88 lines
2.9 KiB
Python
88 lines
2.9 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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import os
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from typing import Dict, Optional
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import pytorch_lightning as pl
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from pytorch_lightning.plugins.io.xla_plugin import XLACheckpointIO
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from pytorch_lightning.plugins.precision import PrecisionPlugin
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from pytorch_lightning.strategies.single_device import SingleDeviceStrategy
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from pytorch_lightning.utilities import _TPU_AVAILABLE, find_shared_parameters, set_shared_parameters
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from pytorch_lightning.utilities.model_helpers import is_overridden
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if _TPU_AVAILABLE:
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import torch_xla.core.xla_model as xm
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class SingleTPUStrategy(SingleDeviceStrategy):
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"""Strategy for training on a single TPU device."""
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strategy_name = "single_tpu"
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def __init__(
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self,
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device: int,
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accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
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checkpoint_io: Optional[XLACheckpointIO] = None,
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precision_plugin: Optional[PrecisionPlugin] = None,
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debug: bool = False,
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):
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checkpoint_io = checkpoint_io or XLACheckpointIO()
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super().__init__(
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accelerator=accelerator,
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device=xm.xla_device(device),
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checkpoint_io=checkpoint_io,
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precision_plugin=precision_plugin,
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)
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self.debug = debug
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self.tpu_local_core_rank = 0
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self.tpu_global_core_rank = 0
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@property
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def is_distributed(self) -> bool:
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return False
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def setup(self, trainer: "pl.Trainer") -> None:
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shared_params = find_shared_parameters(self.model)
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self.model_to_device()
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if is_overridden("on_post_move_to_device", self.lightning_module):
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self.model.on_post_move_to_device()
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else:
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set_shared_parameters(self.model, shared_params)
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super().setup(trainer)
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if self.debug:
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os.environ["PT_XLA_DEBUG"] = str(1)
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self.tpu_local_core_rank = xm.get_local_ordinal()
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self.tpu_global_core_rank = xm.get_ordinal()
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def model_to_device(self) -> None:
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self.model.to(self.root_device)
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@classmethod
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def register_strategies(cls, strategy_registry: Dict) -> None:
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strategy_registry.register(
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cls.strategy_name,
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cls,
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description=f"{cls.__class__.__name__}",
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)
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def teardown(self) -> None:
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super().teardown()
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# TPU teardown
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os.environ.pop("PT_XLA_DEBUG", None)
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