62 lines
2.8 KiB
Python
62 lines
2.8 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 typing import Any, Callable, Optional
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import torch
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from torch.optim import Optimizer
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import pytorch_lightning as pl
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from pytorch_lightning.accelerators.accelerator import Accelerator
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from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
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from pytorch_lightning.plugins.training_type.single_tpu import SingleTPUPlugin
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from pytorch_lightning.plugins.training_type.tpu_spawn import TPUSpawnPlugin
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from pytorch_lightning.utilities import _XLA_AVAILABLE
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from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if _XLA_AVAILABLE:
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import torch_xla.core.xla_model as xm
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class TPUAccelerator(Accelerator):
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""" Accelerator for TPU devices. """
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def setup(self, trainer: 'pl.Trainer', model: 'pl.LightningModule') -> None:
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"""
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Raises:
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MisconfigurationException:
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If AMP is used with TPU, or if TPUs are not using a single TPU core or TPU spawn training.
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"""
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if isinstance(self.precision_plugin, MixedPrecisionPlugin):
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raise MisconfigurationException(
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"amp + tpu is not supported. Only bfloats are supported on TPU. Consider using TPUHalfPrecisionPlugin"
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)
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if not isinstance(self.training_type_plugin, (SingleTPUPlugin, TPUSpawnPlugin)):
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raise MisconfigurationException("TPUs only support a single tpu core or tpu spawn training.")
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return super().setup(trainer, model)
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def run_optimizer_step(
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self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs: Any
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) -> None:
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xm.optimizer_step(optimizer, optimizer_args={'closure': lambda_closure, **kwargs})
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def _move_optimizer_state(self, device: Optional[torch.device] = None) -> None:
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""" Moves the state of the optimizers to the TPU if needed. """
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# TODO: `self.root_device` would raise error if called outside the spawn process
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# while training on 8 and more cores.
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for opt in self.optimizers:
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for p, v in opt.state.items():
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opt.state[p] = apply_to_collection(v, torch.Tensor, move_data_to_device, self.root_device)
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