Source code for openspeech.criterion.perplexity.perplexity

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# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
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import torch
import torch.nn as nn
from torch import Tensor
from omegaconf import DictConfig

from .. import register_criterion
from ..perplexity.configuration import PerplexityLossConfigs
from ...tokenizers.tokenizer import Tokenizer


[docs]@register_criterion("perplexity", dataclass=PerplexityLossConfigs) class Perplexity(nn.Module): r""" Language model perplexity loss. Perplexity is the token averaged likelihood. When the averaging options are the same, it is the exponential of negative log-likelihood. Args: configs (DictConfig): hydra configuration set tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model. Inputs: logits, targets - **logits** (torch.FloatTensor): probability distribution value from model and it has a logarithm shape. The `FloatTensor` of size ``(batch, seq_length, num_classes)`` - **targets** (torch.LongTensor): ground-truth encoded to integers which directly point a word in label The `LongTensor` of size ``(batch, target_length)`` Returns: loss - loss (float): loss for training """ def __init__( self, configs: DictConfig, tokenizer: Tokenizer, ) -> None: super(Perplexity, self).__init__() self.cross_entropy_loss = nn.CrossEntropyLoss( reduction=configs.criterion.reduction, ignore_index=tokenizer.pad_id, ) def forward(self, logits: Tensor, targets: Tensor) -> Tensor: max_target_length = targets.size(1) max_logits_length = logits.size(1) if max_logits_length > max_target_length: logits = logits[:, :max_target_length, :] elif max_target_length > max_logits_length: targets = targets[:, :max_logits_length] logits = logits.contiguous().view(-1, logits.size(-1)) cross_entropy_loss = self.cross_entropy_loss( logits.contiguous().view(-1, logits.size(-1)), targets.contiguous().view(-1), ) return torch.exp(cross_entropy_loss)