# -*- coding: utf-8 -*-
"""Training loops for KGE models using multi-modal information."""
import gc
import logging
import os
import pathlib
import pickle
import random
import time
from abc import ABC, abstractmethod
from contextlib import ExitStack
from datetime import datetime
from hashlib import md5
from tempfile import NamedTemporaryFile, TemporaryDirectory
from typing import IO, Any, ClassVar, Generic, List, Mapping, Optional, Tuple, TypeVar, Union
import numpy as np
import torch
from class_resolver import HintOrType, OptionalKwargs
from class_resolver.contrib.torch import lr_scheduler_resolver, optimizer_resolver
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader
from tqdm.autonotebook import tqdm, trange
from .callbacks import (
GradientAbsClippingTrainingCallback,
GradientNormClippingTrainingCallback,
LearningRateSchedulerTrainingCallback,
MultiTrainingCallback,
OptimizerTrainingCallback,
StopperTrainingCallback,
TrackerTrainingCallback,
TrainingCallback,
TrainingCallbackHint,
TrainingCallbackKwargsHint,
)
from ..constants import PYKEEN_CHECKPOINTS, PYKEEN_DEFAULT_CHECKPOINT
from ..lr_schedulers import LRScheduler
from ..models import RGCN, Model
from ..stoppers import Stopper
from ..trackers import ResultTracker, tracker_resolver
from ..triples import CoreTriplesFactory, TriplesFactory
from ..typing import InductiveMode
from ..utils import (
format_relative_comparison,
get_batchnorm_modules,
get_preferred_device,
is_cuda_oom_error,
is_cudnn_error,
normalize_string,
)
__all__ = [
"TrainingLoop",
"NonFiniteLossError",
"SubBatchingNotSupportedError",
]
logger = logging.getLogger(__name__)
SampleType = TypeVar("SampleType")
BatchType = TypeVar("BatchType")
[docs]class NonFiniteLossError(RuntimeError):
"""An exception raised for non-finite loss values."""
class CheckpointMismatchError(RuntimeError):
"""An exception when a provided checkpoint file does not match the current training loop setup."""
class NoTrainingBatchError(RuntimeError):
"""An exception when a no training batch was available."""
def __init__(self):
"""Initialize the error."""
super().__init__(
"Did not have a single training batch! This typically happens if the batch_size is set larger "
"than the number of training instances, and drop_last is set to True. The latter happens by default, if "
"the model uses batch norm layers. You can try to fix this problem, by explicitly setting drop_last=False. "
"If you are using the pipeline, you find the parameter in the training_kwargs. Further information can be "
"found at https://github.com/pykeen/pykeen/issues/828 ."
)
class SubBatchingNotSupportedError(NotImplementedError):
"""An exception raised when sub batching is not implemented."""
def __init__(self, model: Model):
"""
Initialize the error.
:param model:
the unsupported model
"""
super().__init__(model)
self.model = model
def __str__(self): # noqa: D105
return (
f"No sub-batching support for {self.model.__class__.__name__} due to modules "
f"{get_batchnorm_modules(self.model)}."
)
def _get_optimizer_kwargs(optimizer: Optimizer) -> Mapping[str, Any]:
optimizer_kwargs = optimizer.state_dict()
optimizer_kwargs = {
key: value for key, value in optimizer_kwargs["param_groups"][0].items() if key not in ["params", "initial_lr"]
}
return optimizer_kwargs
def _get_lr_scheduler_kwargs(lr_scheduler: LRScheduler) -> Mapping[str, Any]:
lr_scheduler_kwargs = lr_scheduler.state_dict()
lr_scheduler_kwargs = {
key: value
for key, value in lr_scheduler_kwargs.items()
if not key.startswith("_") and key not in ["base_lrs", "last_epoch"]
}
return lr_scheduler_kwargs
[docs]class TrainingLoop(Generic[SampleType, BatchType], ABC):
"""A training loop."""
lr_scheduler: Optional[LRScheduler]
model: Model
optimizer: Optimizer
losses_per_epochs: List[float]
hpo_default = dict(
num_epochs=dict(type=int, low=100, high=1000, q=100),
batch_size=dict(type=int, low=4, high=12, scale="power_two"), # [16, 4096]
)
supports_slicing: ClassVar[bool] = False
def __init__(
self,
model: Model,
triples_factory: CoreTriplesFactory,
optimizer: HintOrType[Optimizer] = None,
optimizer_kwargs: OptionalKwargs = None,
lr_scheduler: HintOrType[LRScheduler] = None,
lr_scheduler_kwargs: OptionalKwargs = None,
automatic_memory_optimization: bool = True,
mode: Optional[InductiveMode] = None,
result_tracker: HintOrType[ResultTracker] = None,
result_tracker_kwargs: OptionalKwargs = None,
) -> None:
"""Initialize the training loop.
:param model: The model to train
:param triples_factory: The training triples factory
:param optimizer: The optimizer to use while training the model
:param optimizer_kwargs:
additional keyword-based parameters to instantiate the optimizer (if necessary). `params` will be added
automatically based on the `model`.
:param lr_scheduler: The learning rate scheduler you want to use while training the model
:param lr_scheduler_kwargs:
additional keyword-based parameters to instantiate the LR scheduler (if necessary). `optimizer` will be
added automatically.
:param automatic_memory_optimization: bool
Whether to automatically optimize the sub-batch size during
training and batch size during evaluation with regards to the hardware at hand.
:param mode: The inductive training mode. None if transductive.
