CLScalarBroadcastPower.java

package neureka.backend.main.implementations.broadcast;

import neureka.Tensor;
import neureka.backend.api.ExecutionCall;
import neureka.math.args.Arg;
import neureka.devices.opencl.OpenCLDevice;

public class CLScalarBroadcastPower extends CLScalarBroadcast
{
    public CLScalarBroadcastPower( String id ) {
        super(
            id,
            "output = ("+TYPE+") pow( (float) input1, (float) value );",
            "   if ( d == 0 )                                                            \n" +
            "       output = ("+TYPE+")( value * pow( (float) input1, (float)( value - 1 ) ) );   \n" +
            "   else                                                                              \n" +
            "       output = ("+TYPE+") ( pow( (float) input1, (float) value ) * log( (float) value ) );  \n"
        );
    }
    @Override
    public Tensor<?> run(ExecutionCall<OpenCLDevice> call) {
        int offset = (call.input( Number.class, 2 ).isVirtual() || call.input( Number.class, 2 ).size() == 1)?1:0;
        int gwz = call.input( Number.class, 0 ).size();
        call.getDevice()
                .getKernel( call )
                .passAllOf(call.input( Number.class, 0 ))
                .passAllOf(call.input( Number.class, 0 ))
                .pass( call.input( Number.class, 1 + offset ).at( 0 ).get().floatValue() )
                .pass( call.input( Number.class, 0 ).rank() )
                .pass( call.getValOf( Arg.DerivIdx.class ) )
                .call( gwz );

        return call.input(0);
    }
}