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
|
"""H2: Recursive PPGN (higher-order, 3-WL) backbone on graph 3-coloring.
State is a pair-tensor X[i,j,:] (not node features). The powerful block multiplies two
channel-wise n x n matrices (the 3-WL operation): P = M1 @ M2; out = m3([X, P]). Recurse the
pair-tensor (TRM-style deep supervision), pool to nodes (mean over j), decode colors.
Self-contained: trains (EMA, best solve), then LE diagnostic + PTRM noise/lambda-select;
writes color_/le_/ptrm_ JSONs (conv='ppgn') so diag/aggregate.py folds it into the big table.
Run: PYTHONPATH=/home/yurenh2/rrog python3 diag/ppgn_color.py --grad_mode full --seed 0
"""
import argparse, json, os, time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from diag.train_color import make_split, featurize, OUT
try:
from sklearn.metrics import roc_auc_score
except Exception:
roc_auc_score = None
def dense_A(edge_index, n):
A = torch.zeros(n, n)
if edge_index.shape[1]:
A[edge_index[0], edge_index[1]] = 1.0
return torch.maximum(A, A.t())
def mlp(di, dh, do):
return nn.Sequential(nn.Linear(di, dh), nn.ReLU(), nn.Linear(dh, do))
class RecPPGN(nn.Module):
def __init__(self, in_dim, hidden=64, k=3, T=3, n_sup=3, grad_mode='full', sigma=0.0):
super().__init__()
self.node_in = nn.Linear(in_dim, hidden)
self.adj_emb = nn.Embedding(2, hidden)
self.m1 = mlp(hidden, hidden, hidden); self.m2 = mlp(hidden, hidden, hidden)
self.m3 = mlp(2 * hidden, hidden, hidden)
self.ln = nn.LayerNorm(hidden)
self.head = nn.Linear(hidden, k)
self.T, self.n_sup, self.grad_mode, self.sigma = T, n_sup, grad_mode, sigma
def init_pair(self, xin, A): # xin [B,n,in], A [B,n,n]
h = self.node_in(xin)
X = h.unsqueeze(2) + h.unsqueeze(1) # [B,n,n,hidden]
return X + self.adj_emb(A.long())
def block(self, X): # X [B,n,n,h]
M1, M2 = self.m1(X), self.m2(X)
P = torch.einsum('bikc,bkjc->bijc', M1, M2) / X.shape[1]
return self.ln(self.m3(torch.cat([X, P], dim=-1)))
def _inner(self, X, X0, noise):
X = self.block(X + X0)
if noise and self.sigma > 0:
X = X + self.sigma * torch.randn_like(X)
return X
def recurse(self, X, X0, noise, one_step=False):
if one_step:
with torch.no_grad():
for _ in range(self.T - 1):
X = self._inner(X, X0, noise)
X = X.detach()
return self._inner(X, X0, noise)
for _ in range(self.T):
X = self._inner(X, X0, noise)
return X
def forward(self, xin, A, noise=False):
X0 = self.init_pair(xin, A)
X = torch.zeros_like(X0)
outs = []
for s in range(self.n_sup):
X = self.recurse(X, X0, noise, one_step=(self.grad_mode == '1step'))
outs.append(self.head(X.mean(2))) # pool over j -> [B,n,k]
X = X.detach()
return outs
def conflict_loss(logits, A): # logits [B,n,k], A [B,n,n]
p = F.softmax(logits, dim=-1)
return (torch.einsum('bik,bjk->bij', p, p) * A).sum() / (A.sum() + 1e-9)
def batches(graphs, bs, shuffle, dev):
idx = np.arange(len(graphs))
if shuffle:
np.random.shuffle(idx)
for s in range(0, len(idx) - (len(idx) % bs if shuffle else 0), bs):
sel = idx[s:s + bs]
if len(sel) == 0:
continue
X = torch.stack([graphs[i]['xin'] for i in sel]).to(dev)
A = torch.stack([graphs[i]['A'] for i in sel]).to(dev)
yield X, A
@torch.no_grad()
def solve_stats(model, graphs, dev, sample=300):
model.eval(); solved = 0; tot = 0
for g in graphs[:sample]:
A = g['A'].unsqueeze(0).to(dev)
col = model(g['xin'].unsqueeze(0).to(dev), A)[-1][0].argmax(-1)
conf = ((col.unsqueeze(0) == col.unsqueeze(1)) & (A[0] > 0)).sum().item() // 2
solved += int(conf == 0); tot += 1
return solved / tot
def lyap1_and_solved(model, g, dev, seed, sigma=0.0):
gen = torch.Generator(device=dev).manual_seed(seed)
xin = g['xin'].unsqueeze(0).to(dev); A = g['A'].unsqueeze(0).to(dev)
X0 = model.init_pair(xin, A)
X = torch.zeros_like(X0)
v = torch.randn(X.shape, generator=gen, device=dev); v = v / (v.norm() + 1e-12)
lam = 0.0
for _ in range(model.n_sup * model.T):
Xd, Jv = torch.autograd.functional.jvp(lambda XX: model.block(XX + X0), X, v)
nv = Jv.norm(); lam += torch.log(nv + 1e-12).item(); v = (Jv / (nv + 1e-12)).detach()
X = Xd.detach()
if sigma > 0:
X = X + sigma * torch.randn(X.shape, generator=gen, device=dev)
col = model.head(X.mean(2))[0].argmax(-1)
conf = ((col.unsqueeze(0) == col.unsqueeze(1)) & (A[0] > 0)).sum().item() // 2
