From 69314ce4dee3b76225a434263e305cbd2cdb04ae Mon Sep 17 00:00:00 2001 From: Yuren Hao Date: Sat, 18 Jul 2026 22:36:45 -0500 Subject: =?UTF-8?q?RESULT=2047+48:=20=E6=B6=A8=E7=9A=84=E9=87=8F=3Dblocks8?= =?UTF-8?q?-11=E5=8D=95=E6=A8=A1=E6=80=81=E5=A2=9E=E7=9B=8A(=CF=81?= =?UTF-8?q?=E5=A4=8D=E5=88=BB=E6=8E=A2=E9=92=88,=E4=B8=A4=E8=A1=80?= =?UTF-8?q?=E7=BB=9F=E5=90=8C=E6=9E=84=E5=9E=8B90%=E8=B4=A8=E9=87=8F,plain?= =?UTF-8?q?=20205-210k=E8=BF=871,cent=E5=85=A8=E7=A8=8B=E5=B9=B3)+b8=20log?= =?UTF-8?q?it=E8=B7=91=E9=A3=9E70=E2=86=9294;=20BBP=E5=AE=A1=E8=AE=A1=3Dfp?= =?UTF-8?q?32=E6=A8=A1=E6=8B=9F=E5=99=A8=E6=97=A0=E5=8A=A0=E6=80=A7?= =?UTF-8?q?=E4=B8=8B=E7=95=8C(a=3D0,overlap=201.000@3e-4)=E2=86=92BBP?= =?UTF-8?q?=E6=98=AF=E7=A1=AC=E4=BB=B6=E8=AE=BE=E8=AE=A1=E6=96=B9=E7=A8=8B?= =?UTF-8?q?;=20=E4=B8=89=E6=8E=A2=E9=92=88=E5=85=A5=E5=BA=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn --- ep_run/probe_bbp.py | 176 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 176 insertions(+) create mode 100644 ep_run/probe_bbp.py (limited to 'ep_run/probe_bbp.py') diff --git a/ep_run/probe_bbp.py b/ep_run/probe_bbp.py new file mode 100644 index 0000000..053f5e5 --- /dev/null +++ b/ep_run/probe_bbp.py @@ -0,0 +1,176 @@ +"""BBP floor: is wall-1 a spiked-matrix detectability transition? Per-layer model + g_hat(beta) = g_true + Xi/beta, Xi = EP-specific error (additive component). +Measure across batches x betas: (i) entry-std s(beta) of (g_EP - g_BP), fit s = a/beta (+) b +to split additive a (BBP-active) from multiplicative b (co-scaling, exempt per r-sweep); +(ii) sigma1(g_BP) per layer; -> BBP/BGN threshold beta*_l = a_l*(sqrt(m)+sqrt(n))/2 / sigma1_l +(iid-noise convention: bulk edge of Xi/beta at std nu=a/beta per entry is nu*(sqrt(m)+sqrt(n))/ +sqrt(mn)*sqrt(mn)= a/beta*(sqrt m + sqrt n); spike detaches iff sigma1 > that /2..1 band — +report both edge conventions); (iii) EMPIRICAL overlap cos(u1(g_hat), u1(g_BP)) vs beta — +the BBP order parameter, compare its rise against beta*. +Layers: per-block attn.qkv + ff.w2 (the two families), blocks 0..11.""" +import argparse, pickle +import numpy as np, torch, torch.nn as nn, torch.nn.functional as F +from pathlib import Path + +ap = argparse.ArgumentParser() +ap.add_argument('--ckpt', default='runs/fw72m_plain_s150000.pt') +ap.add_argument('--K', type=int, default=3) +ap.add_argument('--betas', default='3e-4,1e-3,3e-3,1e-2') +ap.add_argument('--nb', type=int, default=6) +a = ap.parse_args() +dev = 'cuda' +torch.manual_seed(11) +B, T = 8, 256 + +class RMSNorm(nn.Module): + def __init__(self, C, eps=1e-6): + super().__init__(); self.g = nn.Parameter(torch.ones(C)); self.eps = eps + def forward(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.g + +class SwiGLU(nn.Module): + def __init__(self, C): + super().