ml-sdk/docs/references/api/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, ]], Union[ModelProto, NoneType], 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:

• ONNXAndNumpyForwards: The function that will execute the equivalent numpy code to the passed torch_module and the generated ONNX model.

---

function preprocess_onnx_model

preprocess_onnx_model(
    onnx_model: ModelProto,
    check_model: bool
) → Tuple[Union[ModelProto, NoneType], ModelProto]

Preprocess the ONNX model to be used for numpy execution.

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:

• Tuple[Optional[onnx.ModelProto], onnx.ModelProto]: The preprocessing ONNX model and preprocessed ONNX model. The preprocessing model is None if there is no preprocessing required.

---

function get_equivalent_numpy_forward_from_onnx

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

Returns:

• ONNXAndNumpyForwards: The function that will execute the equivalent numpy function.

---

function get_equivalent_numpy_forward_from_onnx_tree

get_equivalent_numpy_forward_from_onnx_tree(
    onnx_model: ModelProto,
    check_model: bool = True,
    lsbs_to_remove_for_trees: Optional[Tuple[int, int]] = None
) → Tuple[Callable[, Tuple[ndarray, ]], ModelProto]

Get the numpy equivalent forward of the provided ONNX model for tree-based models only.

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.
• lsbs_to_remove_for_trees (Optional[Tuple[int, int]]): This parameter is exclusively used for optimizing tree-based models. It contains the values of the least significant bits to remove during the tree traversal, where the first value refers to the first comparison (either "less" or "less_or_equal"), while the second value refers to the "Equal" comparison operation. Default to None, as it is not applicable to other types of models.

Returns:

• Tuple[NumpyForwardCallable, onnx.ModelProto]: The function that will execute the equivalent numpy function.