File size: 2,470 Bytes
05ab4b5 | 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 | """§1.1 acceptance check (a): two-hop intermediate concepts light up at mid
layers under the fitted lens, and J-lens beats logit-lens on pass@k.
Protocol per data/evaluations/README.md: readout at the single position
immediately preceding `target` (= last prompt token); metric pass@k = mean
fraction of intermediates with min-over-layers rank <= k.
"""
import argparse
import json
import numpy as np
import torch
import transformers
import jlens
from jlens.hooks import ActivationRecorder
EVAL = "/workspace/jacobian-lens/data/evaluations/lens-eval-multihop.json"
def first_token(tok, word):
return tok(f" {word}", add_special_tokens=False).input_ids[0]
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--model_dir", required=True)
ap.add_argument("--lens", required=True)
args = ap.parse_args()
items = json.load(open(EVAL))["items"]
tok = transformers.AutoTokenizer.from_pretrained(args.model_dir)
hf = transformers.AutoModelForCausalLM.from_pretrained(
args.model_dir, dtype=torch.bfloat16
).cuda()
hf.eval()
model = jlens.from_hf(hf, tok)
lens = jlens.JacobianLens.load(args.lens)
lens.jacobians = {l: J.cuda() for l, J in lens.jacobians.items()}
layers = list(lens.source_layers)
ranks_j = np.zeros((len(items), len(layers)), dtype=np.int64)
ranks_l = np.zeros((len(items), len(layers)), dtype=np.int64)
for ii, it in enumerate(items):
tid = first_token(tok, it["intermediates"][0])
ids = tok(it["prompt"], return_tensors="pt").input_ids.cuda()
with torch.no_grad(), ActivationRecorder(model.layers, at=layers) as rec:
model.forward(ids)
acts = {l: rec.activations[l][0, -1].detach().float() for l in layers}
for li, l in enumerate(layers):
for use_j, out in ((True, ranks_j), (False, ranks_l)):
r = lens.transport(acts[l], l) if use_j else acts[l]
logits = model.unembed(r).float()
out[ii, li] = int((logits > logits[tid]).sum())
for k in (1, 10, 50):
pj = (ranks_j.min(1) < k).mean()
pl = (ranks_l.min(1) < k).mean()
print(f"pass@{k}: jlens={pj:.3f} logitlens={pl:.3f}")
print("\nper-layer median rank of intermediate (jlens | logitlens):")
for li, l in enumerate(layers):
print(f" L{l:2d}: {np.median(ranks_j[:, li]):8.0f} | {np.median(ranks_l[:, li]):8.0f}")
if __name__ == "__main__":
main()
|