bp_lm twin control + env forensics probes + holo_a_track_avg (semi-convergence fix, ungated)

BP+EMA 2x2 (local, healthy env): qknorm {1.9951, 1.9977}, no-qknorm {1.9728,
1.9732} -> parameterization-matched BP twin tops at ~1.97 vs EP 1.7888: the
equilibrium computation's iteration/depth dividend = 0.18 CE from identical
parameters. External anchor (tuned depth-1 BP, 1.7921): EP at parity.
bp_lm on 107/2.3.1 gave 1.9823 ~= local -> plain backward exonerated on the
pascal env; the 107 divergence (pair/floss/resreg dead by step 600) narrows
to the EP reg/estimator loop. Single-step fingerprints all match (a/b/c/d) ->
suspected intermittent kernel issue; discriminators in flight (torch-2.7 env
probe + delay_hr2's step-2000 reg-on transition).

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

1 files changed, 75 insertions, 0 deletions

diff --git a/ep_run/bp_lm.py b/ep_run/bp_lm.py
new file mode 100644
index 0000000..7fe0bdd
--- /dev/null
+++ b/ep_run/bp_lm.py
@@ -0,0 +1,75 @@
+"""bp_lm.py — the plain-BP standard-transformer reference with weight-EMA (task #34), rebuilt for
+metric parity with the EP lineage: SAME parameter tensors as EQBlock (7.48M), SAME data + eval
+protocol, and the SAME pema weight-EMA so both lineages report best-of(raw, ema) val. Forward = one
+standard pre-LN residual block (no relaxation): h = x + attn(LN1 x) + ffn(LN2 x); logits = h @ Wh.
+Historical anchor (no EMA, no qknorm): 'BP standard transformer (7.48M, lr 3e-3) best 1.7921 (20k)'
+(FINDINGS:355). Run with --qknorm for architecture parity with the current EP recipe."""
+import argparse, time, torch, torch.nn.functional as F
+import lt_ep_train as L
+
+
+def fwd(blk, idx):
+    x = blk.embed(idx)
+    h1 = F.layer_norm(x, (blk.C,), blk.ln1g, blk.ln1b)
+    h2 = F.layer_norm(x, (blk.C,), blk.ln2g, blk.ln2b)
+    h = x + blk.attn(h1) + (F.gelu(h2 @ blk.fc + blk.fcb, approximate='tanh') @ blk.pj + blk.pjb)
+    return h @ blk.Wh
+
+
+def evaluate(blk, nb=8, B=32):
+    tot = 0.0
+    with torch.no_grad():
+        for _ in range(nb):
+            idx, y = L.get_batch('val', B, blk.T)
+            tot += float(F.cross_entropy(fwd(blk, idx).reshape(-1, L.vocab), y.reshape(-1)))
+    return tot / nb
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument('--steps', type=int, default=32000)
+    ap.add_argument('--B', type=int, default=24)
+    ap.add_argument('--lr', type=float, default=3e-3)
+    ap.add_argument('--wd', type=float, default=1e-4)
+    ap.add_argument('--pema', type=float, default=0.999)
+    ap.add_argument('--qknorm', action='store_true')
+    ap.add_argument('--log', type=int, default=200)
+    ap.add_argument('--ckpt', type=str, default='runs/bp_lm.pt')
+    cfg = ap.parse_args()
+    torch.manual_seed(0)
+    blk = L.EQBlock(512, 16, 256, 256, c=1.0, attn_mode='thick')
+    blk.qknorm = cfg.qknorm
+    opt = torch.optim.AdamW(blk.allp, lr=cfg.lr, weight_decay=cfg.wd)
+    sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, cfg.steps, eta_min=cfg.lr * 0.05)
+    ema = [p.detach().clone() for p in blk.allp]
+    best, t0 = float('inf'), time.time()
+    for step in range(1, cfg.steps + 1):
+        idx, y = L.get_batch('train', cfg.B, blk.T)
+        loss = F.cross_entropy(fwd(blk, idx).reshape(-1, L.vocab), y.reshape(-1))
+        opt.zero_grad(set_to_none=True)
+        loss.backward()
+        opt.step(); sched.step()
+        with torch.no_grad():
+            for e, p in zip(ema, blk.allp):
+                e.mul_(cfg.pema).add_(p, alpha=1 - cfg.pema)
+        if step % cfg.log == 0:
+            raw = evaluate(blk)
+            bak = [p.detach().clone() for p in blk.allp]
+            with torch.no_grad():
+                for p, e in zip(blk.allp, ema):
+                    p.copy_(e)
+            emav = evaluate(blk)
+            with torch.no_grad():
+                for p, b in zip(blk.allp, bak):
+                    p.copy_(b)
+            m = min(raw, emav)
+            if m < best:
+                best = m
+                torch.save({'allp': [p.detach().cpu() for p in blk.allp], 'step': step, 'best': best}, cfg.ckpt)
+            print(f"step {step:5d}/{cfg.steps} | val {raw:.4f} ema {emav:.4f} (best {best:.4f}) "
+                  f"| {step / (time.time() - t0):.2f} it/s", flush=True)
+    print(f"[bp_lm] DONE best {best:.4f}", flush=True)
+
+
+if __name__ == '__main__':
+    main()