:param result_tracker:
the result tracker
:param result_tracker_kwargs:
additional keyword-based parameters to instantiate the result tracker
"""
self.model = model
self.optimizer = optimizer_resolver.make(optimizer, pos_kwargs=optimizer_kwargs, params=model.get_grad_params())
self.lr_scheduler = lr_scheduler_resolver.make_safe(
lr_scheduler, pos_kwargs=lr_scheduler_kwargs, optimizer=self.optimizer
)
self.losses_per_epochs = []
self._should_stop = False
self.automatic_memory_optimization = automatic_memory_optimization
self.mode = mode
self.result_tracker = tracker_resolver.make(query=result_tracker, pos_kwargs=result_tracker_kwargs)
logger.debug("we don't really need the triples factory: %s", triples_factory)
# The internal epoch state tracks the last finished epoch of the training loop to allow for
# seamless loading and saving of training checkpoints
self._epoch = 0
[docs] @classmethod
def get_normalized_name(cls) -> str:
"""Get the normalized name of the training loop."""
return normalize_string(cls.__name__, suffix=TrainingLoop.__name__)
@property
def device(self): # noqa: D401
"""The device used by the model."""
return self.model.device
@property
def loss(self): # noqa: D401
"""The loss used by the model."""
return self.model.loss
@property
def checksum(self) -> str: # noqa: D401
"""The checksum of the model and optimizer the training loop was configured with."""
h = md5() # noqa: S303
h.update(str(self.model).encode("utf-8"))
h.update(str(self.optimizer).encode("utf-8"))
return h.hexdigest()
[docs] def train(
self,
triples_factory: CoreTriplesFactory,
num_epochs: int = 1,
batch_size: Optional[int] = None,
slice_size: Optional[int] = None,
label_smoothing: float = 0.0,
sampler: Optional[str] = None,
continue_training: bool = False,
only_size_probing: bool = False,
use_tqdm: bool = True,
use_tqdm_batch: bool = True,
tqdm_kwargs: Optional[Mapping[str, Any]] = None,
stopper: Optional[Stopper] = None,
sub_batch_size: Optional[int] = None,
num_workers: Optional[int] = None,
clear_optimizer: bool = False,
checkpoint_directory: Union[None, str, pathlib.Path] = None,
checkpoint_name: Optional[str] = None,
checkpoint_frequency: Optional[int] = None,
checkpoint_on_failure: bool = False,
drop_last: Optional[bool] = None,
callbacks: TrainingCallbackHint = None,
callbacks_kwargs: TrainingCallbackKwargsHint = None,
gradient_clipping_max_norm: Optional[float] = None,
gradient_clipping_norm_type: Optional[float] = None,
gradient_clipping_max_abs_value: Optional[float] = None,
pin_memory: bool = True,
) -> Optional[List[float]]:
"""Train the KGE model.
.. note ::
Gradient clipping is a technique to avoid the exploding gradient problem. Clip by norm and clip by value
are two alternative implementations.
:param triples_factory:
The training triples.
:param num_epochs:
The number of epochs to train the model.
:param batch_size:
If set the batch size to use for mini-batch training. Otherwise find the largest possible batch_size
automatically.
:param slice_size: >0
The divisor for the scoring function when using slicing. This is only possible for LCWA training loops in
general and only for models that have the slicing capability implemented.
:param label_smoothing: (0 <= label_smoothing < 1)
If larger than zero, use label smoothing.
:param sampler: (None or 'schlichtkrull')
The type of sampler to use. At the moment sLCWA in R-GCN is the only user of schlichtkrull sampling.
:param continue_training:
If set to False, (re-)initialize the model's weights. Otherwise continue training.
:param only_size_probing:
The evaluation is only performed for two batches to test the memory footprint, especially on GPUs.
:param use_tqdm: Should a progress bar be shown for epochs?
:param use_tqdm_batch: Should a progress bar be shown for batching (inside the epoch progress bar)?
:param tqdm_kwargs:
Keyword arguments passed to :mod:`tqdm` managing the progress bar.
:param stopper:
An instance of :class:`pykeen.stopper.EarlyStopper` with settings for checking
if training should stop early
:param sub_batch_size:
If provided split each batch into sub-batches to avoid memory issues for large models / small GPUs.
:param num_workers:
The number of child CPU workers used for loading data. If None, data are loaded in the main process.
:param clear_optimizer:
Whether to delete the optimizer instance after training (as the optimizer might have additional memory
consumption due to e.g. moments in Adam).
:param checkpoint_directory:
An optional directory to store the checkpoint files. If None, a subdirectory named ``checkpoints`` in the
directory defined by :data:`pykeen.constants.PYKEEN_HOME` is used. Unless the environment variable
``PYKEEN_HOME`` is overridden, this will be ``~/.pykeen/checkpoints``.
:param checkpoint_name:
The filename for saving checkpoints. If the given filename exists already, that file will be loaded and used
to continue training.
:param checkpoint_frequency:
The frequency of saving checkpoints in minutes. Setting it to 0 will save a checkpoint after every epoch.
:param checkpoint_on_failure:
Whether to save a checkpoint in cases of a RuntimeError or MemoryError. This option differs from ordinary
checkpoints, since ordinary checkpoints are only saved after a successful epoch. When saving checkpoints
due to failure of the training loop there is no guarantee that all random states can be recovered correctly,
which might cause problems with regards to the reproducibility of that specific training loop. Therefore,
these checkpoints are saved with a distinct checkpoint name, which will be
``PyKEEN_just_saved_my_day_{datetime}.pt`` in the given checkpoint_root.
:param drop_last:
Whether to drop the last batch in each epoch to prevent smaller batches. Defaults to False, except if the
model contains batch normalization layers. Can be provided explicitly to override.
:param callbacks:
An optional :class:`pykeen.training.TrainingCallback` or collection of callback instances that define
one of several functionalities. Their interface was inspired by Keras.
:param callbacks_kwargs:
additional keyword-based parameter to instantiate the training callback.
