src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp

/*
 * Copyright (c) 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
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"

#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/AccessWindowTranspose.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/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"

#include <set>
#include <sstream>
#include <string>

using namespace arm_compute;

CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel()
    : _input0(nullptr), _input1(nullptr), _output(nullptr)
{
}

void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha)
{
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::F16, DataType::F32);
    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);

    if(output->info()->dimension(1) == 1)
    {
        ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
    }

    _input0 = input0;
    _input1 = input1;
    _output = output;

    if(output->info()->dimension(1) == 196)
    {
        _lws_hint = cl::NDRange(1, 7);
    }
    else
    {
        _lws_hint = cl::NDRange(8, 8);
    }

    std::ostringstream mm_arguments;
    mm_arguments << "-DWIDTH_MATRIX_B=" << input1->info()->dimension(0) << " ";
    mm_arguments << "-DALPHA=" << (input0->info()->data_type() == DataType::QS8 ? scvt_qs8_f32(alpha, input0->info()->fixed_point_position()) : alpha) << " ";
    mm_arguments << "-DFIXED_POINT_POSITION=" << input0->info()->fixed_point_position() << " ";
    std::set<std::string> build_opts;

    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
    if(output->info()->dimension(1) == 1)
    {
        mm_arguments << "-DWIDTH_VECTOR_A=" << input0->info()->dimension(0) << " ";
        build_opts.emplace(mm_arguments.str());

        // Create kernel
        std::string data_type_name = lower_string(string_from_data_type(input0->info()->data_type()));
        _kernel                    = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(("gemm_vm_" + data_type_name), build_opts));

        // Configure window kernel
        const unsigned int num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->info()->data_type());

        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));

        AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_processed_per_iteration_x, 1);
        AccessWindowRectangle input1_access(input1->info(), 0, 0, num_elems_processed_per_iteration_x, 1);
        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, 1);

        update_window_and_padding(win, input0_access, input1_access, output_access);

        Coordinates coord;
        coord.set_num_dimensions(output->info()->num_dimensions());
        output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape()));

        ICLKernel::configure(win);
    }
    else
    {
        build_opts.emplace(mm_arguments.str());

        // Create kernel
        std::string data_type_name = lower_string(string_from_data_type(input0->info()->data_type()));

        if(data_type_name == "f32")
        {
            GPUTarget arch_target = get_arch_from_target(get_target());
            _kernel               = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_mm_f32_" + string_from_target(arch_target), build_opts));
        }
        else
        {
            _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_mm_" + data_type_name, build_opts));
        }

        // Configure window kernel
        const unsigned int     num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->info()->data_type());
        constexpr unsigned int num_elems_processed_per_iteration_y = 4;

        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));

        AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f);
        AccessWindowTranspose input1_access(input1->info(), 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f);
        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);

        update_window_and_padding(win, input0_access, input1_access, output_access);

        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));

        ICLKernel::configure(win);
    }
}

void CLGEMMMatrixMultiplyKernel::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_2D();
    Window slice_matrix_b = slice;
    slice_matrix_b.set(Window::DimX, Window::Dimension(0, _input1->info()->dimension(0), 1));
    slice_matrix_b.set(Window::DimY, Window::Dimension(0, _input1->info()->dimension(1), 1));
    slice_matrix_b.set(Window::DimZ, Window::Dimension(0, 1, 1));

    do
    {
        Window slice_b = slice;
        // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
        if(_input1->info()->num_dimensions() < 3)
        {
            slice_b = slice_matrix_b;
        }

        unsigned int idx = 0;
        add_2D_tensor_argument(idx, _input0, slice);
        add_2D_tensor_argument(idx, _input1, slice_b);
        add_2D_tensor_argument(idx, _output, slice);
        enqueue(queue, *this, slice, _lws_hint);
    }
    while(window.slide_window_slice_2D(slice));
}