RESULT 76: 谱仪读出 — 逐块泄漏剖面(C768上6块-0.70..-0.23,C512块8-11也病)+偶性实锤(-β同号)+disp_ratio阈值跨宽度塌缩(~2e-7≈2×fp32 eps)+fp32地板假说(一举解释全部约束); 预注册双证伪已发射(β-scan平移+fp64决定臂); --probe_f64实装

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

1 files changed, 11 insertions, 5 deletions

diff --git a/ep_run/casc_eq_train.py b/ep_run/casc_eq_train.py
index a9218a0..76b96fd 100644
--- a/ep_run/casc_eq_train.py
+++ b/ep_run/casc_eq_train.py
@@ -54,7 +54,8 @@ ap.add_argument('--dgain_geo', type=float, default=0.0)   # >0: per-layer geomet
 ap.add_argument('--dgain_geo_cap', type=float, default=0.0) # >0: cap for the geometric profile
 ap.add_argument('--dgain_rand', type=float, default=0.0)
 ap.add_argument('--probe_dgspec', type=int, default=0)      # >0: M1 spectroscopy, value = n batches; exits before training
-ap.add_argument('--probe_gains', default='1,2,4,8,16,32,64,128,256')  # >1: per-STEP log-uniform dgain_top in
+ap.add_argument('--probe_gains', default='1,2,4,8,16,32,64,128,256')
+ap.add_argument('--probe_f64', action='store_true')  # fp64 states+model in the probe: the fp-floor decisive arm  # >1: per-STEP log-uniform dgain_top in
                                                           # [1, this] (spread-spectrum probing of the
                                                           # decade-spread threshold distribution)
 ap.add_argument('--dgain_top', type=float, default=1.0)  # amplify d in STATE FORMATION for blocks
@@ -291,6 +292,7 @@ class Olmo2Block(nn.Module):
         z = z + self.na(self.attn(z))
         return z + self.nf(self.ff(z))
 
+SDT = torch.float64 if args.probe_f64 else torch.float32   # state dtype; fp64 only in probe mode
 tok = nn.Embedding(vocab, args.C).to(dev)
 pos = nn.Embedding(args.T, args.C).to(dev)
 if args.tok_init > 0:
@@ -375,7 +377,7 @@ def free_states_graphed(x):
         for b in blocks:
             i = prev.detach().requires_grad_(True)
             o = b(i, mask)
-            ins.append(i); outs.append(o); zs.append(o.detach().float())
+            ins.append(i); outs.append(o); zs.append(o.detach().to(SDT))
             prev = zs[-1]
     return z0, zs, ins, outs
 
@@ -410,7 +412,7 @@ def relax(z0, zs, ins, outs, y, beta, K, x, bmask=None):
             if bmask is not None: g = g * bmask
             d[args.L - 1] = (-beta * nbt_loc * g).detach()
         for l in range(args.L - 2, -1, -1):
-            d[l] = torch.autograd.grad(outs[l + 1], ins[l + 1], grad_outputs=d[l + 1].to(outs[l + 1].dtype))[0].detach().float()
+            d[l] = torch.autograd.grad(outs[l + 1], ins[l + 1], grad_outputs=d[l + 1].to(outs[l + 1].dtype))[0].detach().to(SDT)
 
     def rebuild(last):
         nonlocal ins, outs
@@ -435,7 +437,7 @@ def relax(z0, zs, ins, outs, y, beta, K, x, bmask=None):
                     if args.dgain_geo_cap > 0: _dg = min(_dg, args.dgain_geo_cap)
                 else:
                     _dg = args.dgain_all * ((GOV.get('dgcur') or args.dgain_top) if l >= args.L // 2 else 1.0)
-                znew = o.detach().float() + (_dg * d[l] if _dg != 1.0 else d[l])
+                znew = o.detach().to(SDT) + (_dg * d[l] if _dg != 1.0 else d[l])
                 # damped (under-relaxed) mixing: geta<1 restores contraction on stiff operators
                 # (wall-2 toolkit); fixed point unchanged (z = z + geta*(o+d-z) <=> z = o+d)
                 mixed = znew if g_eff >= 1.0 else (zs[l] + g_eff * (znew - zs[l]))
@@ -855,6 +857,10 @@ if args.probe_dgspec > 0:
     # splits odd-in-beta FD bias from even-in-|displacement| leak. Run WITHOUT --amp: the
     # bf16 floor is width-blind (b15 lesson); fp32 is the instrument-grade path.
     assert WORLD == 1, 'probe_dgspec is single-GPU'
+    if args.probe_f64:
+        tok.double(); blocks.double(); ln_f.double()
+        if W_out is not None: W_out.data = W_out.data.double()
+        print('[dgspec] FP64 states+model active', flush=True)
     import json, sys
     gains = [float(t) for t in args.probe_gains.split(',')]
     cfgs = [(f'g{g:g}', g, +1.0) for g in gains] + [('g1neg', 1.0, -1.0)]
@@ -886,7 +892,7 @@ if args.probe_dgspec > 0:
                     for l in range(args.L):
                         disp[l] += float(GOV['_last_d'][l].norm() / max(float(zp[l].norm()), 1e-12)) / NB
             E = 0.0
-            for z, o in zip(zp, lo): E = E + 0.5 * ((z.detach().float() - o.float()) ** 2).sum()
+            for z, o in zip(zp, lo): E = E + 0.5 * ((z.detach().to(SDT) - o.to(SDT)) ** 2).sum()
             obj = E / (NBT * bt) + obj_loss(readout(zp[-1].detach()).reshape(-1, vocab), y.reshape(-1))
             gs = torch.autograd.grad(obj, all_params, allow_unused=True)
             for j, ix in enumerate(bix):