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Running
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Zero
| # Copyright 2023 (authors: Feiteng Li) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import math | |
| import torch | |
| import torch.nn as nn | |
| class TokenEmbedding(nn.Module): | |
| def __init__( | |
| self, | |
| dim_model: int, | |
| vocab_size: int, | |
| dropout: float = 0.0, | |
| ): | |
| super().__init__() | |
| self.vocab_size = vocab_size | |
| self.dim_model = dim_model | |
| self.dropout = torch.nn.Dropout(p=dropout) | |
| self.word_embeddings = nn.Embedding(self.vocab_size, self.dim_model) | |
| 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, | |
| 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) | |
| def infer(self, x, position_ids): | |
| """ | |
| infer only a single or a few tokens to save time | |
| """ | |
| output = x.unsqueeze(-1) if x.ndim == 2 else x | |
| output = output * self.x_scale + self.alpha * self.pe[:, position_ids] | |
| return self.dropout(output) |