How to Implement Custom GPU Operations#

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

Python

    core = ov.Core()
    core.set_property("GPU", {"CONFIG_FILE": "<path_to_the_xml_file>"})

C++

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

All OpenVINO samples, except the trivial hello_classification, 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 type CustomLayer for every custom operation you provide.

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

Notation	Description
(0/1)	Can have zero or one instance of this node or attribute
	Must have only one instance of this node or attribute
(0+)	Can have any number of instances of this node or attribute
(1+)	Can have one or more instances of this node or attribute

CustomLayer Node and Sub-Node Structure#

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

Attribute Name	#	Description
`name`		The name of the operation type to be used. This name should be identical to the type used in the IR.
`type`		Must be `SimpleGPU` .
`version`		Must be `1` .

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

Kernel Node and Sub-Node Structure#

The Kernel node contains all kernel source code configuration.

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

Source Node and Sub-Node Structure#

The Source node points to a single OpenCL source file.

Attribute Name	#	Description
`filename`		Name of the file containing OpenCL source code. 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#

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

Attribute Name	#	Description
`name`		The name of the defined JIT. For static constants, this can include the value as well, which is taken as a string.
`param`	(0/1)	This parameter value is used as the value of this JIT definition.
`type`	(0/1)	The parameter type. Accepted values: `int` , `float` , and `int[]` , `float[]` for arrays.
`default`	(0/1)	The default value to be used if the specified parameters are missing from the operation in the OpenVINO IR.

Sub-nodes: None

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

Buffers Node and Sub-Node Structure#

The 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#

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

Attribute Name	#	Description
`name`		Name of a blob attached to an operation in the OpenVINO IR.
`arg-index`		0-based index in the entry function arguments to be bound to.

Sub-nodes: None

Tensor Node and Sub-Node Structure#

The Tensor node configures a single input or output tensor.

Attribute Name	#	Description
`arg-index`		0-based index in the entry function arguments to be bound to.
`type`		`input` or `output`
`port-index`		0-based index in the operation input/output ports in the OpenVINO IR
`format`	(0/1)	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#

The CompilerOptions node configures the compilation flags for the OpenCL sources.

Attribute Name	#	Description
`options`		Options string to be passed to the OpenCL compiler

Sub-nodes: None

WorkSizes Node and Sub-Node Structure#

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

Attribute Name	#	Description
`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=””`
`dim`	(0/1)	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 the Configuration File Format.

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

Built-In Definitions for Custom Layers#

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

For an example, see Example Kernel.

Name	Value
`NUM_INPUTS`	Number of the input tensors bound to this kernel
`GLOBAL_WORKSIZE`	An array of global work sizes used to execute this kernel
`GLOBAL_WORKSIZE_SIZE`	The size of the `GLOBAL_WORKSIZE` array
`LOCAL_WORKSIZE`	An array of local work sizes used to execute this kernel
`LOCAL_WORKSIZE_SIZE`	The size of the `LOCAL_WORKSIZE` array
`<TENSOR>_DIMS`	An array of the tensor dimension sizes. Always ordered as `BFYX`
`<TENSOR>_DIMS_SIZE`	The size of the `<TENSOR>_DIMS` array.
`<TENSOR>_TYPE`	The datatype of the tensor: `float` , `half` , or `char`
`<TENSOR>_FORMAT_<TENSOR_FORMAT>`	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` .
`<TENSOR>_LOWER_PADDING`	An array of padding elements used for the tensor dimensions before they start. Always ordered as BFYX.
`<TENSOR>_LOWER_PADDING_SIZE`	The size of the `<TENSOR>_LOWER_PADDING` array
`<TENSOR>_UPPER_PADDING`	An array of padding elements used for the tensor dimensions after they end. Always ordered as BFYX.
`<TENSOR>_UPPER_PADDING_SIZE`	The size of the `<TENSOR>_UPPER_PADDING` array
`<TENSOR>_PITCHES`	The offset (in elements) between adjacent elements in each dimension. Always ordered as BFYX.
`<TENSOR>_PITCHES_SIZE`	The size of the `<TENSOR>_PITCHES` array
`<TENSOR>_OFFSET`	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;
}

Note

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

Debugging Tips#

Using printf in the OpenCL™ Kernels.

To debug the specific values, 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.

Additional Resources#

Models in the OpenVINO IR format published on Hugging Face.