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"""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=0.003); ap.add_argument('--seed', type=int, default=0)
ap.add_argument('--K', type=int, default=3) # fb (message-passing) rounds
ap.add_argument('--geta', type=float, default=1.0) # fb mixing (1.0 = undamped)
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='')
ap.add_argument('--kmax', type=int, default=8) # adaptive fb rounds cap
ap.add_argument('--gate_every', type=int, default=200) # in-training cos(EP,BP) telemetry
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
@torch.no_grad()
def tok_sigma(iters=8):
"""top singular value of tok.weight (power iteration on the raw matrix)."""
W = tok.weight
v = torch.randn(W.shape[1], device=dev); v /= v.norm()
sig = 1.0
for _ in range(iters):
u = W @ v; u /= max(u.norm(), 1e-12)
v = W.t() @ u; sig = v.norm(); v /= max(sig, 1e-12)
return float(sig)
def relax(z0, zs_free, y, beta, K):
"""K fb rounds: backward feedback refresh + forward rebuild. State oscillation is
HARMLESS for the theta-readout (probe-verified); no contraction verdict."""
zs = [z.clone() for z in zs_free]
d = [None] * args.L
for k in range(K):
zc = zs[args.L - 1].detach().requires_grad_(True)
ce = F.cross_entropy(readout(zc).reshape(-1, vocab), y.reshape(-1))
d[args.L - 1] = (-beta * NBT * torch.autograd.grad(ce, zc)[0]).detach()
for l in range(args.L - 2, -1, -1):
zc = zs[l].detach().requires_grad_(True)
fnext = blocks[l + 1](zc, mask)
d[l] = torch.autograd.grad(fnext, zc, grad_outputs=d[l + 1])[0].detach()
with torch.no_grad():
prev = z0
for l in range(args.L):
rebuilt = blocks[l](prev, mask) + d[l]
zs[l] = (1 - args.geta) * zs[l] + args.geta * rebuilt if args.geta < 1.0 else rebuilt
prev = zs[l]
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]
SIG0 = None
GOV = {'K': None, 'bscale': 1.0, 'gema': None}
def ep_step(x, y):
"""single-sided EP with a QUALITY-GOVERNED estimator: beta_t = beta0*bscale*sig0^2/sig^2,
K = GOV['K'] fb rounds; guard = finiteness + drift + grad-norm sanity only."""
global SIG0
if GOV['K'] is None: GOV['K'] = args.K
sig = tok_sigma()
if SIG0 is None: SIG0 = sig
beta_t = args.beta * GOV['bscale'] * (SIG0 * SIG0) / max(sig * sig, 1e-9)
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, +beta_t, GOV['K'])
with torch.no_grad():
drift = sum(float((a - b).norm()) for a, b in zip(zp, zs_free)) / max(
sum(float(b.norm()) for b in zs_free), 1e-9)
if (not math.isfinite(drift)) or drift > 0.5:
for p in all_params: p.grad = None
return free_ce, beta_t, GOV['K'], False
prev = tok(x) + pos(torch.arange(args.T, device=dev))[None]
E = 0.0
for z, b in zip(zp, blocks): E = E + 0.5 * ((z - b(prev, mask)) ** 2).sum(); prev = z
obj = E / (NBT * beta_t) + F.cross_entropy(readout(zp[-1]).reshape(-1, vocab), y.reshape(-1))
gs = torch.autograd.grad(obj, all_params, allow_unused=True)
gn = 0.0
for g in gs:
if g is not None: gn += float((g ** 2).sum())
gn = gn ** 0.5
if GOV['gema'] is None: GOV['gema'] = gn
if not math.isfinite(gn) or gn > 8 * GOV['gema']:
for p in all_params: p.grad = None
return free_ce, beta_t, GOV['K'], False
GOV['gema'] = 0.99 * GOV['gema'] + 0.01 * gn
for p, g in zip(all_params, gs):
p.grad = g
return free_ce, beta_t, GOV['K'], True
def bp_gate(x, y):
"""true BP grads for telemetry cos (called before opt.step; reads p.grad separately)."""
z = tok(x) + pos(torch.arange(args.T, device=dev))[None]
for b in blocks: z = b(z, mask)
ce = F.cross_entropy(readout(z).reshape(-1, vocab), y.reshape(-1))
return list(torch.autograd.grad(ce, all_params, allow_unused=True))
@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/fb) 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()
skips = 0
for step in range(args.steps + 1):
x, y = get_batch('train')
ce, beta_t, rounds, ok = ep_step(x, y)
if not ok: skips += 1
gcos = float('nan')
if step % args.gate_every == 0 and ok:
gbp = bp_gate(x, y)
num = den1 = den2 = 0.0
for p, g in zip(all_params, gbp):
if p.grad is None or g is None: continue
num += float((p.grad * g).sum()); den1 += float((p.grad ** 2).sum()); den2 += float((g ** 2).sum())
gcos = num / max((den1 ** 0.5) * (den2 ** 0.5), 1e-12)
if gcos < 0.97: # estimator governor: spend more
GOV['K'] = min(GOV['K'] + 2, args.kmax); GOV['bscale'] = max(GOV['bscale'] * 0.7, 0.05)
elif gcos > 0.995 and GOV['K'] > args.K: # relax back when quality is abundant
GOV['K'] -= 1; GOV['bscale'] = min(GOV['bscale'] * 1.05, 1.0)
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)
gtag = '' if math.isnan(gcos) else f' cos={gcos:.4f}'
print(f'step {step:5d}/{args.steps} | train {ce:.4f} val {val:.4f} (best {best:.4f}) '
f'| beta={beta_t:.2e} K={rounds} skips={skips}{gtag} | {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, 'beta_t': beta_t,
'rounds': rounds, 'skips': skips, 'gate_cos': (None if math.isnan(gcos) else gcos)}, 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
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