Source code for pykeen.training.training_loop

# -*- 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 datetime import datetime
from hashlib import md5
from tempfile import NamedTemporaryFile
from typing import IO, Any, ClassVar, Generic, List, Mapping, Optional, Tuple, Type, TypeVar, Union

import numpy as np
import torch
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader
from tqdm.autonotebook import tqdm, trange

from .callbacks import (
    GradientAbsClippingCallback,
    GradientNormClippingCallback,
    MultiTrainingCallback,
    TrackerCallback,
    TrainingCallbackHint,
)
from ..constants import PYKEEN_CHECKPOINTS, PYKEEN_DEFAULT_CHECKPOINT
from ..losses import Loss
from ..lr_schedulers import LRScheduler
from ..models import RGCN, Model
from ..stoppers import Stopper
from ..trackers import ResultTracker
from ..training.schlichtkrull_sampler import SLCWASubGraphInstances
from ..triples import CoreTriplesFactory, Instances, TriplesFactory
from ..triples.instances import SLCWAInstances
from ..utils import (
    format_relative_comparison,
    get_batchnorm_modules,
    is_cuda_oom_error,
    is_cudnn_error,
    normalize_string,
)

__all__ = [
    "TrainingLoop",
    "NonFiniteLossError",
    "TrainingApproachLossMismatchError",
    "SubBatchingNotSupportedError",
]

logger = logging.getLogger(__name__)

SampleType = TypeVar("SampleType")
BatchType = TypeVar("BatchType")