:param gradient_clipping_max_norm:
The maximum gradient norm for use with gradient clipping. If None, no gradient norm clipping is used.
:param gradient_clipping_norm_type:
The gradient norm type to use for maximum gradient norm, cf. :func:`torch.nn.utils.clip_grad_norm_`
:param gradient_clipping_max_abs_value:
The maximum absolute value in gradients, cf. :func:`torch.nn.utils.clip_grad_value_`. If None, no
gradient clipping will be used.
:param pin_memory:
whether to use memory pinning in the data loader, cf.
https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-pinning
:return:
The losses per epoch.
"""
self._should_stop = False
# In some cases, e.g. using Optuna for HPO, the cuda cache from a previous run is not cleared
torch.cuda.empty_cache()
# A checkpoint root is always created to ensure a fallback checkpoint can be saved
if checkpoint_directory is None:
checkpoint_directory = PYKEEN_CHECKPOINTS
checkpoint_directory = pathlib.Path(checkpoint_directory)
checkpoint_directory.mkdir(parents=True, exist_ok=True)
logger.debug("using checkpoint_root at %s", checkpoint_directory)
# If a checkpoint file is given, it must be loaded if it exists already
save_checkpoints = False
checkpoint_path = None
best_epoch_model_file_path = None
last_best_epoch = None
if checkpoint_name:
checkpoint_path = checkpoint_directory.joinpath(checkpoint_name)
if checkpoint_path.is_file():
best_epoch_model_file_path, last_best_epoch = self._load_state(
path=checkpoint_path,
triples_factory=triples_factory,
)
if stopper is not None:
stopper_dict = stopper.load_summary_dict_from_training_loop_checkpoint(path=checkpoint_path)
# If the stopper dict has any keys, those are written back to the stopper
if stopper_dict:
stopper._write_from_summary_dict(**stopper_dict)
else:
logger.warning(
"the training loop was configured with a stopper but no stopper configuration was "
"saved in the checkpoint",
)
continue_training = True
else:
logger.info(f"=> no checkpoint found at '{checkpoint_path}'. Creating a new file.")
# The checkpoint frequency needs to be set to save checkpoints
if checkpoint_frequency is None:
checkpoint_frequency = 30
save_checkpoints = True
elif checkpoint_frequency is not None:
logger.warning(
"A checkpoint frequency was set, but no checkpoint file was given. No checkpoints will be created",
)
checkpoint_on_failure_file_path = None
if checkpoint_on_failure:
# In case a checkpoint frequency was set, we warn that no checkpoints will be saved
date_string = datetime.now().strftime("%Y%m%d_%H_%M_%S")
# If no checkpoints were requested, a fallback checkpoint is set in case the training loop crashes
checkpoint_on_failure_file_path = checkpoint_directory.joinpath(
PYKEEN_DEFAULT_CHECKPOINT.replace(".", f"_{date_string}."),
)
# If the stopper loaded from the training loop checkpoint stopped the training, we return those results
if getattr(stopper, "stopped", False):
result: Optional[List[float]] = self.losses_per_epochs
else:
# send model to device before going into the internal training loop
self.model = self.model.to(get_preferred_device(self.model, allow_ambiguity=True))
# the exit stack ensure that we clean up temporary files when an error occurs
with ExitStack() as exit_stack:
# When using early stopping models have to be saved separately at the best epoch, since the training
# loop will due to the patience continue to train after the best epoch and thus alter the model
if (
stopper is not None
and not only_size_probing
and last_best_epoch is None
and best_epoch_model_file_path is None
):
# note: NamedTemporaryFile does not seem to work
# Create a path
temporary_directory = exit_stack.enter_context(TemporaryDirectory())
best_epoch_model_file_path = pathlib.Path(temporary_directory).joinpath("best_model.pt")
result = self._train(
num_epochs=num_epochs,
batch_size=batch_size,
slice_size=slice_size,
label_smoothing=label_smoothing,
sampler=sampler,
continue_training=continue_training,
only_size_probing=only_size_probing,
use_tqdm=use_tqdm,
use_tqdm_batch=use_tqdm_batch,
tqdm_kwargs=tqdm_kwargs,
stopper=stopper,
sub_batch_size=sub_batch_size,
num_workers=num_workers,
save_checkpoints=save_checkpoints,
checkpoint_path=checkpoint_path,
checkpoint_frequency=checkpoint_frequency,
checkpoint_on_failure_file_path=checkpoint_on_failure_file_path,
best_epoch_model_file_path=best_epoch_model_file_path,
last_best_epoch=last_best_epoch,
drop_last=drop_last,
callbacks=callbacks,
callbacks_kwargs=callbacks_kwargs,
gradient_clipping_max_norm=gradient_clipping_max_norm,
gradient_clipping_norm_type=gradient_clipping_norm_type,
gradient_clipping_max_abs_value=gradient_clipping_max_abs_value,
triples_factory=triples_factory,
pin_memory=pin_memory,
)
# Ensure the release of memory
torch.cuda.empty_cache()
# Clear optimizer
if clear_optimizer:
self.optimizer = None
self.lr_scheduler = None
return result
def _train_epoch(
self,
batches: DataLoader,
callbacks: MultiTrainingCallback,
sub_batch_size: Optional[int],
label_smoothing: float,
slice_size: Optional[int],
epoch: int,
only_size_probing: bool,
backward: bool = True,
) -> float:
"""
Run one epoch.
