path: root/ep_run/lt_ep_train.py

"""option 2 / H2: train a full EP equilibrium transformer block on Shakespeare char-LM.

One block = token state z relaxed to a fixed point of
  F(z) = -(z - x_in)               (input clamp; x_in = embed(idx))
       - dE_mem/dz                 (Hopfield memory  E_mem = -sum relu(z Wm)^2 ; CONSERVATIVE = FFN)
       + s*(causal_attn(z) - c*z)  (damped causal attention ; NON-conservative)
Readout logits = z* Whead ; loss = next-token cross-entropy.

Train modes:
  ep    : free + +/-beta nudged equilibria. Block+embed params via vector-field gradient
          <a, dF/dtheta(z*)> with the AEP correction (clipped) on the attention part; readout
          head via its own local gradient dCE/dWhead. NO backprop through the relaxation.
  bptt  : backprop through the unrolled relaxation (exact-gradient reference, same architecture).
Stabilisation from G: damping c, clipped AEP correction, weight-norm caps, best-val checkpoint.
"""
import argparse, math, pickle, time, json, os, numpy as np, torch, torch.nn.functional as F
from pathlib import Path

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, B, T):
    data = np.memmap(DD / ('train.bin' if split == 'train' else 'val.bin'), dtype=np.uint16, mode='r')
    ix = torch.randint(len(data) - T - 1, (B,))
    x = torch.stack([torch.from_numpy(data[i:i + T].astype(np.int64)) for i in ix])
    y = torch.stack([torch.from_numpy(data[i + 1:i + 1 + T].astype(np.int64)) for i in ix])
    return x.to(dev), y.to(dev)


class EQBlock:
    def __init__(self, C, H, Mm, T, s=1.0, c=1.0, attn_mode='real', gamma=0.25):
        self.C, self.H, self.dh, self.s, self.c, self.T = C, H, C // H, s, c, T
        self.attn_mode, self.gamma = attn_mode, gamma
        self.fnoise = 0.0                                    # optics model: mult. noise per force eval
        g = lambda *sh, sc: (torch.randn(*sh, device=dev) * sc).requires_grad_(True)
        self.tok = g(vocab, C, sc=0.02); self.pos = g(T, C, sc=0.02)
        self.WQ = g(C, C, sc=1 / math.sqrt(C)); self.WK = g(C, C, sc=1 / math.sqrt(C))
        self.WV = g(C, C, sc=1 / math.sqrt(C)); self.WO = g(C, C, sc=1 / math.sqrt(C))
        self.Wm = g(C, Mm, sc=0.3 / math.sqrt(C)); self.Wh = g(C, vocab, sc=1 / math.sqrt(C))
        self.P = g(C, C, sc=1 / math.sqrt(C)); self.Q = g(C, C, sc=1 / math.sqrt(C))  # monDEQ monotone op
        self.mono_m = 1.0
        z1 = lambda n, v: torch.full((n,), float(v), device=dev).requires_grad_(True)
        self.ln1g = z1(C, 1); self.ln1b = z1(C, 0); self.ln2g = z1(C, 1); self.ln2b = z1(C, 0)  # LN affine
        self.fc = g(C, 4 * C, sc=1 / math.sqrt(C)); self.fcb = z1(4 * C, 0)                      # untied 4x FFN
        self.pj = g(4 * C, C, sc=1 / math.sqrt(4 * C)); self.pjb = z1(C, 0)
        self.cmask = torch.tril(torch.ones(T, T, dtype=torch.bool, device=dev))
        self.block = [self.tok, self.pos, self.WQ, self.WK, self.WV, self.WO, self.Wm, self.P, self.Q,
                      self.ln1g, self.ln1b, self.ln2g, self.ln2b, self.fc, self.fcb, self.pj, self.pjb]  # in the force
        self.allp = self.block + [self.Wh]
        self.capw = (self.WQ, self.WK, self.WV, self.WO, self.Wm, self.Wh, self.fc, self.pj)
        self.caps = {id(w): w.detach().norm().item() * 3.0 for w in self.capw}

    def embed(self, idx):
        return self.tok[idx] + self.pos[None]

    def attn(self, z):
        B = z.size(0)
        q = (z @ self.WQ).view(B, self.T, self.H, self.dh).transpose(1, 2)
        k = (z @ self.WK).view(B, self.T, self.H, self.dh).transpose(1, 2)
        v = (z @ self.WV).view(B, self.T, self.H, self.dh).transpose(1, 2)
        if getattr(self, 'qknorm', False):                   # Qwen3-style q/k RMSNorm: bounds logits, tames J
            q = q * torch.rsqrt(q.pow(2).mean(-1, keepdim=True) + 1e-6)
            k = k * torch.rsqrt(k.pow(2).mean(-1, keepdim=True) + 1e-6)
        a = (q @ k.transpose(-2, -1)) / math.sqrt(self.dh)
        a = torch.softmax(a.masked_fill(~self.cmask, float('-inf')), -1)
        return (a @ v).transpose(1, 2).reshape(B, self.T, self.C) @ self.WO

