89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import Any
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import torch
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import pytorch_lightning as pl
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from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
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from pytorch_lightning.plugins.precision import PrecisionPlugin
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from pytorch_lightning.strategies.strategy import Strategy
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from pytorch_lightning.utilities.types import _DEVICE
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class SingleDeviceStrategy(Strategy):
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"""Strategy that handles communication on a single device."""
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def __init__(
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self,
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device: _DEVICE,
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accelerator: pl.accelerators.accelerator.Accelerator | None = None,
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checkpoint_io: CheckpointIO | None = None,
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precision_plugin: PrecisionPlugin | None = None,
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):
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super().__init__(accelerator=accelerator, checkpoint_io=checkpoint_io, precision_plugin=precision_plugin)
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self._root_device = torch.device(device)
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self.global_rank = 0
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self.local_rank = 0
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self.world_size = 1
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def reduce(self, tensor: Any | torch.Tensor, *args: Any, **kwargs: Any) -> Any | torch.Tensor:
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"""Reduces a tensor from several distributed processes to one aggregated tensor. As this plugin only
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operates with a single device, the reduction is simply the identity.
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Args:
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tensor: the tensor to sync and reduce
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*args: ignored
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**kwargs: ignored
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Return:
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the unmodified input as reduction is not needed for single process operation
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"""
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return tensor
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def all_gather(self, tensor: torch.Tensor, group: Any | None = None, sync_grads: bool = False) -> torch.Tensor:
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"""Perform a all_gather on all processes."""
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return tensor
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@property
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def root_device(self) -> torch.device:
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return self._root_device
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def model_to_device(self) -> None:
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self.model.to(self.root_device)
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def setup(self, trainer: pl.Trainer) -> None:
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self.model_to_device()
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super().setup(trainer)
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@property
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def is_global_zero(self) -> bool:
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return True
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def barrier(self, *args, **kwargs) -> None:
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pass
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def broadcast(self, obj: object, src: int = 0) -> object:
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return obj
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def teardown(self) -> None:
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super().teardown()
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if self.root_device.type == "cuda":
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# GPU teardown
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self.lightning_module.cpu()
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# clean up memory
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torch.cuda.empty_cache()
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