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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>
397 lines
12 KiB
Python
397 lines
12 KiB
Python
"""
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SAE training and evaluation.
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This module provides a trainer for Sparse Autoencoders with support for:
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- Dead latent resampling
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- Learning rate warmup and decay
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- Evaluation metrics
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- Checkpointing
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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 torch.utils.data import DataLoader, TensorDataset
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from pathlib import Path
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from typing import Dict, Optional, List, Tuple
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from tqdm import tqdm
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import json
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from sae.config import SAEConfig
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from sae.models import BaseSAE, create_sae
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from sae.evaluator import SAEEvaluator
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class SAETrainer:
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"""Trainer for Sparse Autoencoders.
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Handles training loop, optimization, dead latent resampling, and checkpointing.
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"""
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def __init__(
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self,
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sae: BaseSAE,
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config: SAEConfig,
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activations: torch.Tensor,
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val_activations: Optional[torch.Tensor] = None,
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device: str = "cuda",
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save_dir: Optional[Path] = None,
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):
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"""Initialize SAE trainer.
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Args:
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sae: SAE model to train
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config: SAE configuration
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activations: Training activations, shape (num_activations, d_in)
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val_activations: Optional validation activations
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device: Device to train on
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save_dir: Directory to save checkpoints and logs
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"""
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self.sae = sae.to(device)
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self.config = config
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self.device = device
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self.save_dir = Path(save_dir) if save_dir else None
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# Create dataloaders
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self.train_loader = self._create_dataloader(activations, shuffle=True)
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self.val_loader = None
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if val_activations is not None:
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self.val_loader = self._create_dataloader(val_activations, shuffle=False)
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# Setup optimizer
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self.optimizer = torch.optim.Adam(
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sae.parameters(),
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lr=config.learning_rate,
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betas=(0.9, 0.999),
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)
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# Learning rate scheduler with warmup
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self.scheduler = self._create_scheduler()
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# Evaluator
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self.evaluator = SAEEvaluator(sae, config)
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# Training state
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self.step = 0
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self.epoch = 0
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self.best_val_loss = float('inf')
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# Statistics for dead latent detection
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self.feature_counts = torch.zeros(config.d_sae, device=device)
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# Logging
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self.train_losses = []
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self.val_losses = []
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def _create_dataloader(self, activations: torch.Tensor, shuffle: bool) -> DataLoader:
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"""Create dataloader from activations."""
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dataset = TensorDataset(activations)
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return DataLoader(
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dataset,
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batch_size=self.config.batch_size,
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shuffle=shuffle,
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num_workers=0, # Keep simple for now
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pin_memory=True if self.device == "cuda" else False,
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)
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def _create_scheduler(self):
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"""Create learning rate scheduler with warmup."""
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def lr_lambda(step):
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if step < self.config.warmup_steps:
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return step / self.config.warmup_steps
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return 1.0
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return torch.optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda)
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def train_epoch(self, verbose: bool = True) -> Dict[str, float]:
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"""Train for one epoch.
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Args:
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verbose: Whether to show progress bar
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Returns:
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Dictionary of average training metrics
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"""
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self.sae.train()
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epoch_metrics = {
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"mse_loss": 0.0,
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"l0": 0.0,
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"total_loss": 0.0,
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}
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num_batches = 0
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iterator = tqdm(self.train_loader, desc=f"Epoch {self.epoch}") if verbose else self.train_loader
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for batch in iterator:
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x = batch[0].to(self.device)
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# Forward pass
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reconstruction, features, metrics = self.sae(x)
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# Backward pass
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loss = metrics["total_loss"]
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self.optimizer.zero_grad()
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loss.backward()
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self.optimizer.step()
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self.scheduler.step()
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# Normalize decoder weights if configured
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if self.config.normalize_decoder:
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self.sae.normalize_decoder_weights()
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# Update feature counts for dead latent detection
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with torch.no_grad():
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active_features = (features != 0).float().sum(dim=0)
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self.feature_counts += active_features
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# Log metrics
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for key in epoch_metrics:
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if key in metrics:
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epoch_metrics[key] += metrics[key].item()
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num_batches += 1
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# Update progress bar
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if verbose:
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iterator.set_postfix({
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"loss": f"{metrics['total_loss'].item():.4f}",
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"l0": f"{metrics['l0'].item():.1f}",
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})
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# Periodic evaluation and checkpointing
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self.step += 1
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if self.step % self.config.eval_every == 0:
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self._evaluate_and_log()
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if self.step % self.config.save_every == 0:
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self._save_checkpoint()
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# Dead latent resampling
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if self.step % self.config.resample_interval == 0:
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self._resample_dead_latents()
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# Average metrics
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for key in epoch_metrics:
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epoch_metrics[key] /= num_batches
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self.epoch += 1
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return epoch_metrics
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@torch.no_grad()
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def evaluate(self) -> Dict[str, float]:
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"""Evaluate SAE on validation set.
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Returns:
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Dictionary of validation metrics
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"""
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if self.val_loader is None:
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return {}
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self.sae.eval()
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val_metrics = {
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"mse_loss": 0.0,
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"l0": 0.0,
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"total_loss": 0.0,
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}
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num_batches = 0
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for batch in self.val_loader:
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x = batch[0].to(self.device)
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reconstruction, features, metrics = self.sae(x)
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for key in val_metrics:
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if key in metrics:
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val_metrics[key] += metrics[key].item()
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num_batches += 1
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# Average metrics
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for key in val_metrics:
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val_metrics[key] /= num_batches
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return val_metrics
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def _evaluate_and_log(self):
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"""Evaluate and log metrics."""
