Source code for

import warnings
from typing import Any, Callable, Iterator, List, Optional, Union

import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer
from import DataLoader, Dataset, IterableDataset
from import DistributedSampler
from import Sampler

from ignite.distributed import utils as idist
from ignite.distributed.comp_models import horovod as idist_hvd
from ignite.distributed.comp_models import native as idist_native
from ignite.distributed.comp_models import xla as idist_xla
from ignite.utils import setup_logger

__all__ = ["auto_dataloader", "auto_model", "auto_optim", "DistributedProxySampler"]

[docs]def auto_dataloader(dataset: Dataset, **kwargs: Any) -> Union[DataLoader, "_MpDeviceLoader"]: """Helper method to create a dataloader adapted for non-distributed and distributed configurations (supporting all available backends from :meth:`~ignite.distributed.utils.available_backends()`). Internally, we create a dataloader with provided kwargs while applying the following updates: - batch size is scaled by world size: ``batch_size / world_size`` if larger or equal world size. - number of workers is scaled by number of local processes: ``num_workers / nprocs`` if larger or equal world size. - if no sampler provided by user, `torch DistributedSampler`_ is setup. - if a sampler is provided by user, it is wrapped by :class:``. - if the default device is 'cuda', `pin_memory` is automatically set to `True`. .. warning:: Custom batch sampler is not adapted for distributed configuration. Please, make sure that provided batch sampler is compatible with distributed configuration. Examples: .. code-block:: python import ignite.distribted as idist train_loader = idist.auto_dataloader( train_dataset, batch_size=32, num_workers=4, shuffle=True, pin_memory="cuda" in idist.device().type, drop_last=True, ) Args: dataset: input torch dataset. If input dataset is `torch IterableDataset`_ then dataloader will be created without any distributed sampling. Please, make sure that the dataset itself produces different data on different ranks. kwargs: keyword arguments for `torch DataLoader`_. Returns: `torch DataLoader`_ or `XLA MpDeviceLoader`_ for XLA devices .. _torch DataLoader: .. _XLA MpDeviceLoader: .. _torch DistributedSampler: .. _torch IterableDataset: """ rank = idist.get_rank() world_size = idist.get_world_size() logger = setup_logger(__name__ + ".auto_dataloader") if world_size > 1: if "batch_size" in kwargs and kwargs["batch_size"] >= world_size: kwargs["batch_size"] //= world_size nproc = idist.get_nproc_per_node() if "num_workers" in kwargs and kwargs["num_workers"] >= nproc: kwargs["num_workers"] = (kwargs["num_workers"] + nproc - 1) // nproc if "batch_sampler" not in kwargs: if isinstance(dataset, IterableDataset): "Found iterable dataset, dataloader will be created without any distributed sampling. " "Please, make sure that the dataset itself produces different data on different ranks." ) else: if kwargs.get("sampler", None) is not None: sampler = DistributedProxySampler( kwargs["sampler"], num_replicas=world_size, rank=rank ) # type: Union[DistributedProxySampler, DistributedSampler, Sampler] else: sampler = DistributedSampler( dataset, num_replicas=world_size, rank=rank, shuffle=kwargs.get("shuffle", True) ) # we need to remove "shuffle" from kwargs if sampler is used if "shuffle" in kwargs: del kwargs["shuffle"] kwargs["sampler"] = sampler else: warnings.warn( "Found batch_sampler in provided kwargs. Please, make sure that it is compatible " "with distributed configuration" ) if idist.has_xla_support and idist.backend() == idist_xla.XLA_TPU and kwargs.get("pin_memory", False): # TODO: How about XLA GPU ? warnings.warn( "Found incompatible options: xla support and pin_memory args equal True. " "Argument `pin_memory=False` will be used to construct data loader." ) kwargs["pin_memory"] = False else: kwargs["pin_memory"] = kwargs.get("pin_memory", "cuda" in idist.device().type)"Use data loader kwargs for dataset '{repr(dataset)[:20].strip()}': \n\t{kwargs}") dataloader = DataLoader(dataset, **kwargs) if idist.has_xla_support and idist.backend() == idist_xla.XLA_TPU and world_size > 1:"DataLoader is wrapped by `MpDeviceLoader` on XLA") mp_device_loader_cls = _MpDeviceLoader try: from torch_xla.distributed.parallel_loader import MpDeviceLoader mp_device_loader_cls = MpDeviceLoader except ImportError: pass mp_dataloader = mp_device_loader_cls(dataloader, idist.device()) mp_dataloader.sampler = dataloader.sampler # type: ignore[attr-defined] return mp_dataloader return dataloader
[docs]def auto_model(model: nn.Module, sync_bn: bool = False, **kwargs: Any) -> nn.Module: """Helper method to adapt provided model for non-distributed and distributed configurations (supporting all available backends from :meth:`~ignite.distributed.utils.available_backends()`). Internally, we perform to following: - send model to current :meth:`~ignite.distributed.utils.device()` if model's parameters are not on the device. - wrap the model to `torch DistributedDataParallel`_ for native torch distributed if world size is larger than 1. - wrap the model to `torch DataParallel`_ if no distributed context found and more than one CUDA devices available. - broadcast the initial variable states from rank 0 to all other processes if Horovod distributed framework is used. Examples: .. code-block:: python import ignite.distribted as idist model = idist.auto_model(model) In addition with NVidia/Apex, it can be used in the following way: .. code-block:: python import ignite.distribted as idist model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level) model = idist.auto_model(model) Args: model: model to adapt. sync_bn: if True, applies `torch convert_sync_batchnorm`_ to the model for native torch distributed only. Default, False. Note, if using