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454ecdd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | """VAE-style Manifold Projection Layer for learning behavior manifold."""
from __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Dict, Any
class MPLEncoder(nn.Module):
"""Encode input to latent distribution parameters (mu, logvar)."""
def __init__(
self,
input_dim: int = 256,
hidden_dim: int = 256,
latent_dim: int = 64,
):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.latent_dim = latent_dim
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc_mu = nn.Linear(hidden_dim, latent_dim)
self.fc_logvar = nn.Linear(hidden_dim, latent_dim)
self._init_weights()
def _init_weights(self) -> None:
"""Initialize weights for stable training."""
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.zeros_(self.fc1.bias)
nn.init.xavier_uniform_(self.fc_mu.weight)
nn.init.zeros_(self.fc_mu.bias)
nn.init.xavier_uniform_(self.fc_logvar.weight, gain=0.1)
nn.init.zeros_(self.fc_logvar.bias)
def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
"""
Encode input to latent distribution parameters.
Args:
x: Input features [batch, seq, input_dim]
Returns:
(mu, logvar): Distribution parameters [batch, seq, latent_dim]
"""
h = F.gelu(self.fc1(x))
mu = self.fc_mu(h)
logvar = self.fc_logvar(h)
return mu, logvar
class MPLDecoder(nn.Module):
"""Decode latent samples back to input space."""
def __init__(
self,
latent_dim: int = 64,
hidden_dim: int = 256,
output_dim: int = 256,
):
super().__init__()
self.latent_dim = latent_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.fc1 = nn.Linear(latent_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, output_dim)
self._init_weights()
def _init_weights(self) -> None:
"""Initialize weights for stable training."""
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.zeros_(self.fc1.bias)
nn.init.xavier_uniform_(self.fc2.weight)
nn.init.zeros_(self.fc2.bias)
def forward(self, z: torch.Tensor) -> torch.Tensor:
"""
Decode latent samples to reconstruction.
Args:
z: Latent samples [batch, seq, latent_dim]
Returns:
Reconstruction [batch, seq, output_dim]
"""
h = F.gelu(self.fc1(z))
return self.fc2(h)
class ManifoldProjectionLayer(nn.Module):
"""
VAE-style manifold projection for learning behavior manifold.
Projects high-dimensional behavior to low-dimensional manifold,
then reconstructs. KL divergence regularizes latent space.
"""
def __init__(
self,
input_dim: int = 256,
latent_dim: int = 64,
hidden_dim: int = 256,
kl_weight: float = 0.001,
):
super().__init__()
self.input_dim = input_dim
self.latent_dim = latent_dim
self.hidden_dim = hidden_dim
self.kl_weight = kl_weight
self.encoder = MPLEncoder(
input_dim=input_dim,
hidden_dim=hidden_dim,
latent_dim=latent_dim,
)
self.decoder = MPLDecoder(
latent_dim=latent_dim,
hidden_dim=hidden_dim,
output_dim=input_dim,
)
def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:
"""
Reparameterization trick for backprop through sampling.
z = mu + std * eps, where eps ~ N(0, I)
Args:
mu: Mean of latent distribution [batch, seq, latent_dim]
logvar: Log variance of latent distribution [batch, seq, latent_dim]
Returns:
Sampled latent [batch, seq, latent_dim]
"""
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
"""
Forward pass through VAE-style manifold projection.
Args:
x: Input features [batch, seq, input_dim]
Returns:
Dict with:
- "latent": sampled latent [batch, seq, latent_dim]
- "mu": mean [batch, seq, latent_dim]
- "logvar": log variance [batch, seq, latent_dim]
- "reconstruction": reconstructed input [batch, seq, input_dim]
- "kl_loss": KL divergence loss scalar
"""
mu, logvar = self.encoder(x)
z = self.reparameterize(mu, logvar)
reconstruction = self.decoder(z)
# KL(q(z|x) || p(z)) = -0.5 * sum(1 + logvar - mu^2 - exp(logvar))
kl_loss = -0.5 * torch.mean(
torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=-1)
)
return {
"latent": z,
"mu": mu,
"logvar": logvar,
"reconstruction": reconstruction,
"kl_loss": kl_loss * self.kl_weight,
}
@classmethod
def from_config(cls, config: Any) -> "ManifoldProjectionLayer":
"""
Create ManifoldProjectionLayer from ModelConfig.
Args:
config: ModelConfig instance with mpl parameters
Returns:
Configured ManifoldProjectionLayer instance
"""
return cls(
input_dim=config.embed_dim,
latent_dim=config.latent_dim,
hidden_dim=config.mpl_hidden,
kl_weight=config.kl_weight,
)
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