concrete.ml.onnx.convert.md

module concrete.ml.onnx.convert

ONNX conversion related code.

Global Variables

• IMPLEMENTED_ONNX_OPS

• OPSET_VERSION_FOR_ONNX_EXPORT

---

function fuse_matmul_bias_to_gemm

fuse_matmul_bias_to_gemm(onnx_model: ModelProto)

Fuse sequence of matmul -> add into a gemm node.

Args:

• onnx_model (onnx.ModelProto): A onnx model to optimize using Mat-Mult + Add -> Gemm

Returns:

• onnx.ModelProto: the optimized onnx model

---

function get_equivalent_numpy_forward_from_torch

get_equivalent_numpy_forward_from_torch(
    torch_module: Module,
    dummy_input: Union[Tensor, Tuple[Tensor, ]],
    output_onnx_file: Union[NoneType, Path, str] = None
) → Tuple[Callable[, Tuple[ndarray, ]], ModelProto]

Get the numpy equivalent forward of the provided torch Module.

Args:

• torch_module (torch.nn.Module): the torch Module for which to get the equivalent numpy forward.

• dummy_input (Union[torch.Tensor, Tuple[torch.Tensor, ...]]): dummy inputs for ONNX export.

• output_onnx_file (Optional[Union[Path, str]]): Path to save the ONNX file to. Will use a temp file if not provided. Defaults to None.

Returns:

• Tuple[Callable[..., Tuple[numpy.ndarray, ...]], onnx.GraphProto]: The function that will execute the equivalent numpy code to the passed torch_module and the generated ONNX model.

---

function get_equivalent_numpy_forward_from_onnx

get_equivalent_numpy_forward_from_onnx(
    onnx_model: ModelProto,
    check_model: bool = True
) → Tuple[Callable[, Tuple[ndarray, ]], ModelProto]

Get the numpy equivalent forward of the provided ONNX model.

Args:

• onnx_model (onnx.ModelProto): the ONNX model for which to get the equivalent numpy forward.

• check_model (bool): set to True to run the onnx checker on the model. Defaults to True.

Raises:

• ValueError: Raised if there is an unsupported ONNX operator required to convert the torch model to numpy.

Returns:

• Callable[..., Tuple[numpy.ndarray, ...]]: The function that will execute the equivalent numpy function.