Source code for pytorch_lightning.plugins.training_type.parallel
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from abc import ABC, abstractmethod
from contextlib import contextmanager
from typing import Any, List, Optional
import torch
from torch.nn.parallel import DistributedDataParallel
import pytorch_lightning as pl
from pytorch_lightning.overrides.base import unwrap_lightning_module
from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
from pytorch_lightning.utilities import _XLA_AVAILABLE
from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available, ReduceOp
[docs]class ParallelPlugin(TrainingTypePlugin, ABC):
"""Plugin for training with multiple processes in parallel."""
def __init__(
self,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
checkpoint_io: Optional[CheckpointIO] = None,
):
super().__init__(checkpoint_io)
self.parallel_devices = parallel_devices
self.cluster_environment = cluster_environment
@property
@abstractmethod
def root_device(self) -> torch.device:
"""Return the root device."""
@property
def on_gpu(self) -> bool:
return self.root_device.type == "cuda" and torch.cuda.is_available()
@property
def on_tpu(self) -> bool:
return self.root_device.type == "xla" and _XLA_AVAILABLE
@property
def lightning_module(self):
return unwrap_lightning_module(self._model)
@property
def global_rank(self) -> int:
return self.cluster_environment.global_rank() if self.cluster_environment is not None else 0
@property
def local_rank(self) -> int:
return self.cluster_environment.local_rank() if self.cluster_environment is not None else 0
@property
def node_rank(self) -> int:
return self.cluster_environment.node_rank() if self.cluster_environment is not None else 0
@property
def world_size(self) -> int:
return self.cluster_environment.world_size() if self.cluster_environment is not None else 1
@property
def is_global_zero(self) -> bool:
return self.global_rank == 0
@property
def distributed_sampler_kwargs(self):
distributed_sampler_kwargs = dict(num_replicas=len(self.parallel_devices), rank=self.global_rank)
return distributed_sampler_kwargs
[docs] def reconciliate_processes(self, trace: str):
"""Function to re-conciliate processes on failure."""
[docs] def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
"""Perform a all_gather on all processes."""
return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
[docs] def reduce_boolean_decision(self, decision: bool) -> bool:
decision = torch.tensor(int(decision), device=self.lightning_module.device)
decision = self.reduce(decision, reduce_op=ReduceOp.SUM)
decision = bool(decision == self.world_size)
return decision
@property
def torch_distributed_backend(self):
torch_backend = os.getenv("PL_TORCH_DISTRIBUTED_BACKEND")
if torch_backend is None:
torch_backend = "nccl" if self.on_gpu else "gloo"
return torch_backend
[docs] @staticmethod
def configure_sync_batchnorm(model: "pl.LightningModule") -> "pl.LightningModule":
"""Add global batchnorm for a model spread across multiple GPUs and nodes.
Override to synchronize batchnorm between specific process groups instead
of the whole world or use a different sync_bn like `apex`'s version.
Args:
model: pointer to current :class:`LightningModule`.
Return:
LightningModule with batchnorm layers synchronized between process groups
"""
return torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
[docs] @contextmanager
def block_backward_sync(self):
"""Blocks ddp sync gradients behaviour on backwards pass.
This is useful for skipping sync when accumulating gradients, reducing communication overhead
Returns: context manager with sync behaviour off
"""
if isinstance(self.model, DistributedDataParallel):
with self.model.no_sync():
yield None
else:
yield None