CLAUDE: emit model meta sidecar (.meta.json) on L1/L2 TorchScript export

Each export now writes a small <model>.ts.pt.meta.json so the native side
(tp_dnn.cpp / CuasDnnLocal, pieces 3-4) reads the model's params from a bundled
sidecar instead of hardcoding them — single source of truth.
  L1: {N, P, vr, out_ch, velocity_mode, vmax}
  L2: {ch_hidden, vmax, grid, ch_in}
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

export_l2_torchscript.py

@@ -36,7 +36,7 @@ Usage:  python export_l2_torchscript.py runs/mexhat_gaps_boost40/model.pt [out.t
 Also writes <out>.test_*.bin reference vectors (a T-step recurrence) for the C++ probe to verify
 the native output against PyTorch.  By Claude on 06/26/2026.
 """
-import sys, struct, torch
+import sys, struct, json, torch
 import torch.nn as nn
 from layer2 import Layer2Net
 
@@ -119,6 +119,13 @@ def main():
     print(f"wrote reference vectors: seq{tuple(seq.shape)} det{tuple(det_eager.shape)} "
           f"vel{tuple(vel_eager.shape)} h{tuple(h_eager.shape)}")
 
+    # Model self-describes its params so native LibTorch (tp_dnn.cpp / CuasDnnLocal) reads ch_hidden
+    # (the L2 init param) from a bundled sidecar instead of hardcoding. By Claude on 06/26/2026.
+    meta = {"kind": "L2", "ch_hidden": int(ch_hidden), "vmax": float(vmax),
+            "grid": int(grid), "ch_in": 3}
+    open(out + ".meta.json", "w").write(json.dumps(meta))
+    print("wrote", out + ".meta.json", meta)
+
 
 if __name__ == "__main__":
     main()

export_torchscript.py

@@ -24,7 +24,7 @@ Usage:  python export_torchscript.py runs/weighted9_pm_s/model.pt [out.ts.pt]
 Also writes <out>.test_input.bin / <out>.test_output.bin (raw little-endian f32) reference
 vectors for the C++ probe to verify the native output against PyTorch.  By Claude on 06/27/2026.
 """
-import sys, struct, torch
+import sys, struct, json, torch
 from model import RawFCN
 
 ckpt = sys.argv[1]
@@ -55,3 +55,10 @@ def _w(path, t):
 _w(out + ".test_input.bin",  x)
 _w(out + ".test_output.bin", y)
 print(f"wrote reference vectors: {out}.test_input.bin {tuple(x.shape)}, {out}.test_output.bin {tuple(y.shape)}")
+
+# Model self-describes its params so native LibTorch (tp_dnn.cpp / CuasDnnLocal) reads them from a
+# bundled sidecar instead of hardcoding (single source of truth). By Claude on 06/26/2026.
+meta = {"kind": "L1", "N": kw["n_frames"], "P": kw["patch"], "vr": kw["vel_radius"],
+        "out_ch": int(m.out_ch), "velocity_mode": kw["velocity_mode"], "vmax": kw["vmax"]}
+open(out + ".meta.json", "w").write(json.dumps(meta))
+print("wrote", out + ".meta.json", meta)