code/set1_slerp.py

"""The operator practitioners actually use: SLERP.

Every rung in the main SET 1 table is a lab operator. A census of community merges on the Hub finds
SLERP on ~25% of them -- more than TIES, DARE-TIES and task arithmetic combined -- and it needs no
shared base, which is exactly why it gets reached for when merging two models with no common
ancestor. That is the PolyPythia seed case. This adds it, on the same pairs, before and after unit
alignment, with both metrics.

Rungs: M0 naive average - M1 permutation-aligned average - M6 SLERP - M7 permutation-aligned SLERP.
"""
import os, sys, json, time, glob, itertools, argparse, gc, csv
sys.path.insert(0, "/root/compose-audit")
from common import *
from transformers import AutoModelForCausalLM, AutoTokenizer
import pyarrow.parquet as pq

ap = argparse.ArgumentParser()
ap.add_argument("--size", default="14m")
ap.add_argument("--seeds", default="1,2,3,4,5,6,7,8,9")
ap.add_argument("--blocks", type=int, default=48)
ap.add_argument("--bs", type=int, default=16)
ap.add_argument("--n_per_paradigm", type=int, default=200)
ap.add_argument("--acts_rows", type=int, default=2048)
A = ap.parse_args()
SEEDS = [int(s) for s in A.seeds.split(",")]
OUT = f"/root/compose-audit/results/slerp_{A.size}.jsonl"
DEV = "cuda"
BLIMP = glob.glob("/root/hf_cache_brainalign/hub/datasets--nyu-mll--blimp/snapshots/*/")[0]


def log(*a):
    print(f"[{time.strftime('%H:%M:%S')}]", *a, flush=True)


def neox_head_match(sd_a, sd_b, d, nh, nl):
    hd, perms = d // nh, {}
    for L in range(nl):
        qk = f"gpt_neox.layers.{L}.attention.query_key_value.weight"
        de = f"gpt_neox.layers.{L}.attention.dense.weight"
        if qk not in sd_a: continue
        Aq = np.asarray(sd_a[qk], float).reshape(nh, 3 * hd, d)
        Bq = np.asarray(sd_b[qk], float).reshape(nh, 3 * hd, d)
        g = np.einsum("ixy,jxy->ij", Aq, Bq)
        Ad = np.asarray(sd_a[de], float).reshape(d, nh, hd)
        Bd = np.asarray(sd_b[de], float).reshape(d, nh, hd)
        perms[L] = AL._assignment(g + np.einsum("xiy,xjy->ij", Ad, Bd))
    return perms


def neox_apply_head(sd, perms, d, nh):
    hd, out = d // nh, dict(sd)
    for L, h in perms.items():
        qk = f"gpt_neox.layers.{L}.attention.query_key_value.weight"
        qb = f"gpt_neox.layers.{L}.attention.query_key_value.bias"
        de = f"gpt_neox.layers.{L}.attention.dense.weight"
        out[qk] = np.asarray(sd[qk], float).reshape(nh, 3 * hd, d)[h].reshape(3 * d, d)
        if qb in sd: out[qb] = np.asarray(sd[qb], float).reshape(nh, 3 * hd)[h].reshape(3 * d)
        out[de] = np.asarray(sd[de], float).reshape(d, nh, hd)[:, h].reshape(d, d)
    return out


def align_perm(sd_a, sd_b, d, aa, ab, nh, nl):
    sd, _ = AL.align_weights_full(sd_a, sd_b, d, acts_a=aa, acts_b=ab, n_heads=None,
                                  method="permutation", strict=True, accept_each=True)
    hp = neox_head_match(sd_a, sd, d, nh, nl)
    if hp:
        cand = neox_apply_head(sd, hp, d, nh)
        if AL.block_normalised_distance(sd_a, cand) <= AL.block_normalised_distance(sd_a, sd):
            sd = cand
    return sd


# ------------------------------------------------------------------ REPAIR
TARGETS = ("mlp.dense_h_to_4h", "attention.query_key_value")


@torch.no_grad()
def preact_stats(model, blocks, dev, bs, nl):
    """{(layer, target): (mean, std)} of each Linear's OUTPUT (= pre-activation), per unit."""
    acc = {}
    hs = []

    def mk(key):
        def hook(mod, inp, out):
            o = out.detach().float().reshape(-1, out.shape[-1])
            s = acc.setdefault(key, [0.0, None, None])
            s[0] += o.shape[0]
            s[1] = o.sum(0) if s[1] is None else s[1] + o.sum(0)
            s[2] = (o * o).sum(0) if s[2] is None else s[2] + (o * o).sum(0)
        return hook

    for L in range(nl):
        blk = model.gpt_neox.layers[L]
        hs.append(blk.mlp.dense_h_to_4h.register_forward_hook(mk((L, "mlp.dense_h_to_4h"))))
        hs.append(blk.attention.query_key_value.register_forward_hook(mk((L, "attention.query_key_value"))))
    for i in range(0, blocks.shape[0], bs):
        model(blocks[i:i + bs].to(dev))
    for h in hs: h.remove()
    out = {}
    for k, (n, s1, s2) in acc.items():
        m = s1 / n
        v = (s2 / n - m * m).clamp_min(1e-12)
        out[k] = (m.cpu().numpy().astype(np.float64), v.sqrt().cpu().numpy().astype(np.float64))
    return out


def repair(sd_merged, stats_a, stats_b, shell, blocks, dev, bs, nl):
    """Walk layers in order; after fixing layers < L the inputs to layer L are already corrected, so
    layer L's own statistics are re-measured before it is corrected. Affine correction on the
    Linear's weight/bias, so the model stays exactly a model of the same architecture."""
    sd = {k: np.array(v, dtype=np.float64, copy=True) for k, v in sd_merged.items()}
    for L in range(nl):
        sd_load(shell, sd, dev)
        cur = preact_stats(shell, blocks, dev, bs, nl)
        for t in TARGETS:
            mu_t = 0.5 * (stats_a[(L, t)][0] + stats_b[(L, t)][0])
            sd_t = 0.5 * (stats_a[(L, t)][1] + stats_b[(L, t)][1])
            mu_m, sd_m = cur[(L, t)]
            g = sd_t / np.maximum(sd_m, 1e-8)
            wk, bk = f"gpt_neox.layers.{L}.{t}.weight", f"gpt_neox.layers.{L}.{t}.bias"
            sd[wk] = sd[wk] * g[:, None]
            sd[bk] = (sd[bk] - mu_m) * g + mu_t
    return sd


