cascade: zil interleaved scheme = exact gradients at all depths (cos 1.0000 L6-24, trajectory 0.9998+); casc_ep_train zil trainer; naive-relaxation depth failure documented

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn

1 files changed, 133 insertions, 0 deletions

diff --git a/ep_run/casc_ep_train.py b/ep_run/casc_ep_train.py
new file mode 100644
index 0000000..a5a5286
--- /dev/null
+++ b/ep_run/casc_ep_train.py
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+"""Cascade-EP trainer (zil mode): train L DISTINCT standard transformer blocks with the
+interleaved reverse-sweep energy rule — per layer: update z_l (gamma=1, SUM units), then
+read that layer's theta-grad from its LOCAL energy term. Numerically == BP restructured
+as local two-factor rules; inference = plain forward (standard LLM).
+Twin of casc_bp_train.py (same seed => same init & data stream)."""
+import argparse, math, pickle, time
+import numpy as np, torch, torch.nn as nn, torch.nn.functional as F
+from pathlib import Path
+
+ap = argparse.ArgumentParser()
+ap.add_argument('--tag', default='casc_ep6')
+ap.add_argument('--L', type=int, default=6); ap.add_argument('--C', type=int, default=256)
+ap.add_argument('--H', type=int, default=8); ap.add_argument('--T', type=int, default=256)
+ap.add_argument('--B', type=int, default=24); ap.add_argument('--steps', type=int, default=4000)
+ap.add_argument('--lr', type=float, default=3e-4); ap.add_argument('--warmup', type=int, default=200)
+ap.add_argument('--beta', type=float, default=0.03); ap.add_argument('--seed', type=int, default=0)
+ap.add_argument('--save_every', type=int, default=1000); ap.add_argument('--log', type=int, default=200)
+ap.add_argument('--wandb', default=''); ap.add_argument('--wandb_run', default='')
+args = ap.parse_args()
+torch.manual_seed(args.seed)
+dev = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+DD = Path('/home/yurenh2/ept/ep_run/data/tinystories_bpe')
+vocab = pickle.load(open(DD / 'meta.pkl', 'rb'))['vocab_size']
+
+def get_batch(split):
+    data = np.memmap(DD / ('train.bin' if split == 'train' else 'val.bin'), dtype=np.uint16, mode='r')
+    ix = torch.randint(len(data) - args.T - 1, (args.B,))
+    x = torch.stack([torch.from_numpy(data[i:i + args.T].astype(np.int64)) for i in ix])
+    y = torch.stack([torch.from_numpy(data[i + 1:i + 1 + args.T].astype(np.int64)) for i in ix])
+    return x.to(dev), y.to(dev)
+
+class Block(nn.Module):
+    def __init__(self, C, H):
+        super().__init__()
+        self.ln1, self.ln2 = nn.LayerNorm(C), nn.LayerNorm(C)
+        self.attn = nn.MultiheadAttention(C, H, batch_first=True)
+        self.ff = nn.Sequential(nn.Linear(C, 4 * C), nn.GELU(), nn.Linear(4 * C, C))
+    def forward(self, z, mask):
+        h = self.ln1(z); a, _ = self.attn(h, h, h, attn_mask=mask, need_weights=False)
+        z = z + a; return z + self.ff(self.ln2(z))
+
+tok = nn.Embedding(vocab, args.C).to(dev)
+pos = nn.Embedding(args.T, args.C).to(dev)
+blocks = nn.ModuleList([Block(args.C, args.H) for _ in range(args.L)]).to(dev)
+mask = torch.triu(torch.full((args.T, args.T), float('-inf'), device=dev), 1)
+readout = lambda z: z @ tok.weight.t()
+io_params = list(tok.parameters()) + list(pos.parameters())
+all_params = io_params + list(blocks.parameters())
+blk_params = [list(b.parameters()) for b in blocks]
+opt = torch.optim.AdamW(all_params, lr=args.lr, weight_decay=1e-4)
+sched = torch.optim.lr_scheduler.LambdaLR(opt, lambda s: min(1.0, (s + 1) / max(args.warmup, 1)))
+NBT = args.B * args.T
+
+def zil_step(x, y):
+    """One cascade-EP (zil) gradient computation. Returns free-phase CE for logging;