    def attn_energy(self, z):                       # conservative LSE attention energy (tied value)
        B = z.size(0)
        q = (z @ self.WQ).view(B, self.T, self.H, self.dh).transpose(1, 2)
        k = (z @ self.WK).view(B, self.T, self.H, self.dh).transpose(1, 2)
        a = (q @ k.transpose(-2, -1)) / math.sqrt(self.dh)
        a = a.masked_fill(~self.cmask, float('-inf'))
        return -(1.0 / self.gamma) * torch.logsumexp(self.gamma * a, dim=-1).sum()

    def Emem(self, z):
        return -(F.relu(z @ self.Wm) ** 2).sum()

    def tforce(self, z, xin):                            # pure thick force (no grad machinery) -> torch.compile
        h1 = F.layer_norm(z, (self.C,), self.ln1g, self.ln1b)
        h2 = F.layer_norm(z, (self.C,), self.ln2g, self.ln2b)
        ff = F.gelu(h2 @ self.fc + self.fcb, approximate='tanh') @ self.pj + self.pjb
        return -(z - xin) + self.attn(h1) + ff - self.c * z

    def _noisy(self, t):                                 # optics model: per-pass multiplicative noise
        if self.fnoise > 0:
            return t * (1 + self.fnoise * torch.randn_like(t))
        return t

    def nc_force(self, z):                               # non-conservative part of the force (for AEP/jacreg)
        if self.attn_mode == 'thick':
            h1 = F.layer_norm(z, (self.C,), self.ln1g, self.ln1b)
            h2 = F.layer_norm(z, (self.C,), self.ln2g, self.ln2b)
            return self._noisy(self.attn(h1) + (F.gelu(h2 @ self.fc + self.fcb, approximate='tanh') @ self.pj + self.pjb))
        return self._noisy(self.s * self.attn(z))

    def force(self, z, xin, cg=False):
        with torch.enable_grad():
            zr = z if (cg and z.requires_grad) else z.detach().requires_grad_(True)
            if self.attn_mode == 'thick':                        # DEQ-transformer block: LN + untied 4x FFN + residual
                h1 = F.layer_norm(zr, (self.C,), self.ln1g, self.ln1b)
                h2 = F.layer_norm(zr, (self.C,), self.ln2g, self.ln2b)
                ff = F.gelu(h2 @ self.fc + self.fcb, approximate='tanh') @ self.pj + self.pjb
                return -(zr - xin) + self._noisy(self.attn(h1) + ff) - self.c * zr   # -c*z: initial contraction
            if self.attn_mode == 'mono':                         # monDEQ: structurally-monotone contraction
                gm, = torch.autograd.grad(self.Emem(zr), zr, create_graph=cg)
                PtP = self.P.t() @ self.P                        # PSD -> sym(J) = -(mI+PtP) < 0 (guaranteed)
                f = (-(self.mono_m * zr + zr @ PtP) + zr @ (self.Q - self.Q.t()).t()
                     + xin - gm + self.s * self.attn(zr))
                return f
            E = 0.5 * ((zr - xin) ** 2).sum() + self.Emem(zr)
            if self.attn_mode == 'energy':                       # attention folded into the energy (conservative)
                E = E + self.attn_energy(zr) + 0.5 * self.c * (zr ** 2).sum()   # confinement -> bounded below
            gz, = torch.autograd.grad(E, zr, create_graph=cg)
            f = -gz
            if self.attn_mode == 'real':                         # non-conservative attention + damping
                f = f + self.s * (self.attn(zr) - self.c * zr)
            return f


def relax(blk, z, xin, steps, eps):
    cstep = getattr(blk, '_cstep', None)
    if cstep is not None and blk.fnoise == 0.0:          # compiled pure-thick free-phase fast path
        with torch.no_grad():
            for _ in range(steps):
                z = cstep(z, xin)
        return z.detach()
    for _ in range(steps):
        with torch.no_grad():
            z = z + eps * blk.force(z, xin).detach()
    return z.detach()


def ce(blk, z, y):
    return F.cross_entropy((z @ blk.Wh).reshape(-1, vocab), y.reshape(-1))