# ------------------------------------------------------------------ BLiMP
def load_blimp(n_per):
    out = []
    for d in sorted(glob.glob(BLIMP + "*/")):
        f = glob.glob(d + "*.parquet")
        if not f: continue
        t = pq.read_table(f[0]).to_pydict()
        out.append((os.path.basename(d.rstrip("/")), t["sentence_good"][:n_per], t["sentence_bad"][:n_per]))
    return out


tok = AutoTokenizer.from_pretrained(f"EleutherAI/pythia-{A.size}")
if tok.pad_token is None: tok.pad_token = tok.eos_token


def enc(sents, maxlen=48):
    e = tok(sents, return_tensors="pt", padding=True, truncation=True, max_length=maxlen)
    return e["input_ids"], e["attention_mask"]


ENC = [(n, enc(g), enc(b)) for n, g, b in load_blimp(A.n_per_paradigm)]
lines = flores_lines("eng_Latn")
blocks = make_blocks(tok, lines, block=512, max_blocks=A.blocks)
shell = AutoModelForCausalLM.from_pretrained(f"EleutherAI/pythia-{A.size}-seed{SEEDS[0]}",
                                             dtype=torch.float32).to(DEV).eval()
cfg = shell.config
D, NH, NL = cfg.hidden_size, cfg.num_attention_heads, cfg.num_hidden_layers
log(f"size={A.size} d={D} heads={NH} layers={NL} blimp_paradigms={len(ENC)}")


@torch.no_grad()
def bscore(ids, am):
    o = []
    for i in range(0, ids.shape[0], 128):
        x, m = ids[i:i + 128].to(DEV), am[i:i + 128].to(DEV)
        lp = torch.log_softmax(shell(x, attention_mask=m).logits.float()[:, :-1], -1)
        o.append((lp.gather(-1, x[:, 1:].unsqueeze(-1)).squeeze(-1) * m[:, 1:].float()).sum(1).cpu())
    return torch.cat(o).numpy()


def evaluate(sd):
    sd_load(shell, sd, DEV)
    nll = nll_nats(shell, blocks, DEV, bs=A.bs)
    cor = tot = 0
    for name, (gi, gm), (bi, bm) in ENC:
        sg, sb = bscore(gi, gm), bscore(bi, bm)
        cor += int((sg > sb).sum()); tot += len(sg)
    return nll, cor / tot


SDS, ACTS, PAR, STATS = {}, {}, {}, {}
for s in SEEDS:
    m = AutoModelForCausalLM.from_pretrained(f"EleutherAI/pythia-{A.size}-seed{s}", dtype=torch.float32).to(DEV).eval()
    SDS[s] = sd_np(m); ACTS[s] = capture_acts(m, blocks, DEV, n_rows=A.acts_rows, bs=A.bs)
    del m; torch.cuda.empty_cache()
    PAR[s] = evaluate(SDS[s])
    log(f"  seed{s} nll={PAR[s][0]:.4f} blimp={PAR[s][1]:.4f}")

done = set()
if os.path.exists(OUT):
    for l in open(OUT):
        try: done.add(tuple(json.loads(l)["pair"]))
        except Exception: pass
fh = open(OUT, "a")
for a, b in itertools.combinations(SEEDS, 2):
    if (a, b) in done: continue
    t0 = time.time()
    sa, sb = SDS[a], SDS[b]
    sbp = align_perm(sa, sb, D, ACTS[a], ACTS[b], NH, NL)
    rungs = {"M0_naive_avg": MG.average([sa, sb]), "M1_perm_avg": MG.average([sa, sbp]),
             "M6_slerp": MG.slerp(sa, sb, t=0.5), "M7_perm_slerp": MG.slerp(sa, sbp, t=0.5)}
    floor = min(PAR[a][0], PAR[b][0]); ceil = max(PAR[a][1], PAR[b][1])
    res = {}
    for k, sd in rungs.items():
        nll, acc = evaluate(sd)
        res[k] = {"nll": nll, "delta_floor": nll - floor, "blimp_acc": acc,
                  "blimp_delta_vs_ceiling": acc - ceil}
    r = {"set": "set1_slerp", "size": A.size, "pair": [a, b], "floor": floor, "blimp_ceiling": ceil,
         "parent_nll": {"a": PAR[a][0], "b": PAR[b][0]},
         "parent_blimp": {"a": PAR[a][1], "b": PAR[b][1]}, "rungs": res, "secs": time.time() - t0}
    fh.write(json.dumps(r) + "\n"); fh.flush()
    log(f"pair {a},{b} floor={floor:.2f}/ceil={ceil:.3f} | " +
        " | ".join(f"{k}: {v['delta_floor']:+.2f}n {v['blimp_acc']:.3f}" for k, v in res.items()) +
        f" ({r['secs']:.0f}s)")
    del rungs, sbp; gc.collect()
fh.close()
log("DONE slerp", A.size)