PolyBake
純粋関数によって構成されるプログラムを、訓練なしに 意味論を保存または制御された誤差内で近似するニューラル回路へ決定的に変換するコンパイラ。
from polybake import Baker, TensorSpec
baker = Baker()
@baker.collect()
def f(x):
return max(2 * x + 1, 0)
compiled = baker.bake(inputs=[TensorSpec(shape=(4,), dtype="float32")])
compiled(x) # Neural IR を NumPy で実行
compiled.verify() # 元の Python 関数との差分検査 (note.md §20)
compiled.export_onnx("f.onnx") # ONNX 書き出し
近似が要る関数も 入力領域を明示すれば誤差証明書付きで 変換できる。
from math import exp
@baker.collect()
def gaussian(x):
return exp(-(x * x))
compiled = baker.bake(inputs=[...], domain=[(-2.0, 2.0)], error=1e-2)
compiled.error_certificate # -> interval bound<=0.00889 domain=[-2, 2]
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