Spaces:
Sleeping
Sleeping
File size: 4,528 Bytes
b7e1c46 0f64adf b7e1c46 0f64adf b7e1c46 9e67a8e b7e1c46 0f64adf b7e1c46 af9dbe3 b7e1c46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import pandas as pd
from functools import lru_cache
# ----------------------------------------------------------------------
# IMPORTANT: This version uses the PatchscopesRetriever implementation
# from the Tokens2Words paper (https://github.com/schwartz-lab-NLP/Tokens2Words)
# ----------------------------------------------------------------------
try:
from inner_lexicon.word_retriever import PatchscopesRetriever # pip install tokens2words
except ImportError:
PatchscopesRetriever = None
DEFAULT_MODEL = "meta-llama/Llama-3.1-8B" # light default so the demo boots everywhere
DEVICE = (
"cuda" if torch.cuda.is_available() else 'cpu'
)
@lru_cache(maxsize=4)
def get_model_and_tokenizer(model_name: str):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16 ,
output_hidden_states=True,
).to(DEVICE)
model.eval()
return model, tokenizer
def find_last_token_index(full_ids, word_ids):
"""Locate end position of word_ids inside full_ids (first match)."""
for i in range(len(full_ids) - len(word_ids) + 1):
if full_ids[i : i + len(word_ids)] == word_ids:
return i + len(word_ids) - 1
return None
def analyse_word(model_name: str, extraction_template: str, word: str, patchscopes_template: str):
if PatchscopesRetriever is None:
return (
"<p style='color:red'>❌ Patchscopes library not found. Run:<br/>"
"<code>pip install git+https://github.com/schwartz-lab-NLP/Tokens2Words</code></p>"
)
model, tokenizer = get_model_and_tokenizer(model_name)
# Build extraction prompt (where hidden states will be collected)
extraction_prompt ="X"
# Identify last token position of the *word* inside the prompt IDs
word_token_ids = tokenizer.encode(word, add_special_tokens=False)
# Instantiate Patchscopes retriever
patch_retriever = PatchscopesRetriever(
model,
tokenizer,
extraction_prompt,
patchscopes_template,
prompt_target_placeholder="X",
)
# Run retrieval for the word across all layers (one pass)
retrieved_words = patch_retriever.get_hidden_states_and_retrieve_word(
word,
num_tokens_to_generate=len(tokenizer.tokenize(word)),
)[0]
# Build a table summarising which layers match
records = []
matches = 0
for layer_idx, ret_word in enumerate(retrieved_words):
match = ret_word.strip(" ") == word.strip(" ")
if match:
matches += 1
records.append({"Layer": layer_idx, "Retrieved": ret_word, "Match?": "✓" if match else ""})
df = pd.DataFrame(records)
def _style(row):
color = "background-color: lightgreen" if row["Match?"] else ""
return [color] * len(row)
html_table = df.style.apply(_style, axis=1).hide(axis="index").to_html(escape=False)
sub_tokens = tokenizer.convert_ids_to_tokens(word_token_ids)
top = (
f"<p><b>Sub‑word tokens:</b> {' , '.join(sub_tokens)}</p>"
f"<p><b>Total matched layers:</b> {matches} / {len(retrieved_words)}</p>"
)
return top + html_table
# ----------------------------- GRADIO UI -------------------------------
with gr.Blocks(theme="soft") as demo:
gr.Markdown(
"""# Tokens→Words Viewer\nInteractively inspect how hidden‑state patching (Patchscopes) reveals a word's detokenised representation across model layers."""
)
with gr.Row():
model_name = gr.Dropdown(
label="🤖 Model",
choices=[DEFAULT_MODEL, "mistralai/Mistral-7B-v0.1", "meta-llama/Llama-2-7b", "Qwen/Qwen2-7B"],
value=DEFAULT_MODEL,
)
extraction_template = gr.Textbox(
label="Extraction prompt (use X as placeholder)",
value="repeat the following word X twice: 1)X 2)",
)
patchscopes_template = gr.Textbox(
label="Patchscopes prompt (use X as placeholder)",
value="repeat the following word X twice: 1)X 2)",
)
word_box = gr.Textbox(label="Word to test", value="interpretable")
run_btn = gr.Button("Analyse")
out_html = gr.HTML()
run_btn.click(
analyse_word,
inputs=[model_name, extraction_template, word_box, patchscopes_template],
outputs=out_html,
)
if __name__ == "__main__":
demo.launch()
|