86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# Copyright (c) 2023 Amphion.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import math
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import torch
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import torch.nn as nn
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from modules.general.utils import Linear
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class PositionEncoder(nn.Module):
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r"""Encoder of positional embedding, generates PE and then
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feed into 2 full-connected layers with ``SiLU``.
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Args:
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d_raw_emb: The dimension of raw embedding vectors.
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d_out: The dimension of output embedding vectors, default to ``d_raw_emb``.
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d_mlp: The dimension of hidden layer in MLP, default to ``d_raw_emb`` * 4.
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activation_function: The activation function used in MLP, default to ``SiLU``.
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n_layer: The number of layers in MLP, default to 2.
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max_period: controls the minimum frequency of the embeddings.
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"""
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def __init__(
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self,
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d_raw_emb: int = 128,
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d_out: int = None,
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d_mlp: int = None,
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activation_function: str = "SiLU",
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n_layer: int = 2,
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max_period: int = 10000,
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):
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super().__init__()
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self.d_raw_emb = d_raw_emb
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self.d_out = d_raw_emb if d_out is None else d_out
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self.d_mlp = d_raw_emb * 4 if d_mlp is None else d_mlp
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self.n_layer = n_layer
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self.max_period = max_period
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if activation_function.lower() == "silu":
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self.activation_function = "SiLU"
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elif activation_function.lower() == "relu":
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self.activation_function = "ReLU"
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elif activation_function.lower() == "gelu":
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self.activation_function = "GELU"
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else:
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raise ValueError("activation_function must be one of SiLU, ReLU, GELU")
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self.activation_function = activation_function
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tmp = [Linear(self.d_raw_emb, self.d_mlp), getattr(nn, activation_function)()]
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for _ in range(self.n_layer - 1):
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tmp.append(Linear(self.d_mlp, self.d_mlp))
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tmp.append(getattr(nn, activation_function)())
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tmp.append(Linear(self.d_mlp, self.d_out))
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self.out = nn.Sequential(*tmp)
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def forward(self, steps: torch.Tensor) -> torch.Tensor:
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r"""Create and return sinusoidal timestep embeddings directly.
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Args:
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steps: a 1D Tensor of N indices, one per batch element.
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These may be fractional.
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Returns:
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an [N x ``d_out``] Tensor of positional embeddings.
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"""
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half = self.d_raw_emb // 2
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freqs = torch.exp(
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-math.log(self.max_period)
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/ half
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* torch.arange(half, dtype=torch.float32, device=steps.device)
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)
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args = steps[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if self.d_raw_emb % 2:
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embedding = torch.cat(
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[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
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)
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return self.out(embedding)
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