[docs]class NonFiniteLossError(RuntimeError): """An exception raised for non-finite loss values."""
class TrainingApproachLossMismatchError(TypeError): """An exception when an illegal loss function is used with a given training approach.""" class CheckpointMismatchError(RuntimeError): """An exception when a provided checkpoint file does not match the current training loop setup.""" class SubBatchingNotSupportedError(NotImplementedError): """An exception raised when sub batching is not implemented.""" def __init__(self, model: 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] loss_blacklist: ClassVar[Optional[List[Type[Loss]]]] = None hpo_default = dict( num_epochs=dict(type=int, low=100, high=1000, q=100), batch_size=dict(type=int, low=32, high=4000, q=100), ) def __init__( self, model: Model, triples_factory: CoreTriplesFactory, optimizer: Optional[Optimizer] = None, lr_scheduler: Optional[LRScheduler] = None, automatic_memory_optimization: bool = True, ) -> 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 lr_scheduler: The learning rate scheduler you want to use while training the model :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. """ self.model = model self.optimizer = optimizer self.lr_scheduler = lr_scheduler self.losses_per_epochs = [] self.automatic_memory_optimization = automatic_memory_optimization logger.debug("we don't really need the triples factory: %s", triples_factory) if self.loss_blacklist and isinstance(self.model.loss, tuple(self.loss_blacklist)): raise TrainingApproachLossMismatchError( f"Can not use loss {self.model.loss.__class__.__name__}" f" with training approach {self.__class__.__name__}", ) # 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, result_tracker: Optional[ResultTracker] = 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, gradient_clipping_max_norm: Optional[float] = None, gradient_clipping_norm_type: Optional[float] = None, gradient_clipping_max_abs_value: Optional[float] = None, ) -> 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 result_tracker: The result tracker. :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 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. :return: The losses per epoch. """ # Create training instances. Use the _create_instances function to allow subclasses # to modify this behavior training_instances = self._create_instances(triples_factory) # 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: 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, result_tracker=result_tracker, 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, 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, training_instances=training_instances, ) # 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( # noqa: C901 self, triples_factory: CoreTriplesFactory, training_instances: Instances, 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, result_tracker: Optional[ResultTracker] = 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, gradient_clipping_max_norm: Optional[float] = None, gradient_clipping_norm_type: Optional[float] = None, gradient_clipping_max_abs_value: Optional[float] = None, ) -> 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 if ( stopper is not None and not only_size_probing and last_best_epoch is None and best_epoch_model_file_path is None ): # Create a path best_epoch_model_file_path = pathlib.Path(NamedTemporaryFile().name) best_epoch_model_checkpoint_file_path: Optional[pathlib.Path] = 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) # Register a callback for the result tracker, if given if result_tracker is not None: callback.register_callback(TrackerCallback(result_tracker)) if gradient_clipping_max_norm is not None: callback.register_callback( GradientNormClippingCallback( max_norm=gradient_clipping_max_norm, norm_type=gradient_clipping_norm_type, ) ) if gradient_clipping_max_abs_value is not None: callback.register_callback(GradientAbsClippingCallback(clip_value=gradient_clipping_max_abs_value)) 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, training_instances=training_instances, ) 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, training_instances=training_instances, ) 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 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(training_instances)), ) # Force weight initialization if training continuation is not explicitly requested. if not continue_training: # Reset the weights self.model.reset_parameters_() # 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 = self.model.to(self.device) # Create Sampler if sampler == "schlichtkrull": if triples_factory is None: raise ValueError("need to pass triples_factory when using graph sampling") if not isinstance(training_instances, SLCWAInstances): raise NotImplementedError("Subgraph sampling is currently only supported for SLCWA training.") # wrap training instances training_instances = SLCWASubGraphInstances( mapped_triples=triples_factory.mapped_triples, sub_graph_size=sub_batch_size, ) # disable automatic batching batch_size = None # no support for sub-batching sub_batch_size = None sampler = None shuffle = False # this is already done drop_last = False else: shuffle = True if num_workers is None: num_workers = 0 # Bind num_training_instances = len(training_instances) _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 = DataLoader( dataset=training_instances, num_workers=num_workers, batch_size=batch_size, drop_last=drop_last, shuffle=shuffle, ) # Save the time to track when the saved point was available last_checkpoint = time.time() # 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() # Accumulate loss over epoch current_epoch_loss = 0.0 # 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 # Flag to check when to quit the size probing evaluated_once = False for batch in batches: # Recall that torch *accumulates* gradients. Before passing in a # new instance, you need to zero out the gradients from the old instance self.optimizer.zero_grad() # 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, ) current_epoch_loss += batch_loss callback.on_batch(epoch=epoch, batch=batch, batch_loss=batch_loss) # when called by batch_size_search(), the parameter update should not be applied. if not only_size_probing: callback.pre_step() # update parameters according to optimizer self.optimizer.step() # After changing applying the gradients to the embeddings, the model is notified that the forward # constraints are no longer applied self.model.post_parameter_update() # For testing purposes we're only interested in processing one batch if only_size_probing and evaluated_once: break callback.post_batch(epoch=epoch, batch=batch) evaluated_once = True del batch del batches gc.collect() self.optimizer.zero_grad() self._free_graph_and_cache() # When size probing we don't need the losses if only_size_probing: return None # Update learning rate scheduler if self.lr_scheduler is not None: self.lr_scheduler.step(epoch=epoch) # Track epoch loss if self.model.loss.reduction == "mean": epoch_loss = current_epoch_loss / num_training_instances else: epoch_loss = current_epoch_loss / len(train_data_loader) 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 should_stop = False if stopper is not None and stopper.should_evaluate(epoch): if stopper.should_stop(epoch): should_stop = True # Since the model is also used within the stopper, its graph and cache have to be cleared self._free_graph_and_cache() # When the stopper obtained a new best epoch, this model has to be saved for reconstruction if ( stopper is not None and stopper.best_epoch != last_best_epoch and best_epoch_model_file_path is not None ): self._save_state(path=best_epoch_model_file_path, triples_factory=triples_factory) last_best_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 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 should_stop 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 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 def _forward_pass( self, batch: BatchType, start: int, stop: int, current_batch_size: int, label_smoothing: float, slice_size: Optional[int], ) -> 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 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 _create_instances(self, triples_factory: CoreTriplesFactory) -> Instances: """Create the training instances at the beginning of the training loop.""" 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 sub_batch_and_slice( self, *, batch_size: int, sampler: Optional[str], triples_factory: CoreTriplesFactory, training_instances: Instances, ) -> 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, training_instances=training_instances, ) # 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, training_instances=training_instances, 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, training_instances: Instances, 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 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, training_instances: Instances, ) -> 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. :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 """ 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, training_instances=training_instances, 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, training_instances=training_instances, ) 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. """ 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 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