Nvidia/Apex, batchnorm conversion should be applied before calling ``amp.initialize``. kwargs: kwargs to model's wrapping class: `torch DistributedDataParallel`_ or `torch DataParallel`_ if applicable. Please, make sure to use acceptable kwargs for given backend. Returns: torch.nn.Module .. _torch DistributedDataParallel: DistributedDataParallel.html .. _torch DataParallel: .. _torch convert_sync_batchnorm: torch.nn.SyncBatchNorm.convert_sync_batchnorm .. versionchanged:: 0.4.2 - Added Horovod distributed framework. - Added ``sync_bn`` argument. .. versionchanged:: 0.4.3 Added kwargs to ``idist.auto_model``. """ logger = setup_logger(__name__ + ".auto_model") # Put model's parameters to device if its parameters are not on the device device = idist.device() if not all([p.device == device for p in model.parameters()]): # distributed data parallel model if idist.get_world_size() > 1: bnd = idist.backend() if idist.has_native_dist_support and bnd in (idist_native.NCCL, idist_native.GLOO, idist_native.MPI): if sync_bn:"Convert batch norm to sync batch norm") model = nn.SyncBatchNorm.convert_sync_batchnorm(model) if torch.cuda.is_available(): if "device_ids" in kwargs: raise ValueError(f"Argument kwargs should not contain 'device_ids', but got {kwargs}") lrank = idist.get_local_rank()"Apply torch DistributedDataParallel on model, device id: {lrank}") kwargs["device_ids"] = [ lrank, ] else:"Apply torch DistributedDataParallel on model") model = torch.nn.parallel.DistributedDataParallel(model, **kwargs) elif idist.has_hvd_support and bnd == idist_hvd.HOROVOD: import horovod.torch as hvd"Broadcast the initial variable states from rank 0 to all other processes") hvd.broadcast_parameters(model.state_dict(), root_rank=0) # not distributed but multiple GPUs reachable so data parallel model elif torch.cuda.device_count() > 1 and "cuda" in idist.device().type:"Apply torch DataParallel on model") model = torch.nn.parallel.DataParallel(model, **kwargs) return model
[docs]def auto_optim(optimizer: Optimizer) -> Optimizer: """Helper method to adapt optimizer for non-distributed and distributed configurations (supporting all available backends from :meth:`~ignite.distributed.utils.available_backends()`). Internally, this method is no-op for non-distributed and torch native distributed configuration. For XLA distributed configuration, we create a new class that inherits from provided optimizer. The goal is to override the `step()` method with specific `xm.optimizer_step`_ implementation. For Horovod distributed configuration, optimizer is wrapped with Horovod Distributed Optimizer and its state is broadcasted from rank 0 to all other processes. Examples: .. code-block:: python import ignite.distributed as idist optimizer = idist.auto_optim(optimizer) Args: optimizer: input torch optimizer Returns: Optimizer .. _xm.optimizer_step: .. versionchanged:: 0.4.2 Added Horovod distributed optimizer. """ bnd = idist.backend() if idist.has_xla_support and bnd == idist_xla.XLA_TPU: cls = type(optimizer.__class__.__name__, (optimizer.__class__,), dict(_XLADistributedOptimizer.__dict__)) return cls(optimizer) if idist.has_hvd_support and bnd == idist_hvd.HOROVOD: import horovod.torch as hvd optimizer = hvd.DistributedOptimizer(optimizer) hvd.broadcast_optimizer_state(optimizer, root_rank=0) return optimizer return optimizer
[docs]class DistributedProxySampler(DistributedSampler): """Distributed sampler proxy to adapt user's sampler for distributed data parallelism configuration. Code is based on .. note:: Input sampler is assumed to have a constant size. Args: sampler: Input torch data sampler. num_replicas: Number of processes participating in distributed training. rank: Rank of the current process within ``num_replicas``. """ def __init__(self, sampler: Sampler, num_replicas: Optional[int] = None, rank: Optional[int] = None) -> None: if not isinstance(sampler, Sampler): raise TypeError(f"Argument sampler should be instance of torch Sampler, but given: {type(sampler)}") if not hasattr(sampler, "__len__"): raise TypeError("Argument sampler should have length") super(DistributedProxySampler, self).__init__( sampler, num_replicas=num_replicas, rank=rank, shuffle=False # type: ignore[arg-type] ) self.sampler = sampler def __iter__(self) -> Iterator: # deterministically shuffle based on epoch torch.manual_seed(self.epoch) indices = [] # type: List while len(indices) < self.total_size: indices += list(self.sampler) if len(indices) > self.total_size: indices = indices[: self.total_size] # subsample indices = indices[self.rank : self.total_size : self.num_replicas] if len(indices) != self.num_samples: raise RuntimeError(f"{len(indices)} vs {self.num_samples}") return iter(indices)
if idist.has_xla_support: import torch_xla.core.xla_model as xm from torch_xla.distributed.parallel_loader import ParallelLoader class _MpDeviceLoader: # # From pytorch/xla if `torch_xla.distributed.parallel_loader.MpDeviceLoader` is not available def __init__(self, loader: Any, device: torch.device, **kwargs: Any) -> None: self._loader = loader self._device = device self._parallel_loader_kwargs = kwargs def __iter__(self) -> Iterator: parallel_loader = ParallelLoader(self._loader, [self._device], **self._parallel_loader_kwargs) return parallel_loader.per_device_loader(self._device) def __len__(self) -> int: return len(self._loader) class _XLADistributedOptimizer(Optimizer): def __init__(self, optimizer: Optimizer) -> None: super(self.__class__, self).__init__(optimizer.param_groups) # type: ignore[call-arg] self.wrapped_optimizer = optimizer def step(self, closure: Optional[Callable] = None) -> None: xm.optimizer_step(self.wrapped_optimizer, barrier=True)

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 10/03/2022, 1:27:29 PM.

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