From b83947778e2c776f757a07d4719b7ce961d7ed55 Mon Sep 17 00:00:00 2001
From: Yuren Hao <yurenh2@illinois.edu>
Date: Fri, 3 Jul 2026 05:56:50 -0500
Subject: =?UTF-8?q?Initial=20commit:=20ept=20=E2=80=94=20backprop-free=20e?=
 =?UTF-8?q?quilibrium=20transformer=20(EP)?=
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Code (ep_run/), organized docs (docs/{method,campaign,hardware,outreach,paper}),
analysis scripts (scripts/), ONBOARDING.md entry point. Large data/checkpoints
git-ignored (share separately).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn
---
 ep_run/train_local_ce.py | 580 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 580 insertions(+)
 create mode 100644 ep_run/train_local_ce.py

(limited to 'ep_run/train_local_ce.py')

diff --git a/ep_run/train_local_ce.py b/ep_run/train_local_ce.py
new file mode 100644
index 0000000..b8f790a
--- /dev/null
+++ b/ep_run/train_local_ce.py
@@ -0,0 +1,580 @@
+"""Local CE exit training — each block gets a vocab-space CE loss via shared unembedding.
+
+Each block l computes:
+  z_l = W_U @ T_l(h_l)     (local logits via shared unembedding + optional translator)
+  L_l = λ_gt * CE(z_l, y) + λ_kd * τ² * KL(sg(p_L^τ) || p_l^τ)
+
+Forward weights updated per-block via local CE gradient (intra-block only).
+No inter-block chain rule. Fused attention backward within each block.
+
+This replaces the hidden-space MSE target-matching that failed at scale.
+"""
+import argparse
+import json
+import math
+import pickle
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from model_local import LocalGPTConfig, LocalBlock, LocalLinear, _make_ln
+from factorized_exit import FactorizedExitHead, ExactParallelExitHead
+from local_layers import initialize_dfa_block_targets, apply_dfa_block_update
+
+
+def get_batch(split, data_dir, block_size, batch_size, device, n_pred=1):
+    fn = "train.bin" if split == "train" else "val.bin"
+    data = np.memmap(data_dir / fn, dtype=np.uint16, mode="r")
+    ix = torch.randint(len(data) - block_size - n_pred, (batch_size,))
+    x = torch.stack([torch.from_numpy(data[i : i + block_size].astype(np.int64)) for i in ix])
+    if n_pred == 1:
+        y = torch.stack([torch.from_numpy(data[i + 1 : i + 1 + block_size].astype(np.int64)) for i in ix])
+        return x.to(device, non_blocking=True), y.to(device, non_blocking=True)
+    # n_pred > 1: targets shape (B, T, n_pred). Y[..., k-1] = next-k target.
+    y_multi = torch.stack([
+        torch.stack([
+            torch.from_numpy(data[i + k : i + k + block_size].astype(np.int64))
+            for k in range(1, n_pred + 1)
+        ], dim=-1)
+        for i in ix
+    ])
+    return x.to(device, non_blocking=True), y_multi.to(device, non_blocking=True)
+
+
+class LowRankTranslator(nn.Module):
+    """T_l(h) = h + A @ B @ h + b. Low-rank affine residual translator."""
+    def __init__(self, d_model, rank=32):
+        super().__init__()
+        self.A = nn.Parameter(torch.zeros(d_model, rank))
+        self.B = nn.Parameter(torch.zeros(rank, d_model))
+        self.bias = nn.Parameter(torch.zeros(d_model))
+        nn.init.normal_(self.A, std=0.01)
+        nn.init.normal_(self.B, std=0.01)
+
+    def forward(self, h):
+        return h + h @ self.B.T @ self.A.T + self.bias
+
+
+class LocalCETransformer(nn.Module):
+    """Transformer with per-block local CE exits via shared unembedding."""
+
+    def __init__(self, config: LocalGPTConfig, translator_rank: int = 0, n_pred_tokens: int = 1,
+                 shared_blocks: bool = False):
+        super().__init__()
+        self.config = config
+        self.n_pred_tokens = n_pred_tokens
+        self.shared_blocks = shared_blocks
+        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
+        self.pos_emb = nn.Embedding(config.block_size, config.n_embd)
+        self.drop = nn.Dropout(config.dropout)
+        if shared_blocks:
+            # Universal Transformer: one block applied n_layer times.
