Seal Phase 4AK frozen D18 independent validation

2026-09-30 13:18:18 +08:00
parent cb6937666f
commit c6444255fe
1313 changed files with 154250 additions and 15 deletions
@@ -0,0 +1,49 @@
+"""Unchanged deterministic-v2 reference rerun, exclusively into this new stage."""
+import os
+import sys
+import time
+import numpy as np
+import pandas as pd
+import yaml
+from context import ROOT, EXP, CFG, check, save, bit_equal
+
+
+def main():
+    check()
+    assert not (EXP / 'regression/frozen-five.json').exists()
+    from run_case import preflight
+    preflight()
+    runtime_path = ROOT / 'models/male-cns-lif/deterministic-runtime-v2'
+    pc = yaml.safe_load((runtime_path / 'phase4p.yaml').read_text())
+    sys.path.insert(0, str(ROOT / 'models/male-cns-lif'))
+    sys.path.insert(0, str(runtime_path))
+    import torch
+    import phase4p
+    assert torch.cuda.mem_get_info()[0] > 2 * 1024**3
+    torch.cuda.reset_peak_memory_stats()
+    started = time.perf_counter()
+    runtime = phase4p.CanonicalRuntime(pc, runtime_path, EXP)
+    rows = []
+    for spec in pc['reference_trials']:
+        preflight()
+        reference_dir = ROOT / 'experiments/phase4p-deterministic-runtime/phase4c-v2/reference' / spec['name']
+        events = pd.read_parquet(reference_dir / 'spike-events.parquet')
+        reference = dict(step=events.step.to_numpy(), neuron_idx=events.neuron_idx.to_numpy(),
+            counts=np.load(reference_dir / 'per-neuron-spike-counts.int32.npy'),
+            v=np.load(reference_dir / 'final-voltage.float32.npy'), g=np.load(reference_dir / 'final-g.float32.npy'))
+        result = runtime.run(np.flatnonzero(runtime.pops[spec['population']]), spec['rate_hz'], spec['seed'], spec['duration_ms'])
+        row = dict(trial=spec['name'], exact={key: bool(bit_equal(value, result[key])) for key, value in reference.items()},
+            dtype_shape={key: dict(reference_dtype=str(value.dtype), actual_dtype=str(result[key].dtype),
+                reference_shape=list(value.shape), actual_shape=list(result[key].shape)) for key, value in reference.items()},
+            wall_seconds=result['metrics']['wall_seconds'])
+        rows.append(row)
+        print(row, flush=True)
+    passed = len(rows) == 5 and all(all(row['exact'].values()) for row in rows)
+    save(EXP / 'regression/frozen-five.json', dict(pass_all=passed, trials=rows,
+        wall_seconds=time.perf_counter() - started, peak_allocated_vram=int(torch.cuda.max_memory_allocated()),
+        scope='Old frozen deterministic-v2 five references only; not the new D18 hypothesis'))
+    assert passed
+
+
+if __name__ == '__main__':
+    main()