Source code for pytorch_lightning.callbacks.xla_stats_monitor
# 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,
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# See the License for the specific language governing permissions and
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"""
XLA Stats Monitor
=================
Monitor and logs XLA stats during training.
"""
import time
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities import _TPU_AVAILABLE, DeviceType, rank_zero_deprecation, rank_zero_info
from pytorch_lightning.utilities.exceptions import MisconfigurationException
if _TPU_AVAILABLE:
import torch_xla.core.xla_model as xm
[docs]class XLAStatsMonitor(Callback):
r"""
.. deprecated:: v1.5
The `XLAStatsMonitor` callback was deprecated in v1.5 and will be removed in v1.7.
Please use the `DeviceStatsMonitor` callback instead.
Automatically monitors and logs XLA stats during training stage. ``XLAStatsMonitor`` is a callback and in
order to use it you need to assign a logger in the ``Trainer``.
Args:
verbose: Set to ``True`` to print average peak and free memory, and epoch time
every epoch.
Raises:
MisconfigurationException:
If not running on TPUs, or ``Trainer`` has no logger.
Example::
>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import XLAStatsMonitor
>>> xla_stats = XLAStatsMonitor() # doctest: +SKIP
>>> trainer = Trainer(callbacks=[xla_stats]) # doctest: +SKIP
"""
def __init__(self, verbose: bool = True) -> None:
super().__init__()
rank_zero_deprecation(
"The `XLAStatsMonitor` callback was deprecated in v1.5 and will be removed in v1.7."
" Please use the `DeviceStatsMonitor` callback instead."
)
if not _TPU_AVAILABLE:
raise MisconfigurationException("Cannot use XLAStatsMonitor with TPUs are not available")
self._verbose = verbose
[docs] def on_train_start(self, trainer, pl_module) -> None:
if not trainer.logger:
raise MisconfigurationException("Cannot use XLAStatsMonitor callback with Trainer that has no logger.")
if trainer._device_type != DeviceType.TPU:
raise MisconfigurationException(
"You are using XLAStatsMonitor but are not running on TPU"
f" since `tpu_cores` attribute in Trainer is set to {trainer.tpu_cores}."
)
memory_info = xm.get_memory_info(pl_module.device)
total_memory = trainer.training_type_plugin.reduce(memory_info["kb_total"]) * 0.001
rank_zero_info(f"Average Total memory: {total_memory:.2f} MB")
[docs] def on_train_epoch_end(self, trainer, pl_module) -> None:
logs = {}
memory_info = xm.get_memory_info(pl_module.device)
epoch_time = time.time() - self._start_time
free_memory = memory_info["kb_free"]
peak_memory = memory_info["kb_total"] - free_memory
free_memory = trainer.training_type_plugin.reduce(free_memory) * 0.001
peak_memory = trainer.training_type_plugin.reduce(peak_memory) * 0.001
epoch_time = trainer.training_type_plugin.reduce(epoch_time)
logs["avg. free memory (MB)"] = free_memory
logs["avg. peak memory (MB)"] = peak_memory
trainer.logger.log_metrics(logs, step=trainer.current_epoch)
if self._verbose:
rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
rank_zero_info(f"Average Peak memory: {peak_memory:.2f} MB")
rank_zero_info(f"Average Free memory: {free_memory:.2f} MB")