+            # All entries point to the SAME module — gradient accumulates from all "depths".
+            shared = LocalBlock(config)
+            self.blocks = nn.ModuleList([shared for _ in range(config.n_layer)])
+        else:
+            self.blocks = nn.ModuleList([LocalBlock(config) for _ in range(config.n_layer)])
+        self.ln_f = _make_ln(config)
+        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # Auxiliary unembedding heads for next-2..next-N prediction (multi-token training).
+        # Used only as gradient-source heads at training time; inference still uses self.head.
+        if n_pred_tokens > 1:
+            self.aux_heads = nn.ModuleList([
+                nn.Linear(config.n_embd, config.vocab_size, bias=False)
+                for _ in range(n_pred_tokens - 1)
+            ])
+        else:
+            self.aux_heads = None
+
+        # Per-block translators (logit lens = rank 0 = identity)
+        if translator_rank > 0:
+            self.translators = nn.ModuleList([
+                LowRankTranslator(config.n_embd, translator_rank)
+                for _ in range(config.n_layer)
+            ])
+        else:
+            self.translators = None
+
+        self.apply(self._init_weights)
+        for pn, p in self.named_parameters():
+            if pn.endswith("o_proj.weight") or pn.endswith("mlp.proj.weight"):
+                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Linear, LocalLinear)):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+            if getattr(m, "bias", None) is not None:
+                nn.init.zeros_(m.bias)
+        elif isinstance(m, nn.Embedding):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+
+    def forward_activations(self, idx):
+        B, T = idx.shape
+        pos = torch.arange(T, device=idx.device)
+        h = self.drop(self.tok_emb(idx) + self.pos_emb(pos))
+        activations = [h]
+        for block in self.blocks:
+            h = block(h)
+            activations.append(h)
+        return activations
+
+    def local_logits(self, h, layer_idx):
+        """h → local logits via optional translator + shared unembedding."""
+        if self.translators is not None:
+            h = self.translators[layer_idx](h)
+        return F.linear(h, self.head.weight)  # shared W_U, no separate head
+
+    def final_logits(self, h):
+        return self.head(self.ln_f(h))
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--run_name", type=str, required=True)
+    p.add_argument("--seed", type=int, default=1337)
+    p.add_argument("--data_dir", type=str, default="data/shakespeare_char")
+    p.add_argument("--out_dir", type=str, default="runs_local")
+    p.add_argument("--block_size", type=int, default=256)
+    p.add_argument("--batch_size", type=int, default=64)
+    p.add_argument("--n_layer", type=int, default=6)
+    p.add_argument("--n_head", type=int, default=6)
+    p.add_argument("--n_embd", type=int, default=384)
+    p.add_argument("--dropout", type=float, default=0.2)
+    p.add_argument("--max_iters", type=int, default=5000)
+    p.add_argument("--warmup_iters", type=int, default=100)
+    p.add_argument("--max_lr", type=float, default=1e-3)
+    p.add_argument("--min_lr", type=float, default=1e-4)
+    p.add_argument("--eval_interval", type=int, default=250)
+    p.add_argument("--eval_iters", type=int, default=100)
+    p.add_argument("--log_interval", type=int, default=50)
+    p.add_argument("--attn_mode", type=str, default="softmax")
+    p.add_argument("--translator_rank", type=int, default=0, help="0=identity (logit lens), >0=low-rank affine")
+    p.add_argument("--kd_weight", type=float, default=1.0, help="weight for KL distillation from final layer")
+    p.add_argument("--kd_temp", type=float, default=2.0, help="temperature for KD")
+    p.add_argument("--gt_weight", type=float, default=1.0, help="weight for ground-truth CE")
+    p.add_argument("--nbr_weight", type=float, default=0.0, help="weight for neighbor KL (sg(p_{l+1}) || p_l)")
+    p.add_argument("--layer_weighting", type=str, default="uniform", choices=["uniform", "linear"],
+                   help="per-layer loss weight: uniform=all 1.0, linear=l/L")
+    p.add_argument("--bp_free_exit", type=str, default="none",
+                   choices=["none", "dense", "hybrid", "parallel_only", "parallel_gold", "parallel_topmass"],
