# 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 torch from torch import optim from pytorch_lightning.accelerators.base_backend import Accelerator from pytorch_lightning.distributed import LightningDistributed from pytorch_lightning.core.step_result import Result from pytorch_lightning.overrides.data_parallel import LightningDataParallel from pytorch_lightning.utilities import AMPType from pytorch_lightning.utilities.exceptions import MisconfigurationException class DataParallelBackend(Accelerator): def __init__(self, trainer): super().__init__(trainer) self.model_autocast_original_forward = None self.dist = LightningDistributed() def setup(self, model): # call setup after the ddp process has connected self.trainer.call_setup_hook(model) # put model on correct device model.cuda(self.trainer.root_gpu) # CHOOSE OPTIMIZER # allow for lr schedulers as well self.setup_optimizers(model) # init torch data parallel model = self.__init_torch_data_parallel(model) # hack forward to do autocast for the user self.model_autocast_original_forward = model.forward # init half precision if self.trainer.amp_backend: model = self.__init_half_precision(model) self.trainer.model = model def __init_torch_data_parallel(self, model): # create list of device ids device_ids = self.trainer.data_parallel_device_ids if isinstance(device_ids, int): device_ids = list(range(device_ids)) # set dp device torch.cuda.set_device(self.trainer.root_gpu) model = LightningDataParallel(model, device_ids=device_ids) return model def __init_half_precision(self, model): if self.trainer.amp_backend == AMPType.NATIVE: self.__init_native_amp(model) else: model = self.__init_nvidia_apex(model) return model def __init_native_amp(self, model): model.forward = torch.cuda.amp.autocast()(model.forward) def __init_nvidia_apex(self, model): # check for this bug (amp + dp + !01 doesn't work) # https://github.com/NVIDIA/apex/issues/227 if self.trainer.amp_level == 'O2': raise MisconfigurationException( f'Amp level {self.trainer.amp_level} with DataParallel is not supported.' f' See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.' f' We recommend you switch to ddp if you want to use amp') else: model = self.trainer.precision_connector.connect(model) return model def train(self): model = self.trainer.model # set up training routine self.trainer.train_loop.setup_training(model) # train or test results = self.train_or_test() return results def teardown(self): # replace the original fwd function self.trainer.model.forward = self.model_autocast_original_forward def training_step(self, args): if self.trainer.amp_backend == AMPType.NATIVE: with torch.cuda.amp.autocast(): output = self.trainer.model(*args) else: output = self.trainer.model(*args) return output def validation_step(self, args): output = self.training_step(args) return output def test_step(self, args): output = self.training_step(args) return output def training_step_end(self, output): if isinstance(output, Result): output.dp_reduce() elif isinstance(output, torch.Tensor): output = output.mean() return output def validation_step_end(self, output): if isinstance(output, Result): output.dp_reduce() elif isinstance(output, torch.Tensor): output = output.mean() return output def test_step_end(self, output): if isinstance(output, Result): output.dp_reduce() elif isinstance(output, torch.Tensor): output = output.mean() return output def reinit_scheduler_properties(self, optimizers: list, schedulers: list): """ Reinitialize optimizer.step properties added by schedulers """ for scheduler in schedulers: scheduler = scheduler['scheduler'] for optimizer in optimizers: # check that we dont mix users optimizers and schedulers if scheduler.optimizer == optimizer: # Find the mro belonging to the base lr scheduler class for i, mro in enumerate(scheduler.__class__.__mro__): is_regular_scheduler = optim.lr_scheduler._LRScheduler is_lr_reduce_on_plateau = optim.lr_scheduler.ReduceLROnPlateau if is_regular_scheduler or is_lr_reduce_on_plateau: idx = i state = scheduler.state_dict() else: state = None scheduler.__class__.__mro__[idx].__init__(scheduler, optimizer) if state is not None: scheduler.load_state_dict(state)