:param batches:
the batches to process
:param callbacks:
the callbacks to apply (wrapped into a single object)
:param sub_batch_size:
the sub-batch size to use
:param label_smoothing:
the label-smoothing to apply
:param slice_size:
the optional slice size
:param epoch:
the current epoch (only used to forward to callbacks)
:param only_size_probing:
whether to stop after the second batch
:param backward:
whether to calculate gradients via backward
:return:
the epoch loss
"""
# Accumulate loss over epoch
current_epoch_loss = 0.0
# Flag to check when to quit the size probing
evaluated_once = False
for batch in batches:
# apply callbacks before starting with batch
callbacks.pre_batch()
# Get batch size of current batch (last batch may be incomplete)
current_batch_size = self._get_batch_size(batch)
_sub_batch_size = sub_batch_size or current_batch_size
# accumulate gradients for whole batch
for start in range(0, current_batch_size, _sub_batch_size):
stop = min(start + _sub_batch_size, current_batch_size)
# forward pass call
batch_loss = self._forward_pass(
batch,
start,
stop,
current_batch_size,
label_smoothing,
slice_size,
backward=backward,
)
current_epoch_loss += batch_loss
callbacks.on_batch(epoch=epoch, batch=batch, batch_loss=batch_loss)
callbacks.pre_step()
callbacks.post_batch(epoch=epoch, batch=batch)
# For testing purposes we're only interested in processing one batch
if only_size_probing and evaluated_once:
break
evaluated_once = True
# note: this epoch loss can be slightly biased towards the last batch, if this is smaller than the rest
# in practice, this should have a minor effect, since typically batch_size << num_instances
current_epoch_loss = current_epoch_loss / len(batches)
# TODO: is this necessary?
del batch
del batches
gc.collect()
self.optimizer.zero_grad()
self._free_graph_and_cache()
return current_epoch_loss
def _train( # noqa: C901
self,
triples_factory: CoreTriplesFactory,
num_epochs: int = 1,
batch_size: Optional[int] = None,
slice_size: Optional[int] = None,
label_smoothing: float = 0.0,
sampler: Optional[str] = None,
continue_training: bool = False,
only_size_probing: bool = False,
use_tqdm: bool = True,
use_tqdm_batch: bool = True,
tqdm_kwargs: Optional[Mapping[str, Any]] = None,
stopper: Optional[Stopper] = None,
sub_batch_size: Optional[int] = None,
num_workers: Optional[int] = None,
save_checkpoints: bool = False,
checkpoint_path: Union[None, str, pathlib.Path] = None,
checkpoint_frequency: Optional[int] = None,
checkpoint_on_failure_file_path: Union[None, str, pathlib.Path] = None,
best_epoch_model_file_path: Optional[pathlib.Path] = None,
last_best_epoch: Optional[int] = None,
drop_last: Optional[bool] = None,
callbacks: TrainingCallbackHint = None,
callbacks_kwargs: TrainingCallbackKwargsHint = None,
gradient_clipping_max_norm: Optional[float] = None,
gradient_clipping_norm_type: Optional[float] = None,
gradient_clipping_max_abs_value: Optional[float] = None,
pin_memory: bool = True,
) -> Optional[List[float]]:
"""Train the KGE model, see docstring for :func:`TrainingLoop.train`."""
if self.optimizer is None:
raise ValueError("optimizer must be set before running _train()")
# When using early stopping models have to be saved separately at the best epoch, since the training loop will
# due to the patience continue to train after the best epoch and thus alter the model
# -> the temporay file has to be created outside, which we assert here
if stopper is not None and not only_size_probing and last_best_epoch is None:
assert best_epoch_model_file_path is not None
if isinstance(self.model, RGCN) and sampler != "schlichtkrull":
logger.warning(
'Using RGCN without graph-based sampling! Please select sampler="schlichtkrull" instead of %s.',
sampler,
)
# Prepare all of the callbacks
callback = MultiTrainingCallback(callbacks=callbacks, callbacks_kwargs=callbacks_kwargs)
# Register a callback for the result tracker, if given
if self.result_tracker is not None:
callback.register_callback(TrackerTrainingCallback())
# Register a callback for the early stopper, if given
# TODO should mode be passed here?
if stopper is not None:
callback.register_callback(
StopperTrainingCallback(
stopper,
triples_factory=triples_factory,
last_best_epoch=last_best_epoch,
best_epoch_model_file_path=best_epoch_model_file_path,
)
)
callback.register_training_loop(self)
# Take the biggest possible training batch_size, if batch_size not set
batch_size_sufficient = False
if batch_size is None:
if self.automatic_memory_optimization:
# Using automatic memory optimization on CPU may result in undocumented crashes due to OS' OOM killer.
if self.model.device.type == "cpu":
batch_size = 256
batch_size_sufficient = True
logger.info(
"Currently automatic memory optimization only supports GPUs, but you're using a CPU. "
"Therefore, the batch_size will be set to the default value '{batch_size}'",
)
else:
batch_size, batch_size_sufficient = self.batch_size_search(triples_factory=triples_factory)
else:
batch_size = 256
logger.info(f"No batch_size provided. Setting batch_size to '{batch_size}'.")
# This will find necessary parameters to optimize the use of the hardware at hand
if (
not only_size_probing
and self.automatic_memory_optimization
and not batch_size_sufficient
and not continue_training
):
# return the relevant parameters slice_size and batch_size
sub_batch_size, slice_size = self.sub_batch_and_slice(
batch_size=batch_size, sampler=sampler, triples_factory=triples_factory
)
if sub_batch_size is None or sub_batch_size == batch_size: # by default do not split batches in sub-batches
sub_batch_size = batch_size
elif get_batchnorm_modules(self.model): # if there are any, this is truthy
raise SubBatchingNotSupportedError(self.model)
model_contains_batch_norm = bool(get_batchnorm_modules(self.model))
if batch_size == 1 and model_contains_batch_norm:
raise ValueError("Cannot train a model with batch_size=1 containing BatchNorm layers.")