+                   help="BP-free exit: none=W_U^T, dense/hybrid=compressor, parallel_*=exact parallel term")
+    p.add_argument("--exit_rank", type=int, default=128, help="rank for BP-free exit compressor")
+    p.add_argument("--exit_rank_exact", type=int, default=32, help="exact rank for hybrid compressor")
+    p.add_argument("--exit_topk", type=int, default=8, help="top-k for hybrid compressor")
+    p.add_argument("--exit_residual_rank", type=int, default=32,
+                   help="residual_rank for ExactParallelExitHead (parallel_gold/topmass): code dim for h-perp residual")
+    p.add_argument("--intra_block_method", type=str, default="bp", choices=["bp", "fa", "sign_sym", "dfa_block"],
+                   help="intra-block: bp=W^T, fa=seq random B, sign_sym=sign(W)·rescale, dfa_block=direct from block-output-error")
+    p.add_argument("--mlp_topk", type=int, default=0,
+                   help="if >0, apply hard k-WTA to MLP hidden activation (4*n_embd dim)")
+    p.add_argument("--resid_topk", type=int, default=0,
+                   help="if >0, apply hard k-WTA to residual stream output of each block (n_embd dim)")
+    p.add_argument("--vq_codes", type=int, default=0,
+                   help="if >0, apply directional VQ to residual stream at each block (K codebook entries, frozen)")
+    p.add_argument("--subspace_rank", type=int, default=0,
+                   help="if >0, project residual stream to fixed r-dim orthonormal subspace at each block")
+    p.add_argument("--subspace_per_layer", action="store_true",
+                   help="use DIFFERENT random Q per layer (ablation: tests if shared Q is necessary)")
+    p.add_argument("--fa_init_sign", action="store_true",
+                   help="init FA's fixed B as sign(W_init)*rescale instead of random (frozen sign_sym)")
+    p.add_argument("--shared_blocks", action="store_true",
+                   help="Universal Transformer: all blocks share the same parameters (single block applied n_layer times)")
+    p.add_argument("--fa_init", type=str, default="gaussian",
+                   choices=["gaussian", "orthogonal", "ortho_he", "sparse"],
+                   help="FA's fixed B init mode (gaussian=Lillicrap, orthogonal=JL-isometric, ortho_he=He-init backward, sparse=structured)")
+    p.add_argument("--fa_sparse_k", type=int, default=0,
+                   help="for fa_init=sparse: non-zero entries per row (0 = auto = in_features/16)")
+    p.add_argument("--gated_blocks", action="store_true",
+                   help="Path IV: learned per-block residual gates (α_attn, α_mlp). Lets useless layers self-deactivate.")
+    p.add_argument("--progression_targets", action="store_true",
+                   help="Path I: each block l predicts next-(l+1) token (progressive prediction horizons per layer)")
+    p.add_argument("--weight_normalize", action="store_true",
+                   help="Meta-PCN style WN: after each optimizer step, normalize LocalLinear's W by (sqrt(m)+sqrt(n))*std(W) to keep ||W||_2 ~= 1")
+    p.add_argument("--pc_inference", type=int, default=0,
+                   help="Predictive coding inference steps T (T=0 disables PC mode, uses standard local CE)")
+    p.add_argument("--pc_inference_lr", type=float, default=0.1,
+                   help="Inference step size η for PC z updates")
+    p.add_argument("--pc_top_weight", type=float, default=1.0,
+                   help="Weight of top-down CE term in PC energy F")
+    p.add_argument("--fa_grape", action="store_true",
+                   help="GrAPE: per-step JVP-based cosine alignment of FA's B toward true Jacobian (Caillon et al. 2026)")
+    p.add_argument("--fa_grape_lr", type=float, default=0.01,
+                   help="Learning rate for GrAPE B alignment update")
+    p.add_argument("--fa_grape_n_probe", type=int, default=32,
+                   help="Number of probe samples for JVP rank-1 Jacobian estimate")
+    p.add_argument("--save_ckpt", action="store_true",
+                   help="save final model state to run_dir/ckpt.pt for downstream probing")
+    p.add_argument("--n_pred_tokens", type=int, default=1,
+                   help="multi-token prediction: predict next-1..next-N (N=1 disables, default)")
+    p.add_argument("--aux_weight", type=float, default=0.3,
+                   help="weight for aux next-k losses (k=2..N). Primary next-1 always weight 1.0.")
