jasonlim131/sae_gpt2_small

Replication of dictionary learning in gpt2-small

Sparse autoencoder for GPT2 small

This repository hosts a sparse autoencoder trained on the GPT2-small model's activations. The autoencoder's purpose is to expand the MLP layer activations into a larger number of dimensions, providing an overcomplete basis of the MLP activation space. The learned dimensions have been shown to be more interpretable than the original MLP dimensions.

Install

pip install git+https://github.com/openai/sparse_autoencoder.git

Example usage

import torch
import blobfile as bf
import transformer_lens
import sparse_autoencoder

# Load the autoencoder
layer_index = 0  # in range(12)
autoencoder_input = ["mlp_post_act", "resid_delta_mlp"][0]
filename = f"az://openaipublic/sparse-autoencoder/gpt2-small/{autoencoder_input}/autoencoders/{layer_index}.pt"
with bf.BlobFile(filename, mode="rb") as f:
    state_dict = torch.load(f)
    autoencoder = sparse_autoencoder.Autoencoder.from_state_dict(state_dict)

# Extract neuron activations with transformer_lens
model = transformer_lens.HookedTransformer.from_pretrained("gpt2", center_writing_weights=False)
prompt = "This is an example of a prompt that"
tokens = model.to_tokens(prompt)  # (1, n_tokens)
print(model.to_str_tokens(tokens))
with torch.no_grad():
    logits, activation_cache = model.run_with_cache(tokens, remove_batch_dim=True)
if autoencoder_input == "mlp_post_act":
    input_tensor = activation_cache[f"blocks.{layer_index}.mlp.hook_post"]  # (n_tokens, n_neurons)
elif autoencoder_input == "resid_delta_mlp":
    input_tensor = activation_cache[f"blocks.{layer_index}.hook_mlp_out"]  # (n_tokens, n_residual_channels)

# Encode neuron activations with the autoencoder
device = next(model.parameters()).device
autoencoder.to(device)
with torch.no_grad():
    latent_activations = autoencoder.encode(input_tensor)  # (n_tokens, n_latents)

Autoencoder settings

• Model used: "gpt2-small", 12 layers
• Autoencoder architecture: see model.py
• Autoencoder input: "mlp_post_act" (3072 dimensions) or "resid_delta_mlp" (768 dimensions)
• Number of autoencoder latents: 32768
• Loss function: see loss.py
• Number of training tokens: ~64M
• L1 regularization strength: 0.01

Data files

• autoencoder_input is in ["mlp_post_act", "resid_delta_mlp"]
• layer_index is in range(12) (GPT2-small)
• latent_index is in range(32768)

Autoencoder files: az://openaipublic/sparse-autoencoder/gpt2-small/{autoencoder_input}/autoencoders/{layer_index}.pt

NeuronRecord files: az://openaipublic/sparse-autoencoder/gpt2-small/{autoencoder_input}/collated_activations/{layer_index}/{latent_index}.json