Initial release: GRAFT (KAFT) — NeurIPS 2026 submission code

Topology-factorized Jacobian-aligned feedback for deep GNNs. Includes:
- src/: GraphGrAPETrainer (KAFT) + BP / DFA / DFA-GNN / VanillaGrAPE baselines
        + multi-probe alignment estimator + dataset / sparse-mm utilities.
- experiments/: 19 runners reproducing every figure / table in the paper.
- figures/: 4 generators + the 4 PDFs cited in the report.
- paper/: NeurIPS .tex and consolidated experiments_master notes.

Smoke test: 50-epoch Cora GCN L=4 gives BP 77.3% / KAFT 79.0%.

1 files changed, 198 insertions, 0 deletions

diff --git a/experiments/run_cafo_baseline.py b/experiments/run_cafo_baseline.py
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+++ b/experiments/run_cafo_baseline.py
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+#!/usr/bin/env python3
+"""H3: CaFo+CE (Cascaded Forward Learning with Top-Down Feedback, Park et al. 2023).
+
+Greedy layer-wise training for GCN L=6:
+  - Each hidden layer l has an auxiliary classifier W_aux_l: hidden → num_classes
+  - Forward through all layers with .detach() between layers (blocks upstream gradient)
+  - Per-layer CE loss on labeled nodes via auxiliary classifier
+  - Output layer uses standard cross-entropy
+  - No global backprop — each W_l only sees its local loss
+
+Tests CaFo on Cora/CiteSeer/PubMed/DBLP × 20 seeds, 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 run_dblp_depth import load_dblp
+
+device = 'cuda:0'
+SEEDS = list(range(20))
+EPOCHS = 200
+OUT_DIR = 'results/cafo_baseline_20seeds'
+
+
+class CaFoTrainer:
+    """CaFo+CE: greedy layer-wise training with per-layer CE loss."""
+
+    def __init__(self, data, hidden_dim, lr, weight_decay,
+                 num_layers=2, residual_alpha=0.0, backbone='gcn', **_kw):
+        dev = data['X'].device
+        self.data = data
+        self.device = dev
+        self.lr = lr
+        self.wd = weight_decay
+        self.num_layers = num_layers
+        self.residual_alpha = residual_alpha
+        self.backbone = backbone
+        self._training = True
+
+        d_in = data['num_features']
+        d_out = data['num_classes']
+        self.d_out = d_out
+
+        dims = [d_in] + [hidden_dim] * (num_layers - 1) + [d_out]
+        # Main layer weights — autograd Parameters
+        self.weights = []
+        for i in range(num_layers):
+            w = torch.empty(dims[i], dims[i + 1], device=dev)
+            torch.nn.init.xavier_uniform_(w)
+            w.requires_grad_(True)
+            self.weights.append(w)
+
+        # Auxiliary classifier per hidden layer: hidden_dim -> d_out
+        self.W_aux = []
+        for i in range(num_layers - 1):
+            w_aux = torch.empty(hidden_dim, d_out, device=dev)
+            torch.nn.init.xavier_uniform_(w_aux)
+            w_aux.requires_grad_(True)
+            self.W_aux.append(w_aux)
+
+        params = self.weights + self.W_aux
+        self.optim = torch.optim.Adam(params, lr=lr, weight_decay=weight_decay)
+
+    def _gcn_conv(self, H, W):
+        """GCN conv: A_hat @ (H @ W)."""
+        return spmm(self.data['A_hat'], H @ W)
+
+    def train_step(self):
+        X = self.data['X']
+        y = self.data['y']
+        mask = self.data['train_mask']
+
+        self.optim.zero_grad()
+
+        H = X
+        total_loss = 0.0
+        for l in range(self.num_layers):
+            if l > 0:
+                H = H.detach()  # block grad flow upstream
+
+            Z = self._gcn_conv(H, self.weights[l])
+
+            if l < self.num_layers - 1:
+                H_new = F.relu(Z)
+                # Auxiliary classifier (projects hidden to classes)
+                Z_aux = H_new @ self.W_aux[l]
+                loss_l = F.cross_entropy(Z_aux[mask], y[mask])
+                loss_l.backward()
+                total_loss += loss_l.item()
+                H = H_new
+            else:
+                # Output layer: standard CE
+                loss_final = F.cross_entropy(Z[mask], y[mask])
+                loss_final.backward()
+                total_loss += loss_final.item()
+
+        self.optim.step()
+
+        with torch.no_grad():
+            Z_out = self._forward_full_detached()
+            acc = (Z_out[mask].argmax(1) == y[mask]).float().mean().item()
+        return total_loss, acc, {}
+
+    def _forward_full_detached(self):
+        """Full forward pass with no_grad for evaluation."""
+        X = self.data['X']
+        H = X
+        for l in range(self.num_layers):
+            Z = self._gcn_conv(H, self.weights[l].detach())
+            if l < self.num_layers - 1:
+                H = F.relu(Z)
+        return Z
+
+    @torch.no_grad()
+    def evaluate(self, mask_name='test_mask'):
+        self._training = False
+        Z = self._forward_full_detached()
+        self._training = True
+        mask = self.data[mask_name]
+        return (Z[mask].argmax(1) == self.data['y'][mask]).float().mean().item()
+
+
+def train_one(seed, data, num_layers=6):
+    torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
+    t = CaFoTrainer(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
+                    num_layers=num_layers, residual_alpha=0.0, backbone='gcn')
+    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()
+        key = f"{ds_name}_CaFo+CE"
+        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(seed, data)
+                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}\nCaFo+CE summary (20 seeds, GCN L=6)\n{'=' * 70}")
+    results = {}
+    for ds in datasets_cfg:
+        key = f"{ds}_CaFo+CE"
+        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()