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| # modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/embedding.py | |
| import math | |
| import torch | |
| from torch import nn | |
| class TokenEmbedding(nn.Module): | |
| def __init__( | |
| self, | |
| embedding_dim: int, | |
| vocab_size: int, | |
| dropout: float = 0.0, | |
| ): | |
| super().__init__() | |
| self.vocab_size = vocab_size | |
| self.embedding_dim = embedding_dim | |
| self.dropout = torch.nn.Dropout(p=dropout) | |
| self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim) | |
| def weight(self) -> torch.Tensor: | |
| return self.word_embeddings.weight | |
| def embedding(self, index: int) -> torch.Tensor: | |
| return self.word_embeddings.weight[index : index + 1] | |
| def forward(self, x: torch.Tensor): | |
| x = self.word_embeddings(x) | |
| x = self.dropout(x) | |
| return x | |
| class SinePositionalEmbedding(nn.Module): | |
| def __init__( | |
| self, | |
| embedding_dim: int, | |
| dropout: float = 0.0, | |
| scale: bool = False, | |
| alpha: bool = False, | |
| ): | |
| super().__init__() | |
| self.embedding_dim = embedding_dim | |
| self.x_scale = math.sqrt(embedding_dim) if scale else 1.0 | |
| self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha) | |
| self.dropout = torch.nn.Dropout(p=dropout) | |
| self.reverse = False | |
| self.div_term = torch.exp(torch.arange(0, self.embedding_dim, 2) * -(math.log(10000.0) / self.embedding_dim)) | |
| def extend_pe(self, x): | |
| position = torch.cumsum(torch.ones_like(x[:,:,0]), dim=1).transpose(0, 1) | |
| scpe = (position * self.div_term).unsqueeze(0) | |
| pe = torch.cat([torch.sin(scpe), torch.cos(scpe)]).permute(1, 2, 0) | |
| pe = pe.contiguous().view(1, -1, self.embedding_dim) | |
| return pe | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| pe = self.extend_pe(x) | |
| output = x.unsqueeze(-1) if x.ndim == 2 else x | |
| output = output * self.x_scale + self.alpha * pe | |
| return self.dropout(output) | |