if drop_last is None:
drop_last = model_contains_batch_norm
# Force weight initialization if training continuation is not explicitly requested.
if not continue_training:
# Reset the weights
self.model.reset_parameters_()
# afterwards, some parameters may be on the wrong device
self.model.to(get_preferred_device(self.model, allow_ambiguity=True))
# Create new optimizer
optimizer_kwargs = _get_optimizer_kwargs(self.optimizer)
self.optimizer = self.optimizer.__class__(
params=self.model.get_grad_params(),
**optimizer_kwargs,
)
if self.lr_scheduler is not None:
# Create a new lr scheduler and add the optimizer
lr_scheduler_kwargs = _get_lr_scheduler_kwargs(self.lr_scheduler)
self.lr_scheduler = self.lr_scheduler.__class__(self.optimizer, **lr_scheduler_kwargs)
elif not self.optimizer.state:
raise ValueError("Cannot continue_training without being trained once.")
# Ensure the model is on the correct device
self.model.to(get_preferred_device(self.model, allow_ambiguity=True))
if num_workers is None:
num_workers = 0
_use_outer_tqdm = not only_size_probing and use_tqdm
_use_inner_tqdm = _use_outer_tqdm and use_tqdm_batch
# When size probing, we don't want progress bars
if _use_outer_tqdm:
# Create progress bar
_tqdm_kwargs = dict(desc=f"Training epochs on {self.device}", unit="epoch")
if tqdm_kwargs is not None:
_tqdm_kwargs.update(tqdm_kwargs)
epochs = trange(self._epoch + 1, 1 + num_epochs, **_tqdm_kwargs, initial=self._epoch, total=num_epochs)
elif only_size_probing:
epochs = range(1, 1 + num_epochs)
else:
epochs = range(self._epoch + 1, 1 + num_epochs)
logger.debug(f"using stopper: {stopper}")
train_data_loader = self._create_training_data_loader(
triples_factory,
batch_size=batch_size,
drop_last=drop_last,
num_workers=num_workers,
pin_memory=pin_memory,
shuffle=True, # always shuffle during training
sampler=sampler,
)
if len(train_data_loader) == 0:
raise NoTrainingBatchError()
if drop_last and not only_size_probing:
logger.info(
"Dropping last (incomplete) batch each epoch (%s batches).",
format_relative_comparison(part=1, total=len(train_data_loader)),
)
# optimizer callbacks
pre_step_callbacks: List[TrainingCallback] = []
if gradient_clipping_max_norm is not None:
pre_step_callbacks.append(
GradientNormClippingTrainingCallback(
max_norm=gradient_clipping_max_norm,
norm_type=gradient_clipping_norm_type,
)
)
if gradient_clipping_max_abs_value is not None:
pre_step_callbacks.append(GradientAbsClippingTrainingCallback(clip_value=gradient_clipping_max_abs_value))
callback.register_callback(
OptimizerTrainingCallback(only_size_probing=only_size_probing, pre_step_callbacks=pre_step_callbacks)
)
callback.register_callback(LearningRateSchedulerTrainingCallback())
# Save the time to track when the saved point was available
last_checkpoint = time.time()
best_epoch_model_checkpoint_file_path: Optional[pathlib.Path] = None
# Training Loop
for epoch in epochs:
# When training with an early stopper the memory pressure changes, which may allow for errors each epoch
try:
# Enforce training mode
self.model.train()
# Batching
# Only create a progress bar when not in size probing mode
if _use_inner_tqdm:
batches = tqdm(
train_data_loader,
desc=f"Training batches on {self.device}",
leave=False,
unit="batch",
)
else:
batches = train_data_loader
epoch_loss = self._train_epoch(
batches=batches,
callbacks=callback,
sub_batch_size=sub_batch_size,
label_smoothing=label_smoothing,
slice_size=slice_size,
epoch=epoch,
only_size_probing=only_size_probing,
)
# When size probing we don't need the losses
if only_size_probing:
return None
# Track epoch loss
self.losses_per_epochs.append(epoch_loss)
# Print loss information to console
if _use_outer_tqdm:
epochs.set_postfix(
{
"loss": self.losses_per_epochs[-1],
"prev_loss": self.losses_per_epochs[-2] if epoch > 1 else float("nan"),
}
)
# Save the last successful finished epoch
self._epoch = epoch
# When the training loop failed, a fallback checkpoint is created to resume training.
except (MemoryError, RuntimeError) as e:
# During automatic memory optimization only the error message is of interest
if only_size_probing:
raise e
logger.warning(f"The training loop just failed during epoch {epoch} due to error {str(e)}.")
