(l-onnx-doc-HardSwish)=

# HardSwish


(l-onnx-op-hardswish-22)=

## HardSwish - 22

### Version

- **name**: [HardSwish (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#HardSwish)
- **domain**: `main`
- **since_version**: `22`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 22**.

### Summary

HardSwish takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where
the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid<alpha, beta>(x),
where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 22]
>
HardSwish (X) => (Y)
{
   HS_X = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> (X)
   Y = Mul (X, HS_X)
}
```

### Inputs

- **X** (heterogeneous) - **T**:

  Input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  Output tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.

```{toctree}
text_diff_HardSwish_14_22
```

(l-onnx-op-hardswish-14)=

## HardSwish - 14

### Version

- **name**: [HardSwish (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#HardSwish)
- **domain**: `main`
- **since_version**: `14`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 14**.

### Summary

HardSwish takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where
the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid<alpha, beta>(x),
where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 14]
>
HardSwish (X) => (Y)
{
   HS_X = HardSigmoid <alpha: float = 0.166667, beta: float = 0.5> (X)
   Y = Mul (X, HS_X)
}
```

### Inputs

- **X** (heterogeneous) - **T**:

  Input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  Output tensor

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.