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e87a0a7 e5ef3bb e87a0a7 e5ef3bb e87a0a7 e5ef3bb e87a0a7 e5ef3bb e87a0a7 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | """SET 4: Goldfish monolingual -> bilingual merge on the REAL composition models.
goldfish-models/eng_latn_1000mb x goldfish-models/{nld,spa,ell,pol}_*_1000mb (GPT-2, 125M each,
SEPARATE monolingual tokenizers). Rungs: M0 naive average (the merge the manuscript reports as
failing) vs M1 vocab-remapped + unit-aligned (permutation / Procrustes on the residual basis,
free MLP axis, attention heads).
METRIC: Delta-floor in NATS PER UTF-8 BYTE on FLORES-200 devtest. Bytes, not tokens: the two
parents use different tokenizers, so nats/token is not comparable across them. This is a
LIKELIHOOD metric, not benchmark accuracy."""
import os, sys, json, time, argparse, gc
sys.path.insert(0, "/root/compose-audit")
from common import *
import gpt2_align as G2
from mergeschool.core.models import load_hf
ap = argparse.ArgumentParser()
ap.add_argument("--pairs", default="nld_Latn:nld_latn,spa_Latn:spa_latn,ell_Grek:ell_grek,pol_Latn:pol_latn")
ap.add_argument("--n_sent", type=int, default=500)
ap.add_argument("--bs", type=int, default=8)
ap.add_argument("--barrier_n", type=int, default=7)
A = ap.parse_args()
OUT = "/root/compose-audit/results/set4_goldfish.jsonl"
DEV = "cuda"
ENG_REPO = "goldfish-models/eng_latn_1000mb"
def log(*a):
print(f"[{time.strftime('%H:%M:%S')}]", *a, flush=True)
# ------------------------------------------------------------------ tokenizer-invariant eval
def build_blocks(tok, text, block=512, max_blocks=64):
ids = tok(text)["input_ids"]
n = max(1, min(max_blocks, len(ids) // block))
ids = ids[: n * block]
arr = torch.from_numpy(np.asarray(ids, dtype=np.int64).reshape(n, block))
nbytes = sum(len(tok.decode(list(arr[i, 1:].numpy())).encode("utf-8")) for i in range(n))
return arr, nbytes
@torch.no_grad()
def nll_total(model, blocks, dev, bs=8):
tot, ntok = 0.0, 0
for i in range(0, blocks.shape[0], bs):
x = blocks[i:i + bs].to(dev)
lp = torch.log_softmax(model(x).logits.float()[:, :-1], -1)
tgt = x[:, 1:]
tot += (-lp.gather(-1, tgt.unsqueeze(-1)).squeeze(-1)).sum().item()
ntok += tgt.numel()
return tot, ntok
@torch.no_grad()
def sent_acts(model, tok, lines, dev, bs=16, maxlen=128):
"""Mean-pooled per-sentence residual activations, {layer: (n_sent, d)} -- rows are matched
ACROSS LANGUAGES by FLORES sentence id, which is what makes a cross-lingual basis map fittable."""
outs = None
for i in range(0, len(lines), bs):
enc = tok(lines[i:i + bs], return_tensors="pt", padding=True, truncation=True, max_length=maxlen)
ids = enc["input_ids"].to(dev); am = enc["attention_mask"].to(dev).float()
hs = model(ids, attention_mask=enc["attention_mask"].to(dev), output_hidden_states=True).hidden_states
if outs is None:
outs = [[] for _ in hs]
w = am / am.sum(1, keepdim=True).clamp(min=1)
for j, h in enumerate(hs):
outs[j].append((h.float() * w.unsqueeze(-1)).sum(1).cpu())
return {j: torch.cat(o).numpy().astype(np.float64) for j, o in enumerate(outs)}
# ------------------------------------------------------------------ load English parent
log("loading eng parent")
m_e, tok_e = load_hf(ENG_REPO, dtype=torch.float32, device=DEV)
m_e.eval()
cfg = m_e.config
D, NH, NL, V = cfg.n_embd, cfg.n_head, cfg.n_layer, cfg.vocab_size
SD_E = sd_np(m_e)
log(f"gpt2 d={D} heads={NH} layers={NL} vocab={V}")
eng_lines = flores_lines("eng_Latn")[: A.n_sent]
eng_text = "\n".join(eng_lines)
bl_e_e, by_e_e = build_blocks(tok_e, eng_text) # eng text, eng tokenizer
