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Diffstat (limited to 'experiments/run_ff_baseline.py')
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diff --git a/experiments/run_ff_baseline.py b/experiments/run_ff_baseline.py new file mode 100644 index 0000000..095b811 --- /dev/null +++ b/experiments/run_ff_baseline.py @@ -0,0 +1,282 @@ +#!/usr/bin/env python3 +"""H4: Forward-Forward with Virtual-Node variant (FF+VN, Hinton 2022 + graph adaptation). + +Each layer trained locally to discriminate positive vs negative samples via a +"goodness" function (sum of squared activations). For graph data with virtual +node: + - Positive sample: augment graph with a virtual node connected to all real + nodes. The VN feature encodes the CORRECT class label (one-hot). + - Negative sample: same graph augmentation but VN feature encodes a WRONG + (random) class label. + - Goodness at layer l: g_l = mean(H_l^2) (clamped via sigmoid threshold θ) + - Local loss: binary cross-entropy on goodness, positive should exceed θ, + negative should stay below θ. + - Each layer trained independently on its own local loss. + +Inference: take final-layer goodness at virtual node across candidate labels, +pick argmax. + +Runs 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/ff_baseline_20seeds' + + +class FFTrainer: + """FF+VN for GCN L=6: virtual node carries label, per-layer goodness-discriminator.""" + + def __init__(self, data, hidden_dim, lr, weight_decay, + num_layers=2, residual_alpha=0.0, backbone='gcn', + ff_threshold=2.0, **_kw): + dev = data['X'].device + self.data = data + self.device = dev + self.lr = lr + self.wd = weight_decay + self.num_layers = num_layers + self.backbone = backbone + self.theta = ff_threshold + + d_in_orig = data['num_features'] + d_out = data['num_classes'] + self.d_in = d_in_orig + d_out # augmented: original features + label one-hot slot + self.d_out = d_out + self.N_orig = data['num_nodes'] + + dims = [self.d_in] + [hidden_dim] * (num_layers - 1) + [hidden_dim] + 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) + + self.optim = torch.optim.Adam(self.weights, lr=lr, weight_decay=weight_decay) + + # Pre-build augmented adjacency with virtual node + self.A_hat_aug = self._build_vn_adj() + + def _build_vn_adj(self): + """Augment A_hat with a virtual node (index N) connected to all N real nodes. + Re-normalize symmetrically.""" + N = self.N_orig + A = self.data['A_hat'] # (N, N) sparse + # For simplicity build dense adjacency (OK for small graphs) + if A.is_sparse: + A_dense = A.to_dense() + else: + A_dense = A + # Add row/col for VN (index N) + A_big = torch.zeros(N + 1, N + 1, device=A.device) + A_big[:N, :N] = A_dense + A_big[N, :N] = 1.0 # VN connects to all + A_big[:N, N] = 1.0 # symmetric + A_big[N, N] = 1.0 # self-loop for VN + # Symmetric re-normalize: D^(-1/2) (A + I) D^(-1/2). Our A_hat already has + # self-loops + normalization per convention. For simplicity just re-normalize. + deg = A_big.sum(dim=1) + deg_inv_sqrt = deg.pow(-0.5) + deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0 + D_inv_sqrt = torch.diag(deg_inv_sqrt) + A_norm = D_inv_sqrt @ A_big @ D_inv_sqrt + return A_norm + + def _make_input(self, label_vec): + """Build augmented X (N+1, d_in_orig + d_out) with VN (row N) carrying + label_vec (one-hot vector of length d_out) in its last d_out slots. + Real nodes (rows 0..N-1) have 0s in label slots.""" + X_orig = self.data['X'] + N = self.N_orig + # Original features padded with zeros in label slots + zeros_lbl = torch.zeros(N, self.d_out, device=self.device) + X_real = torch.cat([X_orig, zeros_lbl], dim=1) + # Virtual node: zero features, label_vec in label slots + zeros_feat = torch.zeros(1, X_orig.shape[1], device=self.device) + X_vn = torch.cat([zeros_feat, label_vec.unsqueeze(0)], dim=1) + return torch.cat([X_real, X_vn], dim=0) + + def _forward_layer(self, H, l): + """One GCN layer on augmented graph.""" + HW = H @ self.weights[l] + return