-
Notifications
You must be signed in to change notification settings - Fork 387
/
perplexity.py
130 lines (104 loc) · 4.95 KB
/
perplexity.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
# 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 typing import Any, Dict, Optional, Sequence, 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 torchmetrics.text import Perplexity
>>> import torch
>>> gen = torch.manual_seed(42)
>>> preds = torch.rand(2, 8, 5, generator=gen)
>>> target = torch.randint(5, (2, 8), generator=gen)
>>> 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)