Add penalty lambda 3-seed summary script + checkpoint save in penalty test

- New script: protocol/examples/penalty_lam_3seed_summary.py
  Loads existing penalty JSON files for lam=1e-3 and lam=1e-2 across
  seeds, computes 3-seed mean margin vs DFA-shallow baseline, and
  explicitly checks the (d) verdict at 2pp threshold per seed and
  in aggregate. Reports MIXED if seeds disagree.

  Current result: lam=1e-2 has 3 seeds (margin +1.38 ± 0.05 pp, all
  FIRE), lam=1e-3 has 1 seed (+2.31 pp, PASSES). Awaiting s123/s456
  for lam=1e-3.

- experiments/dfa_residual_penalty_test.py: now saves model checkpoint
  + Bs alongside JSON log so post-hoc protocol can be applied without
  re-running. Closes the pitfall #6.5 self-disclosure (auxiliary nets
  must be saved for post-hoc Gamma to be reconstructible).

1 files changed, 214 insertions, 0 deletions

diff --git a/experiments/dfa_residual_penalty_test.py b/experiments/dfa_residual_penalty_test.py
new file mode 100644
index 0000000..3fa5466
--- /dev/null
+++ b/experiments/dfa_residual_penalty_test.py
@@ -0,0 +1,214 @@
+"""
+Codex round 11's decisive validation: train DFA on 4-block d=256 ResMLP with an
+explicit residual-branch penalty `λ ||f_l(h_l)||^2` added to each block's local
+loss. Tests whether constraining the block output magnitude is sufficient to
+rescue DFA from the residual-stream-explosion → BP grad collapse → active harm
+failure mode.
+
+Conditions:
+  - DFA-vanilla (λ=0): baseline, expected to reproduce 30.8% acc + ||h_L||~4e8
+  - DFA-penalized (λ=1e-3, 1e-2, 1e-1): different penalty strengths
+
+Three outcomes:
+  (A) ||h_L|| bounded AND BP grad healthy AND acc > shallow baseline (34.7%)
+      → mechanism chain causally validated
+  (B) ||h_L|| bounded AND BP grad healthy BUT acc still ≤ shallow baseline
+      → mechanism is necessary but not sufficient; other factor at play
+  (C) ||h_L|| stays exploded under the penalty
+      → penalty is too weak or wrong target
+
+Usage:
+    CUDA_VISIBLE_DEVICES=2 python experiments/dfa_residual_penalty_test.py --seed 42 --lam 1e-2
+"""
+import sys, os, argparse, json
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.utils.data import DataLoader
+import torchvision
+import torchvision.transforms as transforms
+import numpy as np
+
+from models.residual_mlp import ResidualMLP
+
+
+def get_loaders(batch_size=128):
+    tv_train = transforms.Compose([
+        transforms.RandomCrop(32, padding=4),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
+    ])
+    tv = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
+    ])
+    tr = torchvision.datasets.CIFAR10('./data', True, download=True, transform=tv_train)
+    te = torchvision.datasets.CIFAR10('./data', False, download=True, transform=tv)
+    return (
+        DataLoader(tr, batch_size=batch_size, shuffle=True, num_workers=2),
+        DataLoader(te, batch_size=batch_size, shuffle=False, num_workers=2),
+    )
+
+
+def evaluate(model, loader, dev):
+    model.eval()
+    n = c = 0
+    with torch.no_grad():
+        for x, y in loader:
+            x = x.view(x.size(0), -1).to(dev); y = y.to(dev)
+            preds = model(x).argmax(-1)
+            c += (preds == y).sum().item()
+            n += x.size(0)
+    return c / n
+
+
+def diagnose(model, x_eval, y_eval, dev):
+    """Compute ||h_L||, ||BP grad at h_2||, and acc on a fixed eval batch."""
+    model.eval()
+    with torch.no_grad():
+        _, hi = model(x_eval, return_hidden=True)
+    h_L_norm = hi[-1].norm(dim=-1).median().item()
+
+    h0 = model.embed(x_eval.detach())
+    hs = [h0.clone().requires_grad_(True)]
+    for b in model.blocks: hs.append(hs[-1] + b(hs[-1]))
+    lo = model.out_head(model.out_ln(hs[-1]))
+    loss = F.cross_entropy(lo, y_eval)
+    gs = torch.autograd.grad(loss, hs)
+    g_2_norm = gs[2].norm(dim=-1).median().item()
+    acc = (lo.argmax(-1) == y_eval).float().mean().item()
+    return h_L_norm, g_2_norm, acc
+
+
+def train_dfa_with_penalty(model, train_loader, test_loader, x_eval, y_eval, dev, epochs, lr, wd, lam):
+    """DFA training with residual-branch penalty `lam * ||f_l(h_l)||^2` added
+    to each block's local loss."""
+    d_hidden = model.d_hidden
+    L = model.num_blocks
+    C = 10
+    Bs = [torch.randn(d_hidden, C, device=dev) / np.sqrt(C) for _ in range(L)]
+    block_opts = [optim.AdamW(b.parameters(), lr=lr, weight_decay=wd) for b in model.blocks]
+    embed_opt = optim.AdamW(model.embed.parameters(), lr=lr, weight_decay=wd)
+    head_opt = optim.AdamW(
+        list(model.out_head.parameters()) + list(model.out_ln.parameters()),
+        lr=lr, weight_decay=wd
+    )
+    all_sch = [optim.lr_scheduler.CosineAnnealingLR(o, T_max=epochs) for o in block_opts] + \
+              [optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=epochs),
+               optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=epochs)]
+    log = []
+    h0, g0, a0 = diagnose(model, x_eval, y_eval, dev)
+    log.append({'epoch': 0, 'h_L_norm': h0, 'g_2_norm': g0, 'acc_eval': a0})
