Source code for pytorch_lightning.plugins.training_type.dp
# 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.
from typing import List, Optional
import torch
from torch.nn import DataParallel, Module
from pytorch_lightning.overrides.data_parallel import LightningParallelModule
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.plugins.training_type.parallel import ParallelPlugin
from pytorch_lightning.utilities.apply_func import apply_to_collection
from pytorch_lightning.utilities.enums import DistributedType
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.types import _METRIC_COLLECTION
[docs]class DataParallelPlugin(ParallelPlugin):
"""Implements data-parallel training in a single process, i.e., the model gets replicated to each device and
each gets a split of the data."""
distributed_backend = DistributedType.DP
def __init__(
self,
parallel_devices: Optional[List[torch.device]] = None,
checkpoint_io: Optional[CheckpointIO] = None,
):
super().__init__(parallel_devices=parallel_devices, cluster_environment=None, checkpoint_io=checkpoint_io)
@property
def global_rank(self) -> int:
return 0
@property
def local_rank(self) -> int:
return 0
@property
def node_rank(self) -> int:
return 0
@property
def world_size(self) -> int:
return 1
[docs] def setup(self) -> None:
# model needs to be moved to the device before it is wrapped
self.model_to_device()
self._model = self._setup_model(LightningParallelModule(self._model))
def _setup_model(self, model: Module) -> DataParallel:
"""Wraps the given model into a :class:`~torch.nn.parallel.DataParallel` module."""
return DataParallel(module=model, device_ids=self.parallel_devices)
[docs] def reduce(self, collection: _METRIC_COLLECTION, *args, **kwargs) -> _METRIC_COLLECTION:
"""Reduces a collection of tensors from all processes. It can be applied to just a single tensor.
Args:
collection: The collection of tensors to sync and reduce.
*args: ignored for DP
**kwargs: ignored for DP
Return:
Reduced tensor values or the same value if it was not or did not contain a tensor.
"""
def mean(t: torch.Tensor) -> torch.Tensor:
original_dtype = t.dtype
return t.float().mean().to(original_dtype)
return apply_to_collection(collection, torch.Tensor, mean)
@property
def root_device(self):
return self.parallel_devices[0]
def training_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def validation_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def test_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def predict_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def training_step_end(self, output):
if not is_overridden("training_step_end", self.lightning_module):
return self.reduce(output)
return output
def validation_step_end(self, output):
if not is_overridden("validation_step_end", self.lightning_module):
return self.reduce(output)
return output
def test_step_end(self, output):
if not is_overridden("test_step_end", self.lightning_module):
return self.reduce(output)
return output
[docs] def teardown(self) -> None:
if self.on_gpu:
# GPU teardown
self.lightning_module.cpu()
# clean up memory
torch.cuda.empty_cache()