import streamlit as st from transformer_lens import HookedTransformer, utils from io import StringIO import sys import torch from functools import partial import plotly.offline as pyo import plotly.graph_objs as go import numpy as np import plotly.express as px import circuitsvis as cv # Little bit of front end for model selector # Radio buttons model_name = st.sidebar.radio("Model (only use patching for\nsmall (<4L) models due to memory limits)", [ "gelu-1l", "gelu-2l", "gelu-3l", "gelu-4l", "attn-only-1l", "attn-only-2l", "attn-only-3l", "attn-only-4l", "solu-1l", "solu-2l", "solu-3l", "solu-4l", "solu-6l", "solu-8l", "solu-10l", "solu-12l", "gpt2-small", "gpt2-medium", #"gpt2-large", #"gpt2-xl", ], index=1) # Backend code model = HookedTransformer.from_pretrained(model_name) def predict_next_token(prompt): logits = model(prompt)[0,-1] answer_index = logits.argmax() answer = model.tokenizer.decode(answer_index) answer = f"|{answer}| (answer by {model.cfg.model_name})" return answer def test_prompt(prompt, answer): output = StringIO() sys.stdout = output utils.test_prompt(prompt, answer, model) output = output.getvalue() return output def compute_residual_stream_patch(clean_prompt=None, answer=None, corrupt_prompt=None, corrupt_answer=None, layers=None): model.reset_hooks() clean_answer_index = model.tokenizer.encode(answer)[0] corrupt_answer_index = model.tokenizer.encode(corrupt_answer)[0] clean_tokens = model.to_str_tokens(clean_prompt) _, corrupt_cache = model.run_with_cache(corrupt_prompt) # Patching function def patch_residual_stream(activations, hook, layer="blocks.6.hook_resid_post", pos=5): activations[:, pos, :] = corrupt_cache[layer][:, pos, :] return activations # Compute logit diffs n_layers = len(layers) n_pos = len(clean_tokens) patching_effect = torch.zeros(n_layers, n_pos) for l, layer in enumerate(layers): for pos in range(n_pos): fwd_hooks = [(layer, partial(patch_residual_stream, layer=layer, pos=pos))] prediction_logits = model.run_with_hooks(clean_prompt, fwd_hooks=fwd_hooks)[0, -1] patching_effect[l, pos] = prediction_logits[clean_answer_index] - prediction_logits[corrupt_answer_index] return patching_effect def compute_attn_patch(clean_prompt=None, answer=None, corrupt_prompt=None, corrupt_answer=None): use_attn_result_prev = model.cfg.use_attn_result model.cfg.use_attn_result = True clean_answer_index = model.tokenizer.encode(answer)[0] corrupt_answer_index = model.tokenizer.encode(corrupt_answer)[0] clean_tokens = model.to_str_tokens(clean_prompt) _, corrupt_cache = model.run_with_cache(corrupt_prompt) # Patching function def patch_head_result(activations, hook, head=None, pos=None): activations[:, pos, head, :] = corrupt_cache[hook.name][:, pos, head, :] return activations n_layers = model.cfg.n_layers n_heads = model.cfg.n_heads n_pos = len(clean_tokens) patching_effect = torch.zeros(n_layers*n_heads, n_pos) for layer in range(n_layers): for head in range(n_heads): for pos in range(n_pos): fwd_hooks = [(f"blocks.{layer}.attn.hook_result", partial(patch_head_result, head=head, pos=pos))] prediction_logits = model.run_with_hooks(clean_prompt, fwd_hooks=fwd_hooks)[0, -1] patching_effect[n_heads*layer+head, pos] = prediction_logits[clean_answer_index] - prediction_logits[corrupt_answer_index] model.cfg.use_attn_result = use_attn_result_prev return patching_effect def imshow(tensor, xlabel="X", ylabel="Y", zlabel=None, xticks=None, yticks=None, c_midpoint=0.0, c_scale="RdBu", **kwargs): tensor = utils.to_numpy(tensor) xticks = [str(x) for x in xticks] yticks = [str(y) for y in yticks] labels = {"x": xlabel, "y": ylabel} if zlabel is not None: labels["color"] = zlabel fig = px.imshow(tensor, x=xticks, y=yticks, labels=labels, color_continuous_midpoint=c_midpoint, color_continuous_scale=c_scale, **kwargs) return fig def plot_residual_stream_patch(clean_prompt=None, answer=None, corrupt_prompt=None, corrupt_answer=None): layers = ["blocks.0.hook_resid_pre", *[f"blocks.{i}.hook_resid_post" for i in range(model.cfg.n_layers)]] clean_tokens = model.to_str_tokens(clean_prompt) token_labels = [f"(pos {i:2}) {t}" for i, t in enumerate(clean_tokens)] patching_effect = compute_residual_stream_patch(clean_prompt=clean_prompt, answer=answer, corrupt_prompt=corrupt_prompt, corrupt_answer=corrupt_answer, layers=layers) fig = imshow(patching_effect, xticks=token_labels, yticks=layers, xlabel="Position", ylabel="Layer", zlabel="Logit Difference", title="Patching residual stream at specific layer and position") return fig def plot_attn_patch(clean_prompt=None, answer=None, corrupt_prompt=None, corrupt_answer=None): clean_tokens = model.to_str_tokens(clean_prompt) n_layers = model.cfg.n_layers n_heads = model.cfg.n_heads layerhead_labels = [f"{l}.