__init__() + h = ((8 * C // 3) + 63) // 64 * 64 + self.w1 = nn.Linear(C, h, bias=False); self.w3 = nn.Linear(C, h, bias=False) + self.w2 = nn.Linear(h, C, bias=False) + def forward(self, x): + return self.w2(F.silu(self.w1(x)) * self.w3(x)) + +class Olmo2Attn(nn.Module): + def __init__(self, C, H, T): + super().__init__() + self.H, self.hd = H, C // H + self.qkv = nn.Linear(C, 3 * C, bias=False); self.proj = nn.Linear(C, C, bias=False) + self.qn, self.kn = RMSNorm(C), RMSNorm(C) + inv = 1.0 / (500000.0 ** (torch.arange(0, self.hd, 2).float() / self.hd)) + fr = torch.outer(torch.arange(T).float(), inv) + self.register_buffer('rc', fr.cos(), persistent=False) + self.register_buffer('rs', fr.sin(), persistent=False) + def rope(self, x): + Tn = x.shape[2] + x1, x2 = x[..., ::2], x[..., 1::2] + c, s = self.rc[None, None, :Tn].to(x.dtype), self.rs[None, None, :Tn].to(x.dtype) + return torch.stack((x1 * c - x2 * s, x1 * s + x2 * c), dim=-1).flatten(-2) + def forward(self, x): + Bn, Tn, C = x.shape + q, k, v = self.qkv(x).split(C, dim=2) + q, k = self.qn(q), self.kn(k) + q = self.rope(q.view(Bn, Tn, self.H, self.hd).transpose(1, 2)) + k = self.rope(k.view(Bn, Tn, self.H, self.hd).transpose(1, 2)) + v = v.view(Bn, Tn, self.H, self.hd).transpose(1, 2) + y = F.scaled_dot_product_attention(q, k, v, is_causal=True) + return self.proj(y.transpose(1, 2).contiguous().view(Bn, Tn, C)) + +class Olmo2Block(nn.Module): + def __init__(self, C, H, T): + super().__init__() + self.attn = Olmo2Attn(C, H, T); self.ff = SwiGLU(C) + self.na, self.nf = RMSNorm(C), RMSNorm(C) + def forward(self, z): + z = z + self.na(self.attn(z)) + return z + self.nf(self.ff(z)) + +ck = torch.load(a.ckpt, map_location='cpu', weights_only=False) +cfg = ck['config']; C, H, L = cfg['C'], cfg['H'], cfg['L'] +DD = Path('/home/yurenh2/ept/ep_run/data') / cfg.get('data', 'fineweb_edu') +vocab = pickle.load(open(DD / 'meta.pkl', 'rb'))['vocab_size'] +data = np.memmap(DD / 'val.bin', dtype=np.uint16, mode='r') +tok = nn.Embedding(vocab, C).to(dev); tok.load_state_dict(ck['tok']) +blocks = nn.ModuleList([Olmo2Block(C, H, T) for _ in range(L)]).to(dev) +blocks.load_state_dict(ck['blocks'], strict=False) +W_out = ck['wout'].to(dev) +ln_f = RMSNorm(C).to(dev); ln_f.load_state_dict(ck['lnf']) +NBT = B * T +params = list(blocks.parameters()) +names = [n for n, _ in blocks.named_parameters()] + +def readout(z): return ln_f(z) @ W_out.t() + +def get_batch(): + 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) + +def grads_from(outs_list, cots): + obj = sum((o * c.detach()).sum() for o, c in zip(outs_list, cots)) + gs = torch.autograd.grad(obj, params, allow_unused=True, retain_graph=True) + return [g.float() if g is not None else torch.zeros_like(p) for g, p in zip(gs, params)] + +def ep_and_bp(x, y, beta): + z = tok(x) + zs_bp = [] + for b in blocks: + z = b(z); zs_bp.append(z) + ce = F.cross_entropy(readout(zs_bp[-1]).reshape(-1, vocab), y.reshape(-1)) + g_bp = [g.float() for g in torch.autograd.grad(ce, params, retain_graph=True, allow_unused=False)] + f_ins, f_outs = [], [] + prev = tok(x).detach() + for b in blocks: + i = prev.detach().requires_grad_(True) + o = b(i) + f_ins.append(i); f_outs.append(o) + prev = o.detach() + ins, outs = f_ins, f_outs + zs = [o.detach().float() for o in f_outs] + d = [None] * L + for k in range(a.K): + zc = zs[L - 1].detach().requires_grad_(True) + ce_k = F.cross_entropy(readout(zc).reshape(-1, vocab), y.reshape(-1)) + d[L - 1] = (-beta * NBT * torch.autograd.grad(ce_k, zc)[0]).detach().float() + for l in range(L - 2, -1, -1): + d[l] = torch.autograd.grad(outs[l + 1], ins[l + 1], grad_outputs=d[l + 1], + retain_graph=True)[0].detach().float() + prev = tok(x).detach() + n_ins, n_outs = [], [] + for l in range(L): + i = prev.detach().requires_grad_(True) + o = blocks[l](i) + n_ins.append(i); n_outs.append(o) + zs[l] = o.detach().float() + d[l] + prev = zs[l] + ins, outs = n_ins, n_outs + md = [-di / (beta * NBT) for di in d] # normalize so EP grad is on BP scale + g_ep = grads_from(outs, md) + return g_bp, g_ep + +SEL = [i for i, n in enumerate(names) if n.endswith('attn.qkv.weight') or n.endswith('ff.w2.weight')] +betas = [float(s) for s in a.betas.split(',')] +batches = [get_batch() for _ in range(a.nb)] +# collect per layer: err entry-std per beta (across batches), sigma1(gbp), top-vec overlap per beta +acc = {i: {b: {'s': [], 'ov': []} for b in betas} for i in SEL} +sig1 = {i: [] for i in SEL} +for (x, y) in batches: + ref = None + for b in betas: + g_bp, g_ep = ep_and_bp(x, y, b) + for i in SEL: + gb, ge = g_bp[i], g_ep[i] + if ref is None: pass + err = ge - gb + acc[i][b]['s'].append(float(err.std())) + try: + ub = torch.linalg.svd(gb, full_matrices=False).U[:, 0] + ue = torch.linalg.svd(ge, full_matrices=False).U[:, 0] + acc[i][b]['ov'].append(abs(float(ub @ ue))) + except Exception: + acc[i][b]['ov'].append(float('nan')) + for i in SEL: + if b == betas[0]: sig1[i].append(float(torch.linalg.svdvals(g_bp[i])[0])) + del g_bp, g_ep + torch.cuda.empty_cache() +print('layer m x n sigma1(gBP) a(add) b(mult) beta*_edge ov@' + + ' ov@'.join(f'{b:g}' for b in betas), flush=True) +for i in SEL: + m, n = params[i].shape + s1 = float(np.mean(sig1[i])) + ss = np.array([np.mean(acc[i][b]['s']) for b in betas]) + X = np.vstack([1.0 / np.array(betas), np.ones(len(betas))]).T + coef, *_ = np.linalg.lstsq(X, ss, rcond=None) + aa, bb = max(coef[0], 0.0), max(coef[1], 0.0) + edge = aa * (np.sqrt(m) + np.sqrt(n)) + bstar = edge / max(s1, 1e-30) + ovs = ' '.join(f'{np.nanmean(acc[i][b]["ov"]):.3f}' for b in betas) + print(f'{names[i]:22s} {m:5d}x{n:<5d} {s1:11.4g} {aa:10.3g} {bb:10.3g} {bstar:11.3g} {ovs}', flush=True) +print('BBP_DONE', flush=True) -- cgit v1.2.3