"""Cascade-EP gradient gate v2 — L distinct standard transformer blocks, layered energy
E = sum_l 0.5||z_l - f_l(z_{l-1})||^2.  Free equilibrium == the standard forward pass.
Two-phase (+-beta) relaxation of states -> local weight gradient; gate vs true BP.

v2: relaxation schemes (jacobi = physical parallel | gsf/gsr = Gauss-Seidel fwd/reverse sweeps),
    --include_io (emb/pos/tied-readout grads via the full EP formula), multi-batch gates,
    machine-parseable SUMMARY line.  Usage examples:
      python3 cascade_probe.py --L 12 --K 480
      python3 cascade_probe.py --L 6 --scheme gsr --K 12 --include_io
"""
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('--L', type=int, default=3); ap.add_argument('--C', type=int, default=128)
ap.add_argument('--H', type=int, default=4); ap.add_argument('--T', type=int, default=32)
ap.add_argument('--B', type=int, default=4); ap.add_argument('--beta', type=float, default=0.03)
ap.add_argument('--K', type=int, default=400); ap.add_argument('--eta', type=float, default=0.3)
ap.add_argument('--mom', type=float, default=0.9)
ap.add_argument('--scheme', choices=['jacobi', 'gsf', 'gsr'], default='jacobi')
ap.add_argument('--batches', type=int, default=4)
ap.add_argument('--include_io', action='store_true')   # gate emb/pos/tied-readout too
ap.add_argument('--seed', type=int, default=0); ap.add_argument('--warp', type=float, default=1.0)
ap.add_argument('--ckpt', type=str, default='')         # gate at a casc_bp_train checkpoint
args = ap.parse_args()
torch.manual_seed(args.seed)
dev = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.set_float32_matmul_precision('highest')

DD = Path('/home/yurenh2/ept/ep_run/data/tinystories_bpe')
vocab = pickle.load(open(DD / 'meta.pkl', 'rb'))['vocab_size']
data = np.memmap(DD / 'val.bin', dtype=np.uint16, mode='r')

class Block(nn.Module):
    """Standard pre-LN transformer block (distinct weights per layer, no recurrence)."""
    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)
if args.ckpt:
    sd = torch.load(args.ckpt, map_location=dev)
    tok.load_state_dict(sd['tok']); pos.load_state_dict(sd['pos']); blocks.load_state_dict(sd['blocks'])
    print(f"[gate] loaded {args.ckpt} (step {sd.get('step')}, val {sd.get('val', float('nan')):.4f})")
elif args.warp != 1.0:
    with torch.no_grad():
        for p in blocks.parameters(): p.mul_(args.warp)
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())
blk_params = list(blocks.parameters())
gate_params = blk_params + (io_params if args.include_io else [])
NBT = args.B * args.T

def get_batch(i):
    g = torch.Generator().manual_seed(1000 + i)
    ix = torch.randint(len(data) - args.T - 1, (args.B,), generator=g)
    x = torch.stack([torch.from_numpy(data[j:j + args.T].astype(np.int64)) for j in ix]).to(dev)
    y = torch.stack([torch.from_numpy(data[j + 1:j + 1 + args.T].astype(np.int64)) for j in ix]).to(dev)
    return x, y

def fwd_states(z0):
    zs = [z0]
    for b in blocks: zs.append(b(zs[-1], mask))
    return zs

def local_grad(zs, z0, l, beta, y):
    """dF/dz_l with neighbors fixed (zs: list of L free states, 0-indexed block l)."""
    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() / NBT
    if l + 1 < args.L:
        obj = obj + 0.5 * ((zs[l + 1].detach() - blocks[l + 1](zl, mask)) ** 2).sum() / NBT
    else:
        obj = obj + beta * F.cross_entropy(readout(zl).reshape(-1, vocab), y.reshape(-1))
    return torch.autograd.grad(obj, zl)[0]

