lightning/pytorch_lightning/accelerators/cpu_accelerator.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.
optimizer clean up (#4658) * add LightningOptimizer * typo * add mock closure * typo * remove logic in optimizer_step * update * update * update * desactivate LightningOptimizer for hovorod * resolve flake * typo * check optimizer name * change name * added backward to LightningOptimizer * remove use_lightning_optimizer * move update * simplify init * resolve comments * resolve bug * update * update * resolve bugs * resolve flake8 * set state * work manual_optimizer_step * add doc * add enable_pl_optimizer * make optimizer_step * add make_optimizer_step * add examples * resolve test * add test_optimizer_return_options_enable_pl_optimizer * add enable_pl_optimizer=True * update * update tests * resolve bugs * update * set Trainer to False * update * resolve bugs * update * remove from doc * resolve bug * typo * update * set to True * simplification * typo * resolve horovod * unwrap horovod * remove Optimizer * resolve horovod * move logic to amp_backend * doesn't seem to be pickable * update * add again * resolve some bugs * cleanup * resolve bug with AMP * change __repr__ * round at -12 * udpate * update * update * remove from horovod * typo * add convert_to_lightning_optimizers in each accelerators * typo * forgot * forgot a convert_to_lightning_optimizers * update * update * update * increase coverage * update * resolve flake8 * update * remove useless code * resolve comments + add support for LightningOptimizer base class * resolve flake * check optimizer get wrapped back * resolve DDPSharded * reduce code * lightningoptimizer * Update pytorch_lightning/core/optimizer.py Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> * Update pytorch_lightning/core/lightning.py * remove reference to step function * Apply suggestions from code review * update on comments * resolve * Update CHANGELOG.md * add back training_step in apex and native_amp * rename optimizer_step Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: William Falcon <waf2107@columbia.edu> Co-authored-by: Sean Naren <sean.narenthiran@gmail.com>
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from typing import Any, Optional, Union
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import torch
from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
from pytorch_lightning.utilities import AMPType, rank_zero_warn
from pytorch_lightning.utilities.exceptions import MisconfigurationException
class CPUAccelerator(Accelerator):
def __init__(self, trainer, cluster_environment=None):
"""
Runs training on CPU
Example::
# default
trainer = Trainer(accelerator=CPUAccelerator())
"""
super().__init__(trainer, cluster_environment)
self.nickname = None
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
self.setup_optimizers(model)
optimizer clean up (#4658) * add LightningOptimizer * typo * add mock closure * typo * remove logic in optimizer_step * update * update * update * desactivate LightningOptimizer for hovorod * resolve flake * typo * check optimizer name * change name * added backward to LightningOptimizer * remove use_lightning_optimizer * move update * simplify init * resolve comments * resolve bug * update * update * resolve bugs * resolve flake8 * set state * work manual_optimizer_step * add doc * add enable_pl_optimizer * make optimizer_step * add make_optimizer_step * add examples * resolve test * add test_optimizer_return_options_enable_pl_optimizer * add enable_pl_optimizer=True * update * update tests * resolve bugs * update * set Trainer to False * update * resolve bugs * update * remove from doc * resolve bug * typo * update * set to True * simplification * typo * resolve horovod * unwrap horovod * remove Optimizer * resolve horovod * move logic to amp_backend * doesn't seem to be pickable * update * add again * resolve some bugs * cleanup * resolve bug with AMP * change __repr__ * round at -12 * udpate * update * update * remove from horovod * typo * add convert_to_lightning_optimizers in each accelerators * typo * forgot * forgot a convert_to_lightning_optimizers * update * update * update * increase coverage * update * resolve flake8 * update * remove useless code * resolve comments + add support for LightningOptimizer base class * resolve flake * check optimizer get wrapped back * resolve DDPSharded * reduce code * lightningoptimizer * Update pytorch_lightning/core/optimizer.py Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> * Update pytorch_lightning/core/lightning.py * remove reference to step function * Apply suggestions from code review * update on comments * resolve * Update CHANGELOG.md * add back training_step in apex and native_amp * rename optimizer_step Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: William Falcon <waf2107@columbia.edu> Co-authored-by: Sean Naren <sean.narenthiran@gmail.com>
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self.trainer.convert_to_lightning_optimizers()
self.trainer.model = model
def train(self):
model = self.trainer.model
# set up training routine
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self.trainer.train_loop.setup_training(model)
# train or test
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results = self.train_or_test()
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
def sync_tensor(self,
tensor: Union[torch.Tensor],
group: Optional[Any] = None,
reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
return tensor