+    args = p.parse_args()
+
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    data_dir = Path(args.data_dir)
+    with open(data_dir / "meta.pkl", "rb") as f:
+        meta = pickle.load(f)
+    vocab_size = meta["vocab_size"]
+
+    run_dir = Path(args.out_dir) / args.run_name
+    run_dir.mkdir(parents=True, exist_ok=True)
+    log_path = run_dir / "log.jsonl"
+    log_path.write_text("")
+
+    cfg = LocalGPTConfig(
+        block_size=args.block_size, vocab_size=vocab_size,
+        n_layer=args.n_layer, n_head=args.n_head, n_embd=args.n_embd,
+        dropout=args.dropout, attn_mode=args.attn_mode,
+        method=args.intra_block_method,
+        fuse_attn_local=True, ste_gelu=True, ln_mode="center_scale",
+        mlp_topk=args.mlp_topk, resid_topk=args.resid_topk,
+        vq_codes=args.vq_codes, subspace_rank=args.subspace_rank,
+        fa_init_mode=args.fa_init, fa_sparse_k=args.fa_sparse_k,
+        gated_blocks=args.gated_blocks,
+        fa_grape=args.fa_grape, fa_grape_n_probe=args.fa_grape_n_probe,
+    )
+    model = LocalCETransformer(cfg, translator_rank=args.translator_rank,
+                                n_pred_tokens=args.n_pred_tokens,
+                                shared_blocks=args.shared_blocks).to(device)
+
+    # Frozen sign_sym: replace FA's random B with sign(W_init)*rescale, then freeze.
+    # B is still a fixed buffer (BP-free by definition B), just structured init.
+    if args.fa_init_sign and args.intra_block_method == "fa":
+        with torch.no_grad():
+            for module in model.modules():
+                if isinstance(module, LocalLinear) and module.method == "fa":
+                    scale = module.weight.norm() / (module.weight.numel() ** 0.5 + 1e-8)
+                    module.B.copy_(torch.sign(module.weight) * scale)
+
+    # Per-layer different Q ablation: replace each block's shared-seed subspace
+    # with independently-seeded subspace (tests if shared Q is the mechanism)
+    if args.subspace_per_layer and args.subspace_rank > 0:
+        from model_local import FrozenSubspace
+        for i, block in enumerate(model.blocks):
+            block.subspace = FrozenSubspace(args.n_embd, args.subspace_rank, seed=1000 + i).to(device)
+
+    # Initialize DFA-block targets if needed
+    if args.intra_block_method == "dfa_block":
+        initialize_dfa_block_targets(model, args.n_embd)
+
+    # BP-free exit heads (one per block)
+    exit_heads = None
+    if args.bp_free_exit in ("dense", "hybrid"):
+        exit_heads = nn.ModuleList([
+            FactorizedExitHead(
+                args.n_embd, vocab_size, mode=args.bp_free_exit,
+                rank=args.exit_rank, rank_exact=args.exit_rank_exact, topk=args.exit_topk,
+            ) for _ in range(cfg.n_layer)
+        ]).to(device)
+    elif args.bp_free_exit.startswith("parallel"):
+        exit_heads = nn.ModuleList([
+            ExactParallelExitHead(
+                args.n_embd, vocab_size, mode=args.bp_free_exit,
+                residual_rank=args.exit_residual_rank,
+            ) for _ in range(cfg.n_layer)
+        ]).to(device)
+
+    n_params = sum(p.numel() for p in model.parameters())
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.max_lr, weight_decay=0.1)
+
+    t0 = time.time()
+
+    def log(rec):
+        rec["t"] = time.time() - t0
+        with open(log_path, "a") as f:
+            f.write(json.dumps(rec) + "\n")
+
+    log({"event": "start", "method": "local_ce", "params": n_params,
