File size: 7,323 Bytes
7e354e4 | 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 | """Per-pair worker: pre-merge diagnostic -> recorded prediction -> merge (naive / aligned) ->
DOWNSTREAM ACCURACY for parent A, parent B, naive merge, aligned merge.
usage: run_pairs.py <pairs.json> <gpu> [<shard> <nshards>]
"""
from __future__ import annotations
import os, sys, json, time, gc, traceback
gpu = sys.argv[2]
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
import numpy as np, torch
import ma_common as C
import tasks as TK
PAIRS = json.load(open(sys.argv[1]))
SHARD, NSH = (int(sys.argv[3]), int(sys.argv[4])) if len(sys.argv) > 4 else (0, 1)
LEDGER = os.environ.get("MA_LEDGER", "/root/merge-accuracy/results/ledger.jsonl")
NDOC = int(os.environ.get("MA_NDOC", "500"))
CORE = os.environ.get("MA_TASKS", "sciq,piqa,arc_easy,lambada").split(",")
DEV = "cuda"
_docs = {}
def docs(t):
if t not in _docs:
_docs[t] = TK.TASKS[t](NDOC)
return _docs[t]
done = C.jload(LEDGER)
def have(k): return k in done
def put(k, rec):
rec["key"] = k; rec["t"] = time.time()
C.jappend(LEDGER, rec); done[k] = rec
print(f"[{time.strftime('%H:%M:%S')}] {k} " +
" ".join(f"{t}={rec['acc'][t]:.4f}" for t in rec.get("acc", {})), flush=True)
def eval_sd(model, tok, sd=None):
if sd is not None:
C.sd_load(model, sd)
out, items = {}, {}
for t in CORE:
r = C.eval_task(model, tok, docs(t), DEV, bs=int(os.environ.get("MA_BS", "16")))
out[t] = r["acc"]; out[t + "_norm"] = r["acc_norm"]; items[t] = r["items"]
out["mean"] = float(np.mean([out[t] for t in CORE]))
return out, items
for pi, P in enumerate(PAIRS):
if pi % NSH != SHARD:
continue
name = P["name"]; rung = P["rung"]
t_pair = time.time()
try:
# ---------------------------------------------------------- parents
ma = C.load_model(P["a"]["repo"], P["a"].get("rev"))
ta = C.load_tok(P["a"]["repo"], P["a"].get("rev"))
sents = C.flores_lines("eng_Latn", 256)
acts_a = C.capture_acts_sent(ma, ta, sents, DEV)
sd_a = C.sd_np(ma)
ka = f"{rung}|{name}|parentA"
if not have(ka):
acc, _ = eval_sd(ma, ta)
put(ka, {"rung": rung, "pair": name, "arm": "parentA", "alpha": None,
"model": P["a"]["repo"], "rev": P["a"].get("rev"), "acc": acc})
accA = done[ka]["acc"]
mb = C.load_model(P["b"]["repo"], P["b"].get("rev"))
tb = C.load_tok(P["b"]["repo"], P["b"].get("rev"))
acts_b = C.capture_acts_sent(mb, tb, sents, DEV)
sd_b = C.sd_np(mb)
kb = f"{rung}|{name}|parentB"
if not have(kb):
acc, _ = eval_sd(mb, tb)
put(kb, {"rung": rung, "pair": name, "arm": "parentB", "alpha": None,
"model": P["b"]["repo"], "rev": P["b"].get("rev"), "acc": acc})
accB = done[kb]["acc"]
del mb; gc.collect(); torch.cuda.empty_cache()
cfg = ma.config
hid = getattr(cfg, "hidden_size", None); nh = getattr(cfg, "num_attention_heads", None)
# ---------------------------------------------------------- mergeable keys
mk = C.body_keys(sd_a, sd_b)
full = C.shared_keys(sd_a, sd_b)
emb_ok = len(full) > len(mk) and P.get("same_tokenizer", True)
keys = full if emb_ok else mk
scope = "full" if emb_ok else "body_only"
# ---------------------------------------------------------- ALIGN (B -> A's frame)
t0 = time.time()
sd_b_perm, info_p = C.align_pair(sd_a, sd_b, hid, nh, acts_a, acts_b, "permutation")
sd_b_orth, info_o = C.align_pair(sd_a, sd_b, hid, nh, acts_a, acts_b, "orthogonal")
t_align = time.time() - t0
# ---------------------------------------------------------- PRE-MERGE DIAGNOSTIC
kd = f"{rung}|{name}|diag"
if not have(kd):
d = C.diagnostics({k: sd_a[k] for k in keys}, {k: sd_b[k] for k in keys},
{k: sd_b_perm[k] for k in keys}, {k: sd_b_orth[k] for k in keys},
acts_a, acts_b)
d["align_info_perm"] = info_p; d["align_info_orth"] = info_o
d["align_seconds"] = t_align; d["merge_scope"] = scope; d["n_merge_keys"] = len(keys)