acts_e = sent_acts(m_e, tok_e, eng_lines, DEV)
shell = m_e # reuse as the eval shell (eng tokenizer space)
def ev_np(sd, blocks):
sd_load(shell, sd, DEV)
t, n = nll_total(shell, blocks, DEV, bs=A.bs)
return t, n
nll_e_eng_t, nll_e_eng_n = nll_total(m_e, bl_e_e, DEV, bs=A.bs)
PARENT_ENG = {"nats_per_byte": nll_e_eng_t / by_e_e, "nats_per_token": nll_e_eng_t / nll_e_eng_n}
log(f"eng parent on eng: {PARENT_ENG}")
done = set()
if os.path.exists(OUT):
for line in open(OUT):
try: done.add(json.loads(line)["lang"])
except Exception: pass
fh = open(OUT, "a")
for spec in A.pairs.split(","):
fcode, gcode = spec.split(":")
if fcode in done:
log("skip", fcode); continue
t0 = time.time()
repo = f"goldfish-models/{gcode}_1000mb"
log(f"=== {fcode} <- {repo}")
m_x, tok_x = load_hf(repo, dtype=torch.float32, device=DEV); m_x.eval()
SD_X = sd_np(m_x)
x_lines = flores_lines(fcode)[: A.n_sent]
x_text = "\n".join(x_lines)
bl_x_x, by_x_x = build_blocks(tok_x, x_text) # X text, X tokenizer (X parent's own floor)
bl_x_e, by_x_e = build_blocks(tok_e, x_text) # X text, ENG tokenizer (merged model's space)
acts_x = sent_acts(m_x, tok_x, x_lines, DEV)
tx, nx = nll_total(m_x, bl_x_x, DEV, bs=A.bs)
parent_x = {"nats_per_byte": tx / by_x_x, "nats_per_token": tx / nx}
del m_x; torch.cuda.empty_cache()
te, ne = ev_np(SD_E, bl_x_e) # eng parent on X text
eng_on_x = {"nats_per_byte": te / by_x_e, "nats_per_token": te / ne}
log(f" parents: eng/eng={PARENT_ENG['nats_per_byte']:.4f} x/x={parent_x['nats_per_byte']:.4f} "
f"eng-on-x={eng_on_x['nats_per_byte']:.4f} nats/byte")
# ------------- vocabulary transport (the OTHER axis: token ids, not the residual basis)
vkeys = [k for k in SD_X if k.endswith("wte.weight") or k.endswith("lm_head.weight")]
SD_X_V, cov = AL.remap_vocab_rows(SD_X, tok_e, tok_x, V, keys=vkeys)
for k in vkeys:
W = np.asarray(SD_X_V[k], float)
bad = ~np.isfinite(W).all(axis=1) if W.shape[0] == V else ~np.isfinite(W).all(axis=0)
if W.shape[0] == V:
W[bad] = np.asarray(SD_E[k], float)[bad] # unshared ids: keep English's row (no-op merge)
SD_X_V[k] = W
anchors = AL.vocab_anchors(tok_e, tok_x)
log(f" vocab anchors={len(anchors)} ({len(anchors)/V:.1%} of English ids)")
BODY = [k for k in SD_E if not (k.endswith("wte.weight") or k.endswith("lm_head.weight"))]
# ------------- alignments (fitted BEFORE merging)
R_emb, n_anch = G2.emb_procrustes(SD_E, SD_X, tok_e, tok_x)
sd_emb = G2.apply_resid(SD_X_V, D, R=R_emb)
sd_emb2, _ = G2.align_full(SD_E, sd_emb, D, NH, None, None, "permutation", body_keys=BODY)
sdp, ip = G2.align_full(SD_E, SD_X_V, D, NH, acts_e, acts_x, "permutation", body_keys=BODY)
sdo, io = G2.align_full(SD_E, SD_X_V, D, NH, acts_e, acts_x, "orthogonal", body_keys=BODY)
sdpf, ipf = G2.align_full(SD_E, SD_X_V, D, NH, acts_e, acts_x, "permutation", body_keys=BODY, accept_each=False)
sdof, iof = G2.align_full(SD_E, SD_X_V, D, NH, acts_e, acts_x, "orthogonal", body_keys=BODY, accept_each=False)
log(f" align perm={ip} orth={io}")
# ------------- predictors (pre-merge)
KEYS = shared_keys(SD_E, SD_X)
fa, fb = flat(SD_E, KEYS), flat(SD_X, KEYS)
p = {"n_emb_anchors": n_anch, "weight_cosine": float(fa @ fb / (np.linalg.norm(fa) * np.linalg.norm(fb))),
"vocab_overlap": len(anchors) / V}
p["weight_cosine_body"] = float(np.mean([
float(np.asarray(SD_E[k], float).ravel() @ np.asarray(SD_X[k], float).ravel() /
(np.linalg.norm(SD_E[k]) * np.linalg.norm(SD_X[k]) + 1e-12)) for k in BODY]))
q_p = MT.quotient_weight_distance(SD_E, SD_X_V, sdp, BODY)
q_o = MT.quotient_weight_distance(SD_E, SD_X_V, sdo, BODY)
