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# Inspired by https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/metrics/perplexity.py
# But we compute the perplexity correctly: exp(average(nll)), not average(exp(nll))
# Also adapted from https://github.com/Lightning-AI/metrics/blob/master/src/torchmetrics/text/perplexity.py
# But we pass in the loss to avoid recomputation
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import Tensor
from torchmetrics import Metric
try:
from flash_attn.losses.cross_entropy import CrossEntropyLoss
except ImportError:
CrossEntropyLoss = torch.nn.CrossEntropyLoss
__all__ = ['Perplexity']
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.
Args:
kwargs:
Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Examples:
>>> import torch
>>> preds = torch.rand(2, 8, 5, generator=torch.manual_seed(22))
>>> target = torch.randint(5, (2, 8), generator=torch.manual_seed(22))
>>> target[0, 6:] = -100
>>> metric = Perplexity(ignore_index=-100)
>>> metric(preds, target)
tensor(5.2545)
"""
is_differentiable = True
higher_is_better = False
full_state_update = False
total_log_probs: Tensor
count: Tensor
def __init__(self, **kwargs: Dict[str, Any]):
super().__init__(**kwargs)
self.add_state("total_log_probs", default=torch.tensor(0.0, dtype=torch.float64),
dist_reduce_fx="sum")
self.add_state("count", default=torch.tensor(0, dtype=torch.int64), dist_reduce_fx="sum")
self.loss_fn = CrossEntropyLoss()
def update(self, preds: Tensor, target: Tensor, loss: Optional[Tensor] = None) -> None: # type: ignore
"""Compute and store intermediate statistics for Perplexity.
Args:
preds:
Probabilities assigned to each token in a sequence with shape [batch_size, seq_len, vocab_size].
target:
Ground truth values with a shape [batch_size, seq_len].
"""
count = target.numel()
if loss is None:
loss = self.loss_fn(preds, target)
self.total_log_probs += loss.double() * count
self.count += count
def compute(self) -> Tensor:
"""Compute the Perplexity.
Returns:
Perplexity
"""
return torch.exp(self.total_log_probs / self.count)
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