def ep_step(blk, idx, y, T1, T2, eps, beta, jacreg=0.0, holo=0, hr=0.02, t1max=0, res_est=1e-4, t2sel=0,
            corr_every=1, res_gate=0.0, resreg=0.0, eigreg=0.0, eig_margin=1.0):
    xin0 = blk.embed(idx).detach()
    zs = relax(blk, xin0.clone(), xin0, T1, eps)
    res = (relax(blk, zs, xin0, 1, eps) - zs).norm().item() / (zs.norm().item() + 1e-9)
    res_used = res
    zT, resT1 = zs, res                           # the T1 free-phase state (what eval/BPTT use), BEFORE refinement
    if t1max > T1:                                # estimator refinement: relax further until tight
        rnow, t = res, T1                         # (controller signal `res` stays measured at T1)
        while t < t1max and rnow > res_est:
            zs = relax(blk, zs, xin0, 50, eps); t += 50
            rnow = (relax(blk, zs, xin0, 1, eps) - zs).norm().item() / (zs.norm().item() + 1e-9)
        res_used = rnow
    if res_gate > 0 and res_used > res_gate:      # validity gate: off-equilibrium the EP update is
        grads = {}                                # undefined -> apply ONLY the homeostat (jacreg) and
        if jacreg > 0:                            # skip the nudge entirely (fast recovery steps)
            er = torch.randn_like(zs)
            with torch.enable_grad():
                Jv = torch.autograd.functional.jvp(blk.nc_force, zs.detach(), er, create_graph=True)[1]
                R = jacreg * (Jv ** 2).sum() / (er ** 2).sum()
                gr = torch.autograd.grad(R, blk.block, allow_unused=True)
            grads = {id(p): g for p, g in zip(blk.block, gr) if g is not None}
        return grads, res
    def nudge(sign):
        z = zs.clone()
        for _ in range(T2):
            with torch.enable_grad():
                zz = z.detach().requires_grad_(True)
                g, = torch.autograd.grad(ce(blk, zz, y), zz)
                g = g.clamp(-2.0, 2.0)                          # clip nudge so it can't blow up relax
            with torch.no_grad():
                f = blk.force(z, xin0).detach() - sign * beta * g
                if blk.attn_mode in ('real', 'thick'):            # AEP correction (full non-conservative part)
                    v = (z - zs).detach()
                    Jv = torch.autograd.functional.jvp(blk.nc_force, zs, v)[1]
                    JTv = torch.autograd.functional.vjp(blk.nc_force, zs, v)[1]
                    corr = Jv - JTv
                    cn, fn = corr.norm(), f.norm() + 1e-8
                    f = f - (corr * (fn / cn) if cn > fn else corr)
                z = z + eps * f
        return z.detach()
    if holo == 2 and t2sel > 0:                           # adaptive-T2, phase-batched fast path (validated ==)
        from holo_ep import holo_a_select2, holo_a_track
        K = max(1, getattr(blk, 'navg', 1))               # restart-averaging: noise / sqrt(K)
        acc = None
        for _ in range(K):
            if getattr(blk, 'track', False):              # common-mode-tracking AEP (loose-tolerant)
                ai, _ = holo_a_track(blk, zs, xin0, y, hr, t2sel, eps)
            else:
                ai, _ = holo_a_select2(blk, zs, xin0, y, hr, t2sel, eps, li=getattr(blk, 'li_avg', 0))
            acc = ai if acc is None else acc + ai
        a = acc / K
    elif holo > 0 and t2sel > 0:                          # adaptive-T2 via hindsight snapshot selection
        from holo_ep import holo_a_select
        a, _ = holo_a_select(blk, zs, xin0, y, holo, hr, t2sel, eps, corr_every=corr_every)
    elif holo > 0:                                        # holomorphic nudge (clamp-free, Cauchy readout)
        from holo_ep import holo_a
        a, _ = holo_a(blk, zs, xin0, y, holo, hr, T2, eps)
    else:
        zp, zm = nudge(+1), nudge(-1)
        a = ((zm - zp) / (2 * beta)).detach()
    grads = {}
    with torch.enable_grad():
        xin = blk.embed(idx)                                  # live (for tok/pos grad through clamp)
        f = blk.force(zs.detach(), xin, cg=True)
        gblk = torch.autograd.grad((a * f).sum(), blk.block, allow_unused=True)
    for p, gv in zip(blk.block, gblk):
        grads[id(p)] = gv
    with torch.enable_grad():
        gh, = torch.autograd.grad(ce(blk, zs.detach(), y), blk.Wh)  # readout local gradient
    grads[id(blk.Wh)] = gh
    if jacreg > 0:                                        # soft Lyapunov: penalize non-conservative Jacobian norm
        er = torch.randn_like(zs)
        with torch.enable_grad():
            Jv = torch.autograd.functional.jvp(blk.nc_force, zs.detach(), er, create_graph=True)[1]
            R = jacreg * (Jv ** 2).sum() / (er ** 2).sum()      # Hutchinson est of ||J_nc||_F^2
            gr = torch.autograd.grad(R, blk.block, allow_unused=True)
        for p, g in zip(blk.block, gr):
            if g is not None:
                grads[id(p)] = g if grads.get(id(p)) is None else grads[id(p)] + g
    if resreg > 0 and resT1 > 7e-4:                      # defend z_T1 (BPTT gets this implicitly; EP at z* doesn't)
        with torch.enable_grad():
            Fz = blk.tforce(zT, xin0)                     # deterministic thick force at z_T1 (params live, zT/xin0 detached)
            Rr = (eps * Fz).pow(2).sum() / (zT.pow(2).sum() + 1e-9)    # ~ (T1 residual)^2
            grr = torch.autograd.grad(Rr, blk.block, allow_unused=True)
        ratio = resreg * min(1.0, resT1 / 2e-2)          # ramp 0->resreg as res 7e-4->2e-2, capped
        gtask = math.sqrt(sum(float((grads[id(p)] ** 2).sum()) for p in blk.block if grads.get(id(p)) is not None) + 1e-20)
        gres = math.sqrt(sum(float((g ** 2).sum()) for g in grr if g is not None) + 1e-20)
        lam = ratio * gtask / gres                       # scale penalty to `ratio` of the task-grad norm
        for p, g in zip(blk.block, grr):
            if g is not None:
                grads[id(p)] = g * lam if grads.get(id(p)) is None else grads[id(p)] + lam * g
    if eigreg > 0:                                       # #2: leading-abscissa control (surgical, one-sided; alt to jacreg)
        from eig_control import eig_penalty
        ge, _om = eig_penalty(blk, zs, eigreg, eig_margin, blk.__dict__.setdefault('_eigcache', {}))
        for pid, g in ge.items():
            grads[pid] = g if grads.get(pid) is None else grads[pid] + g
    return grads, res


