelWrapper, OptimizerWrapper from .plugin_base import Plugin __all__ = ['TorchDDPPlugin'] class TorchDDPCheckpointIO(GeneralCheckpointIO): def __init__(self) -> None: super().__init__() self.coordinator = DistCoordinator() def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool = True): """ Load model from checkpoint with automatic unwrapping. """ # the model should be unwrapped in self.load_model via ModelWrapper.unwrap return super().load_unsharded_model(model, checkpoint, strict=strict) def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool): """ Save model to checkpoint but only on master process. """ # the model should be unwrapped in self.load_model via ModelWrapper.unwrap if self.coordinator.is_master(): super().save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors) def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): """ Save optimizer to checkpoint but only on master process. """ if self.coordinator.is_master(): super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor) def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): """ Save model to checkpoint but only on master process. """ if self.coordinator.is_master(): super().save_lr_scheduler(lr_scheduler, checkpoint) class TorchDDPModel(ModelWrapper): def __init__(self, module: nn.Module, *args, **kwargs) -> None: super().__init__(module) self.module = DDP(module, *args, **kwargs) def unwrap(self): return self.module.module class TorchDDPPlugin(Plugin): """ Plugin for PyTorch DDP. Example: >>> from colossalai.booster import Booster >>> from colossalai.booster.plugin import TorchDDPPlugin >>> >>> model, train_dataset, optimizer, criterion = ... >>> plugin = TorchDDPPlugin() >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8) >>> booster = Booster(plugin=plugin) >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion) Args: broadcast_buffers (bool, optional): Whether to broadcast buffers in the beginning of training. Defaults to True. bucket_cap_mb (int, optional): The bucket size in MB. Defaults to 25. find_unused_parameters (bool, optional): Whether to find unused parameters. Defaults to False. check_reduction (bool, optional): Whether to check reduction. Defaults to False. gradient_as_bucket_view (bool, optional): Whether to use gradient as bucket view. Defaults to False. static_graph (bool, optional): Whether to use static graph. Defaults to False. """ def __init__(self, broadcast_buffers: bool = True, bucket_cap_mb: int = 25, find_unused_parameters: bool = False, check_reduction: bool = False, gradient_as_bucket_view: bool = False, static_graph: bool = False) -> None: assert dist.is_initialized( ), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment' self.rank = dist.get_rank() self.world_size = dist.get_world_size() self.ddp_kwargs = dict(broadcast_buffers=broadcast_buffers, bucket_cap_mb=bucket_cap_mb, find_unused_parameters=find_unused_parameters, check_reduction=check_reduction, gradient_as_bucket_view=gradient_as_bucket_view, static_graph=static_graph) def support_no_sync(self) -> bool: return True def control_precision(self) -> bool: return False def supported_precisions(self) -> List[str]: return ['fp16', 'fp16_apex', 'bf16', 'fp8'] def control_device(self) -> bool: return True def supported_devices(self) -> List[str]: return ['cuda'] def prepare_train_dataloader(self, dataset, batch_size, shuffle=False, seed=1024, drop_last=False, pin_memory=False, num_workers=0, **kwargs): r""" Prepare a dataloader for distributed training. The dataloader will be wrapped by `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. Note: 1. Evaluation datasets should not be passed to this function. Args: dataset (`torch.utils.data.Dataset`): The dataset to be loaded. shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. seed (int, optional): Random worker seed for sampling, defaults to 1024. add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller, defaults to False. pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in `DataLoader `_. Returns: :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. """ _kwargs = kwargs.copy() sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle) # Deterministic dataloader def seed_worker(worker_id): worker_seed = seed np.random.seed(worker_seed) torch.manual_seed(worker_seed) random.seed(worker_seed) return DataLoader(dataset, batch_size=batch_size, sampler=sampler, worker_init_fn=seed_worker, drop_last=drop_last, pin_memory=pin_memory, num_workers=num_workers, **_kwargs) def configure( self, model: nn.Module, optimizer: Optimizer, criterion: Callable = None, dataloader: DataLoader = None, lr_scheduler: LRScheduler = None, ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: # cast model to cuda model = model.cuda() # convert model to sync bn model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None) # wrap the model with PyTorch DDP model = TorchDDPModel(model, **self.ddp_kwargs) if not isinstance(optimizer, OptimizerWrapper): optimizer = OptimizerWrapper(optimizer) return model, optimizer, criterion, dataloader, lr_scheduler def control_checkpoint_io(self) -> bool: return True def get_checkpoint_io(self) -> CheckpointIO: return TorchDDPCheckpointIO()