Source code for pytorch_lightning.trainer.trainer
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# http://www.apache.org/licenses/LICENSE-2.0
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# THIS FILE MUST READ EASILY, FOR UNDERSTANDING AND DEBUGGING PURPOSES.
# DO NOT OBSCURE THE TRAINING LOOP
# THIS IS A HARD REQUIREMENT TO CONTRIBUTING TO LIGHTNING
# WE FAVOR READABILITY OVER ENGINEERING-CONSTRUCTS BY DESIGN
# DO NOT REMOVE THIS NOTICE
# - WILLIAM FALCON
"""Trainer to automate the training."""
import inspect
import logging
import math
import os
import warnings
from argparse import _ArgumentGroup, ArgumentParser, Namespace
from contextlib import contextmanager
from copy import deepcopy
from datetime import timedelta
from pathlib import Path
from typing import Any, Dict, Generator, Iterable, List, Optional, Type, Union
from weakref import proxy
import torch
import torch.distributed as dist
from lightning_utilities.core.apply_func import apply_to_collection
from lightning_utilities.core.imports import module_available
from packaging.version import Version
from torch import Tensor
from torch.optim import Optimizer
from torch.utils.data import DataLoader
from typing_extensions import Literal
import pytorch_lightning as pl
from lightning_fabric.utilities.cloud_io import get_filesystem
from lightning_fabric.utilities.data import _auto_add_worker_init_fn
from lightning_fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
from lightning_fabric.utilities.types import _PATH
from lightning_fabric.utilities.warnings import PossibleUserWarning
from pytorch_lightning.accelerators import Accelerator, TPUAccelerator
from pytorch_lightning.callbacks import Callback, Checkpoint, EarlyStopping, ProgressBarBase
from pytorch_lightning.callbacks.prediction_writer import BasePredictionWriter
from pytorch_lightning.core.datamodule import LightningDataModule
from pytorch_lightning.loggers import Logger
from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
from pytorch_lightning.loops import PredictionLoop, TrainingEpochLoop
from pytorch_lightning.loops.dataloader.evaluation_loop import EvaluationLoop
from pytorch_lightning.loops.fit_loop import FitLoop
from pytorch_lightning.loops.utilities import _parse_loop_limits, _reset_progress
from pytorch_lightning.plugins import ApexMixedPrecisionPlugin, MixedPrecisionPlugin, PLUGIN_INPUT, PrecisionPlugin
from pytorch_lightning.profilers import Profiler
from pytorch_lightning.strategies import (
DDPFullyShardedNativeStrategy,
DDPStrategy,
ParallelStrategy,
SingleDeviceStrategy,
Strategy,
)
from pytorch_lightning.trainer import call, setup
from pytorch_lightning.trainer.configuration_validator import verify_loop_configurations
from pytorch_lightning.trainer.connectors.accelerator_connector import (
_LITERAL_WARN,
_PRECISION_INPUT,
_PRECISION_INPUT_STR,
AcceleratorConnector,
)
from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
from pytorch_lightning.trainer.connectors.data_connector import DataConnector
from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
from pytorch_lightning.trainer.connectors.logger_connector.result import _OUT_DICT, _PBAR_DICT, _ResultCollection
from pytorch_lightning.trainer.connectors.signal_connector import SignalConnector
from pytorch_lightning.trainer.states import RunningStage, TrainerFn, TrainerState, TrainerStatus
from pytorch_lightning.trainer.supporters import CombinedLoader
from pytorch_lightning.tuner.tuning import _TunerResult, Tuner
from pytorch_lightning.utilities import GradClipAlgorithmType, parsing
from pytorch_lightning.utilities.argparse import (
_defaults_from_env_vars,
add_argparse_args,
from_argparse_args,
parse_argparser,
parse_env_variables,
)
from pytorch_lightning.utilities.auto_restart import _add_capture_metadata_collate
from pytorch_lightning.utilities.data import has_len_all_ranks
from pytorch_lightning.utilities.exceptions import ExitGracefullyException, MisconfigurationException
from pytorch_lightning.utilities.imports import _fault_tolerant_training
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_warn
from pytorch_lightning.utilities.seed import isolate_rng
from pytorch_lightning.utilities.types import (
_EVALUATE_OUTPUT,
_PREDICT_OUTPUT,
EVAL_DATALOADERS,
LRSchedulerConfig,
TRAIN_DATALOADERS,
)
log = logging.getLogger(__name__)
# warnings to ignore in trainer
warnings.filterwarnings(
"ignore", message="torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead"
)
[docs]class Trainer:
[docs] @_defaults_from_env_vars
def __init__(
self,
logger: Union[Logger, Iterable[Logger], bool] = True,
enable_checkpointing: bool = True,
callbacks: Optional[Union[List[Callback], Callback]] = None,
default_root_dir: Optional[_PATH] = None,
gradient_clip_val: Optional[Union[int, float]] = None,
gradient_clip_algorithm: Optional[str] = None,
num_nodes: int = 1,
num_processes: Optional[int] = None, # TODO: Remove in 2.0
devices: Optional[Union[List[int], str, int]] = None,
gpus: Optional[Union[List[int], str, int]] = None, # TODO: Remove in 2.0
auto_select_gpus: Optional[bool] = None, # TODO: Remove in 2.0
tpu_cores: Optional[Union[List[int], str, int]] = None, # TODO: Remove in 2.0
ipus: Optional[int] = None, # TODO: Remove in 2.0
enable_progress_bar: bool = True,
overfit_batches: Union[int, float] = 0.0,
track_grad_norm: Union[int, float, str] = -1,
check_val_every_n_epoch: Optional[int] = 1,
fast_dev_run: Union[int, bool] = False,
accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None,
max_epochs: Optional[int] = None,
min_epochs: Optional[int] = None,
max_steps: int = -1,
min_steps: Optional[int] = None,
max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None,
limit_train_batches: Optional[Union[int, float]] = None,
limit_val_batches: Optional[Union[int, float]] = None,
limit_test_batches: Optional[Union[int, float]] = None,
limit_predict_batches: Optional[Union[int, float]] = None,
val_check_interval: Optional[Union[int, float]] = None,
log_every_n_steps: int = 50,
accelerator: Optional[Union[str, Accelerator]] = None,
strategy: Optional[Union[str, Strategy]] = None,
sync_batchnorm: bool = False,
precision: _PRECISION_INPUT = 32,
enable_model_summary: bool = True,
num_sanity_val_steps: int = 2,
resume_from_checkpoint: Optional[Union[Path, str]] = None,
profiler: Optional[Union[Profiler, str]] = None,
benchmark: Optional[bool] = None,
deterministic: Optional[Union[bool, _LITERAL_WARN]] = None,
reload_dataloaders_every_n_epochs: int = 0,
auto_lr_find: Union[bool, str] = False,
replace_sampler_ddp: bool = True,
detect_anomaly: bool = False,
auto_scale_batch_size: Union[str, bool] = False,
plugins: Optional[Union[PLUGIN_INPUT, List[PLUGIN_INPUT]]] = None,
amp_backend: Optional[str] = None, # TODO: Remove in v2.0.0
amp_level: Optional[str] = None, # TODO: Remove in v2.0.0
move_metrics_to_cpu: bool = False,
multiple_trainloader_mode: str = "max_size_cycle",
inference_mode: bool = True,
) -> None:
r"""
Customize every aspect of training via flags.
Args:
accelerator: Supports passing different accelerator types ("cpu", "gpu", "tpu", "ipu", "hpu", "mps", "auto")
as well as custom accelerator instances.
accumulate_grad_batches: Accumulates grads every k batches or as set up in the dict.
Default: ``None``.
amp_backend: The mixed precision backend to use ("native" or "apex").
Default: ``'native''``.
.. deprecated:: v1.9
Setting ``amp_backend`` inside the ``Trainer`` is deprecated in v1.8.0 and will be removed
in v2.0.0. This argument was only relevant for apex which is being removed.
amp_level: The optimization level to use (O1, O2, etc...). By default it will be set to "O2"
if ``amp_backend`` is set to "apex".
.. deprecated:: v1.8
Setting ``amp_level`` inside the ``Trainer`` is deprecated in v1.8.0 and will be removed
in v2.0.0.
auto_lr_find: If set to True, will make trainer.tune() run a learning rate finder,
trying to optimize initial learning for faster convergence. trainer.tune() method will
set the suggested learning rate in self.lr or self.learning_rate in the LightningModule.
To use a different key set a string instead of True with the key name.
Default: ``False``.
auto_scale_batch_size: If set to True, will `initially` run a batch size
finder trying to find the largest batch size that fits into memory.
The result will be stored in self.batch_size in the LightningModule
or LightningDataModule depending on your setup.
Additionally, can be set to either `power` that estimates the batch size through
a power search or `binsearch` that estimates the batch size through a binary search.
Default: ``False``.
auto_select_gpus: If enabled and ``gpus`` or ``devices`` is an integer, pick available
gpus automatically. This is especially useful when
GPUs are configured to be in "exclusive mode", such
that only one process at a time can access them.
Default: ``False``.
.. deprecated:: v1.9
``auto_select_gpus`` has been deprecated in v1.9.0 and will be removed in v2.0.0.
Please use the function :func:`~lightning_fabric.accelerators.cuda.find_usable_cuda_devices`
instead.
benchmark: The value (``True`` or ``False``) to set ``torch.backends.cudnn.benchmark`` to.
The value for ``torch.backends.cudnn.benchmark`` set in the current session will be used
(``False`` if not manually set). If :paramref:`~pytorch_lightning.trainer.Trainer.deterministic` is set
to ``True``, this will default to ``False``. Override to manually set a different value.
