156 lines
6.7 KiB
Python
156 lines
6.7 KiB
Python
"""E2 stage A eval (item 21): GSM8K accuracy for short-CoT-trained arms.
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Generates with generate_carry_c (prefill loop k + pause carry + per-token
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carry) or feedforward mode for the control arm; scores last_number vs
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gold, by STaR label. Grid over (k,p) cells.
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"""
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import argparse
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import json
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import os
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import sys
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import time
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from pathlib import Path
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import torch
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from carry_common import generate_carry_c
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from loop_common import (BandLooper, MergeAdapter, chat_prompt,
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DIRECT_SUFFIX, last_number, num_eq)
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from jlens.core import load_model # noqa: E402
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OUT = Path(os.environ.get("LOOP_OUT",
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Path(__file__).resolve().parent.parent / "results-loop"))
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@torch.no_grad()
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--adapter", required=True)
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ap.add_argument("--tag", required=True)
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ap.add_argument("--grid", default="0:0,2:2,2:6",
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help="comma list of k:p cells")
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ap.add_argument("--n", type=int, default=256)
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ap.add_argument("--batch", type=int, default=8)
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ap.add_argument("--feedforward", action="store_true")
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ap.add_argument("--max-new", type=int, default=160)
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ap.add_argument("--bandlora", default=None, metavar="LORA_PT",
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help="load a lora_*_e*.pt loop-only band-LoRA checkpoint")
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ap.add_argument("--inner-iters", type=int, default=0, metavar="M",
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help="M in-place band iterations at the last pause")
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ap.add_argument("--kvmem", default=None, metavar="KVMEM_PT",
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help="KVMemoryAdapter checkpoint: burst states become "
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"band-layer KV prefix entries at generation")
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ap.add_argument("--symchain", default=None, metavar="PROJ_PT",
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help="item 32: discrete latent chain — zero-init "
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"projector checkpoint; generation snaps hard argmax")
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args = ap.parse_args()
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model, tok = load_model(dtype=torch.bfloat16)
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tok.padding_side = "left"
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looper = BandLooper(model)
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kvmem = None
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if args.kvmem:
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from kv_memory import install, KVMemoryAdapter
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from loop_common import BAND
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install(model)
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sd = torch.load(args.kvmem, map_location="cuda")
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code = sd["trunk.1.weight"].shape[0]
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kvmem = KVMemoryAdapter(model, band=BAND, code=code).cuda()
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kvmem.load_state_dict(sd)
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kvmem.eval()
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print(f"kv-memory loaded: {args.kvmem} (code={code})", flush=True)
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if args.bandlora:
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from lora_band import inject_band_lora
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ck = torch.load(args.bandlora, map_location="cuda")
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scales = {l: 1.0 for l in ck["band"]}
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ps = inject_band_lora(looper.tm, ck["band"][0], scales,
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rank=ck["rank"])
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assert len(ps) == len(ck["tensors"]), (len(ps), len(ck["tensors"]))
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for pr, t in zip(ps, ck["tensors"]):
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pr.data = t.cuda()
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print(f"band-lora loaded: {args.bandlora} "
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f"(r={ck['rank']}, layers {ck['band'][0]}-{ck['band'][-1]})",
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flush=True)
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symchain = None
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if args.symchain:
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d_ = model.config.get_text_config().hidden_size
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proj = torch.nn.Linear(d_, d_).cuda()
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proj.load_state_dict(torch.load(args.symchain, map_location="cuda"))
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proj.eval()
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jbar = torch.load(Path(__file__).resolve().parent.parent
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/ "results/jbar.pt", map_location="cuda")["Jbar"]
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from loop_common import BAND
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J30 = jbar[BAND[1]].float()
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tm = model.model.language_model
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softcap = model.config.get_text_config().final_logit_softcapping
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def lens_fn(h):
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x = tm.norm((h.float() @ J30.T).to(tm.norm.weight.dtype))
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lg = model.lm_head(x)
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return softcap * torch.tanh(lg / softcap) if softcap else lg
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symchain = {"proj": proj, "lens_fn": lens_fn,
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"embed_w": model.get_input_embeddings().weight.detach(),
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"start_id": tok("\n",
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add_special_tokens=False)["input_ids"][0]}
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print(f"symchain loaded: {args.symchain}", flush=True)
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adapter = MergeAdapter(
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d=model.config.get_text_config().hidden_size).cuda()
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adapter.load_state_dict(torch.load(args.adapter, map_location="cuda"))
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adapter.eval()
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items = [it for it in json.load(open(OUT / "star_data.json"))
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if it["split"] == "test"][: args.n]
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print(f"[{args.tag}] GSM carry-cot eval on {len(items)}, "
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f"grid={args.grid} ff={args.feedforward}", flush=True)
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res = {"tag": args.tag, "grid": {}, "n": len(items)}
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for cell in args.grid.split(","):
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k, p = (int(x) for x in cell.split(":"))
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t0 = time.time()
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hits, per_label, per_item = 0, {}, []
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for i in range(0, len(items), args.batch):
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chunk = items[i : i + args.batch]
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enc = tok([chat_prompt(tok, it["question"], DIRECT_SUFFIX)
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for it in chunk], return_tensors="pt", padding=True,
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add_special_tokens=False).to("cuda")
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if k == 0 and p == 0:
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gen = model.generate(**enc, max_new_tokens=args.max_new,
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do_sample=False)
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else:
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gen = generate_carry_c(looper, adapter, tok,
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enc["input_ids"],
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enc["attention_mask"], k, p,
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max_new_tokens=args.max_new,
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feedforward=args.feedforward,
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inner_iters=args.inner_iters,
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kvmem=kvmem, symchain=symchain)
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if kvmem is not None:
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from kv_memory import arm_memory
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arm_memory(None)
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for j, it in enumerate(chunk):
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txt = tok.decode(gen[j, enc["input_ids"].shape[1]:],
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skip_special_tokens=True)
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ok = num_eq(last_number(txt), it["gold"])
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hits += ok
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d = per_label.setdefault(it["label"], [0, 0])
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d[0] += ok
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d[1] += 1
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per_item.append({"idx": it["idx"], "ok": bool(ok)})
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acc = hits / len(items)
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by = {l: c / n for l, (c, n) in per_label.items()}
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res["grid"][cell] = {"acc": acc, "by_label": by,
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"per_item": per_item}
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print(f"{cell}: acc={acc:.3f} "
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f"by_label={ {l: round(v,3) for l,v in by.items()} }"
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f" ({time.time()-t0:.0f}s)", flush=True)
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json.dump(res, open(OUT / f"eval_{args.tag}.json", "w"), indent=1)
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print("wrote", OUT / f"eval_{args.tag}.json")
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if __name__ == "__main__":
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main()
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