timandClaude Fable 5
9a750756c5
py -m rl.export vyexportuje checkpoint do rl/weights/neural-bot.json (bit-exact float32, 1.3 MB) a rl/pure_net.py ho hra bez torch/numpy (stdlib forward pass, ~16 ms/tah). Testy parity: logity aj akcie sa zhoduju s torch, identicke trajektorie celych kol. Natrenovany model: 6.7-6.9 b/kolo proti vsetkym baseline-om (heuristika prekonana). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
"""Export vah natrenovanej siete do formatu pre cisto-Python inferenciu.
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Torch je len trenovacia zavislost (host); produkcia hra cez rl/pure_net.py,
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ktory cita tento subor bez torch/numpy. Vahy sa uladaju ako base64 float32
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little-endian (bit-exact kopia checkpointu, ziadna strata presnosti).
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Pouzitie:
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py -m rl.export rl/checkpoints/latest.pt rl/weights/neural-bot.json
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"""
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import argparse
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import base64
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import json
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import os
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from rl.encoding import OBS_DIM
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from rl.train import load_checkpoint
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DEFAULT_WEIGHTS_PATH = os.path.join('rl', 'weights', 'neural-bot.json')
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def _pack(tensor) -> dict:
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"""Tensor -> {shape, base64 float32 LE}. Cez struct, bez numpy -- tolist()
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vracia presne hodnoty float32, takze zapis je bit-exact."""
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import struct
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data = tensor.detach().to('cpu').contiguous().float()
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flat = data.reshape(-1).tolist()
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return {
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'shape': list(data.shape),
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'data': base64.b64encode(struct.pack(f'<{len(flat)}f', *flat)).decode('ascii'),
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}
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def export(checkpoint_path: str, out_path: str) -> dict:
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net = load_checkpoint(checkpoint_path)
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hidden = net.trunk[0].out_features
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payload = {
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'obs_dim': OBS_DIM,
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'hidden': hidden,
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'weights': {
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'trunk0_w': _pack(net.trunk[0].weight),
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'trunk0_b': _pack(net.trunk[0].bias),
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'trunk2_w': _pack(net.trunk[2].weight),
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'trunk2_b': _pack(net.trunk[2].bias),
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'guess_w': _pack(net.guess_head.weight),
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'guess_b': _pack(net.guess_head.bias),
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'play_w': _pack(net.play_head.weight),
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'play_b': _pack(net.play_head.bias),
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},
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}
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os.makedirs(os.path.dirname(out_path), exist_ok=True)
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with open(out_path, 'w') as f:
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json.dump(payload, f)
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return payload
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def main():
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parser = argparse.ArgumentParser(description='Export vah pre pure-Python inferenciu')
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parser.add_argument('checkpoint', nargs='?', default='rl/checkpoints/latest.pt')
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parser.add_argument('out', nargs='?', default=DEFAULT_WEIGHTS_PATH)
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args = parser.parse_args()
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payload = export(args.checkpoint, args.out)
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size = os.path.getsize(args.out)
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print(f'Exportovane: {args.checkpoint} (hidden={payload["hidden"]}) '
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f'-> {args.out} ({size / 1024:.0f} kB)')
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if __name__ == '__main__':
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main()
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