Masked KL Divergence loss with label smoothing

I am trying to implement a masked KL divergence loss with label smoothing. Below is the code. Can anyone attest if this is the right way to go about it? Thanks!
Context: Calculating loss in Sequence2Sequence architecture:
Decoder output format: Batch x Sequence Length x Vocab size
Input format: Batch x Sequence Length (Note: this is not one hot encoded)

class KLDivergenceLossWithMask(nn.Module):
  def __init__(self, label_smoothing, target_vocab, ignore_value=102):
    self.ignore_value = ignore_value
    self.label_smoothing = label_smoothing
    self.confidence = 1.0 - label_smoothing
    self.target_vocab = target_vocab
    smoothing_value = self.label_smoothing / (self.target_vocab - 2)
    self.one_hot = torch.full((self.target_vocab,), smoothing_value)
    self.one_hot = self.one_hot.unsqueeze(0)

  def forward(self, inp, tgt):
    model_prob = self.one_hot.repeat(tgt.size(0),tgt.size(1), 1)
    model_prob = model_prob.scatter_(2,tgt.unsqueeze(2), value=self.confidence)
    mask = (tgt != self.ignore_value)*1
    model_prob_masked = model_prob*mask.unsqueeze(2)
    inp = (inp*mask.unsqueeze(2))
    return nn.KLDivLoss(reduction = 'batchmean')(F.log_softmax(inp, dim=2), 
inp = torch.randn(4,7,12000).float() #batch,seq_len,vocab
tgt = torch.LongTensor([[23,34,22,67,10122,11343,102],
print(inp.shape, tgt.shape)
label_smoothing = 0.2

target_vocab = 12000

ignore_value = 102

kl_loss_with_mask = KLDivergenceLossWithMask(label_smoothing, target_vocab, ignore_value)

loss = kl_loss_with_mask(inp, tgt)