Source code for lightning.pytorch.callbacks.device_stats_monitor
# Copyright The Lightning AI team.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Device Stats Monitor
====================
Monitors and logs device stats during training.
"""
from typing import Any, Optional
from typing_extensions import override
import lightning.pytorch as pl
from lightning.pytorch.accelerators.cpu import _PSUTIL_AVAILABLE
from lightning.pytorch.callbacks.callback import Callback
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.types import STEP_OUTPUT
[docs]class DeviceStatsMonitor(Callback):
r"""Automatically monitors and logs device stats during training, validation and testing stage.
``DeviceStatsMonitor`` is a special callback as it requires a ``logger`` to passed as argument to the ``Trainer``.
**Logged Metrics**
Logs device statistics with keys prefixed as ``DeviceStatsMonitor.{hook_name}/{base_metric_name}``.
The actual metrics depend on the active accelerator and the ``cpu_stats`` flag. Below are an overview of the
possible available metrics and their meaning.
- CPU (via ``psutil``)
- ``cpu_percent`` — System-wide CPU utilization (%)
- ``cpu_vm_percent`` — System-wide virtual memory (RAM) utilization (%)
- ``cpu_swap_percent`` — System-wide swap memory utilization (%)
- CUDA GPU (via ``torch.cuda.memory_stats``)
Logs memory statistics from PyTorch caching allocator (all in bytes).
GPU compute utilization is not logged by default.
- General Memory Usage:
- ``allocated_bytes.all.current`` — Current allocated GPU memory
- ``allocated_bytes.all.peak`` — Peak allocated GPU memory
- ``reserved_bytes.all.current`` — Current reserved GPU memory (allocated + cached)
- ``reserved_bytes.all.peak`` — Peak reserved GPU memory
- ``active_bytes.all.current`` — Current GPU memory in active use
- ``active_bytes.all.peak`` — Peak GPU memory in active use
- ``inactive_split_bytes.all.current`` — Memory in inactive, splittable blocks
- Allocator Pool Statistics* (for ``small_pool`` and ``large_pool``):
- ``allocated_bytes.{pool_type}.current`` / ``allocated_bytes.{pool_type}.peak``
- ``reserved_bytes.{pool_type}.current`` / ``reserved_bytes.{pool_type}.peak``
- ``active_bytes.{pool_type}.current`` / ``active_bytes.{pool_type}.peak``
- Allocator Events:
- ``num_ooms`` — Cumulative out-of-memory errors
- ``num_alloc_retries`` — Number of allocation retries
- ``num_device_alloc`` — Number of device allocations
- ``num_device_free`` — Number of device deallocations
For a full list of CUDA memory stats, see the
`PyTorch documentation <https://docs.pytorch.org/docs/stable//generated/torch.cuda.device_memory_used.html>`_.
- TPU (via ``torch_xla``)
- *Memory Metrics* (per device, e.g., ``xla:0``):
- ``memory.free.xla:0`` — Free HBM memory (MB)
- ``memory.used.xla:0`` — Used HBM memory (MB)
- ``memory.percent.xla:0`` — Percentage of HBM memory used (%)
- *XLA Operation Counters*:
- ``CachedCompile.xla``
- ``CreateXlaTensor.xla``
- ``DeviceDataCacheMiss.xla``
- ``UncachedCompile.xla``
- ``xla::add.xla``, ``xla::addmm.xla``, etc.
These counters can be retrieved using: ``torch_xla.debug.metrics.counter_names()``
Args:
cpu_stats: if ``None``, it will log CPU stats only if the accelerator is CPU.
If ``True``, it will log CPU stats regardless of the accelerator.
If ``False``, it will not log CPU stats regardless of the accelerator.
Raises:
MisconfigurationException:
If ``Trainer`` has no logger.
ModuleNotFoundError:
If ``psutil`` is not installed and CPU stats are monitored.
Example::
from lightning import Trainer
from lightning.pytorch.callbacks import DeviceStatsMonitor
device_stats = DeviceStatsMonitor()
trainer = Trainer(callbacks=[device_stats])
"""
def __init__(self, cpu_stats: Optional[bool] = None) -> None:
self._cpu_stats = cpu_stats
[docs] @override
def setup(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
stage: str,
) -> None:
if stage != "fit":
return
if not trainer.loggers:
raise MisconfigurationException("Cannot use `DeviceStatsMonitor` callback with `Trainer(logger=False)`.")
# warn in setup to warn once
device = trainer.strategy.root_device
if self._cpu_stats is None and device.type == "cpu" and not _PSUTIL_AVAILABLE:
raise ModuleNotFoundError(
f"`DeviceStatsMonitor` cannot log CPU stats as `psutil` is not installed. {str(_PSUTIL_AVAILABLE)} "
)
def _get_and_log_device_stats(self, trainer: "pl.Trainer", key: str) -> None:
if not trainer._logger_connector.should_update_logs:
return
device = trainer.strategy.root_device
if self._cpu_stats is False and device.type == "cpu":
# cpu stats are disabled
return
device_stats = trainer.accelerator.get_device_stats(device)
if self._cpu_stats and device.type != "cpu":
# Don't query CPU stats twice if CPU is accelerator
from lightning.pytorch.accelerators.cpu import get_cpu_stats
device_stats.update(get_cpu_stats())
for logger in trainer.loggers:
separator = logger.group_separator
prefixed_device_stats = _prefix_metric_keys(device_stats, f"{self.__class__.__qualname__}.{key}", separator)
logger.log_metrics(prefixed_device_stats, step=trainer.fit_loop.epoch_loop._batches_that_stepped)
[docs] @override
def on_train_batch_start(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int
) -> None:
self._get_and_log_device_stats(trainer, "on_train_batch_start")
[docs] @override
def on_train_batch_end(
self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
) -> None:
self._get_and_log_device_stats(trainer, "on_train_batch_end")
[docs] @override
def on_validation_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
self._get_and_log_device_stats(trainer, "on_validation_batch_start")
[docs] @override
def on_validation_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: STEP_OUTPUT,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
self._get_and_log_device_stats(trainer, "on_validation_batch_end")
[docs] @override
def on_test_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
self._get_and_log_device_stats(trainer, "on_test_batch_start")
[docs] @override
def on_test_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: STEP_OUTPUT,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
self._get_and_log_device_stats(trainer, "on_test_batch_end")
def _prefix_metric_keys(metrics_dict: dict[str, float], prefix: str, separator: str) -> dict[str, float]:
return {prefix + separator + k: v for k, v in metrics_dict.items()}