Default: ``None``.
callbacks: Add a callback or list of callbacks.
Default: ``None``.
enable_checkpointing: If ``True``, enable checkpointing.
It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in
:paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`.
Default: ``True``.
check_val_every_n_epoch: Perform a validation loop every after every `N` training epochs. If ``None``,
validation will be done solely based on the number of training batches, requiring ``val_check_interval``
to be an integer value.
Default: ``1``.
default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed.
Default: ``os.getcwd()``.
Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/'
detect_anomaly: Enable anomaly detection for the autograd engine.
Default: ``False``.
deterministic: If ``True``, sets whether PyTorch operations must use deterministic algorithms.
Set to ``"warn"`` to use deterministic algorithms whenever possible, throwing warnings on operations
that don't support deterministic mode (requires PyTorch 1.11+). If not set, defaults to ``False``.
Default: ``None``.
devices: Will be mapped to either `gpus`, `tpu_cores`, `num_processes` or `ipus`,
based on the accelerator type.
fast_dev_run: Runs n if set to ``n`` (int) else 1 if set to ``True`` batch(es)
of train, val and test to find any bugs (ie: a sort of unit test).
Default: ``False``.
gpus: Number of GPUs to train on (int) or which GPUs to train on (list or str) applied per node
Default: ``None``.
.. deprecated:: v1.7
``gpus`` has been deprecated in v1.7 and will be removed in v2.0.
Please use ``accelerator='gpu'`` and ``devices=x`` instead.
gradient_clip_val: The value at which to clip gradients. Passing ``gradient_clip_val=None`` disables
gradient clipping. If using Automatic Mixed Precision (AMP), the gradients will be unscaled before.
Default: ``None``.
gradient_clip_algorithm: The gradient clipping algorithm to use. Pass ``gradient_clip_algorithm="value"``
to clip by value, and ``gradient_clip_algorithm="norm"`` to clip by norm. By default it will
be set to ``"norm"``.
limit_train_batches: How much of training dataset to check (float = fraction, int = num_batches).
Default: ``1.0``.
limit_val_batches: How much of validation dataset to check (float = fraction, int = num_batches).
Default: ``1.0``.
limit_test_batches: How much of test dataset to check (float = fraction, int = num_batches).
Default: ``1.0``.
limit_predict_batches: How much of prediction dataset to check (float = fraction, int = num_batches).
Default: ``1.0``.
logger: Logger (or iterable collection of loggers) for experiment tracking. A ``True`` value uses
the default ``TensorBoardLogger`` if it is installed, otherwise ``CSVLogger``.
``False`` will disable logging. If multiple loggers are provided, local files
(checkpoints, profiler traces, etc.) are saved in the ``log_dir`` of he first logger.
Default: ``True``.
log_every_n_steps: How often to log within steps.
Default: ``50``.
enable_progress_bar: Whether to enable to progress bar by default.
Default: ``True``.
profiler: To profile individual steps during training and assist in identifying bottlenecks.
Default: ``None``.
overfit_batches: Overfit a fraction of training/validation data (float) or a set number of batches (int).
Default: ``0.0``.
plugins: Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins.
Default: ``None``.
precision: Double precision (64), full precision (32), half precision (16) or bfloat16 precision (bf16).
Can be used on CPU, GPU, TPUs, HPUs or IPUs.
Default: ``32``.
max_epochs: Stop training once this number of epochs is reached. Disabled by default (None).
If both max_epochs and max_steps are not specified, defaults to ``max_epochs = 1000``.
To enable infinite training, set ``max_epochs = -1``.
min_epochs: Force training for at least these many epochs. Disabled by default (None).
max_steps: Stop training after this number of steps. Disabled by default (-1). If ``max_steps = -1``
and ``max_epochs = None``, will default to ``max_epochs = 1000``. To enable infinite training, set
``max_epochs`` to ``-1``.
min_steps: Force training for at least these number of steps. Disabled by default (``None``).
max_time: Stop training after this amount of time has passed. Disabled by default (``None``).
The time duration can be specified in the format DD:HH:MM:SS (days, hours, minutes seconds), as a
:class:`datetime.timedelta`, or a dictionary with keys that will be passed to
:class:`datetime.timedelta`.
num_nodes: Number of GPU nodes for distributed training.
Default: ``1``.
num_processes: Number of processes for distributed training with ``accelerator="cpu"``.
Default: ``1``.
.. deprecated:: v1.7
``num_processes`` has been deprecated in v1.7 and will be removed in v2.0.
Please use ``accelerator='cpu'`` and ``devices=x`` instead.
num_sanity_val_steps: Sanity check runs n validation batches before starting the training routine.
Set it to `-1` to run all batches in all validation dataloaders.
Default: ``2``.
reload_dataloaders_every_n_epochs: Set to a non-negative integer to reload dataloaders every n epochs.
Default: ``0``.
replace_sampler_ddp: Explicitly enables or disables sampler replacement. If not specified this
will toggled automatically when DDP is used. By default it will add ``shuffle=True`` for
train sampler and ``shuffle=False`` for val/test sampler. If you want to customize it,
you can set ``replace_sampler_ddp=False`` and add your own distributed sampler.
resume_from_checkpoint: Path/URL of the checkpoint from which training is resumed. If there is
no checkpoint file at the path, an exception is raised. If resuming from mid-epoch checkpoint,
training will start from the beginning of the next epoch.
.. deprecated:: v1.5
``resume_from_checkpoint`` is deprecated in v1.5 and will be removed in v2.0.
Please pass the path to ``Trainer.fit(..., ckpt_path=...)`` instead.
strategy: Supports different training strategies with aliases
as well custom strategies.
Default: ``None``.
sync_batchnorm: Synchronize batch norm layers between process groups/whole world.
Default: ``False``.
tpu_cores: How many TPU cores to train on (1 or 8) / Single TPU to train on (1)
Default: ``None``.
.. deprecated:: v1.7
``tpu_cores`` has been deprecated in v1.7 and will be removed in v2.0.
Please use ``accelerator='tpu'`` and ``devices=x`` instead.
ipus: How many IPUs to train on.
Default: ``None``.
.. deprecated:: v1.7
``ipus`` has been deprecated in v1.7 and will be removed in v2.0.
Please use ``accelerator='ipu'`` and ``devices=x`` instead.
track_grad_norm: -1 no tracking. Otherwise tracks that p-norm. May be set to 'inf' infinity-norm. If using
Automatic Mixed Precision (AMP), the gradients will be unscaled before logging them.
Default: ``-1``.
val_check_interval: How often to check the validation set. Pass a ``float`` in the range [0.0, 1.0] to check
after a fraction of the training epoch. Pass an ``int`` to check after a fixed number of training
batches. An ``int`` value can only be higher than the number of training batches when
``check_val_every_n_epoch=None``, which validates after every ``N`` training batches
across epochs or during iteration-based training.
Default: ``1.0``.
enable_model_summary: Whether to enable model summarization by default.
Default: ``True``.
move_metrics_to_cpu: Whether to force internal logged metrics to be moved to cpu.
This can save some gpu memory, but can make training slower. Use with attention.
Default: ``False``.
multiple_trainloader_mode: How to loop over the datasets when there are multiple train loaders.
In 'max_size_cycle' mode, the trainer ends one epoch when the largest dataset is traversed,
and smaller datasets reload when running out of their data. In 'min_size' mode, all the datasets
reload when reaching the minimum length of datasets.
Default: ``"max_size_cycle"``.
inference_mode: Whether to use :func:`torch.inference_mode` or :func:`torch.no_grad` during
evaluation (``validate``/``test``/``predict``).
"""
super().__init__()
Trainer._log_api_event("init")
log.detail(f"{self.__class__.__name__}: Initializing trainer with parameters: {locals()}")
self.state = TrainerState()
if default_root_dir is not None:
default_root_dir = os.fspath(default_root_dir)
# init connectors
self._data_connector = DataConnector(self, multiple_trainloader_mode)
self._accelerator_connector = AcceleratorConnector(
num_processes=num_processes,
devices=devices,
tpu_cores=tpu_cores,
ipus=ipus,
accelerator=accelerator,
strategy=strategy,
gpus=gpus,
num_nodes=num_nodes,
sync_batchnorm=sync_batchnorm,
benchmark=benchmark,
replace_sampler_ddp=replace_sampler_ddp,
deterministic=deterministic,
auto_select_gpus=auto_select_gpus,
precision=precision,
amp_type=amp_backend,
amp_level=amp_level,
plugins=plugins,
)
self._logger_connector = LoggerConnector(self)
self._callback_connector = CallbackConnector(self)
self._checkpoint_connector = CheckpointConnector(self, resume_from_checkpoint)
self._signal_connector = SignalConnector(self)
self.tuner = Tuner(self)
fit_loop = FitLoop(min_epochs=min_epochs, max_epochs=max_epochs)
training_epoch_loop = TrainingEpochLoop(min_steps=min_steps, max_steps=max_steps)
fit_loop.connect(epoch_loop=training_epoch_loop)
# default .fit() loop
self.fit_loop = fit_loop
# default .validate() loop
self.validate_loop = EvaluationLoop()
# default .test() loop
self.test_loop = EvaluationLoop()
# default .predict() loop
self.predict_loop = PredictionLoop()
# set when a checkpoint is loaded via `Trainer.{fit,validate,test,predict}`.
self._ckpt_path: Optional[str] = None
# init callbacks
# Declare attributes to be set in _callback_connector on_trainer_init
self._callback_connector.on_trainer_init(
callbacks,
enable_checkpointing,
enable_progress_bar,
default_root_dir,
enable_model_summary,
max_time,
accumulate_grad_batches,
)
# init data flags
self.check_val_every_n_epoch: Optional[int]
self._data_connector.on_trainer_init(
val_check_interval,
reload_dataloaders_every_n_epochs,
check_val_every_n_epoch,
)
# gradient clipping
if gradient_clip_val is not None and not isinstance(gradient_clip_val, (int, float)):
raise TypeError(f"`gradient_clip_val` should be an int or a float. Got {gradient_clip_val}.")
if gradient_clip_algorithm is not None and not GradClipAlgorithmType.supported_type(
gradient_clip_algorithm.lower()
):
raise MisconfigurationException(
f"`gradient_clip_algorithm` {gradient_clip_algorithm} is invalid. "
f"Allowed algorithms: {GradClipAlgorithmType.supported_types()}."
