SPIRA v2: entropy-patch tokenization + hierarchical dopamine-gated spiking cortex + hippocampal one-shot memory with replay consolidation
032a961 verified | #!/usr/bin/env python3 | |
| """Post-training sanity: hierarchy ablation + generation quality.""" | |
| import sys | |
| import time | |
| sys.path.insert(0, "/home/z/my-project/download/spira") | |
| from spira import SPIRA | |
| BASE = "/home/z/my-project" | |
| m = SPIRA.load(f"{BASE}/download/spira/artifacts/spira_model.npz") | |
| print(f"vocab={m.cfg.vocab_size} hier={m.core.hier} beta={m.core.beta:.3f} " | |
| f"thr_G={float(m.core.thr_G.mean()):.3f} rhat_G={float(m.core.rhat_G.mean()):.3f}") | |
| m._ids_train = None | |
| train = open(f"{BASE}/data/train.bin", "rb").read() | |
| test = open(f"{BASE}/data/test.bin", "rb").read() | |
| import numpy as np | |
| from spira.hashing import fnv1a64 | |
| ids = np.fromiter((fnv1a64(p) for p in m.ept.segment(train)), dtype=np.uint64, count=len(m.ept.segment(train))) | |
| m._ids_train = ids | |
| for name, data, pos in [("train-region", train[10_000_000:10_200_000], 5000), | |
| ("test", test[:200_000], 8000)]: | |
| ev = m.eval_stream(data, max_positions=pos) | |
| print(f"{name}: cov={ev['vocab_coverage']:.3f} fast={ev['snn_acc']:.4f} " | |
| f"hier={ev['hier_acc']:.4f} am={ev['am_acc']:.4f} fused={ev['fused_acc']:.4f} " | |
| f"| invocab: fast={ev['snn_acc_invocab']:.4f} hier={ev['hier_acc_invocab']:.4f} " | |
| f"am={ev['am_acc_invocab']:.4f}") | |
| t0 = time.perf_counter() | |
| g = m.generate(b"The old man looked at the ", n_tokens=120, temperature=0.7, seed=42) | |
| dt = time.perf_counter() - t0 | |
| print(f"\ngeneration ({len(g)} B, {len(g)/max(dt,1e-9):.0f} B/s):") | |
| print(g.decode("utf-8", "replace")[:400]) | |
| g2 = m.generate(b"def process(data):\n ", n_tokens=100, temperature=0.7, seed=7) | |
| print("\ncode:", g2.decode("utf-8", "replace")[:300]) | |