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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.
from collections.abc import Sequence
from typing import Any, Optional, Union
from torch import Tensor, tensor
from torchmetrics.functional.text.perplexity import _perplexity_compute, _perplexity_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["Perplexity.plot"]
class Perplexity(Metric):
r"""Perplexity measures how well a language model predicts a text sample.
It's calculated as the average number of bits per word a model needs to represent the sample.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): Logits or a unnormalized score assigned to each token in a sequence with shape
[batch_size, seq_len, vocab_size], which is the output of a language model. Scores will be normalized internally
using softmax.
- ``target`` (:class:`~torch.Tensor`): Ground truth values with a shape [batch_size, seq_len]
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``perp`` (:class:`~torch.Tensor`): A tensor with the perplexity score
Args:
ignore_index: Integer specifying a target class to ignore.
If given, this class index does not contribute to the returned score.
kwargs:
Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Examples:
>>> from torch import rand, randint
>>> from torchmetrics.text import Perplexity
>>> preds = rand(2, 8, 5)
>>> target = randint(5, (2, 8))
>>> target[0, 6:] = -100
>>> perp = Perplexity(ignore_index=-100)
>>> perp(preds, target)
tensor(5.8540)
"""
is_differentiable = True
higher_is_better = False
full_state_update = False
total_log_probs: Tensor
count: Tensor
def __init__(
self,
ignore_index: Optional[int] = None,
**kwargs: dict[str, Any],
) -> None:
super().__init__(**kwargs)
if ignore_index is not None and not isinstance(ignore_index, int):
raise ValueError(f"Argument `ignore_index` expected to either be `None` or an `int` but got {ignore_index}")
self.ignore_index = ignore_index
self.add_state("total_log_probs", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("count", default=tensor(0.0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
total_log_probs, count = _perplexity_update(preds, target, self.ignore_index)
self.total_log_probs += total_log_probs
self.count += count
def compute(self) -> Tensor:
"""Compute the Perplexity."""
return _perplexity_compute(self.total_log_probs, self.count)
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
>>> import torch
>>> from torchmetrics.text import Perplexity
>>> metric = Perplexity()
>>> metric.update(torch.rand(2, 8, 5), torch.randint(5, (2, 8)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.text import Perplexity
>>> metric = Perplexity()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(2, 8, 5), torch.randint(5, (2, 8))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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