src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp

/*
 * Copyright (c) 2016, 2017 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/core/CL/kernels/CLPixelWiseMultiplicationKernel.h"

#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"

#include <cmath>
#include <cstdlib>
#include <set>
#include <string>

using namespace arm_compute;

CLPixelWiseMultiplicationKernel::CLPixelWiseMultiplicationKernel()
    : _input1(nullptr), _input2(nullptr), _output(nullptr)
{
}

void CLPixelWiseMultiplicationKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float scale,
                                                ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);

    // Auto initialize output if not initialized
    {
        set_shape_if_empty(*output->info(), input1->info()->tensor_shape());

        if(input1->info()->data_type() == DataType::S16 || input2->info()->data_type() == DataType::S16)
        {
            set_format_if_unknown(*output->info(), Format::S16);
        }
        else if(input1->info()->data_type() == DataType::F32 || input2->info()->data_type() == DataType::F32)
        {
            set_format_if_unknown(*output->info(), Format::F32);
        }
    }

    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output);
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
    ARM_COMPUTE_ERROR_ON_MSG(output->info()->data_type() == DataType::U8 && (input1->info()->data_type() != DataType::U8 || input2->info()->data_type() != DataType::U8),
                             "Output can only be U8 if both inputs are U8");
    ARM_COMPUTE_ERROR_ON_MSG(scale < 0, "Scale cannot be negative. ");
    if(is_data_type_fixed_point(input1->info()->data_type()))
    {
        // All data types must be all QS8 or all QS16
        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2, output);
        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input1, input2, output);
        ARM_COMPUTE_ERROR_ON_MSG(scale != 1, "Unsupported scaling factor for QS8/QS16. Scale must be 1.");
    }

    _input1 = input1;
    _input2 = input2;
    _output = output;

    int scale_int = -1;
    // Extract sign, exponent and mantissa
    int   exponent            = 0;
    float normalized_mantissa = std::frexp(scale, &exponent);
    // Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15
    // frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14
    // Moreover, it will be negative as we deal with 1/2^n
    if((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1))
    {
        // Store the positive exponent. We know that we compute 1/2^n
        // Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5
        scale_int = std::abs(exponent - 1);
    }

    std::string data_type;
    std::string compute_type;
    // Check if it has float inputs and output
    if(is_data_type_float(input1->info()->data_type()) || is_data_type_float(input2->info()->data_type()))
    {
        scale_int    = -1;
        compute_type = (input1->info()->data_type() == DataType::F32 || input2->info()->data_type() == DataType::F32) ? "float" : "half";
        data_type    = "DATA_TYPE_FLOAT";
    }
    else
    {
        if(input1->info()->data_type() == DataType::S16 || input2->info()->data_type() == DataType::S16)
        {
            compute_type = "int";
        }
        else if(input1->info()->data_type() == DataType::QS8)
        {
            compute_type = "qs8";
        }
        else if(input1->info()->data_type() == DataType::QS16)
        {
            compute_type = "qs16";
        }
        else
        {
            compute_type = "ushort";
        }
        data_type = "DATA_TYPE_INT";
    }

    // Construct kernel name
    std::string kernel_name = "pixelwise_mul";
    kernel_name += (scale_int >= 0) ? "_int" : "_float";

    // Set kernel build options
    std::set<std::string> build_opts;
    build_opts.emplace((overflow_policy == ConvertPolicy::WRAP || is_data_type_float(output->info()->data_type())) ? "-DWRAP" : "-DSATURATE");
    build_opts.emplace((rounding_policy == RoundingPolicy::TO_ZERO) ? "-DROUND=_rtz" : "-DROUND=_rte");
    if(is_data_type_fixed_point(input1->info()->data_type()))
    {
        build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input1->info()->fixed_point_position()));
    }
    build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
    build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
    build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
    build_opts.emplace("-DDATA_TYPE_RES=" + compute_type);
    build_opts.emplace("-D" + data_type);

    // Create kernel
    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));

    // Set scale argument
    unsigned int idx = 3 * num_arguments_per_3D_tensor(); //Skip the inputs and output parameters

    if(scale_int >= 0)
    {
        _kernel.setArg(idx++, scale_int);
    }
    else
    {
        _kernel.setArg(idx++, scale);
    }

    // Configure kernel window
    constexpr unsigned int num_elems_processed_per_iteration = 16;

    Window win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration));

    AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_processed_per_iteration);
    AccessWindowHorizontal input2_access(input2->info(), 0, num_elems_processed_per_iteration);
    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);

    update_window_and_padding(win, input1_access, input2_access, output_access);

    ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
                                                       input2->info()->valid_region());
    output_access.set_valid_region(win, valid_region);

    ICLKernel::configure(win);
}

void CLPixelWiseMultiplicationKernel::run(const Window &window, cl::CommandQueue &queue)
{
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

    Window slice = window.first_slice_window_3D();

    do
    {
        unsigned int idx = 0;
        add_3D_tensor_argument(idx, _input1, slice);
        add_3D_tensor_argument(idx, _input2, slice);
        add_3D_tensor_argument(idx, _output, slice);
        enqueue(queue, *this, slice);
    }
    while(window.slide_window_slice_3D(slice));
}