# -*- coding: utf-8 -*-
"""Training KGE models based on the LCWA."""
import logging
from math import ceil
from typing import Callable, Optional, Union
import torch
from torch.utils.data import DataLoader
from .training_loop import TrainingLoop
from ..losses import Loss
from ..models import Model
from ..triples import CoreTriplesFactory
from ..triples.instances import LCWABatchType, LCWASampleType
from ..typing import InductiveMode
__all__ = [
"LCWATrainingLoop",
]
logger = logging.getLogger(__name__)
name_to_index = {name: index for index, name in enumerate("hrt")}
[docs]class LCWATrainingLoop(TrainingLoop[LCWASampleType, LCWABatchType]):
r"""A training loop that is based upon the local closed world assumption (LCWA).
Under the LCWA, for a given true training triple $(h, r, t) \in \mathcal{T}_{train}$, all triples
$(h, r, t') \notin \mathcal{T}_{train}$ are assumed to be false. The training approach thus uses a 1-n scoring,
where it efficiently computes scores for all triples $(h, r, t')$ for $t' \in \mathcal{E}$, i.e., sharing the
same (head, relation)-pair.
This implementation slightly generalizes the original LCWA, and allows to make the same assumption for relation, or
head entity. In particular the second, i.e., predicting the relation, is commonly encountered in visual relation
prediction.
[ruffinelli2020]_ call the LCWA ``KvsAll`` in their work.
"""
def __init__(
self,
*,
target: Union[None, str, int] = None,
**kwargs,
):
"""
Initialize the training loop.
:param target:
The target column. From {0, 1, 2} for head/relation/tail prediction. Defaults to 2, i.e., tail prediction.
:param kwargs:
Additional keyword-based parameters passed to TrainingLoop.__init__
:raises ValueError:
If an invalid target column is given
"""
super().__init__(**kwargs)
# normalize target column
if target is None:
target = 2
if isinstance(target, str):
target = name_to_index[target]
self.target = target
# The type inference is so confusing between the function switching
# and polymorphism introduced by slicability that these need to be ignored
if self.target == 0:
self.score_method = self.model.score_h # type: ignore
elif self.target == 1:
self.score_method = self.model.score_r # type: ignore
elif self.target == 2:
self.score_method = self.model.score_t # type: ignore
else:
raise ValueError(f"Invalid target column: {self.target}. Must be from {{0, 1, 2}}.")
# Explicit mentioning of num_transductive_entities since in the evaluation there will be a different number
# of total entities from another inductive inference factory
self.num_targets = self.model.num_relations if self.target == 1 else self.model._get_entity_len(mode=self.mode)
# docstr-coverage: inherited
def _create_training_data_loader(
self,
triples_factory: CoreTriplesFactory,
batch_size: int,
drop_last: bool,
num_workers: int,
pin_memory: bool,
sampler: Optional[str],
) -> DataLoader[LCWABatchType]: # noqa: D102
if sampler:
raise NotImplementedError(
f"LCWA training does not support non-default batch sampling. Expected sampler=None, but got "
f"sampler='{sampler}'.",
)
dataset = triples_factory.create_lcwa_instances(target=self.target)
return DataLoader(
dataset=dataset,
num_workers=num_workers,
batch_size=batch_size,
drop_last=drop_last,
shuffle=True,
pin_memory=pin_memory,
collate_fn=dataset.get_collator(),
)
@staticmethod
# docstr-coverage: inherited
def _get_batch_size(batch: LCWABatchType) -> int: # noqa: D102
return batch[0].shape[0]
@staticmethod
def _process_batch_static(
model: Model,
score_method: Callable,
loss: Loss,
num_targets: Optional[int],
mode: Optional[InductiveMode],
batch: LCWABatchType,
start: Optional[int],
stop: Optional[int],
label_smoothing: float = 0.0,
slice_size: Optional[int] = None,
) -> torch.FloatTensor:
# Split batch components
batch_pairs, batch_labels_full = batch
# Send batch to device
batch_pairs = batch_pairs[start:stop].to(device=model.device)
batch_labels_full = batch_labels_full[start:stop].to(device=model.device)
predictions = score_method(batch_pairs, slice_size=slice_size, mode=mode)
return (
loss.process_lcwa_scores(
predictions=predictions,
labels=batch_labels_full,
label_smoothing=label_smoothing,
num_entities=num_targets,
)
+ model.collect_regularization_term()
)
# docstr-coverage: inherited
def _process_batch(
self,
batch: LCWABatchType,
start: int,
stop: int,
label_smoothing: float = 0.0,
slice_size: Optional[int] = None,
) -> torch.FloatTensor: # noqa: D102
return self._process_batch_static(
model=self.model,
score_method=self.score_method,
loss=self.loss,
num_targets=self.num_targets,
mode=self.mode,
batch=batch,
start=start,
stop=stop,
label_smoothing=label_smoothing,
slice_size=slice_size,
)
# docstr-coverage: inherited
def _slice_size_search(
self,
*,
triples_factory: CoreTriplesFactory,
batch_size: int,
sub_batch_size: int,
supports_sub_batching: bool,
) -> int: # noqa: D102
self._check_slicing_availability(supports_sub_batching)
reached_max = False
evaluated_once = False
logger.info("Trying slicing now.")
# Since the batch_size search with size 1, i.e. one tuple ((h, r) or (r, t)) scored on all entities,
# must have failed to start slice_size search, we start with trying half the entities.
slice_size = ceil(self.model.num_entities / 2)
while True:
try:
logger.debug(f"Trying slice size {slice_size} now.")
self._train(
triples_factory=triples_factory,
num_epochs=1,
batch_size=batch_size,
sub_batch_size=sub_batch_size,
slice_size=slice_size,
only_size_probing=True,
)
except RuntimeError as e:
self._free_graph_and_cache()
if "CUDA out of memory." not in e.args[0]:
raise e
if evaluated_once:
slice_size //= 2
logger.info(f"Concluded search with slice_size {slice_size}.")
break
if slice_size == 1:
raise MemoryError(
f"Even slice_size={slice_size} doesn't fit into your memory with these" f" parameters.",
) from e
logger.debug(
f"The slice_size {slice_size} was too big, trying less now.",
)
slice_size //= 2
reached_max = True
else:
self._free_graph_and_cache()
if reached_max:
logger.info(f"Concluded search with slice_size {slice_size}.")
break
slice_size *= 2
evaluated_once = True
return slice_size
def _check_slicing_availability(self, supports_sub_batching: bool):
if self.target == 0 and self.model.can_slice_h:
return
if self.target == 1 and self.model.can_slice_r:
return
if self.target == 2 and self.model.can_slice_t:
return
elif supports_sub_batching:
report = (
"This model supports sub-batching, but it also requires slicing,"
" which is not implemented for this model yet."
)
else:
report = "This model doesn't support sub-batching and slicing is not" " implemented for this model yet."
logger.warning(report)
raise MemoryError("The current model can't be trained on this hardware with these parameters.")