concrete.ml.onnx.ops_impl.md

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)

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

Args:

• *args (tuple[Any]): function argument names

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_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

• gamma (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_celu

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

Compute celu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Celu-12

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_leakyrelu

numpy_leakyrelu(x: ndarray, alpha: float = 0.01) → Tuple[ndarray]

Compute leakyrelu in numpy according to ONNX spec.

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

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_thresholdedrelu

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

Compute thresholdedrelu in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#ThresholdedRelu-10

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_hardsigmoid

numpy_hardsigmoid(
    x: ndarray,
    alpha: float = 0.2,
    beta: float = 0.5
) → Tuple[ndarray]

Compute hardsigmoid in numpy according to ONNX spec.

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

Args:

• x (numpy.ndarray): Input tensor

• alpha (float): Coefficient

• beta (float): Coefficient

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_softplus

numpy_softplus(x: ndarray) → Tuple[ndarray]

Compute softplus in numpy according to ONNX spec.

See https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Softplus-1

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_abs

numpy_abs(x: ndarray) → Tuple[ndarray]

Compute abs in numpy according to ONNX spec.

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

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_div

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

Compute div in numpy according to ONNX spec.

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

Args:

• a (numpy.ndarray): Input tensor

• b (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_mul

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

Compute mul in numpy according to ONNX spec.

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

Args:

• a (numpy.ndarray): Input tensor

• b (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_sub

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

Compute sub in numpy according to ONNX spec.

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

Args:

• a (numpy.ndarray): Input tensor

• b (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_log

numpy_log(x: ndarray) → Tuple[ndarray]

Compute log in numpy according to ONNX spec.

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

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_erf

numpy_erf(x: ndarray) → Tuple[ndarray]

Compute erf in numpy according to ONNX spec.

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

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_hardswish

numpy_hardswish(x: ndarray) → Tuple[ndarray]

Compute hardswish in numpy according to ONNX spec.

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

Args:

• x (numpy.ndarray): Input tensor

Returns:

• Tuple[numpy.ndarray]: Output tensor

function numpy_exp