casc_eq_train: equilibrium-mode cascade-EP trainer (GS-reverse solver K sweeps, two-phase ±β, EP readout at relaxed states) — the true-EP route C1

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

1 files changed, 140 insertions, 0 deletions

diff --git a/ep_run/casc_eq_train.py b/ep_run/casc_eq_train.py
new file mode 100644
index 0000000..d2da0ce
--- /dev/null
+++ b/ep_run/casc_eq_train.py
@@ -0,0 +1,140 @@
+"""Cascade-EP trainer — EQUILIBRIUM MODE (the true-EP route).
+Two-phase (+-beta) relaxation of all layer states to the nudged equilibria via
+Gauss-Seidel reverse sweeps (solver choice only; readout is taken AT the relaxed
+states with the standard EP formula), weight grad = (1/2beta)[dF/dtheta|+ - dF/dtheta|-].
+Inference = plain forward (standard LLM). Twin of casc_bp_train.py (same seed/data)."""
+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_eq6')
+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('--K', type=int, default=20)            # GS reverse sweeps per phase
+ap.add_argument('--geta', type=float, default=0.8)      # GS state step (sum units)
+ap.add_argument('--save_every', type=int, default=1000); ap.add_argument('--log', type=int, default=100)
+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()
+all_params = list(tok.parameters()) + list(pos.parameters()) + list(blocks.parameters())
+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 free_states(x):
+    with torch.no_grad():
+        z = tok(x) + pos(torch.arange(args.T, device=dev))[None]
+        z0 = z.clone(); zs = []
+        for b in blocks:
+            z = b(z, mask); zs.append(z)
+    return z0, zs
+
+def relax(z0, zs_free, y, beta):
+    """GS reverse sweeps to the nudged equilibrium (SUM-unit local grads, step geta)."""
+    zs = [z.clone() for z in zs_free]
+    for _ in range(args.K):
+        for l in range(args.L - 1, -1, -1):
+            zl = zs[l].detach().requires_grad_(True)
+            prev = z0 if l == 0 else zs[l - 1].detach()
+            obj = 0.5 * ((zl - blocks[l](prev, mask)) ** 2).sum()
+            if l + 1 < args.L:
+                obj = obj + 0.5 * ((zs[l + 1].detach() - blocks[l + 1](zl, mask)) ** 2).sum()
+            else:
+                obj = obj + beta * NBT * F.cross_entropy(readout(zl).reshape(-1, vocab), y.reshape(-1))
+            g = torch.autograd.grad(obj, zl)[0]
+            zs[l] = (zl - args.geta * g).detach()
+    return zs
+
+def dFdtheta(zs, x, y, beta):
+    """dF/dtheta at fixed relaxed states (z0 rebuilt WITH graph so emb gets its E-path grad)."""
+    prev = tok(x) + pos(torch.arange(args.T, device=dev))[None]
+    E = 0.0
+    for z, b in zip(zs, blocks): E = E + 0.5 * ((z - b(prev, mask)) ** 2).sum(); prev = z
+    obj = E / NBT + beta * F.cross_entropy(readout(zs[-1]).reshape(-1, vocab), y.reshape(-1))
+    gs = torch.autograd.grad(obj, all_params, allow_unused=True)
+    return [g if g is not None else None for g in gs]
+
+def ep_step(x, y):
+    z0, zs_free = free_states(x)
+    free_ce = F.cross_entropy(readout(zs_free[-1]).reshape(-1, vocab), y.reshape(-1)).item()
+    zp = relax(z0, zs_free, y, +args.beta)
+    zm = relax(z0, zs_free, y, -args.beta)
+    gp = dFdtheta(zp, x, y, +args.beta)
+    gm = dFdtheta(zm, x, y, -args.beta)
+    for p, a, b in zip(all_params, gp, gm):
+        p.grad = None if (a is None or b is None) else (a - b) / (2 * 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(EQUILIBRIUM) L{args.L} C{args.C} T{args.T} beta={args.beta} '
+      f'K={args.K} geta={args.geta} | {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 = ep_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):.3f} 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 2.9746; zil-diagnostic 3.3236)', flush=True)
+if wb is not None:
+    try: wb.summary['best_val_ce'] = best; wb.finish()
+    except Exception: pass