class Lion(torch.optim.Optimizer):
    """Chen et al. 2023. Analog-hardware rationale: sign updates = fixed-amplitude pulses
    (kills device write-nonlinearity), magnitude-noise immune, one momentum cap per weight."""
    def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0, lars=False):
        super().__init__(params, dict(lr=lr, betas=betas, weight_decay=weight_decay, lars=lars))

    @torch.no_grad()
    def step(self):
        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                b1, b2 = group['betas']
                st = self.state.setdefault(p, {})
                if 'm' not in st:
                    st['m'] = torch.zeros_like(p)
                u = (b1 * st['m'] + (1 - b1) * p.grad).sign()
                lr = group['lr']
                if group['lars']:                 # per-tensor trust ratio: one gain line per array
                    lr = lr * (p.norm() / (u.norm() + 1e-12)).item()
                p.mul_(1 - lr * group['weight_decay'])
                p.add_(u, alpha=-lr)
                st['m'].mul_(b2).add_(p.grad, alpha=1 - b2)


def bptt_step(blk, idx, y, T1, eps, jacreg=0.0):
    xin = blk.embed(idx)
    z = xin.detach().requires_grad_(True) * 0 + xin            # init = embedding (keeps graph to emb)
    for _ in range(T1):
        z = z + eps * blk.force(z, xin, cg=True)
    g = torch.autograd.grad(ce(blk, z, y), blk.allp, allow_unused=True)
    gd = {id(p): gv for p, gv in zip(blk.allp, g)}
    if jacreg > 0:                                # same soft Lyapunov penalty as ep mode (fair control)
        er = torch.randn_like(z)
        with torch.enable_grad():
            Jv = torch.autograd.functional.jvp(blk.nc_force, z.detach(), er, create_graph=True)[1]
            R = jacreg * (Jv ** 2).sum() / (er ** 2).sum()
            gr = torch.autograd.grad(R, blk.block, allow_unused=True)
        for p, gv in zip(blk.block, gr):
            if gv is not None:
                gd[id(p)] = gv if gd.get(id(p)) is None else gd[id(p)] + gv
    return gd


@torch.no_grad()
def evaluate(blk, T1, eps, nb=8, B=32):
    tot = 0.0
    for _ in range(nb):
        idx, y = get_batch('val', B, blk.T)
        xin = blk.embed(idx).detach()
        z = relax(blk, xin.clone(), xin, T1, eps)
        tot += ce(blk, z, y).item()
    return tot / nb


def specnorm_weight_items(blk):
    items = []
    qkv = None
    for name in ('WQKV', 'Wqkv', 'W_qkv', 'qkv'):
        if hasattr(blk, name):
            qkv = (name, getattr(blk, name))
            break
    if qkv is not None:
        items.append(qkv)
    else:
        items.extend((name, getattr(blk, name)) for name in ('WQ', 'WK', 'WV') if hasattr(blk, name))
    items.extend((name, getattr(blk, name)) for name in ('WO', 'fc', 'pj') if hasattr(blk, name))
    return [(name, w) for name, w in items if w.ndim >= 2]


@torch.no_grad()
def power_sigma(W, u, iters=2):
    M = W.detach().reshape(W.shape[0], -1)
    if u is None or u.shape != (M.shape[0],) or u.device != W.device or u.dtype != W.dtype:
        u = F.normalize(torch.randn(M.shape[0], device=W.device, dtype=W.dtype), dim=0, eps=1e-12)
    for _ in range(iters):
        v = F.normalize(M.t().mv(u), dim=0, eps=1e-12)
        u = F.normalize(M.mv(v), dim=0, eps=1e-12)
    sigma = u.dot(M.mv(v)).abs()
    return sigma, u.detach()


@torch.no_grad()
def project_specnorm_(items, cache, bound):
    max_before, max_after, clamped = 0.0, 0.0, []
    for name, W in items:
        sigma, u = power_sigma(W, cache.get(id(W)))
        cache[id(W)] = u
        before = float(sigma)
        after = before
        if before > bound:
            scale = bound / (before + 1e-12)
            W.mul_(scale)
            after = bound
            clamped.append(name)
        max_before = max(max_before, before)
        max_after = max(max_after, after)
    return max_before, max_after, clamped