)
# gradient norm tracking
if track_grad_norm != -1 and not (
(isinstance(track_grad_norm, (int, float)) or track_grad_norm == "inf") and float(track_grad_norm) > 0
):
raise MisconfigurationException(
f"`track_grad_norm` must be a positive number or 'inf' (infinity norm). Got {track_grad_norm}."
)
self.gradient_clip_val: Optional[Union[int, float]] = gradient_clip_val
self.gradient_clip_algorithm: Optional[GradClipAlgorithmType] = (
GradClipAlgorithmType(gradient_clip_algorithm.lower()) if gradient_clip_algorithm is not None else None
)
self.track_grad_norm: float = float(track_grad_norm)
self._inference_mode: bool = inference_mode
self._detect_anomaly: bool = detect_anomaly
self._setup_on_init()
# configure tuner
self.tuner.on_trainer_init(auto_lr_find, auto_scale_batch_size)
# configure profiler
setup._init_profiler(self, profiler)
# init logger flags
self._loggers: List[Logger]
self._logger_connector.on_trainer_init(logger, log_every_n_steps, move_metrics_to_cpu)
# init debugging flags
self.val_check_batch: Union[int, float]
self.val_check_interval: Union[int, float]
self.num_sanity_val_steps: Union[int, float]
self.limit_train_batches: Union[int, float]
self.limit_val_batches: Union[int, float]
self.limit_test_batches: Union[int, float]
self.limit_predict_batches: Union[int, float]
setup._init_debugging_flags(
self,
limit_train_batches,
limit_val_batches,
limit_test_batches,
limit_predict_batches,
fast_dev_run,
overfit_batches,
val_check_interval,
num_sanity_val_steps,
)
def _setup_on_init(self) -> None:
setup._log_device_info(self)
self.should_stop = False
self.state = TrainerState()
self.num_training_batches = float("inf")
self.train_dataloader: Optional[Union[CombinedLoader, TRAIN_DATALOADERS]] = None
self.num_sanity_val_batches: List[Union[int, float]] = []
self.num_test_batches: List[Union[int, float]] = []
self.num_val_batches: List[Union[int, float]] = []
self.num_predict_batches: List[Union[int, float]] = []
self.test_dataloaders: Optional[List[DataLoader]] = None
self.val_dataloaders: Optional[List[DataLoader]] = None
self.predict_dataloaders: Optional[List[DataLoader]] = None
self._last_train_dl_reload_epoch = float("-inf")
self._last_val_dl_reload_epoch = float("-inf")
def _maybe_unwrap_optimized(self, model: object) -> "pl.LightningModule":
if not _TORCH_GREATER_EQUAL_2_0:
if not isinstance(model, pl.LightningModule):
raise TypeError(f"`model` must be a `LightningModule`, got `{type(model).__qualname__}`")
return model
from torch._dynamo import OptimizedModule
if isinstance(model, OptimizedModule):
return model.from_compiled(model)
if isinstance(model, pl.LightningModule):
return model
raise TypeError(
f"`model` must be a `LightningModule` or `torch._dynamo.OptimizedModule`, got `{type(model).__qualname__}`"
)
[docs] def fit(
self,
model: "pl.LightningModule",
train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
val_dataloaders: Optional[EVAL_DATALOADERS] = None,
datamodule: Optional[LightningDataModule] = None,
ckpt_path: Optional[str] = None,
) -> None:
r"""
Runs the full optimization routine.
Args:
model: Model to fit.
train_dataloaders: A collection of :class:`torch.utils.data.DataLoader` or a
:class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying training samples.
In the case of multiple dataloaders, please see this :ref:`section <multiple-dataloaders>`.
val_dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
ckpt_path: Path/URL of the checkpoint from which training is resumed. Could also be one of two special
keywords ``"last"`` and ``"hpc"``. If there is no checkpoint file at the path, an exception is raised.
If resuming from mid-epoch checkpoint, training will start from the beginning of the next epoch.
datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
"""
model = self._maybe_unwrap_optimized(model)
self.strategy._lightning_module = model
call._call_and_handle_interrupt(
self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
)
def _fit_impl(
self,
model: "pl.LightningModule",
train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
val_dataloaders: Optional[EVAL_DATALOADERS] = None,
datamodule: Optional[LightningDataModule] = None,
ckpt_path: Optional[str] = None,
) -> None:
Trainer._log_api_event("fit")
log.detail(f"{self.__class__.__name__}: trainer fit stage")
self.state.fn = TrainerFn.FITTING
self.state.status = TrainerStatus.RUNNING
self.training = True
# if a datamodule comes in as the second arg, then fix it for the user
if isinstance(train_dataloaders, LightningDataModule):
datamodule = train_dataloaders
train_dataloaders = None
# If you supply a datamodule you can't supply train_dataloader or val_dataloaders
if (train_dataloaders is not None or val_dataloaders is not None) and datamodule is not None:
raise MisconfigurationException(
"You cannot pass `train_dataloader` or `val_dataloaders` to `trainer.fit(datamodule=...)`"
)
# links data to the trainer
self._data_connector.attach_data(
model, train_dataloaders=train_dataloaders, val_dataloaders=val_dataloaders, datamodule=datamodule
)
# TODO: ckpt_path only in v2.0
ckpt_path = ckpt_path or self.resume_from_checkpoint
self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
self.state.fn,
ckpt_path, # type: ignore[arg-type]
model_provided=True,
model_connected=self.lightning_module is not None,
)
self._run(model, ckpt_path=self.ckpt_path)
assert self.state.stopped
self.training = False
return
[docs] def validate(
self,
model: Optional["pl.LightningModule"] = None,
dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
ckpt_path: Optional[str] = None,
verbose: bool = True,
datamodule: Optional[LightningDataModule] = None,
) -> _EVALUATE_OUTPUT:
r"""
Perform one evaluation epoch over the validation set.
Args:
model: The model to validate.
dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them,
or a :class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying validation samples.
ckpt_path: Either ``"best"``, ``"last"``, ``"hpc"`` or path to the checkpoint you wish to validate.
If ``None`` and the model instance was passed, use the current weights.
Otherwise, the best model checkpoint from the previous ``trainer.fit`` call will be loaded
if a checkpoint callback is configured.
verbose: If True, prints the validation results.
datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
Returns:
List of dictionaries with metrics logged during the validation phase, e.g., in model- or callback hooks
like :meth:`~pytorch_lightning.core.module.LightningModule.validation_step`,
:meth:`~pytorch_lightning.core.module.LightningModule.validation_epoch_end`, etc.
The length of the list corresponds to the number of validation dataloaders used.
"""
if model is None:
# do we still have a reference from a previous call?
if self.lightning_module is None:
raise TypeError(
"`Trainer.validate()` requires a `LightningModule` when it hasn't been passed in a previous run"
)
else:
model = self._maybe_unwrap_optimized(model)
self.strategy._lightning_module = model
return call._call_and_handle_interrupt(
self, self._validate_impl, model, dataloaders, ckpt_path, verbose, datamodule
)
def _validate_impl(
self,
model: Optional["pl.LightningModule"] = None,
dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
ckpt_path: Optional[str] = None,
verbose: bool = True,
datamodule: Optional[LightningDataModule] = None,
) -> Optional[Union[_PREDICT_OUTPUT, _EVALUATE_OUTPUT]]:
# --------------------
# SETUP HOOK
# --------------------
Trainer._log_api_event("validate")
log.detail(f"{self.__class__.__name__}: trainer validate stage")
self.state.fn = TrainerFn.VALIDATING
self.state.status = TrainerStatus.RUNNING
self.validating = True
# if a datamodule comes in as the second arg, then fix it for the user
if isinstance(dataloaders, LightningDataModule):
datamodule = dataloaders
dataloaders = None
# If you supply a datamodule you can't supply val_dataloaders
if dataloaders is not None and datamodule:
raise MisconfigurationException("You cannot pass both `trainer.validate(dataloaders=..., datamodule=...)`")
if model is None:
model = self.lightning_module
model_provided = False
else:
model_provided = True
self.validate_loop.verbose = verbose
# links data to the trainer
self._data_connector.attach_data(model, val_dataloaders=dataloaders, datamodule=datamodule)
self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
self.state.fn, ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
)
self._validated_ckpt_path = self.ckpt_path # TODO: remove in v1.8
# run validate
results = self._run(model, ckpt_path=self.ckpt_path)
assert self.state.stopped
self.validating = False
return results
[docs] def test(
self,
model: Optional["pl.LightningModule"] = None,
dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
ckpt_path: Optional[str] = None,
verbose: bool = True,
datamodule: Optional[LightningDataModule] = None,
) -> _EVALUATE_OUTPUT:
r"""
Perform one evaluation epoch over the test set.
