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
|
"""d=512 sparsity analysis. One method+seed per invocation."""
import os, sys, json, argparse, numpy as np, torch, torch.nn.functional as F
from torch.utils.data import DataLoader
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from models.residual_mlp import ResidualMLP
import torchvision, torchvision.transforms as transforms
def main():
p = argparse.ArgumentParser()
p.add_argument('--method', type=str, required=True)
p.add_argument('--seed', type=int, required=True)
p.add_argument('--gpu', type=int, default=0)
p.add_argument('--output_dir', type=str, default='results/confirmatory/d512_sparsity')
args = p.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
device = torch.device(f'cuda:{args.gpu}')
L, d = 4, 512
thresholds = [1e-8, 1e-7, 1e-6, 1e-5, 1e-4]
tv = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.4914,0.4822,0.4465),(0.2470,0.2435,0.2616))])
tel = DataLoader(torchvision.datasets.CIFAR10('./data', False, download=True, transform=tv),
256, False, num_workers=0)
for x, y in tel: x = x.view(x.size(0),-1).to(device); y = y.to(device); break
model = ResidualMLP(3072, d, 10, L).to(device)
model.load_state_dict(torch.load(
f'results/confirmatory/cifar_d512/{args.method}_s{args.seed}.pt',
map_location=device), strict=True)
model.eval()
h0 = model.embed(x.detach())
hs = [h0.clone().requires_grad_(True)]
for b in model.blocks: hs.append(hs[-1] + b(hs[-1]))
lo = model.out_head(model.out_ln(hs[-1]))
loss = F.cross_entropy(lo, y)
acc = (lo.argmax(1) == y).float().mean().item()
gs = torch.autograd.grad(loss, hs)
result = {'method': args.method, 'seed': args.seed, 'd': d, 'acc': acc, 'per_layer': []}
for l in range(L):
g = gs[l].detach(); norms = g.norm(dim=-1)
ninf = g.abs().max(-1).values; n2 = norms.clamp(min=1e-30)
n4 = (g.abs()**4).sum(-1)**(1/4); n1 = g.abs().sum(-1)
r_inf = (ninf / n2); pr = (n2**4 / (n4**4).clamp(min=1e-60)) / d
hoyer = (n1 / (n2 * d**0.5).clamp(min=1e-30))**2
gsq = g**2; te = gsq.sum(-1, keepdim=True).clamp(min=1e-60)
ssq, _ = gsq.sort(dim=-1, descending=True); cs = ssq.cumsum(-1)
topk = {}
for k in [1, 5, 10, 25]:
idx = max(1, int(d * k / 100)) - 1
topk[str(k)] = (cs[:, idx:idx+1] / te).squeeze(-1).mean().item()
support = {str(tau): (norms > tau).float().mean().item() for tau in thresholds}
result['per_layer'].append({
'layer': l, 'mean_norm': norms.mean().item(), 'median_norm': norms.median().item(),
'support': support, 'r_inf_mean': r_inf.mean().item(), 'pr_mean': pr.mean().item(),
'hoyer_mean': hoyer.mean().item(), 'topk_energy': topk})
out = os.path.join(args.output_dir, f'{args.method}_s{args.seed}.json')
with open(out, 'w') as f: json.dump(result, f, indent=2, default=float)
s16 = np.mean([ld['support']['1e-06'] for ld in result['per_layer']])
mn = np.mean([ld['mean_norm'] for ld in result['per_layer']])
ri = np.mean([ld['r_inf_mean'] for ld in result['per_layer']])
pr_v = np.mean([ld['pr_mean'] for ld in result['per_layer']])
print(f"[{args.method} s={args.seed}] acc={acc:.4f} s(1e-6)={s16:.4f} norm={mn:.2e} r_inf={ri:.4f} PR={pr_v:.4f}", flush=True)
if __name__ == '__main__': main()
|