p.update({"d_raw": q_p["d_raw"], "qmd_perm": q_p["qmd"], "coord_share_perm": q_p["coord_fraction"],
"qmd_orth": q_o["qmd"], "coord_share_orth": q_o["coord_fraction"]})
b_raw = AL.block_normalised_distance(SD_E, SD_X_V, BODY)
b_p = AL.block_normalised_distance(SD_E, sdp, BODY)
b_o = AL.block_normalised_distance(SD_E, sdo, BODY)
p.update({"bnd_raw": b_raw, "bnd_perm": b_p, "bnd_orth": b_o,
"coord_share_bnd_perm": float((b_raw - b_p) / b_raw),
"coord_share_bnd_orth": float((b_raw - b_o) / b_raw)})
ck, ckby = mean_cka(acts_e, acts_x)
p["cka_mean"] = ck; p["cka_last"] = ckby[max(ckby)]
for g in ("perm", "procrustes", "ot"):
try:
qr = MT.quotient_residual(acts_e[NL // 2], acts_x[NL // 2], group=g)
p[f"qmd_act_{g}"] = qr["distance"]; p[f"aligned_cka_{g}"] = qr["aligned_cka"]
except Exception:
p[f"qmd_act_{g}"] = float("nan")
# ------------- merge rungs
rungs = {"M0_naive_avg": MG.average([SD_E, SD_X]),
"M1a_vocab_avg": MG.average([SD_E, SD_X_V]),
"M1b_vocab_perm_avg": MG.average([SD_E, sdp]),
"M1c_vocab_orth_avg": MG.average([SD_E, sdo]),
"M1d_vocab_perm_forced": MG.average([SD_E, sdpf]),
"M1e_vocab_orth_forced": MG.average([SD_E, sdof]),
"M1g_emb_procrustes": MG.average([SD_E, sd_emb]),
"M1h_emb_proc_units": MG.average([SD_E, sd_emb2]),
"M1f_perm_novocab": MG.average([SD_E, G2.align_full(SD_E, SD_X, D, NH, acts_e, acts_x, "permutation", body_keys=BODY)[0]])}
res = {}
for name, sd in rungs.items():
t_e, n_e = ev_np(sd, bl_e_e)
t_x, n_x = ev_np(sd, bl_x_e)
res[name] = {
"eng": {"nats_per_byte": t_e / by_e_e, "nats_per_token": t_e / n_e},
"x": {"nats_per_byte": t_x / by_x_e, "nats_per_token": t_x / n_x},
"delta_floor_eng": t_e / by_e_e - PARENT_ENG["nats_per_byte"],
"delta_floor_x": t_x / by_x_e - min(parent_x["nats_per_byte"], eng_on_x["nats_per_byte"]),
}
res[name]["delta_floor_mean"] = 0.5 * (res[name]["delta_floor_eng"] + res[name]["delta_floor_x"])
for name in res:
res[name]["delta_vs_naive_mean"] = res[name]["delta_floor_mean"] - res["M0_naive_avg"]["delta_floor_mean"]
r = {"set": "set4_goldfish", "lang": fcode, "repo_a": ENG_REPO, "repo_b": repo,
"corpus": "flores200_devtest", "n_sent": A.n_sent,
"metric": "nats_per_utf8_byte (likelihood, NOT benchmark accuracy)",
"parents": {"eng_on_eng": PARENT_ENG, "x_on_x": parent_x, "eng_on_x": eng_on_x},
"floor_eng": PARENT_ENG["nats_per_byte"],
"floor_x": min(parent_x["nats_per_byte"], eng_on_x["nats_per_byte"]),
"align_info": {"perm": ip, "orth": io, "perm_forced": ipf, "orth_forced": iof}, "predictors": p, "rungs": res}
# ------------- barriers on the mean nats/byte
def ev_mean(sd):
t_e, _ = ev_np(sd, bl_e_e); t_x, _ = ev_np(sd, bl_x_e)
return 0.5 * (t_e / by_e_e + t_x / by_x_e)
try:
bn = EV.merge_barrier(SD_E, SD_X, ev_mean, n=A.barrier_n)
r["barrier_naive"] = {"barrier": bn["barrier"], "losses": list(map(float, bn["losses"]))}
bp = EV.merge_barrier(SD_E, sdp, ev_mean, n=A.barrier_n)
r["barrier_perm"] = {"barrier": bp["barrier"], "losses": list(map(float, bp["losses"]))}
except Exception as e:
log("barrier failed", e)
r["secs"] = time.time() - t0
fh.write(json.dumps(r) + "\n"); fh.flush()
log(f" {fcode}: M0 dfloor_mean={res['M0_naive_avg']['delta_floor_mean']:+.4f} "
f"M1a={res['M1a_vocab_avg']['delta_floor_mean']:+.4f} "
f"M1b_perm={res['M1b_vocab_perm_avg']['delta_floor_mean']:+.4f} "
f"M1c_orth={res['M1c_vocab_orth_avg']['delta_floor_mean']:+.4f} ({r['secs']:.0f}s)")
del rungs, sdp, sdo, sdpf, sdof, sd_emb, sd_emb2, SD_X, SD_X_V; gc.collect()
fh.close()
log("DONE set4")
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