76 lines
3.1 KiB
Python
76 lines
3.1 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, Dict, Optional, Union
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import torch
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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 TPUPrecisionPlugin
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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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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") -> None:
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"""
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Raises:
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ValueError:
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If the precision or training type plugin are unsupported.
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"""
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if not isinstance(self.precision_plugin, TPUPrecisionPlugin):
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# this configuration should have been avoided in the accelerator connector
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raise ValueError(
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f"The `TPUAccelerator` can only be used with a `TPUPrecisionPlugin`, found: {self.precision_plugin}."
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)
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if not isinstance(self.training_type_plugin, (SingleTPUPlugin, TPUSpawnPlugin)):
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raise ValueError(
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"The `TPUAccelerator` can only be used with a `SingleTPUPlugin` or `TPUSpawnPlugin,"
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f" found {self.training_type_plugin}."
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)
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return super().setup(trainer)
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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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def get_device_stats(self, device: Union[str, torch.device]) -> Dict[str, Any]:
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"""Gets stats for the given TPU device.
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Args:
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device: TPU device for which to get stats
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Returns:
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A dictionary mapping the metrics (free memory and peak memory) to their values.
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"""
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memory_info = xm.get_memory_info(device)
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free_memory = memory_info["kb_free"]
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peak_memory = memory_info["kb_total"] - free_memory
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device_stats = {
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"avg. free memory (MB)": free_memory,
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"avg. peak memory (MB)": peak_memory,
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}
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return device_stats
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