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument('--mode', choices=['ep', 'bptt'], required=True)
    ap.add_argument('--steps', type=int, default=2000); ap.add_argument('--B', type=int, default=32)
    ap.add_argument('--T', type=int, default=64); ap.add_argument('--C', type=int, default=128)
    ap.add_argument('--H', type=int, default=4); ap.add_argument('--Mm', type=int, default=256)
    ap.add_argument('--T1', type=int, default=80); ap.add_argument('--T2', type=int, default=15)
    ap.add_argument('--eps', type=float, default=0.1); ap.add_argument('--beta', type=float, default=0.02)
    ap.add_argument('--lr', type=float, default=1e-3); ap.add_argument('--log', type=int, default=100)
    ap.add_argument('--warmup', type=int, default=0)           # linear lr warmup steps (big-model stability)
    ap.add_argument('--state', type=str, default='')           # periodic FULL-state path (weights+opt+sched+step)
    ap.add_argument('--resume', action='store_true')           # resume from --state if it exists (Colab timeouts)
    ap.add_argument('--save_every', type=int, default=0)        # full-state save cadence (0=every --log); set small on Colab
    ap.add_argument('--c', type=float, default=1.0); ap.add_argument('--capx', type=float, default=3.0)
    ap.add_argument('--attn_mode', choices=['real', 'energy', 'mono', 'thick'], default='real')
    ap.add_argument('--ccap', type=float, default=8.0)
    ap.add_argument('--specnorm', type=float, default=0.0)  # hard post-step spectral projection bound; 0=off
    ap.add_argument('--jacreg', type=float, default=0.0)   # Bai 2021: soft Jacobian-norm (Lyapunov) penalty
    ap.add_argument('--jr_max', type=float, default=16.0)  # adaptive jacreg ceiling (ramps up vs residual)
    ap.add_argument('--res_target', type=float, default=5e-3)  # continuous controller target residual
    ap.add_argument('--jr_floor', type=float, default=None)    # controller floor; default=--jacreg (legacy: never off)
    ap.add_argument('--res_ema', type=float, default=0.0)      # EMA on residual signal (0=off); kills controller thrash
    ap.add_argument('--jr_lrcouple', action='store_true')      # anneal the λ floor with the lr schedule (late-drift fix)
    ap.add_argument('--holo', type=int, default=0)             # holomorphic EP: N circle points (0=off)
    ap.add_argument('--hr', type=float, default=0.02)          # holomorphic nudge radius |beta|
    ap.add_argument('--pema', type=float, default=0.0)         # parameter EMA decay (0=off); tames late wander
    ap.add_argument('--t1max', type=int, default=0)            # adaptive free phase: extend up to t1max...
    ap.add_argument('--res_est', type=float, default=1e-4)     # ...until this residual (estimator validity)
    ap.add_argument('--t2sel', type=int, default=0)            # adaptive T2: snapshot-selection cap (0=off)
    ap.add_argument('--seed', type=int, default=0)
    ap.add_argument('--data', type=str, default='/tmp/lt_ep/data/shakespeare_char')
    ap.add_argument('--ckpt', type=str, default='')            # save best weights (raw+ema) here
    ap.add_argument('--corr_every', type=int, default=1)       # recompute AEP corr every k nudge steps
    ap.add_argument('--tf32', action='store_true')             # tf32 matmuls (check res floor first!)
    ap.add_argument('--abort_res', type=float, default=0.1)    # kill switch: res above this 100 steps straight
    ap.add_argument('--res_gate', type=float, default=0.0)     # validity gate: skip task grads above this res
    ap.add_argument('--wsd', type=float, default=0.0)          # WSD: hold peak lr, cosine-decay only the last wsd fraction
    ap.add_argument('--resreg', type=float, default=0.0)       # T1-residual penalty: defend z_T1 (cap ratio vs task grad); run res_gate=0
    ap.add_argument('--eigreg', type=float, default=0.0)       # #2: leading-abscissa (numerical-abscissa) control — surgical alt to jacreg
    ap.add_argument('--eig_margin', type=float, default=1.0)   # penalize omega(J_nc) above this (free-phase Hopf boundary ~ 1+c)
    ap.add_argument('--diag_cos', type=int, default=0)         # #1: every N steps, log cos(EP grad, exact BPTT grad) + res
    ap.add_argument('--fingerprint', action='store_true')      # load --init_ckpt, print (res,cos,abscissa,val) fingerprint, exit
    ap.add_argument('--opt', choices=['adamw', 'lion', 'lionlars', 'sgdm', 'sgdsai'], default='adamw')
    ap.add_argument('--wd', type=float, default=1e-4)
    ap.add_argument('--fnoise', type=float, default=0.0)       # optics/device twin: mult. noise per force eval
    ap.add_argument('--wq_bits', type=int, default=0)          # weights projected to N bits each step (0=off)
    ap.add_argument('--wmis', type=float, default=0.0)         # static per-device mismatch sigma (0=off)
    ap.add_argument('--li_avg', type=int, default=0)           # lock-in integration window (0=snapshot mode)
    ap.add_argument('--navg', type=int, default=1)             # restart-averaged contrast estimates per update
    ap.add_argument('--track', action='store_true')            # common-mode-tracking AEP correction
    ap.add_argument('--rt_final', type=float, default=0.0)     # anneal res_target to this (0=off), 25%-75% of run