It's separated from fit to make sure you never run on your test set until you want to.
Args:
model: The model to test.
dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them,
or a :class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying test samples.
ckpt_path: Either ``"best"``, ``"last"``, ``"hpc"`` or path to the checkpoint you wish to test.
If ``None`` and the model instance was passed, use the current weights.
Otherwise, the best model checkpoint from the previous ``trainer.fit`` call will be loaded
if a checkpoint callback is configured.
verbose: If True, prints the test results.
datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
Returns:
List of dictionaries with metrics logged during the test phase, e.g., in model- or callback hooks
like :meth:`~pytorch_lightning.core.module.LightningModule.test_step`,
:meth:`~pytorch_lightning.core.module.LightningModule.test_epoch_end`, etc.
The length of the list corresponds to the number of test dataloaders used.
"""
if model is None:
# do we still have a reference from a previous call?
if self.lightning_module is None:
raise TypeError(
"`Trainer.test()` requires a `LightningModule` when it hasn't been passed in a previous run"
)
else:
model = self._maybe_unwrap_optimized(model)
self.strategy._lightning_module = model
return call._call_and_handle_interrupt(
self, self._test_impl, model, dataloaders, ckpt_path, verbose, datamodule
)
def _test_impl(
self,
model: Optional["pl.LightningModule"] = None,
dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
ckpt_path: Optional[str] = None,
verbose: bool = True,
datamodule: Optional[LightningDataModule] = None,
) -> Optional[Union[_PREDICT_OUTPUT, _EVALUATE_OUTPUT]]:
# --------------------
# SETUP HOOK
# --------------------
Trainer._log_api_event("test")
log.detail(f"{self.__class__.__name__}: trainer test stage")
self.state.fn = TrainerFn.TESTING
self.state.status = TrainerStatus.RUNNING
self.testing = True
# if a datamodule comes in as the second arg, then fix it for the user
if isinstance(dataloaders, LightningDataModule):
datamodule = dataloaders
dataloaders = None
# If you supply a datamodule you can't supply test_dataloaders
if dataloaders is not None and datamodule:
raise MisconfigurationException("You cannot pass both `trainer.test(dataloaders=..., datamodule=...)`")
if model is None:
model = self.lightning_module
model_provided = False
else:
model_provided = True
self.test_loop.verbose = verbose
# links data to the trainer
self._data_connector.attach_data(model, test_dataloaders=dataloaders, datamodule=datamodule)
self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
self.state.fn, ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
)
self._tested_ckpt_path = self.ckpt_path # TODO: remove in v1.8
# run test
results = self._run(model, ckpt_path=self.ckpt_path)
assert self.state.stopped
self.testing = False
return results
[docs] def predict(
self,
model: Optional["pl.LightningModule"] = None,
dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
datamodule: Optional[LightningDataModule] = None,
return_predictions: Optional[bool] = None,
ckpt_path: Optional[str] = None,
) -> Optional[_PREDICT_OUTPUT]:
r"""
Run inference on your data.
This will call the model forward function to compute predictions. Useful to perform distributed
and batched predictions. Logging is disabled in the predict hooks.
Args:
model: The model to predict with.
dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them,
or a :class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying prediction samples.
datamodule: The datamodule with a predict_dataloader method that returns one or more dataloaders.
return_predictions: Whether to return predictions.
``True`` by default except when an accelerator that spawns processes is used (not supported).
ckpt_path: Either ``"best"``, ``"last"``, ``"hpc"`` or path to the checkpoint you wish to predict.
If ``None`` and the model instance was passed, use the current weights.
Otherwise, the best model checkpoint from the previous ``trainer.fit`` call will be loaded
if a checkpoint callback is configured.
Returns:
Returns a list of dictionaries, one for each provided dataloader containing their respective predictions.
See :ref:`Lightning inference section<deploy/production_basic:Predict step with your LightningModule>` for more.
"""
if model is None:
# do we still have a reference from a previous call?
if self.lightning_module is None:
raise TypeError(
"`Trainer.predict()` requires a `LightningModule` when it hasn't been passed in a previous run"
)
else:
model = self._maybe_unwrap_optimized(model)
self.strategy._lightning_module = model
return call._call_and_handle_interrupt(
self, self._predict_impl, model, dataloaders, datamodule, return_predictions, ckpt_path
)
def _predict_impl(
self,
model: Optional["pl.LightningModule"] = None,
dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
datamodule: Optional[LightningDataModule] = None,
return_predictions: Optional[bool] = None,
ckpt_path: Optional[str] = None,
) -> Optional[_PREDICT_OUTPUT]:
# --------------------
# SETUP HOOK
# --------------------
Trainer._log_api_event("predict")
log.detail(f"{self.__class__.__name__}: trainer predict stage")
self.state.fn = TrainerFn.PREDICTING
self.state.status = TrainerStatus.RUNNING
self.predicting = True
self.predict_loop.return_predictions = return_predictions # type: ignore[assignment]
# if a datamodule comes in as the second arg, then fix it for the user
if isinstance(dataloaders, LightningDataModule):
datamodule = dataloaders
dataloaders = None
if dataloaders is not None and datamodule:
raise MisconfigurationException("You cannot pass both `trainer.predict(dataloaders=..., datamodule=...)`")
if model is None:
model = self.lightning_module
model_provided = False
else:
model_provided = True
# links data to the trainer
self._data_connector.attach_data(model, predict_dataloaders=dataloaders, datamodule=datamodule)
self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
self.state.fn, ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
)
self._predicted_ckpt_path = self.ckpt_path # TODO: remove in v1.8
results = self._run(model, ckpt_path=self.ckpt_path)
assert self.state.stopped
self.predicting = False
return results
[docs] def tune(
self,
model: "pl.LightningModule",
train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
val_dataloaders: Optional[EVAL_DATALOADERS] = None,
dataloaders: Optional[EVAL_DATALOADERS] = None,
datamodule: Optional[LightningDataModule] = None,
scale_batch_size_kwargs: Optional[Dict[str, Any]] = None,
lr_find_kwargs: Optional[Dict[str, Any]] = None,
method: Literal["fit", "validate", "test", "predict"] = "fit",
) -> _TunerResult:
r"""
Runs routines to tune hyperparameters before training.
Args:
model: Model to tune.
train_dataloaders: A collection of :class:`torch.utils.data.DataLoader` or a
:class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying training samples.
In the case of multiple dataloaders, please see this :ref:`section <multiple-dataloaders>`.
val_dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying val/test/predict
samples used for running tuner on validation/testing/prediction.
datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
scale_batch_size_kwargs: Arguments for :func:`~pytorch_lightning.tuner.batch_size_scaling.scale_batch_size`
lr_find_kwargs: Arguments for :func:`~pytorch_lightning.tuner.lr_finder.lr_find`
method: Method to run tuner on. It can be any of ``("fit", "validate", "test", "predict")``.
"""
model = self._maybe_unwrap_optimized(model)
Trainer._log_api_event("tune")
with isolate_rng():
result = self.tuner._tune(
model,
train_dataloaders,
val_dataloaders,
dataloaders,
datamodule,
scale_batch_size_kwargs=scale_batch_size_kwargs,
lr_find_kwargs=lr_find_kwargs,
method=method,
)
return result
def _restore_modules_and_callbacks(self, checkpoint_path: Optional[_PATH] = None) -> None:
# restore modules after setup
self._checkpoint_connector.resume_start(checkpoint_path)
self._checkpoint_connector._restore_quantization_callbacks()
self._checkpoint_connector.restore_model()
self._checkpoint_connector.restore_datamodule()
if self.state.fn == TrainerFn.FITTING:
# restore callback states
self._checkpoint_connector.restore_callbacks()
def _run(
self, model: "pl.LightningModule", ckpt_path: Optional[str] = None
) -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
if model._compiler_ctx is not None:
supported_strategies = [SingleDeviceStrategy, DDPStrategy, DDPFullyShardedNativeStrategy]
if self.strategy is not None and not any(isinstance(self.strategy, s) for s in supported_strategies):
supported_strategy_names = ", ".join(s.__name__ for s in supported_strategies)
raise RuntimeError(
"Using a compiled model is incompatible with the current strategy: "
f"{self.strategy.__class__.__name__}. "
f"Only {supported_strategy_names} support compilation. "
"Either switch to one of the supported strategies or avoid passing in "
"a compiled model."
)
if self.state.fn == TrainerFn.FITTING:
min_epochs, max_epochs = _parse_loop_limits(
self.min_steps, self.max_steps, self.min_epochs, self.max_epochs, self
)
self.fit_loop.min_epochs = min_epochs
self.fit_loop.max_epochs = max_epochs
# clean hparams
if hasattr(model, "hparams"):
parsing.clean_namespace(model.hparams)
# attach model to the strategy
self.strategy.connect(model)
self._callback_connector._attach_model_callbacks()
self._callback_connector._attach_model_logging_functions()
verify_loop_configurations(self)
# hook
log.detail(f"{self.__class__.__name__}: preparing data")
self._data_connector.prepare_data()
# ----------------------------
# SET UP TRAINING
# ----------------------------
log.detail(f"{self.__class__.__name__}: setting up strategy environment")
self.strategy.setup_environment()
self.__setup_profiler()
self._call_setup_hook() # allow user to setup lightning_module in accelerator environment
# check if we should delay restoring checkpoint till later
if not self.strategy.restore_checkpoint_after_setup:
log.detail(f"{self.__class__.__name__}: restoring module and callbacks from checkpoint path: {ckpt_path}")
self._restore_modules_and_callbacks(ckpt_path)
log.detail(f"{self.__class__.__name__}: configuring sharded model")
self._call_configure_sharded_model() # allow user to setup in model sharded environment
# ----------------------------
# INSPECT THE CORE LOOPS
# ----------------------------
rf"""
Lightning internal flow looks like this:
{Trainer.fit} or {Trainer.test} or {Trainer.predict} ||
| ||
spawn processes ||
{self.strategy.setup_environment} ||
| ||
setup accelerator ||
and strategy || LIGHTNING
| ||
{self._run_stage} || FLOW
| ||
{self._run_train} || DIRECTION
or {self._run_evaluate} ||
or {self._run_predict} ||
| ||
results \/
This is used to guide readers to the core loops: train, test, predict.
