Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
TransformerLens: Mechanistic Interpretability for Transformers
TransformerLens is the de facto standard library for mechanistic interpretability research on GPT-style language models. Created by Neel Nanda and maintained by Bryce Meyer, it provides clean interfaces to inspect and manipulate model internals via HookPoints on every activation.
The main class that wraps transformer models with HookPoints on every activation:
from transformer_lens import HookedTransformer
# Load a model
model = HookedTransformer.from_pretrained("gpt2-small")
# For gated models (LLaMA, Mistral)import os
os.environ["HF_TOKEN"] = "your_token"
model = HookedTransformer.from_pretrained("meta-llama/Llama-2-7b-hf")
Supported Models (50+)
Family
Models
GPT-2
gpt2, gpt2-medium, gpt2-large, gpt2-xl
LLaMA
llama-7b, llama-13b, llama-2-7b, llama-2-13b
EleutherAI
pythia-70m to pythia-12b, gpt-neo, gpt-j-6b
Mistral
mistral-7b, mixtral-8x7b
Others
phi, qwen, opt, gemma
Activation Caching
Run the model and cache all intermediate activations:
# Get all activations
tokens = model.to_tokens("The Eiffel Tower is in")
logits, cache = model.run_with_cache(tokens)
# Access specific activations
residual = cache["resid_post", 5] # Layer 5 residual stream
attn_pattern = cache["pattern", 3] # Layer 3 attention pattern
mlp_out = cache["mlp_out", 7] # Layer 7 MLP output# Filter which activations to cache (saves memory)
logits, cache = model.run_with_cache(
tokens,
names_filter=lambda name: "resid_post"in name
)
ActivationCache Keys
Key Pattern
Shape
Description
resid_pre, layer
[batch, pos, d_model]
Residual before attention
resid_mid, layer
[batch, pos, d_model]
Residual after attention
resid_post, layer
[batch, pos, d_model]
Residual after MLP
attn_out, layer
[batch, pos, d_model]
Attention output
mlp_out, layer
[batch, pos, d_model]
MLP output
pattern, layer
[batch, head, q_pos, k_pos]
Attention pattern (post-softmax)
q, layer
[batch, pos, head, d_head]
Query vectors
k, layer
[batch, pos, head, d_head]
Key vectors
v, layer
[batch, pos, head, d_head]
Value vectors
Workflow 1: Activation Patching (Causal Tracing)
Identify which activations causally affect model output by patching clean activations into corrupted runs.
Step-by-Step
from transformer_lens import HookedTransformer, patching
import torch
model = HookedTransformer.from_pretrained("gpt2-small")
# 1. Define clean and corrupted prompts
clean_prompt = "The Eiffel Tower is in the city of"
corrupted_prompt = "The Colosseum is in the city of"
clean_tokens = model.to_tokens(clean_prompt)
corrupted_tokens = model.to_tokens(corrupted_prompt)
# 2. Get clean activations
_, clean_cache = model.run_with_cache(clean_tokens)
# 3. Define metric (e.g., logit difference)
paris_token = model.to_single_token(" Paris")
rome_token = model.to_single_token(" Rome")
defmetric(logits):
return logits[0, -1, paris_token] - logits[0, -1, rome_token]
# 4. Patch each position and layer
results = torch.zeros(model.cfg.n_layers, clean_tokens.shape[1])
for layer inrange(model.cfg.n_layers):
for pos inrange(clean_tokens.shape[1]):
defpatch_hook(activation, hook):
activation[0, pos] = clean_cache[hook.name][0, pos]
return activation
patched_logits = model.run_with_hooks(
corrupted_tokens,
fwd_hooks=[(f"blocks.{layer}.hook_resid_post", patch_hook)]
)
results[layer, pos] = metric(patched_logits)
# 5. Visualize results (layer x position heatmap)
Checklist
Define clean and corrupted inputs that differ minimally
Choose metric that captures behavior difference
Cache clean activations
Systematically patch each (layer, position) combination
Replicate the IOI circuit discovery from "Interpretability in the Wild".
