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
|
"""
Clean BP gradient check — run in independent Python process per method.
Usage: python clean_gradient_check.py --method bp --seed 42 --gpu 1
"""
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, default=42)
p.add_argument('--gpu', type=int, default=1)
p.add_argument('--output_dir', type=str, default='results/confirmatory/clean_grads')
args = p.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
device = torch.device(f'cuda:{args.gpu}')
# 1. Load eval data (256 samples, first batch, no shuffle)
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) # num_workers=0 for determinism
for x, y in tel:
x = x.view(x.size(0), -1).to(device)
y = y.to(device)
break
batch = x.size(0)
print(f"[{args.method} s={args.seed}] Batch: {batch}, y[:5]={y[:5].tolist()}", flush=True)
# 2. Create model from scratch, load checkpoint (strict=True)
L, d, C = 4, 256, 10
ckpt_path = f'results/confirmatory/checkpoints_A2/{args.method}_s{args.seed}.pt'
assert os.path.exists(ckpt_path), f"Checkpoint not found: {ckpt_path}"
model = ResidualMLP(3072, d, C, L).to(device)
sd = torch.load(ckpt_path, map_location=device)
model.load_state_dict(sd, strict=True)
model.eval()
# Verify: print first param norm and checkpoint hash
first_param = list(model.parameters())[0]
print(f" First param norm: {first_param.norm().item():.6f}", flush=True)
print(f" Checkpoint: {ckpt_path}", flush=True)
# 3. Method A: manual forward + autograd.grad
h0 = model.embed(x.detach())
hs = [h0.clone().requires_grad_(True)]
for b in model.blocks:
hs.append(hs[-1] + b(hs[-1]))
lo_a = model.out_head(model.out_ln(hs[-1]))
loss_a = F.cross_entropy(lo_a, y)
acc_a = (lo_a.argmax(1) == y).float().mean().item()
gs_a = torch.autograd.grad(loss_a, hs)
print(f" Method A (manual+autograd.grad): loss={loss_a.item():.6f} acc={acc_a:.4f}", flush=True)
for l in range(L):
n = gs_a[l].norm(dim=-1)
print(f" layer {l}: mean_norm={n.mean():.2e} median={n.median():.2e} "
f"max={n.max():.2e} s(1e-6)={(n>1e-6).float().mean():.4f}", flush=True)
# 4. Method B: retain_grad + backward
model.zero_grad()
for param in model.parameters():
param.requires_grad_(True)
lo_b, hi_b = model(x, return_hidden=True)
for l in range(L + 1):
hi_b[l].retain_grad()
loss_b = F.cross_entropy(lo_b, y)
acc_b = (lo_b.argmax(1) == y).float().mean().item()
loss_b.backward()
print(f" Method B (retain_grad+backward): loss={loss_b.item():.6f} acc={acc_b:.4f}", flush=True)
for l in range(L):
if hi_b[l].grad is not None:
n = hi_b[l].grad.norm(dim=-1)
print(f" layer {l}: mean_norm={n.mean():.2e} median={n.median():.2e} "
f"max={n.max():.2e} s(1e-6)={(n>1e-6).float().mean():.4f}", flush=True)
else:
print(f" layer {l}: grad is None!", flush=True)
# 5. Method C: full model backward (no detach)
model.zero_grad()
lo_c = model(x)
loss_c = F.cross_entropy(lo_c, y)
loss_c.backward()
# Get embedding gradient as proxy
embed_grad_norm = model.embed.weight.grad.norm().item() if model.embed.weight.grad is not None else 0
print(f" Method C (full backward): loss={loss_c.item():.6f} embed_grad_norm={embed_grad_norm:.2e}", flush=True)
# 6. Save results
result = {
'method': args.method, 'seed': args.seed, 'batch_size': batch,
'y_first5': y[:5].tolist(),
'first_param_norm': first_param.norm().item(),
'method_A': {
'loss': loss_a.item(), 'acc': acc_a,
'per_layer': [{
'mean_norm': gs_a[l].norm(-1).mean().item(),
'median_norm': gs_a[l].norm(-1).median().item(),
'max_norm': gs_a[l].norm(-1).max().item(),
's_1e6': (gs_a[l].norm(-1) > 1e-6).float().mean().item(),
} for l in range(L)]
},
'method_B': {
'loss': loss_b.item(), 'acc': acc_b,
'per_layer': [{
'mean_norm': hi_b[l].grad.norm(-1).mean().item() if hi_b[l].grad is not None else None,
'median_norm': hi_b[l].grad.norm(-1).median().item() if hi_b[l].grad is not None else None,
'max_norm': hi_b[l].grad.norm(-1).max().item() if hi_b[l].grad is not None else None,
's_1e6': (hi_b[l].grad.norm(-1) > 1e-6).float().mean().item() if hi_b[l].grad is not None else None,
} for l in range(L)]
},
'method_C_embed_grad_norm': embed_grad_norm,
}
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)
print(f" Saved to {out}", flush=True)
if __name__ == '__main__':
main()
|