    ap.add_argument('--nudge_brake', type=float, default=0.0)  # kappa: anchor spring during nudge (Tikhonov adjoint)
    ap.add_argument('--init_ckpt', type=str, default='')       # warm-start weights from a saved ckpt
    ap.add_argument('--qknorm', action='store_true')           # Qwen3-style q/k RMSNorm in attention
    ap.add_argument('--compile', action='store_true')          # torch.compile the free-phase relaxation (thick)
    ap.add_argument('--resinit', type=float, default=1.0)      # scale WO,pj at init (ReZero/Fixup: small=near-identity block)
    cfg = ap.parse_args()
    if cfg.specnorm < 0:
        raise SystemExit("--specnorm must be non-negative")
    global DD, vocab
    DD = Path(cfg.data)
    vocab = pickle.load(open(DD / 'meta.pkl', 'rb'))['vocab_size']
    if cfg.tf32:
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True
    torch.manual_seed(cfg.seed)
    blk = EQBlock(cfg.C, cfg.H, cfg.Mm, cfg.T, s=1.0, c=cfg.c, attn_mode=cfg.attn_mode)
    for w in blk.capw:
        blk.caps[id(w)] = w.detach().norm().item() * cfg.capx
    if cfg.opt in ('lion', 'lionlars'):
        opt = Lion(blk.allp, lr=cfg.lr, weight_decay=cfg.wd, lars=(cfg.opt == 'lionlars'))
    elif cfg.opt == 'sgdm':
        opt = torch.optim.SGD(blk.allp, lr=cfg.lr, momentum=0.95, weight_decay=cfg.wd, nesterov=True)
    elif cfg.opt == 'sgdsai':
        # EP-SaI (SGD-SaI, arXiv:2412.11768, adapted to EP gradients): per-tensor lr from the
        # init-time gradient SNR, frozen — hardware: one gain line per array, set at calibration.
        gs = {id(p): [] for p in blk.allp}
        for _ in range(12):
            idx0, y0 = get_batch('train', cfg.B, cfg.T)
            g0, _ = ep_step(blk, idx0, y0, cfg.T1, cfg.T2, cfg.eps, cfg.beta, 0.0, cfg.holo, cfg.hr,
                            cfg.t1max, cfg.res_est, cfg.t2sel, cfg.corr_every, 0.0)
            for p in blk.allp:
                if g0.get(id(p)) is not None:
                    gs[id(p)].append(g0[id(p)].detach().clone())
        sc = {}
        for p in blk.allp:
            if gs[id(p)]:
                S = torch.stack(gs[id(p)])
                sc[id(p)] = (S.mean(0).norm() / (S.std(0).norm() + 1e-12)).item()
        mx = max(sc.values())
        print("[sgdsai] per-tensor lr scales: " + " ".join(f"{v/mx:.3f}" for v in sc.values()), flush=True)
        opt = torch.optim.SGD([dict(params=[p], lr=cfg.lr * sc.get(id(p), mx) / mx) for p in blk.allp],
                              momentum=0.95, weight_decay=cfg.wd, nesterov=True)
    else:
        opt = torch.optim.AdamW(blk.allp, lr=cfg.lr, weight_decay=cfg.wd)
    if cfg.warmup > 0 or cfg.wsd > 0:              # warmup -> (WSD hold peak) -> cosine decay
        _w = cfg.warmup                            # contraction before large steps kick weights out of basin
        def _lrl(s):
            if s < _w:
                return (s + 1) / _w
            _ds = int((1 - cfg.wsd) * cfg.steps) if cfg.wsd > 0 else _w   # WSD decay-start: hold peak lr until here
            if s < _ds:
                return 1.0
            p = (s - _ds) / max(1, cfg.steps - _ds)
            return 0.05 + 0.475 * (1 + math.cos(math.pi * min(1.0, p)))
        sched = torch.optim.lr_scheduler.LambdaLR(opt, _lrl)
    else:
        sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, cfg.steps, eta_min=cfg.lr * 0.05)
    if cfg.init_ckpt:
        ckl = torch.load(cfg.init_ckpt, map_location=dev)
        with torch.no_grad():
            for p, w in zip(blk.allp, ckl['allp']):
                p.copy_(w.to(dev))
        print(f"[init] warm-start from {cfg.init_ckpt} (step {ckl.get('step')}, best {ckl.get('best', float('nan')):.4f})", flush=True)
    xin = blk.embed(*get_batch('train', cfg.B, cfg.T)[:1]).detach()
    r = (relax(blk, relax(blk, xin.clone(), xin, 200, cfg.eps), xin, 1, cfg.eps) - relax(blk, xin.clone(), xin, 200, cfg.eps)).norm().item()
    print(f"[{cfg.mode}] residual~{r:.1e} | C={cfg.C} H={cfg.H} Mm={cfg.Mm} T1={cfg.T1} T2={cfg.T2}", flush=True)
    best, t0, jr, rs = 9.9, time.time(), cfg.jacreg, None
    pema = [p.detach().clone() for p in blk.allp] if cfg.pema > 0 else None
    badct = 0
    blk.fnoise = cfg.fnoise
    blk.li_avg = cfg.li_avg
    blk.navg = cfg.navg
    blk.track = cfg.track
    blk.nbrake = cfg.nudge_brake
    blk.qknorm = cfg.qknorm
    if cfg.resinit != 1.0:                          # near-identity block at init (contractive) -> stable big-width start
        with torch.no_grad():
            blk.WO.mul_(cfg.resinit); blk.pj.mul_(cfg.resinit)
    spec_items = specnorm_weight_items(blk)
    spec_cache = {}
    if cfg.specnorm > 0:
        shapes = " ".join(f"{name}{tuple(W.shape)}" for name, W in spec_items)
        print(f"[specnorm] hard post-step projection: sigma_max <= {cfg.specnorm:g} on {shapes}", flush=True)
    blk._cstep = None
    if cfg.compile and cfg.attn_mode == 'thick':
        _ee = cfg.eps
        blk._cstep = torch.compile(lambda z, xin: z + _ee * blk.tforce(z, xin))
    mis = None
    if cfg.wmis > 0:                                # fixed fabrication mismatch (same devices all run)
        gm = torch.Generator().manual_seed(1234)
        mis = [(1 + cfg.wmis * torch.randn(p.shape, generator=gm)).clamp(0.2, 5.0).to(dev) for p in blk.allp]
    hw_on = cfg.wq_bits > 0 or mis is not None

