How to Implement Custom GPU Operations

To enable operations not supported by OpenVINO out of the box, you may need an extension for an OpenVINO operation set, and a custom kernel for the device you will target. This page describes custom kernel support for the GPU device.

The GPU codepath abstracts many details about OpenCL. You need to provide the kernel code in OpenCL C and an XML configuration file that connects the kernel and its parameters to the parameters of the operation.

There are two options for using the custom operation configuration file:

  • Include a section with your kernels into the automatically-loaded <lib_path>/cldnn_global_custom_kernels/cldnn_global_custom_kernels.xml file.

  • Call the ov::Core::set_property() method from your application with the "CONFIG_FILE" key and the configuration file name as a value before loading the network that uses custom operations to the plugin:

ov::Core core;
// Load GPU Extensions
core.set_property("GPU", {{ CONFIG_KEY(CONFIG_FILE), "<path_to_the_xml_file>" }});
core = Core()
core.set_property("GPU", {"CONFIG_FILE": "<path_to_the_xml_file>"})

All OpenVINO samples, except the trivial hello_classification, and most Open Model Zoo demos feature a dedicated command-line option -c to load custom kernels. For example, to load custom operations for the classification sample, run the command below:

$ ./classification_sample -m <path_to_model>/bvlc_alexnet_fp16.xml -i ./validation_set/daily/227x227/apron.bmp -d GPU
 -c <absolute_path_to_config>/custom_layer_example.xml

Configuration File Format

The configuration file is expected to follow the .xml file structure with a node of the CustomLayer type for every custom operation you provide.

The definitions described in the sections below use the following notations:




Can have zero or one instance of this node or attribute

Must have only one instance of this node or attribute


Can have any number of instances of this node or attribute


Can have one or more instances of this node or attribute

CustomLayer Node and Sub-Node Structure

CustomLayer node contains the entire configuration for a single custom operation.

Attribute Name




The name of the operation type to be used. This name should be identical to the type used in the IR.


Must be SimpleGPU .


Must be 1 .

Sub-nodes : Kernel (1), Buffers (1), CompilerOptions (0+), WorkSizes (0/1)

Kernel Node and Sub-Node Structure

Kernel node contains all kernel source code configuration.

Sub-nodes : Source (1+), Define (0+)

Source Node and Sub-Node Structure

Source node points to a single OpenCL source file.

Attribute Name




Name of the file containing OpenCL source code. Note that the path is relative to your executable. Multiple source nodes will have their sources concatenated in order.

Sub-nodes : None

Define Node and Sub-Node Structure

Define node configures a single #define instruction to be added to the sources during compilation (JIT).

Attribute Name




The name of the defined JIT. For static constants, this can include the value as well, which is taken as a string.



This parameter value is used as the value of this JIT definition.



The parameter type. Accepted values: int , float , and int[] , float[] for arrays.



The default value to be used if the specified parameters are missing from the operation in the IR.

Sub-nodes: None

The resulting JIT has the following form: #define [name] [type] [value/default].

Buffers Node and Sub-Node Structure

Buffers node configures all input/output buffers for the OpenCL entry function. No buffers node structure exists.

Sub-nodes: Data (0+), Tensor (1+)

Data Node and Sub-Node Structure

Data node configures a single input with static data, for example, weights or biases.

Attribute Name




Name of a blob attached to an operation in the IR


0-based index in the entry function arguments to be bound to

Sub-nodes : None

Tensor Node and Sub-Node Structure

Tensor node configures a single input or output tensor.

Attribute Name




0-based index in the entry function arguments to be bound to.


input or output


0-based index in the operation input/output ports in the IR



Data layout declaration for the tensor. Accepted values: BFYX , BYXF , YXFB , FYXB , and same values in all lowercase. Default value: BFYX

CompilerOptions Node and Sub-Node Structure

CompilerOptions node configures the compilation flags for the OpenCL sources.

Attribute Name




Options string to be passed to the OpenCL compiler

Sub-nodes : None

WorkSizes Node and Sub-Node Structure

WorkSizes node configures the global/local work sizes to be used when queuing an OpenCL program for execution.

