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58e7bb7 | 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 | """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)
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