Leaky ReLU Prototype and Function List¶

Description¶

This kernel performs Rectified Linear Unit (ReLU) with a negative slope activation function. It transforms each element of input tensor according to the following formula:

\[\begin{split}y_{i} = \Big\{ {\begin{matrix} x_{i}\text{ if }x_{i} \geq 0 \\ {\alpha}*x_{i}\text{ if }x_{i} < 0 \\ \end{matrix}}\end{split}\]

Where:

\(x_{i}\) – \(i_{\text{th}}\) value in input tensor

\(y_{i}\) – \(i_{\text{th}}\) value in output tensor

\(\alpha\) - coefficient of the negative slope

Functions¶

Kernels which implement Leaky ReLU functions have the following prototype:

mli_status mli_krn_leaky_relu_<data_format>(
   const mli_tensor *in,
   const mli_tensor *slope_coeff,
   mli_tensor *out);

where data_format is one of the data formats listed in Table MLI Data Formats and the function parameters are shown in the following table:

Leaky ReLU Function Parameters¶
Parameter	Type	Description
`in`	`mli_tensor *`	[IN] Pointer to constant input tensor.
`slope_coeff`	`mli_tensor *`	[IN] Pointer to tensor-scalar with negative slope coefficient.
`out`	`mli_tensor *`	[IN \| OUT] Pointer to output tensor. Result is stored here

List of Available Leaky ReLU Functions¶
Function Name	Details
`mli_krn_leaky_relu_sa8`	All tensors data format: sa8
`mli_krn_leaky_relu_fx16`	All tensors data format: fx16

Conditions¶

Ensure that you satisfy the following general conditions before calling the function:

in and out tensors must be valid (see mli_tensor Structure Field Descriptions) and satisfy data requirements of the selected version of the kernel.

slope_coeff tensor must be a valid tensor-scalar (see data field description in the Table mli_tensor Structure Field Descriptions).

in and out tensors must be of the same shapes

mem_stride of the innermost dimension must be equal to 1 for all the tensors.

For fx16 versions of kernel, in addition to general conditions, ensure that you satisfy the following quantization conditions before calling the function:

The number of frac_bits in the in and out tensors must be equal.

For sa8 versions of kernel, in addition to general conditions, ensure that you satisfy the following quantization conditions before calling the function:

in and out tensor must be quantized on the tensor level. This implies that each tensor contains a single scale factor and a single zero offset.

Zero offset of in and out tensors must be within [-128, 127] range.

Zero offset of slope_coeffs tensor must be within [-16384, 16383] range.

Ensure that you satisfy the platform-specific conditions in addition to those listed above (see the Platform Specific Details chapter).

Result¶

These functions only modify the memory pointed by out.data.mem field. It is assumed that all the other fields of out tensor are properly populated to be used in calculations and are not modified by the kernel.

The kernel supports in-place computation. It means that out and in tensor structures can point to the same memory with the same memory strides but without shift. It can affect performance for some platforms.

Warning

Only an exact overlap of starting address and memory stride of the in and out tensors is acceptable. Partial overlaps result in undefined behavior.

Depending on the debug level (see section Error Codes) this function performs a parameter check and returns the result as an mli_status code as described in section Kernel Specific Configuration Structures.