unilm/unilm-v1/src/pytorch_pretrained_bert/loss.py

# coding=utf-8

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss


class LabelSmoothingLoss(_Loss):
    """
    With label smoothing,
    KL-divergence between q_{smoothed ground truth prob.}(w)
    and p_{prob. computed by model}(w) is minimized.
    """

    def __init__(self, label_smoothing=0, tgt_vocab_size=0, ignore_index=0, size_average=None, reduce=None, reduction='mean'):
        assert 0.0 < label_smoothing <= 1.0
        self.ignore_index = ignore_index
        super(LabelSmoothingLoss, self).__init__(
            size_average=size_average, reduce=reduce, reduction=reduction)

        assert label_smoothing > 0
        assert tgt_vocab_size > 0

        smoothing_value = label_smoothing / (tgt_vocab_size - 2)
        one_hot = torch.full((tgt_vocab_size,), smoothing_value)
        one_hot[self.ignore_index] = 0
        self.register_buffer('one_hot', one_hot.unsqueeze(0))
        self.confidence = 1.0 - label_smoothing
        self.tgt_vocab_size = tgt_vocab_size

    def forward(self, output, target):
        """
        output (FloatTensor): batch_size * num_pos * n_classes
        target (LongTensor): batch_size * num_pos
        """
        assert self.tgt_vocab_size == output.size(2)
        batch_size, num_pos = target.size(0), target.size(1)
        output = output.view(-1, self.tgt_vocab_size)
        target = target.view(-1)
        model_prob = self.one_hot.repeat(target.size(0), 1)
        model_prob.scatter_(1, target.unsqueeze(1), self.confidence)
        model_prob.masked_fill_((target == self.ignore_index).unsqueeze(1), 0)

        return F.kl_div(output, model_prob, reduction='none').view(batch_size, num_pos, -1).sum(2)