Stage-1 epoch blowup @12100: sig story REFUTED (only +8%, cos fine till after); leading indicator=drift-guard skips -> contractivity bifurcation in nudged relaxation (cascade Hopf wall); add resume/sig0/final_ln; A/B/C diagnostic launched

1 files changed, 4 insertions, 2 deletions

diff --git a/ep_run/casc_bp_train.py b/ep_run/casc_bp_train.py
index c056fed..7647fae 100644
--- a/ep_run/casc_bp_train.py
+++ b/ep_run/casc_bp_train.py
@@ -20,6 +20,7 @@ ap.add_argument('--tok_init', type=float, default=0.0)  # >0: init tok/pos std (
 ap.add_argument('--cosine', action='store_true')         # warmup then cosine decay to lr_min_ratio*lr over --steps (long runs)
 ap.add_argument('--lr_min_ratio', type=float, default=0.1)
 ap.add_argument('--qk_norm', action='store_true')        # RMS-norm q,k per head before scores (OLMo2-style; bounds logits, analog-friendly)
+ap.add_argument('--final_ln', action='store_true')       # final LayerNorm before readout (standard GPT; bounds sig_tok growth -> keeps beta/estimator healthy on long runs)
 args = ap.parse_args()
 torch.manual_seed(args.seed)
 dev = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -73,7 +74,8 @@ if args.tok_init > 0:
         tok.weight.normal_(0, args.tok_init); pos.weight.normal_(0, args.tok_init)
 blocks = nn.ModuleList([Block(args.C, args.H, args.qk_norm) for _ in range(args.L)]).to(dev)
 mask = torch.triu(torch.full((args.T, args.T), float('-inf'), device=dev), 1)
-params = list(tok.parameters()) + list(pos.parameters()) + list(blocks.parameters())
+ln_f = nn.LayerNorm(args.C).to(dev) if args.final_ln else nn.Identity()
+params = list(tok.parameters()) + list(pos.parameters()) + list(blocks.parameters()) + list(ln_f.parameters())
 if args.opt == 'muon':
     from muon import build_hybrid
     opt, sched = build_hybrid(blocks, params, args.lr, args.muon_lr, args.warmup)
@@ -91,7 +93,7 @@ else:
 def fwd(x):
     z = tok(x) + pos(torch.arange(args.T, device=dev))[None]
     for b in blocks: z = b(z, mask)
-    return z @ tok.weight.t()
+    return ln_f(z) @ tok.weight.t()
 
 @torch.no_grad()
 def evaluate(nb=6):