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| """ | |
| Taken from ESPNet | |
| """ | |
| import math | |
| import torch | |
| class PositionalEncoding(torch.nn.Module): | |
| """ | |
| Positional encoding. | |
| Args: | |
| d_model (int): Embedding dimension. | |
| dropout_rate (float): Dropout rate. | |
| max_len (int): Maximum input length. | |
| reverse (bool): Whether to reverse the input position. | |
| """ | |
| def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): | |
| """ | |
| Construct an PositionalEncoding object. | |
| """ | |
| super(PositionalEncoding, self).__init__() | |
| self.d_model = d_model | |
| self.reverse = reverse | |
| self.xscale = math.sqrt(self.d_model) | |
| self.dropout = torch.nn.Dropout(p=dropout_rate) | |
| self.pe = None | |
| self.extend_pe(torch.tensor(0.0, device=d_model.device).expand(1, max_len)) | |
| 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.d_model) | |
| 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.d_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.d_model)) | |
| 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) | |
| def forward(self, x): | |
| """ | |
| Add positional encoding. | |
| Args: | |
| x (torch.Tensor): Input tensor (batch, time, `*`). | |
| Returns: | |
| torch.Tensor: Encoded tensor (batch, time, `*`). | |
| """ | |
| self.extend_pe(x) | |
| x = x * self.xscale + self.pe[:, : x.size(1)] | |
| return self.dropout(x) | |
| class RelPositionalEncoding(torch.nn.Module): | |
| """ | |
| Relative positional encoding module (new implementation). | |
| Details can be found in https://github.com/espnet/espnet/pull/2816. | |
| See : Appendix B in https://arxiv.org/abs/1901.02860 | |
| Args: | |
| d_model (int): Embedding dimension. | |
| dropout_rate (float): Dropout rate. | |
| max_len (int): Maximum input length. | |
| """ | |
| def __init__(self, d_model, dropout_rate, max_len=5000): | |
| """ | |
| Construct an PositionalEncoding object. | |
| """ | |
| super(RelPositionalEncoding, self).__init__() | |
| self.d_model = d_model | |
| self.xscale = math.sqrt(self.d_model) | |
| self.dropout = torch.nn.Dropout(p=dropout_rate) | |
| self.pe = None | |
| self.extend_pe(torch.tensor(0.0).expand(1, max_len)) | |
| def extend_pe(self, x): | |
| """Reset the positional encodings.""" | |
| if self.pe is not None: | |
| # self.pe contains both positive and negative parts | |
| # the length of self.pe is 2 * input_len - 1 | |
| if self.pe.size(1) >= x.size(1) * 2 - 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 | |
| # Suppose `i` means to the position of query vecotr and `j` means the | |
| # position of key vector. We use position relative positions when keys | |
| # are to the left (i>j) and negative relative positions otherwise (i<j). | |
| pe_positive = torch.zeros(x.size(1), self.d_model, device=x.device) | |
| pe_negative = torch.zeros(x.size(1), self.d_model, device=x.device) | |
| position = torch.arange(0, x.size(1), dtype=torch.float32, device=x.device).unsqueeze(1) | |
| div_term = torch.exp(torch.arange(0, self.d_model, 2, dtype=torch.float32, device=x.device) * -(math.log(10000.0) / self.d_model)) | |
| pe_positive[:, 0::2] = torch.sin(position * div_term) | |
| pe_positive[:, 1::2] = torch.cos(position * div_term) | |
| pe_negative[:, 0::2] = torch.sin(-1 * position * div_term) | |
| pe_negative[:, 1::2] = torch.cos(-1 * position * div_term) | |
| # Reserve the order of positive indices and concat both positive and | |
| # negative indices. This is used to support the shifting trick | |
| # as in https://arxiv.org/abs/1901.02860 | |
| pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0) | |
| pe_negative = pe_negative[1:].unsqueeze(0) | |
| pe = torch.cat([pe_positive, pe_negative], dim=1) | |
| self.pe = pe.to(dtype=x.dtype) | |
| def forward(self, x): | |
| """ | |
| Add positional encoding. | |
| Args: | |
| x (torch.Tensor): Input tensor (batch, time, `*`). | |
| Returns: | |
| torch.Tensor: Encoded tensor (batch, time, `*`). | |
| """ | |
| self.extend_pe(x) | |
| x = x * self.xscale | |
| pos_emb = self.pe[:, self.pe.size(1) // 2 - x.size(1) + 1: self.pe.size(1) // 2 + x.size(1), ] | |
| return self.dropout(x), self.dropout(pos_emb) | |
| class ScaledPositionalEncoding(PositionalEncoding): | |
| """ | |
| Scaled positional encoding module. | |
| See Sec. 3.2 https://arxiv.org/abs/1809.08895 | |
| Args: | |
| d_model (int): Embedding dimension. | |
| dropout_rate (float): Dropout rate. | |
| max_len (int): Maximum input length. | |
| """ | |
| def __init__(self, d_model, dropout_rate, max_len=5000): | |
| super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) | |
| self.alpha = torch.nn.Parameter(torch.tensor(1.0)) | |
| def reset_parameters(self): | |
| self.alpha.data = torch.tensor(1.0) | |
| def forward(self, x): | |
| """ | |
| Add positional encoding. | |
| Args: | |
| x (torch.Tensor): Input tensor (batch, time, `*`). | |
| Returns: | |
| torch.Tensor: Encoded tensor (batch, time, `*`). | |
| """ | |
| self.extend_pe(x) | |
| x = x + self.alpha * self.pe[:, : x.size(1)] | |
| return self.dropout(x) | |