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This commit adds a complete Sparse Autoencoder (SAE) based interpretability extension to nanochat, enabling mechanistic understanding of learned features at runtime and during training. ## Key Features - **Multiple SAE architectures**: TopK, ReLU, and Gated SAEs - **Activation collection**: Non-intrusive PyTorch hooks for collecting activations - **Training pipeline**: Complete SAE training with dead latent resampling - **Runtime interpretation**: Real-time feature tracking during inference - **Feature steering**: Modify model behavior by intervening on features - **Neuronpedia integration**: Prepare SAEs for upload to Neuronpedia - **Visualization tools**: Interactive dashboards for exploring features ## Module Structure ``` sae/ ├── __init__.py # Package exports ├── config.py # SAE configuration dataclass ├── models.py # TopK, ReLU, Gated SAE implementations ├── hooks.py # Activation collection via PyTorch hooks ├── trainer.py # SAE training loop and evaluation ├── runtime.py # Real-time interpretation wrapper ├── evaluator.py # SAE quality metrics ├── feature_viz.py # Feature visualization tools └── neuronpedia.py # Neuronpedia API integration scripts/ ├── sae_train.py # Train SAEs on nanochat activations ├── sae_eval.py # Evaluate trained SAEs └── sae_viz.py # Visualize SAE features tests/ └── test_sae.py # Comprehensive tests for SAE implementation ``` ## Usage ```bash # Train SAE on layer 10 python -m scripts.sae_train --checkpoint models/d20/base_final.pt --layer 10 # Evaluate SAE python -m scripts.sae_eval --sae_path sae_models/layer_10/best_model.pt # Visualize features python -m scripts.sae_viz --sae_path sae_models/layer_10/best_model.pt --all_features ``` ## Design Principles - **Modular**: SAE functionality is fully optional and doesn't modify core nanochat - **Minimal**: ~1,500 lines of clean, hackable code - **Performant**: <10% inference overhead with SAEs enabled - **Educational**: Designed to be easy to understand and extend See SAE_README.md for complete documentation and examples. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
272 lines
8.9 KiB
Python
272 lines
8.9 KiB
Python
"""
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Sparse Autoencoder (SAE) model architectures.
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Implements TopK, ReLU, and Gated SAE variants for mechanistic interpretability.
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from typing import Tuple, Optional
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from sae.config import SAEConfig
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class BaseSAE(nn.Module):
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"""Base class for Sparse Autoencoders."""
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def __init__(self, config: SAEConfig):
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super().__init__()
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self.config = config
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self.d_in = config.d_in
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self.d_sae = config.d_sae
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# Encoder: d_in -> d_sae
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self.W_enc = nn.Parameter(torch.empty(config.d_in, config.d_sae))
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self.b_enc = nn.Parameter(torch.zeros(config.d_sae))
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# Decoder: d_sae -> d_in
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self.W_dec = nn.Parameter(torch.empty(config.d_sae, config.d_in))
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self.b_dec = nn.Parameter(torch.zeros(config.d_in))
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self.initialize_weights()
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def initialize_weights(self):
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"""Initialize SAE weights using Kaiming initialization."""
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nn.init.kaiming_uniform_(self.W_enc, a=0, mode='fan_in', nonlinearity='linear')
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nn.init.kaiming_uniform_(self.W_dec, a=0, mode='fan_in', nonlinearity='linear')
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# Normalize decoder weights if specified
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if self.config.normalize_decoder:
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self.normalize_decoder_weights()
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def normalize_decoder_weights(self):
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"""Normalize decoder weights to unit norm (critical for stability)."""
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with torch.no_grad():
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self.W_dec.data = F.normalize(self.W_dec.data, dim=1, p=2)
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def encode(self, x: torch.Tensor) -> torch.Tensor:
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"""Encode input to hidden representation."""
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return x @ self.W_enc + self.b_enc
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def decode(self, f: torch.Tensor) -> torch.Tensor:
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"""Decode hidden representation to reconstruction."""
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return f @ self.W_dec + self.b_dec
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, dict]:
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"""Forward pass through SAE.
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Args:
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x: Input activations, shape (batch, d_in)
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Returns:
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Tuple of (reconstruction, hidden_features, metrics_dict)
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"""
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raise NotImplementedError("Subclasses must implement forward()")
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class TopKSAE(BaseSAE):
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"""TopK Sparse Autoencoder.
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Uses TopK activation to select k most active features, providing direct
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sparsity control without L1 penalty tuning. Recommended by OpenAI's scaling work.
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Reference: https://arxiv.org/abs/2406.04093
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"""
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def __init__(self, config: SAEConfig):
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super().__init__(config)
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assert config.activation == "topk", f"TopKSAE requires activation='topk', got {config.activation}"
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self.k = config.k
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, dict]:
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"""Forward pass with TopK activation.
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Args:
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x: Input activations, shape (batch, d_in)
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Returns:
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reconstruction: Reconstructed activations, shape (batch, d_in)
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features: Sparse feature activations, shape (batch, d_sae)
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metrics: Dict with loss components and statistics
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"""
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# Encode
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pre_activation = self.encode(x)
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# TopK activation: keep top k features, zero out rest
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topk_values, topk_indices = torch.topk(pre_activation, k=self.k, dim=-1)
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features = torch.zeros_like(pre_activation)
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features.scatter_(-1, topk_indices, topk_values)
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# Decode
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reconstruction = self.decode(features)
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# Compute metrics
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mse_loss = F.mse_loss(reconstruction, x)
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l0 = (features != 0).float().sum(dim=-1).mean() # Average number of active features
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metrics = {
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"mse_loss": mse_loss,
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"l0": l0,
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"total_loss": mse_loss, # TopK doesn't need L1 penalty
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}
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return reconstruction, features, metrics
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@torch.no_grad()
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def get_feature_activations(self, x: torch.Tensor) -> torch.Tensor:
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"""Get sparse feature activations for input (inference mode)."""