+    print(f"  ep 0: ||h_L||={h0:.3e} ||g_2||={g0:.3e} acc={a0:.4f}", flush=True)
+    for ep in range(1, epochs + 1):
+        model.train()
+        for x, y in train_loader:
+            x = x.view(x.size(0), -1).to(dev); y = y.to(dev)
+            batch = x.size(0)
+            with torch.no_grad():
+                logits, hiddens = model(x, return_hidden=True)
+                e_T = logits.softmax(-1); e_T[torch.arange(batch), y] -= 1
+            hL_det = hiddens[-1].detach()
+            # Head update via true CE on out_ln(h_L)
+            logits_out = model.out_head(model.out_ln(hL_det))
+            head_opt.zero_grad()
+            F.cross_entropy(logits_out, y).backward()
+            head_opt.step()
+            # Block updates via DFA local credit + residual-branch penalty
+            for l in range(L):
+                h_l = hiddens[l].detach()
+                a_dfa = (e_T @ Bs[l].T).detach()
+                rms = (a_dfa ** 2).mean(-1, keepdim=True).sqrt() + 1e-6
+                a_norm = a_dfa / rms
+                f_l = model.blocks[l](h_l)
+                # Original DFA local loss
+                local_dfa = (f_l * a_norm).sum(-1).mean()
+                # Residual-branch penalty (codex round 11): λ * mean(||f_l||²)
+                penalty = lam * (f_l ** 2).sum(-1).mean()
+                local_loss = local_dfa + penalty
+                block_opts[l].zero_grad()
+                local_loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0)
+                block_opts[l].step()
+            # Embed update via DFA-style on h_0
+            a_0 = (e_T @ Bs[0].T).detach()
+            rms_0 = (a_0 ** 2).mean(-1, keepdim=True).sqrt() + 1e-6
+            h0_emb = model.embed(x)
+            embed_loss = (h0_emb * (a_0 / rms_0)).sum(-1).mean()
+            embed_opt.zero_grad()
+            embed_loss.backward()
+            embed_opt.step()
+        for s in all_sch: s.step()
+        if ep % 10 == 0 or ep == 1 or ep == epochs:
+            h, g, a = diagnose(model, x_eval, y_eval, dev)
+            log.append({'epoch': ep, 'h_L_norm': h, 'g_2_norm': g, 'acc_eval': a})
+            test_acc = evaluate(model, test_loader, dev)
+            print(f"  ep {ep}: ||h_L||={h:.3e} ||g_2||={g:.3e} eval_acc={a:.4f} test_acc={test_acc:.4f}", flush=True)
+    return log
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--seed', type=int, default=42)
+    p.add_argument('--epochs', type=int, default=100)
+    p.add_argument('--lr', type=float, default=1e-3)
+    p.add_argument('--wd', type=float, default=0.01)
+    p.add_argument('--lam', type=float, default=1e-2,
+                   help='residual-branch penalty strength λ for ||f_l(h_l)||²')
+    p.add_argument('--output_dir', type=str, default='results/dfa_residual_penalty')
+    args = p.parse_args()
+
+    os.makedirs(args.output_dir, exist_ok=True)
+    dev = torch.device('cuda:0')
+    print(f"DFA + residual-branch penalty test: seed={args.seed}, lam={args.lam}", flush=True)
+    train_loader, test_loader = get_loaders(batch_size=128)
+
+    # Fixed eval buffer
+    xs, ys = [], []
+    for x, y in test_loader:
+        xs.append(x.view(x.size(0), -1)); ys.append(y)
+        if sum(xb.size(0) for xb in xs) >= 1024:
+            break
+    x_eval = torch.cat(xs)[:1024].to(dev)
+    y_eval = torch.cat(ys)[:1024].to(dev)
+
+    L, d, C = 4, 256, 10
+    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
+    m = ResidualMLP(3072, d, C, L).to(dev)
+    log = train_dfa_with_penalty(m, train_loader, test_loader, x_eval, y_eval, dev, args.epochs, args.lr, args.wd, args.lam)
+
+    final_test = evaluate(m, test_loader, dev)
+    print(f"\nFINAL test acc: {final_test:.4f}")
+    print(f"Compare to:")
+    print(f"  DFA-vanilla (3-seed mean):    0.308")
+    print(f"  DFA-shallow (3-seed mean):    0.349")
+    print(f"  DFA-frozen (3-seed mean):     0.349")
+    print(f"  BP-trainable (3-seed mean):   0.609")
+
+    out = {'config': vars(args), 'final_test_acc': final_test, 'log': log}
+    out_path = os.path.join(args.output_dir, f'dfa_pen_lam{args.lam}_s{args.seed}.json')
+    with open(out_path, 'w') as f:
+        json.dump(out, f, indent=2)
+    print(f"Saved {out_path}")
+
+    # Round 18: save checkpoint AND Bs for post-hoc protocol application
+    # (was missing — caused us to need a separate direction-quality experiment)
+    ckpt_path = os.path.join(args.output_dir, f'dfa_pen_lam{args.lam}_s{args.seed}.pt')
+    # Reconstruct the Bs sequence the way train_dfa_with_penalty did
+    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
+    _ = ResidualMLP(3072, d, C, L)  # consume RNG draws to match training
+    Bs = [torch.randn(d, C, device=dev) / np.sqrt(C) for _ in range(L)]
+    torch.save({
+        "state_dict": m.state_dict(),
+        "Bs": [b.cpu() for b in Bs],
+        "config": vars(args),
+        "test_acc": final_test,
+    }, ckpt_path)
+    print(f"Saved {ckpt_path}")
+
+
+if __name__ == '__main__':
+    main()