+         "translator_rank": args.translator_rank, "config": vars(args)})
+    print(f"[{args.run_name}] local_ce, params={n_params/1e6:.2f}M, translator_rank={args.translator_rank}")
+
+    def lr_schedule(it):
+        if it < args.warmup_iters:
+            return args.max_lr * (it + 1) / (args.warmup_iters + 1)
+        decay = 0.5 * (1 + math.cos(math.pi * (it - args.warmup_iters) /
+                                      max(1, args.max_iters - args.warmup_iters)))
+        return args.min_lr + decay * (args.max_lr - args.min_lr)
+
+    @torch.no_grad()
+    def eval_loss():
+        model.eval()
+        losses = torch.zeros(args.eval_iters)
+        for k in range(args.eval_iters):
+            X, Y = get_batch("val", data_dir, args.block_size, args.batch_size, device)
+            acts = model.forward_activations(X)
+            logits = model.final_logits(acts[-1])
+            loss = F.cross_entropy(logits.view(-1, vocab_size), Y.view(-1))
+            losses[k] = loss.item()
+        model.train()
+        return losses.mean().item()
+
+    model.train()
+    for it in range(args.max_iters + 1):
+        lr = lr_schedule(it)
+        for g in optimizer.param_groups:
+            g["lr"] = lr
+
+        if it % args.eval_interval == 0 or it == args.max_iters:
+            val = eval_loss()
+            log({"event": "eval", "iter": it, "val_loss": val, "lr": lr})
+            print(f"[{args.run_name}] iter {it:5d}  val {val:.4f}  lr {lr:.4g}")
+
+        if it == args.max_iters:
+            break
+
+        # Determine n_pred for batch fetch
+        # - n_pred_tokens > 1: multi-token MTP aux losses (each block predicts N targets via N heads)
+        # - progression_targets: each block l predicts next-(l+1) (so need n_pred = n_layer)
+        if args.n_pred_tokens > 1:
+            n_pred = args.n_pred_tokens
+        elif args.progression_targets:
+            n_pred = cfg.n_layer
+        else:
+            n_pred = 1
+
+        if n_pred > 1:
+            X, Y_multi = get_batch("train", data_dir, args.block_size, args.batch_size, device,
+                                   n_pred=n_pred)
+            Y = Y_multi[..., 0]  # (B, T) — next-1 target for default
+        else:
+            X, Y = get_batch("train", data_dir, args.block_size, args.batch_size, device)
+            Y_multi = None
+
+        # ============================================================
+        # PC mode: predictive coding inference + Hebbian-style updates
+        # (when --pc_inference T > 0, replaces standard local CE)
+        # ============================================================
+        if args.pc_inference > 0:
+            optimizer.zero_grad()
+            Y_flat = Y.view(-1)
+
+            # 1. Forward init (no autograd graph during init)
+            with torch.no_grad():
+                init_acts = model.forward_activations(X)
+            # z[0] = embedding, clamped (no grad). z[1..L] = block outputs, evolve.
+            z = [init_acts[0].detach()]
+            for l in range(1, len(init_acts)):
+                z.append(init_acts[l].detach().clone().requires_grad_(True))
+
+            # 2. PC inference: T iterations of z updates via ∂F/∂z
+            #    F = Σ_{l<L} (1/2) ||z_l - block_{l-1}(z_{l-1})||² / d  +  λ·CE(W_U @ z_L, y)
+            #    Skip PE_L (Meta-PCN trick: CE replaces last-layer squared error)
+            #    Use mean over hidden dim (per-token PE²) for scale-invariance.