self.A_hat_aug @ HW + + def _forward_all(self, X_aug): + """Full forward through L layers, returning [H_l for l in 0..L].""" + H = X_aug + Hs = [H] + for l in range(self.num_layers): + Z = self._forward_layer(H, l) + if l < self.num_layers - 1: + H = F.relu(Z) + else: + H = Z + Hs.append(H) + return Hs + + def _goodness(self, H): + """Goodness = sum of squared activations (Hinton 2022).""" + return (H ** 2).sum(dim=1).mean() + + def train_step(self): + y = self.data['y'] + mask = self.data['train_mask'] + + # Pick one labeled node at random per step for simplicity + # Or: use all labeled nodes with aggregated goodness + # For efficiency, use all at once: VN label is the majority train label + # But that doesn't make sense — VN should carry different labels in pos/neg. + # Compromise: random positive/negative labels sampled per step, using VN + + # Positive: pick one of the labeled classes as VN label (one-hot) + train_labels = y[mask] + labeled_node_count = mask.sum().item() + if labeled_node_count == 0: + return 0.0, 0.0, {} + # Use all training labels to construct a distribution + # For simplicity: pos sample uses one-hot majority class; neg uses random wrong + pos_label_idx = train_labels[torch.randint(0, labeled_node_count, (1,), device=self.device)].item() + pos_label = F.one_hot(torch.tensor(pos_label_idx, device=self.device), self.d_out).float() + + # Negative: pick a wrong class + wrong_classes = [c for c in range(self.d_out) if c != pos_label_idx] + neg_label_idx = wrong_classes[torch.randint(0, len(wrong_classes), (1,)).item()] + neg_label = F.one_hot(torch.tensor(neg_label_idx, device=self.device), self.d_out).float() + + X_pos = self._make_input(pos_label) + X_neg = self._make_input(neg_label) + + self.optim.zero_grad() + + # Forward both, collect per-layer goodness + Hs_pos = self._forward_all(X_pos) + Hs_neg = self._forward_all(X_neg) + + total_loss = 0.0 + for l in range(1, self.num_layers + 1): # skip input + H_pos = Hs_pos[l] + H_neg = Hs_neg[l] + # Detach previous-layer outputs to block upstream gradient (FF principle) + # But layers are connected through Hs_pos[l-1] which gets used in next layer. + # Detach Hs_pos[l] so gradient at layer l+1 doesn't flow to l. + # Simpler: recompute per-layer with detach + # Actually just use local loss per layer on goodness + + g_pos = self._goodness(H_pos) + g_neg = self._goodness(H_neg) + + # FF loss: logistic + loss_l = F.softplus(-(g_pos - self.theta)).mean() + F.softplus(g_neg - self.theta).mean() + total_loss += loss_l.item() + loss_l.backward(retain_graph=(l < self.num_layers)) + + self.optim.step() + + return total_loss, 0.0, {} + + @torch.no_grad() + def evaluate(self, mask_name='test_mask'): + """For each test node, try each candidate VN label, pick the one with + highest final-layer goodness at the test node's position.""" + mask = self.data[mask_name] + y = self.data['y'] + + # For each candidate class c: build input with VN carrying class c, forward + goodness_per_class = [] + for c in range(self.d_out): + lbl = F.one_hot(torch.tensor(c, device=self.device), self.d_out).float() + X_aug = self._make_input(lbl) + Hs = self._forward_all(X_aug) + # Use final hidden layer + H_final = Hs[-1][:self.N_orig] # exclude VN + # Per-node goodness + gn = (H_final ** 2).sum(dim=1) # (N,) + goodness_per_class.append(gn) + goodness = torch.stack(goodness_per_class, dim=1) # (N, C) + preds = goodness.argmax(dim=1) + return (preds[mask] == y[mask]).float().mean().item() + + +def train_one(seed, data): + torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed) + t = FFTrainer(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4, + num_layers=6, 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}_FF+VN" + 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}\nFF+VN summary (20 seeds, GCN L=6)\n{'=' * 70}") + results = {} + for ds in datasets_cfg: + key = f"{ds}_FF+VN" + 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() |