+    leaves grads in p.grad (normalized to CE_mean units)."""
+    z0g = tok(x) + pos(torch.arange(args.T, device=dev))[None]
+    z0 = z0g.detach()
+    with torch.no_grad():
+        zs, z = [], z0
+        for b in blocks:
+            z = b(z, mask); zs.append(z)
+        free_ce = F.cross_entropy(readout(zs[-1]).reshape(-1, vocab), y.reshape(-1)).item()
+    for p in all_params: p.grad = None
+    for l in range(args.L - 1, -1, -1):
+        zl = zs[l].detach().requires_grad_(True)
+        # 1) state update: gamma=1 in SUM units (top gets the beta-nudge)
+        if l + 1 < args.L:
+            obj_u = 0.5 * ((zs[l + 1].detach() - blocks[l + 1](zl, mask)) ** 2).sum()
+        else:
+            obj_u = args.beta * NBT * F.cross_entropy(readout(zl).reshape(-1, vocab), y.reshape(-1))
+        g = torch.autograd.grad(obj_u, zl)[0]
+        zs[l] = (zl - g).detach()
+        # 2) immediate local read: this block's params (+ io at the ends)
+        prev = z0g if l == 0 else zs[l - 1].detach()      # l=0 keeps emb graph
+        params_l = blk_params[l] + (io_params if l == 0 else [])
+        obj_r = 0.5 * ((zs[l] - blocks[l](prev, mask)) ** 2).sum() / NBT
+        if l + 1 == args.L:
+            obj_r = obj_r + args.beta * F.cross_entropy(readout(zs[l]).reshape(-1, vocab), y.reshape(-1))
+            params_l = params_l + [tok.weight]
+        seen, uniq = set(), []
+        for p in params_l:
+            if id(p) not in seen: seen.add(id(p)); uniq.append(p)
+        gs = torch.autograd.grad(obj_r, uniq, allow_unused=True)
+        for p, gg in zip(uniq, gs):
+            if gg is None: continue
+            p.grad = gg if p.grad is None else p.grad + gg
+    for p in all_params:
+        if p.grad is not None: p.grad /= args.beta
+    return free_ce
+
+@torch.no_grad()
+def evaluate(nb=6):
+    tot = 0.0
+    for _ in range(nb):
+        x, y = get_batch('val')
+        z = tok(x) + pos(torch.arange(args.T, device=dev))[None]
+        for b in blocks: z = b(z, mask)
+        tot += F.cross_entropy(readout(z).reshape(-1, vocab), y.reshape(-1)).item()
+    return tot / nb
+
+wb = None
+if args.wandb:
+    try:
+        import wandb as _w
+        wb = _w.init(project=args.wandb, name=args.wandb_run or args.tag, id=args.wandb_run or args.tag,
+                     resume='allow', config=vars(args))
+    except Exception as e:
+        print(f'[wandb] disabled ({e})', flush=True)
+
+n = sum(p.numel() for p in all_params)
+print(f'[{args.tag}] cascade-EP(zil) L{args.L} C{args.C} H{args.H} T{args.T} beta={args.beta} | {n/1e6:.2f}M | {dev}', flush=True)
+best, t0 = 1e9, time.time()
+for step in range(args.steps + 1):
+    x, y = get_batch('train')
+    ce = zil_step(x, y)
+    torch.nn.utils.clip_grad_norm_(all_params, 1.0)
+    opt.step(); sched.step(); opt.zero_grad(set_to_none=True)
+    if step % args.log == 0:
+        val = evaluate(); best = min(best, val)
+        print(f'step {step:5d}/{args.steps} | train {ce:.4f} val {val:.4f} (best {best:.4f}) '
+              f'| {step/max(time.time()-t0,1e-9):.2f} it/s', flush=True)
+        if wb is not None:
+            try: wb.log({'train_ce': ce, 'val_ce': val, 'best': best}, step=step)
+            except Exception: pass
+    if step % args.save_every == 0 and step > 0:
+        torch.save({'tok': tok.state_dict(), 'pos': pos.state_dict(), 'blocks': blocks.state_dict(),
+                    'step': step, 'val': best, 'config': vars(args)}, Path('runs') / f'{args.tag}_s{step}.pt')
+print(f'[{args.tag}] DONE best val CE {best:.4f} (BP twin reference: casc_bp6)', flush=True)
+if wb is not None:
+    try: wb.summary['best_val_ce'] = best; wb.finish()
+    except Exception: pass