+            for t in range(args.pc_inference):
+                F_energy = 0.0
+                for l in range(1, len(z) - 1):  # l = 1..L-1, skip PE_L
+                    z_hat = model.blocks[l - 1](z[l - 1])
+                    pe = z[l] - z_hat
+                    F_energy = F_energy + 0.5 * (pe ** 2).mean()  # mean over (B,T,d) → scale-invariant
+                # Top-down: CE at z_L (replaces PE_L per Meta-PCN convention)
+                logits_top = F.linear(z[-1], model.head.weight)
+                CE_top = F.cross_entropy(logits_top.view(-1, vocab_size), Y_flat)
+                F_total = F_energy + args.pc_top_weight * CE_top
+                # Compute ∂F/∂z[1..L] (FA-flavored due to LocalLinear FA backward inside blocks)
+                grads = torch.autograd.grad(F_total, z[1:], create_graph=False, retain_graph=False)
+                # SGD update on z's
+                with torch.no_grad():
+                    new_z = [z[0]]
+                    for i, g in enumerate(grads):
+                        new_z.append((z[i + 1] - args.pc_inference_lr * g).detach().requires_grad_(True))
+                    z = new_z
+
+            # 3. Weight update via per-block PE loss using converged z's
+            #    For block l-1: minimize ||sg(z_l) - block_{l-1}(sg(z_{l-1}))||²
+            #    backward gives FA-flavored W gradients (Hebbian-equivalent at equilibrium)
+            total_loss = 0.0
+            for l in range(1, len(z)):
+                z_hat = model.blocks[l - 1](z[l - 1].detach())
+                target = z[l].detach()
+                pe_loss = 0.5 * ((target - z_hat) ** 2).mean()
+                pe_loss.backward()
+                total_loss += pe_loss.item()
+
+            # Final head + ln_f via CE on converged z[-1]
+            final_z = model.final_logits(z[-1].detach())
+            head_loss = F.cross_entropy(final_z.view(-1, vocab_size), Y_flat)
+            head_loss.backward()
+            total_loss += head_loss.item()
+
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step()
+
+            # Optional WN
+            if args.weight_normalize:
+                with torch.no_grad():
+                    for module in model.modules():
+                        if isinstance(module, LocalLinear):
+                            m, n = module.weight.shape
+                            sigma_w = module.weight.std()
+                            scale = (m ** 0.5 + n ** 0.5) * sigma_w
+                            if scale > 1e-8:
+                                module.weight.div_(scale)
+
+            if it % args.log_interval == 0:
+                log({"event": "step", "iter": it,
+                     "total_loss": total_loss / (cfg.n_layer + 1),
+                     "head_loss": head_loss.item(), "lr": lr})
+            continue
+        # ============================================================
+        # End PC mode; standard local CE follows
+        # ============================================================
+
+        # Forward: compute all activations
+        activations = model.forward_activations(X)
+
+        # Final logits (for KD teacher + eval)
+        with torch.no_grad():
+            final_logits = model.final_logits(activations[-1].detach())
+            teacher_probs = F.softmax(final_logits / args.kd_temp, dim=-1)
+
+        # Per-block local CE losses (no inter-block gradient)
+        optimizer.zero_grad()
+        total_loss = 0.0
+        Y_flat = Y.view(-1)
+
+        # Neighbor KL teachers are computed on-the-fly inside the per-block loop
+        # (avoid pre-computing all 6 × (B,T,V) tensors which OOM at V=50k)
+
+        for l in range(cfg.n_layer):
+            # Block l: h_l → block → h_{l+1}
+            h_l = activations[l] if l == 0 else activations[l].detach()
+            # For dfa_block mode, need h_lp1 with retain_grad to capture block-output-error
+            if args.intra_block_method == "dfa_block":
+                h_lp1 = model.blocks[l](h_l)
+                h_lp1.retain_grad()
+            else:
+                h_lp1 = model.blocks[l](h_l)
+
+            # Path I: progression targets — block l predicts next-(l+1) instead of next-1
+            if args.progression_targets and Y_multi is not None:
+                Y_block = Y_multi[..., l]  # (B, T) — block l's specific target
+            else:
+                Y_block = Y  # default: all blocks predict next-1
+            Y_block_flat = Y_block.reshape(-1)
+
+            # Local logits via shared unembedding (exact or BP-free)
+            if exit_heads is not None:
+                local_z = exit_heads[l](h_lp1, model.head.weight, Y_block)
+            else:
+                local_z = model.local_logits(h_lp1, l)
+            local_z_flat = local_z.view(-1, vocab_size)
+
+            # Per-layer weight
+            if args.layer_weighting == "linear":
+                layer_w = (l + 1) / cfg.n_layer
+            else:
+                layer_w = 1.0
+
+            # Ground-truth CE (uses Y_block_flat which respects progression mode)
+            loss_gt = F.cross_entropy(local_z_flat, Y_block_flat)
+
+            # KD from final layer (skip when both kd_weight and nbr_weight are 0 to save 3.3GB/block)
+            loss_kd = 0.0
+            loss_nbr = 0.0
+            if args.kd_weight > 0 or args.nbr_weight > 0:
+                local_log_probs = F.log_softmax(local_z / args.kd_temp, dim=-1)
+                if args.kd_weight > 0:
+                    loss_kd = F.kl_div(
+                        local_log_probs.view(-1, vocab_size),
+                        teacher_probs.view(-1, vocab_size),
+                        reduction="batchmean",
+                    ) * (args.kd_temp ** 2)
+                # Neighbor KL: match next block's prediction (stop-grad), computed on-the-fly
+                if args.nbr_weight > 0 and l < cfg.n_layer - 1:
+                    with torch.no_grad():
+                        nbr_z = model.local_logits(activations[l + 2].detach(), l + 1)
+                        nbr_probs = F.softmax(nbr_z / args.kd_temp, dim=-1)
+                        del nbr_z
+                    loss_nbr = F.kl_div(
+                        local_log_probs.view(-1, vocab_size),
+                        nbr_probs.view(-1, vocab_size),
+                        reduction="batchmean",
+                    ) * (args.kd_temp ** 2)
+                    del nbr_probs
+                del local_log_probs
+
+            # Multi-token aux losses: predict next-2..next-N via aux_heads
+            # Each aux head provides an independent gradient direction (different W_k column space).