if checkpoint_on_failure_file_path:
# When there wasn't a best epoch the checkpoint path should be None
if last_best_epoch is not None and best_epoch_model_file_path is not None:
best_epoch_model_checkpoint_file_path = best_epoch_model_file_path
self._save_state(
path=checkpoint_on_failure_file_path,
stopper=stopper,
best_epoch_model_checkpoint_file_path=best_epoch_model_checkpoint_file_path,
triples_factory=triples_factory,
)
logger.warning(
"However, don't worry we got you covered. PyKEEN just saved a checkpoint when this "
f"happened at '{checkpoint_on_failure_file_path}'. To resume training from the checkpoint "
f"file just restart your code and pass this file path to the training loop or pipeline you "
f"used as 'checkpoint_file' argument.",
)
# Delete temporary best epoch model
if best_epoch_model_file_path is not None and best_epoch_model_file_path.is_file():
os.remove(best_epoch_model_file_path)
raise e
# Includes a call to result_tracker.log_metrics
callback.post_epoch(epoch=epoch, epoch_loss=epoch_loss)
# If a checkpoint file is given, we check whether it is time to save a checkpoint
if save_checkpoints and checkpoint_path is not None:
minutes_since_last_checkpoint = (time.time() - last_checkpoint) // 60
# MyPy overrides are because you should
if (
minutes_since_last_checkpoint >= checkpoint_frequency # type: ignore
or self._should_stop
or epoch == num_epochs
):
# When there wasn't a best epoch the checkpoint path should be None
if last_best_epoch is not None and best_epoch_model_file_path is not None:
best_epoch_model_checkpoint_file_path = best_epoch_model_file_path
self._save_state(
path=checkpoint_path,
stopper=stopper,
best_epoch_model_checkpoint_file_path=best_epoch_model_checkpoint_file_path,
triples_factory=triples_factory,
) # type: ignore
last_checkpoint = time.time()
if self._should_stop:
if last_best_epoch is not None and best_epoch_model_file_path is not None:
self._load_state(path=best_epoch_model_file_path)
# Delete temporary best epoch model
if pathlib.Path.is_file(best_epoch_model_file_path):
os.remove(best_epoch_model_file_path)
return self.losses_per_epochs
callback.post_train(losses=self.losses_per_epochs)
# If the stopper didn't stop the training loop but derived a best epoch, the model has to be reconstructed
# at that state
if stopper is not None and last_best_epoch is not None and best_epoch_model_file_path is not None:
self._load_state(path=best_epoch_model_file_path)
# Delete temporary best epoch model
if pathlib.Path.is_file(best_epoch_model_file_path):
os.remove(best_epoch_model_file_path)
return self.losses_per_epochs
@abstractmethod
def _create_training_data_loader(
self, triples_factory: CoreTriplesFactory, *, sampler: Optional[str], batch_size: int, drop_last: bool, **kwargs
) -> DataLoader[BatchType]:
"""
Create a data loader over training instances.
:param triples_factory:
the training triples' factory
:param batch_size:
the batch size to use
:param drop_last:
whether to drop the last (incomplete) batch, cf. torch.utils.data.DataLoader
:param sampler:
the batch sampler to use. Either None, or "schlichtkrull".
:param kwargs:
additional keyword-based parameters passed to :meth:`torch.utils.data.DataLoader.__init__`
:return:
a data loader over training instances.
"""
raise NotImplementedError
def _forward_pass(
self,
batch: BatchType,
start: int,
stop: int,
current_batch_size: int,
label_smoothing: float,
slice_size: Optional[int],
backward: bool = True,
) -> float:
# forward pass
loss = self._process_batch(
batch=batch,
start=start,
stop=stop,
label_smoothing=label_smoothing,
slice_size=slice_size,
)
# raise error when non-finite loss occurs (NaN, +/-inf)
if not torch.isfinite(loss):
raise NonFiniteLossError("Loss is non-finite.")
# correction for loss reduction
if self.model.loss.reduction == "mean":
this_sub_batch_size = stop - start
loss *= this_sub_batch_size / current_batch_size
# backward pass
if backward:
loss.backward()
current_epoch_loss = loss.item()
self.model.post_forward_pass()
# TODO why not call torch.cuda.empty_cache()? or call self._free_graph_and_cache()?
return current_epoch_loss
@staticmethod
@abstractmethod
def _get_batch_size(batch: BatchType) -> int:
"""Get the batch size from a (sub-) batch."""
raise NotImplementedError
@abstractmethod
def _process_batch(
self,
batch: BatchType,
start: int,
stop: int,
label_smoothing: float = 0.0,
slice_size: Optional[int] = None,
) -> torch.FloatTensor:
"""Process a single batch and returns the loss."""
raise NotImplementedError
[docs] def batch_size_search(
self,
*,
triples_factory: CoreTriplesFactory,
batch_size: Optional[int] = None,
) -> Tuple[int, bool]:
"""Find the maximum batch size for training with the current setting.
This method checks how big the batch size can be for the current model with the given training data and the
hardware at hand. If possible, the method will output the determined batch size and a boolean value indicating
that this batch size was successfully evaluated. Otherwise, the output will be batch size 1 and the boolean
value will be False.
:param triples_factory:
The triples factory over which search is run
:param batch_size:
The batch size to start the search with. If None, set batch_size=num_triples (i.e. full batch training).
:return:
Tuple containing the maximum possible batch size as well as an indicator if the evaluation with that size
was successful.
:raises RuntimeError:
If a runtime error is raised during training
"""
if batch_size is None:
batch_size = 8192
# Set upper bound
batch_size = min(batch_size, triples_factory.num_triples)
reached_max = False
evaluated_once = False
logger.info("Starting batch_size search for training now...")
while True:
logger.debug(f"Trying batch_size={batch_size}.")
try:
self._free_graph_and_cache()
self._train(
num_epochs=1,
batch_size=batch_size,
sub_batch_size=None,
only_size_probing=True,
triples_factory=triples_factory,
)
except RuntimeError as runtime_error:
self._free_graph_and_cache()
if not is_cudnn_error(runtime_error) and not is_cuda_oom_error(runtime_error):
raise runtime_error
if batch_size == 1:
logger.debug(f"batch_size={batch_size} does not fit into your memory with these parameters.")
break
reached_max = True
batch_size //= 2
if evaluated_once:
logger.info(f"Concluded batch_size search with batch_size={batch_size}.")
break
logger.debug(f"batch_size={batch_size} was too big, trying less now.")
else:
self._free_graph_and_cache()
if not reached_max and batch_size <= triples_factory.num_triples:
batch_size *= 2
else:
logger.info(f"Concluded batch_size search with batch_size={batch_size}.")
evaluated_once = True
break
return batch_size, evaluated_once
[docs] def sub_batch_and_slice(
self,
*,
batch_size: int,
sampler: Optional[str],
triples_factory: CoreTriplesFactory,
) -> Tuple[int, Optional[int]]:
"""Check if sub-batching and/or slicing is necessary to train the model on the hardware at hand."""
