CLConvolution.java

package neureka.backend.main.implementations.convolution;

import neureka.Neureka;
import neureka.backend.main.implementations.ParsedCLImplementation;
import neureka.math.args.Arg;
import neureka.devices.opencl.KernelCode;

public class CLConvolution extends ParsedCLImplementation
{
    public CLConvolution( String id ) {
        super( call -> {
                int offset = ( call.input( Number.class, 0 ) != null ) ? 0 : 1;
                int gwz = ( call.input( Number.class, 0 ) != null ) ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size();
                call.getDevice()
                        .getKernel(call)
                        .passAllOf( call.input( Number.class, offset ) )
                        .passAllOf( call.input( Number.class, offset + 1 ) )
                        .passAllOf( call.input( Number.class, offset + 2 ) )
                        .pass( call.input( Number.class, 0 ).rank() )
                        .pass( call.getValOf( Arg.DerivIdx.class ) )
                        .call( gwz );

                return call.input( 0 );
            },
            3,
            Neureka.get().utility().readResource("kernels/convolution_template.cl"),
            "value = src1 * src2;\n",
            "value += handle * drain;\n",
            id,
            kernelCode -> new KernelCode[]{kernelCode}
        );
    }

}