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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 math | |
| import torch | |
| import torch.nn as nn | |
| from modules.general.utils import Linear | |
| class PositionEncoder(nn.Module): | |
| r"""Encoder of positional embedding, generates PE and then | |
| feed into 2 full-connected layers with ``SiLU``. | |
| Args: | |
| d_raw_emb: The dimension of raw embedding vectors. | |
| d_out: The dimension of output embedding vectors, default to ``d_raw_emb``. | |
| d_mlp: The dimension of hidden layer in MLP, default to ``d_raw_emb`` * 4. | |
| activation_function: The activation function used in MLP, default to ``SiLU``. | |
| n_layer: The number of layers in MLP, default to 2. | |
| max_period: controls the minimum frequency of the embeddings. | |
| """ | |
| def __init__( | |
| self, | |
| d_raw_emb: int = 128, | |
| d_out: int = None, | |
| d_mlp: int = None, | |
| activation_function: str = "SiLU", | |
| n_layer: int = 2, | |
| max_period: int = 10000, | |
| ): | |
| super().__init__() | |
| self.d_raw_emb = d_raw_emb | |
| self.d_out = d_raw_emb if d_out is None else d_out | |
| self.d_mlp = d_raw_emb * 4 if d_mlp is None else d_mlp | |
| self.n_layer = n_layer | |
| self.max_period = max_period | |
| if activation_function.lower() == "silu": | |
| self.activation_function = "SiLU" | |
| elif activation_function.lower() == "relu": | |
| self.activation_function = "ReLU" | |
| elif activation_function.lower() == "gelu": | |
| self.activation_function = "GELU" | |
| else: | |
| raise ValueError("activation_function must be one of SiLU, ReLU, GELU") | |
| self.activation_function = activation_function | |
| tmp = [Linear(self.d_raw_emb, self.d_mlp), getattr(nn, activation_function)()] | |
| for _ in range(self.n_layer - 1): | |
| tmp.append(Linear(self.d_mlp, self.d_mlp)) | |
| tmp.append(getattr(nn, activation_function)()) | |
| tmp.append(Linear(self.d_mlp, self.d_out)) | |
| self.out = nn.Sequential(*tmp) | |
| def forward(self, steps: torch.Tensor) -> torch.Tensor: | |
| r"""Create and return sinusoidal timestep embeddings directly. | |
| Args: | |
| steps: a 1D Tensor of N indices, one per batch element. | |
| These may be fractional. | |
| Returns: | |
| an [N x ``d_out``] Tensor of positional embeddings. | |
| """ | |
| half = self.d_raw_emb // 2 | |
| freqs = torch.exp( | |
| -math.log(self.max_period) | |
| / half | |
| * torch.arange(half, dtype=torch.float32, device=steps.device) | |
| ) | |
| args = steps[:, None].float() * freqs[None] | |
| embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) | |
| if self.d_raw_emb % 2: | |
| embedding = torch.cat( | |
| [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 | |
| ) | |
| return self.out(embedding) | |