Step-by-Step
from transformer_lens import HookedTransformer
import torch
model = HookedTransformer.from_pretrained("gpt2-small")
# IOI task: "When John and Mary went to the store, Mary gave a bottle to"# Model should predict "John" (indirect object)
prompt = "When John and Mary went to the store, Mary gave a bottle to"
tokens = model.to_tokens(prompt)
# 1. Get baseline logits
logits, cache = model.run_with_cache(tokens)
john_token = model.to_single_token(" John")
mary_token = model.to_single_token(" Mary")
# 2. Compute logit difference (IO - S)
logit_diff = logits[0, -1, john_token] - logits[0, -1, mary_token]
print(f"Logit difference: {logit_diff.item():.3f}")
# 3. Direct logit attribution by headdefget_head_contribution(layer, head):
# Project head output to logits
head_out = cache["z", layer][0, :, head, :] # [pos, d_head]
W_O = model.W_O[layer, head] # [d_head, d_model]
W_U = model.W_U # [d_model, vocab]# Head contribution to logits at final position
contribution = head_out[-1] @ W_O @ W_U
return contribution[john_token] - contribution[mary_token]
# 4. Map all heads
head_contributions = torch.zeros(model.cfg.n_layers, model.cfg.n_heads)
for layer inrange(model.cfg.n_layers):
for head inrange(model.cfg.n_heads):
head_contributions[layer, head] = get_head_contribution(layer, head)
# 5. Identify top contributing heads (name movers, backup name movers)
Find induction heads that implement [A][B]...[A] → [B] pattern.
from transformer_lens import HookedTransformer
import torch
model = HookedTransformer.from_pretrained("gpt2-small")
# Create repeated sequence: [A][B][A] should predict [B]
repeated_tokens = torch.tensor([[1000, 2000, 1000]]) # Arbitrary tokens
_, cache = model.run_with_cache(repeated_tokens)
# Induction heads attend from final [A] back to first [B]# Check attention from position 2 to position 1
induction_scores = torch.zeros(model.cfg.n_layers, model.cfg.n_heads)
for layer inrange(model.cfg.n_layers):
pattern = cache["pattern", layer][0] # [head, q_pos, k_pos]# Attention from pos 2 to pos 1
induction_scores[layer] = pattern[:, 2, 1]
# Heads with high scores are induction heads
top_heads = torch.topk(induction_scores.flatten(), k=5)
Common Issues & Solutions
Issue: Hooks persist after debugging
# WRONG: Old hooks remain active
model.run_with_hooks(tokens, fwd_hooks=[...]) # Debug, add new hooks
model.run_with_hooks(tokens, fwd_hooks=[...]) # Old hooks still there!# RIGHT: Always reset hooks
model.reset_hooks()
model.run_with_hooks(tokens, fwd_hooks=[...])
# Use selective caching
logits, cache = model.run_with_cache(
tokens,
names_filter=lambda n: "resid_post"in n or"pattern"in n,
device="cpu"# Cache on CPU
)
Key Classes Reference
Class
Purpose
HookedTransformer
Main model wrapper with hooks
ActivationCache
Dictionary-like cache of activations
HookedTransformerConfig
Model configuration
FactoredMatrix
Efficient factored matrix operations
Integration with SAELens
TransformerLens integrates with SAELens for Sparse Autoencoder analysis:
from transformer_lens import HookedTransformer
from sae_lens import SAE
model = HookedTransformer.from_pretrained("gpt2-small")
sae = SAE.from_pretrained("gpt2-small-res-jb", "blocks.8.hook_resid_pre")
# Run with SAE
tokens = model.to_tokens("Hello world")
_, cache = model.run_with_cache(tokens)
sae_acts = sae.encode(cache["resid_pre", 8])
Reference Documentation
For detailed API documentation, tutorials, and advanced usage, see the references/ folder:
