lightning/pytorch_lightning/accelerators/cpu_backend.py

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# 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.
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import torch
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.accelerators.base_backend import Accelerator
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from pytorch_lightning.utilities import AMPType
class CPUBackend(Accelerator):
def __init__(self, trainer):
super().__init__(trainer)
def setup(self, model):
# run through amp wrapper
if self.trainer.amp_backend:
raise MisconfigurationException('amp + cpu is not supported. Please use a GPU option')
# call setup after the ddp process has connected
self.trainer.call_setup_hook(model)
# CHOOSE OPTIMIZER
# allow for lr schedulers as well
optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
self.trainer.optimizers = optimizers
self.trainer.lr_schedulers = lr_schedulers
self.trainer.optimizer_frequencies = optimizer_frequencies
self.trainer.model = model
def train(self):
model = self.trainer.model
results = self.trainer.setup_training(model)
return results
def training_step(self, args):
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if self.trainer.amp_backend == AMPType.NATIVE:
with torch.cuda.amp.autocast():
output = self.trainer.model.training_step(*args)
else:
output = self.trainer.model.training_step(*args)
return output
def validation_step(self, args):
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if self.trainer.amp_backend == AMPType.NATIVE:
with torch.cuda.amp.autocast():
output = self.trainer.model.validation_step(*args)
else:
output = self.trainer.model.validation_step(*args)
return output
def test_step(self, args):
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if self.trainer.amp_backend == AMPType.NATIVE:
with torch.cuda.amp.autocast():
output = self.trainer.model.test_step(*args)
else:
output = self.trainer.model.test_step(*args)
return output