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a80a585 | 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 | from __future__ import annotations
import torch
from torch import nn
class TopKSparseAutoencoder(nn.Module):
def __init__(
self, input_dim: int = 96, features: int = 384, top_k: int = 16
) -> None:
super().__init__()
self.input_dim = input_dim
self.features = features
self.top_k = top_k
self.encoder = nn.Linear(input_dim, features)
self.decoder = nn.Linear(features, input_dim, bias=False)
nn.init.normal_(self.decoder.weight, std=0.05)
self.normalize_dictionary()
@torch.no_grad()
def normalize_dictionary(self) -> None:
norms = self.decoder.weight.norm(dim=0, keepdim=True).clamp_min(1e-8)
self.decoder.weight.div_(norms)
def encode(self, activations: torch.Tensor) -> torch.Tensor:
preactivations = self.encoder(activations)
values, indices = torch.topk(
preactivations, self.top_k, dim=1
)
values = torch.relu(values)
sparse = torch.zeros_like(preactivations)
return sparse.scatter(1, indices, values)
def forward(
self, activations: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
features = self.encode(activations)
return self.decoder(features), features
def parameter_count(module: nn.Module) -> int:
return sum(parameter.numel() for parameter in module.parameters())
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