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val_metrics = self.evaluate()
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if val_metrics:
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print(f"\nStep {self.step} - Validation metrics:")
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for key, value in val_metrics.items():
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print(f" {key}: {value:.4f}")
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# Track best model
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if val_metrics["total_loss"] < self.best_val_loss:
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self.best_val_loss = val_metrics["total_loss"]
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self._save_checkpoint(is_best=True)
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self.val_losses.append(val_metrics)
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def _save_checkpoint(self, is_best: bool = False):
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"""Save model checkpoint.
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Args:
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is_best: Whether this is the best model so far
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"""
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if self.save_dir is None:
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return
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self.save_dir.mkdir(parents=True, exist_ok=True)
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# Save model state
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checkpoint = {
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"step": self.step,
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"epoch": self.epoch,
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"sae_state_dict": self.sae.state_dict(),
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"optimizer_state_dict": self.optimizer.state_dict(),
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"scheduler_state_dict": self.scheduler.state_dict(),
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"config": self.config.to_dict(),
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"feature_counts": self.feature_counts.cpu(),
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"best_val_loss": self.best_val_loss,
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}
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# Save checkpoint
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checkpoint_path = self.save_dir / f"checkpoint_step{self.step}.pt"
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torch.save(checkpoint, checkpoint_path)
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# Save as best if applicable
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if is_best:
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best_path = self.save_dir / "best_model.pt"
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torch.save(checkpoint, best_path)
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print(f"Saved best model to {best_path}")
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# Save config as JSON for easy inspection
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config_path = self.save_dir / "config.json"
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with open(config_path, "w") as f:
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json.dump(self.config.to_dict(), f, indent=2)
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def _resample_dead_latents(self):
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"""Resample dead latents that rarely activate.
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Dead latents are features that activate on fewer than a threshold fraction
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of training examples. We resample them by reinitializing their weights.
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"""
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# Calculate activation frequency
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total_samples = self.step * self.config.batch_size
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activation_freq = self.feature_counts / total_samples
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# Find dead latents
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dead_mask = activation_freq < self.config.dead_latent_threshold
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num_dead = dead_mask.sum().item()
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if num_dead > 0:
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print(f"\nResampling {num_dead} dead latents (threshold: {self.config.dead_latent_threshold})")
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# Reinitialize dead latent weights
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with torch.no_grad():
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# Sample from active latents with high loss
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# For simplicity, just reinitialize randomly
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dead_indices = torch.where(dead_mask)[0]
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for idx in dead_indices:
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# Reinitialize encoder weights
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nn.init.kaiming_uniform_(self.sae.W_enc[:, idx].unsqueeze(1))
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self.sae.b_enc[idx] = 0.0
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# Reinitialize decoder weights
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nn.init.kaiming_uniform_(self.sae.W_dec[idx].unsqueeze(0))
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# Normalize decoder if configured
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if self.config.normalize_decoder:
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self.sae.normalize_decoder_weights()
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# Reset feature counts for resampled latents
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self.feature_counts[dead_mask] = 0
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def train(self, num_epochs: Optional[int] = None, verbose: bool = True):
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"""Train SAE for specified number of epochs.
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Args:
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num_epochs: Number of epochs to train. If None, uses config.num_epochs
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verbose: Whether to show progress bars
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"""
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num_epochs = num_epochs or self.config.num_epochs
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for _ in range(num_epochs):
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epoch_metrics = self.train_epoch(verbose=verbose)
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if verbose:
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print(f"Epoch {self.epoch - 1} summary:")
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for key, value in epoch_metrics.items():
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print(f" {key}: {value:.4f}")
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self.train_losses.append(epoch_metrics)
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# Final evaluation and checkpoint
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self._evaluate_and_log()
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self._save_checkpoint()
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print(f"\nTraining complete! Best validation loss: {self.best_val_loss:.4f}")
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def load_checkpoint(self, checkpoint_path: Path):
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"""Load checkpoint and resume training.
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Args:
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checkpoint_path: Path to checkpoint file
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"""
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checkpoint = torch.load(checkpoint_path, map_location=self.device)
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self.sae.load_state_dict(checkpoint["sae_state_dict"])
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self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
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self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
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self.step = checkpoint["step"]
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self.epoch = checkpoint["epoch"]
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self.feature_counts = checkpoint["feature_counts"].to(self.device)
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self.best_val_loss = checkpoint.get("best_val_loss", float('inf'))
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print(f"Loaded checkpoint from step {self.step}, epoch {self.epoch}")
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def train_sae_from_activations(
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activations: torch.Tensor,
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config: SAEConfig,
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val_split: float = 0.1,
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device: str = "cuda",
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save_dir: Optional[Path] = None,
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verbose: bool = True,
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) -> Tuple[BaseSAE, SAETrainer]:
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"""Train an SAE from activations.
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Convenience function that creates an SAE, splits data, and trains.
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Args:
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activations: Training activations, shape (num_activations, d_in)
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config: SAE configuration
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val_split: Fraction of data to use for validation
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device: Device to train on
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save_dir: Directory to save checkpoints
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verbose: Whether to show progress
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Returns:
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Tuple of (trained_sae, trainer)
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"""
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# Split data into train/val
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num_val = int(len(activations) * val_split)
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indices = torch.randperm(len(activations))
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train_activations = activations[indices[num_val:]]
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val_activations = activations[indices[:num_val]] if num_val > 0 else None
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print(f"Training on {len(train_activations)} activations, validating on {num_val}")
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# Create SAE
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sae = create_sae(config)
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# Create trainer
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trainer = SAETrainer(
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sae=sae,
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config=config,
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activations=train_activations,
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val_activations=val_activations,
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device=device,
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save_dir=save_dir,
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)
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# Train
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trainer.train(verbose=verbose)
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return sae, trainer
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