{self._run_predict} is the simplest to understand, use `Go to Definition` to read it :)
"""
# ----------------------------
# TRAIN
# ----------------------------
# reset logger connector
self._logger_connector.reset_results()
self._logger_connector.reset_metrics()
# strategy will configure model and move it to the device
self.strategy.setup(self)
# hook
if self.state.fn == TrainerFn.FITTING:
self._call_callback_hooks("on_fit_start")
self._call_lightning_module_hook("on_fit_start")
self._log_hyperparams()
if self.strategy.restore_checkpoint_after_setup:
log.detail(f"{self.__class__.__name__}: restoring module and callbacks from checkpoint path: {ckpt_path}")
self._restore_modules_and_callbacks(ckpt_path)
# restore optimizers, etc.
log.detail(f"{self.__class__.__name__}: restoring training state")
self._checkpoint_connector.restore_training_state()
self._checkpoint_connector.resume_end()
results = self._run_stage()
log.detail(f"{self.__class__.__name__}: trainer tearing down")
self._teardown()
# ----------------------------
# POST-Training CLEAN UP
# ----------------------------
# hook
if self.state.fn == TrainerFn.FITTING:
self._call_callback_hooks("on_fit_end")
self._call_lightning_module_hook("on_fit_end")
log.detail(f"{self.__class__.__name__}: calling teardown hooks")
self._call_teardown_hook()
self.state.status = TrainerStatus.FINISHED
self.state.stage = None
return results
def _log_hyperparams(self) -> None:
if not self.loggers:
return
# log hyper-parameters
hparams_initial = None
# save exp to get started (this is where the first experiment logs are written)
datamodule_log_hyperparams = self.datamodule._log_hyperparams if self.datamodule is not None else False
if self.lightning_module._log_hyperparams and datamodule_log_hyperparams:
datamodule_hparams = self.datamodule.hparams_initial
lightning_hparams = self.lightning_module.hparams_initial
inconsistent_keys = []
for key in lightning_hparams.keys() & datamodule_hparams.keys():
lm_val, dm_val = lightning_hparams[key], datamodule_hparams[key]
if type(lm_val) != type(dm_val):
inconsistent_keys.append(key)
elif isinstance(lm_val, Tensor) and id(lm_val) != id(dm_val):
inconsistent_keys.append(key)
elif lm_val != dm_val:
inconsistent_keys.append(key)
if inconsistent_keys:
raise MisconfigurationException(
f"Error while merging hparams: the keys {inconsistent_keys} are present "
"in both the LightningModule's and LightningDataModule's hparams "
"but have different values."
)
hparams_initial = {**lightning_hparams, **datamodule_hparams}
elif self.lightning_module._log_hyperparams:
hparams_initial = self.lightning_module.hparams_initial
elif datamodule_log_hyperparams:
hparams_initial = self.datamodule.hparams_initial
for logger in self.loggers:
if hparams_initial is not None:
logger.log_hyperparams(hparams_initial)
logger.log_graph(self.lightning_module)
logger.save()
def _teardown(self) -> None:
"""This is the Trainer's internal teardown, unrelated to the `teardown` hooks in LightningModule and
Callback; those are handled by :meth:`_call_teardown_hook`."""
self.strategy.teardown()
loop = self._active_loop
# loop should never be `None` here but it can because we don't know the trainer stage with `ddp_spawn`
if loop is not None:
loop.teardown()
self._logger_connector.teardown()
self._signal_connector.teardown()
def _run_stage(self) -> Optional[Union[_PREDICT_OUTPUT, _EVALUATE_OUTPUT]]:
self.strategy.barrier("run-stage")
self.strategy.dispatch(self)
if self.evaluating:
return self._run_evaluate()
if self.predicting:
return self._run_predict()
self._run_train()
def _pre_training_routine(self) -> None:
# wait for all to join if on distributed
self.strategy.barrier("setup_training")
# register signals
self._signal_connector.register_signal_handlers()
def _run_train(self) -> None:
self._pre_training_routine()
with isolate_rng():
self._run_sanity_check()
# enable train mode
assert self.model is not None
self.model.train()
torch.set_grad_enabled(True)
self.fit_loop.trainer = self
with torch.autograd.set_detect_anomaly(self._detect_anomaly):
self.fit_loop.run()
def _run_evaluate(self) -> _EVALUATE_OUTPUT:
assert self.evaluating
# reload dataloaders
self._evaluation_loop._reload_evaluation_dataloaders()
# reset trainer on this loop and all child loops in case user connected a custom loop
self._evaluation_loop.trainer = self
with self.profiler.profile(f"run_{self.state.stage}_evaluation"), _evaluation_context(
self.accelerator, self._inference_mode
):
eval_loop_results = self._evaluation_loop.run()
# remove the tensors from the eval results
for result in eval_loop_results:
if isinstance(result, dict):
for k, v in result.items():
if isinstance(v, Tensor):
result[k] = v.cpu().item()
return eval_loop_results
def _run_predict(self) -> Optional[_PREDICT_OUTPUT]:
self.reset_predict_dataloader(self.lightning_module)
# reset trainer on this loop and all child loops in case user connected a custom loop
self.predict_loop.trainer = self
with _evaluation_context(self.accelerator, self._inference_mode):
return self.predict_loop.run()
def _run_sanity_check(self) -> None:
val_loop = self.fit_loop.epoch_loop.val_loop
should_sanity_check = (
self.enable_validation
and self.num_sanity_val_steps > 0
# do not sanity check if restarting because it would mess up the loaded state
and not val_loop.restarting
)
# run tiny validation (if validation defined)
# to make sure program won't crash during val
if should_sanity_check:
stage = self.state.stage
self.sanity_checking = True
# reset logger connector
self._logger_connector.reset_results()
self._logger_connector.reset_metrics()
self._call_callback_hooks("on_sanity_check_start")
# reload dataloaders
val_loop._reload_evaluation_dataloaders()
self.num_sanity_val_batches = [
min(self.num_sanity_val_steps, val_batches) for val_batches in self.num_val_batches
]
# run eval step
with torch.no_grad():
val_loop.run()
self._call_callback_hooks("on_sanity_check_end")
# reset logger connector
self._logger_connector.reset_results()
self._logger_connector.reset_metrics()
# reset the progress tracking state after sanity checking. we don't need to set the state before
# because sanity check only runs when we are not restarting
_reset_progress(val_loop)
# restore the previous stage when the sanity check if finished
self.state.stage = stage
def _call_setup_hook(self) -> None:
assert self.state.fn is not None
fn = self.state.fn
self.strategy.barrier("pre_setup")
if self.datamodule is not None:
self._call_lightning_datamodule_hook("setup", stage=fn)
self._call_callback_hooks("setup", stage=fn)
self._call_lightning_module_hook("setup", stage=fn)
self.strategy.barrier("post_setup")
def _call_configure_sharded_model(self) -> None:
with self.strategy.model_sharded_context():
# experimental support for torchdistx
if module_available("torchdistx.deferred_init"):
from torchdistx.deferred_init import materialize_module
materialize_module(self.lightning_module)
self._call_lightning_module_hook("configure_sharded_model")
def _call_teardown_hook(self) -> None:
assert self.state.fn is not None
fn = self.state.fn
if self.datamodule is not None:
self._call_lightning_datamodule_hook("teardown", stage=fn)
self._call_callback_hooks("teardown", stage=fn)
self._call_lightning_module_hook("teardown", stage=fn)
self.lightning_module._current_fx_name = None
# these could have become stale if metrics are defined in `setup`
self.lightning_module._metric_attributes = None
# todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
# It might be related to xla tensors blocked when moving the cpu kill loggers.
for logger in self.loggers:
logger.finalize("success")
# summarize profile results
self.profiler.describe()
def _call_lightning_module_hook(
self,
hook_name: str,
*args: Any,
pl_module: Optional["pl.LightningModule"] = None,
**kwargs: Any,
) -> Any:
pl_module = pl_module or self.lightning_module
if pl_module is None:
raise TypeError("No `LightningModule` is available to call hooks on.")
fn = getattr(pl_module, hook_name)
if not callable(fn):
return
prev_fx_name = pl_module._current_fx_name
pl_module._current_fx_name = hook_name
with self.profiler.profile(f"[LightningModule]{pl_module.__class__.__name__}.{hook_name}"):
output = fn(*args, **kwargs)
# restore current_fx when nested context
pl_module._current_fx_name = prev_fx_name
return output
def _call_lightning_datamodule_hook(
self,
hook_name: str,
*args: Any,
**kwargs: Any,
) -> Any:
if self.datamodule is None:
raise TypeError("No `LightningDataModule` is available to call hooks on.")
