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, 144 insertions, 0 deletions

diff --git a/ep_run/probe_rhorelax.py b/ep_run/probe_rhorelax.py
new file mode 100644
index 0000000..20acbe5
--- /dev/null
+++ b/ep_run/probe_rhorelax.py
@@ -0,0 +1,144 @@
+"""Replicate the governor's rho meter offline: trainer-faithful NUDGED relax (derive-d via
+top CE grad + down-chain vjp, rebuild via up-chain feedforward + d) at fixed ckpts, K sweeps,
+fixed val batch. Reports per-sweep residual ratio rho (the exact quantity beta_cap gates on)
++ the per-block residual profile of the dominant mode (localization), per lineage x step.
+This is the operator whose contraction collapse killed crown-3; free-state norm audits
+(specaudit, statej) could not see it — the growth may live in curvature/alignment."""
+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('--ckpts', default=('plain:150000,plain:185000,plain:195000,plain:200000,'
+                                    'plain:210000,plain:230000,cent:150000,cent:185000,'
+                                    'cent:195000,cent:200000,cent:210000,cent:230000'))
+ap.add_argument('--K', type=int, default=30)
+ap.add_argument('--beta', type=float, default=3e-3)
+a = ap.parse_args()
+dev = 'cuda'
+torch.manual_seed(7)
+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))
+
+first = True
+for spec in a.ckpts.split(','):
+    lineage, step = spec.split(':'); step = int(step)
+    p = f'runs/fw72m_{lineage}_s{step}.pt'
+    try:
+        ck = torch.load(p, map_location='cpu', weights_only=False)
+    except FileNotFoundError:
+        print(f'{lineage} s{step}: MISSING', flush=True); continue
+    cfg = ck['config']; C, H, L = cfg['C'], cfg['H'], cfg['L']
+    if first:
+        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')
+        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]).to(dev)
+        y = torch.stack([torch.from_numpy(data[i + 1:i + 1 + T].astype(np.int64)) for i in ix]).to(dev)
+        first = False
+    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
+
+    def readout(z): return ln_f(z) @ W_out.t()
+
+    # free feedforward pass (block-wise graphs, EP-style detach between blocks)
+    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
+    rhos, res_list = [], []
+    prof = None
+    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] = (-a.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 = [], []
+        rnum, rden, pblk = 0.0, 0.0, []
+        for l in range(L):
+            i = prev.detach().requires_grad_(True)
+            o = blocks[l](i)
+            n_ins.append(i); n_outs.append(o)
+            znew = o.detach().float() + d[l]
+            dn = float((znew - zs[l]).norm())
+            rnum += dn; rden += float(zs[l].norm()); pblk.append(dn)
+            zs[l] = znew
+            prev = zs[l]
+        ins, outs = n_ins, n_outs
+        res = rnum / max(rden, 1e-9)
+        if res_list: rhos.append(res / max(res_list[-1], 1e-12))
+        res_list.append(res)
+        prof = pblk
+        if not np.isfinite(res) or res > 1e3:
+            print(f'{lineage} s{step//1000}k: DIVERGED at sweep {k} (res {res:.2e})', flush=True)
+            break
+    tail = rhos[-5:] if len(rhos) >= 5 else rhos
+    pn = np.array(prof) / (np.sum(prof) + 1e-30)
+    print(f'{lineage} s{step//1000}k | res0 {res_list[0]:.4f} resK {res_list[-1]:.2e} | '
+          f'rho tail-med {np.median(tail):.4f} max {max(rhos):.4f} | mode blk-profile ' +
+          ' '.join(f'{v:.2f}' for v in pn), flush=True)
+    del tok, blocks, W_out, ln_f, ins, outs, f_ins, f_outs, zs, d
+    torch.cuda.empty_cache()
+print('RHORELAX_DONE', flush=True)