Source code for pytorch_lightning.accelerators.gpu_accelerator

# 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 Any, Callable, Optional, Union

import torch

from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
from pytorch_lightning.cluster_environments import ClusterEnvironment
from pytorch_lightning.distributed.dist import LightningDistributed
from pytorch_lightning.utilities import AMPType


class GPUAccelerator(Accelerator):
    amp_backend: AMPType

    def __init__(self, trainer, cluster_environment: Optional[ClusterEnvironment] = None):
        """
        Runs training using a single GPU

        Example::

            # default
            trainer = Trainer(accelerator=GPUAccelerator())

        """
        super().__init__(trainer, cluster_environment)
        self.dist = LightningDistributed()
        self.nickname = None

    def setup(self, model):

        # call setup
        self.trainer.call_setup_hook(model)

        torch.cuda.set_device(self.trainer.root_gpu)
        model.cuda(self.trainer.root_gpu)

        # CHOOSE OPTIMIZER
        # allow for lr schedulers as well
        self.setup_optimizers(model)

        # 16-bit
        model = self.trainer.precision_connector.connect(model)

        self.trainer.model = model

    def _step(self, model_step: Callable, args):
        args[0] = self.to_device(args[0])

        if self.trainer.amp_backend == AMPType.NATIVE:
            with torch.cuda.amp.autocast():
                output = model_step(*args)
        else:
            output = model_step(*args)

        return output

    def training_step(self, args):
        return self._step(self.trainer.model.training_step, args)

    def validation_step(self, args):
        return self._step(self.trainer.model.validation_step, args)

    def test_step(self, args):
        return self._step(self.trainer.model.test_step, args)

    def to_device(self, batch):
        gpu_id = 0
        if isinstance(self.trainer.data_parallel_device_ids, list):
            gpu_id = self.trainer.data_parallel_device_ids[0]

        # Don't copy the batch since there is a single gpu that the batch could
        # be referenced from and if there are multiple optimizers the batch will
        # wind up copying it to the same device repeatedly.
        return self.batch_to_device(batch, gpu_id)

    def sync_tensor(self,
                    tensor: Union[torch.Tensor],
                    group: Optional[Any] = None,
                    reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
        return tensor

    @property
    def require_distributed_sampler(self):
        return False