Attribute Name



global local

(0/1) (0/1)

An array of up to three integers or formulas for defining OpenCL work-sizes to be used during execution. The formulas can use the values of the B,F,Y,X dimensions and contain the operators: +,-,/,*,%. All operators are evaluated in integer arithmetic. Default value: global=”B\*F\*Y\*X” local=””



A tensor to take the work-size from. Accepted values: input N , output , where N is an index of input tensor starting with 0. Default value: output

Sub-nodes : None

Example Configuration File

The following code sample provides an example configuration file in XML format. For information on the configuration file structure, see Configuration File Format.

<CustomLayer name="ReLU" type="SimpleGPU" version="1">
  <Kernel entry="example_relu_kernel">
    <Source filename=""/>
    <Define name="neg_slope" type="float" param="negative_slope" default="0.0"/>
    <Tensor arg-index="0" type="input" port-index="0" format="BFYX"/>
    <Tensor arg-index="1" type="output" port-index="0" format="BFYX"/>
  <CompilerOptions options="-cl-mad-enable"/>
  <WorkSizes global="X,Y,B\*F"/>

Built-In Definitions for Custom Layers

The following table includes definitions that are attached before user sources.

For an example, see Example Kernel.




Number of the input tensors bound to this kernel


An array of global work sizes used to execute this kernel


The size of the GLOBAL_WORKSIZE array


An array of local work sizes used to execute this kernel


The size of the LOCAL_WORKSIZE array


An array of the tensor dimension sizes. Always ordered as BFYX


The size of the <TENSOR>_DIMS array.


The datatype of the tensor: float , half , or char


The format of the tensor, BFYX, BYXF, YXFB , FYXB, or ANY. The format is concatenated to the defined name. You can use the tensor format to define codepaths in your code with #ifdef/#endif .


An array of padding elements used for the tensor dimensions before they start. Always ordered as BFYX.


The size of the <TENSOR>_LOWER_PADDING array


An array of padding elements used for the tensor dimensions after they end. Always ordered as BFYX.


The size of the <TENSOR>_UPPER_PADDING array


The offset (in elements) between adjacent elements in each dimension. Always ordered as BFYX.


The size of the <TENSOR>_PITCHES array


The number of elements from the start of the tensor to the first valid element, bypassing the lower padding.

All <TENSOR> values are automatically defined for every tensor bound to this operation, such as INPUT0, INPUT1, and OUTPUT0, as shown in the following example:

#define INPUT0_DIMS_SIZE 4
#define INPUT0_DIMS (int []){ 1,96,55,55, }

Example Kernel

#pragma OPENCL EXTENSION cl_khr_fp16 : enable
__kernel void example_relu_kernel(
    const __global INPUT0_TYPE\*  input0,
          __global OUTPUT0_TYPE\* output)
    const uint idx  = get_global_id(0);
    const uint idy  = get_global_id(1);
    const uint idbf = get_global_id(2); // batches\*features, as OpenCL supports 3D nd-ranges only
    const uint feature = idbf % OUTPUT0_DIMS[1];
    const uint batch   = idbf / OUTPUT0_DIMS[1];
    //notice that pitches are in elements, not in bytes!
    const uint in_id  = batch\*INPUT0_PITCHES[0] + feature\*INPUT0_PITCHES[1]   + idy\*INPUT0_PITCHES[2]  + idx\*INPUT0_PITCHES[3]  + INPUT0_OFFSET;
    const uint out_id = batch\*OUTPUT0_PITCHES[0] + feature\*OUTPUT0_PITCHES[1]  + idy\*OUTPUT0_PITCHES[2]  + idx\*OUTPUT0_PITCHES[3]  + OUTPUT0_OFFSET;

    INPUT0_TYPE value = input0[in_id];
    // neg_slope (which is non-zero for leaky ReLU) is put automatically as #define, refer to the config xml
    output[out_id] = value < 0 ? value \* neg_slope : value;


As described in the previous section, all items like INPUT0_TYPE are actually defined as OpenCL (pre-)compiler inputs by OpenVINO for efficiency reasons. See Debugging Tips for information on debugging the results.

Debugging Tips

  • Using printf in the OpenCL™ Kernels. To debug the specific values, you can use printf in your kernels. However, be careful not to output excessively, which could generate too much data. The printf output is typical, so your output can be truncated to fit the buffer. Also, because of buffering, you actually get an entire buffer of output when the execution ends.

For more information, refer to the printf Function.