(l-onnx-doc-Pow)=

# Pow


(l-onnx-op-pow-15)=

## Pow - 15

### Version

- **name**: [Pow (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Pow)
- **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](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

### 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.

```{toctree}
text_diff_Pow_13_15
```

(l-onnx-op-pow-13)=

## Pow - 13

### Version

- **name**: [Pow (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Pow)
- **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

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](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

### 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.

```{toctree}
text_diff_Pow_12_15
text_diff_Pow_12_13
```

(l-onnx-op-pow-12)=

## Pow - 12

### Version

- **name**: [Pow (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Pow)
- **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

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](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

### 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.

```{toctree}
text_diff_Pow_7_15
text_diff_Pow_7_13
text_diff_Pow_7_12
```

(l-onnx-op-pow-7)=

## Pow - 7

### Version

- **name**: [Pow (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Pow)
- **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

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](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

### 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.

```{toctree}
text_diff_Pow_1_15
text_diff_Pow_1_13
text_diff_Pow_1_12
text_diff_Pow_1_7
```

(l-onnx-op-pow-1)=

## Pow - 1

### Version

- **name**: [Pow (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Pow)
- **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 - INT** :

  If set, defines the broadcast dimensions. See doc for details.

* **broadcast - INT** (default is `'0'`):

  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.