    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """Compute loss between x and target.

        The model outputs and data labels tensors are flatten to
        (batch*seqlen, class) shape and a mask is applied to the
        padding part which should not be calculated for loss.

        Args:
            x (torch.Tensor): prediction (batch, seqlen, class)
            target (torch.Tensor):
                target signal masked with self.padding_id (batch, seqlen)
        Returns:
            loss (torch.Tensor) : The KL loss, scalar float value
        """
        assert x.size(2) == self.size
        batch_size = x.size(0)
        x = x.view(-1, self.size)
        target = target.view(-1)
        # use zeros_like instead of torch.no_grad() for true_dist,
        # since no_grad() can not be exported by JIT
        true_dist = torch.zeros_like(x)
        true_dist.fill_(self.smoothing / (self.size - 1))
        ignore = target == self.padding_idx  # (B,)
        total = len(target) - ignore.sum().item()
        target = target.masked_fill(ignore, 0)  # avoid -1 index
        true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
        kl = self.criterion(torch.log_softmax(x, dim=1), true_dist)
        denom = total if self.normalize_length else batch_size
        return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom
Compute loss between x and target.

The model outputs and data labels tensors are flatten to
(batch*seqlen, class) shape and a mask is applied to the
padding part which should not be calculated for loss.

Args:
x (torch.Tensor): prediction (batch, seqlen, class)
target (torch.Tensor):
target signal masked with self.padding_id (batch, seqlen)
Returns:
loss (torch.Tensor) : The KL loss, scalar float value
modules/wenet_extractor/transformer/label_smoothing_loss.py