    def hw_swap():                                  # measure physics on the imperfect device copy;
        saved = [p.detach().clone() for p in blk.allp]   # masters stay fp32 (program-verify model)
        with torch.no_grad():
            for i, p in enumerate(blk.allp):
                w = p * mis[i] if mis is not None else p.detach().clone()
                if cfg.wq_bits > 0:
                    d = w.abs().max() / (2 ** (cfg.wq_bits - 1) - 1) + 1e-12
                    w = torch.round(w / d) * d
                p.copy_(w)
        return saved

    def hw_restore(saved):
        with torch.no_grad():
            for p, s in zip(blk.allp, saved):
                p.copy_(s)

    start_step = 1
    if cfg.resume and cfg.state and os.path.exists(cfg.state):   # Colab-timeout resume: full state
        st = torch.load(cfg.state, map_location=dev)
        with torch.no_grad():
            for p, w in zip(blk.allp, st['allp']):
                p.copy_(w.to(dev))
        if pema is not None and st.get('pema') is not None:
            pema = [s.to(dev) for s in st['pema']]
        opt.load_state_dict(st['opt']); sched.load_state_dict(st['sched'])
        start_step = st['step'] + 1; jr = st['jr']; rs = st['rs']; best = st['best']
        print(f"[resume] from {cfg.state}: step {start_step}, best {best:.4f}, jr {jr:.1f}", flush=True)

    def save_state(step):
        if not cfg.state:
            return
        torch.save({'allp': [p.detach().cpu() for p in blk.allp],
                    'pema': [s.cpu() for s in pema] if pema is not None else None,
                    'opt': opt.state_dict(), 'sched': sched.state_dict(),
                    'step': step, 'jr': jr, 'rs': rs, 'best': best}, cfg.state + '.tmp')
        os.replace(cfg.state + '.tmp', cfg.state)                # atomic: survive a mid-write timeout

