Source code for pytorch_lightning.accelerators.tpu
# 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, Dict, Optional, Union
import torch
import pytorch_lightning as pl
from pytorch_lightning.accelerators.accelerator import Accelerator
from pytorch_lightning.plugins.precision import TPUPrecisionPlugin
from pytorch_lightning.plugins.training_type.single_tpu import SingleTPUPlugin
from pytorch_lightning.plugins.training_type.tpu_spawn import TPUSpawnPlugin
from pytorch_lightning.utilities import _XLA_AVAILABLE
from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
if _XLA_AVAILABLE:
import torch_xla.core.xla_model as xm
[docs]class TPUAccelerator(Accelerator):
"""Accelerator for TPU devices."""
[docs] def setup(self, trainer: "pl.Trainer") -> None:
"""
Raises:
ValueError:
If the precision or training type plugin are unsupported.
"""
if not isinstance(self.precision_plugin, TPUPrecisionPlugin):
# this configuration should have been avoided in the accelerator connector
raise ValueError(
f"The `TPUAccelerator` can only be used with a `TPUPrecisionPlugin`, found: {self.precision_plugin}."
)
if not isinstance(self.training_type_plugin, (SingleTPUPlugin, TPUSpawnPlugin)):
raise ValueError(
"The `TPUAccelerator` can only be used with a `SingleTPUPlugin` or `TPUSpawnPlugin,"
f" found {self.training_type_plugin}."
)
return super().setup(trainer)
def _move_optimizer_state(self, device: Optional[torch.device] = None) -> None:
"""Moves the state of the optimizers to the TPU if needed."""
# TODO: `self.root_device` would raise error if called outside the spawn process
# while training on 8 and more cores.
for opt in self.optimizers:
for p, v in opt.state.items():
opt.state[p] = apply_to_collection(v, torch.Tensor, move_data_to_device, self.root_device)
[docs] def get_device_stats(self, device: Union[str, torch.device]) -> Dict[str, Any]:
"""Gets stats for the given TPU device.
Args:
device: TPU device for which to get stats
Returns:
A dictionary mapping the metrics (free memory and peak memory) to their values.
"""
memory_info = xm.get_memory_info(device)
free_memory = memory_info["kb_free"]
peak_memory = memory_info["kb_total"] - free_memory
device_stats = {
"avg. free memory (MB)": free_memory,
"avg. peak memory (MB)": peak_memory,
}
return device_stats
[docs] @staticmethod
def auto_device_count() -> int:
"""Get the devices when set to auto."""
return 8