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Running
on
Zero
| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import torch.nn as nn | |
| import math | |
| class SinePositionalEmbedding(nn.Module): | |
| def __init__( | |
| self, | |
| dim_model: int, | |
| dropout: float = 0.0, | |
| scale: bool = False, | |
| alpha: bool = False, | |
| ): | |
| super().__init__() | |
| self.dim_model = dim_model | |
| self.x_scale = math.sqrt(dim_model) 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.dim_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.dim_model, 2, dtype=torch.float32) | |
| * -(math.log(10000.0) / self.dim_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).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) | |
| # import torch | |
| # import torch.nn as nn | |
| # import math | |
| # class SinePositionalEmbedding(nn.Module): | |
| # def __init__( | |
| # self, | |
| # dim_model: int, | |
| # dropout: float = 0.0, | |
| # scale: bool = False, | |
| # alpha: bool = False, | |
| # ): | |
| # super().__init__() | |
| # self.dim_model = dim_model | |
| # self.x_scale = math.sqrt(dim_model) 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.zeros(1, 4000)) | |
| # def extend_pe(self, x): | |
| # """Reset the positional encodings.""" | |
| # if self._pe_needs_extension(x): | |
| # self.pe = self._generate_positional_encodings(x) | |
| # def _pe_needs_extension(self, x): | |
| # return self.pe is None or self.pe.size(1) < x.size(1) or self.pe.dtype != x.dtype or self.pe.device != x.device | |
| # def _generate_positional_encodings(self, x): | |
| # pe = torch.zeros(x.size(1), self.dim_model) | |
| # position = self._get_position_tensor(x) | |
| # div_term = self._get_div_term() | |
| # pe[:, 0::2] = torch.sin(position * div_term) | |
| # pe[:, 1::2] = torch.cos(position * div_term) | |
| # return pe.unsqueeze(0).to(device=x.device, dtype=x.dtype).detach() | |
| # def _get_position_tensor(self, x): | |
| # position = torch.arange(x.size(1), dtype=torch.float32).unsqueeze(1) | |
| # return position.flip(0) if self.reverse else position | |
| # def _get_div_term(self): | |
| # return torch.exp(torch.arange(0, self.dim_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.dim_model)) | |
| # 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) | |