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/*
* 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/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::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(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=" << alpha << " ";
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);
output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), 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));
}
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