130 lines
5.4 KiB
Python
130 lines
5.4 KiB
Python
"""E2 stage A eval (item 21): GSM8K accuracy for short-CoT-trained arms.
|
|
|
|
Generates with generate_carry_c (prefill loop k + pause carry + per-token
|
|
carry) or feedforward mode for the control arm; scores last_number vs
|
|
gold, by STaR label. Grid over (k,p) cells.
|
|
"""
|
|
|
|
import argparse
|
|
import json
|
|
import os
|
|
import sys
|
|
import time
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
|
|
from carry_common import generate_carry_c
|
|
from loop_common import (BandLooper, MergeAdapter, chat_prompt,
|
|
DIRECT_SUFFIX, last_number, num_eq)
|
|
|
|
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
|
from jlens.core import load_model # noqa: E402
|
|
|
|
OUT = Path(os.environ.get("LOOP_OUT",
|
|
Path(__file__).resolve().parent.parent / "results-loop"))
|
|
|
|
|
|
@torch.no_grad()
|
|
def main():
|
|
ap = argparse.ArgumentParser()
|
|
ap.add_argument("--adapter", required=True)
|
|
ap.add_argument("--tag", required=True)
|
|
ap.add_argument("--grid", default="0:0,2:2,2:6",
|
|
help="comma list of k:p cells")
|
|
ap.add_argument("--n", type=int, default=256)
|
|
ap.add_argument("--batch", type=int, default=8)
|
|
ap.add_argument("--feedforward", action="store_true")
|
|
ap.add_argument("--max-new", type=int, default=160)
|
|
ap.add_argument("--bandlora", default=None, metavar="LORA_PT",
|
|
help="load a lora_*_e*.pt loop-only band-LoRA checkpoint")
|
|
ap.add_argument("--inner-iters", type=int, default=0, metavar="M",
|
|
help="M in-place band iterations at the last pause")
|
|
ap.add_argument("--kvmem", default=None, metavar="KVMEM_PT",
|
|
help="KVMemoryAdapter checkpoint: burst states become "
|
|
"band-layer KV prefix entries at generation")
|
|
args = ap.parse_args()
|
|
|
|
model, tok = load_model(dtype=torch.bfloat16)
|
|
tok.padding_side = "left"
|
|
looper = BandLooper(model)
|
|
kvmem = None
|
|
if args.kvmem:
|
|
from kv_memory import install, KVMemoryAdapter
|
|
from loop_common import BAND
|
|
install(model)
|
|
sd = torch.load(args.kvmem, map_location="cuda")
|
|
code = sd["trunk.1.weight"].shape[0]
|
|
kvmem = KVMemoryAdapter(model, band=BAND, code=code).cuda()
|
|
kvmem.load_state_dict(sd)
|
|
kvmem.eval()
|
|
print(f"kv-memory loaded: {args.kvmem} (code={code})", flush=True)
|
|
if args.bandlora:
|
|
from lora_band import inject_band_lora
|
|
ck = torch.load(args.bandlora, map_location="cuda")
|
|
scales = {l: 1.0 for l in ck["band"]}
|
|
ps = inject_band_lora(looper.tm, ck["band"][0], scales,
|
|
rank=ck["rank"])
|
|
assert len(ps) == len(ck["tensors"]), (len(ps), len(ck["tensors"]))
|
|
for pr, t in zip(ps, ck["tensors"]):
|
|
pr.data = t.cuda()
|
|
print(f"band-lora loaded: {args.bandlora} "
|
|
f"(r={ck['rank']}, layers {ck['band'][0]}-{ck['band'][-1]})",
|
|
flush=True)
|
|
adapter = MergeAdapter(
|
|
d=model.config.get_text_config().hidden_size).cuda()
|
|
adapter.load_state_dict(torch.load(args.adapter, map_location="cuda"))
|
|
adapter.eval()
|
|
|
|
items = [it for it in json.load(open(OUT / "star_data.json"))
|
|
if it["split"] == "test"][: args.n]
|
|
print(f"[{args.tag}] GSM carry-cot eval on {len(items)}, "
|
|
f"grid={args.grid} ff={args.feedforward}", flush=True)
|
|
|
|
res = {"tag": args.tag, "grid": {}, "n": len(items)}
|
|
for cell in args.grid.split(","):
|
|
k, p = (int(x) for x in cell.split(":"))
|
|
t0 = time.time()
|
|
hits, per_label, per_item = 0, {}, []
|
|
for i in range(0, len(items), args.batch):
|
|
chunk = items[i : i + args.batch]
|
|
enc = tok([chat_prompt(tok, it["question"], DIRECT_SUFFIX)
|
|
for it in chunk], return_tensors="pt", padding=True,
|
|
add_special_tokens=False).to("cuda")
|
|
if k == 0 and p == 0:
|
|
gen = model.generate(**enc, max_new_tokens=args.max_new,
|
|
do_sample=False)
|
|
else:
|
|
gen = generate_carry_c(looper, adapter, tok,
|
|
enc["input_ids"],
|
|
enc["attention_mask"], k, p,
|
|
max_new_tokens=args.max_new,
|
|
feedforward=args.feedforward,
|
|
inner_iters=args.inner_iters,
|
|
kvmem=kvmem)
|
|
if kvmem is not None:
|
|
from kv_memory import arm_memory
|
|
arm_memory(None)
|
|
for j, it in enumerate(chunk):
|
|
txt = tok.decode(gen[j, enc["input_ids"].shape[1]:],
|
|
skip_special_tokens=True)
|
|
ok = num_eq(last_number(txt), it["gold"])
|
|
hits += ok
|
|
d = per_label.setdefault(it["label"], [0, 0])
|
|
d[0] += ok
|
|
d[1] += 1
|
|
per_item.append({"idx": it["idx"], "ok": bool(ok)})
|
|
acc = hits / len(items)
|
|
by = {l: c / n for l, (c, n) in per_label.items()}
|
|
res["grid"][cell] = {"acc": acc, "by_label": by,
|
|
"per_item": per_item}
|
|
print(f"{cell}: acc={acc:.3f} "
|
|
f"by_label={ {l: round(v,3) for l,v in by.items()} }"
|
|
f" ({time.time()-t0:.0f}s)", flush=True)
|
|
json.dump(res, open(OUT / f"eval_{args.tag}.json", "w"), indent=1)
|
|
print("wrote", OUT / f"eval_{args.tag}.json")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|