{h}" for l in range(n_layers) for h in range(n_heads)] token_labels = [f"(pos {i:2}) {t}" for i, t in enumerate(clean_tokens)] patching_effect = compute_attn_patch(clean_prompt=clean_prompt, answer=answer, corrupt_prompt=corrupt_prompt, corrupt_answer=corrupt_answer) return imshow(patching_effect, xticks=token_labels, yticks=layerhead_labels, xlabel="Position", ylabel="Layer.Head", zlabel="Logit Difference", title=f"Patching attention outputs for specific layer, head, and position", width=600, height=300+200*n_layers) # Frontend code st.title("Simple Trafo Mech Int") st.subheader("Transformer Mechanistic Interpretability") st.markdown("Powered by [TransformerLens](https://github.com/neelnanda-io/TransformerLens/)") st.markdown("For _what_ these plots are, and _why_, see this [tutorial](https://docs.google.com/document/d/1e6cs8d9QNretWvOLsv_KaMp6kSPWpJEW0GWc0nwjqxo/).") # Predict next token st.header("Predict the next token") st.markdown("Just a simple test UI, enter a prompt and the model will predict the next token") prompt_simple = st.text_input("Prompt:", "Today, the weather is", key="prompt_simple") if "prompt_simple_output" not in st.session_state: st.session_state.prompt_simple_output = None if st.button("Run model", key="key_button_prompt_simple"): res = predict_next_token(prompt_simple) st.session_state.prompt_simple_output = res if st.session_state.prompt_simple_output: st.markdown(st.session_state.prompt_simple_output, unsafe_allow_html=True) # Test prompt st.header("Verbose test prompt") st.markdown("Enter a prompt and the correct answer, the model will run the prompt and print the results") prompt = st.text_input("Prompt:", "The most popular programming language is", key="prompt") answer = st.text_input("Answer:", " Java", key="answer") if "test_prompt_output" not in st.session_state: st.session_state.test_prompt_output = None if st.button("Run model", key="key_button_test_prompt"): res = test_prompt(prompt, answer) st.session_state.test_prompt_output = res if st.session_state.test_prompt_output: st.code(st.session_state.test_prompt_output) # Residual stream patching st.header("Residual stream patching") st.markdown("Enter a clean prompt, correct answer, corrupt prompt and corrupt answer, the model will compute the patching effect") default_clean_prompt = "Her name was Alex Hart. Tomorrow at lunch time Alex" default_clean_answer = "Hart" default_corrupt_prompt = "Her name was Alex Carroll. Tomorrow at lunch time Alex" default_corrupt_answer = "Carroll" clean_prompt = st.text_input("Clean Prompt:", default_clean_prompt) clean_answer = st.text_input("Correct Answer:", default_clean_answer) corrupt_prompt = st.text_input("Corrupt Prompt:", default_corrupt_prompt) corrupt_answer = st.text_input("Corrupt Answer:", default_corrupt_answer) if "residual_stream_patch_out" not in st.session_state: st.session_state.residual_stream_patch_out = None if st.button("Run model", key="key_button_residual_stream_patch"): fig = plot_residual_stream_patch(clean_prompt=clean_prompt, answer=clean_answer, corrupt_prompt=corrupt_prompt, corrupt_answer=corrupt_answer) st.session_state.residual_stream_patch_out = fig if st.session_state.residual_stream_patch_out: st.plotly_chart(st.session_state.residual_stream_patch_out) # Attention head output st.header("Attention head output patching") st.markdown("Enter a clean prompt, correct answer, corrupt prompt and corrupt answer, the model will compute the patching effect") clean_prompt_attn = st.text_input("Clean Prompt:", default_clean_prompt, key="key2_clean_prompt_attn") clean_answer_attn = st.text_input("Correct Answer:", default_clean_answer, key="key2_clean_answer_attn") corrupt_prompt_attn = st.text_input("Corrupt Prompt:", default_corrupt_prompt, key="key2_corrupt_prompt_attn") corrupt_answer_attn = st.text_input("Corrupt Answer:", default_corrupt_answer, key="key2_corrupt_answer_attn") if "attn_head_patch_out" not in st.session_state: st.session_state.attn_head_patch_out = None if st.button("Run model", key="key_button_attn_head_patch"): fig = plot_attn_patch(clean_prompt=clean_prompt_attn, answer=clean_answer_attn, corrupt_prompt=corrupt_prompt_attn, corrupt_answer=corrupt_answer_attn) st.session_state.attn_head_patch_out = fig if st.session_state.attn_head_patch_out: st.plotly_chart(st.session_state.attn_head_patch_out) # Attention Head Visualization st.header("Attention Pattern Visualization") st.markdown("Powered by [CircuitsVis](https://github.com/alan-cooney/CircuitsVis)") st.markdown("Enter a prompt, show attention patterns") default_prompt_attn = "Her name was Alex Hart. Tomorrow at lunch time Alex" prompt_attn = st.text_input("Prompt:", default_prompt_attn) if "attn_html" not in st.session_state: st.session_state.attn_html = None if st.button("Run model", key="key_button_attention_head"): _, cache = model.run_with_cache(prompt_attn) st.session_state.attn_html = [] for layer in range(model.cfg.n_layers): html = cv.attention.attention_patterns(tokens=model.to_str_tokens(prompt_attn), attention=cache[f'blocks.{layer}.attn.hook_pattern'][0]) st.session_state.attn_html.append(html.show_code()) if st.session_state.attn_html: for layer in range(len(st.session_state.attn_html)): st.write(f"Attention patterns Layer {layer}:") st.components.v1.html(st.session_state.attn_html[layer], height=500)