# PREDICTION, recorded BEFORE any merged model is scored.
d["predicted_align_helps"] = bool(d["coord_share"] >= 0.02)
put(kd, {"rung": rung, "pair": name, "arm": "diag", "diag": d})
diag = done[kd]["diag"]
# pick the better of the two aligners by scale-free residual distance
use_orth = diag.get("qmd_bn_orth", 9e9) < diag.get("qmd_bn_perm", 9e9)
sd_b_al = sd_b_orth if use_orth else sd_b_perm
aligner = "orthogonal" if use_orth else "permutation"
# ---------------------------------------------------------- MERGES
for alpha in P.get("alphas", [0.5]):
for arm, sdb in (("naive", sd_b), ("aligned", sd_b_al)):
k = f"{rung}|{name}|{arm}|a{alpha}"
if have(k):
continue
sd_m = dict(sd_a)
for kk in keys:
sd_m[kk] = (1 - alpha) * sd_a[kk] + alpha * np.asarray(sdb[kk], float)
acc, _ = eval_sd(ma, ta, sd_m)
put(k, {"rung": rung, "pair": name, "arm": arm, "alpha": alpha,
"aligner": aligner if arm == "aligned" else None,
"scope": scope, "acc": acc,
"coord_share": diag["coord_share"],
"accA_mean": accA["mean"], "accB_mean": accB["mean"]})
del sd_m; gc.collect()
C.sd_load(ma, sd_a) # restore A for the next merge
# ---------------------------------------------------------- TIES (alpha-free)
if P.get("ties", True):
for arm, sdb in (("ties_naive", sd_b), ("ties_aligned", sd_b_al)):
k = f"{rung}|{name}|{arm}"
if have(k):
continue
try:
base = {kk: np.zeros_like(sd_a[kk]) for kk in keys}
tv = C.MG.ties(base, [{kk: sd_a[kk] for kk in keys},
{kk: np.asarray(sdb[kk], float) for kk in keys}],
density=0.2)
sd_m = dict(sd_a); sd_m.update(tv)
acc, _ = eval_sd(ma, ta, sd_m)
put(k, {"rung": rung, "pair": name, "arm": arm, "alpha": None,
"scope": scope, "acc": acc, "coord_share": diag["coord_share"],
"accA_mean": accA["mean"], "accB_mean": accB["mean"]})
del sd_m, tv; gc.collect()
C.sd_load(ma, sd_a)
except Exception as e:
print("TIES fail", name, arm, repr(e)[:200], flush=True)
print(f"== pair {name} done in {time.time()-t_pair:.0f}s", flush=True)
except Exception as e:
print(f"!! PAIR FAIL {name}: {traceback.format_exc()[-1500:]}", flush=True)
finally:
for v in ("ma", "mb", "sd_a", "sd_b", "sd_b_perm", "sd_b_orth", "acts_a", "acts_b"):
if v in dir(): pass
try: del ma
except Exception: pass
gc.collect(); torch.cuda.empty_cache()
print("ALLDONE", flush=True)
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