fn = getattr(self.datamodule, hook_name)
if callable(fn):
with self.profiler.profile(f"[LightningDataModule]{self.datamodule.__class__.__name__}.{hook_name}"):
return fn(*args, **kwargs)
def _call_callback_hooks(
self,
hook_name: str,
*args: Any,
**kwargs: Any,
) -> None:
log.debug(f"{self.__class__.__name__}: calling callback hook: {hook_name}")
pl_module = self.lightning_module
if pl_module:
prev_fx_name = pl_module._current_fx_name
pl_module._current_fx_name = hook_name
for callback in self.callbacks:
fn = getattr(callback, hook_name)
if callable(fn):
with self.profiler.profile(f"[Callback]{callback.state_key}.{hook_name}"):
fn(self, self.lightning_module, *args, **kwargs)
if pl_module:
# restore current_fx when nested context
pl_module._current_fx_name = prev_fx_name
def _call_callbacks_state_dict(self) -> Dict[str, dict]:
"""Called when saving a model checkpoint, calls and returns every callback's `state_dict`, keyed by
`Callback.state_key`."""
callback_state_dicts = {}
for callback in self.callbacks:
state_dict = callback.state_dict()
if state_dict:
callback_state_dicts[callback.state_key] = state_dict
return callback_state_dicts
def _call_callbacks_on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
"""Called when saving a model checkpoint, calls every callback's `on_save_checkpoint` hook."""
pl_module = self.lightning_module
if pl_module:
prev_fx_name = pl_module._current_fx_name
pl_module._current_fx_name = "on_save_checkpoint"
for callback in self.callbacks:
with self.profiler.profile(f"[Callback]{callback.state_key}.on_save_checkpoint"):
state = callback.on_save_checkpoint(self, self.lightning_module, checkpoint)
if state is not None:
# TODO: Remove this error message in v2.0
raise ValueError(
f"Returning a value from `{callback.__class__.__name__}.on_save_checkpoint` was deprecated in v1.6"
f" and is no longer supported as of v1.8. Please override `Callback.state_dict` to return state"
f" to be saved."
)
if pl_module:
# restore current_fx when nested context
pl_module._current_fx_name = prev_fx_name
def _call_callbacks_on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
"""Called when loading a model checkpoint.
Calls every callback's `on_load_checkpoint` hook. We have a dedicated function for this rather than using
`_call_callback_hooks` because we have special logic for getting callback_states.
"""
pl_module = self.lightning_module
if pl_module:
prev_fx_name = pl_module._current_fx_name
pl_module._current_fx_name = "on_load_checkpoint"
callback_states: Optional[Dict[Union[Type, str], Dict]] = checkpoint.get("callbacks")
if callback_states is None:
return
is_legacy_ckpt = Version(checkpoint["pytorch-lightning_version"]) < Version("1.5.0dev")
current_callbacks_keys = {cb._legacy_state_key if is_legacy_ckpt else cb.state_key for cb in self.callbacks}
difference = callback_states.keys() - current_callbacks_keys
if difference:
rank_zero_warn(
"Be aware that when using `ckpt_path`,"
" callbacks used to create the checkpoint need to be provided during `Trainer` instantiation."
f" Please add the following callbacks: {list(difference)}.",
)
for callback in self.callbacks:
with self.profiler.profile(f"[Callback]{callback.state_key}.on_load_checkpoint"):
callback.on_load_checkpoint(self, self.lightning_module, checkpoint)
if pl_module:
# restore current_fx when nested context
pl_module._current_fx_name = prev_fx_name
def _call_callbacks_load_state_dict(self, checkpoint: Dict[str, Any]) -> None:
"""Called when loading a model checkpoint, calls every callback's `load_state_dict`."""
callback_states: Optional[Dict[Union[Type, str], Dict]] = checkpoint.get("callbacks")
if callback_states is None:
return
for callback in self.callbacks:
state = callback_states.get(callback.state_key, callback_states.get(callback._legacy_state_key))
if state:
state = deepcopy(state)
callback.load_state_dict(state)
def _call_strategy_hook(
self,
hook_name: str,
*args: Any,
**kwargs: Any,
) -> Any:
pl_module = self.lightning_module
prev_fx_name = pl_module._current_fx_name
pl_module._current_fx_name = hook_name
fn = getattr(self.strategy, hook_name)
if not callable(fn):
return
with self.profiler.profile(f"[Strategy]{self.strategy.__class__.__name__}.{hook_name}"):
output = fn(*args, **kwargs)
# restore current_fx when nested context
pl_module._current_fx_name = prev_fx_name
return output
@staticmethod
def _log_api_event(event: str) -> None:
torch._C._log_api_usage_once("lightning.trainer." + event)
def __setup_profiler(self) -> None:
assert self.state.fn is not None
local_rank = self.local_rank if self.world_size > 1 else None
self.profiler._lightning_module = proxy(self.lightning_module)
self.profiler.setup(stage=self.state.fn, local_rank=local_rank, log_dir=self.log_dir)
"""
Data loading methods
"""
[docs] def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
"""Resets the train dataloader and initialises required variables (number of batches, when to validate,
etc.).
Args:
model: The ``LightningModule`` if calling this outside of the trainer scope.
"""
source = self._data_connector._train_dataloader_source
pl_module = model or self.lightning_module
has_step = is_overridden("training_step", pl_module)
enable_training = self.limit_train_batches > 0
if not (source.is_defined() and has_step and enable_training):
return
self.train_dataloader = self._data_connector._request_dataloader(RunningStage.TRAINING)
if self.overfit_batches > 0:
self.train_dataloader = self._data_connector._resolve_overfit_batches(
self.train_dataloader, mode=RunningStage.TRAINING
)
# automatically add samplers
self.train_dataloader = apply_to_collection(
self.train_dataloader,
(DataLoader, CombinedLoader),
self._data_connector._prepare_dataloader,
mode=RunningStage.TRAINING,
)
loaders = (
self.train_dataloader.loaders
if isinstance(self.train_dataloader, CombinedLoader)
else self.train_dataloader
)
# check the workers recursively
apply_to_collection(loaders, DataLoader, self._data_connector._worker_check, "train_dataloader")
# add worker_init_fn for correct seeding in worker processes
apply_to_collection(loaders, DataLoader, _auto_add_worker_init_fn, rank=self.global_rank)
# add collate_fn to collect metadata for fault tolerant training
if _fault_tolerant_training():
apply_to_collection(loaders, DataLoader, _add_capture_metadata_collate)
# wrap the sequence of train loaders to a CombinedLoader object for computing the num_training_batches
if not isinstance(self.train_dataloader, CombinedLoader):
self.train_dataloader = CombinedLoader(loaders, self._data_connector.multiple_trainloader_mode)
module = model or self.lightning_module or self.datamodule
orig_train_batches = self.num_training_batches = (
len(self.train_dataloader) # type: ignore[arg-type]
if has_len_all_ranks(self.train_dataloader, self.strategy, module)
else float("inf")
)
if orig_train_batches == 0:
return
# store epoch of dataloader reset for reload_dataloaders_every_n_epochs
self._last_train_dl_reload_epoch = self.current_epoch
if isinstance(self.limit_train_batches, int):
self.num_training_batches = min(orig_train_batches, self.limit_train_batches)
elif self.num_training_batches != float("inf"):
self.num_training_batches = int(orig_train_batches * self.limit_train_batches)
elif self.limit_train_batches != 1.0:
raise MisconfigurationException(
"When using an `IterableDataset`, `Trainer(limit_train_batches)` must be `1.0` or an int."
"An int specifies `num_training_batches` to use."
)
if isinstance(self.val_check_interval, int):
self.val_check_batch = self.val_check_interval
if self.val_check_batch > self.num_training_batches and self.check_val_every_n_epoch is not None:
raise ValueError(
f"`val_check_interval` ({self.val_check_interval}) must be less than or equal "
f"to the number of the training batches ({self.num_training_batches}). "
"If you want to disable validation set `limit_val_batches` to 0.0 instead."
"If you want to validate based on the total training batches, set `check_val_every_n_epoch=None`."
)
else:
if not has_len_all_ranks(self.train_dataloader, self.strategy, module):
if self.val_check_interval == 1.0:
self.val_check_batch = float("inf")
else:
raise MisconfigurationException(
"When using an IterableDataset for `train_dataloader`,"
" `Trainer(val_check_interval)` must be `1.0` or an int. An int k specifies"
" checking validation every k training batches."
)
else:
self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
self.val_check_batch = max(1, self.val_check_batch)
if self.loggers and self.num_training_batches < self.log_every_n_steps:
rank_zero_warn(
f"The number of training batches ({self.num_training_batches}) is smaller than the logging interval"
f" Trainer(log_every_n_steps={self.log_every_n_steps}). Set a lower value for log_every_n_steps if"
" you want to see logs for the training epoch.",
category=PossibleUserWarning,
)
if (
self.num_training_batches == 0
and self.limit_train_batches > 0.0
and isinstance(self.limit_train_batches, float)
and orig_train_batches != float("inf")
):
min_percentage = 1.0 / orig_train_batches
raise MisconfigurationException(
f"You requested to check {self.limit_train_batches} of the `train_dataloader` but"
f" {self.limit_train_batches} * {orig_train_batches} < 1. Please increase the"
f" `limit_train_batches` argument. Try at least"
f" `limit_train_batches={min_percentage}`"
)
[docs] def reset_val_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
"""Resets the validation dataloader and determines the number of batches.
Args:
model: The ``LightningModule`` if called outside of the trainer scope.
