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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # Copyright 2019 Shigeki Karita | |
| # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| """Positonal Encoding Module.""" | |
| import math | |
| import torch | |
| def _pre_hook( | |
| state_dict, | |
| prefix, | |
| local_metadata, | |
| strict, | |
| missing_keys, | |
| unexpected_keys, | |
| error_msgs, | |
| ): | |
| """Perform pre-hook in load_state_dict for backward compatibility. | |
| Note: | |
| We saved self.pe until v.0.5.2 but we have omitted it later. | |
| Therefore, we remove the item "pe" from `state_dict` for backward compatibility. | |
| """ | |
| k = prefix + "pe" | |
| if k in state_dict: | |
| state_dict.pop(k) | |
| class PositionalEncoding(torch.nn.Module): | |
| """Positional encoding. | |
| :param int d_model: embedding dim | |
| :param float dropout_rate: dropout rate | |
| :param int max_len: maximum input length | |
| :param reverse: 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).expand(1, max_len)) | |
| self._register_load_state_dict_pre_hook(_pre_hook) | |
| 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: torch.Tensor): | |
| """Add positional encoding. | |
| Args: | |
| x (torch.Tensor): Input. Its shape is (batch, time, ...) | |
| Returns: | |
| torch.Tensor: Encoded tensor. Its shape is (batch, time, ...) | |
| """ | |
| self.extend_pe(x) | |
| x = x * self.xscale + self.pe[:, : x.size(1)] | |
| return self.dropout(x) | |
| class ScaledPositionalEncoding(PositionalEncoding): | |
| """Scaled positional encoding module. | |
| See also: Sec. 3.2 https://arxiv.org/pdf/1809.08895.pdf | |
| """ | |
| def __init__(self, d_model, dropout_rate, max_len=5000): | |
| """Initialize class. | |
| :param int d_model: embedding dim | |
| :param float dropout_rate: dropout rate | |
| :param int max_len: maximum input length | |
| """ | |
| 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): | |
| """Reset parameters.""" | |
| self.alpha.data = torch.tensor(1.0) | |
| def forward(self, x): | |
| """Add positional encoding. | |
| Args: | |
| x (torch.Tensor): Input. Its shape is (batch, time, ...) | |
| Returns: | |
| torch.Tensor: Encoded tensor. Its shape is (batch, time, ...) | |
| """ | |
| self.extend_pe(x) | |
| x = x + self.alpha * self.pe[:, : x.size(1)] | |
| return self.dropout(x) | |
| class RelPositionalEncoding(PositionalEncoding): | |
| """Relitive positional encoding module. | |
| See : Appendix B in https://arxiv.org/abs/1901.02860 | |
| :param int d_model: embedding dim | |
| :param float dropout_rate: dropout rate | |
| :param int max_len: maximum input length | |
| """ | |
| def __init__(self, d_model, dropout_rate, max_len=5000): | |
| """Initialize class. | |
| :param int d_model: embedding dim | |
| :param float dropout_rate: dropout rate | |
| :param int max_len: maximum input length | |
| """ | |
| super().__init__(d_model, dropout_rate, max_len, reverse=True) | |
| def forward(self, x): | |
| """Compute positional encoding. | |
| Args: | |
| x (torch.Tensor): Input. Its shape is (batch, time, ...) | |
| Returns: | |
| torch.Tensor: x. Its shape is (batch, time, ...) | |
| torch.Tensor: pos_emb. Its shape is (1, time, ...) | |
| """ | |
| self.extend_pe(x) | |
| x = x * self.xscale | |
| pos_emb = self.pe[:, : x.size(1)] | |
| return self.dropout(x), self.dropout(pos_emb) | |