da4ml.codegen.cpp package

Submodules

da4ml.codegen.cpp.cpp_codegen module

da4ml.codegen.cpp.cpp_codegen.cpp_logic_and_bridge_gen(sol: Solution, fn_name: str, flavor: str, pragmas: list[str] | None = None, n_indent: int = 4, n_base_indent: int = 0, print_latency: bool = False)
da4ml.codegen.cpp.cpp_codegen.get_io_types(sol: Solution, flavor: str)
da4ml.codegen.cpp.cpp_codegen.get_typestr_fn(flavor: str)
da4ml.codegen.cpp.cpp_codegen.kif_to_hlslib_type(k: bool | int = 1, i: int = 0, f: int = 0)
da4ml.codegen.cpp.cpp_codegen.kif_to_vitis_type(k: bool | int = 1, i: int = 0, f: int = 0)
da4ml.codegen.cpp.cpp_codegen.output_gen(sol: Solution, typestr_fn: Callable[[bool | int, int, int], str])
da4ml.codegen.cpp.cpp_codegen.ssa_gen(sol: Solution, print_latency: bool, typestr_fn: Callable[[bool | int, int, int], str])

da4ml.codegen.cpp.hls_model module

class da4ml.codegen.cpp.hls_model.HLSModel(solution: Solution, prj_name: str, path: str | Path, flavor: str = 'vitis', print_latency: bool = True, part_name: str = 'xcvu13p-flga2577-2-e', pragma: Sequence[str] | None = None, clock_period: int = 5, clock_uncertainty: float = 0.1, io_delay_minmax: tuple[float, float] = (0.2, 0.4))

Bases: object

compile(verbose=False, openmp=True, o3: bool = False, clean=True)

Compile the model to a shared object file

Parameters:

  • verbose (bool, optional) – Verbose output, by default False

  • openmp (bool, optional) – Enable openmp, by default True

  • o3 (bool | None, optional) – Turn on -O3 flag, by default False

  • clean (bool, optional) – Remove obsolete shared object files, by default True

Raises:

RuntimeError – If compilation fails

predict(data: ndarray[tuple[int, ...], dtype[T]]) → ndarray[tuple[int, ...], dtype[T]]

Run the model on the input data.

Parameters:

data (NDArray[np.floating]) – Input data to the model. The shape is ignored, and the number of samples is determined by the size of the data.

Returns:

Output of the model in shape (n_samples, output_size).

Return type:

NDArray[np.floating]

write()

Module contents

class da4ml.codegen.cpp.HLSModel(solution: Solution, prj_name: str, path: str | Path, flavor: str = 'vitis', print_latency: bool = True, part_name: str = 'xcvu13p-flga2577-2-e', pragma: Sequence[str] | None = None, clock_period: int = 5, clock_uncertainty: float = 0.1, io_delay_minmax: tuple[float, float] = (0.2, 0.4))

Bases: object

compile(verbose=False, openmp=True, o3: bool = False, clean=True)

Compile the model to a shared object file

Parameters:

  • verbose (bool, optional) – Verbose output, by default False

  • openmp (bool, optional) – Enable openmp, by default True

  • o3 (bool | None, optional) – Turn on -O3 flag, by default False

  • clean (bool, optional) – Remove obsolete shared object files, by default True

Raises:

RuntimeError – If compilation fails

predict(data: ndarray[tuple[int, ...], dtype[T]]) → ndarray[tuple[int, ...], dtype[T]]

Run the model on the input data.

Parameters:

data (NDArray[np.floating]) – Input data to the model. The shape is ignored, and the number of samples is determined by the size of the data.

Returns:

Output of the model in shape (n_samples, output_size).

Return type:

NDArray[np.floating]

write()
da4ml.codegen.cpp.cpp_logic_and_bridge_gen(sol: Solution, fn_name: str, flavor: str, pragmas: list[str] | None = None, n_indent: int = 4, n_base_indent: int = 0, print_latency: bool = False)