+            # Reuses the same exit_heads[l] (shared codebook) but with different shared_weight + targets.
+            loss_aux = 0.0
+            if args.n_pred_tokens > 1 and args.aux_weight > 0 and model.aux_heads is not None:
+                for k_idx, aux_head in enumerate(model.aux_heads):
+                    Y_k = Y_multi[..., k_idx + 1]  # next-(k_idx+2) target
+                    if exit_heads is not None:
+                        z_k = exit_heads[l](h_lp1, aux_head.weight, Y_k)
+                    else:
+                        z_k = F.linear(h_lp1, aux_head.weight)
+                    loss_k = F.cross_entropy(z_k.view(-1, vocab_size), Y_k.reshape(-1))
+                    loss_aux = loss_aux + loss_k
+                loss_aux = loss_aux * args.aux_weight / (args.n_pred_tokens - 1)
+
+            block_loss = layer_w * (
+                args.gt_weight * loss_gt
+                + args.kd_weight * loss_kd
+                + args.nbr_weight * loss_nbr
+                + loss_aux
+            )
+            block_loss.backward()
+
+            # For dfa_block: overwrite intra-block linears' .grad using block-output-error
+            if args.intra_block_method == "dfa_block" and h_lp1.grad is not None:
+                with torch.no_grad():
+                    apply_dfa_block_update(model.blocks[l], h_lp1.grad)
+
+            total_loss += block_loss.item()
+
+        # Also train head + ln_f via final CE
+        h_L_det = activations[-1].detach()
+        final_z = model.final_logits(h_L_det)
+        head_loss = F.cross_entropy(final_z.view(-1, vocab_size), Y_flat)
+        head_loss.backward()
+        total_loss += head_loss.item()
+
+        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+        optimizer.step()
+
+        # GrAPE: per-step alignment of FA's B toward Jacobian via JVP probes (forward-only)
+        if args.fa_grape:
+            for module in model.modules():
+                if isinstance(module, LocalLinear) and getattr(module, "_fa_grape", False):
+                    module.grape_align_step(lr_b=args.fa_grape_lr)
+
+        # Meta-PCN style weight normalization: rescale each LocalLinear's W to have ||W||_2 ~= 1
+        # via random matrix theory bound ||W||_2 ~= (sqrt(m) + sqrt(n)) * std(W).
+        # Only normalizes LocalLinear W (the trained weight); leaves B (fixed buffer) untouched.
+        if args.weight_normalize:
+            with torch.no_grad():
+                for module in model.modules():
+                    if isinstance(module, LocalLinear):
+                        m, n = module.weight.shape
+                        sigma_w = module.weight.std()
+                        scale = (m ** 0.5 + n ** 0.5) * sigma_w
+                        if scale > 1e-8:
+                            module.weight.div_(scale)
+
+        if it % args.log_interval == 0:
+            log({"event": "step", "iter": it, "total_loss": total_loss / (cfg.n_layer + 1),
+                 "head_loss": head_loss.item(), "lr": lr})
+
+    if args.save_ckpt:
+        ckpt_path = run_dir / "ckpt.pt"
+        torch.save({
+            "model_state": model.state_dict(),
+            "config": vars(cfg),
+            "args": vars(args),
+            "vocab_size": vocab_size,
+        }, ckpt_path)
+        log({"event": "save_ckpt", "path": str(ckpt_path)})
+        print(f"[{args.run_name}] saved ckpt to {ckpt_path}")
+
+
+if __name__ == "__main__":
+    main()
-- 
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