Pow#

Pow - 15 #

Version

name: Pow (GitHub)
domain: main
since_version: 15
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 15.

Summary

Pow takes input data (Tensor<T>) and exponent Tensor, and produces one output data (Tensor<T>) where the function f(x) = x^exponent, is applied to the data tensor elementwise. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

X (heterogeneous) - T: First operand, base of the exponent.
Y (heterogeneous) - T1: Second operand, power of the exponent.

Outputs

Z (heterogeneous) - T: Output tensor

Type Constraints

T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64) ): Constrain input X and output types to float/int tensors.
T1 in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input Y types to float/int tensors.

Examples

default

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Pow",
    inputs=["x", "y"],
    outputs=["z"],
)

x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y)  # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_example")

x = np.arange(60).reshape(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = pow(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_pow")

_pow_broadcast

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Pow",
    inputs=["x", "y"],
    outputs=["z"],
)

x = np.array([1, 2, 3]).astype(np.float32)
y = np.array(2).astype(np.float32)
z = pow(x, y)  # expected output [1., 4., 9.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar")

node = onnx.helper.make_node(
    "Pow",
    inputs=["x", "y"],
    outputs=["z"],
)
x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
y = np.array([1, 2, 3]).astype(np.float32)
# expected output [[1, 4, 27], [4, 25, 216]]
z = pow(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array")

_types

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Pow",
    inputs=["x", "y"],
    outputs=["z"],
)

x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.int64)
z = pow(x, y)  # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64")

x = np.array([1, 2, 3]).astype(np.int64)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y)  # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32")

x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.int32)
z = pow(x, y)  # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32")

x = np.array([1, 2, 3]).astype(np.int32)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y)  # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32")

x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.uint64)
z = pow(x, y)  # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64")

x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.uint32)
z = pow(x, y)  # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32")

x = np.array([1, 2, 3]).astype(np.int64)
y = np.array([4, 5, 6]).astype(np.int64)
z = pow(x, y)  # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64")

x = np.array([1, 2, 3]).astype(np.int32)
y = np.array([4, 5, 6]).astype(np.int32)
z = pow(x, y)  # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32")

Pow - 13 vs 15

Pow - 13 #

Version

name: Pow (GitHub)
domain: main
since_version: 13
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 13.

Summary

Inputs

X (heterogeneous) - T: First operand, base of the exponent.
Y (heterogeneous) - T1: Second operand, power of the exponent.

Outputs

Z (heterogeneous) - T: Output tensor

Type Constraints

T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64) ): Constrain input X and output types to float/int tensors.
T1 in ( tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input Y types to float/int tensors.

Pow - 12 #

Version

name: Pow (GitHub)
domain: main
since_version: 12
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 12.

Summary

Inputs

X (heterogeneous) - T: First operand, base of the exponent.
Y (heterogeneous) - T1: Second operand, power of the exponent.

Outputs

Z (heterogeneous) - T: Output tensor.

Type Constraints

T in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64) ): Constrain input X and output types to float/int tensors.
T1 in ( tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input Y types to float/int tensors.

Pow - 7 #

Version

name: Pow (GitHub)
domain: main
since_version: 7
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 7.

Summary

Inputs

X (heterogeneous) - T: First operand, base of the exponent.
Y (heterogeneous) - T: Second operand, power of the exponent.

Outputs

Z (heterogeneous) - T: Output tensor.

Type Constraints

T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Pow - 1 #

Version

name: Pow (GitHub)
domain: main
since_version: 1
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 1.

Summary

Pow takes input data (Tensor<T>) and exponent Tensor, and produces one output data (Tensor<T>) where the function f(x) = x^exponent, is applied to the data tensor elementwise.

If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.

For example, the following tensor shapes are supported (with broadcast=1):

shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute broadcast=1 needs to be passed to enable broadcasting.

Attributes

axis: If set, defines the broadcast dimensions. See doc for details.
broadcast: Pass 1 to enable broadcasting

Inputs

X (heterogeneous) - T: Input tensor of any shape, base of the exponent.
Y (heterogeneous) - T: Input tensor of any shape broadcastable to X shape, the exponent component.

Outputs

Z (heterogeneous) - T: Output tensor (same size as X)

Type Constraints

T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Pow#

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