sub_batch_size, finished_search, supports_sub_batching = self._sub_batch_size_search(
batch_size=batch_size,
sampler=sampler,
triples_factory=triples_factory,
)
# If the sub_batch_size did not finish search with a possibility that fits the hardware, we have to try slicing
if finished_search:
return sub_batch_size, None
slice_size = self._slice_size_search(
triples_factory=triples_factory,
batch_size=batch_size,
sub_batch_size=sub_batch_size,
supports_sub_batching=supports_sub_batching,
)
return sub_batch_size, slice_size
@abstractmethod
def _slice_size_search(
self,
*,
triples_factory: CoreTriplesFactory,
batch_size: int,
sub_batch_size: int,
supports_sub_batching: bool,
) -> int:
"""Find the maximum slice size for training with the current setting.
This method finds the biggest slice size to train the model with the given training data and the desired batch
and sub_batch size on the hardware at hand. If even the slice size 1 is too high, it will raise an error.
Otherwise it will return the determined slice size.
:param triples_factory:
A triples factory
:param batch_size:
The batch size to use.
:param sub_batch_size:
The sub-batch size to use.
:param supports_sub_batching:
Indicator if the model supports sub-batching. This is used to create appropriate error messages, if needed.
:return:
The slice_size that allows training the model with the given parameters on this hardware.
:raises MemoryError:
If it is not possible to train the model on the hardware at hand with the given parameters.
"""
raise NotImplementedError
def _sub_batch_size_search(
self,
*,
batch_size: int,
sampler: Optional[str],
triples_factory: CoreTriplesFactory,
) -> Tuple[int, bool, bool]:
"""Find the allowable sub batch size for training with the current setting.
This method checks if it is possible to train the model with the given training data and the desired batch size
on the hardware at hand. If possible, the sub-batch size equals the batch size. Otherwise, the maximum
permissible sub-batch size is determined.
:param batch_size:
The initial batch size to start with.
:param sampler:
The sampler (None or schlichtkrull)
:param triples_factory:
A triples factory
:return:
Tuple containing the sub-batch size to use and indicating if the search was finished, i.e. successfully
without hardware errors, as well as if sub-batching is possible
:raises RuntimeError:
If a runtime error is raised during training
"""
sub_batch_size = batch_size
finished_search = False
supports_sub_batching = True
try:
# The cache of the previous run has to be freed to allow accurate memory availability estimates
self._free_graph_and_cache()
logger.debug(f"Trying batch_size {batch_size} for training now.")
self._train(
triples_factory=triples_factory,
num_epochs=1,
batch_size=batch_size,
sub_batch_size=sub_batch_size,
sampler=sampler,
only_size_probing=True,
)
except RuntimeError as runtime_error:
self._free_graph_and_cache()
if not is_cudnn_error(runtime_error) and not is_cuda_oom_error(runtime_error):
raise runtime_error
logger.debug(f"The batch_size {batch_size} was too big, sub_batching is required.")
sub_batch_size //= 2
else:
finished_search = True
logger.debug("No sub-batching required.")
if not finished_search:
logger.info("Starting sub_batch_size search for training now...")
if get_batchnorm_modules(self.model): # if there are any, this is truthy
logger.info("This model does not support sub-batching.")
supports_sub_batching = False
sub_batch_size = batch_size
else:
while True:
logger.debug(f"Trying sub_batch_size {sub_batch_size} now.")
try:
self._free_graph_and_cache()
self._train(
num_epochs=1,
batch_size=batch_size,
sub_batch_size=sub_batch_size,
sampler=sampler,
only_size_probing=True,
triples_factory=triples_factory,
)
except RuntimeError as runtime_error:
self._free_graph_and_cache()
if not is_cudnn_error(runtime_error) and not is_cuda_oom_error(runtime_error):
raise runtime_error
if sub_batch_size == 1:
logger.info(
f"Even sub_batch_size={sub_batch_size} does not fit in memory with these parameters",
)
break
logger.debug(f"The sub_batch_size {sub_batch_size} was too big, trying less now.")
sub_batch_size //= 2
else:
finished_search = True
logger.info(f"Concluded search with sub_batch_size {sub_batch_size}.")
break
self._free_graph_and_cache()
return sub_batch_size, finished_search, supports_sub_batching
def _free_graph_and_cache(self):
self.model._free_graph_and_cache()
# The cache of the previous run has to be freed to allow accurate memory availability estimates
torch.cuda.empty_cache()
def _save_state(
self,
path: Union[IO[bytes], str, pathlib.Path],
stopper: Optional[Stopper] = None,
best_epoch_model_checkpoint_file_path: Optional[pathlib.Path] = None,
triples_factory: Optional[CoreTriplesFactory] = None,
) -> None:
"""Save the state of the training loop.
:param path:
Path of the file where to store the state in.
:param stopper:
An instance of :class:`pykeen.stopper.EarlyStopper` with settings for checking
if training should stop early
:param best_epoch_model_checkpoint_file_path:
The file path for the checkpoint of the best epoch model when using early stopping.
:param triples_factory:
The triples factory being used in the current training loop.
