concrete.ml.onnx.ops_impl.md

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module `concrete.ml.onnx.ops_impl`

ONNX ops implementation in Python + NumPy.

function `cast_to_float`

cast_to_float(inputs)

Cast values to floating points.

Args:

inputs (Tuple[numpy.ndarray]): The values to consider.

Returns:

Tuple[numpy.ndarray]: The float values.

function `onnx_func_raw_args`

onnx_func_raw_args(*args, output_is_raw: bool = False)

Decorate a numpy onnx function to flag the raw/non quantized inputs.

Args:

*args (tuple[Any]): function argument names
output_is_raw (bool): marks the function as returning raw values that should not be quantized

Returns:

result (ONNXMixedFunction): wrapped numpy function with a list of mixed arguments

function `numpy_where_body`

numpy_where_body(c: ndarray, t: ndarray, f: Union[ndarray, int]) → ndarray

Compute the equivalent of numpy.where.

This function is not mapped to any ONNX operator (as opposed to numpy_where). It is usable by functions which are mapped to ONNX operators, e.g., numpy_div or numpy_where.

Args:

c (numpy.ndarray): Condition operand.
t (numpy.ndarray): True operand.
f (numpy.ndarray): False operand.

Returns:

numpy.ndarray: numpy.where(c, t, f)

function `numpy_where`

numpy_where(c: ndarray, t: ndarray, f: ndarray) → Tuple[ndarray]

Compute the equivalent of numpy.where.

Args:

c (numpy.ndarray): Condition operand.
t (numpy.ndarray): True operand.
f (numpy.ndarray): False operand.

Returns:

numpy.ndarray: numpy.where(c, t, f)

function `numpy_add`

numpy_add(a: ndarray, b: ndarray) → Tuple[ndarray]

Compute add in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Add-13

Args:

a (numpy.ndarray): First operand.
b (numpy.ndarray): Second operand.

Returns:

Tuple[numpy.ndarray]: Result, has same element type as two inputs

function `numpy_constant`

numpy_constant(**kwargs)

Return the constant passed as a kwarg.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Constant-13

Args:

**kwargs: keyword arguments

Returns:

Any: The stored constant.

function `numpy_gemm`

numpy_gemm(
    a: ndarray,
    b: ndarray,
    c: Optional[ndarray] = None,
    alpha: float = 1,
    beta: float = 1,
    transA: int = 0,
    transB: int = 0
) → Tuple[ndarray]

Compute Gemm in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Gemm-13

Args:

a (numpy.ndarray): Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
b (numpy.ndarray): Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
c (Optional[numpy.ndarray]): Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N). Defaults to None.
alpha (float): Scalar multiplier for the product of input tensors A * B. Defaults to 1.
beta (float): Scalar multiplier for input tensor C. Defaults to 1.
transA (int): Whether A should be transposed. The type is kept as int as it is the type used by ONNX and it can easily be interpreted by Python as a boolean. Defaults to 0.
transB (int): Whether B should be transposed. The type is kept as int as it is the type used by ONNX and it can easily be interpreted by Python as a boolean. Defaults to 0.

Returns:

Tuple[numpy.ndarray]: The tuple containing the result tensor

function `numpy_matmul`

numpy_matmul(a: ndarray, b: ndarray) → Tuple[ndarray]

Compute matmul in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#MatMul-13

Args:

a (numpy.ndarray): N-dimensional matrix A
b (numpy.ndarray): N-dimensional matrix B

Returns:

Tuple[numpy.ndarray]: Matrix multiply results from A * B

function `numpy_relu`

numpy_relu(x: ndarray) → Tuple[ndarray]

Compute relu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Relu-14

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_sigmoid`

numpy_sigmoid(x: ndarray) → Tuple[ndarray]

Compute sigmoid in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sigmoid-13

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_softmax`

numpy_softmax(x, axis=1, keepdims=True)

Compute softmax in numpy according to ONNX spec.

Softmax is currently not supported in FHE.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#softmax-13

Args:

x (numpy.ndarray): Input tensor
axis (None, int, tuple of int): Axis or axes along which a softmax's sum is performed. If None, it will sum all of the elements of the input array. If axis is negative it counts from the last to the first axis. Default to 1.
keepdims (bool): If True, the axes which are reduced along the sum are left in the result as dimensions with size one. Default to True.

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_cos`

numpy_cos(x: ndarray) → Tuple[ndarray]

Compute cos in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Cos-7

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_cosh`

numpy_cosh(x: ndarray) → Tuple[ndarray]

Compute cosh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Cosh-9

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_sin`

numpy_sin(x: ndarray) → Tuple[ndarray]

Compute sin in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sin-7

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_sinh`

numpy_sinh(x: ndarray) → Tuple[ndarray]

Compute sinh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Sinh-9

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_tan`

numpy_tan(x: ndarray) → Tuple[ndarray]

Compute tan in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Tan-7

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_tanh`

numpy_tanh(x: ndarray) → Tuple[ndarray]

Compute tanh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Tanh-13

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_acos`

numpy_acos(x: ndarray) → Tuple[ndarray]

Compute acos in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Acos-7

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_acosh`

numpy_acosh(x: ndarray) → Tuple[ndarray]

Compute acosh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Acosh-9

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_asin`

numpy_asin(x: ndarray) → Tuple[ndarray]

Compute asin in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Asin-7

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_asinh`

numpy_asinh(x: ndarray) → Tuple[ndarray]

Compute sinh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Asinh-9

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_atan`

numpy_atan(x: ndarray) → Tuple[ndarray]

Compute atan in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Atan-7

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_atanh`

numpy_atanh(x: ndarray) → Tuple[ndarray]

Compute atanh in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Atanh-9

Args:

x (numpy.ndarray): Input tensor

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_elu`

numpy_elu(x: ndarray, alpha: float = 1) → Tuple[ndarray]

Compute elu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Elu-6

Args:

x (numpy.ndarray): Input tensor
alpha (float): Coefficient

Returns:

Tuple[numpy.ndarray]: Output tensor

function `numpy_selu`

numpy_selu(
    x: ndarray,
    alpha: float = 1.6732632423543772,
    gamma: float = 1.0507009873554805
) → Tuple[ndarray]

Compute selu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Selu-6

Args:

x (numpy.ndarray): Input tensor
alpha (float): Coefficient

module concrete.ml.onnx.ops_impl

function cast_to_float

function onnx_func_raw_args

function numpy_where_body

function numpy_where

function numpy_add

function numpy_constant

function numpy_gemm

function numpy_matmul

function numpy_relu

function numpy_sigmoid

function numpy_softmax

function numpy_cos

function numpy_cosh

function numpy_sin

function numpy_sinh

function numpy_tan

function numpy_tanh

function numpy_acos

function numpy_acosh

function numpy_asin

function numpy_asinh

function numpy_atan

function numpy_atanh

function numpy_elu

function numpy_selu

module `concrete.ml.onnx.ops_impl`

function `cast_to_float`

function `onnx_func_raw_args`

function `numpy_where_body`

function `numpy_where`

function `numpy_add`

function `numpy_constant`

function `numpy_gemm`

function `numpy_matmul`

function `numpy_relu`

function `numpy_sigmoid`

function `numpy_softmax`

function `numpy_cos`

function `numpy_cosh`

function `numpy_sin`

function `numpy_sinh`

function `numpy_tan`

function `numpy_tanh`

function `numpy_acos`

function `numpy_acosh`

function `numpy_asin`

function `numpy_asinh`

function `numpy_atan`

function `numpy_atanh`

function `numpy_elu`

function `numpy_selu`