RESULT 47+48: 涨的量=blocks8-11单模态增益(ρ复刻探针,两血统同构型90%质量,plain 205-210k过1,cent全程平)+b8 logit跑飞70→94; BBP审计=fp32模拟器无加性下界(a=0,overlap 1.000@3e-4)→BBP是硬件设计方程; 三探针入库

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

1 files changed, 176 insertions, 0 deletions

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)