    if cfg.fingerprint:                                  # study s2000 vs other ckpts: print the operator's 4-D fingerprint
        from diag_cos import fingerprint
        fp = fingerprint(blk, cfg.T1, cfg.T2, cfg.eps, cfg.beta, cfg.holo, cfg.hr, cfg.t1max, cfg.res_est, cfg.t2sel)
        print(f"[fingerprint] ckpt={cfg.init_ckpt or 'scratch'} | res={fp['res']:.2e} cos(EP,BPTT)={fp['cos']:.4f} "
              f"num_abscissa={fp['num_abscissa']:+.4f} val={fp['val']:.4f}", flush=True)
        return
    for step in range(start_step, cfg.steps + 1):
        idx, y = get_batch('train', cfg.B, cfg.T)
        if cfg.mode == 'ep':
            sw = hw_swap() if hw_on else None
            grads, res = ep_step(blk, idx, y, cfg.T1, cfg.T2, cfg.eps, cfg.beta, jr, cfg.holo, cfg.hr,
                                 cfg.t1max, cfg.res_est, cfg.t2sel, cfg.corr_every, cfg.res_gate, cfg.resreg,
                                 cfg.eigreg, cfg.eig_margin)
            if sw is not None:
                hw_restore(sw)
            if cfg.jacreg > 0:                    # continuous controller: drive residual -> res_target (smooth)
                flo = cfg.jacreg if cfg.jr_floor is None else cfg.jr_floor
                if cfg.jr_lrcouple:
                    flo *= sched.get_last_lr()[0] / cfg.lr
                rtgt = cfg.res_target
                if cfg.rt_final > 0:              # stiffness anneal: tight start -> loose mid/late
                    u = min(1.0, max(0.0, (step / cfg.steps - 0.25) / 0.5))
                    rtgt = math.exp((1 - u) * math.log(cfg.res_target) + u * math.log(cfg.rt_final))
                rs = res if rs is None else cfg.res_ema * rs + (1 - cfg.res_ema) * res
                jr = min(cfg.jr_max, max(flo, jr * math.exp(0.3 * math.log((rs + 1e-9) / rtgt))))
            else:                                 # damping feedback (no jacreg)
                if res > 1e-3:
                    blk.c = min(cfg.ccap, blk.c * 1.3)
                elif res < 2e-4:
                    blk.c = max(0.5, blk.c * 0.97)
        else:
            grads = bptt_step(blk, idx, y, cfg.T1, cfg.eps, jr if cfg.jacreg > 0 else 0.0)
            with torch.no_grad():                # is BPTT's optimum contractive? (free-phase residual)
                xinb = blk.embed(idx).detach()
                zsb = relax(blk, xinb.clone(), xinb, cfg.T1, cfg.eps)
                res = (relax(blk, zsb, xinb, 1, cfg.eps) - zsb).norm().item() / (zsb.norm().item() + 1e-9)
            if cfg.jacreg > 0:                   # same residual-driven λ controller as ep mode
                flo = cfg.jacreg if cfg.jr_floor is None else cfg.jr_floor
                if cfg.jr_lrcouple:
                    flo *= sched.get_last_lr()[0] / cfg.lr
                rs = res if rs is None else cfg.res_ema * rs + (1 - cfg.res_ema) * res
                jr = min(cfg.jr_max, max(flo, jr * math.exp(0.3 * math.log((rs + 1e-9) / cfg.res_target))))
        badct = badct + 1 if (cfg.abort_res > 0 and res > cfg.abort_res) else 0
        if badct >= 100:                          # containment lost and not recovering: stop, keep best ckpt
            print(f"  ABORT at step {step}: res>{cfg.abort_res} for 100 consecutive steps (best {best:.4f})", flush=True)
            break
        ok = all((g is None) or torch.isfinite(g).all() for g in grads.values())
        if not ok:
            print(f"  step {step}: non-finite, skip", flush=True); continue
        opt.zero_grad(set_to_none=True)
        for p in blk.allp:
            p.grad = grads.get(id(p))
        torch.nn.utils.clip_grad_norm_(blk.allp, 5.0)
        opt.step()
        spec_stats = None
        with torch.no_grad():
            if cfg.specnorm > 0:
                spec_stats = project_specnorm_(spec_items, spec_cache, cfg.specnorm)
            else:
                for p in blk.capw:
                    pn = p.norm(); cap = blk.caps[id(p)]
                    if pn > cap:
                        p.mul_(cap / pn)
        sched.step()
        if spec_stats is not None and (step == start_step or step % cfg.log == 0):
            sb, sa, names = spec_stats
            cname = ",".join(names) if names else "none"
            print(f"  specnorm step {step}: max sigma before={sb:.4f} after={sa:.4f} bound={cfg.specnorm:.4f} clamped={cname}", flush=True)
        if pema is not None:
            with torch.no_grad():
                for s, p in zip(pema, blk.allp):
                    s.mul_(cfg.pema).add_(p.detach(), alpha=1 - cfg.pema)
        if cfg.save_every and step % cfg.save_every == 0 and step % cfg.log != 0:
            save_state(step)                       # mid-interval state save (Colab: cap worst-case loss)
        if cfg.diag_cos and step % cfg.diag_cos == 0:        # #1: gradient-alignment trajectory (scratch vs warm)
            from diag_cos import cos_ep_bptt
            _c, _r = cos_ep_bptt(blk, idx, y, cfg.T1, cfg.T2, cfg.eps, cfg.beta, cfg.holo, cfg.hr,
                                 cfg.t1max, cfg.res_est, cfg.t2sel)
            print(f"  [diag] step {step}: cos(EP,BPTT)={_c:.4f} res={_r:.1e}", flush=True)
        if step % cfg.log == 0:
            prevb = best
            sw = hw_swap() if hw_on else None
            v = evaluate(blk, cfg.T1, cfg.eps)
            if sw is not None:
                hw_restore(sw)
            best = min(best, v)
            etag = ""
            if pema is not None:
                with torch.no_grad():
                    raw = [p.detach().clone() for p in blk.allp]
                    for p, s in zip(blk.allp, pema):
                        p.copy_(s)
                    ve = evaluate(blk, cfg.T1, cfg.eps)
                    for p, r in zip(blk.allp, raw):
                        p.copy_(r)
                best = min(best, ve); etag = f" ema={ve:.4f}"
            if cfg.ckpt and best < prevb:
                torch.save({'allp': [p.detach().cpu() for p in blk.allp],
                            'pema': [s.cpu() for s in pema] if pema is not None else None,
                            'step': step, 'best': best}, cfg.ckpt)
            print(f"step {step:4d}/{cfg.steps} | val CE {v:.4f}{etag} (best {best:.4f}) | jr={jr:.1f} res={res:.1e} | {step/(time.time()-t0):.2f} it/s", flush=True)
            save_state(step)                       # full-state checkpoint each log interval (Colab resume)
    print(f"[{cfg.mode}] DONE best val CE {best:.4f}  (random baseline ln({vocab})={math.log(vocab):.3f})", flush=True)
    out_dir = Path('runs')
    out_dir.mkdir(exist_ok=True)
    json.dump({'mode': cfg.mode, 'best_val_ce': best}, open(out_dir / f'H2_{cfg.mode}.json', 'w'))


if __name__ == '__main__':
    main()