:raises ValueError: if the internal optimizer is not set
"""
if self.optimizer is None:
raise ValueError
logger.debug("=> Saving checkpoint.")
if stopper is None:
stopper_dict: Mapping[str, Any] = dict()
else:
stopper_dict = stopper.get_summary_dict()
# Only if a cuda device is available, the random state is accessed
if torch.cuda.is_available():
torch_cuda_random_state = torch.cuda.get_rng_state()
else:
torch_cuda_random_state = None
if best_epoch_model_checkpoint_file_path is not None:
best_epoch_model_checkpoint = torch.load(best_epoch_model_checkpoint_file_path)
else:
best_epoch_model_checkpoint = None
if self.lr_scheduler is None:
lr_scheduler_state_dict = None
else:
lr_scheduler_state_dict = self.lr_scheduler.state_dict()
relation_to_id_dict = None
entity_to_id_dict = None
if triples_factory is not None and isinstance(triples_factory, TriplesFactory):
relation_to_id_dict = triples_factory.relation_to_id
entity_to_id_dict = triples_factory.entity_to_id
torch.save(
{
"epoch": self._epoch,
"loss": self.losses_per_epochs,
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.optimizer.state_dict(),
"lr_scheduler_state_dict": lr_scheduler_state_dict,
"checksum": self.checksum,
"random_seed": self.model._random_seed,
"stopper_dict": stopper_dict,
"random_state": random.getstate(),
"np_random_state": np.random.get_state(),
"torch_random_state": torch.random.get_rng_state(),
"torch_cuda_random_state": torch_cuda_random_state,
# This is an entire checkpoint for the optional best model when using early stopping
"best_epoch_model_checkpoint": best_epoch_model_checkpoint,
# Saving triples factory related states
"relation_to_id_dict": relation_to_id_dict,
"entity_to_id_dict": entity_to_id_dict,
},
path,
pickle_protocol=pickle.HIGHEST_PROTOCOL,
)
logger.info(f"=> Saved checkpoint after having finished epoch {self._epoch}.")
def _load_state(
self,
path: Union[str, pathlib.Path],
triples_factory: Optional[CoreTriplesFactory] = None,
) -> Tuple[Optional[pathlib.Path], Optional[int]]:
"""Load the state of the training loop from a checkpoint.
:param path:
Path of the file where to load the state from.
:param triples_factory:
The triples factory being used in the current training loop. This is being used to check whether the
entity and relation to id mappings from the checkpoint match those provided by the current triples
factory.
:return:
Temporary file path of the best epoch model and the best epoch when using early stoppers, None otherwise.
:raises ValueError:
If the internal optimizer is none
:raises CheckpointMismatchError:
If the given checkpoint file has a non-matching checksum, i.e. it was saved with a different configuration.
"""
if self.optimizer is None:
raise ValueError
logger.info(f"=> loading checkpoint '{path}'")
checkpoint = torch.load(path)
if checkpoint["checksum"] != self.checksum:
raise CheckpointMismatchError(
f"The checkpoint file '{path}' that was provided already exists, but seems to be "
f"from a different training loop setup.",
)
# Cuda requires its own random state, which can only be set when a cuda device is available
torch_cuda_random_state = checkpoint["torch_cuda_random_state"]
if torch_cuda_random_state is not None and torch.cuda.is_available():
torch.cuda.set_rng_state(torch_cuda_random_state)
elif torch_cuda_random_state is not None and not torch.cuda.is_available():
logger.warning(
"You're currently trying to resume the training loop on a CPU from a checkpoint that was saved "
"with a GPU. Therefore, the random state for the CUDA devices can't be set and results may not "
"be deterministic.",
)
elif torch_cuda_random_state is None and torch.cuda.is_available():
logger.warning(
"You're currently trying to resume the training loop on a GPU from a checkpoint that was saved "
"without a GPU. Therefore, the random state for the CUDA devices won't be set and results may not "
"be deterministic.",
)
# If the checkpoint was saved with a best epoch model from the early stopper, this model has to be retrieved
best_epoch_model_file_path = None
best_epoch = None
if checkpoint.get("best_epoch_model_checkpoint"):
best_epoch_model_file_path = pathlib.Path(NamedTemporaryFile().name)
best_epoch = checkpoint["best_epoch_model_checkpoint"]["epoch"]
torch.save(
checkpoint["best_epoch_model_checkpoint"],
best_epoch_model_file_path,
pickle_protocol=pickle.HIGHEST_PROTOCOL,
)
# Check whether the triples factory mappings match those from the checkpoints
relation_to_id_dict = checkpoint.get("relation_to_id_dict")
entity_to_id_dict = checkpoint.get("entity_to_id_dict")
if (
relation_to_id_dict is not None
and entity_to_id_dict is not None
and triples_factory is not None
and isinstance(triples_factory, TriplesFactory)
):
if relation_to_id_dict != triples_factory.relation_to_id:
logger.warning(
"The model provided by the checkpoint was trained on different relation_to_id mappings than the "
"ones provided by the current triples factory. This will most likely render the current learning "
"state of your model useless. This is usually caused by using a completely different dataset "
"or sampling a sub-dataset from a bigger dataset before handing it to the PyKEEN triples factory.",
)
if entity_to_id_dict != triples_factory.entity_to_id:
logger.warning(
"The model provided by the checkpoint was trained on different entity_to_id mappings than the "
"ones provided by the current triples factory. This will most likely render the current learning "
"state of your model useless. This is usually caused by using a completely different dataset "
"or sampling a sub-dataset from a bigger dataset before handing it to the PyKEEN triples factory.",
)
self._epoch = checkpoint["epoch"]
self.losses_per_epochs = checkpoint["loss"]
self.model.load_state_dict(checkpoint["model_state_dict"])
self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
if self.lr_scheduler is not None:
self.lr_scheduler.load_state_dict(checkpoint["lr_scheduler_state_dict"])
random.setstate(checkpoint["random_state"])
np.random.set_state(checkpoint["np_random_state"])
torch.random.set_rng_state(checkpoint["torch_random_state"])
logger.info(f"=> loaded checkpoint '{path}' stopped after having finished epoch {checkpoint['epoch']}")
return best_epoch_model_file_path, best_epoch