"""
source = self._data_connector._val_dataloader_source
pl_module = self.lightning_module or model
has_step = is_overridden("validation_step", pl_module)
enable_validation = self.limit_val_batches > 0
if source.is_defined() and has_step and enable_validation:
# store epoch of dataloader reset for reload_dataloaders_every_n_epochs
# it should not reload again if it has already reloaded during sanity_check
if self.state.fn == TrainerFn.FITTING and (
(self.sanity_checking and self.fit_loop.epoch_loop._should_check_val_epoch())
or not self.sanity_checking
):
self._last_val_dl_reload_epoch = self.current_epoch
self.num_val_batches, self.val_dataloaders = self._data_connector._reset_eval_dataloader(
RunningStage.VALIDATING, model=pl_module
)
[docs] def reset_test_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
"""Resets the test dataloader and determines the number of batches.
Args:
model: The ``LightningModule`` if called outside of the trainer scope.
"""
source = self._data_connector._test_dataloader_source
pl_module = self.lightning_module or model
has_step = is_overridden("test_step", pl_module)
enable_testing = self.limit_test_batches > 0
if source.is_defined() and has_step and enable_testing:
self.num_test_batches, self.test_dataloaders = self._data_connector._reset_eval_dataloader(
RunningStage.TESTING, model=pl_module
)
[docs] def reset_predict_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
"""Resets the predict dataloader and determines the number of batches.
Args:
model: The ``LightningModule`` if called outside of the trainer scope.
"""
source = self._data_connector._predict_dataloader_source
pl_module = self.lightning_module or model
enable_prediction = self.limit_predict_batches > 0
if source.is_defined() and enable_prediction:
self.num_predict_batches, self.predict_dataloaders = self._data_connector._reset_eval_dataloader(
RunningStage.PREDICTING, model=pl_module
)
"""
Accelerator properties
"""
@property
def accelerator(self) -> Accelerator:
assert self.strategy.accelerator
return self.strategy.accelerator
@property
def strategy(self) -> Strategy:
# TODO(fabric): remove ignore after merging Fabric and PL strategies
return self._accelerator_connector.strategy # type: ignore[return-value]
@property
def precision_plugin(self) -> PrecisionPlugin:
return self.strategy.precision_plugin
@property
def global_rank(self) -> int:
return self.strategy.global_rank
@property
def local_rank(self) -> int:
# some strategies define a local rank
return getattr(self.strategy, "local_rank", 0)
@property
def node_rank(self) -> int:
# some strategies define a node rank
return getattr(self.strategy, "node_rank", 0)
@property
def world_size(self) -> int:
# some strategies define a world size
return getattr(self.strategy, "world_size", 1)
@property
def num_nodes(self) -> int:
return getattr(self.strategy, "num_nodes", 1)
@property
def device_ids(self) -> List[int]:
"""List of device indexes per node."""
devices = (
self.strategy.parallel_devices
if isinstance(self.strategy, ParallelStrategy)
else [self.strategy.root_device]
)
assert devices is not None
device_ids = []
for idx, device in enumerate(devices):
if isinstance(device, torch.device):
device_ids.append(device.index or idx)
elif isinstance(device, int):
device_ids.append(device)
return device_ids
@property
def num_devices(self) -> int:
"""Number of devices the trainer uses per node."""
return len(self.device_ids)
@property
def lightning_module(self) -> "pl.LightningModule":
# TODO: this is actually an optional return
return self.strategy.lightning_module # type: ignore[return-value]
@property
def optimizers(self) -> List[Optimizer]:
return self.strategy.optimizers
@optimizers.setter
def optimizers(self, new_optims: List[Optimizer]) -> None:
self.strategy.optimizers = new_optims
@property
def lr_scheduler_configs(self) -> List[LRSchedulerConfig]:
return self.strategy.lr_scheduler_configs
@property
def optimizer_frequencies(self) -> List[int]:
return self.strategy.optimizer_frequencies
@optimizer_frequencies.setter
def optimizer_frequencies(self, new_freqs: List[int]) -> None:
self.strategy.optimizer_frequencies = new_freqs
@property
def amp_backend(self) -> Optional[str]:
rank_zero_deprecation(
"The NVIDIA/apex AMP implementation has been deprecated upstream. Consequently, its integration inside"
" PyTorch Lightning has been deprecated in v1.9.0 and will be removed in v2.0.0."
" Accessing `Trainer.amp_backend` will not be supported. You can assume it will be `'native'`",
stacklevel=6,
)
if isinstance(self.precision_plugin, ApexMixedPrecisionPlugin):
return "apex"
if isinstance(self.precision_plugin, MixedPrecisionPlugin):
return "native"
return None
@property
def precision(self) -> _PRECISION_INPUT_STR:
return self.strategy.precision_plugin.precision
@property
def scaler(self) -> Optional[Any]:
return getattr(self.precision_plugin, "scaler", None)
@property
def model(self) -> Optional[torch.nn.Module]:
"""The LightningModule, but possibly wrapped into DataParallel or DistributedDataParallel.
To access the pure LightningModule, use
:meth:`~pytorch_lightning.trainer.trainer.Trainer.lightning_module` instead.
"""
return self.strategy.model
@model.setter
def model(self, model: torch.nn.Module) -> None:
"""Setter for the model, pass-through to accelerator and plugin where the model reference is stored. Used
by the Tuner to reset the state of Trainer and Accelerator.
Args:
model: The LightningModule, possibly wrapped into DataParallel or DistributedDataParallel, depending
on the backend.
"""
self.strategy.model = model
"""
General properties
"""
@property
def log_dir(self) -> Optional[str]:
if len(self.loggers) > 0:
if not isinstance(self.loggers[0], TensorBoardLogger):
dirpath = self.loggers[0].save_dir
else:
dirpath = self.loggers[0].log_dir
else:
dirpath = self.default_root_dir
dirpath = self.strategy.broadcast(dirpath)
return dirpath
@property
def is_global_zero(self) -> bool:
return self.strategy.is_global_zero
@property
def distributed_sampler_kwargs(self) -> Optional[Dict[str, Any]]:
if isinstance(self.strategy, ParallelStrategy):
return self.strategy.distributed_sampler_kwargs
@property
def data_parallel(self) -> bool:
return isinstance(self.strategy, ParallelStrategy)
@property
def enable_validation(self) -> bool:
"""Check if we should run validation during training."""
return (
self._data_connector._val_dataloader_source.is_defined()
and is_overridden("validation_step", self.lightning_module)
and self.limit_val_batches > 0
)
@property
def default_root_dir(self) -> str:
"""The default location to save artifacts of loggers, checkpoints etc.
It is used as a fallback if logger or checkpoint callback do not define specific save paths.
"""
if get_filesystem(self._default_root_dir).protocol == "file":
return os.path.normpath(self._default_root_dir)
return self._default_root_dir
@property
def early_stopping_callback(self) -> Optional[EarlyStopping]:
"""The first :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` callback in the
Trainer.callbacks list, or ``None`` if it doesn't exist."""
callbacks = self.early_stopping_callbacks
return callbacks[0] if len(callbacks) > 0 else None
@property
def early_stopping_callbacks(self) -> List[EarlyStopping]:
"""A list of all instances of :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` found in
the Trainer.callbacks list."""
return [c for c in self.callbacks if isinstance(c, EarlyStopping)]
@property
def prediction_writer_callbacks(self) -> List[BasePredictionWriter]:
"""A list of all instances of :class:`~pytorch_lightning.callbacks.prediction_writer.BasePredictionWriter`
found in the Trainer.callbacks list."""
return [cb for cb in self.callbacks if isinstance(cb, BasePredictionWriter)]
@property
def checkpoint_callback(self) -> Optional[Checkpoint]:
"""The first :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` callback in the
Trainer.callbacks list, or ``None`` if it doesn't exist."""
callbacks = self.checkpoint_callbacks
return callbacks[0] if len(callbacks) > 0 else None
@property
def checkpoint_callbacks(self) -> List[Checkpoint]:
"""A list of all instances of :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` found
in the Trainer.callbacks list."""
return [c for c in self.callbacks if isinstance(c, Checkpoint)]
@property
def progress_bar_callback(self) -> Optional[ProgressBarBase]:
"""An instance of :class:`~pytorch_lightning.callbacks.progress.base.ProgressBarBase` found in the
Trainer.callbacks list, or ``None`` if one doesn't exist."""
for c in self.callbacks:
if isinstance(c, ProgressBarBase):
return c
return None
@property
def resume_from_checkpoint(self) -> Optional[Union[str, Path]]:
resume_from_checkpoint = self._checkpoint_connector.resume_from_checkpoint_fit_path
if resume_from_checkpoint is not None:
rank_zero_deprecation(
"`trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v2.0."
" Specify the fit checkpoint path with `trainer.fit(ckpt_path=)` instead.",
stacklevel=5,
)
return resume_from_checkpoint
@property
def ckpt_path(self) -> Optional[str]:
"""Set to the path/URL of a checkpoint loaded via :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`,
:meth:`~pytorch_lightning.trainer.trainer.Trainer.validate`,
:meth:`~pytorch_lightning.trainer.trainer.Trainer.test`, or
:meth:`~pytorch_lightning.trainer.trainer.Trainer.predict`. ``None`` otherwise."""
return self._ckpt_path
[docs] def save_checkpoint(
self, filepath: _PATH, weights_only: bool = False, storage_options: Optional[Any] = None
) -> None:
r"""
Runs routine to create a checkpoint.
Args:
filepath: Path where checkpoint is saved.
weights_only: If ``True``, will only save the model weights.
storage_options: parameter for how to save to storage, passed to ``CheckpointIO`` plugin
"""
if self.model is None:
raise AttributeError(
"Saving a checkpoint is only possible if a model is attached to the Trainer. Did you call"
" `Trainer.save_checkpoint()` before calling `Trainer.{fit,validate,test,predict}`?"
