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import copy
from typing import List, Optional
import torch
class AdaptiveLayerNorm1D(torch.nn.Module):
def __init__(self, data_dim: int, norm_cond_dim: int):
super().__init__()
if data_dim <= 0:
raise ValueError(f"data_dim must be positive, but got {data_dim}")
if norm_cond_dim <= 0:
raise ValueError(f"norm_cond_dim must be positive, but got {norm_cond_dim}")
self.norm = torch.nn.LayerNorm(
data_dim
) # TODO: Check if elementwise_affine=True is correct
self.linear = torch.nn.Linear(norm_cond_dim, 2 * data_dim)
torch.nn.init.zeros_(self.linear.weight)
torch.nn.init.zeros_(self.linear.bias)
def forward(self, x: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
# x: (batch, ..., data_dim)
# t: (batch, norm_cond_dim)
# return: (batch, data_dim)
x = self.norm(x)
alpha, beta = self.linear(t).chunk(2, dim=-1)
# Add singleton dimensions to alpha and beta
if x.dim() > 2:
alpha = alpha.view(alpha.shape[0], *([1] * (x.dim() - 2)), alpha.shape[1])
beta = beta.view(beta.shape[0], *([1] * (x.dim() - 2)), beta.shape[1])
return x * (1 + alpha) + beta
class SequentialCond(torch.nn.Sequential):
def forward(self, input, *args, **kwargs):
for module in self:
if isinstance(module, (AdaptiveLayerNorm1D, SequentialCond, ResidualMLPBlock)):
# print(f'Passing on args to {module}', [a.shape for a in args])
input = module(input, *args, **kwargs)
else:
# print(f'Skipping passing args to {module}', [a.shape for a in args])
input = module(input)
return input
def normalization_layer(norm: Optional[str], dim: int, norm_cond_dim: int = -1):
if norm == "batch":
return torch.nn.BatchNorm1d(dim)
elif norm == "layer":
return torch.nn.LayerNorm(dim)
elif norm == "ada":
assert norm_cond_dim > 0, f"norm_cond_dim must be positive, got {norm_cond_dim}"
return AdaptiveLayerNorm1D(dim, norm_cond_dim)
elif norm is None:
return torch.nn.Identity()
else:
raise ValueError(f"Unknown norm: {norm}")
def linear_norm_activ_dropout(
input_dim: int,
output_dim: int,
activation: torch.nn.Module = torch.nn.ReLU(),
bias: bool = True,
norm: Optional[str] = "layer", # Options: ada/batch/layer
dropout: float = 0.0,
norm_cond_dim: int = -1,
) -> SequentialCond:
layers = []
layers.append(torch.nn.Linear(input_dim, output_dim, bias=bias))
if norm is not None:
layers.append(normalization_layer(norm, output_dim, norm_cond_dim))
layers.append(copy.deepcopy(activation))
if dropout > 0.0:
layers.append(torch.nn.Dropout(dropout))
return SequentialCond(*layers)
def create_simple_mlp(
input_dim: int,
hidden_dims: List[int],
output_dim: int,
activation: torch.nn.Module = torch.nn.ReLU(),
bias: bool = True,
norm: Optional[str] = "layer", # Options: ada/batch/layer
dropout: float = 0.0,
norm_cond_dim: int = -1,
) -> SequentialCond:
layers = []
prev_dim = input_dim
for hidden_dim in hidden_dims:
layers.extend(
linear_norm_activ_dropout(
prev_dim, hidden_dim, activation, bias, norm, dropout, norm_cond_dim
)
)
prev_dim = hidden_dim
layers.append(torch.nn.Linear(prev_dim, output_dim, bias=bias))
return SequentialCond(*layers)
class ResidualMLPBlock(torch.nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
num_hidden_layers: int,
output_dim: int,
activation: torch.nn.Module = torch.nn.ReLU(),
bias: bool = True,
norm: Optional[str] = "layer", # Options: ada/batch/layer
dropout: float = 0.0,
norm_cond_dim: int = -1,
):
super().__init__()
if not (input_dim == output_dim == hidden_dim):
raise NotImplementedError(
f"input_dim {input_dim} != output_dim {output_dim} is not implemented"
)
layers = []
prev_dim = input_dim
for i in range(num_hidden_layers):
layers.append(
linear_norm_activ_dropout(
prev_dim, hidden_dim, activation, bias, norm, dropout, norm_cond_dim
)
)
prev_dim = hidden_dim
self.model = SequentialCond(*layers)
self.skip = torch.nn.Identity()
def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
return x + self.model(x, *args, **kwargs)
class ResidualMLP(torch.nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
num_hidden_layers: int,
output_dim: int,
activation: torch.nn.Module = torch.nn.ReLU(),
bias: bool = True,
norm: Optional[str] = "layer", # Options: ada/batch/layer
dropout: float = 0.0,
num_blocks: int = 1,
norm_cond_dim: int = -1,
):
super().__init__()
self.input_dim = input_dim
self.model = SequentialCond(
linear_norm_activ_dropout(
input_dim, hidden_dim, activation, bias, norm, dropout, norm_cond_dim
),
*[
ResidualMLPBlock(
hidden_dim,
hidden_dim,
num_hidden_layers,
hidden_dim,
activation,
bias,
norm,
dropout,
norm_cond_dim,
)
for _ in range(num_blocks)
],
torch.nn.Linear(hidden_dim, output_dim, bias=bias),
)
def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
return self.model(x, *args, **kwargs)
class FrequencyEmbedder(torch.nn.Module):
def __init__(self, num_frequencies, max_freq_log2):
super().__init__()
frequencies = 2 ** torch.linspace(0, max_freq_log2, steps=num_frequencies)
self.register_buffer("frequencies", frequencies)
def forward(self, x):
# x should be of size (N,) or (N, D)
N = x.size(0)
if x.dim() == 1: # (N,)
x = x.unsqueeze(1) # (N, D) where D=1
x_unsqueezed = x.unsqueeze(-1) # (N, D, 1)
scaled = self.frequencies.view(1, 1, -1) * x_unsqueezed # (N, D, num_frequencies)
s = torch.sin(scaled)
c = torch.cos(scaled)
embedded = torch.cat([s, c, x_unsqueezed], dim=-1).view(
N, -1
) # (N, D * 2 * num_frequencies + D)
return embedded
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