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#!/usr/bin/env python3
"""§2.3 diagnostic v2 — faithful reproduction of original methodology.
Uses src.trainers.BPTrainer (the actual training stack used in the paper),
matching results/gradient_reach_20seeds/per_seed_data.json which shows
GCN L=10 weight grad norms = 0.0 for all 20 seeds × 10 layers.
Adds beyond the original:
- pre-activation grad G_Z[l] = ||dL/dZ_l||_F and RMS-normed variant
- forward magnitudes M[l] = ||H_l||_F and RMS-normed
- centered dispersion D[l] = ||H_l - mean||_F / D_0
- frozen linear probe probe_acc[l] on H_l
Backbone: GCN. Cora. 100 epochs (matches original). 20 seeds. Depths {6, 10, 20}.
Output: results/diag_section23/diag_data_v2.json
"""
import json, os, sys
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
sys.path.insert(0, '/home/yurenh2/graph-grape')
from src.data import load_dataset, spmm
from src.trainers import BPTrainer
DEVICE = 'cuda:0' # CUDA_VISIBLE_DEVICES=2 → cuda:0
HIDDEN = 64
LR = 0.01
WD = 5e-4
EPOCHS = 100
SEEDS = list(range(20))
OUT_DIR = '/home/yurenh2/graph-grape/results/diag_section23'
os.makedirs(OUT_DIR, exist_ok=True)
def forward_with_intermediates(bp, capture_for_grad=False):
"""Re-implement BPTrainer.forward() but capture per-layer Z (pre-act) and H (post-act).
H[0] = X (input features). For l = 1..L: H[l] = relu(Z[l-1]) (or Z[l-1] for last layer).
Z[0..L-1] are pre-activation outputs of each conv.
"""
X = bp.data['X']
H_list = [X]
Z_list = []
H = X
H0 = None
for l in range(bp.num_layers):
if l > 0 and l < bp.num_layers - 1 and bp.residual_alpha > 0 and H0 is not None:
H = (1 - bp.residual_alpha) * H + bp.residual_alpha * H0
Z = bp._graph_conv(H, bp.weights[l], l)
if capture_for_grad:
Z.retain_grad()
Z_list.append(Z)
if l < bp.num_layers - 1:
H = F.relu(Z)
if l == 0:
H0 = H
else:
H = Z # final logits, no relu
H_list.append(H)
return H_list[-1], Z_list, H_list # logits, Z's, H's
def diagnose(seed, L, data):
torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
bp = BPTrainer(data=data, hidden_dim=HIDDEN, lr=LR, weight_decay=WD,
num_layers=L, residual_alpha=0.0, backbone='gcn')
for _ in range(EPOCHS):
bp.train_step()
# Diagnostic forward at epoch 100
bp.optimizer.zero_grad()
logits, Zs, Hs = forward_with_intermediates(bp, capture_for_grad=True)
mask = data['train_mask']
loss = F.cross_entropy(logits[mask], data['y'][mask])
loss.backward(retain_graph=False)
# Weight gradients (original methodology)
W_grads_F = [float(bp.weights[l].grad.detach().norm().item()) for l in range(L)]
W_grads_rms = [g / np.sqrt(bp.weights[l].numel()) for g, l in zip(W_grads_F, range(L))]
# Pre-activation gradients on Z_l (l=0..L-1)
Z_grads_F = []
Z_grads_rms = []
for z in Zs:
if z.grad is None:
Z_grads_F.append(0.0); Z_grads_rms.append(0.0); continue
N, d_ = z.shape
gf = float(z.grad.detach().norm().item())
Z_grads_F.append(gf)
Z_grads_rms.append(gf / np.sqrt(N * d_))
# Forward state metrics on H_l (l=0..L)
M_F, M_rms = [], []
D_raw = []
for H in Hs:
N, d_ = H.shape
mf = float(H.detach().norm().item())
M_F.append(mf)
M_rms.append(mf / np.sqrt(N * d_))
mu = H.detach().mean(0, keepdim=True)
D_raw.append(float((H.detach() - mu).norm().item()))
D0 = D_raw[0] if D_raw[0] > 0 else 1.0
D_norm = [d / D0 for d in D_raw]
# Frozen linear probe on each H_l
probe_acc = []
ytr = data['y'][data['train_mask']].cpu().numpy()
yte = data['y'][data['test_mask']].cpu().numpy()
train_mask_b = data['train_mask']
test_mask_b = data['test_mask']
for H in Hs:
Xtr = H.detach()[train_mask_b].cpu().numpy()
Xte = H.detach()[test_mask_b].cpu().numpy()
try:
sc = StandardScaler().fit(Xtr)
Xtr_s = sc.transform(Xtr)
Xte_s = sc.transform(Xte)
clf = LogisticRegression(max_iter=2000, C=1.0).fit(Xtr_s, ytr)
acc = float(clf.score(Xte_s, yte))
except Exception:
acc = float('nan')
probe_acc.append(acc)
bp_acc = bp.evaluate('test_mask')
del bp; torch.cuda.empty_cache()
return dict(L=L, seed=seed, bp_acc=bp_acc,
W_grads_F=W_grads_F, W_grads_rms=W_grads_rms,
Z_grads_F=Z_grads_F, Z_grads_rms=Z_grads_rms,
M_F=M_F, M_rms=M_rms, D_raw=D_raw, D_norm=D_norm,
probe_acc=probe_acc)
def main():
data = load_dataset('Cora', device=DEVICE)
print(f"Cora: N={data['X'].shape[0]}, F={data['X'].shape[1]}, "
f"C={data['num_classes']}", flush=True)
all_results = {}
for L in [20, 10, 6]:
print(f'\n=== L={L} ===', flush=True)
rows = []
for s in SEEDS:
r = diagnose(s, L, data)
rows.append(r)
wg = r['W_grads_F']
print(f" L={L} s={s:2d} acc={r['bp_acc']:.4f} "
f"W_grads[0,mid,-1]=[{wg[0]:.2e}, {wg[len(wg)//2]:.2e}, {wg[-1]:.2e}] "
f"Z_grad[out]={r['Z_grads_F'][-1]:.2e}", flush=True)
all_results[f'L={L}'] = rows
out_path = os.path.join(OUT_DIR, 'diag_data_v2.json')
with open(out_path, 'w') as f:
json.dump(all_results, f, indent=2)
print(f'\nSaved {out_path}')
print('\n=== summary ===')
for k, rows in all_results.items():
Wg = np.array([r['W_grads_F'] for r in rows])
n_under = int((Wg < 1e-38).sum())
n_total = Wg.size
accs = np.array([r['bp_acc'] for r in rows])
print(f' {k}: BP acc {accs.mean():.4f}±{accs.std():.4f} '
f'W_grads_F median={np.median(Wg):.3e} '
f'<1e-38: {n_under}/{n_total} cells')
if __name__ == '__main__':
main()
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