return lam / (model.n_sup * model.T), conf
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--grad_mode', choices=['full', '1step'], default='full')
ap.add_argument('--epochs', type=int, default=150); ap.add_argument('--bs', type=int, default=16)
ap.add_argument('--hidden', type=int, default=64); ap.add_argument('--seed', type=int, default=0)
ap.add_argument('--sigma', type=float, default=0.2); ap.add_argument('--K', type=int, default=16)
ap.add_argument('--n_graphs', type=int, default=150)
args = ap.parse_args()
torch.manual_seed(args.seed); np.random.seed(args.seed)
dev = 'cuda' if torch.cuda.is_available() else 'cpu'
tr = featurize(make_split('train', 50, 3, 0.2, 8, 2000, 0), 'none', 16)
te = featurize(make_split('test', 50, 3, 0.2, 8, 500, 100000), 'none', 16)
for g in tr + te:
g['A'] = dense_A(g['edge_index'], g['n'])
in_dim = tr[0]['xin'].shape[1]
model = RecPPGN(in_dim, args.hidden, 3, grad_mode=args.grad_mode).to(dev)
opt = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)
sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, args.epochs)
ema = {kk: v.detach().clone() for kk, v in model.state_dict().items()}
t0 = time.time(); best = -1; best_state = None
for ep in range(args.epochs):
model.train()
for X, A in batches(tr, args.bs, True, dev):
opt.zero_grad()
outs = model(X, A, noise=False)
loss = sum(conflict_loss(o, A) for o in outs) / len(outs)
loss.backward(); torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step()
with torch.no_grad():
for kk, v in model.state_dict().items():
ema[kk].mul_(0.999).add_(v.detach(), alpha=0.001) if torch.is_floating_point(v) else ema[kk].copy_(v)
sched.step()
if (ep + 1) % 20 == 0 or ep == args.epochs - 1:
bk = {kk: v.detach().clone() for kk, v in model.state_dict().items()}
model.load_state_dict(ema); sr = solve_stats(model, te, dev)
if sr > best:
best = sr; best_state = {kk: ema[kk].detach().cpu().clone() for kk in ema}
model.load_state_dict(bk)
print(f"ep{ep+1} solve={sr:.3f}", flush=True)
model.load_state_dict({kk: best_state[kk].to(dev) for kk in best_state}); model.eval()
# LE + PTRM (noise + lambda-select) on test
lams, fails = [], []
passk = lamsel = rand = 0
Lr, Sr = [], []
for gi, g in enumerate(te[:args.n_graphs]):
lam0, c0 = lyap1_and_solved(model, g, dev, seed=gi, sigma=0.0)
lams.append(lam0); fails.append(int(c0 > 0))
res = [lyap1_and_solved(model, g, dev, seed=1000 * gi + j, sigma=args.sigma) for j in range(args.K)]
confs = np.array([c for _, c in res]); rl = np.array([l for l, _ in res])
solved = confs == 0
passk += int(solved.any()); lamsel += int(solved[rl.argmin()]); rand += int(solved[0])
Lr += rl.tolist(); Sr += solved.tolist()
lams, fails = np.array(lams), np.array(fails)
s, f = lams[fails == 0], lams[fails == 1]
auc = (roc_auc_score(fails, lams) if roc_auc_score and len(s) and len(f) else float('nan'))
Lr, Sr = np.array(Lr), np.array(Sr)
pauc = (roc_auc_score(Sr.astype(int), -Lr) if roc_auc_score and Sr.any() and (~Sr).any() else float('nan'))
n = len(te[:args.n_graphs])
print(f"[ppgn/{args.grad_mode}] solve={best:.3f} | LE AUROC={auc:.3f} mean_lam={lams.mean():+.3f} | "
f"PTRM det={1 - fails.mean():.3f} passK={passk/n:.3f} lamsel={lamsel/n:.3f} ({time.time()-t0:.0f}s)")
base = f"ppgn_full_none_n50_k3_p0.2_T3_ns3_s{args.seed}" if args.grad_mode == 'full' \
else f"ppgn_1step_none_n50_k3_p0.2_T3_ns3_s{args.seed}"
com = {'conv': 'ppgn', 'pe': 'none', 'grad_mode': args.grad_mode, 'contract': False, 'seed': args.seed}
json.dump({**com, 'solve_rate': best}, open(os.path.join(OUT, f"color_{base}.json"), 'w'), indent=2)
json.dump({**com, 'auroc': float(auc), 'mean_lam': float(lams.mean()),
'lam_solved': (float(s.mean()) if len(s) else None),
'lam_unsolved': (float(f.mean()) if len(f) else None)},
open(os.path.join(OUT, f"le_color_{base}.json"), 'w'), indent=2)
json.dump({**com, 'det': 1 - float(fails.mean()),
'sigmas': {'0.2': {'passk': passk / n, 'lamsel': lamsel / n, 'random': rand / n,
'perRoll': float(Sr.mean()), 'auroc': float(pauc)}}},
open(os.path.join(OUT, f"ptrm_color_{base}.json"), 'w'), indent=2)
print(" wrote color_/le_color_/ptrm_color_", base)
if __name__ == "__main__":
main()
|
