File size: 4,958 Bytes
9497609 | 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 | """Does L20's advantage over L47 survive more training data?
A head fitted on 4,000 images reads 0.293 at L20 against 0.230 at L47, which
puts the shipped tap layer in question. Before re-encoding train2017 at a new
depth, which is an overnight job, this asks the cheaper question: is the gap
stable as the fit gets more data, or is it a small-sample effect that closes?
Same held-out 1,000 images throughout, so only the training size moves. Several
seeds per point, because a gap of 0.06 read off one fit is not a gap.
python scripts/tap_layer_scaling.py
"""
from __future__ import annotations
import argparse
import json
import numpy as np
import torch
import torch.nn.functional as F
LAYERS = [4, 12, 20, 28, 36, 44, 47, 54, 60]
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--cache", default="/root/depth_states_all9.npz")
p.add_argument("--cache-layers", default="4,12,20,28,36,44,47,54,60",
help="layer order the cache was written with")
p.add_argument("--layers", default="20,47")
p.add_argument("--sizes", default="250,500,1000,2000,4000")
p.add_argument("--seeds", type=int, default=5)
p.add_argument("--holdout", type=int, default=1000)
p.add_argument("--dim", type=int, default=1024)
p.add_argument("--epochs", type=int, default=40)
p.add_argument("--lr", type=float, default=1e-3)
p.add_argument("--tau", type=float, default=0.05)
p.add_argument("--out", default="/root/tap_layer_scaling.json")
return p.parse_args()
def fit_and_score(I_tr, T_tr, I_te, T_te, dim, epochs, lr, tau, seed):
torch.manual_seed(seed)
mu_i, mu_t = I_tr.mean(0, keepdim=True), T_tr.mean(0, keepdim=True)
Wi = torch.nn.Linear(I_tr.shape[1], dim).cuda()
Wt = torch.nn.Linear(T_tr.shape[1], dim).cuda()
opt = torch.optim.Adam(list(Wi.parameters()) + list(Wt.parameters()), lr=lr)
n = len(I_tr)
for _ in range(epochs):
perm = torch.randperm(n, device="cuda")
for i in range(0, n, 1024):
idx = perm[i:i + 1024]
if len(idx) < 8:
continue
a = F.normalize(Wi(I_tr[idx] - mu_i), dim=1)
b = F.normalize(Wt(T_tr[idx] - mu_t), dim=1)
logits = a @ b.T / tau
lab = torch.arange(len(idx), device="cuda")
loss = 0.5 * (F.cross_entropy(logits, lab) + F.cross_entropy(logits.T, lab))
opt.zero_grad()
loss.backward()
opt.step()
with torch.no_grad():
a = F.normalize(Wi(I_te - mu_i), dim=1)
b = F.normalize(Wt(T_te - mu_t), dim=1)
S = a @ b.T
d = torch.arange(len(a), device="cuda")
rank = (S > S[d, d][:, None]).sum(1) + 1
return float((rank == 1).float().mean())
def main() -> None:
a = parse_args()
layers = [int(x) for x in a.layers.split(",")]
cache_layers = [int(x) for x in a.cache_layers.split(",")]
sizes = [int(x) for x in a.sizes.split(",")]
z = np.load(a.cache, allow_pickle=True)
img, txt = z["img"], z["txt"]
n_all = img.shape[0]
rng = np.random.default_rng(0)
order = rng.permutation(n_all)
te, pool = order[:a.holdout], order[a.holdout:]
print(f"{n_all} images, {len(pool)} available to train on, {len(te)} held out")
out = {}
for L in layers:
li = cache_layers.index(L)
I = torch.tensor(img[:, li], dtype=torch.float32, device="cuda")
T = torch.tensor(txt[:, li], dtype=torch.float32, device="cuda")
out[f"L{L}"] = {}
for n in sizes:
if n > len(pool):
continue
vals = []
for s in range(a.seeds):
sub = np.random.default_rng(100 + s).choice(pool, size=n, replace=False)
vals.append(fit_and_score(I[sub], T[sub], I[te], T[te],
a.dim, a.epochs, a.lr, a.tau, s))
out[f"L{L}"][str(n)] = {"mean_r@1": float(np.mean(vals)),
"std": float(np.std(vals)),
"seeds": vals}
print(f" L{L:<3d} n={n:<5d} r@1 {np.mean(vals):.3f} +/- {np.std(vals):.3f}",
flush=True)
print()
print(f"{'n':>7s} {'L20':>16s} {'L47':>16s} {'gap':>8s}")
for n in sizes:
k = str(n)
if k in out.get("L20", {}) and k in out.get("L47", {}):
g = out["L20"][k]["mean_r@1"] - out["L47"][k]["mean_r@1"]
print(f"{n:>7d} {out['L20'][k]['mean_r@1']:>8.3f}+/-{out['L20'][k]['std']:<6.3f} "
f"{out['L47'][k]['mean_r@1']:>8.3f}+/-{out['L47'][k]['std']:<6.3f} {g:>+8.3f}")
with open(a.out, "w") as f:
json.dump({"question": "does the L20 advantage over L47 survive more training data",
"holdout": a.holdout, "seeds_per_point": a.seeds,
"results": out}, f, indent=1)
print(f"\nwrote {a.out}")
if __name__ == "__main__":
main()
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