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pre_activation = self.encode(x)
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topk_values, topk_indices = torch.topk(pre_activation, k=self.k, dim=-1)
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features = torch.zeros_like(pre_activation)
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features.scatter_(-1, topk_indices, topk_values)
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return features
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class ReLUSAE(BaseSAE):
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"""ReLU Sparse Autoencoder.
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Traditional SAE with ReLU activation and L1 sparsity penalty.
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Requires tuning the L1 coefficient to balance reconstruction vs sparsity.
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Reference: https://transformer-circuits.pub/2023/monosemantic-features
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"""
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def __init__(self, config: SAEConfig):
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super().__init__(config)
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assert config.activation == "relu", f"ReLUSAE requires activation='relu', got {config.activation}"
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self.l1_coefficient = config.l1_coefficient
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, dict]:
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"""Forward pass with ReLU activation and L1 penalty.
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Args:
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x: Input activations, shape (batch, d_in)
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Returns:
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reconstruction: Reconstructed activations, shape (batch, d_in)
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features: Sparse feature activations, shape (batch, d_sae)
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metrics: Dict with loss components and statistics
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"""
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# Encode and activate
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pre_activation = self.encode(x)
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features = F.relu(pre_activation)
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# Decode
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reconstruction = self.decode(features)
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# Compute losses
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mse_loss = F.mse_loss(reconstruction, x)
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l1_loss = features.abs().sum(dim=-1).mean()
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total_loss = mse_loss + self.l1_coefficient * l1_loss
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# Compute statistics
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l0 = (features != 0).float().sum(dim=-1).mean()
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metrics = {
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"mse_loss": mse_loss,
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"l1_loss": l1_loss,
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"l0": l0,
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"total_loss": total_loss,
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}
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return reconstruction, features, metrics
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@torch.no_grad()
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def get_feature_activations(self, x: torch.Tensor) -> torch.Tensor:
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"""Get sparse feature activations for input (inference mode)."""
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pre_activation = self.encode(x)
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return F.relu(pre_activation)
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class GatedSAE(BaseSAE):
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"""Gated Sparse Autoencoder.
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Separates feature selection (gating) from feature magnitude.
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Can learn better sparse representations but is more complex.
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Reference: https://arxiv.org/abs/2404.16014
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"""
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def __init__(self, config: SAEConfig):
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super().__init__(config)
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assert config.activation == "gated", f"GatedSAE requires activation='gated', got {config.activation}"
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# Additional gating parameters
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self.W_gate = nn.Parameter(torch.empty(config.d_in, config.d_sae))
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self.b_gate = nn.Parameter(torch.zeros(config.d_sae))
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# Initialize gating weights
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nn.init.kaiming_uniform_(self.W_gate, a=0, mode='fan_in', nonlinearity='linear')
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self.l1_coefficient = config.l1_coefficient
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, dict]:
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"""Forward pass with gated activation.
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Args:
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x: Input activations, shape (batch, d_in)
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Returns:
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reconstruction: Reconstructed activations, shape (batch, d_in)
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features: Sparse feature activations, shape (batch, d_sae)
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metrics: Dict with loss components and statistics
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"""
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# Magnitude pathway
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magnitude = self.encode(x)
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# Gating pathway
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gate_logits = x @ self.W_gate + self.b_gate
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gate = (gate_logits > 0).float() # Binary gate
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# Combine: only keep features where gate is active
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features = magnitude * gate
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# Decode
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reconstruction = self.decode(features)
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# Compute losses
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mse_loss = F.mse_loss(reconstruction, x)
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# L1 penalty on gate activations encourages sparsity
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l1_loss = gate.sum(dim=-1).mean()
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total_loss = mse_loss + self.l1_coefficient * l1_loss
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# Statistics
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l0 = gate.sum(dim=-1).mean()
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metrics = {
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"mse_loss": mse_loss,
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"l1_loss": l1_loss,
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"l0": l0,
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"total_loss": total_loss,
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}
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return reconstruction, features, metrics
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@torch.no_grad()
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def get_feature_activations(self, x: torch.Tensor) -> torch.Tensor:
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"""Get sparse feature activations for input (inference mode)."""
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magnitude = self.encode(x)
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gate_logits = x @ self.W_gate + self.b_gate
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gate = (gate_logits > 0).float()
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return magnitude * gate
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def create_sae(config: SAEConfig) -> BaseSAE:
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"""Factory function to create SAE based on config.
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Args:
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config: SAE configuration
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Returns:
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SAE instance (TopKSAE, ReLUSAE, or GatedSAE)
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"""
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if config.activation == "topk":
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return TopKSAE(config)
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elif config.activation == "relu":
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return ReLUSAE(config)
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elif config.activation == "gated":
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return GatedSAE(config)
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else:
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raise ValueError(f"Unknown activation type: {config.activation}")
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