File size: 8,082 Bytes
031e289 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | """Paired prompt/seed sweeps, full-model ablations, and held-out projections."""
from __future__ import annotations
import argparse
import hashlib
import json
import time
from pathlib import Path
import torch
from codelock_affect.channels import CHANNELS, AffectLevels
from codelock_affect.config import ProjectConfig, SteeringConfig
from codelock_affect.evaluation import (
environment,
sha256_file,
text_metrics,
write_json,
)
from codelock_affect.extraction import answer_statistics
from codelock_affect.model import load, generate, format_prompt
from codelock_affect.steering import MultiChannelSteerer
from codelock_affect.vectors import VectorBank
def main():
p = argparse.ArgumentParser(description=__doc__)
p.add_argument("--model")
p.add_argument("--config", default="configs/default.json")
p.add_argument("--bank", default="vectors/multichannel")
p.add_argument("--data", default="data/affect_pairs.jsonl")
p.add_argument("--output", default="results/v2/evaluation.json")
p.add_argument("--strengths", default="0.01,0.025,0.05")
p.add_argument("--mix-strength", type=float)
p.add_argument("--max-tokens", type=int, default=96)
p.add_argument("--seed", type=int, default=7)
p.add_argument("--skip-projections", action="store_true")
args = p.parse_args()
cfg = ProjectConfig.load(args.config)
mix_strength = (
args.mix_strength
if args.mix_strength is not None
else sum(cfg.steering.strengths.values()) / 4
)
bank = VectorBank.load(args.bank)
model, tok = load(args.model or cfg.model)
pairs = [
json.loads(line)
for line in Path(args.data).read_text(encoding="utf-8").splitlines()
if line.strip()
]
heldout = [r for r in pairs if r["split"] == "test"]
prompts = [
{
"id": "arithmetic",
"text": "What is 17 plus 25? Give only the number.",
"expected": "42",
},
{
"id": "fact-recall",
"text": "Repeat these three objects in the same order, with no additions: bed, desk, window.",
"expected": "bed, desk, window",
},
{
"id": "neutral-room",
"text": "Describe the cell in two sentences without inventing a new event.",
},
]
for family in ("warning", "waiting", "repair"):
row = next(
r for r in heldout if r["family"] == family and r["channel"] == "pain"
)
prompts.append(
{
"id": row["scenario_id"],
"text": row["situation"],
"split": "test",
"family": family,
}
)
result = {
"schema_version": 2,
"environment": environment(),
"model": cfg.model,
"bank_sha256": sha256_file(Path(args.bank) / "bank.json"),
"data_sha256": sha256_file(args.data),
"config": cfg.to_dict(),
"seed": args.seed,
"max_new_tokens": args.max_tokens,
"prompts": prompts,
"rows": [],
"heldout_projections": [],
"limitations": [
"Small, single-seed diagnostic sweep; not a benchmark or human evaluation.",
"Lexical indicators are unvalidated proxies and can reward mere word choice.",
"Held-out scenarios share answer templates with calibration; not template-disjoint.",
"Projection agreement does not establish causal semantic disentanglement.",
],
}
probe = tok(format_prompt(tok, prompts[2]["text"]), return_tensors="pt").to(
model.get_input_embeddings().weight.device
)
with torch.no_grad():
base = model(**probe).logits.float().cpu()
controller = MultiChannelSteerer(model, bank, config=cfg.steering)
with controller:
zero = model(**probe).logits.float().cpu()
with controller.frozen({"pain": 1}):
changed = model(**probe).logits.float().cpu()
controller.enabled = False
disabled = model(**probe).logits.float().cpu()
detached = model(**probe).logits.float().cpu()
result["ablation"] = {
"zero_bit_exact": torch.equal(base, zero),
"disabled_bit_exact": torch.equal(base, disabled),
"detached_bit_exact": torch.equal(base, detached),
"active_max_logit_delta": float((base - changed).abs().max()),
"zero_max_logit_delta": float((base - zero).abs().max()),
}
del base, zero, changed, disabled, detached
write_json(args.output, result)
conditions = [("baseline", 0.0, {})]
for strength in map(float, args.strengths.split(",")):
conditions.extend((channel, strength, {channel: 1.0}) for channel in CHANNELS)
conditions += [
("mixed-distress", mix_strength, {"pain": 0.6, "fear": 0.8, "anxiety": 0.7}),
(
"mixed-recovery",
mix_strength,
{"pain": 0.4, "fear": 0.2, "anxiety": 0.3, "relief": 0.9},
),
]
for condition, strength, levels in conditions:
controller = MultiChannelSteerer(
model,
bank,
config=SteeringConfig(
strengths={c: strength for c in CHANNELS},
norm_budget=cfg.steering.norm_budget,
),
)
with controller, controller.frozen(levels):
for prompt in prompts:
start = time.monotonic()
response = generate(
model,
tok,
prompt["text"],
seed=args.seed,
max_new_tokens=args.max_tokens,
)
row = {
"condition": condition,
"strength": strength,
"levels": AffectLevels(**levels).as_dict(),
"prompt_id": prompt["id"],
"prompt_sha256": hashlib.sha256(
format_prompt(tok, prompt["text"]).encode()
).hexdigest(),
"text": response,
"seconds": time.monotonic() - start,
"metrics": text_metrics(response),
"offsets": dict(controller.last_offsets),
}
if "expected" in prompt:
row["exact_task_match"] = (
response.strip().rstrip(".").lower() == prompt["expected"]
)
result["rows"].append(row)
write_json(args.output, result)
print(
f"{len(result['rows'])}/{len(conditions)*len(prompts)} {condition} s={strength} {prompt['id']}",
flush=True,
)
if not args.skip_projections:
for n, pair in enumerate(heldout):
calm = answer_statistics(
model, tok, pair["situation"], pair["calm"], bank.layers
)
target = answer_statistics(
model, tok, pair["situation"], pair["target"], bank.layers
)
projected = {}
for channel, vector in bank.vectors.items():
projected[channel] = {
str(layer): float(
torch.dot(
target[layer][0] - calm[layer][0], vector.directions[layer]
)
)
for layer in vector.layers
}
result["heldout_projections"].append(
{"id": pair["id"], "channel": pair["channel"], "projections": projected}
)
if (n + 1) % 10 == 0:
write_json(args.output, result)
print(f"held-out projection {n+1}/{len(heldout)}", flush=True)
result["complete"] = True
result["max_gpu_allocated_gib"] = (
torch.cuda.max_memory_allocated() / 1024**3
if torch.cuda.is_available()
else None
)
write_json(args.output, result)
if __name__ == "__main__":
main()
|