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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections.abc import Sequence
from typing import Any, ClassVar, List, Optional, Union
import torch
from torch import Tensor
from torchmetrics.functional.text.helper_embedding_metric import _load_tokenizer_and_model
from torchmetrics.functional.text.infolm import (
_ALLOWED_INFORMATION_MEASURE_LITERAL,
_get_dataloader,
_get_special_tokens_map,
_infolm_compute,
_infolm_update,
_InformationMeasure,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_4
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["InfoLM.plot"]
if not _TRANSFORMERS_GREATER_EQUAL_4_4:
__doctest_skip__ = ["InfoLM", "InfoLM.plot"]
class InfoLM(Metric):
"""Calculate `InfoLM`_.
InfoLM measures a distance/divergence between predicted and reference sentence discrete distribution using one of
the following information measures:
- `KL divergence`_
- `alpha divergence`_
- `beta divergence`_
- `AB divergence`_
- `Rényi divergence`_
- L1 distance
- L2 distance
- L-infinity distance
- `Fisher-Rao distance`_
`InfoLM`_ is a family of untrained embedding-based metrics which addresses some famous flaws of standard
string-based metrics thanks to the usage of pre-trained masked language models. This family of metrics is mainly
designed for summarization and data-to-text tasks.
The implementation of this metric is fully based HuggingFace ``transformers``' package.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~Sequence`): An iterable of hypothesis corpus
- ``target`` (:class:`~Sequence`): An iterable of reference corpus
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``infolm`` (:class:`~torch.Tensor`): If `return_sentence_level_score=True` return a tuple with a tensor
with the corpus-level InfoLM score and a list of sentence-level InfoLM scores, else return a corpus-level
InfoLM score
Args:
model_name_or_path:
A name or a model path used to load ``transformers`` pretrained model.
By default the `"bert-base-uncased"` model is used.
temperature:
A temperature for calibrating language modelling. For more information, please reference `InfoLM`_ paper.
information_measure:
A name of information measure to be used. Please use one of: ['kl_divergence', 'alpha_divergence',
'beta_divergence', 'ab_divergence', 'renyi_divergence', 'l1_distance', 'l2_distance', 'l_infinity_distance',
'fisher_rao_distance']
idf:
An indication of whether normalization using inverse document frequencies should be used.
alpha:
Alpha parameter of the divergence used for alpha, AB and Rényi divergence measures.
beta:
Beta parameter of the divergence used for beta and AB divergence measures.
device:
A device to be used for calculation.
max_length:
A maximum length of input sequences. Sequences longer than ``max_length`` are to be trimmed.
batch_size:
A batch size used for model processing.
num_threads:
A number of threads to use for a dataloader.
verbose:
An indication of whether a progress bar to be displayed during the embeddings calculation.
return_sentence_level_score:
An indication whether a sentence-level InfoLM score to be returned.
Example:
>>> from torchmetrics.text.infolm import InfoLM
>>> preds = ['he read the book because he was interested in world history']
>>> target = ['he was interested in world history because he read the book']
>>> infolm = InfoLM('google/bert_uncased_L-2_H-128_A-2', idf=False)
>>> infolm(preds, target)
tensor(-0.1784)
"""
is_differentiable = False
preds_input_ids: List[Tensor]
preds_attention_mask: List[Tensor]
target_input_ids: List[Tensor]
target_attention_mask: List[Tensor]
_information_measure_higher_is_better: ClassVar = {
# following values are <0
"kl_divergence": True,
"alpha_divergence": True,
# following values are >0
"beta_divergence": False,
"ab_divergence": False,
"renyi_divergence": False,
"l1_distance": False,
"l2_distance": False,
"l_infinity_distance": False,
"fisher_rao_distance": False,
}
def __init__(
self,
model_name_or_path: Union[str, os.PathLike] = "bert-base-uncased",
temperature: float = 0.25,
information_measure: _ALLOWED_INFORMATION_MEASURE_LITERAL = "kl_divergence",
idf: bool = True,
alpha: Optional[float] = None,
beta: Optional[float] = None,
device: Optional[Union[str, torch.device]] = None,
max_length: Optional[int] = None,
batch_size: int = 64,
num_threads: int = 0,
verbose: bool = True,
return_sentence_level_score: bool = False,
**kwargs: dict[str, Any],
) -> None:
super().__init__(**kwargs)
self.model_name_or_path = model_name_or_path
self.temperature = temperature
self.information_measure = information_measure
self.idf = idf
self.alpha = alpha
self.beta = beta
self._device = torch.device(device or "cpu")
self.batch_size = batch_size
self.num_threads = num_threads
self.verbose = verbose
self.return_sentence_level_score = return_sentence_level_score
self.tokenizer, self.model = _load_tokenizer_and_model(model_name_or_path, device)
self.information_measure_cls = _InformationMeasure(information_measure, alpha, beta)
self.max_length = max_length or self.model.config.max_length
self.special_tokens_map = _get_special_tokens_map(self.tokenizer)
self.add_state("preds_input_ids", [], dist_reduce_fx="cat")
self.add_state("preds_attention_mask", [], dist_reduce_fx="cat")
self.add_state("target_input_ids", [], dist_reduce_fx="cat")
self.add_state("target_attention_mask", [], dist_reduce_fx="cat")
@property
def higher_is_better(self) -> bool: # type: ignore[override]
"""Returns a bool indicating whether a higher value of the information measure is better.
Done this way as depends on if the information measure is positive or negative.
"""
return self._information_measure_higher_is_better[self.information_measure]
def update(self, preds: Union[str, Sequence[str]], target: Union[str, Sequence[str]]) -> None:
"""Update state with predictions and targets."""
preds_input_ids, preds_attention_mask, target_input_ids, target_attention_mask = _infolm_update(
preds, target, self.tokenizer, self.max_length
)
self.preds_input_ids.append(preds_input_ids)
self.preds_attention_mask.append(preds_attention_mask)
self.target_input_ids.append(target_input_ids)
self.target_attention_mask.append(target_attention_mask)
def compute(self) -> Union[Tensor, tuple[Tensor, Tensor]]:
"""Calculate selected information measure using the pre-trained language model."""
preds_dataloader = _get_dataloader(
input_ids=dim_zero_cat(self.preds_input_ids),
attention_mask=dim_zero_cat(self.preds_attention_mask),
idf=self.idf,
batch_size=self.batch_size,
num_workers=self.num_threads,
)
target_dataloader = _get_dataloader(
input_ids=dim_zero_cat(self.target_input_ids),
attention_mask=dim_zero_cat(self.target_attention_mask),
idf=self.idf,
batch_size=self.batch_size,
num_workers=self.num_threads,
)
info_lm_score = _infolm_compute(
self.model,
preds_dataloader,
target_dataloader,
self.temperature,
self.idf,
self.information_measure_cls,
self.special_tokens_map,
self.verbose,
)
if self.return_sentence_level_score:
return info_lm_score.mean(), info_lm_score
return info_lm_score.mean()
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> from torchmetrics.text.infolm import InfoLM
>>> metric = InfoLM('google/bert_uncased_L-2_H-128_A-2', idf=False)
>>> preds = ['he read the book because he was interested in world history']
>>> target = ['he was interested in world history because he read the book']
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torchmetrics.text.infolm import InfoLM
>>> metric = InfoLM('google/bert_uncased_L-2_H-128_A-2', idf=False)
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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