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#!/usr/bin/env python3
"""H2: PEPITA (Dellaferrera & Kreiman 2022) adapted to GCN L=6.
Algorithm (per batch / full graph):
1. Forward pass 1 (clean): X -> H_0, ..., H_{L-2}, Z_out
2. Compute E0 = softmax(Z_out) - y_onehot (masked to train nodes, unscaled)
3. Project error to input: X_mod = X - E0 @ F (F is fixed random: C × d_in)
4. Forward pass 2 (modulated): X_mod -> H_0^m, ..., H_{L-2}^m
5. Weight updates:
Output layer W_{L-1}: standard gradient via E0 (only place BP-like)
Hidden layer W_l (l < L-1): gradient ~ (agg_input_l)^T @ relu_gate * (H_l^clean - H_l^mod)
Runs on 4 datasets × 20 seeds at GCN L=6.
"""
import torch
import torch.nn.functional as F
import numpy as np
import json
import os
from src.data import load_dataset, spmm
from src.trainers import _FeedbackTrainerBase, label_spreading
from run_dblp_depth import load_dblp
device = 'cuda:0'
SEEDS = list(range(20))
EPOCHS = 200
OUT_DIR = 'results/pepita_baseline_20seeds'
class PEPITATrainer(_FeedbackTrainerBase):
"""PEPITA backward rule for GCN."""
def __init__(self, data, hidden_dim, lr, weight_decay,
diffusion_alpha=0.5, diffusion_iters=10,
num_layers=2, residual_alpha=0.0, backbone='gcn',
pepita_fb_scale=0.05, **_kw):
super().__init__(data, hidden_dim, lr, weight_decay,
diffusion_alpha, diffusion_iters,
num_layers, residual_alpha, backbone,
_kw.get('use_batchnorm', False), _kw.get('dropout', 0.0))
# Fixed random feedback: C × d_in, projects output error back to input
self.F_fb = torch.randn(self.d_out, self.d_in, device=self.device) * pepita_fb_scale
def _pepita_output_error_unscaled(self, Z_out):
"""Raw error (not divided by n_labeled) for perturbation purposes."""
mask = self.data['train_mask']
y = self.data['y']
probs = F.softmax(Z_out.detach(), dim=1)
y_oh = F.one_hot(y, self.d_out).float()
E = torch.zeros_like(probs)
E[mask] = probs[mask] - y_oh[mask]
return E
def train_step(self):
# Pass 1: clean forward
Z_out_clean, inter_clean = self.forward()
# Perturbation error (unscaled)
E_unscaled = self._pepita_output_error_unscaled(Z_out_clean)
# Gradient error (scaled by n_labeled) for output layer
E0_scaled, _ = self._output_error(Z_out_clean)
# Modulate input
X_orig = self.data['X']
X_mod = X_orig - E_unscaled @ self.F_fb
# Pass 2: modulated forward
self.data['X'] = X_mod
try:
Z_out_mod, inter_mod = self.forward()
finally:
self.data['X'] = X_orig
# Per-layer gradients
grads = []
for l in range(self.num_layers):
if l == self.num_layers - 1:
# Output layer: standard gradient via scaled E0
H_prev = inter_clean['Hs'][-1] if inter_clean['Hs'] else X_orig
g = H_prev.t() @ self._graph_conv_T(E0_scaled, l)
else:
# Hidden layer: activity difference, relu-gated
if l == 0:
H_prev = X_orig
else:
H_prev = inter_clean['Hs'][l - 1]
relu_gate = (inter_clean['Zs'][l].detach() > 0).float()
# activity difference (post-ReLU)
delta_post = inter_clean['Hs'][l] - inter_mod['Hs'][l]
# scale by n_labeled like BP does
n_labeled = self.data['train_mask'].sum().float().clamp(min=1.0)
delta = relu_gate * delta_post / n_labeled
g = H_prev.t() @ self._graph_conv_T(delta, l)
grads.append(g)
# Apply Adam
if self._use_adam:
self._adam_t += 1
for i in range(self.num_layers):
self.weights[i] = self.weights[i] - self._adam_step(i, grads[i])
else:
for i in range(self.num_layers):
self.weights[i] = self.weights[i] - self.lr * (grads[i] + self.wd * self.weights[i])
with torch.no_grad():
mask = self.data['train_mask']
loss = F.cross_entropy(Z_out_clean[mask], self.data['y'][mask]).item()
acc = (Z_out_clean[mask].argmax(1) == self.data['y'][mask]).float().mean().item()
return loss, acc, {}
def train_one(cls, common, extra, seed):
torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
t = cls(**common, **extra)
bv, bt = 0, 0
for ep in range(EPOCHS):
t.train_step()
if ep % 5 == 0:
v = t.evaluate('val_mask')
te = t.evaluate('test_mask')
if v > bv: bv, bt = v, te
del t; torch.cuda.empty_cache()
return bt
def main():
os.makedirs(OUT_DIR, exist_ok=True)
per_seed_file = os.path.join(OUT_DIR, 'per_seed_data.json')
if os.path.exists(per_seed_file):
with open(per_seed_file) as f:
per_seed_data = json.load(f)
else:
per_seed_data = {}
datasets_cfg = {
'Cora': lambda: load_dataset('Cora', device=device),
'CiteSeer': lambda: load_dataset('CiteSeer', device=device),
'PubMed': lambda: load_dataset('PubMed', device=device),
'DBLP': lambda: load_dblp(),
}
for ds_name, loader in datasets_cfg.items():
data = loader()
common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
num_layers=6, residual_alpha=0.0, backbone='gcn')
key = f"{ds_name}_PEPITA"
if key not in per_seed_data:
per_seed_data[key] = {}
print(f"\n=== {key} (20 seeds, GCN L=6) ===", flush=True)
for seed in SEEDS:
sk = str(seed)
if sk in per_seed_data[key]:
print(f" seed {seed}: cached ({per_seed_data[key][sk]*100:.1f}%)", flush=True)
continue
try:
acc = train_one(PEPITATrainer, common, {}, seed)
per_seed_data[key][sk] = acc
print(f" seed {seed}: {acc*100:.1f}%", flush=True)
except Exception as e:
print(f" seed {seed}: FAILED - {e}", flush=True)
per_seed_data[key][sk] = 0.0
with open(per_seed_file, 'w') as f:
json.dump(per_seed_data, f, indent=2)
del data; torch.cuda.empty_cache()
# Summary
print(f"\n{'=' * 70}\nPEPITA summary (20 seeds, GCN L=6)\n{'=' * 70}")
results = {}
for ds in datasets_cfg:
key = f"{ds}_PEPITA"
vals = np.array([per_seed_data[key][str(s)] for s in SEEDS]) * 100
results[key] = {'mean': float(vals.mean()), 'std': float(vals.std()),
'per_seed': vals.tolist()}
print(f" {ds:<12} {vals.mean():5.1f} ± {vals.std():4.1f}")
with open(os.path.join(OUT_DIR, 'results.json'), 'w') as f:
json.dump(results, f, indent=2)
print(f"\nSaved to {OUT_DIR}/results.json")
if __name__ == '__main__':
main()
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