98 lines
3.6 KiB
Python
98 lines
3.6 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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"""
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XLA Stats Monitor
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=================
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Monitor and logs XLA stats during training.
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"""
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import time
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.utilities import _TPU_AVAILABLE, DeviceType, rank_zero_info
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if _TPU_AVAILABLE:
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import torch_xla.core.xla_model as xm
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class XLAStatsMonitor(Callback):
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"""
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Automatically monitors and logs XLA stats during training stage. ``XLAStatsMonitor``
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is a callback and in order to use it you need to assign a logger in the ``Trainer``.
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Args:
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verbose: Set to ``True`` to print average peak and free memory, and epoch time
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every epoch.
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Raises:
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MisconfigurationException:
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If not running on TPUs, or ``Trainer`` has no logger.
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Example::
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>>> from pytorch_lightning import Trainer
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>>> from pytorch_lightning.callbacks import XLAStatsMonitor
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>>> xla_stats = XLAStatsMonitor() # doctest: +SKIP
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>>> trainer = Trainer(callbacks=[xla_stats]) # doctest: +SKIP
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"""
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def __init__(self, verbose: bool = True) -> None:
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super().__init__()
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if not _TPU_AVAILABLE:
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raise MisconfigurationException("Cannot use XLAStatsMonitor with TPUs are not available")
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self._verbose = verbose
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def on_train_start(self, trainer, pl_module) -> None:
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if not trainer.logger:
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raise MisconfigurationException("Cannot use XLAStatsMonitor callback with Trainer that has no logger.")
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if trainer._device_type != DeviceType.TPU:
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raise MisconfigurationException(
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"You are using XLAStatsMonitor but are not running on TPU"
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f" since `tpu_cores` attribute in Trainer is set to {trainer.tpu_cores}."
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)
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memory_info = xm.get_memory_info(pl_module.device)
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total_memory = trainer.training_type_plugin.reduce(memory_info["kb_total"]) * 0.001
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rank_zero_info(f"Average Total memory: {total_memory:.2f} MB")
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def on_train_epoch_start(self, trainer, pl_module) -> None:
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self._start_time = time.time()
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def on_train_epoch_end(self, trainer, pl_module) -> None:
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logs = {}
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memory_info = xm.get_memory_info(pl_module.device)
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epoch_time = time.time() - self._start_time
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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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free_memory = trainer.training_type_plugin.reduce(free_memory) * 0.001
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peak_memory = trainer.training_type_plugin.reduce(peak_memory) * 0.001
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epoch_time = trainer.training_type_plugin.reduce(epoch_time)
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logs["avg. free memory (MB)"] = free_memory
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logs["avg. peak memory (MB)"] = peak_memory
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trainer.logger.log_metrics(logs, step=trainer.current_epoch)
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if self._verbose:
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rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
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rank_zero_info(f"Average Peak memory: {peak_memory:.2f} MB")
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rank_zero_info(f"Average Free memory: {free_memory:.2f} MB")
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