def relax(z0, y, beta):
    """minimize F_beta over states; free init = forward pass. Returns relaxed states."""
    with torch.no_grad():
        zs = [z.detach().clone() for z in fwd_states(z0)[1:]]
    if args.scheme == 'jacobi':
        for z in zs: z.requires_grad_(True)
        opt = torch.optim.SGD(zs, lr=args.eta, momentum=args.mom)
        for _ in range(args.K):
            opt.zero_grad()
            E = 0.0; prev = z0
            for z, b in zip(zs, blocks): E = E + 0.5 * ((z - b(prev, mask)) ** 2).sum(); prev = z
            (E / NBT + beta * F.cross_entropy(readout(zs[-1]).reshape(-1, vocab), y.reshape(-1))).backward()
            opt.step()
        return [z.detach() for z in zs]
    order = range(args.L) if args.scheme == 'gsf' else range(args.L - 1, -1, -1)
    bufs = [torch.zeros_like(z) for z in zs]
    for _ in range(args.K):
        for l in order:
            g = local_grad(zs, z0, l, beta, y)
            bufs[l].mul_(args.mom).add_(g)
            zs[l] = (zs[l] - args.eta * bufs[l]).detach()
    return zs

def dFdtheta(zs, z0, y, beta, params):
    """dF/dtheta at FIXED states (E-term local reads + beta*CE readout term for io params)."""
    E = 0.0; prev = z0
    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, params, allow_unused=True)
    return [g if g is not None else torch.zeros(1, device=dev) for g in gs]

flat = lambda gs: torch.cat([g.reshape(-1) for g in gs])
names = ([f'blk.{n}' for n, _ in blocks.named_parameters()] +
         ([f'io.{n}' for n, _ in list(tok.named_parameters()) + list(pos.named_parameters())] if args.include_io else []))

cos_b, shr_b, blkcos = [], [], [[] for _ in range(args.L)]
t0 = time.time()
for bi in range(args.batches):
    x, y = get_batch(bi)
    z0 = (tok(x) + pos(torch.arange(args.T, device=dev))[None]).detach()
    # BP reference (embedding z0 grad flows for io gate)
    z0ref = tok(x) + pos(torch.arange(args.T, device=dev))[None]
    zs = [z0ref]
    for b in blocks: zs.append(b(zs[-1], mask))
    ce = F.cross_entropy(readout(zs[-1]).reshape(-1, vocab), y.reshape(-1))
    gbp = list(torch.autograd.grad(ce, gate_params, allow_unused=True))
    gbp = [g if g is not None else torch.zeros(1, device=dev) for g in gbp]
    # two-phase
    zp = relax(z0, y, +args.beta); zm = relax(z0, y, -args.beta)
    gp = dFdtheta(zp, z0, y, +args.beta, gate_params)
    gm = dFdtheta(zm, z0, y, -args.beta, gate_params)
    gep = [(a - b) / (2 * args.beta) for a, b in zip(gp, gm)]
    cos_b.append(F.cosine_similarity(flat(gep), flat(gbp), dim=0).item())
    shr_b.append((flat(gep).norm() / flat(gbp).norm()).item())
    for l in range(args.L):
        idx = [i for i, n in enumerate(names) if n.startswith(f'blk.{l}.')]
        blkcos[l].append(F.cosine_similarity(flat([gep[i] for i in idx]), flat([gbp[i] for i in idx]), dim=0).item())

cm, cmin = float(np.mean(cos_b)), float(np.min(cos_b))
sm = float(np.mean(shr_b))
print('per-block cos: ' + '  '.join(f'{l}:{np.mean(v):.4f}' for l, v in enumerate(blkcos)))
if args.include_io:
    idx = [i for i, n in enumerate(names) if n.startswith('io.')]
    print(f'io cos (last batch): {F.cosine_similarity(flat([gep[i] for i in idx]), flat([gbp[i] for i in idx]), dim=0).item():.4f}')
print(f'SUMMARY scheme={args.scheme} L={args.L} C={args.C} K={args.K} eta={args.eta} beta={args.beta} '
      f'io={int(args.include_io)} warp={args.warp} ckpt={args.ckpt or "-"} '
      f'cos={cm:.4f} cosmin={cmin:.4f} shrink={sm:.3f} t={time.time()-t0:.1f}s')