)
self._checkpoint_connector.save_checkpoint(filepath, weights_only=weights_only, storage_options=storage_options)
"""
Parsing properties
"""
@classmethod
def default_attributes(cls) -> dict:
init_signature = inspect.signature(cls)
return {k: v.default for k, v in init_signature.parameters.items()}
@classmethod
def from_argparse_args(cls: Any, args: Union[Namespace, ArgumentParser], **kwargs: Any) -> Any:
return from_argparse_args(cls, args, **kwargs)
@classmethod
def parse_argparser(cls, arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:
return parse_argparser(cls, arg_parser)
@classmethod
def match_env_arguments(cls) -> Namespace:
return parse_env_variables(cls)
@classmethod
def add_argparse_args(cls, parent_parser: ArgumentParser, **kwargs: Any) -> Union[_ArgumentGroup, ArgumentParser]:
return add_argparse_args(cls, parent_parser, **kwargs)
"""
State properties
"""
@property
def interrupted(self) -> bool:
return self.state.status == TrainerStatus.INTERRUPTED
@property
def training(self) -> bool:
return self.state.stage == RunningStage.TRAINING
@training.setter
def training(self, val: bool) -> None:
if val:
self.state.stage = RunningStage.TRAINING
elif self.training:
self.state.stage = None
@property
def testing(self) -> bool:
return self.state.stage == RunningStage.TESTING
@testing.setter
def testing(self, val: bool) -> None:
if val:
self.state.stage = RunningStage.TESTING
elif self.testing:
self.state.stage = None
@property
def predicting(self) -> bool:
return self.state.stage == RunningStage.PREDICTING
@predicting.setter
def predicting(self, val: bool) -> None:
if val:
self.state.stage = RunningStage.PREDICTING
elif self.predicting:
self.state.stage = None
@property
def tuning(self) -> bool:
rank_zero_deprecation("`Trainer.tuning` has been deprecated in v1.8.0 and will be removed in v2.0.0.")
return self.state.stage == RunningStage.TUNING
@tuning.setter
def tuning(self, val: bool) -> None:
rank_zero_deprecation("Setting `Trainer.tuning` has been deprecated in v1.8.0 and will be removed in v2.0.0.")
if val:
self.state.stage = RunningStage.TUNING
elif self.tuning:
self.state.stage = None
@property
def validating(self) -> bool:
return self.state.stage == RunningStage.VALIDATING
@validating.setter
def validating(self, val: bool) -> None:
if val:
self.state.stage = RunningStage.VALIDATING
elif self.validating:
self.state.stage = None
@property
def evaluating(self) -> bool:
return self.state.stage is not None and self.state.stage.evaluating
@property
def sanity_checking(self) -> bool:
return self.state.stage == RunningStage.SANITY_CHECKING
@sanity_checking.setter
def sanity_checking(self, val: bool) -> None:
if val:
self.state.stage = RunningStage.SANITY_CHECKING
elif self.sanity_checking:
self.state.stage = None
"""
Loop properties
"""
@property
def global_step(self) -> int:
"""The number of optimizer steps taken (does not reset each epoch).
This includes multiple optimizers and TBPTT steps (if enabled).
"""
return self.fit_loop.epoch_loop.global_step
@property
def current_epoch(self) -> int:
"""The current epoch, updated after the epoch end hooks are run."""
return self.fit_loop.epoch_progress.current.completed
@property
def max_epochs(self) -> Optional[int]:
return self.fit_loop.max_epochs
@property
def min_epochs(self) -> Optional[int]:
return self.fit_loop.min_epochs
@property
def max_steps(self) -> int:
return self.fit_loop.max_steps
@property
def min_steps(self) -> Optional[int]:
return self.fit_loop.min_steps
@property
def is_last_batch(self) -> bool:
"""Whether trainer is executing the last batch."""
return self.fit_loop.epoch_loop.batch_progress.is_last_batch
@property
def fit_loop(self) -> FitLoop:
return self._fit_loop
@fit_loop.setter
def fit_loop(self, loop: FitLoop) -> None:
"""Attach a custom fit loop to this Trainer.
It will run with
:meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`.
"""
loop.trainer = self
self._fit_loop = loop
@property
def validate_loop(self) -> EvaluationLoop:
return self._validate_loop
@validate_loop.setter
def validate_loop(self, loop: EvaluationLoop) -> None:
"""Attach a custom validation loop to this Trainer.
It will run with
:meth:`~pytorch_lightning.trainer.trainer.Trainer.validate`. Note that this loop is different from the one
running during training inside the :meth:`pytorch_lightning.trainer.trainer.Trainer.fit` call.
"""
loop.trainer = self
self._validate_loop = loop
@property
def test_loop(self) -> EvaluationLoop:
return self._test_loop
@test_loop.setter
def test_loop(self, loop: EvaluationLoop) -> None:
"""Attach a custom test loop to this Trainer.
It will run with
:meth:`~pytorch_lightning.trainer.trainer.Trainer.test`.
"""
loop.trainer = self
self._test_loop = loop
@property
def predict_loop(self) -> PredictionLoop:
return self._predict_loop
@predict_loop.setter
def predict_loop(self, loop: PredictionLoop) -> None:
"""Attach a custom prediction loop to this Trainer.
It will run with
:meth:`~pytorch_lightning.trainer.trainer.Trainer.predict`.
"""
loop.trainer = self
self._predict_loop = loop
@property
def _evaluation_loop(self) -> EvaluationLoop:
if self.state.fn == TrainerFn.FITTING:
return self.fit_loop.epoch_loop.val_loop
if self.state.fn == TrainerFn.VALIDATING:
return self.validate_loop
if self.state.fn == TrainerFn.TESTING:
return self.test_loop
raise RuntimeError("The `Trainer._evaluation_loop` property isn't defined. Accessed outside of scope")
@property
def _active_loop(self) -> Optional[Union[FitLoop, EvaluationLoop, PredictionLoop]]:
if self.training:
return self.fit_loop
if self.sanity_checking or self.evaluating:
return self._evaluation_loop
if self.predicting:
return self.predict_loop
"""
Logging properties
"""
@property
def logger(self) -> Optional[Logger]:
return self.loggers[0] if len(self.loggers) > 0 else None
@logger.setter
def logger(self, logger: Optional[Logger]) -> None:
if not logger:
self.loggers = []
else:
self.loggers = [logger]
@property
def loggers(self) -> List[Logger]:
return self._loggers
@loggers.setter
def loggers(self, loggers: Optional[List[Logger]]) -> None:
self._loggers = loggers if loggers else []
@property
def callback_metrics(self) -> Dict:
# TODO: the true typing return can include dictionaries as defined in
# `pytorch_lightning.trainer.connectors.logger_connector.result._OUT_DICT`
return self._logger_connector.callback_metrics
@property
def logged_metrics(self) -> _OUT_DICT:
return self._logger_connector.logged_metrics
@property
def progress_bar_metrics(self) -> _PBAR_DICT:
return self._logger_connector.progress_bar_metrics
@property
def _results(self) -> Optional[_ResultCollection]:
active_loop = self._active_loop
if active_loop is not None:
return active_loop._results
def _exit_gracefully_on_signal(self) -> None:
if not _fault_tolerant_training() or not self._should_terminate_gracefully():
return
raise ExitGracefullyException(0)
def _should_terminate_gracefully(self) -> bool:
value = torch.tensor(int(self._terminate_gracefully), device=self.strategy.root_device)
return bool(self.strategy.reduce(value, reduce_op="sum") > 0)
"""
Other
"""
@property
def estimated_stepping_batches(self) -> Union[int, float]:
r"""
Estimated stepping batches for the complete training inferred from DataLoaders, gradient
accumulation factor and distributed setup.
Examples::
def configure_optimizers(self):
optimizer = ...
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer, max_lr=1e-3, total_steps=self.trainer.estimated_stepping_batches
)
return [optimizer], [scheduler]
"""
accumulation_scheduler = self.accumulation_scheduler
if accumulation_scheduler.epochs != [0]:
raise MisconfigurationException(
"Estimated stepping batches cannot be computed with different"
" `accumulate_grad_batches` at different epochs."
)
# infinite training
if self.max_epochs == -1:
return float("inf") if self.max_steps == -1 else self.max_steps
if self.train_dataloader is None:
rank_zero_info("Loading `train_dataloader` to estimate number of stepping batches.")
self.reset_train_dataloader()
total_batches = self.num_training_batches
# iterable dataset
if total_batches == float("inf"):
return self.max_steps
assert self.max_epochs is not None
self.accumulate_grad_batches = accumulation_scheduler.get_accumulate_grad_batches(self.current_epoch)
effective_batch_size = self.accumulate_grad_batches
max_estimated_steps = math.ceil(total_batches / effective_batch_size) * max(self.max_epochs, 1)
max_estimated_steps = min(max_estimated_steps, self.max_steps) if self.max_steps != -1 else max_estimated_steps
return max_estimated_steps
@contextmanager
def _evaluation_context(accelerator: Accelerator, inference_mode: bool = True) -> Generator:
# inference mode is not supported with gloo backend (#9431) and TPU accelerators.
context_manager_class = (
torch.inference_mode
if inference_mode
and not (dist.is_available() and dist.is_initialized() and dist.get_backend() == "gloo")
and not isinstance(accelerator, TPUAccelerator)
else torch.no_grad
)
with context_manager_class():
yield
