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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.pe = None | |
| self.extend_pe(torch.tensor(0.0).expand(1, 4000)) | |
| def extend_pe(self, x): | |
| """Reset the positional encodings.""" | |
| if self.pe is not None: | |
| if self.pe.size(1) >= x.size(1): | |
| if self.pe.dtype != x.dtype or self.pe.device != x.device: | |
| self.pe = self.pe.to(dtype=x.dtype, device=x.device) | |
| return | |
| pe = torch.zeros(x.size(1), self.embedding_dim) | |
| if self.reverse: | |
| position = torch.arange( | |
| x.size(1) - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1) | |
| else: | |
| position = torch.arange( | |
| 0, x.size(1), dtype=torch.float32).unsqueeze(1) | |
| div_term = torch.exp( | |
| torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) * | |
| -(math.log(10000.0) / self.embedding_dim)) | |
| pe[:, 0::2] = torch.sin(position * div_term) | |
| pe[:, 1::2] = torch.cos(position * div_term) | |
| pe = pe.unsqueeze(0) | |
| self.pe = pe.to(device=x.device, dtype=x.dtype).detach() | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| self.extend_pe(x) | |
| output = x.unsqueeze(-1) if x.ndim == 2 else x | |
| output = output * self.x_scale + self.alpha * self.pe[:, :x.size(1)] | |
| return self.dropout(output) | |